Medicine, Faculty of
Population and Public Health (SPPH), School of
DSpace
UBCV
Guthrie, Jennifer L.
Doctor of Philosophy - PhD
University of British Columbia
Background: In Canada, TB remains a public health concern, with the disease becoming increasingly entrenched in our most vulnerable populations. In 2012, British Columbia (BC) prepared a strategic plan with the aim of reducing incidence in the province by 50% over 10 years. A key aspect of this is preventing person-to-person spread of TB within BC—challenging, as our understanding of endemic transmission is incomplete. The objective of this dissertation is to use new advances in technology, including whole genome sequencing (WGS), to address the knowledge gaps around who, where, and how transmission occurs in BC and provide the foundation upon which new TB prevention policies and programs will be developed. Methods: This dissertation draws on data routinely collected from persons diagnosed with culture-confirmed TB in BC (2005–2014) and linked to laboratory data from their corresponding Mycobacterium tuberculosis (Mtb) isolate(s). Collaborations with Yukon and Ontario provided additional data. 24-locus MIRU-VNTR genotype results were available for each Mtb isolate across all three study populations. WGS of genotypically clustered BC isolates and all Yukon isolates was carried out and the data analyzed using a bioinformatics pipeline developed at Oxford University. Descriptive and inferential statistics were used to examine clinical and demographic characteristics of persons with Mtb isolates belonging to a cluster according to genotyping and WGS methods. Results: Universal genotyping of all Mtb isolates collected in BC over a ten-year period revealed that 57.6% of the study population had a genotypically unique Mtb isolate. Sixteen large genotype clusters were identified, nine in predominately Canadian-born (CB) persons. Application of WGS indicated the large genotypic clusters comprised of mainly non-Canadian-born (nCB) persons did not represent recent, endemic transmission within BC, and WGS additionally refined many of the CB clusters to smaller sub-clusters. The WGS clustered proportion was 25.8%. Conclusions: Approximately one in four of BC’s TB cases occur in CB persons and are largely the result of local transmission. WGS represents a new and important tool for understanding the spread of TB within a population, and using this technology paired with routinely collected case-level data provided significant insights to transmission in BC.
Unravelling the Thread of Transmission: Pairing Traditional Epidemiology with Genomics to Understand Tuberculosis Transmission Dynamics by Jennifer L. Guthrie B.Sc. (Honours), Brock University, 2000 M.Sc. (Distinction), Brock University, 2003 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Population and Public Health) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2018 © Jennifer L. Guthrie, 2018 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Unravelling the Thread of Transmission: Pairing Traditional Epidemiology with Genomics to Understand Tuberculosis Transmission Dynamics submitted by Jennifer L. Guthrie in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Population and Public Health Examining Committee: Dr. Jennifer Gardy, School of Population and Public Health Supervisor Dr. James Johnston, TB Services Evaluation Lead, BC Centre for Disease Control Supervisory Committee Member Dr. Bonnie Henry, BC Provincial Health Officer Supervisory Committee Member Dr. Martin Hirst, Microbiology and Immunology University Examiner Dr. Richard Harrigan, Experimental Medicine University Examiner Additional Supervisory Committee Members: Dr. Lindsay Eltis, Microbiology & Immunology Supervisory Committee Member Supervisory Committee Member iii Abstract Background: In Canada, TB remains a public health concern, with the disease becoming increasingly entrenched in our most vulnerable populations. In 2012, British Columbia (BC) prepared a strategic plan with the aim of reducing incidence in the province by 50% over 10 years. A key aspect of this is preventing person-to-person spread of TB within BC—challenging, as our understanding of endemic transmission is incomplete. The objective of this dissertation is to use new advances in technology, including whole genome sequencing (WGS), to address the knowledge gaps around who, where, and how transmission occurs in BC and provide the foundation upon which new TB prevention policies and programs will be developed. Methods: This dissertation draws on data routinely collected from persons diagnosed with culture-confirmed TB in BC (2005–2014) and linked to laboratory data from their corresponding Mycobacterium tuberculosis (Mtb) isolate(s). Collaborations with Yukon and Ontario provided additional data. 24-locus MIRU-VNTR genotype results were available for each Mtb isolate across all three study populations. WGS of genotypically clustered BC isolates and all Yukon isolates was carried out and the data analyzed using a bioinformatics pipeline developed at Oxford University. Descriptive and inferential statistics were used to examine clinical and demographic characteristics of persons with Mtb isolates belonging to a cluster according to genotyping and WGS methods. Results: Universal genotyping of all Mtb isolates collected in BC over a ten-year period revealed that 57.6% of the study population had a genotypically unique Mtb isolate. Sixteen large genotype clusters were identified, nine in predominately Canadian-born (CB) persons. Application of WGS indicated the large genotypic clusters comprised of mainly non-Canadian-born (nCB) persons did not represent recent, endemic transmission within BC, and WGS additionally refined many of the CB clusters to smaller sub-clusters. The WGS clustered proportion was 25.8%. Conclusions: Approximately one in four of BC’s TB cases occur in CB persons and are largely the result of local transmission. WGS represents a new and important tool for understanding the spread of TB within a population, and using this technology paired with routinely collected case-level data provided significant insights to transmission in BC. iv Lay Summary With an estimated 1.7 million deaths annually, tuberculosis (TB) is the number one infectious disease killer worldwide. In Canada, TB remains a public health concern, with the disease becoming increasingly entrenched in our most vulnerable populations. Recent advances now allow the full DNA sequence to be read from TB bacteria isolated from persons diagnosed with the disease. Analysis and comparison of tuberculosis DNA sequences from persons with TB in British Columbia (BC) over a ten-year period was used to detect small changes in the DNA as TB is spread (transmitted) from person-to-person. By combining this information with case-level data important insights to tuberculosis transmission were gained, including risk factors related to TB spread and a more accurate estimate of disease resulting from transmission within BC. Understanding person-to-person spread of TB is crucial to improving awareness, education, early detection, and ultimately preventing the spread of tuberculosis in the most at-risk communities. v Preface All the works presented henceforth were conducted at the University of British Columbia (UBC), Vancouver, and at the British Columbia Centre for Disease Control (BCCDC), Vancouver, BC, Canada, and conceived, undertaken, and written by the candidate, Jennifer L. Guthrie (JLGu). Data for the research projects presented within have been obtained from several distinct sources: (1) BCCDC Public Health Laboratory (2) BC’s tuberculosis registry—Integrated Public Health Information System (iPHIS) (3) Yukon’s tuberculosis registry (iPHIS) (4) Immigration, Refugees and Citizenship Canada Permanent Residents database records, linked to data from Population Data BC health administrative databases, and (5) Public Health Ontario Laboratories. Data linkage for each study was performed under the BCCDC’s public health mandate with approval of the Privacy and Access Committee. The UBC Clinical Research Ethics Board provided ethical approvals (#H12-00910, #H16–00265). An additional ethics approval (#2016-058.0) was obtained for the collaboration with Public Health Ontario (Chapter 4). The co-authors of the manuscripts, including Dr. Jennifer L. Gardy (JLGa), Dr. James Johnston (JJ), Dr. Victoria J. Cook (VJC), Dr. Patrick Tang (PT), Dr. Linda Hoang (LH), Dr. Kevin Elwood (KE), Dr. David Roth (DR), Dr. Bonnie Henry (BHe), Dr. Andy Delli Pizzi (ADP), Dr. Sarah Cherian (SC), Dr. Lisa Ronald (LR), Dr. Mabel Rodrigues (MR), Clare Kong (CK), Danielle Jorgensen (DJ), Maichael Thejoe (MT), Dr. Frances B. Jamieson (FBJ), Alex Marchand Austin (AMA), Karen Lam (KL), Daria Pyskir (DP), Kirby Cronin (KC), Dr. Brendan Hanley (BHa), Lori Strudwick (LS), Beth Roberts (BR), Meadow Allen (MA), Jan McFadzen (JM), Dr. Timothy Walker (TW) and Dona Foster (DF) made contributions only as is commensurate with collegial or co-investigator duties. Relative contributions of the author, collaborators, and co-authors are described in detail below. Chapter 1, Sections 1.1 – 1.5 and 0, Sections 9.1 – 9.3, 9.5 – 9.6 are the original, unpublished intellectual products of the candidate. With guidance and input from my supervisor (JLGa), the literature was searched and reviewed, and the findings of these chapters synthesized. vi A version of Chapter 1, Section 1.6 has been published. Guthrie JL, and Gardy, JL. (2017). A brief primer on genomic epidemiology: lessons learned from Mycobacterium tuberculosis. Annals of the New York Academy of Sciences. 1388:59–77. dx.doi.org/10.1111/nyas.13273. JLGu in collaboration with JLGa developed the manuscript concept, conducted a literature review, drafted and revised the manuscript. A version of Chapter 1, Section 1.7 has been published. Guthrie JL, and Gardy JL. (2015). Accelerating tuberculosis elimination in low-incidence settings: the role of genomics. European Respiratory Journal. 46:1840–1841. dx.doi.org/10.1183/13993003.00788-2015. JLGu in collaboration with JLGa developed the manuscript concept, conducted a literature review, drafted and revised the manuscript. A version of Chapter 2 has been published. Guthrie JL, Kong C, Roth D, Jorgensen D, Rodrigues M, Tang P, Thejoe M, Elwood K, Cook VJ, Johnston J, Gardy JL. (2018). Universal Genotyping for Tuberculosis Prevention Programs: A Five-Year Comparison with On-Request Genotyping. Journal of Clinical Microbiology. 56:e01778-17. JLGu developed the study, linked, cleaned and analyzed the data, drafted and revised the manuscript. JLGa supervised the study. CK carried out the laboratory work under the supervision of MR and PT. DR extracted the provincial case data. CK, DR, DJ, MR, PT, MT, KE, VJC, JJ, and JLGa provided input to the draft and contributed to the revision of the final manuscript. A version of Chapter 3 has been published. Guthrie, JL, Kong, C, Roth, D, Jorgensen, D, Rodrigues, M, Hoang, L, Tang, P, Cook, V, Johnston, J, and Gardy, JL. (2017). Molecular Epidemiology of Tuberculosis in British Columbia, Canada: A 10-Year Retrospective Study. Clinical Infectious Diseases. 66:849–856. JLGu conceived of and designed the study, linked, cleaned and analyzed the data, drafted and revised the manuscript. JLGa supervised the study. CK carried out the laboratory work under the supervision of MR and PT. DR extracted the provincial case data. CK, DR, DJ, MR, LH, PT, VJC, JJ, and JLGa provided input to the draft and contributed to the revision of the final manuscript. vii A version of Chapter 4 has been submitted for peer review. Guthrie JL, Marchand-Austin A, Cronin K, Lam K, Pyskir D, Kong C, Jorgensen D, Rodrigues M, Roth D, Tang P, Cook VJ, Johnston J, Jamieson FB, Gardy JL. (2018). Universal Genotyping Reveals Province-Level Differences in the Molecular Epidemiology of Tuberculosis. Manuscript submitted. This study is based on a collaboration with Public Health Ontario (PHO) and lead by JLGu. A collaboration agreement between BCCDC and PHO was approved on May 31, 2018 (#RRB-17-025). JLGu conceived of and designed the study, linked, cleaned and analyzed the data, drafted and revised the manuscript. JLGa supervised the study. CK carried out the BC laboratory work under the supervision of MR and PT. KL and DP carried out the ON laboratory work under the supervision of FJ. KC extracted the ON provincial case data under the supervision of AMA. AMA, KC, KL, DP, CK, DJ, MR, DR, PT, VJC, JJ, FBJ and JLGa provided input to the draft and contributed to the revision of the final manuscript. A version of Chapter 5 has been published. Guthrie JL, Delli Pizzi A, Roth D, Kong C, Jorgensen D, Rodrigues M, Tang P, Cook VJ, Johnston J, and Gardy JL. (2018). Genotyping and Whole Genome Sequencing to Identify Tuberculosis Transmission to Pediatric Patients in British Columbia, Canada, 2005–2014. The Journal of Infectious Diseases. 218:1155–1163. JLGu conceived of and designed the study, linked, cleaned and analyzed the data, drafted and revised the manuscript. ADP reviewed pediatric case notes and provided a summary of contact investigations. JLGa supervised the study. CK carried out the laboratory work under the supervision of MR and PT. DR extracted the provincial case data. ADP, DR, CK, DJ, MR, PT, VJC, JJ and JLGa provided input to the draft and contributed to the revision of the final manuscript. A version of Chapter 6 has been submitted for peer review. Guthrie JL, Strudwick L, Roberts B, Allen M, McFadzen J, Roth D, Jorgensen D, Rodrigues M, Tang P, Hanley B, Johnston J, Cook VJ, Gardy JL. (2018). Whole Genome Sequencing for Improved Understanding of Mycobacterium tuberculosis Transmission in a Remote Circumpolar Region. Submitted. Chapter 6 is based on a collaboration with Yukon Communicable Disease Control (YCDC) and lead by JLGu under the supervision of JLGa, VJC (BCCDC Physician Consultant to Yukon) and viii BH (Yukon Chief Medical Officer of Health). YCDC contracts BCCDC for laboratory work and TB services support; however, to formalize the collaboration for this research project an information sharing agreement between BCCDC and YCDC was prepared and approved on October 5, 2017 (#2017-008). JLGu conceived of and designed the study, linked, cleaned and analyzed the data, drafted and revised the manuscript. CK carried out the laboratory work under the supervision of MR and PT. LR and DR extracted the YT provincial case data. LS, BR, MA, and JM reviewed YT cases and summarized contact investigation notes. LS, BR, MA, JM, DR, DJ, MR, PT, BH, JJ, VJC and JLGa provided input to the draft and contributed to the revision of the final manuscript. A version of Chapter 7 is currently being finalized for submission. Guthrie JL, Strudwick L, Roberts B, Allen M, McFadzen J, Roth D, Jorgensen D, Rodrigues M, Tang P, Hanley B, Johnston J, Cook VJ, Gardy JL. (2018). Comparison of Traditional Field Epidemiology and Whole Genome Sequencing to Understand Tuberculosis Transmission in a Remote. Manuscript in preparation. Chapter 7 is based on a collaboration with Yukon Communicable Disease Control (YCDC) and lead by JLGu under the supervision of JLGa, VJC (BCCDC Physician Consultant to Yukon) and BH (Yukon Chief Medical Officer of Health). YCDC contracts BCCDC for laboratory work and TB services support; however, to formalize the collaboration for this research project an information sharing agreement between BCCDC and YCDC was prepared and approved on October 5, 2017 (#2017-008). JLGu conceived of and designed the study, linked, cleaned and analyzed the data, drafted and revised the manuscript. JLGu developed the online and in-person surveys with input from JLGa and VJC. LS, BR, MA, and JM participated in the online and in-person surveys. JLGu and JLGa lead in-person case discussions, qualitative survey and data collection.CK carried out the laboratory work under the supervision of MR and PT. LR and DR extracted the YT provincial case data. LS, BR, MA, and JM reviewed YT cases and summarized contact investigation notes. LS, BR, MA, JM, DR, DJ, MR, PT, BH, JJ, VJC and JLGa provided input to the draft and contributed to the revision of the final manuscript. ix A version of Chapter 8 is currently being prepared for submission. Guthrie JL, Cherian S, Kong C, Roth D, Jorgensen D, Rodrigues M, Walker T, Foster D, Henry B, Cook VJ, Johnston J, Tang P, Gardy JL. (2018). Whole Genome Sequencing as a Tool to Understand and Quantify Active Tuberculosis Arising from Local Transmission. Manuscript in preparation. JLGu conceived of the study with JLGa, and designed the analysis protocols in consultation with JLGa, PT, VJC, and JJ. SC reviewed case notes and extracted case data. JLGu linked, cleaned and analyzed the data, drafted and revised the manuscript. JLGa supervised the study. CK carried out the laboratory work under the supervision of MR and PT. DR extracted the provincial case data. JLGu and JLGa reviewed case notes where necessary. TM and DF provided support and coordination of genomic analysis using the Oxford University bioinformatics pipeline. SC, CK, DR, DJ, MR, TW, DF, BH, VJC, JJ, PT and JLGa are providing input to the draft and will contribute to the revision of the final manuscript. A version of Chapter 9, Section 9.3.1 is currently undergoing peer review. Guthrie JL, Ronald L, Cook VJ, Johnston J, Gardy, JL. (2018). Homogeneous Group or a Multicultural Mosaic? The Challenge with Reporting Birth Outside Canada as a Tuberculosis Risk. Submitted. JLGu in collaboration with JLGa developed the manuscript concept with input from LR, VJC and JJ. JLGu analyzed the genotyped data set. LR contributed results from the CIC/PopData BC data set. JLGu drafted and revised the manuscript. LR, VJC, JJ, and JLGa provided input to the draft and contributed to the revision of the final manuscript. x Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ...........................................................................................................................x List of Tables ............................................................................................................................ xviii List of Figures ...............................................................................................................................xx List of Abbreviations ............................................................................................................... xxiii Glossary .................................................................................................................................... xxvi Acknowledgements ................................................................................................................. xxvii Chapter 1: Introduction ................................................................................................................1 1.1 Background and Rationale .............................................................................................. 1 1.2 Research Objectives ........................................................................................................ 3 1.3 Tuberculosis .................................................................................................................... 5 1.3.1 Etiology ....................................................................................................................... 5 1.3.2 Diagnosis and treatment .............................................................................................. 6 1.3.3 Mycobacterium tuberculosis lineages ......................................................................... 8 1.3.4 Transmission ............................................................................................................... 9 1.3.5 Monitoring, surveillance and investigation............................................................... 10 1.3.6 Epidemiology of tuberculosis in Canada .................................................................. 11 1.4 Genotyping of Mycobacterium tuberculosis ................................................................. 13 1.4.1 RFLP ......................................................................................................................... 13 xi 1.4.2 MIRU-VNTR ............................................................................................................ 13 1.4.3 Spoligotyping ............................................................................................................ 14 1.5 Molecular Epidemiology and Public Health ................................................................. 15 1.6 A Brief Primer on Genomic Epidemiology: Lessons Learned from Mycobacterium tuberculosis ............................................................................................................................... 16 1.6.1 The Mycobacterium tuberculosis genome ................................................................ 16 1.6.2 Leveraging genomics for epidemiology ................................................................... 17 1.6.3 Step 1: look before you leap ..................................................................................... 23 1.6.4 Step 2: from sample to sequence .............................................................................. 26 1.6.5 Step 3: bases to bytes ................................................................................................ 29 1.6.6 Step 4: rapid resistance prediction ............................................................................ 35 1.6.7 Step 5: making the links ............................................................................................ 37 1.6.8 Concluding thoughts ................................................................................................. 40 1.7 Accelerating TB Elimination in Low-incidence Settings: the Role of Genomics ........ 41 Chapter 2: Universal Genotyping for Tuberculosis Prevention Programs: A Five-Year Comparison with On-Request Genotyping ...............................................................................44 2.1 Background ................................................................................................................... 44 2.2 Materials and Methods .................................................................................................. 45 2.2.1 On-request genotyping data ...................................................................................... 45 2.2.2 Universal genotyping data ........................................................................................ 45 2.2.3 Statistical analysis ..................................................................................................... 46 2.3 Results ........................................................................................................................... 47 xii 2.3.1 The genotype request proportion was smaller than the genotypic clustering proportion .............................................................................................................................. 47 2.3.2 Requests reflected suspected community transmission and known risk factors ....... 49 2.3.3 Universal genotyping improves cluster identification .............................................. 51 2.3.4 Growing clusters were variably identified by on-request genotyping ...................... 55 2.4 Discussion ..................................................................................................................... 57 Chapter 3: Molecular Epidemiology of Tuberculosis in British Columbia, Canada—A 10-Year Retrospective Study ............................................................................................................60 3.1 Background ................................................................................................................... 60 3.2 Materials and Methods .................................................................................................. 61 3.2.1 Study setting and design ........................................................................................... 61 3.2.2 Case data ................................................................................................................... 61 3.2.3 Laboratory analysis ................................................................................................... 62 3.2.4 Statistical analysis ..................................................................................................... 63 3.3 Results ........................................................................................................................... 63 3.3.1 Lineage analysis ........................................................................................................ 68 3.3.2 MIRU-VNTR identifies discrete subgroups amongst BC’s TB cases ...................... 72 3.3.3 MIRU-VNTR identifies drivers of large transmission clusters ................................ 75 3.4 Discussion ..................................................................................................................... 76 Chapter 4: Universal Genotyping Reveals Province-Level Differences in the Molecular Epidemiology of Tuberculosis .....................................................................................................81 4.1 Background ................................................................................................................... 81 4.2 Methods......................................................................................................................... 82 xiii 4.2.1 Study setting and design ........................................................................................... 82 4.2.2 Diagnosis and case information ................................................................................ 83 4.2.3 Genotyping by 24-locus MIRU-VNTR .................................................................... 83 4.2.4 Statistical analysis ..................................................................................................... 84 4.3 Results ........................................................................................................................... 84 4.3.1 Descriptive epidemiology ......................................................................................... 84 4.3.2 TB isolates in BC are more likely to be clustered by MIRU-VNTR ........................ 86 4.3.3 Interprovincial clustering occurs frequently between Ontario and BC .................... 87 4.4 Discussion ..................................................................................................................... 96 Chapter 5: Genotyping and Whole-Genome Sequencing to Identify Tuberculosis Transmission to Pediatric Patients in British Columbia ..........................................................99 5.1 Background ................................................................................................................... 99 5.2 Methods....................................................................................................................... 100 5.2.1 Study setting and design ......................................................................................... 100 5.2.2 Case data ................................................................................................................. 101 5.2.3 Laboratory methods ................................................................................................ 101 5.2.4 Whole genome sequencing analysis ....................................................................... 102 5.2.5 Transmission classification ..................................................................................... 103 5.2.6 Statistical analysis ................................................................................................... 103 5.3 Results ......................................................................................................................... 104 5.3.1 Demographics, clinical presentation, and epidemiology ........................................ 104 5.3.2 Molecular and genomic epidemiology investigation of putative sources ............... 107 5.3.3 Identification of infections acquired out of province .............................................. 108 xiv 5.3.4 Identification of locally acquired infections ........................................................... 110 5.3.5 Household transmission of multidrug resistant tuberculosis .................................. 113 5.4 Discussion ................................................................................................................... 113 Chapter 6: Whole Genome Sequencing for Improved Understanding of Mycobacterium tuberculosis Transmission in a Remote Circumpolar Region ................................................117 6.1 Background ................................................................................................................. 117 6.2 Methods....................................................................................................................... 118 6.2.1 Study setting and design ......................................................................................... 118 6.2.2 Case-level information ............................................................................................ 119 6.2.3 Laboratory methods ................................................................................................ 120 6.2.4 WGS analysis .......................................................................................................... 120 6.2.5 Statistical Analysis .................................................................................................. 121 6.3 Results ......................................................................................................................... 121 6.3.1 MIRU-VNTR and WGS provide different estimates of clustering ........................ 121 6.3.2 Genomically related cases across jurisdictions are similar clinically ..................... 125 6.3.3 Transmission reconstruction ................................................................................... 127 6.4 Discussion ................................................................................................................... 130 Chapter 7: Comparison of Traditional Field Epidemiology and Whole Genome Sequencing to Understand Tuberculosis Transmission in a Remote Setting ...........................................133 7.1 Background ................................................................................................................. 133 7.2 Methods....................................................................................................................... 135 7.2.1 Study setting and design ......................................................................................... 135 7.2.2 Bacterial culture, genotyping and whole genome sequencing ................................ 135 xv 7.2.3 Source identification by field and molecular epidemiology ................................... 136 7.2.4 Source identification by genomic epidemiology .................................................... 137 7.2.5 Source identification consensus .............................................................................. 137 7.2.6 Qualitative assessment ............................................................................................ 138 7.2.7 Statistical methods .................................................................................................. 138 7.3 Results ......................................................................................................................... 139 7.3.1 Good agreement around clusters and location of TB exposure between methods . 139 7.3.2 Low genomic variability within clusters limited of an exact source ...................... 143 7.3.3 Confidence in correct source identification varied between teams ........................ 144 7.3.4 Preference for genomics over genotyping .............................................................. 146 7.4 Discussion ................................................................................................................... 149 Chapter 8: Whole Genome Sequencing as a Tool to Understand and Quantify Active Tuberculosis Arising from Local Transmission ......................................................................152 8.1 Background ................................................................................................................. 152 8.2 Methods....................................................................................................................... 153 8.2.1 Study population ..................................................................................................... 153 8.2.2 Case data ................................................................................................................. 153 8.2.3 Laboratory analysis ................................................................................................. 153 8.2.4 WGS analysis and genomic clustering ................................................................... 154 8.2.5 Transmission across population groups .................................................................. 155 8.2.6 Tuberculosis reoccurrences ..................................................................................... 155 8.2.7 Characterization of large clusters and transmission reconstruction ........................ 156 8.2.8 Statistical analysis ................................................................................................... 156 xvi 8.3 Results ......................................................................................................................... 157 8.3.1 Whole genome sequencing reduces the local transmission estimate ...................... 161 8.3.2 Transmitted Mtb isolates belong largely to the Euro-American lineage ................ 164 8.3.3 Risk factors for local transmission .......................................................................... 164 8.3.4 Transmission occurs in both directions between Canadian-born and non-Canadian-born persons ........................................................................................................................ 168 8.3.5 TB relapse vs reinfection ........................................................................................ 171 8.3.6 Characterization of large genomic clusters ............................................................. 173 8.4 Discussion ................................................................................................................... 178 Chapter 9: Conclusion ...............................................................................................................182 9.1 Summary of findings................................................................................................... 182 9.2 Unique contributions, implications and impact .......................................................... 185 9.3 Strengths and limitations............................................................................................. 187 9.3.1 Homogeneous Group or a Multicultural Mosaic? The Challenge with Reporting Birth Outside Canada as a Tuberculosis Risk ..................................................................... 190 9.4 Knowledge translation ................................................................................................ 197 9.5 Future research and recommendations........................................................................ 198 9.5.1 Prospective provincial MIRU-VNTR genotyping .................................................. 198 9.5.2 Standardization of WGS bioinformatics pipelines ................................................. 198 9.5.3 WGS as a tool for TB prevention ........................................................................... 199 9.6 Final Conclusions........................................................................................................ 200 References ...................................................................................................................................201 Appendices ..................................................................................................................................238 xvii Online Pre- and Post-Meeting Survey Questions.............................................. 238 Presentations ...................................................................................................... 242 B.1 Oral presentations ................................................................................................... 242 B.2 Poster Presentations ................................................................................................ 243 B.3 Media ...................................................................................................................... 243 Genotyping Summary Report ............................................................................ 244 xviii List of Tables Table 1-1. Genomic epidemiology TB studies examining transmission between individuals ..... 22 Table 1-2. Sequencing platforms currently deployed for genomic epidemiology studies ........... 28 Table 2-1. Genotype request reasons ............................................................................................ 48 Table 2-2. Study sample characteristics........................................................................................ 50 Table 2-3. Characteristics of MIRU-VNTR clusters .................................................................... 51 Table 2-4. Logistic regression....................................................................................................... 53 Table 2-5. Logistic regression with a restricted dataset ................................................................ 54 Table 2-6. Request status, risk factor, and clustering ................................................................... 55 Table 2-7. Genotype cluster characteristics .................................................................................. 56 Table 3-1. Study population .......................................................................................................... 65 Table 3-2. Multi-drug resistant isolates ........................................................................................ 67 Table 3-3. Lineage by anatomical disease site .............................................................................. 70 Table 3-4. Large cluster characteristics ........................................................................................ 71 Table 3-5. Genotype cluster sizes ................................................................................................. 73 Table 3-6. Risk factors for genotypic clustering ........................................................................... 74 Table 3-7. Risk factors associated with cluster size ..................................................................... 76 Table 4-1. Study population .......................................................................................................... 85 Table 4-2. Genotype cluster sizes ................................................................................................. 87 Table 4-3. Multivariable logistic regression ................................................................................. 90 Table 4-4. Multivariable logistic regression according to size ..................................................... 91 Table 4-5. Large genotypic clusters .............................................................................................. 94 xix Table 5-1. Demographic and clinical characteristics of culture-positive pediatric TB cases ..... 106 Table 5-2. Factors associated with locally acquired pediatric tuberculosis ................................ 112 Table 6-1. Characteristics of Yukon study population ............................................................... 126 Table 7-1. Location of TB Acquisition ....................................................................................... 142 Table 7-2. High level concordance between methods ................................................................ 142 Table 7-3. Concordance between methods at a case-level ......................................................... 143 Table 7-4. Accuracy of source case identification ...................................................................... 145 Table 8-1. Characteristics of the study sample ........................................................................... 159 Table 8-2. Genomic clustering logistic regression ..................................................................... 166 Table 8-3. Logistic regression for risk factors for genomic clustering—various thresholds ...... 167 Table 8-4. Large genomic clusters .............................................................................................. 174 xx List of Figures Figure 1-1. Tuberculosis incidence (per 100,000 population) in Canada, 1924–2014 ................. 11 Figure 1-2. Tuberculosis incidence (per 100,000 population) by Canadian province/territory, 2014............................................................................................................................................... 12 Figure 1-3. Molecular epidemiology methods used in tuberculosis surveillance ......................... 18 Figure 1-4. The basic principle of genomic epidemiology ........................................................... 20 Figure 1-5. Identifying transmission-informative variation .......................................................... 34 Figure 2-1. Study sample request status ....................................................................................... 47 Figure 2-2. Quarterly genotype requests ....................................................................................... 48 Figure 2-3. Proportion of each cluster requested by cluster growth over time ............................. 52 Figure 2-4. Cluster growth by genotype request status................................................................. 57 Figure 3-1. Molecular epidemiology study inclusion/exclusion criteria ...................................... 62 Figure 3-2. Population structure of Mycobacterium tuberculosis genotypes in BC ..................... 69 Figure 3-3. Mycobacterium tuberculosis lineage by continent of birth ........................................ 70 Figure 4-1. Genotypes shared between provinces ........................................................................ 88 Figure 4-2. Proportion of genotypic clustering ............................................................................. 88 Figure 4-3. Interprovincial genotype matches .............................................................................. 92 Figure 4-4. Single contributors to clusters .................................................................................... 92 Figure 4-5. Population structure of Mycobacterium tuberculosis isolates shared between BC and Ontario .......................................................................................................................................... 95 Figure 5-1. Pediatric TB study inclusion/exclusion criteria ....................................................... 104 Figure 5-2. Pediatric age distribution by birthplace.................................................................... 105 xxi Figure 5-3. Number of contacts .................................................................................................. 107 Figure 5-4. Pediatric tuberculosis investigation summary .......................................................... 109 Figure 5-5. Pediatric analysis phylogenetic tree ......................................................................... 111 Figure 6-1. Study sample ............................................................................................................ 119 Figure 6-2. Population structure of Mycobacterium tuberculosis in Yukon Territory ............... 122 Figure 6-3. Yukon Territory Mycobacterium tuberculosis isolates in the context of related BC isolates......................................................................................................................................... 124 Figure 6-4. Yukon WGS transmission reconstructions .............................................................. 128 Figure 6-5. Transmission clusters—SNV alignments ................................................................ 130 Figure 7-1. MIRU-VNTR cluster summary example ................................................................. 136 Figure 7-2. Yukon cases over time ............................................................................................. 139 Figure 7-3. Whole genome sequencing-based population structure of Yukon Territory Mycobacterium tuberculosis isolates .......................................................................................... 141 Figure 7-4. Certainty assigned to identified sources ................................................................... 144 Figure 7-5. Frequency of certainty categories assigned for each source identified, divided by cluster .......................................................................................................................................... 145 Figure 8-1. Study sample inclusion/exclusion criteria ................................................................ 158 Figure 8-2. Maximum-likelihood phylogenetic tree of study isolates ........................................ 162 Figure 8-3. Genomic cluster sizes ............................................................................................... 163 Figure 8-4. Pairwise SNV distances between study isolates ...................................................... 163 Figure 8-5. Genomic cluster sizes and birthplace composition .................................................. 169 Figure 8-6. Mixed cluster transmission ...................................................................................... 170 Figure 8-7. Recurrent tuberculosis characteristics ...................................................................... 171 xxii Figure 8-8. Timeline of case diagnosis ....................................................................................... 176 Figure 8-9. Characterization of WClust-2 .................................................................................. 177 Figure 9-1. Trends in active tuberculosis diagnoses in British Columbia (BC), Canada ........... 192 Figure 9-2. Tuberculosis incidence rates in British Columbia (BC) for persons born outside of Canada......................................................................................................................................... 193 Figure 9-3. Number of tuberculosis cases for each large (≥10 persons) genotypic cluster ........ 194 xxiii List of Abbreviations AFB acid-fast bacilli AIC Akaike’s Information Criterion aOR adjusted odds ratio BC British Columbia BCG Bacillus Calmette–Guérin BCCDC British Columbia Centre for Disease Control BCPHL British Columbia Centre for Disease Control Public Health Laboratory bp base pair BED browser extensible data BAM Binary Alignment Map CAN-Marg Canadian Marginalization Index CBP Canadian-born parents CDC Centers for Disease Control and Prevention CI confidence interval CIs contact investigations CRyPTIC Comprehensive Resistance Prediction for Tuberculosis: an International Consortium DA dissemination area DNA deoxyribonucleic acid DR direct repeat DST drug susceptibility testing DTES Downtown Eastside GVR Greater Vancouver Region HIV human immunodeficiency virus INH isoniazid IGRA Interferon gamma release assay iPHIS Integrated Public Health Information System IQR interquartile range xxiv LJ Lowenstein-Jensen LIMS laboratory information management systems LTBI latent tuberculosis infection MCAR missing completely at random MClustID MIRU-VNTR cluster identifier MDR multi-drug resistance MGEs mobile genetic elements MGIT Mycobacteria growth indicator tube MIRU-VNTR Mycobacterial Interspersed Repetitive Units-Variable Number Tandem Repeats MST minimum-spanning tree Mtb Mycobacterium tuberculosis nCB non-Canadian-born NCBI National Center for Biotechnology Information nCBP non-Canadian-born parents NRTB exclusively non-respiratory tuberculosis ON Ontario OR odds ratio PCR polymerase chain reaction PHAC Public Health Agency of Canada PHE Public Health England PHO Public Health Ontario PZA pyrazinamide RFLP restriction fragment length polymorphism RIF rifampin RTB exclusively respiratory tuberculosis SD standard deviation SNV(s) single nucleotide variant(s) TB tuberculosis TST tuberculin skin test UBC University of British Columbia xxv UK United Kingdom USA United States of America VCF variant call format WClustID whole genome sequencing cluster identifier WGS whole genome sequencing YCDC Yukon Communicable Disease Control YT Yukon Territory xxvi Glossary Acid-fast bacilli (AFB) smear microscopy Microscopic examination of a clinical specimen (e.g. sputum) prepared using a fluorochrome stain and smeared onto a glass slide to detect acid-fast bacilli. Bacille Calmette-Guérin (BCG) vaccine BCG is a live attenuated vaccine named after the doctors who developed it. Derived from a strain of Mycobacterium bovis, BCG is mainly used as a vaccination against tuberculosis, although there are widely varying results in BCG efficacy studies. Downtown Eastside (DTES) Neighbourhood in Vancouver with extreme poverty, homelessness, illicit drug use, mental illness and sex work. Endemic Disease or bacterial strain that is regularly found in an area or very common among a particular group. Induration A palpable, raised, hardened area. Multidrug-resistant tuberculosis (MDR-TB) Tuberculosis resistant to isoniazid and rifampin with or without resistance to other anti-tuberculosis drugs. Reactivation The development of active disease after a period of latent tuberculosis infection. Reinfection Individual previously treated (cure or completed) for active TB, in whom active tuberculosis is detected ≥6 months following previous treatment completion, and the new isolate is confirmed to have a difference genotype from the original organism. Relapse Individual previously treated (cure or completed) for active TB, in whom active tuberculosis is detected ≥6 months following previous treatment completion, and the new isolate is confirmed to have the same genotype as the original organism. Reoccurrence Individual previously treated (cure or completed) for active TB, in whom active tuberculosis is detected ≥6 months following previous treatment completion. Super-spreader An individual who transmits TB to a greater number of secondary cases than the average infected person xxvii Acknowledgements I would first like to thank my supervisor, Dr. Jennifer Gardy, for the patient guidance, encouragement and advice given for all things thesis and not. I have been extremely lucky to have a supervisor who cares so much—not just about the work—but also the student. Jenn fosters an open, ambitious and collaborative research culture that along with her eternal optimism and ability to deal with any obstacle, is an inspiration to my future endeavors. I am forever grateful to her for providing me with the opportunity to make my PhD goal come true. I would also like to acknowledge the support, and guidance of my supervisory committee, Drs. James Johnston, Lindsay Eltis and Bonnie Henry. I am very much appreciative of their time, insightful comments and questions that most definitely improved my research. A big thank you to the BC Centre for Disease Control TB Services and Public Health Laboratory personnel for all their efforts, without which there would have been no thesis. In particular, Dr. Patrick Tang for his work to make this very large laboratory intensive study happen, Clare Kong who was so key to the laboratory efforts, and to Dr. David Roth for his assistance with the provincial data extraction. Also, a huge thank you to Dr. Victoria Cook for her support and big picture vision, and ever amazing discussion points. Additionally, I would like to say thank you to my collaborators at Yukon Communicable Disease Control and Public Health Ontario. Funding was generously provided by the Canadian Institutes for Health Research through a Banting and Best Canada Graduate Scholarship, Killam Doctoral Scholarship, and the University of British Columbia Doctoral Fellowship. I also thank the BCCDC Foundation for Population and Public Health for providing project support. I would like to express my utmost gratitude for the support and patience of my friends and family throughout my studies. This journey would not have been possible without them. Especially, my brother Dennis, Sandy, Olga, Angela, Sujay, Uncle Chuck, Aunt Gerry and the rest of the crazy but lovable Guthrie clan. My wonderful little nephews whose smiles always brighten my day, D., Hunter, Joshua and James. Also, my friends who are like family, Erynne, Lesley, Rian, Beth, Rema, Alex, David A., Christine, Ace and all the way in Wales—Charlotte and Andrew. Lastly, I would like to acknowledge my parents, my brother David, and grandma C., who always believed in my ability to persevere no matter the challenges. Although gone their belief in me remains—I know they would be proud of my accomplishments. 1 Chapter 1: Introduction An individual with active respiratory tuberculosis disease, left untreated, has been estimated to infect 2–12 people per year.1–3 Investigating the contacts of a person with TB is a fundamental cornerstone of TB prevention and care programs, and is the key to identifying infected individuals with active disease requiring treatment, or latent TB infection that may merit prophylaxis. While it is recognized that tuberculosis transmission is driven by the interaction between host, pathogen, and environmental factors,4 our understanding of TB transmission in low-TB incidence settings like British Columbia is incomplete. This dissertation pairs new molecular and genomic technologies with traditional epidemiological contact investigation data to improve our knowledge and understanding of tuberculosis transmission in a low-incidence setting. An overview of the relevant history, etiology and epidemiology of tuberculosis are provided in this introductory chapter. 1.1 Background and Rationale Tuberculosis (TB), a bacterial infection caused by Mycobacterium tuberculosis (Mtb), is a major cause of disease and death in much of the world. More than 20 years after the World Health Organization declared tuberculosis a global emergency, TB remains entrenched in the world’s population. TB disease affects nine million people per year and was estimated to have caused 1.7 million deaths in 2016.5 While the highest burden of disease is seen in developing countries, TB remains a major public health issue in Canada. After decades of decline in TB incidence, progress towards elimination has stalled. Those born outside Canada, Indigenous persons, as well as incarcerated and under-housed individuals are at the greatest risk of presenting with active TB disease.6 Indeed, the TB incidence rate in certain sectors of the Canadian population can exceed that in developing countries.7 British Columbia has one of the highest provincial TB rates in Canada, with 6.3 cases per 100,000 population (293 cases) in 2014.8 Of these, persons born outside Canada accounted for 2 approximately 80% of BC’s tuberculosis cases,9 many of which likely result from reactivation of latent TB infections (LTBI) acquired in the individual’s country of birth. Canadian-born individuals accounted for ~20% of BC’s cases, and with few exceptions these are the result of local acquisition, also referred to as endemic transmission. Nationally, endemic transmission accounts for anywhere from 10–100% of new TB cases in a province or territory.7 Even within a province/territory rates can vary. For example, Vancouver’s Downtown Eastside (DTES), an impoverished, high-density, urban neighbourhood with a high prevalence of illicit drug use, has a very high TB incidence rate, at ~40 cases per 100,000 people,10 due almost entirely to endemic transmission. Significant public health resources are required for TB treatment, follow-up, and contact tracing. Persons with active TB disease are often hospitalized in airborne infection isolation rooms for weeks to months and are subjected to multi-drug therapy for six months or longer with antibiotics that may have harmful side effects. Directly observed therapy may be required to facilitate individuals completing their full course of treatment, as failure to take anti-tuberculosis drugs can lead to longer illness and antibiotic-resistant TB, at which point a cure becomes significantly more challenging, if at all possible. Treating each uncomplicated active TB case is estimated to cost Canada $47,290;11 complex cases involving drug resistance can cost significantly more, and the disease exacts a physical and emotional toll on affected individuals, their contacts, and their caregivers. In 2006, Canada set the goal of reducing the national incidence of reported TB to 3.6 cases per 100,000 population or less by 2015.12 Although rates decreased over subsequent years, a reported incidence rate of 4.6 per 100,000 in 2015 and an increase to 4.8 the following year indicates that we failed to achieve this goal, and that we must adopt innovative new strategies to manage this disease.13 TB prevention and care is a shared responsibility between various levels of government, including individual provincial/territorial governments, and as such, BC developed a Provincial TB Strategy in 2012, aiming to reduce TB incidence by 50% over 10 years.14 Achieving this ambitious goal will take considerable effort, requiring innovative public health interventions specifically tailored to both prevent person-to-person transmission of tuberculosis 3 within BC, as well as prevent the development of active disease in individuals with LTBI by improving screening and therapy. The latter is a comparatively straightforward proposition; however, reducing active transmission within Canada is more complex, as our understanding of endemic transmission is incomplete. It is unknown precisely how many cases result from transmission of active disease and, because TB disease may not occur until years after exposure, individuals are often unable to identify the true source of their infection. This information is key to the design and delivery of effective evidence-based interventions and to prevent the continuing spread of TB. This dissertation relies on a novel approach utilizing the new technique of genomic epidemiology to fully describe the when, where, and how of endemic transmission within and between the varied regional, socioeconomic, and cultural settings of BC. The findings of this research will directly impact public health policy and practice within BC and potentially across Canada. 1.2 Research Objectives The overarching aim of this dissertation is to describe the molecular and genomic epidemiology of TB in British Columbia and increase our understanding of the patterns underlying the person-to-person spread of TB in the province. “Molecular epidemiology” refers to the use of a rapid, low-resolution genotyping method to identify clusters of cases that might represent endemic transmission, one that is routinely used in BC’s Public Health Laboratory. “Genomic epidemiology” refers to the use of higher-resolution whole genome sequencing to more accurately identify clusters representing true endemic transmission, including inferring specific person-to-person transmission events where possible. Specific aims and objectives include: 1. To identify gaps in the literature with regards to the use of genomics for enhanced TB care and treatment and contribute expertise in this area. a. Review the literature and provide background on the use of genomic epidemiology in TB. (Manuscript 1) 4 b. Communicate the role of genomics in accelerating TB elimination in low-incidence settings. (Manuscript 2) 2. To describe the molecular epidemiology of TB in British Columbia. a. Assess the value of universal genotyping for the identification of clustered TB cases potentially representing local transmission. (Manuscript 3) b. Describe the molecular epidemiology of TB in British Columbia using 24-locus MIRU-VNTR genotyping linked to key clinical and demographic data. (Manuscript 4) c. Compare province-level TB molecular epidemiology between BC and Ontario—a similarly large, immigrant-receiving province—to identify interprovincial genotype clusters and improve our understanding of BC’s molecular epidemiology in the larger Canadian context. (Manuscript 5) 3. To calibrate molecular tools using small, well-defined populations for a more refined understanding of transmission. a. Using pediatric TB cases, which often have well-defined TB exposures and extensive contact investigation data, examine person-to-person TB transmission using genotyping and WGS data. (Manuscript 6) b. Combine case-level epidemiological data with genotyping and WGS analysis to identify chains of transmission within and across Yukon/BC borders. (Manuscript 7) c. Using the small, well-defined cohort of Yukon TB cases, compare the insights into transmission provided by genomic versus traditional epidemiological methods and determine the added value of molecular/genomic technologies in a setting with rich epidemiological data. (Manuscript 8) 5 4. To quantify the extent of local transmission of TB within BC over a ten-year period using genomic epidemiology, and demonstrate the utility of WGS through the reconstruction of likely transmission events within a large genomic cluster, while additionally characterizing all large clusters to reveal common trends in TB outbreaks. (Manuscript 9) 5. To address any remaining limitations of the previous manuscripts that could be investigated with available study data. (Manuscript 10) 1.3 Tuberculosis 1.3.1 Etiology Tuberculosis is caused by the bacterium Mycobacterium tuberculosis (Mtb). When an individual with respiratory TB coughs, sneezes, or sings, droplets containing infectious bacilli become airborne.15 Following inhalation into the lungs, alveolar macrophages engulf the Mtb bacterium; however, Mtb has adapted to this hostile environment and are able to proliferate within the macrophage.16 Infected macrophages may carry bacilli to nearby lymph nodes, from whence they may migrate into the blood and disseminate throughout the body.17 Consequently, Mtb may infect multiple organs, or establish a localized infection in a particular organ or tissue.18 However, most cases of TB are confined to the lungs. Here, the immune system attempts to contain the bacteria by forming structures known as granulomas—an agglomerate of immune cells intended to “wall off” Mtb infection. Unsuccessful containment may support bacterial growth and lead to tissue necrosis, resulting in the formation of cavities in the lung tissue.19 There are three possible outcomes following Mtb exposure: bacilli clearance, latent TB infection (LTBI), or progression to active disease.20 The frequency of successful clearance is unknown, and most exposures are assumed to result in LTBI—a clinically asymptomatic, non-infectious form of TB in which the bacilli has been successfully contained.20 Characterized by the absence of clinical and radiographical signs of active disease, and an immune response detectable using a 6 tuberculin skin test (TST) or interferon gamma release assay (IGRA), LTBI is the most common manifestation of TB, representing 90–95% of infected individuals.21,22 The lifetime risk of progression to active TB disease amongst persons with LTBI is estimated at 5–10%, and the risk is highest within the first two years following infection.22 Active disease, which can develop within 1–2 months of exposure, is the potentially infectious form of TB, and symptoms include fever, chills, night sweats, weight loss and cough.18 1.3.2 Diagnosis and treatment The tuberculin skin test, or TST, remains the most widely used diagnostic tool to determine whether a person has been infected with TB, though the test cannot distinguish between LTBI and active disease.23 The test involves an intradermal injection of a small amount of tuberculin (an extract of the tubercle bacillus) into the inside forearm.24 If a person has previously been exposed to a mycobacterial species resulting in an adaptive immune response, a swelling will appear at the site of injection. The size of the induration, if any, is measured 48–72 hours later by a healthcare professional. Various factors, such as prior BCG vaccine exposure, age, and immunocompromising conditions, will affect the size at which an induration is considered positive evidence for TB infection.25,26 In recent years, the IGRA blood test has also been used to diagnose LTBI. By evaluating the levels of interferon gamma produced in response to antigens specific to Mtb and not BCG or other non-tuberculous mycobacteria, IGRA may be preferred for diagnosing LTBI in certain populations.27,28 However, both IGRA and TST rely on an immune response and each has their own unique advantages and limitations.23 For individuals with positive TST or IGRA results, a chest X-ray should be ordered to differentiate between latent TB infection and active pulmonary disease.6 Where there are radiographic signs of potential active TB disease, sputum specimens will be submitted for laboratory testing. Smear microscopy results indicating the presence or absence of acid-fast bacilli (AFB) are often the first test result obtained, with turnaround times of 1–2 days following receipt of the specimen by a specialized reference mycobacteriology laboratory. This test, if positive, provides an indication of bacterial load, scored from 1+ to 4+—the greater the number, the more bacilli are likely expelled and hence, the more infectious the individual.29 Because AFB 7 microscopy is not specific for Mtb—other mycobacterial species will yield a positive result if present—a combination of molecular and culture-based methods are used to diagnose active Mtb infection. PCR assays to identify Mtb and Mycobacterium avium complex—a frequently observed non-tuberculous mycobacterium—may be performed on a smear-positive sample and give a result within days. All specimens, whether smear-positive or negative, are inoculated into culture, both in liquid and/or solid media; however, culture can take up to eight weeks to yield a positive result6 due to the lengthy doubling time of Mtb (~24 hours).30 Upon culture confirmation, phenotypic drug susceptibility testing (DST) is recommended for the first culture-positive isolate from each new TB case to determine the most appropriate therapy.6 It should be noted that it is not always possible to obtain laboratory confirmation, particularly with respect to culture growth and some individuals will be clinically diagnosed with active TB. Treatment of tuberculosis depends on whether the diagnosis is latent or active TB. Individuals with LTBI may be prescribed antibiotics that will substantially reduce the risk of progression to active TB in the future.31 However, the lengthy treatment time—typically six to nine months with one or more antibiotics—and the potential side effects of the drugs results in low uptake and completion rates.32 Estimates are that fewer than half of those initiating LTBI prophylaxis in North America complete the full regimen;32 however, new, shorter-course regimens requiring fewer and less frequent doses are now available and may improve completion rates. Treatment for active tuberculosis is not optional; however, the specific antibiotics and length of treatment may depend on an individual’s age, comorbidities, anatomical site of infection, and possible Mtb antibiotic resistance.33 Unless there is a high suspicion of multi-drug resistance (MDR)—defined as resistance to isoniazid and rifampin—initial empiric treatment using a combination of isoniazid, rifampin, pyrazinamide, and ethambutol is recommended for a minimum of six months.33 Inadequate treatment of TB can quickly give rise to antibiotic resistance and poses a major global threat.34,35 Resistance is due to the acquisition of chromosomal mutations by Mtb; these mutations can be quickly detected with molecular tests such as PCR or line-probe assays, and large collaborations, such as Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC), are combining genomic data with phenotypic laboratory results to catalogue and discover new resistance-conferring mutations.36 8 1.3.3 Mycobacterium tuberculosis lineages The Mycobacterium tuberculosis complex is a genetically related group of Mycobacterium species and includes the human-adapted lineages M. tuberculosis sensu stricto and M. africanum, along with several lineages adapted to mammalian species.37 Also included in the Mtb complex, M. bovis has the distinction of infecting both animals and humans. Close contact with infected animals and consumption of contaminated unpasteurized milk are the main routes of transmission to humans.38 Genomic studies have enhanced our understanding of the global Mtb population structure, revealing a long history of host-pathogen co-evolution, following patterns of human migration resulting in a strong phylogeographic structure, meaning that genetically distinct lineages are associated with specific geographical regions.39 Mtb has been classified into seven major phylogenetic lineages: lineage 1 (Indo-Oceanic), lineage 2 (East Asian), lineage 3 (East African-Indian), lineage 4 (Euro-American), lineage 5 (West African 1), lineage 6 (West African 2), and the recently identified lineage 7 found in Ethiopia or recent Ethiopian emigrants.40,41 The genetic diversity across Mtb lineages manifests as phenotypic and epidemiological differences. For example, animal models have shown a more rapid progression to active disease and increased virulence for the East-Asian and Euro-American lineages.42,43 In addition, these two lineages are more globally widespread, are often responsible for large outbreaks, and are more likely to be associated with respiratory TB.37,44–46 It has been well established that East-Asian strains—specifically the Beijing sub-lineage—have a higher propensity for drug resistance.43,47 Overall, the characteristics of the pathogen are clearly linked to its ability to infect hosts, cause disease and spread from person-to-person. 9 1.3.4 Transmission Various host, environmental, and pathogen factors influence the likelihood of TB transmission from an infected individual to another person.44,48,49 A host with active TB disease is the first element necessary for transmission. Whether or not an individual with active TB disease is infectious depends on multiple factors. Non-respiratory forms of the disease are assumed to be non-infectious, and the infectiousness of an individual with respiratory disease is influenced by their AFB smear status and the presence of cavitary disease,50 although it should be noted that transmission from non-cavitary, smear-negative persons can occur,51,52 and precautionary measures, such as use of a mask or isolation, should be taken to minimize the possibility of transmission from all active TB cases until they are deemed non-infectious.6 Delays in diagnosis and treatment initiation also influence infectivity—they may increase the burden of disease and the length of time an individual is infectious, thereby increasing the number of social contacts that may result in TB transmission.53,54 Beyond infectiousness of the source case, environmental factors, such as poorly ventilated indoor spaces,55 duration of exposure, and physical proximity to the source case, may increase the probability of transmission.4,6,55 For this reason, congregate settings such as schools, long-term care facilities, prisons, and homeless shelters have been associated with TB outbreaks. Pathogen-level factors also play a role in transmission— as described in the previous section the East-Asian and Euro-American Mtb lineages demonstrate higher in vitro growth rates and virulence, and have been linked to higher numbers of secondary cases and more outbreaks in human populations, as compared to other lineages.37,44 In addition to the factors listed above, a susceptible host is a further requirement for person-to-person transmission. Because the BCG vaccine does not effectively prevent infection—it instead prevents TB meningitis or disseminated disease in children exposed to TB56—and because previous infection does not protect against reinfection,57 all individuals are theoretically potentially susceptible to TB. However, evidence suggests that some individuals can clear TB infections despite exposure—for example, within a household, not everyone exposed to an infectious household member will become infected.58 The proportion of close or household 10 contacts of an infectious source case that remain persistently TST-negative has been estimated at approximately half of those exposed.59 While factors such as smoking,60 substance use,61,62 malnutrition,63 and immunocompromising conditions—particularly HIV64—have been reported to increase susceptibility, other factors, including genetics, likely play a role in influencing an individual’s susceptibility.65 The TB vaccine—Bacillus Calmette-Guérin (BCG)— was developed in 1921 and while it does provide some protection against severe forms of pediatric non-respiratory TB, it does not effectively prevent infection in adults nor does it prevent reactivation of LTBI.66 Consequently, there is likely little to no impact of BCG vaccination on TB transmission rates, and a number of countries with low TB prevalence have discontinued universal BCG vaccination, including Canada.6 1.3.5 Monitoring, surveillance and investigation Monitoring, surveillance, and investigation of active tuberculosis cases are essential elements of effective TB prevention and care programs. As a reportable disease, physicians in Canada are required to notify the appropriate local health authority of all new TB diagnoses. Each province independently maintains a provincial TB registry, which may be used to identify patterns and trends in TB epidemiology that inform planning around programmatic monitoring and disease surveillance initiatives.14 At the case level, local health authorities conduct contact investigations with the aim of identifying undiagnosed active cases and exposed individuals with LTBI in order to prevent the further spread of disease.67 Establishing that a transmission between two individuals has occurred—particularly in an outbreak situation where there are multiple transmission events—is often possible only through interviewing newly diagnosed individuals to request the names of their close and casual contacts for TB screening.68 Before the era of genomic epidemiology, identifying epidemiological linkages between cases through such interviews was considered sufficient for concluding that transmission had occurred; however, genomic studies have shown that these epidemiological assumptions may not always hold true.69 Conversely, molecular and genomic methods have confirmed person-to-person transmission in instances where individuals were unwilling or unable to name contacts,70,71 and have also 11 revealed previously unsuspected routes of transmission.72–74 Given the potential for improving contact investigations and revealing patterns of local transmission, many low-incidence settings now, as standard practice, use molecular methods together with clinical and epidemiological information to identify transmission events.75–77 1.3.6 Epidemiology of tuberculosis in Canada In recent decades, the decline in TB incidence has plateaued in Canada, seeing little change from 2005 through 2014 (Figure 1-1). Over this period, an average of 1,634 active TB cases were reported each year, corresponding to an average ten-year incidence rate of 4.8 per 100,000 persons.78 While this rate categorizes Canada as a low-incidence country—similar to the United States and many Western European countries79—the rate is not uniform across the country (Figure 1-2). The Atlantic provinces have the fewest cases, together averaging 24 TB diagnoses annually (2005–2014), and with an incidence rate well below the Canadian average.78 In contrast, 70% of Canada’s ~1,600 annual TB cases occur in Ontario, Quebec and British Columbia;78 the metropolitan areas of Toronto, Montreal, and Vancouver account for most of these cases.8,80,81 The highest incidence rate for a province or territory is observed in Nunavut, where in 2012, the rate of 226 cases per 100,000 population was comparable to rates in sub-Saharan Africa.82 Figure 1-1. Tuberculosis incidence (per 100,000 population) in Canada, 1924–2014. Data source: Public Health Agency of Canada;78,83 Created with: R (v3.4.1). 12 Figure 1-2. Tuberculosis incidence (per 100,000 population) by Canadian province/territory, 2014. Data source: Public Health Agency of Canada;78 Created with: R (v3.4.1). Persons born outside Canada make up the largest proportion of TB diagnoses overall, accounting for 70% of all active cases diagnosed in 2016.13 The proportion of TB cases in persons born outside Canada varies across the country, with the largest immigrant-receiving provinces (Ontario, Quebec and British Columbia) reporting the highest number of cases in migrants to Canada.6 Many of these cases are the result of LTBI reactivation from infections acquired outside Canada,6 and while migrants diagnosed with TB in Canada come from all nine World Health Organization regions, slightly more than half come from only four countries, all of which have high TB incidence rates: India, Philippines, China, and Vietnam.13 TB notifications differ by age and gender, with males accounting for a slightly higher proportion of TB cases (56%) compared to females (44%).13 With respect to age, persons 25 to 34 years of age proportionally represent the largest number of cases (18%).84 However, TB rates in Canada are highest for those 75 years of age and older, at 10.4 cases per 100,000 population.13 Despite the global threat of multidrug-resistant TB,85 rates of resistance remain low in Canada. Mono-resistance to any first-line antibiotic (isoniazid, rifampin, ethambutol, pyrazinamide) was detected in 7.5% of isolates and multi-drug resistance in 1.2% of isolates (2006–2010).6 13 1.4 Genotyping of Mycobacterium tuberculosis Genotyping refers to a suite of molecular fingerprinting methods, in which specific DNA sequences are interrogated to identify genetically related isolates that may come from a common source. This ability to identify closely related TB isolates that might reflect recent endemic transmission is useful for population-level epidemiological studies, identifying person-to-person transmission, outbreak detection, identifying laboratory cross-contamination events, and differentiating between TB relapse and reinfection.86–93 Three tools are frequently used for Mtb strain typing: restriction fragment length polymorphism (RFLP), mycobacterial interspersed repetitive unit–variable-number tandem repeat (MIRU-VNTR), and spoligotyping. 1.4.1 RFLP Fragmenting chromosomal DNA using restriction endonucleases and comparing the banding patterns that result from gel electrophoresis of the fragments is known as restriction fragment length polymorphism (RFLP) and is a commonly used technique in bacterial strain typing.94 The RFLP protocol for Mtb leverages the insertional sequence IS6110, a 1,361-bp mobile element specific to the Mtb complex—this is detected via a labeled probe following gel electrophoresis.95,96 The number and location of IS6110 elements varies (0–27 copies/genome),97 resulting in a digital image of a banding pattern that can be used to compare isolates to each other. The estimated rate of evolution (3.2–8.7 years) of IS6110 makes RFLP well-suited for epidemiological studies and outbreak detection,98–100 and it has the highest discriminatory power of the commonly used Mtb genotyping methods.101 However, the ability of RFLP to distinguish between strains is diminished in Mtb isolates with <6 copies of IS6110.77,102 Furthermore, RFLP is a labour-intensive method with a slow turnaround time, and the resulting gel images are difficult to compare between laboratories.96 1.4.2 MIRU-VNTR Two decades ago, 41 distinct mini-satellite-like structures were identified across the Mtb genome.103,104 Composed of 40–100-bp variable number tandem repeats (VNTRs), these structures, located mainly in intergenic regions, are known as mycobacterial interspersed 14 repetitive units (MIRUs).103 MIRU-VNTR is a PCR-based method used to determine the number of these repetitive units at specific loci.103 The technique involves PCR amplification using fluorescently labeled primers complementary to flanking DNA regions, and sizing of the resulting amplicons using an automated capillary electrophoresis DNA analyzer.105 Software such as GeneMapper (Applied Biosystems)77,106 or BioNumerics (Applied Maths)107 are used to determine the number of repeats, and the final result is a concatenated string of digits representing the number of repeated sequences at each loci analyzed (e.g. 223454124341). A locus with >9 repeats is represented by a letter code, A=10, B=11, C=12, etc. The discriminatory power of MIRU-VNTR depends upon the number of loci typed. Initially, 12 loci were used, which was then expanded to 15, and currently, a set of 24 loci have been agreed upon as the optimal scheme that balances maximal strain discrimination against the time and costs associated with genotyping.77 24-locus MIRU-VNTR is currently considered the global standard for TB genotyping, and is performed by most well-resourced reference mycobacteriology laboratories.108 Due to the low amount of input DNA needed, the rapid turn-around time, and the portable digital pattern generated, MIRU-VNTR has surpassed RFLP as the preferred method of genotyping.102,108 1.4.3 Spoligotyping Another commonly used PCR-based Mtb genotyping method is spacer oligonucleotide typing, otherwise known as spoligotyping. This technique makes use of polymorphisms in the direct repeat (DR) locus, in which a multiple, highly-conserved 36-bp DRs are interspersed with 35–41bp spacers.109 Following PCR amplification of the DR region, amplicons are hybridized to a set of 43 spacer oligonucleotides—the presence of each is detected by a membrane-based reverse line-probe assay or, more recently, using a microbead system.110 A binary code (1–0) is used to indicate the presence or absence of each of the 43 spacers, which is then converted to a more compact 15-digit octal code (e.g. 777774777303761).111 Spoligotyping’s advantages are similar to those of MIRU-VNTR—a low amount of input DNA is needed, the method has a rapid turnaround time, and it results in a digital code easily comparable between laboratories; however, spoligotyping has considerably lower resolution compared to RFLP and MIRU-VNTR and therefore is most useful when paired with one of these higher-resolution techniques.111–113 15 1.5 Molecular Epidemiology and Public Health The use of molecular biology-based genotyping assays in the context of traditional epidemiological investigations is known as molecular epidemiology, and it greatly improved our understanding of the Mtb population structure both within specific regions and globally.41,114–116 Genotyping facilitates the identification of clusters of TB isolates potentially reflecting recent transmission, and has improved public health’s ability to detect outbreaks and identify person-to-person transmission events, when used in combination with traditional contact investigation methods.74,117,118 Molecular technologies have also proven useful in distinguishing between relapse and exogenous reinfection—key to providing insight into the biology of reoccurrences and understanding treatment failure.119,120 At a population-level, molecular epidemiology has allowed for a more accurate quantification of disease epidemiology, particularly around the estimation of cases attributable to LTBI reactivation versus local transmission.121–124 16 1.6 A Brief Primer on Genomic Epidemiology: Lessons Learned from Mycobacterium tuberculosis 1.6.1 The Mycobacterium tuberculosis genome In 1998, the Wellcome Trust Sanger Centre sequenced the first Mycobacterium tuberculosis (Mtb) genome—the laboratory strain H37Rv.125 That was followed in 2002 by the genome of CDC1551, a clinical isolate sequenced at the Institute for Genomic Research.126 Since then, dramatic improvements in sequencing technology and capacity have led to a wealth of Mtb genomes. The National Center for Biotechnology Information (NCBI) now stores over 3,600 assembled Mtb genomes127—47 are considered complete, with no gaps or ambiguous bases, and many thousands are partially assembled. The number of unassembled Mtb genomes is even larger—NCBI’s Sequence Read Archive stores raw read data from ~22,000 isolates,128 with thousands more data sets deposited each year. It is no surprise that so many Mtb genomes have been sequenced. In its most recent Global Tuberculosis Report,129 the World Health Organization reported that, in 2014, 9,600,000 people had active tuberculosis (TB) disease, with 5% of those cases demonstrating multidrug resistance—resistance to two first-line antibiotics, isoniazid (INH) and rifampin (RIF). As much as one third of the world’s population is thought to harbor latent TB infection,130 with between 5% and 10% of these individuals progressing to active, symptomatic disease at some point in their lives.22 Beyond the scale of the problem, the nature of the Mtb genome itself also promotes genomic inquiry. Mtb is a highly clonal species.131 It comprises seven geographically structured lineages, with a maximum difference of only 1,800 single nucleotide variants (SNVs) between lineages.44 Evolution has largely proceeded through genomic deletions, with minor historical contributions from repetitive and mobile genetic elements (MGEs).44 The obligate intracellular lifestyle of Mtb means that horizontal gene transfer is rare,132 as is recombination.133 Mtb harbors no plasmids, and antibiotic resistance within Mtb arises solely through chromosomal mutations.134 Together, 17 these factors make the assembly and analysis of Mtb genomes a comparatively straightforward proposition. 1.6.2 Leveraging genomics for epidemiology Managing TB, particularly in low- and medium incidence settings, such as North America and Europe, relies on several strategies, from rapid diagnostics and drug sensitivity testing to removing barriers around accessing care and adhering to treatment.135 In addition to appropriate therapy, one of the most fundamental strategies for TB management is epidemiological investigation. The guidelines for investigation were first set out by the American Thoracic Society in 1976,136 and were updated in 2005.137 At its simplest, investigation involves interviewing a newly diagnosed individual to establish a list of their named contacts and places of social aggregation, followed by contact investigation—prioritizing contacts for follow-up and performing a tuberculin skin test (TST) or another screening instrument. At its most complex, an investigation might involve screening thousands of individuals in large institutional settings or across multiple geographic areas and instituting a multiyear outbreak-management plan. TB outbreak investigations have been greatly enhanced by molecular epidemiology methods, reviewed by Kato-Maeda et al.101 and illustrated in Figure 1-3. By examining specific regions of the Mtb genome independently or in combination, including IS6110 insertion elements, the CRISPR direct repeat locus, or 12–24 mycobacterial interspersed repetitive unit (MIRU) variable number tandem repeat (VNTR) loci—TB laboratories can determine whether two or more Mtb isolates have the same genotype, suggesting recent transmission. Prospective genotyping of all culture-positive Mtb isolates138 is now routine practice in many well-resourced mycobacteriology laboratories,139 facilitating the real-time identification and investigation of TB clusters. 18 Figure 1-3. Molecular epidemiology methods used in tuberculosis surveillance. This simple schematic, not to scale, compares four common methods used in TB molecular epidemiology. In IS6110-RFLP, a restriction enzyme cuts the genome (shown as arrows) and the resulting fragments are separated using gel electrophoresis. A probe specific to the IS6110 insertion element (shown as dots), which varies in count and position between isolates, is applied, resulting in a distinct banding pattern. In MIRU-VNTR, PCR is used to amplify between 12 and 24 VNTR loci (ovals) in the genome, and the products are separated by either capillary or gel electrophoresis alongside a size standard to determine the length of the amplicon, allowing for calculation of the number of repeats, which is then converted to a digital code to facilitate comparison against a database. In spoligotyping, a hybridization assay is used to detect the presence or absence of 43 “spacer oligonucleotides” in the direct repeat region (hatched lines), a pattern that is translated into a binary representation and then an octal code. Unlike the other methods, genomics interrogates the entire genome, with SNVs revealing the relationship between isolates. Despite the many insights into TB transmission afforded by molecular epidemiology, there are several attendant limitations. Its resolution depends on which typing method is used, giving varying answers as to the size and membership of clusters—large clusters described by 12-locus MIRU-VNTR often collapse into multiple, smaller clusters when 24-locus MIRU-VNTR was implemented. Genotyping cannot definitively rule in transmission, though it can rule it out,139 and it often overpredicts clustering in certain lineages and sublineages.140 Given that genotyping only interrogates a fraction of the available genomic information in an isolate, an obvious question to ask is “what would happen if we looked at the whole genome?” Because outbreak investigations often involve tens, if not hundreds, of individual bacterial isolates, this notion of genomic epidemiology was simply not possible using the Sanger sequencing-based approaches that generated the first Mtb genomes. It was only with the release 19 of second-generation sequencing platforms,141 with their high throughput and low per-genome cost, that microbial genomics entered the era of large-scale sequencing.142 The concept behind genomic epidemiology is simple. As bacteria replicate, mutations arise. Over the short time scale of an outbreak and absent selective pressures, these accrue in a largely neutral fashion according to a molecular clock. If a mutation is present in the genome of a pathogen infecting person A, that mutation will also likely appear in the genomes of the pathogens isolated from everyone person A has infected. By reading the complete genomes of the pathogens isolated from each case in an outbreak, the patterns of shared mutations suggest transmission events (Figure 1-4).143 It is important to note that, although methods are presently being developed to infer transmission directly from phylogenetic trees derived from whole-genome data,144–146 it is only by assessing the transmission events suggested by genomics in the light of available epidemiological data that investigators can draw reliable conclusions about who infected whom.144 Drawing further conclusions around transmission, such as where and how it might have occurred, will always require comprehensive epidemiological data—in a nosocomial outbreak, for example, genomic data from patient and environmental isolates might suggest the “who” of transmission, but it is only through examining patients’ movements and their exposure to common environments or equipment that the “why” and “how” become clear and can inform infection-control activities. 20 Figure 1-4. The basic principle of genomic epidemiology. In this small transmission tree, six infected individuals, or hosts, are shown. Each host is infected with a simplified “pathogen,” shown here with a simple 6 base pair genome, where gray circles indicate a wild-type base and colored circles represent different mutations (A, C, G, or T). Host 1 transmits their pathogen to host 2, in whom a single mutation arises (pink); this mutation is seen in all cases downstream of host 2. Host 2 infects host 3 and 4. In host 3, a second mutation (green) arises and is passed to host 5, in whom no further mutations accrue. Host 4 is an example of within-host diversity. Owing to a prolonged and/or disseminated infection or a burst of mutation, host 4 harbors a pool of variation, with the infecting strain from host 2 as a common ancestor. Host 4 transmits one of their lineages to host 6. The inset panel shows the results of sequencing the pathogens sampled from each host; these data can be combined with clinical and epidemiological data to reconstruct likely transmission pathways. 21 Some of the earliest works in genomic epidemiology focused on TB. In 2009, Schürch et al.147 sequenced the first and last isolates from a known five-person transmission chain, identifying six variants, which they surveyed in isolates from persons 2, 3, and 4 using amplicon sequencing. They found that the bulk of the variants arose in a single host—person 4—who was noncompliant with treatment. In 2011, the Gardy laboratory sequenced 36 Mtb genomes from a large outbreak in British Columbia, Canada.74 By combining the genomic data with epidemiological information collected through a social network questionnaire,148 plausible transmission events were inferred for most cases in the outbreak and identified super-spreaders who contributed disproportionately to infection of secondary contacts. In 2012, Walker and colleagues scaled up the genomic epidemiology approach by an order of magnitude, sequencing 390 isolates from 254 cases.149 Their work provided several important benchmarks, establishing a mutation rate of 0.5 SNVs per year and suggesting thresholds of five SNVs as a maximum distance between epidemiologically linked cases and 12 SNVs as a threshold above which transmission can be ruled out. These thresholds are unique to TB and its slow mutation rate; for other pathogens with different molecular clocks and shorter outbreaks, other thresholds will apply. Duchêne et al.150 recently calculated evolutionary rates for 35 human pathogens, ranging from the very low (M. tuberculosis at 5.39×10–8 substitutions per site per year, or ~0.3 SNVs per year) to the very high (vancomycin-resistant Enterococcus faecium at 9.35×10–6 substitutions per site per year, or ~26 SNVs per year). Since those first studies, the use of genomics to understand TB transmission has dramatically increased, with 27 papers and over 5000 Mtb genomes to date (Table 1-1). Genomic epidemiology is also being applied in the larger clinical microbiology space, with studies of everything from food- and water-borne pathogens to hospital-acquired infections, as highlighted in several recent reviews.143,151–154 Pathogen genomics has become an integral part of many national public health agencies’ routine practice, with groups, such as Public Health England (PHE), the U.S. Centers for Disease Control and Prevention (CDC), and the Public Health Agency of Canada (PHAC), all engaged in genomic surveillance. 22 Though the technique is now being applied across the microbial landscape, many of the lessons learned around genomic epidemiology arose from work in TB. This review will provide a brief primer on genomic epidemiology, highlighting lessons learned from Mtb that have broader implications for work in other organisms. Table 1-1. Genomic epidemiology TB studies examining transmission between individuals. Publication Mtb isolates sequenced Schürch et al.147 2 Schürch et al.155 3 Gardy et al.74 36 Bryant et al.69 199 Bryant et al.119 94 Clark et al.156 51 Kato-Maeda et al.157 9 Roetzer et al.158 86 Török et al.159 2 Walker et al.149 390 Jamieson et al.140 36 Kohl et al.160 26 Luo et al.161 32 Mehaffy et al.117 61 Pérez-Lago et al.162 36 Walker et al.73 247 Coscolla et al.163 46 Glynn et al.164 170 Guerra-Assunção et al.165 1687 Guerra-Assunção et al.120 1933 Lee et al.166 120 Lee et al.167 163 Regmi et al.168 9 Stucki et al.169 3 Witney et al.170 16 Arnold et al.171 2 Outhred et al.72 22 23 1.6.3 Step 1: look before you leap With the ever-decreasing cost of bacterial genome sequencing—a recent review noted that, in some extreme cases, the cost of generating a single draft assembly is now less than one dollar172—it can be tempting to see sequencing as the hammer for every nail. However, before deciding to launch a genomic epidemiology study, several questions must be answered. First, is sequencing even possible? To infer transmission events, one must have a well-sampled outbreak; in other words, DNA should be available for the majority of the outbreak cases. For a reportable disease like TB, with nearly exclusive human-to human transmission, and in a well-resourced, low incidence setting where culture is routinely used in diagnosis, this is often achievable. For example, in a recent British Columbia TB outbreak,173 Mtb DNA was obtained from 48 of 52 outbreak cases.174 For non-notifiable diseases and/or those involving food- or water-borne transmission or nonhuman hosts, the outbreak may be insufficiently represented by the available samples. A further wrinkle is introduced by the nature of the diagnostic test used. For certain pathogens—largely viruses but also bacteria, such as Treponema pallidum or Streptococcus pyogenes—diagnosis is often based on serology and the pathogen is never cultured. In the recent work on a British Columbia measles outbreak, 45% of the cases were diagnosed on clinical or serological grounds and did not have available nucleic acid, precluding the inference of person-to-person transmission events beyond those already suggested by the field epidemiology.175 Even the use of molecular diagnostics is no guarantee that genomic DNA will be available. As clinical microbiology enters a new era in which the focus is increasingly on rapid and direct molecular identification of pathogens without culture through platforms such as MALDI-TOF,176 we may lose the opportunity to extract DNA in sufficient quantities for sequencing. Nevertheless, the microbial genomics community is rising to meet these challenges. Several groups have developed library preparation methods capable of sequencing from ultra-low-input samples—on the order of picograms of nucleic acid,177 while others are working toward extracting pathogen DNA directly from clinical samples, including urine,178 vaginal swabs,179 24 and even sputum.180 Even more promising is the notion of clinical metagenomics—directly sequencing all of the nucleic acid present in a sample and using the resulting data to both diagnose an infection and assemble a genome that can then be scanned for resistance-associated mutations and/or epidemiological markers. This was first demonstrated by Loman et al.181 in their investigation of an Escherichia coli O104:H4 outbreak, with subsequent proof of principle in Mtb sequencing from sputum.182 Clinical metagenomics was recently reviewed in the context of both Salmonella diagnostics,183 and microbial infections in general.184 Assuming there is DNA to be sequenced, can you afford it? The National Human Genome Research Institute estimates the costs of sequencing, including production costs, at $0.014 per megabase,185 data that give rise to estimates like the previously described sub-$1 bacterial genome.172 Unfortunately, these figures can lead to sometimes unrealistic expectations of the costs of a bacterial genomics project. Such economies are possible in large sequencing centers, but for the typical laboratory outsourcing sequencing to a third-party provider or using commercial reagent kits, is likely the cost will be closer to ~$100–$250 per bacterial genome, not including upstream laboratory costs for culture and extraction and downstream personnel costs for bioinformatics and interpretation. This notion of interpretability is also important to consider from multiple perspectives. First, is the genome itself readily interpretable in an epidemiological context? As noted earlier, Mtb has a single nonrecombining chromosome, no plasmids, and a well-described mutation rate.186 In highly recombinogenic pathogens, such as Neisseria meningitidis, Streptococcus pneumoniae, and Campylobacter jejuni, phylogenetic trees—the foundations of many genomic epidemiology analyses—may be distorted. Recent work has shown that, although the topologies of such trees are robust to recombination, branch lengths and the parameters derived from them—data integral to making epidemiological inferences—are not.187 Important work on the genomic epidemiology of hospital-associated outbreaks of carbapenemase-producing organisms has demonstrated that it is not just strains that move between persons and the environments, but also plasmids,188 and that this plasmid movement depends on a range of factors.189 Reconstructing plasmids from short-read data requires additional bioinformatics steps,190,191 and, even then, the acquisition of 25 exogenous genes through plasmids, integrons, and transposons often complicates the identification of transmission chains.192 At the chromosomal level, mutation rates can vary substantially between pathogens,193 meaning that a simple threshold for the number of SNVs associated with chains of transmission cannot always be determined.194 The second issue around interpretability is whether or not a research group has the capacity for the downstream bioinformatics and epidemiological interpretation. Our experiences have shown that, in order to extract meaningful, actionable information from a genomic epidemiology study, it is not enough just to be able to run the software—instead, the person interpreting the resulting data needs to understand the disease and its epidemiology and appreciate the unique aspects of a pathogen’s genome. In Mtb, for example, repetitive and low-complexity regions make up about 10% of the total genome and are typically discarded from a bioinformatics analysis owing to difficulties in accurately calling SNVs in these regions.195 If a researcher leaves these regions in his or her analyses, they can easily be misled by assembly errors masquerading as mutations, leading to incorrect assumptions around mutation rate and transmission events. A similar caution applies to the recombinogenic pathogens—if a researcher does not understand their pathogen’s genomic quirks and account for them in their bioinformatics analysis, the wrong conclusions may be drawn. Unfortunately, finding a team member with the right combination of epidemiological, computational, and evolutionary biology expertise is extremely difficult, and many groups struggle to even find someone with just the computational background. For this reason, a number of publicly available, pathogen-specific assembly and variant-calling platforms are being released. Tools like TGS-TB for Mtb196 and WGSA.net for Staphylococcus aureus197 facilitate bioinformatics analyses and incorporate the organism-specific knowledge necessary to provide high-quality output that users can have confidence in. Data access is yet another issue to consider. Whether sequencing outbreaks or using genomics as a tool to understand epidemic dynamics,198–200 one must ask whether clinical and epidemiological case-level data are available (e.g., symptom onset, infectious period, named contacts, locations, hospitalization dates), and whether it can be linked to specimen-level results, such as phenotypic drug susceptibility testing (DST) data. 26 Finally, it is helpful to ask a somewhat difficult question—why use genomic epidemiology? Genomic epidemiology is effective when there is a clear outcome with public health relevance, such as implementing infection control procedures,201,202 identifying the source of infection,203 changing outbreak management guidelines,68,204 and creating surveillance resources to support prospective sequencing efforts.205 Where other interventions are better suited to affecting a disease’s epidemiology—particularly in the developing world, where diagnosis, access to care, and adherence to treatment are fundamental issues—we would argue that the resources required for a genomic epidemiology investigation might be better used to address these more foundational issues. 1.6.4 Step 2: from sample to sequence Having decided to launch a genomic epidemiology investigation, the next step is to sequence each sample of interest. As noted earlier, efforts are underway to sequence directly from clinical samples; however, most genomic epidemiology investigations currently begin with an isolate in pure culture. While certain sequencing services accept cultures under some circumstances, sequencing typically proceeds from extracted DNA. The choice of extraction method depends on the nature of the underlying sample,206 employing either in-house protocols, such as ethanol or phenol–chloroform extractions, or commercial kits—either low-throughput kits or high-throughput automated handling systems, such as the MagMAX (ThermoFisher, Waltham, MA) or QIAsymphony (Qiagen, Germany). Extraction may be followed by enrichment, in which a bait is used to capture a specific region of interest207 or remove nonbacterial data from a metagenomic sample,208 or in which a whole-genome amplification technique is used to increase the amount of nucleic acid available for sequencing.209 Extracted DNA is then quantified using a platform such as the Qubit, Nanodrop, or Quant-iT (ThermoFisher, Waltham, MA), though results across platforms tend to vary, and the reported concentrations depend heavily on sample quality.210,211 DNA may also be checked for integrity using the Bioanalyzer or TapeStation (Agilent, Santa Clara, CA); degraded samples are likely to yield poor results downstream. Once each sample has been quantified, a decision can be made as to which to move forward to library preparation. Typically, sequencing centers will request at least 500 ng of total DNA, even though certain library preparation kits, such as Illumina’s Nextera XT, are capable of sequencing from 27 as little as 1 ng of starting material. Both Head et al.212 and van Dijk et al.213 recently reviewed library preparation methods, discussing the range of approaches for fragmentation, size selection, and adaptor ligation. Unlike library preparation methods for more specialized applications (e.g., de novo assembly and finishing of a new genome, RNAseq, ChIP-seq, or methylation studies), issues such as low-concentration starting material or GC-biased genomes do not confound most resequencing studies, in which the resulting reads will be aligned against an existing reference genome. Indeed, for the British Columbia Mtb resequencing work, in which over 1,500 complete genomes have been sequenced to date, excellent results have been achieved using both commercial kits and in-house protocols developed by a local sequencing center, with high-quality data from as little as 2–3 ng starting material. Experience suggests that the most critical part of step 2 is selecting a sequencing platform and a multiplexing scheme. Too little sequencing capacity and/or over multiplexing will result in low-coverage, unusable data, while over sequencing is a poor use of resources, and the resulting files can be too large to manage and transfer efficiently. Table 1-2 briefly summarizes currently available sequencing platforms, their throughputs, and the advantages and disadvantages of each system in the context of a genomic epidemiology study. Thanks to their low error rates and large market penetration, the Illumina platforms dominate the genomic epidemiology market space, with national public health agencies relying on networks of MiSeqs214,215 or centralized HiSeqs216 for their genomic diagnostic and surveillance efforts. Short-read data are frequently analyzed in tandem with long-read data from the PacBio platforms to elucidate plasmid transmission in hospital-associated infections,188 and proof of principle for plasmid sequencing on the Oxford Nanopore was recently demonstrated by PHE in their investigation of an outbreak of ST38 E. coli with a chromosomally integrated blaOXA-48 element.217 Use of the Ion platforms and the Oxford Nanopore MinION is more common in the viral genomic epidemiology space, with the notable exception of the real-time nanopore sequencing and management of a hospital Salmonella outbreak.218 As nanopore technology matures—the first MinION data was only released in 2014219—this is likely to change. 28 Table 1-2. Sequencing platforms currently deployed for genomic epidemiology studies . Platform Throughput Platform Throughput Comments220 Illumina MiniSeq221 7.5 Gb, 25 M reads Max 2×150 bp 4–24 h run time High accuracy reads (0.1% error rate), ideal for identifying variants for a genomic epidemiology study. Range of platforms offered, capable of handling small projects (MiniSeq, MiSeq) to large-scale efforts (HiSeq). Can be coupled to Neoprep automated library prep platform. Illumina MiSeq221 15 Gb, 25 M reads Max 2×300 bp 4–55 h run time Illumina NextSeq221 120 Gb, 400 M reads Max 2×150 bp 12–30 h run time Illumina HiSeq221 1500 Gb, 5 B reads Max 2×150 bp 1–6 days run time Ion PGM222 2 Gb, 400 k–5.5 M reads 200 or 400 bp 2–7 h run time Simple machine—less subject to breakdowns. Low (1%) error rate. Can be coupled to Ion Chef automated library prep platform. Ion S5/S5 XL223 1–15 Gb, 3–80 M reads 200 or 400 bp 3–18 h run time Ion Proton224 10 Gb, 80 M reads Up to 200 bp 2–4 h run time Oxford Nanopore MinION225 Up to 42 Gb, 4.4 M reads Up to 300 kbp reads reported 1 min–48 h run time Small, fast, and portable. Higher error rates (4–12%).226 Not yet widely used in bacterial genomic epidemiology. PacBio RSII227 1–16 cells, 150 k reads/cell Up to 60 kbp reads reported 30 min–6 h run time Short run time. Good for sequencing plasmids. Often used in tandem with Illumina short-read data. PacBio Sequel227 1–16 cells, 1 M reads/cell Up to 60 kbp reads reported 30 min–6 h run time 29 To make the most efficient use of a sequencer’s throughput, it is almost always necessary to multiplex samples. By ligating unique barcodes to DNA fragments during the library preparation stage, as many as 384 samples can be pooled, run in a single sequencing lane, and separated bioinformatically after sequencing. The degree to which one can multiplex a batch of samples depends on both the sequencing platform and the desired coverage—the number of times each base pair in a genome is sequenced. The microbial genomics community has largely gone with a consensus of 50× coverage,228 and, by using a coverage calculator, one can easily calculate how many samples can be multiplexed on a single run.229 In our own work on Mtb in BC, typically no more than 15 genomes are multiplexed on a single 2×150 bp MiSeq run using the v2 reagent kit (Illumina, San Diego, CA), giving ~60× coverage. When outsourcing sequencing to a local sequencing center, their maximum indexing capacity is 92 samples in a single HiSeq lane, which gives us an average coverage of ~150×. 1.6.5 Step 3: bases to bytes Whether sequencing is done in-house or outsourced, the resulting data will almost always be returned as FASTQ files, which store both the sequence of each read and a quality score for each base in the read.230 In order to go from FASTQ files to a set of isolate-specific mutations that can be used to infer transmission events, there are a series of bioinformatics steps—alignment, variant calling (where “variant” means both SNVs and insertions or deletions, collectively referred to as indels), and variant filtering—that must be executed, either via a series of command-line tools or a pre-packaged, organism-specific pipeline. A full discussion of the command-line tools is outside the scope of this review; however, an excellent tutorial on genome assembly and analysis was published by Edwards and Holt in 2013,231 and updated in a 2016 blog post.232 The original publication describes the assembly, annotation, comparative analysis, and typing of an E. coli O104:H4 genome and, in a supplementary file, provides detailed instructions so that the reader can install, run, and understand the output of the many tools highlighted in the tutorial. 30 Alignment Genomic epidemiology studies are resequencing studies, in which multiple isolates of a well characterized species, like M. tuberculosis, E. coli, or Staphylococcus aureus, are sequenced and compared to each other to identify the point mutations or other changes that suggest evolutionary and epidemiological relationships among the samples. In these scenarios, short reads are aligned against a high quality, completely finished reference genome to identify regions of difference—an approach known as reference mapping or reference-guided assembly. This stands in contrast to de novo assembly, in which overlapping short reads are slowly built up into long stretches of contiguous sequence called contigs without the use of a reference genome.233 De novo assembly is typically used for comparative genomics studies, in which many spatially and temporally diverse isolates are sequenced in order to understand a species’ population structure or to identify virulence factors and antimicrobial resistance genes, or in outbreak studies of an emerging pathogen with few reference genomes, as in the case of the recent Elizabethkingia anophelis outbreak.234 In pursuing a reference-mapping strategy, experience with Mtb has suggested a number of best practices. First, recent work from Lee and Behr235 demonstrated that, when the goal of a resequencing study is to identify transmission-informative SNVs, the choice of reference genome in a clonal species like Mtb does not matter. While mapping a set of reads to references from different lineages does result in varying numbers of total SNVs identified, when these total SNV lists are filtered to leave only those SNVs that vary between outbreak isolates, the final count is the same regardless of the reference chosen. In less clonal species, a fast, hash-based distance estimation tool like Mash236 should be used to compare a set of reads to the NCBI RefSeq database and to select an appropriate reference. Second, while initial outbreak investigations used the CDC1551 reference genome, selected for its similarity to an outbreak strain in BC, we have since changed our protocol to map all data against the H37Rv reference for the simple reason that it facilitates interlaboratory comparisons. Nearly every study in Table 1-1 has used H37Rv as its reference, and, in their prospective COMPASS-TB project, PHE selected H37Rv as the reference against which their Mtb reads are 31 aligned.214 Furthermore, many of the Mtb databases warehousing resistance associated SNVs, reviewed by Stucki and Gagneux,237 list SNVs according to their position in the H37Rv reference. Finally, it is helpful to perform a de novo assembly of any unmapped reads remaining after reference mapping—this typically results in at least 5 to 10 contigs of length >1 kb. These regions, which are small stretches of coding sequence present in the outbreak isolates but not the H37Rv reference, occasionally contain informative SNVs. A recent review by Mielczarek and Szyda238 summarizes the tools used in reference mapping and their underlying algorithmic principles; of the many methods they describe, BWA239 is used by the majority of the microbial genomics community. In our own Mtb work in BC, we run the BWAmem algorithm, the most recent implementation of BWA. Before alignment, FastQC240 is used to verify the quality of short-read data and Trimmomatic241 to remove sequencing adaptors and bases with a low-quality score. While trimming is not strictly necessary when using BWAmem, it does improve the performance of other aligners. The output of the alignment stage is typically a Binary Alignment Map (BAM) file, which can be further refined using an assembly improvement tool, such as Pilon,242 or the local realignment workflow in GATK.243 Variant calling and filtering After reference mapping, the next step is to identify variants present in one’s samples but not in the reference genome. There are many approaches to variant calling; a full description of these is provided by Mielczarek and Szyda.238 Pabinger et al.244 offer a similarly detailed review of the many tools available; of these, both SAMtools mpileup245 and GATK243 are widely used in genomic epidemiology. These tools take a BAM file as input and output a list of SNVs and indels as a VCF (variant call format) file,246 in which the genomic coordinates of each variant are provided, along with columns describing the genotype at that position, the read depth, the genotype quality, and many other metrics. Arguably more important than the choice of variant caller is the approach one uses to filter the resulting VCF file, which will invariably contain a number of false-positive SNVs. While a small fraction of errors are introduced during sample preparation or sequencing—these can often be corrected with tools, such as Blue247 or 32 QuorUM248—most SNV-calling errors result from improper mapping.249 When a reference genome contains repetitive regions and/or structural variants, reads may map incorrectly and give rise to false-positive calls. Many variant-calling algorithms employ some degree of filtering, using either hardcoded or learned thresholds on metrics, such as base quality and strand bias, but personal experience suggests that even further filtering is typically necessary. It is here that a combination of both bioinformatics expertise and knowledge of the target genome is most helpful. In our work on Mtb, we call SNVs using SAMtools mpileup, then remove all variants called within the ~10% of the Mtb genome that is highly repetitive; this is done by providing a list of the repetitive region coordinates in BED (browser extensible data) format to the BEDTools subtract program.250 The next step depends on the nature and scale of the genomic epidemiology investigation. When examining a single outbreak or another set of closely related strains, our approach is to use a custom Python script to read in all of the variant positions in all of the VCF files (Figure 1-5) and keep only those that differ in at least one file. In this way, we are able to quickly discard the hundreds or thousands of SNVs that are identical across all sequenced outbreak isolates and are thus not informative for downstream transmission analysis. The script outputs a tab-delimited text file in which each row corresponds to a variant position; there are two columns for each sequenced isolate, one with the base call at that position (either the wild-type base appearing in the reference genome or a SNV) and one with the associated quality score, the scale of which depends on the variant caller used. We next apply a distance filter, looking for any variant positions within 50 bp of another variant; in other words, we search for dense clusters of SNVs that are suggestive of mapping errors. We then apply a quality filter but in a flexible way. Many microbial genomics pipelines employ a strict quality threshold—if a variant’s quality score does not meet or exceed the threshold, it is discarded. However, through frequent manual review of the data, it has often been found that these variants can indeed be trusted and ought not to be thrown out. Thus, it is recommended to 33 examine each row in the table, flagging those where at least one isolate achieves a minimum quality score (typically 200 when SAMtools mpileup is used) and discarding those where no isolate achieves the minimum score. We then manually inspect the remaining rows—usually these amount to no more than 20–100, depending on the scale of the outbreak—and assess each isolate’s base call and associated quality to arrive at a final data set of concatenated variation in FASTA format. While this manual inspection can often be done by examining data in the VCF file alone, it can sometimes be helpful to manually inspect the alignment using a BAM file viewer. In larger studies retrospectively examining the many strains, clustered or otherwise, present in a region over time, or in the real-time prospective sequencing initiatives underway at national public health reference laboratories, this approach, which requires a certain degree of human intervention, is clearly not feasible. Instead, the bioinformatics pipelines supporting these studies use both repeat masking and carefully chosen thresholds on read depth, allele frequency, quality score, and strand bias to automatically filter out likely false positives and arrive at a final set of SNVs relative to the reference strain for each isolate. This approach may filter out some true-positive SNVs not meeting the various thresholds, but when the goal is simply surveillance and cluster identification rather than transmission inference, this automated approach is sufficient. Specific clusters can then be followed up in a second, less stringent analysis using an approach similar to that shown in Figure 1-5. 34 Figure 1-5. Identifying transmission-informative variation. Sequencing pathogen genomes from three cases yields three VCF files (top row). In this simplified example, the first column of each VCF contains the position, the second column contains the reference base, the third column contains the variant base call, and the fourth column contains the variant quality score. The variants called across all three isolates can be summarized in a matrix (second row). Here, the positions 643924 or 3834928 have not been included, as they are identical in all three genomes sequenced and are not informative for transmission—only bases that vary among the outbreak cases are included. In reviewing the quality scores and positions, the grayed-out rows would be excluded, positions 2374827 and 2374824, from subsequent analyses, as they have low-quality scores and are within 50 bp of each other, suggesting a mapping error. The third row indicates a manual review of the base called at positions 43978 in genome 1—although the variant was called with a low score of 92, manual inspection reveals that the low score is likely due to low coverage—only 3×—but that the variant call is true. The final row shows the concatenated variation for each genome in FASTA format. 35 Pipelines Several bioinformatics pipelines have been developed to facilitate reference mapping and variant calling, combining the assembly and variant-calling steps into a single analysis; these might be organism specific, such as TGS-TB196 or COMPASS-TB214 for Mtb or wgsa.net for S. aureus,197 or they might be organism agnostic, such as Snippy,251 the PHEnix252 pipeline used by PHE, the CFSAN SNP253 pipeline used by the U.S. Food and Drug Administration, and SNVPhyl,254 used by PHAC. Multiple commercial solutions (reviewed by Wyres et al.255) are also available, including pipelines developed by the makers of several sequencers. Illumina’s BaseSpace offers cloud-based analytics from both Illumina developers and external collaborators, while TorrentSuite offers analytics directly on the Ion family of sequencers. As genomics moves toward more routine clinical use, benchmarking these pipelines will become critically important. A set of best practices for evaluation was recently described by Olson et al.,249 while Budowle et al.256 describe the criteria for validating sequencing as a microbial forensics tool. Comparing a web-based tool like TGS-TB, targeted at research-oriented users with only a few genomes, to the command-line COMPASS-TB pipeline, designed to handle the real-time processing of every TB genome generated in UK reference laboratories, illustrates the features that make a pipeline scalable to a clinical level: the ability to handle hundreds of input sequences at a time on a high-performance computing back end; a well documented, transparent, and modular workflow; version control for both software tools and databases to promote reproducibility; the ability to integrate with other software platforms used in the clinical laboratory for sample tracking or reporting; and outputting a carefully designed report that meets regulatory standards for medical test reporting. 1.6.6 Step 4: rapid resistance prediction Before discussing how transmission inference proceeds from assembled genomes, it is worth briefly describing how genomic data can be leveraged to predict an isolate’s antibiotic susceptibilities. Appropriate antibiotic therapy is a cornerstone of TB treatment, but Mtb’s slow growth means that gold-standard, culture-based DST results take up to 8 weeks,257 during which time the patient may be on an incorrect treatment regimen. More rapid culture-based methods have been proposed, and molecular tests with turnaround times in hours, not days, are also 36 routinely used, including hybridization-based line-probe assays and real-time polymerase chain reaction (PCR).258 Concordance between molecular and phenotypic assays varies widely—while molecular methods for detecting resistance to INH and RIF are over 92% and 97% concordant with phenotypic results,259 molecular assessments of ethambutol and streptomycin resistance are closer to 50% concordant.260 For a molecular assay to be sensitive enough to replace culture-based DST, two conditions must be met. First, the suite of mutations conferring resistance to a particular drug must be well defined. For RIF, in which ~95% of resistance arises owing to mutations in a single gene (rpoB),261 this is straightforward; for other drugs, genotype–phenotype correlations are not as robust.262 This is particularly problematic for first-line therapies, such as pyrazinamide (PZA). Mutations in pncA constitute the primary mechanism of PZA resistance;263 however, phenotypically resistant isolates have been identified without pncA mutations. In this and many other discordant scenarios, other mutations are clearly involved, but many have yet to be revealed; thus, pncA remains the only target of value for predicting PZA resistance.264 However, the list of resistance-associated mutations in Mtb is growing thanks to large genomic studies265–268 and initiatives like ReSeqTB, a data-sharing platform developed through a unique collaboration among the WHO, CDC, the Stop TB New Diagnostics Working Group, the Critical Path Institute, FIND, and academic TB experts.269 As the number of sequenced Mtb genomes with linked metadata describing their phenotypic DST results grows, algorithms like that proposed by Walker et al.267 will be able to continuously self-update, identifying more and more resistance-associated mutations. Second, for a molecular assay to replace culture-based DSTs, the assay itself must incorporate as many resistance-conferring mutations as possible. Tools like the INNO-LiPA Rif.TB test (Innogenetics NV, Belgium) and the GenoType MTBDRplus assay (Hain Lifescience GmbH, Germany) are limited to specific mutations in first-line drugs,270,271 with the recent GenoType MTBDRsl adding mutations in four genes associated with resistance to second-line antibiotics. Whole-genome sequencing offers a clear advantage here. Because it considers the entire genome, a single genomic analysis can scan for all known resistance mutations. This approach is at the 37 heart of recent online tools for predicting Mtb antibiotic susceptibilities directly from genomic data. KvarQ,272 PhyResSE,273 and Mykrobe274 take FASTQ files as input and perform rapid, in silico phenotyping by scanning the short reads for a catalogue of resistance- and lineage associated mutations. Not having to assemble the reads into complete genomes means results are available within minutes, and the mutation catalogues can easily be updated as more resistance-conferring mutations are identified. While resistance typing from genomic data is straightforward in Mtb, where resistance arises solely through point mutations, for other pathogens, resistance-conferring DNA is acquired from other sources,275 via transmissible plasmids, integrons, and transposons.276 Tools, such as SRST2277 and SEAR,278 take FASTQ files as input but must include a reference mapping step so as to capture resistance genes carried on MGEs. Accurate assembly of MGEs using short-read sequencing technology can be challenging, although hybrid assemblies incorporating long-read data are facilitating plasmid reconstruction.188,279 Predicting drug susceptibility from genomic data can facilitate rapid, targeted prescribing, improving patient outcomes and enhancing antimicrobial stewardship. However, it is not without its challenges. Mutations and gene presence/absence alone do not solely determine phenotypic resistance and the associated success or failure of antibiotic therapy—resistance may depend on gene expression levels,280 and treatment failure can be influenced by host genetic polymorphisms.281 Nevertheless, for slow-growing organisms like Mtb, genomics offers a rapid alternative to traditional DSTs that is already affecting patient care.171,214 1.6.7 Step 5: making the links Returning to our reference-mapped genomes, we now have a FASTA file of the concatenated variation in each pathogen genome taken from our outbreak cases (Figure 1-5). Isolates that may have previously appeared identical via genotyping can now be distinguished from each other owing to the significantly improved resolution provided by genomics140,158,282,283—not only can we now begin to assess which isolates are most closely related, potentially representing person-to-person transmission (Figure 1-4), but we can also distinguish between reinfection and relapse in diseases that may not always achieve cure, including Clostridium difficile and TB.119,120,284 38 In reconstructing an outbreak from genomic data, it is easier to rule out transmission than it is to rule it in—research into HIV phylogenetics in the context of criminal prosecution makes this explicitly clear.285 In Mtb, Walker et al.149 suggest that isolates >12 SNVs apart are not likely to be related epidemiologically. Nevertheless, drawing inferences around the precise route of transmission of closely related isolates—fewer than five SNVs apart for Mtb—is a useful exercise when genomics is used as part of a real-time outbreak-management strategy. In the earliest Mtb genomic epidemiology studies, clinical and epidemiological information, such as AFB smear results, site of infection, presence of cavitary disease, infectious period, named contacts, and named locations, was manually reviewed in the context of genomic data to establish plausible transmission events or clusters.74,149 More recently, automated methods for inferring transmission have been introduced that are extensible to other pathogens, including viruses. Tools like TransPhylo,144 OutbreakTools,286 SEEDY,287 and within-host coalescent analysis in BEAST154 take, as input, concatenated genomic variants from outbreak isolates or a phylogenetic tree showing this variation. These methods combine complex epidemiological and evolutionary models to infer the transmission tree—the chain of person-to-person infection events—that best explains the observed genomic variation; some methods are also able to assign a date interval within which an infection event likely occurred. Several tools also account for within-host genetic diversity, which can complicate manual transmission inference when dealing with pathogens that have latent periods or periods of asymptomatic carriage, including Mtb and S. aureus infection (Figure 1-4).162,288 In our work, we use a hybrid approach to reconstructing transmission. We first use TransPhylo to infer a putative transmission network from our concatenated genomic variation alone.144 This initial network must then be manually refined using our clinical and epidemiological data. This refinement is necessary for two reasons. First, the initial network contains many more edges—potential person-to-person transmission events—than would have actually occurred in the outbreak. In other words, for any one person in the network, the genomic data might suggest multiple different sources of that person’s infection. Edges in automatically inferred networks are also often bidirectional, indicating a potential infection event between persons A and B but not the direction of that event. Second, the evolutionary models underlying automated-39 transmission inference tools are not well suited to capturing the variable tempo of mutation in organisms like Mtb.147 The development of TransPhylo demonstrated that transmissions strongly supported by epidemiology may not be captured in the inferred network because of the model’s parameters.144 In refining the network, we rely on the available clinical and epidemiological data associated with each case. We attempt to prune the network, removing edges that we deem unlikely to represent true transmission events between individuals. Using clinical data, we remove edges emanating from individuals with non-infectious forms of TB, for example non-pulmonary forms of the disease with no evidence of AFB in a patient’s sputum. We also remove edges that are unlikely to represent transmission given what we know about the location and timing of cases. In outbreaks spanning geographically distinct locales, we can eliminate edges between cases without reported travel histories. Similarly, we can use an individual’s prior TST or chest X-ray findings to narrow down transmission possibilities—repeated screening in a large outbreak will often reveal several individuals with a documented window of TST conversion; individuals who had initiated treatment and were therefore removed from the pool of potential infectors before this window can be ruled out as a source of these cases’ infection. We can also confirm edges based on named contact or location data—if an individual has multiple potential sources in the network but only one of those is a named contact or someone known to frequent the same locations, we select that as the most probable edge. The nature of the clinical and epidemiological data that can influence a genomic epidemiology reconstruction will vary from pathogen to pathogen. In outbreaks of hospital-acquired infections, one must consider everything from patient movement between beds and wards to whether specific pieces of equipment were used in an individual’s care,202,289 while in outbreaks of foodborne illness, data collection may have to span international or even intercontinental borders.203,290 Accurately reconstructing an outbreak from genomic data therefore requires thorough knowledge of the pathogen in question and the epidemiology of the resulting disease and a recognition that undertaking such an investigation may have legal, economic, and/or privacy ramifications, as reviewed by Gilchrist et al.151 It is important to note that, just as 40 epidemiological data might influence the decision to use genomics, so can genomics inform the decision to undertake an epidemiological investigation. For example, investigating a nosocomial outbreak of an antibiotic-resistant pathogen can be time consuming and expensive. Genomics can quickly establish whether suspected outbreak isolates are related and an investigation is warranted or whether the putative outbreak represents a series of unrelated introductions into the hospital environment with no onward transmission. 1.6.8 Concluding thoughts Genomics has had a profound impact on our ability to understand infectious disease epidemiology. When coupled with active surveillance programs, genome sequencing is solving more outbreaks,291 suggesting new modes of transmission,292 and revealing new reservoirs of disease.204 Furthermore, the routine, real-time use of genomics within the clinical reference laboratory means that genomics can do more than identify epidemiological links between cases—it can also be used as a tool for the rapid diagnosis and resistance typing of an isolate, as has recently been demonstrated for Mtb.214 And, when the lens of genomics is turned upon a larger set of samples collected across time and space, we gain a deeper understanding of disease ecology. Genomic investigations of Shigella sonnei293—an emerging cause of dysentery in the developing world—and Vibrio cholerae198 have revealed the role that population expansion plays in a disease’s success. Sequencing of S. pneumoniae has shown how capsular switching mediates vaccine escape,294 while work on Neisseria gonorrhoeae charts the dispersal of cefixime resistance.295 As the number of publicly available genome sequences increases, so does our ability to mine sequence data and carry out comparative genomic analyses, leading to vaccine development, new drug targets, improved diagnostics, and the identification of novel drug resistance and virulence loci.296 Despite the promise of genomics,297 the community is currently dealing with several challenges, such as validating genomics against existing molecular tools, accrediting ever-changing bioinformatics pipelines for use in a clinical environment, and communicating complex genomic information to end users. Moving forward, training the next generation of disease detectives represents the next great hurdle—we need analysts with graduate-level skills in bioinformatics, 41 evolutionary biology, and infectious disease microbiology and epidemiology. Challenges aside, the benefits of whole-genome sequencing in a public health environment are clear, and all are looking forward to the exciting and transformative insights yet to come as groups around the world harness the power of sequencing. 1.7 Accelerating TB Elimination in Low-incidence Settings: the Role of Genomics In a recent ERJ article, Lönnroth et al. proposed a framework to accelerate progress towards tuberculosis (TB) elimination in low-incidence settings.135 In it, they outline eight priority areas and multiple interventions that align with the World Health Organization’s post-2015 global TB strategy.298 This framework is to be applauded and the recognition that elimination in low-incidence countries is a unique problem, where infection occurs amongst the most difficult–to–reach individuals. Although “new research and tools” is one of the framework’s areas, it overlooks an important new technology that is changing our understanding of TB and our approaches to diagnosis, phenotyping, and treatment—whole genome sequencing (WGS) of Mycobacterium tuberculosis (Mtb) isolates from cases of active TB.299,300 In contrast to genotyping, which interrogates ~0.5% of the Mtb genome, WGS reads the entire 4.4 Mbp of sequence—with current sequencing technologies this can be done in under a day at a cost of ~$50–$100/genome depending on the laboratory.301 Many federal public health agencies have invested substantially in genomics and are using it routinely in medical microbiology,302 with TB representing the ideal use case. The Mtb genome is uncomplicated—the global population of Mtb is clonal and the genome comprises a single chromosome in which variations arise through point mutations—facilitating downstream bioinformatics analyses such as prediction of antimicrobial resistance phenotypes. Additionally, most TB diagnostic work is performed in well-equipped reference laboratories, where centralization permits integrating WGS data with the data streams necessary for diagnosis, surveillance, outbreak management, and research, and where the need for accredited operating procedures is accelerating the standardization of TB genomics protocols—Public Heath England is already using WGS routinely alongside its traditional mycobacteriology laboratory pipeline in its Birmingham Public Health laboratory.302 42 There are many areas of the framework in which WGS is poised to support efforts in tuberculosis elimination. First, WGS is able to resolve TB transmission dynamics, including individual transmission events, to a degree not possible with traditional genotyping. This genomic epidemiology approach has been used to reconstruct single transmission events, local outbreaks, and regional epidemics,300 and is providing insights into both patterns of spread and characteristics of transmitters. Genomic epidemiology speaks to several priority action areas. Unlike genotyping, which simply clusters active cases, WGS can elucidate the order and direction of transmission, revealing common trends in TB outbreaks and identifying those individuals who are transmitting disease and those who aren’t. This directly informs the framework’s goals of describing local patterns of transmission amongst vulnerable populations and identifying individuals most at risk for transmitting disease (areas 2 and 3); it also enables us to prioritize contacts of these key transmitters for screening (area 4). Additionally, real-time WGS of specimens from new TB cases provides a core indicator of ongoing transmission for surveillance (area 6). Another priority area is the prevention and care of drug-resistant TB (area 5), for which phenotypic methods are the current gold standard. These methods are culture-dependent and have turnaround times (TATs) of weeks to months; even rapid molecular methods with TATs of hours—such as the Cepheid GeneXpert and line probe assays—are limited to detecting only a handful of known resistance mutations. As much as 1/3 of isoniazid resistance cannot be explained by these canonical mutations,303 and no commercially available test can probe resistance to all anti-tuberculous drugs. WGS, in contrast, interrogates the entire genome, identifying resistance-associated mutations in hours to days304 and optimizing the prescription of appropriate treatment and supporting rational drug use (area 1). Although this requires a comprehensive database of well-described resistance-associated mutations, several such efforts are underway and more mutations are likely to be described as ever-increasing numbers of isolates are sequenced. There are other, less obvious, benefits to incorporating WGS into the routine diagnosis, management, and surveillance of TB in low-incidence settings. Chief amongst these is that just 43 as early Mtb genomics efforts led to new molecular diagnostic tools based on a handful of sequenced genomes,299 the generation and sharing of thousands of Mtb genomes will likely lead to new molecular tools for use in higher-incidence settings. Genomics is stimulating TB research and reinvigorating a community that has experienced de-prioritisation (area 1), potentially leading to renewed political interest in TB programs and the specialized training and creation of central repositories. In addition, collaborative efforts—necessary to deploy WGS in the clinical laboratory, will act to build capacity at national and international levels, which is essential for the elimination of TB in low- and high-incidence settings. In conclusion, genomics stands to significantly enhance TB elimination efforts through direct and indirect routes. When combined with the framework’s recommended interventions, it is believed that WGS has the potential to accelerate progress towards TB elimination in low-incidence countries, with the knowledge gained in these settings working to support the final priority action area—informing TB prevention, care, and management in countries with a high burden of disease. 44 Chapter 2: Universal Genotyping for Tuberculosis Prevention Programs: A Five-Year Comparison with On-Request Genotyping 2.1 Background Despite declining case rates, tuberculosis (TB) remains a public health issue in Canada and other low-incidence countries.135 Here, a substantial proportion of TB diagnoses occur in persons born outside Canada and represent reactivation of latent TB infection.6,135 However, outbreaks and endemically circulating strains also contribute to incidence rates.74,166,305 Interruption of these transmission chains requires an understanding of regional epidemiology. Techniques such as 24-locus mycobacterial interspersed repetitive unit–variable-number tandem repeat (MIRU-VNTR) genotyping can provide valuable insights into the potential extent of local TB transmission by using clustering as a proxy; thus, many low-incidence settings have incorporated MIRU-VNTR genotyping into standard practice.121,306,307 Several laboratories now perform universal genotyping,307–312 in which all culture-positive isolates from a region are prospectively genotyped by one or more molecular methods. While published reports have examined clustering rates and other metrics related to universal genotyping programs,313–315 there are no reports directly comparing the results of universal genotyping to those of an on-request genotyping program over the same time period in the same region. In the Province of British Columbia (BC), Canada, Mycobacterium tuberculosis isolates are MIRU-VNTR genotyped by the BC Centre for Disease Control (BCCDC) Public Health Laboratory (BCPHL). From 2009 to 2014, genotyping was done only when requested by BCCDC TB Services personnel. However, a recent province-wide retrospective molecular epidemiology research study later genotyped all culture-positive BC isolates from 2005 to 2014 (n = 2,290) to describe the complete genotypic landscape of TB in BC, the results of which are detailed in Chapter 3. This data set was used to compare the insights derived from the on-45 request genotyping performed between 2009 and 2013 to those later revealed through genotyping of all of the remaining isolates during this period. Given the significant costs, time, and effort associated with the implementation of universal genotyping, it was important to assess the epidemiological value it adds in a low-incidence setting such as BC, where >75% of TB cases occur in persons born outside Canada and are likely not due to local transmission.6,9 2.2 Materials and Methods 2.2.1 On-request genotyping data The BCPHL performs routine TB diagnostics, phenotypic drug susceptibility testing, and 24-locus MIRU-VNTR genotyping for all culture-confirmed cases in BC. Until mid-2014, MIRU-VNTR genotyping was performed only when requested by a clinician—typically to support outbreak investigations and contact tracing efforts—with all requests recorded in a spreadsheet. This spreadsheet was used to identify all of the genotyping requests received between 1 January 2009 and 31 December 2013—the last full calendar year before universal genotyping was implemented. On the basis of the information contained in the spreadsheet, the reason for each request was coded as (i) suspected possible transmission, (ii) distinguishing relapse from reinfection, or (iii) suspected false-positive results. For inquiries regarding possible transmission, it was noted whether the request asked for comparison to a specific individual(s), to a known outbreak, or to the general database. 2.2.2 Universal genotyping data In Chapter 3, a retrospective genotyping analysis of culture-positive M. tuberculosis isolates diagnosed in BC between 2005 and 2014 will be described; here, the subset of isolates received between 2009 and 2013 (n = 1,136) and an additional 39 isolates requested for genotyping during this period but from specimens received prior to 2009 were examined. For case-based analyses, the study sample excluded false-positive TB diagnoses (n = 3) and the second record of a reoccurrence, leaving a total of 1,158 cases. Briefly, M. tuberculosis sensu stricto isolates were genotyped by standard 24-locus MIRU-VNTR methods77 and linked to individual-level clinical, 46 demographic, and epidemiological data extracted from the BCCDC Integrated Public Health Information System (iPHIS). Postal codes were used to obtain the corresponding census dissemination area (DA) for each case, which was then linked to the 2006 Canadian Marginalization Index (CAN-Marg) to determine the deprivation index quintile.316 2.2.3 Statistical analysis Data were analyzed and presented as means with standard deviations and relative frequencies, as appropriate. Logistic regression was used to estimate the odds ratio (OR) and 95% confidence interval (CI) for the association between genotype requested to confirm/refute transmission (yes/no) and MIRU-VNTR genotyping clustered (yes/no). A cluster was defined as ≥2 isolates with identical 24-locus MIRU-VNTR genotyping patterns by using a stringent perfect type match, and each cluster was labeled with a unique identifier (MClustID). To obtain the adjusted OR (aOR), variables were selected a priori, which included age group, gender, birthplace (Canada/outside Canada), and the presence of one or more risk factors (HIV, illicit drug use, or alcohol misuse) known to be associated with TB transmission and therefore genotype clustering.317 Only cases with complete data for all variables were included in the model (n = 910). A secondary analysis was conducted on a subset of the 2009 to 2013 data (2013 quarter 3 [Q3] and Q4 excluded) to explore the possibility that the relationship between genotypic clustering and request status was influenced by the large increase in requests during the last two quarters of 2013. An additional analysis to examine risk factors in relation to genotype requests and clustering status used case records with complete risk factor data (n = 916). Characteristics of all clusters with ≥3 persons (i.e., growing clusters) were analyzed, and the predominant birthplace was assigned as Canada where ≥50% of the persons in the cluster were born in Canada; otherwise, the predominant birthplace was categorized as outside Canada. The cluster growth rate was calculated as the average increase in case counts per quarter over the study period, and linear regression was used to test the relationship of growth rate, cluster size, and birthplace on cluster proportion requested. All analyses were executed in R (v3.3.1). 47 2.3 Results 2.3.1 The genotype request proportion was smaller than the genotypic clustering proportion The study sample included 1,175 isolates, consisting of 1,136 culture-positive M. tuberculosis specimens received by the BCPHL from 2009 through 2013 and 39 isolates requested during the study period that were received prior to 2009 (Figure 2-1). During this time, clinicians submitted 194 genotyping requests involving 309 isolates from 296 individuals, including 13 isolates from TB recurrences. The quarterly request proportion varied over time, averaging 20.5% before 2013 Q3, at which point requests increased (Figure 2-2). Of the 1,136 specimens received by the BCPHL during the study period, only 271 (23.8%) had genotyping requested specifically to confirm or refute suspected transmission (Table 2-1) However, the subsequent universal genotyping analysis revealed an overall provincial genotypic clustering proportion of 38.0%, meaning that prior to universal genotyping, on-request genotyping captured fewer clusters. Figure 2-1. Study sample request status. Diagram representing the request status of the study sample (n = 1,175) for isolates with genotyping requested (all reasons) from 2009 through 2013, which included all genotyped isolates from specimens received at the British Columbia Centre for Disease Control Public Health Laboratory from 2009 through 2013, and those requested for genotyping with a specimen received date prior to 2009. 48 Figure 2-2. Quarterly genotype requests. Percentage of isolates requested for genotyping (all reasons) displayed by year and quarter specimens were received at the British Columbia Centre for Disease Control Public Health Laboratory from 2009 through 2013. Dashed line represents the mean percentage of genotyped isolates requested per quarter. Table 2-1. Genotype request reasons. Frequency of genotype requests by reason, British Columbia, 2009–2013 (n = 300)a. Request Reason n (%)b Transmission Specified case comparison 41 (13.7) Specified outbreak comparison 111 (37.0) General database comparison 119 (39.7) Relapse or reinfection 12 (4.0) Specimen mix-up/cross-contamination 17 (5.7) aIncluded are all cases who were subjects of genotyping requests (n = 296). Four individuals were the subjects of multiple genotyping requests for different reasons; here, each request is counted separately (n = 4). bPercentages have been rounded and may not add up to 100%. 49 2.3.2 Requests reflected suspected community transmission and known risk factors Most requests (90.3%) were made during contact investigations to confirm or refute transmission, although few named specific individuals (Table 2-1). Instead, most requests asked for a comparison against a specific outbreak genotype or the general database. When a specific comparator was identified (n = 152 requests)—either an individual or a specific outbreak genotype—a match between the requested strain and comparator was observed in 83 instances (54.6%). Upon examination of all isolates requested to determine possible transmission, it was found that 67.5% (183/271) matched at least one other isolate by MIRU-VNTR genotyping during the study period. Requests to differentiate relapse from reinfection (n = 12) and requests to investigate potential laboratory errors (n = 17) were less frequent. Next, the characteristics of individuals for whom genotyping was requested to confirm or refute transmission (n = 269 after the exclusion of two individuals whose genotype was requested on more than one occasion to investigate transmission) versus all other cases in the study sample representing true positive TB diagnoses were compared (Table 2-2). It was found that proportionally more requests were made for individuals in the 35- to 54-year age group, males, those born in Canada, and persons with one or more risk factors (HIV, illicit drug use, or alcohol misuse). 50 Table 2-2. Study sample characteristics. Demographic characteristics of the study samplea (n = 1,158), comparing individuals whose isolates were requested for genotyping to confirm/refute transmission (n = 269) versus all other samples (n = 889). Genotyping Requested to Confirm/Refute Transmission Characteristic Yes n (%) No n (%) p-valueb Age, years 0–34 60 (23.6) 194 (76.4) <0.001 35–54 111 (32.5) 231 (67.5) 55–74 66 (21.5) 241 (78.5) 75+ 32 (12.5) 223 (87.5) Gender Male 168 (24.7) 513 (75.3) 0.188 Female 101 (21.2) 376 (78.8) Birthplacec Canada 158 (51.6) 148 (48.4) <0.001 Outside Canada 105 (12.9) 709 (87.1) Risk Factorsd None 131 (16.6) 657 (83.4) <0.001 ≥1 70 (54.7) 58 (45.3) aExcluded false-positive TB diagnoses (n = 3) and counted each individual once by excluding the second record from reoccurrences (n = 14). bChi-square test. cData unavailable (n = 38). dThe risk factors are HIV, illicit drug use, and alcohol misuse. Data unavailable for one or more risk factors, n = 242. 51 2.3.3 Universal genotyping improves cluster identification Province-wide, retrospective universal genotyping revealed how many clusters and how many clustered individuals were missed during the on-request period. From 2009 through 2013, 94 genotypic clusters were observed in BC, ranging in size from 2 to 53 cases (mean = 5) and involving a total of 432 individuals. On-request genotyping missed 54 (57.4%) of these clusters and 130 (30.1%) clustered individuals (Table 2-3). Ten clusters (10.6%), with an average of three isolates per cluster, were fully identified through on-request genotyping; 30 clusters (31.9%) were partially identified (Table 2-3; Figure 2-3). These partial clusters tended to be larger (9.1 ± 10.7 persons/cluster) than those that were either missed or fully identified (≤6 persons/cluster). The mean proportion of requested cases within a partially identified cluster was 40.5%. Clusters described as predominantly Canadian-born (n = 29) were more likely to be partially or fully requested (Table 2-3). Table 2-3. Characteristics of MIRU-VNTR clusters. Characteristics of MIRU-VNTR clusters identified through universal genotyping categorized by the proportion of each cluster (none, partial or all) requested for genotyping to confirm or refute potential transmission. Cluster Requested Proportion No. of Clusters Predominantly Canadian-born n (%) Cluster Size Range Mean Cluster Size (± SD) None (0%) 54 10 (18.5) 2–6 2.4 ± 0.8 Partial (1–99%) 30 14 (46.7) 2–53 9.1 ± 10.7 All (100%) 10 5 (50.0) 2–5 3.0 ± 1.2 Abbreviation: SD, standard deviation. 52 Figure 2-3. Proportion of each cluster requested by cluster growth over time. Bubble plot of the proportion of each cluster requested for genotyping to confirm or refute transmission, with the average cluster growth per quarter in BC from 2009 to 2013. Growing clusters had a minimum of three persons in the cluster over the study period. Bubbles are colored to indicate the predominant birthplace (≥50%) of the individuals in each cluster and sized to represent the total number of genotypically clustered cases. Cluster identifiers are indicated for clusters with five or more cases. Logistic regression analysis was used to examine the characteristics of those in genotypic clusters and found that individuals in the 35- to 54-year age group, males, those born in Canada, and persons with one or more risk factors (HIV, illicit drug use, or alcohol misuse) were more likely to belong to a cluster than to have a unique genotype (Table 2-4). It was observed that isolates that had a historical genotype request had greater odds of belonging to a genotypic cluster (aOR 2.3, 95%CI: 1.5–3.3); this effect size increased (aOR 3.3, 95%CI: 2.0–5.4) when the last two quarters of 2013 from the analysis were excluded (Table 2-5). Risk factors in relation to genotype requests and clustering status were examined and it was found that 258 (72.5%) of the 356 persons with clustered isolates had no risk factors identified (Table 2-6). 53 Table 2-4. Logistic regression. Analysis for the relationship between MIRU-VNTR genotypic clustering, as revealed by universal genotyping, and whether an isolate had originally been requested for genotyping to confirm or refute transmission. Clustereda vs. Unique Characteristic Unadjusted OR (95%CI) Adjusted OR (95%CI) Requested Yes 4.6 (3.3–6.5) 2.3 (1.5–3.3) No Reference Reference Age, years 0–34 Reference Reference 35–54 1.7 (1.2–2.5) 1.5 (1.0–2.3) 55–74 0.9 (0.6–1.4) 1.0 (0.6–1.5) 75+ 0.5 (0.3–0.8) 0.8 (0.5–1.3) Gender Male 1.3 (1.0–1.7) 1.1 (0.8–1.5) Female Reference Reference Birthplace Canada 8.8 (6.2–12.3) 5.3 (3.5–7.8) Outside Canada Reference Reference Risk Factorsb None Reference Reference ≥1 6.6 (4.2–10.2) 1.8 (1.0–3.0) Abbreviations: CI, confidence interval; OR, odds ratio. aCluster: ≥ 2 isolates that share an identical genotype (24-locus MIRU-VNTR). bRisk Factors = HIV positive, illicit drug use, or alcohol misuse. 54 Table 2-5. Logistic regression with a restricted dataset. Logistic regression analysis for the relationship between MIRU-VNTR genotypic clustering and genotyping requested (2009–2013Q2) to confirm or refute transmission (n = 813), British Columbia. Characteristic Clustereda vs. Unique Unadjusted OR (95%CI) Adjusted OR (95%CI) Requested Yes 8.5 (5.5–13.1) 3.3 (2.0–5.4) No Reference Reference Age, years 0–34 Reference Reference 35–54 1.8 (1.3–2.7) 1.6 (1.0–2.5) 55–74 1.0 (0.6–1.4) 1.0 (0.6–1.6) 75+ 0.5 (0.3–0.8) 0.8 (0.4–1.3) Gender Male 1.2 (0.9–1.6) 1.0 (0.7–1.4) Female Reference Reference Birthplace Canada 9.1 (6.3–13.0) 4.9 (3.2–7.3) Outside Canada Reference Reference Risk Factorsb None Reference Reference ≥1 6.8 (4.3–10.8) 1.8 (1.0–3.1) Abbreviations: CI, confidence interval; OR, odds ratio. aCluster: ≥ 2 isolates that share an identical genotype (24-locus MIRU-VNTR). bRisk Factors = HIV positive, illicit drug use, or alcohol misuse. 55 Table 2-6. Request status, risk factor, and clustering. Relationship between genotype request status, risk factorsa and genotypic clustering (Yes/No), British Columbia, 2009–2013. Clustered (n) Characteristic No. Isolates (%) Yes No Requested No Risk Factors 131 (65.2) 73 58 ≥1 Risk Factors 70 (34.8) 62 8 Not Requested No Risk Factors 657 (91.9) 185 472 ≥1 Risk Factors 58 (8.1) 36 22 aRisk Factors = HIV, illicit drug use, or alcohol misuse; data unavailable for 1 or more risk factor (n = 242). 2.3.4 Growing clusters were variably identified by on-request genotyping To examine growing clusters, the data set was pruned to include only the 43 clusters with three or more persons and examined the cluster growth rate and the proportion of requested cases (Figure 2-3 and Figure 2-4). Although request rates varied, Canadian-born clusters with higher growth rates were larger and tended to have proportionally more isolates requested for genotyping (p = 0.003). MClust-002, a previously described TB outbreak in BC,173 was the largest cluster observed during the study period (n = 53) and had the highest average rate of growth (2.3 cases/quarter) and the largest number of clustered cases observed in a single quarter (n = 9). Within this cluster, an additional seven cases were identified through universal genotyping—six of these were early in the outbreak (2009 Q1). Two other recognized outbreaks, one previously described74 (growth rate = 0.8 case/quarter) and the other spanning a more remote part of the province (1.1 cases/quarter), had partially requested isolates (44.4% and 37.5% of cases missed, respectively). MClust-012 involved an urban population with a high material deprivation index (Table 2-7). Here, only five of 28 individuals in the cluster had a genotyping request (Figure 2-4; Table 2-7), three of which were late in the outbreak (2013), and the requests for a 2009 and a 2010 isolate asked for comparisons to outbreak strains other than MClust-012. Requests were less common among clusters involving largely non-Canadian-born individuals, where the request rate in the three largest clusters (≥10 individuals) averaged 22.6% (Table 2-7). 56 Table 2-7. Genotype cluster characteristics. Characteristics of 24-locus MIRU–VNTR clusters comprised of ≥5 individuals, displayed as clusters that were predominantly Canadian- or non-Canadian-born, British Columbia, 2009–2013. Cluster ID Cluster Size (% Requested) Predominant Birthplacea (%) Median Age (IQR) years Gender M:F Predominant Community Type (%) Risk Factorsb (%) Median Deprivationc Quintile Canadian–born MClust-002 53 (86.8) Canada (86.0) 51 (44–58) 12.2 Metro (71.7) 39.6 4.0 MClust-012 28 (17.9) Canada (89.3) 46 (41–51) 1.8 Metro (89.3) 35.7 4.0 MClust-003 24 (62.5) Canada (95.7) 44 (28–52) 2.4 Urban/Rural (41.7/41.7) 45.8 4.0 MClust-001 18 (55.6) Canada (100.0) 46 (37–52) 0.8 Rural (50.0) 44.4 4.0 MClust-008 14 (50.0) Canada (85.7) 51 (37–63) 1.3 Metro (85.7) 42.9 3.0 MClust-004 8 (62.5) Canada (100.0) 47 (40–57) 1.0 Urban (75.0) 37.5 4.0 MClust-005 7 (42.9) Canada (85.7) 57 (46–60) 2.5 Metro (85.7) 14.3 2.0 MClust-006 5 (100.0) Canada (100.0) 25 (23–37) 0.7 Rural (80.0) 0.0 3.0 MClust-052 5 (60.0) Canada (100.0) 53 (43–61) NAd Metro/Urban (40.0/40.0) 100.0 5.0 MClust-055 5 (100.0) Canada (100.0) 42 (34–48) 0.7 Urban (80.0) 20.0 3.0 Non-Canadian-born MClust-011 19 (21.1) Philippines (94.4) 42 (33–53) 2.8 Metro (68.4) 0.0 4.0 MClust-021 12 (16.7) Philippines (100.0) 44 (29–51) 0.7 Metro (83.3) 0.0 3.5 MClust-187 10 (30.0) Mixed (88.8) 80 (55–88) 0.7 Metro (100.0) 0.0 3.0 MClust-046 9 (22.2) India (77.8) 70 (56–75) 1.2 Metro (88.9) 0.0 2.0 MClust-038 8 (12.5) China/Hong Kong (100.0) 79 (72–81) 3.0 Metro (100.0) 0.0 3.5 MClust-033 7 (14.3) Mixed (85.8) 68 (42–76) 0.8 Metro (100.0) 0.0 3.0 MClust-149 6 (0.0) India (83.3) 50 (34–76) 0.5 Metro/Urban (50.0/50.0) 0.0 2.5 MClust-170 6 (33.3) China (83.3) 68 (55–75) 1.0 Metro (100.0) 0.0 3.0 MClust-016 5 (40.0) Philippines (100.0) 39 (32–41) 0.7 Metro/Urban (40.0/40.0) 0.0 3.0 MClust-040 5 (20.0) Mixed (80.0) 75 (35–86) 0.7 Metro (100.0) 0.0 3.0 Abbreviation: IQR, interquartile range. aInformation for birthplace was unknown for six persons; percentage represents those with complete data. Mixed indicates various Asian countries. bOne or more risk factors for transmission (HIV, illicit drug use, or alcohol misuse); data unavailable (n = 50); percentage represents those with complete data. cCanadian Marginalization Index,316 material deprivation (quintile 1: least deprived, quintile 5: most deprived); data unavailable (n = 23). dAll individuals were male. 57 Figure 2-4. Cluster growth by genotype request status. Annual cluster growth and overall cluster size for all clusters with three or more persons in BC from 2009 to 2013. Bars are colored by genotype requested (yes/no). Twenty-four-locus MIRU-VNTR genotyping cluster identifiers (MClustID) in bold italics represent clusters that are composed of predominantly Canadian-born persons. 2.4 Discussion In low TB incidence settings, clinical laboratories considering universal genotyping must demonstrate that it offers substantial epidemiological insights beyond those from an on-request service. This study leveraged a unique situation, in which five years of an on-request genotyping program was compared to the information later gained from retrospective genotyping of all of the isolates during this period, to generate the evidence to justify ongoing universal genotyping. During the on-request period, the existence of many genotypic clusters and the full extent of many other clusters were missed. MIRU-VNTR genotyping overestimates the clustering of related isolates, particularly for clusters involving non-Euro-American M. tuberculosis 58 lineages.318 With 62% of BC’s cases attributable to non-Euro-American lineages (see Chapter 3), some of the missed clusters are likely pseudoclusters and do not reflect true local transmission. However, clusters involving Canadian-born persons that do likely represent local transmission were also partially or fully missed by on-request genotyping. Whole-genome sequencing (WGS) of all clustered isolates was later undertaken to provide a more accurate quantification of local transmission within BC, as well as strain-specific insights into drug resistance and transmissibility (Chapter 8). Genotyping requests were most often used to investigate suspected community transmission, particularly in individuals with known risk factors. MIRU-VNTR genotyping results confirmed many potential transmission events, but specific suspicions, in which an individual or outbreak strain comparator was noted in the request, were less frequently correct. This suggests that clinicians understood the risk factors for transmission but that the underlying epidemiological networks were not as clear. Universal genotyping provides a bias-free method to identify connections between cases and reveal possible transmissions between individuals who do not fit traditional risk profiles. In a secondary analysis, restricting the data to include only dates prior to the spike in requests (2013 Q3 and Q4) increased the odds of belonging to a genotypic cluster in relation to request status. These results indicate a possible shift in reasoning behind genotype requests in 2013. Clinicians were likely recognizing that genotyping provides a deeper understanding of the molecular epidemiology of TB and were thus issuing genotyping requests not only to address a specific hypothesis about transmission but also to understand the overall transmission dynamics of TB in BC. Prospective universal genotyping will enable earlier detection of clusters and allow prompt intervention.313 However, this can only occur if those capable of acting on the information have timely access to it. Universal genotyping requires an efficient and effective means of communicating genotyping results, such as the online tools developed in other jurisdictions.307,319 While implementation of a universal genotyping program incurs additional costs, it is believed 59 that the incremental expenditure associated with additional genotyping and the cost of implementing a new reporting system are minimal on the scale of a provincial public health budget. This is especially true when considered in the context of TB infections prevented, as the average cost of treating a person with active TB in Canada is $47,000,11 and when universal genotyping refutes suspected transmission and large-scale contact tracing and case finding are avoided, especially in complex settings such as homeless shelters.313 Tangible benefits are also realized when specimen cross-contamination events are revealed by universal genotyping and an individual can be taken off unnecessary therapy.320,321 While the data make a strong case for implementing universal genotyping in a low-incidence setting, it is impossible to know with certainty how many new infections would have been prevented if universal genotyping had been in place since 2009; thus, the true public health impact of this intervention cannot be fully assessed. However, universal genotyping of M. tuberculosis in New York City revealed new transmission sites and contributed to the rapid diagnosis and treatment of both active cases and infected contacts.313 It is also difficult to assess the future potential of universal genotyping in well-resourced settings, where WGS is supplanting MIRU-VNTR genotyping as the method of choice for inferring transmission. Until WGS of all isolates is routinely performed, MIRU-VNTR genotyping and other molecular methods provide valuable insight into a region’s TB epidemiology and permit comparison of patterns across jurisdictional boundaries. If countries like Canada are to achieve the ambitious elimination targets set by the World Health Organization, every available tool in our arsenal must be used to accelerate progress toward making TB an infection of the past. 60 Chapter 3: Molecular Epidemiology of Tuberculosis in British Columbia, Canada—A 10-Year Retrospective Study 3.1 Background As tuberculosis (TB) prevention and care programs in low-incidence, well-resourced settings look to accelerate progress towards elimination, it’s clear that different interventions are required for different populations—whether it be enhanced screening and uptake of latent tuberculosis infection (LTBI) preventative therapy or interventions aimed at accelerating diagnosis and reducing person-to-person transmission. To identify discrete groups of individuals with TB and ultimately develop tailored interventions bespoke to each, molecular genotyping methods such as 24-locus mycobacterial interspersed repetitive unit–variable-number tandem repeat (MIRU-VNTR) can be leveraged.77 MIRU-VNTR is a PCR-based technique with high discriminatory power, often used to differentiate relapse from reinfection, detect laboratory cross-contamination events, and identify outbreaks and endemically circulating strains.322 Canada has a low TB incidence rate of 4.4 cases per 100,000 population, but amongst the provinces, British Columbia (BC) has one of the highest rates—6.3 cases per 100,000 population.9 More than 80% of BC’s TB diagnosed individuals live in the Greater Vancouver Region (GVR),9 home to approximately half of BC’s residents and the majority of BC’s immigrant population.323 This latter group represents 81% of BC’s TB diagnoses,9 in whom active TB disease is generally thought to result from reactivation of LTBI acquired in the individual’s country of origin. Risk factors for TB disease in this group are likely markedly different from those in the group whose disease results from a locally transmitted infection. Previous population-based molecular epidemiological studies in Canada have focused largely on specific metropolitan areas,51,324,325 with few province-wide studies,326–328 and no provincial study has used 24-locus MIRU-VNTR; thus, a retrospective genotypic survey of all culture-61 positive TB diagnoses in BC from 2005–2014 was undertaken to better understand the patterns underlying TB transmission in BC. 3.2 Materials and Methods 3.2.1 Study setting and design The British Columbia Centre for Disease Control (BCCDC)’s Public Health Laboratory (BCPHL) receives all Mycobacterium tuberculosis (Mtb) cultures for the province, and oversees routine diagnosis, and phenotypic drug sensitivity testing. Prior to 2014, genotyping was performed on request, with approximately 20% of isolates genotyped annually. Therefore, a retrospective study was designed to include all persons with culture-confirmed TB (79.5% of all 2,915 diagnoses), residing in BC whose first Mtb isolate was received by the BCPHL from 2005 through 2014 (n = 2,318). Mycobacterium africanum (n = 29), Mycobacterium bovis (n = 3), and Mycobacterium bovis bacilli Calmette-Guérin (n = 19) were excluded from the analysis—these are not commonly isolated at BCPHL and local transmission was not expected. For individuals with a recurrence during the study period, data from their first episode only was used if isolates from their first and second episode had matching MIRU-VNTR (n = 11), and data from both episodes where MIRU-VNTR indicated reinfection (n = 2). 3.2.2 Case data Individual-level clinical and demographic data were extracted from BCCDC’s Integrated Public Health Information System (iPHIS), which contains both routinely collected surveillance data as reported to the Public Health Agency of Canada and additional data collected in the course of epidemiological contact investigations. Community type was determined using the population density of the geographic service area in which each individual resided—metro (>190,000), urban/rural (40,001–190,000), rural (10,001–40,000), and remote (≤10,000). Postal codes were used to obtain the corresponding census dissemination area (DA) for each case and linked it to the 2006 Canadian Marginalization Index (CAN-Marg)316 to determine the deprivation index quintile—a neighbourhood-level indicator of socioeconomic status. The CAN-Marg material 62 deprivation index measures relative socioeconomic disadvantage of a DA compared to the rest of Canada, reported as quintile values by DA (quintile 1: least deprived, quintile 5: most deprived). 3.2.3 Laboratory analysis All Mtb isolates were revived from BCPHL’s frozen archival stocks on Lowenstein-Jensen (LJ) slants or in MGIT™ liquid medium (Becton-Dickinson, Sparks, MD). Phenotypic drug susceptibility results (isoniazid, rifampin, ethambutol, and streptomycin) were available for each isolate from routine testing on the BACTEC™ MGIT™ 460 or 960 (Becton-Dickinson, Sparks, MD). DNA was extracted using the MagMAX™ Total Nucleic Acid Isolation Kit (Ambion, Austin, TX). Of the 2,307 culture-positive isolates meeting study criteria (Figure 3-1), 17 isolates had incomplete MIRU-VNTR or were unavailable for genotyping—leaving a total of 2,290 (99.3%) isolates which were successfully genotyped using standard methods.77 Major lineage was predicted for each isolate using TB-Insight’s CBN method.329 Phylogenetic relationships within each lineage were visualized using a minimum-spanning tree (MST) in PHYLOViZ 2.0.330 Figure 3-1. Molecular epidemiology study inclusion/exclusion criteria. All Mtb sensu stricto culture positiveisolates, British Columbia, 2005–2014n = 2,318All Mtb sensu stricto culture positiveisolates, British Columbia 2005–2014with first isolate of a relapse representedn = 2,307Excluded additional recordsfrom relapse patients*n = 11Excluded records withincomplete or no MIRUavailable (n = 17)All Mtb sensu stricto culture positiveisolates, British Columbia, 2005–2014with first isolate of a relapse representedand MIRU availablen = 2,290*First episode for a relapse patient was maintained in the study; relapse was defined asa subsequent episode with a genotype ≤1 MIRU-VNTR locus different to the initialepisode.63 3.2.4 Statistical analysis A cluster was defined as ≥2 isolates with identical MIRU-VNTR patterns. The odds ratio (OR) and 95% confidence interval (CI) was then estimated for the distribution of persons by cluster status (clustered/non-clustered) according to birthplace and other clinical and demographic variables. To examine factors associated with cluster growth a multivariable logistic regression model was constructed with cluster size—large (≥10 persons) versus small (<10 persons)—as the outcome, using backward elimination of factors identified in univariable analysis (p < 0.20), and Akaike’s Information Criterion (AIC) minimisation.331 Because the variables (HIV status, illicit drug and alcohol misuse) had >5% missing values, Little’s test332 was performed to assess whether these data were missing completely at random (MCAR). The test suggested no violation of this assumption and missing values were unrelated to genotypic clustering (p > 0.05). To test the association between TB lineage and disease site, a Chi-square test was used, and to examine time from immigration to active TB disease, as well as median age between clustered and non-clustered individuals, the Mann-Whitney U test was used. All analyses were executed in R (v3.3.1). 3.3 Results Table 3-1 presents an overview of the demographic and clinical characteristics of culture-positive tuberculosis in BC. The median age was 52 years, with the highest proportion of diagnoses occurring in individuals aged 35–54. Males outnumbered females 1.4:1. Country of birth was available for 97.5% of individuals, most of whom (73.7%) were non-Canadian-born. Although 78 countries were represented, most TB cases born outside Canada came from high-incidence settings,333 with 23.2% from India, 20.9% from Philippines, 18.5% from China, and 25.0% from other Asian countries. Most individuals (76.6%) lived in metro regions at the time of TB diagnosis. With respect to clinical characteristics, most (77.2%) had respiratory TB, and of these 16.3% of individuals were characterized as having cavitary disease based on chest radiography. Of the persons for whom HIV status was known (82.4%), 103 individuals were HIV-positive. A small fraction of individuals were recorded as using drugs (5.7%) or alcohol (5.5%). 64 Phenotypic drug susceptibilities were available for all genotyped isolates, with multi-drug resistance (MDR) defined as resistance to at least isoniazid and rifampin found in 18 (0.8%) isolates (Table 3-2). 65 Table 3-1. Study population. Demographic and clinical characteristics of culture positive TB cases, British Columbia 2005–2014 (n = 2,290).a Characteristic No. Cases (%) Age, years 0–14 32 (1.4) 15–34 500 (21.8) 35–54 704 (30.7) 55–74 584 (25.5) 75+ 470 (20.5) Genderb Male 1329 (58.1) Community type Metro 1753 (76.6) Urban/Rural 332 (14.5) Rural 173 (7.6) Remote 32 (1.4) Birthplacec Canada 588 (26.3) Non-Canadian-born continentd Asia 1437 (87.6) Africa 79 (4.8) Europe 69 (4.2) Americas 45 (2.7) Oceania 11 (0.7) Time in Canadae < 5 years 456 (28.6) ≥ 5 years 1141 (71.4) Disease Site Respiratory 1767 (77.2) Non-Respiratory 363 (15.9) Respiratory + Non-Respiratory 160 (7.0) Respiratoryf smear Positive 1152 (62.1) Cavitary disease Yes 315 (13.8) Drug susceptibility MDR 18 (0.8) INH-R (non-MDR) 173 (7.6) Continued on next page 66 Table 3-1 Continued from previous page Characteristic No. Cases (%) HIV Positive 103 (4.5) Negative 1784 (77.9) Unknown 403 (17.6) Illicit drug use Yes 130 (5.7) No 1639 (71.6) Unknown 521 (22.8) Alcohol misuse Yes 125 (5.5) No 1656 (72.3) Unknown 509 (22.2) Material deprivationg Quintile 1 (least) 273 (12.5) Quintile 2 418 (19.2) Quintile 3 529 (24.3) Quintile 4 529 (24.3) Quintile 5 (most) 427 (19.6) Abbreviations: HIV, human immunodeficiency virus; INH-R, isoniazid resistant; MDR, multidrug-resistant (tuberculosis resistant to isoniazid and rifampin). aPercentages have been rounded and may not total 100%. bOne transgender/gender-unknown individual excluded from analysis. cData unavailable for 57 individuals. dData unavailable for 4 individuals. eData unavailable for 48 individuals. f“Other respiratory” sites (e.g. pleura) were excluded. gData unavailable for 114 individuals. 67 Table 3-2. Multi-drug resistant isolates. Characteristics of Mycobacterium tuberculosis multi-drug resistant cases in British Columbia, 2005–2014 (n = 18). Birth Sub-Continent Region Case Type Disease Site Lineage MIRU-VNTR Clustera North America GVR New Resp. East-Asian Yes North America GVR New Resp. East-Asian Yesb East Asia GVR New Resp. East-Asian Yesb East Asia GVR New Resp. East-Asian East Asia GVR Retreatment Resp. East-Asian Yes East Asia GVR Retreatment Resp. East-Asian South-Central Asia GVR New Resp. East-African-Indian Yes South-Eastern Asia GVR Retreatment Resp. Indo-Oceanic South-Eastern Asia GVR New Non-Resp. Indo-Oceanic South-Eastern Asia GVR New Non-Resp. Indo-Oceanic Yes South-Eastern Asia GVR New Resp.+Non-Resp. Indo-Oceanic Northern Europe GVR New Resp. East-Asian Yes East Asia GVR Retreatment Resp. East-Asian East Asia GVR New Resp. East-Asian Yes East Asia non-GVR Retreatment Resp. East-Asian East Asia GVR Retreatment Resp. East-Asian South-Eastern Asia GVR New Resp. East-Asian South-Eastern Asia GVR New Resp. East-Asian Abbreviations: GVR, Greater Vancouver Region; Non-Resp., non-respiratory; Resp., respiratory. aYes indicates that the isolate belongs to a MIRU-VNTR cluster. bSame cluster; known transmission event. 68 3.3.1 Lineage analysis First, the phylogenetic structure of BC’s Mtb population was examined and the association between lineage and the study variables explored. An MST revealed numerous large Euro-American clusters with distinct clades containing sizable clusters (Figure 3-2). Consistent with previous research,41 it was found that lineage reflected birthplace (Figure 3-3), and the Euro-American group contained largely Canadian-born persons (57.7%). The majority (13/18) of MDR isolates belonged to the East-Asian lineage (Table 3-2). Disease site varied by lineage, and it was found that the proportion of exclusively non-respiratory tuberculosis was higher amongst individuals with an Indo-Oceanic lineage (26.7%) versus other lineages: East-Asian Indian (18.2%), East-Asian (12.6%), and Euro-American (10.4%), p < 0.001. Persons with an Indo-Oceanic strain also had the highest proportion of respiratory disease with non-respiratory involvement (Table 3-3). Clustering rates varied between lineages, with 54.5% of Euro-American, 43.3% of East-Asian, 33.8% of Indo-Oceanic and 22.7% of East-African Indian isolates clustering. The five largest clusters belonged to the Euro-American lineage (Table 3-4). 69 Figure 3-2. Population structure of Mycobacterium tuberculosis genotypes in BC. Minimum spanning tree analysis of mycobacterial interspersed repetitive-unit–variable-number tandem repeat (MIRU-VNTR) genotyping for Mycobacterium tuberculosis isolates, British Columbia (2005–2014). The size of each circle is proportional to the number of isolates. Classification of strains by birthplace is visualized with color coding. The inset demonstrates overall cluster composition with respect to birthplace; relative frequency of clusters that were exclusively Canadian-born (Excl.CB), exclusively non-Canadian-born (Excl. nCB), Canadian- and non-Canadian-born (CB and nCB), or where there were cases in a cluster with only CB or nCB identified in addition to ≥1 case of unknown birthplace. Note that percentages have been rounded and may not total to 100%. 70 Figure 3-3. Mycobacterium tuberculosis lineage by continent of birth. Relative frequency of Mycobacterium tuberculosis (Mtb) sensu stricto lineages by continent of birth of cases. The inset map separates the Mtb lineages of Asian-born persons into the relative frequency observed within each Asian sub-continent. Pie chart areas are scaled relative to the number of cases. Table 3-3. Lineage by anatomical disease site. Mycobacterium tuberculosis sensu stricto lineages by anatomical disease site in British Columbia, 2005–2014.a Lineage No. Cases (%) NRTB vs. RTB OR (95%CI) RTB NRTB RTB+NRTB Euro-American 723 (83.0) 91 (10.4) 57 (6.5) Reference East-Asian 456 (81.9) 70 (12.6) 31 (5.6) 1.2 (0.9–1.7) East-African Indian 252 (76.4) 60 (18.2) 18 (5.5) 1.9 (1.3–2.7) Indo-Oceanic 336 (63.2) 142 (26.7) 54 (10.2) 3.4 (2.5–4.5) Abbreviations: CI, confidence interval; NRTB, exclusively non-respiratory; OR, odds ratio; RTB, exclusively respiratory; RTB+NRTB, respiratory+non-respiratory. aPercentages have been rounded and may not total 100%. 71 Table 3-4. Large cluster characteristics. Characteristics of 24-locus MIRU–VNTR clusters comprised of ≥ 10 individuals, displayed as clusters that were predominantly Canadian- or non-Canadian-born: British Columbia, 2005–2014. Cluster ID Cluster Size Predominant Birthplacea (%) Median Age (IQR) years Gender M:F Predominant Community Type (%) Lineage Canadian–born MClust-002 70 Canada (88.1) 50 (43–57) 13.0 Metro (78.6) Euro-American MClust-012 64 Canada (87.1) 48 (40–56) 3.0 Metro (79.7) Euro-American MClust-001 56 Canada (96.4) 40 (29–48) 0.8 Rural (75.0) Euro-American MClust-003 39 Canada (97.4) 45 (29–50) 1.8 Rural (56.4) Euro-American MClust-008 36 Canada (91.7) 41 (35–55) 1.1 Metro (83.3) Euro-American MClust-035 17 Canada (88.2) 39 (32–58) 1.7b Metro (82.4) East-Asian MClust-052 17 Canada (94.1) 49 (46–53) 3.2 Metro (82.4) Euro-American MClust-134 13 Canada (100.0) 59 (46–68) 3.3 Rural (84.6) Euro-American MClust-055 10 Canada (100.0) 38 (31–43) 1.5 Urban/Rural (70.0) Euro-American Non-Canadian-born MClust-011 34 Philippines (97.0) 41 (31–50) 2.1 Metro (76.5) Indo-Oceanic MClust-021 25 Philippines (100.0) 50 (31–54) 0.8 Metro (84.0) Indo-Oceanic MClust-038 16 China/Hong Kong (87.5) 76 (64–81) 1.3 Metro (100.0) East-Asian MClust-187 16 China/Hong Kong (80.0) 66 (47–87) 0.6 Metro (100.0) East-Asian MClust-149 13 India (84.6) 59 (34–79) 0.9 Metro (61.5) East-African Indian MClust-046 12 India (75.0) 71 (64–78) 2.0 Metro (91.7) East-African Indian MClust-032 11 Mixedc 54 (48–82) 1.2 Metro (100.0) East-Asian Abbreviations: IQR, interquartile range; MIRU-VNTR, mycobacterial interspersed repetitive-unit–variable-number tandem repeat. aInformation for birthplace was unknown for 9 individuals; percentage represents those with complete data. bNot included in the ratio, 1 transgender/gender unknown individual. cPredominantly East Asian and South-East Asian countries. 72 3.3.2 MIRU-VNTR identifies discrete subgroups amongst BC’s TB cases Next, individual- and community-level risk factors driving clustering in BC were examined. MIRU-VNTR revealed that of 2,290 isolates, 1,319 (57.6%) were unique profiles, likely reflecting LTBI reactivation, while the remaining 42.4% were grouped into 189 clusters (2–70 isolates/cluster), suggesting potential local transmission (Table 3-5). Via the “n – 1” method88 MIRU-VNTR estimated that 782 (34.1%) of infections could have resulted from local transmission. The median age of non-clustered individuals was higher (56 years) compared to clustered (48 years), p < 0.001. Amongst males, 44.6% were clustered versus 39.3% amongst females. Other factors for clustering included HIV, illicit drug use and alcohol misuse (Table 3-6). Within the group of Canadian-born persons the majority (77.0%) were in a cluster, while only 30.2% of persons born outside Canada belonged to a cluster; indeed, the odds of belonging to a cluster were 7.8 times higher for Canadian-born persons (95%CI, 6.2–9.6), Table 3-6. Interestingly, few (16.4%) clusters were exclusively Canadian-born (Figure 3-2). When stratified by birthplace, risk factors for clustering followed similar trends between Canadian- and non-Canadian-born persons; however, the strength of association differed (Table 3-6). For example, both Canadian- and non-Canadian-born persons residing in remote communities had increased odds of belonging to a cluster compared to individuals in metro areas, but odds were higher amongst the Canadian-born (3.6. vs 1.7). Illicit drug use and alcohol misuse were also significantly associated with clustering in Canadian-born persons, and those living in areas of high material deprivation had 2.3 higher odds of belonging to a cluster (95%CI, 1.2–4.4). 73 Table 3-5. Genotype cluster sizes. Genotyping results (24-locus MIRU-VNTR), including genotype clustersa (n = 189) by size and frequency in British Columbia, 2005–2014.b Characteristic Number Percentage (%) Isolates Unique genotype 1319 57.6 Clustered genotype 971 42.4 Clusters Cluster Size 2 isolates 102 54.0 3 isolates 33 17.5 4 isolates 7 3.7 5–9 isolates 31 16.4 10–29 isolates 10 5.3 30–49 isolates 3 1.6 ≥50 isolates 3 1.6 Abbreviation: MIRU-VNTR, mycobacterial interspersed repetitive-unit–variable-number tandem repeat. aClusters are defined as ≥2 individuals with Mycobacterium tuberculosis infection who share an identical genotype. bPercentages have been rounded and may not total 100%. 74 Table 3-6. Risk factors for genotypic clustering. Distribution and univariable analysis of risk factors associated with Mycobacterium tuberculosis genotypic clustering stratified by birthplace, British Columbia 2005–2014.a All Cases Canadian-born Non-Canadian-born Characteristic Clustered n (%) Unique n (%) Clustered vs. Unique OR (95%CI) Clustered vs. Unique OR (95%CI) Clustered vs. Unique OR (95%CI) Age, years 0–14 16 (50.0) 16 (50.0) 1.3 (0.6–2.6) 0.8 (0.3–2.5) 0.7 (0.2–2.3) 15–34 221 (44.2) 279 (55.8) Reference Reference Reference 35–54 370 (52.6) 334 (47.4) 1.4 (1.1–1.8) 2.4 (1.4–4.2) 1.0 (0.7–1.3) 55–74 237 (40.6) 347 (59.4) 0.9 (0.7–1.1) 0.9 (0.5–1.5) 0.9 (0.6–1.1) 75+ 127 (27.0) 343 (73.0) 0.5 (0.4–0.6) 0.3 (0.2–0.6) 0.6 (0.5–0.9) Gender Male 593 (44.6) 736 (55.4) 1.2 (1.1–1.5) 1.1 (0.7–1.6) 1.1 (0.9–1.4) Female 377 (39.3) 583 (60.7) Reference Reference Reference Community type Metro 678 (38.7) 1075 (61.3) Reference Reference Reference Urban/Rural 142 (42.8) 190 (57.2) 1.2 (0.9–1.5) 0.7 (0.4–1.1) 0.9 (0.7–1.3) Rural 126 (72.8) 47 (27.2) 4.3 (3.0–6.0) 2.1 (1.2–3.6) 0.8 (0.4–1.8) Remote 25 (78.1) 7 (21.9) 5.7 (2.4–13.2) 3.6 (0.8–15.5) 1.7 (0.4–7.7) Birthplace Canada 453 (77.0) 135 (23.0) 7.8 (6.2–9.6) – – Outside Canada 497 (30.2) 1148 (69.8) Reference Disease Site Resp. 776 (43.9) 991 (56.1) 1.5 (1.2–1.9) 1.7 (0.9–3.3) 1.0 (0.8–1.3) Non-Resp. 125 (34.4) 238 (65.6) Reference Reference Reference Resp. + Non-Resp. 70 (43.8) 90 (56.2) 1.5 (1.0–2.2) 2.1 (0.8–5.9) 1.2 (0.7–1.8) Respiratoryb smear Positive 521 (45.2) 631 (54.8) 1.1 (0.9–1.4) 1.6 (1.0–2.4) 0.9 (0.7–1.1) Cavitary disease 156 (49.5) 159 (50.5) 1.4 (1.1–1.8) 0.8 (0.5–1.4) 1.3 (1.0–1.8) HIV positive 66 (64.1) 37 (35.9) 2.6 (1.7–3.9) 1.6 (0.8–3.1) 0.6 (0.3–1.5) Illicit drug use 112 (86.2) 18 (13.8) 10.3 (6.2–17.0) 2.7 (1.5–5.0) 3.8 (0.9–16.1) Alcohol misuse 97 (77.6) 28 (22.4) 5.6 (3.6–8.6) 2.7 (1.4–5.1) 1.4 (0.6–3.2) Material deprivation Quintile 1 (least) 100 (36.6) 173 (63.4) Reference Reference Reference Quintile 2 148 (35.4) 270 (64.6) 0.9 (0.7–1.3) 2.0 (0.9–4.4) 0.9 (0.6–1.4) Quintile 3 196 (37.1) 333 (62.9) 1.0 (0.8–1.4) 1.3 (0.7–2.6) 1.0 (0.7–1.5) Quintile 4 220 (41.6) 309 (58.4) 1.2 (0.9–1.7) 1.8 (0.9–3.7) 1.2 (0.8–1.8) Quintile 5 (most) 224 (52.5) 203 (47.5) 1.9 (1.4–2.6) 2.3 (1.2–4.4) 1.1 (0.7–1.7) Abbreviations: CI, confidence interval, HIV, human immunodeficiency virus; Non-Resp., non-respiratory; OR, odds ratio; Resp., respiratory. aPercentages have been rounded and may not total 100%. b“Other respiratory” sites (e.g. pleura) were excluded. 75 3.3.3 MIRU-VNTR identifies drivers of large transmission clusters Finally, analyses were conducted to explore the differences between large clusters, typically representing outbreaks requiring public health intervention, from smaller clusters. Individuals in large clusters (≥10 persons) were more likely to be Canadian-born (adjusted OR [aOR] 3.3, 95%CI: 2.3–4.8), reside in a rural area (aOR 2.3, 95%CI: 1.2–4.5), or use drugs (aOR 2.0, 95%CI: 1.2–3.4), Table 3-7. Amongst the 16 large clusters (Table 3-4), nine were comprised predominantly of Canadian-born individuals (≥87.1%), and the few non-Canadian-born individuals within these clusters had a median time from immigration to active TB disease of 40 years (IQR: 25–49). Additionally, for these non-Canadian-born persons, where country of birth was known (n = 24), only five (20.8%) individuals emigrated from high-burden TB countries.333 Conversely, amongst the seven large clusters comprising mainly non-Canadian-born individuals, most (89.9%) emigrated from high-burden countries and had a significantly lower median time from immigration to active disease (12 years, IQR: 3–18), p < 0.001. 76 Table 3-7. Risk factors associated with cluster size. Multivariable analysis of factors associated with large and small 24-locus MIRU-VNTR clusters in British Columbia 2005–2014 (n = 971).a Characteristic Large vs. Small OR (95%CI) Large vs. Small aORb (95%CI) Age, years 0–14 0.9 (0.3–2.6) 0.7 (0.2–2.6) 15–34 Reference Reference 35–54 1.4 (1.0–2.0) 1.2 (0.8–1.8) 55–74 0.9 (0.6–1.3) 1.1 (0.7–1.8) 75+ 0.5 (0.3–0.8) 0.9 (0.5–1.6) Gender Male 1.3 (1.0–1.7) 1.4 (1.0–1.9) Community type Metro Reference Reference Urban/Rural 1.4 (0.9–2.0) 0.9 (0.6–1.5) Rural 3.2 (2.1–4.9) 2.3 (1.2–4.5) Remote 0.7 (0.3–1.5) 0.5 (0.2–1.4) Birthplace Canada 4.6 (3.5–6.1) 3.3 (2.3–4.8) Illicit drug use 4.9 (3.1–7.8) 2.0 (1.2–3.4) Abbreviations: aOR, adjusted odds ratio; CI, confidence interval; MIRU-VNTR, mycobacterial interspersed repetitive-unit–variable-number tandem repeat; OR, odds ratio. aLarge clusters were defined as ≥10 persons; small clusters, as <10 persons. bAdjusted for age, gender, community type, birthplace, and illicit drug use. 3.4 Discussion The molecular epidemiology of tuberculosis in British Columbia from 2005 through 2014 was described and demonstrates using a near-complete (99.3%) isolate collection, that BC has notable strain diversity, with >1,500 distinct MIRU-VNTR genotypes. The Mtb population structure reflects the global nature of BC’s residents. Migration to BC has been occurring for several centuries, first by predominately European settlers and later with individuals from all over the world—especially from Asia334—which is reflected in the proportion of each lineage by region of birth. Clustering rates vary between lineages, with the largest clusters belonging to the Euro-American lineage, typical of what has been reported in European and North American-born 77 populations.41 An MST revealed sizable clusters within the Euro-American lineage, and distinct sub-groups, likely reflecting a long history of migration to Canada and independent introduction of strains which have diversified and now circulate endemically, such as those introduced during the fur trade in previous centuries.92 Different Mtb lineages have frequently been associated with phenotypic differences such as propensity for drug resistance, varying pathogenicity, and tendencies towards specific disease sites.46,335 Indeed, it was observed that the bulk of MDR disease occurring in individuals with East-Asian strains, while individuals with Indo-Oceanic and East-African Indian lineages had higher odds of non-respiratory disease and the lowest clustering rates, an observation in line with a large U.S. study.46 Given that non-respiratory TB requires a high index of suspicion for diagnosis and commonly results in diagnostic delays and increased morbidity and mortality,6 the observation here suggests that clinicians treating individuals who have emigrated from countries where Indo-Oceanic and East-African Indian strains circulate, might benefit from educational initiatives urging them to “think TB”. Overall, 189 clusters comprising 42.4% of the study isolates were identified. Clustering rates previously reported from smaller studies in BC have varied substantially, with earlier work in the Greater Vancouver area reporting a much smaller clustering rate of 17.3%,324 and a study of Western Canadian provinces suggesting clustering from 0–82%.328 Given the near complete sampling over a decade-long period that was undertaken, the figure reported here represents the most accurate estimate of genotype-level clustering for this setting. Using the “n – 1” method,88 it was estimated that 34.1% of BC’s cases may be the result of local transmission, a figure identical to a study121 from London, England—a city with a similarly large and ethnically diverse population. This is certainly an overestimation—reports directly comparing MIRU-VNTR to whole genome sequencing (WGS) have shown that genotype-level identity does not always correspond to genomic distances that reflect recent, local transmission.140,336 Indeed, two large Indo-Oceanic clusters were noted whose MIRU-VNTR patterns match those of clusters reported elsewhere in Canada.140 WGS yielded genomic distances incompatible with local transmission, suggesting that these clusters likely represent regionally endemic strains acquired in the country 78 of origin rather than transmission within Canada.140 Subsequent work (Chapter 8) includes sequencing of clustered isolates identified here to further refine the estimate of transmission, and will allow us to prioritise MIRU-VNTR clusters for investigation. Where MIRU-VNTR is most likely to capture true local transmission is amongst the Canadian-born. These individuals had nearly eight times the odds of belonging to a cluster and multiple large clusters were identified—two already characterized by WGS,74,144,174 and most known to public health personnel and involving documented epidemiological links. In a New York City–area study, U.S.-born residents were more likely to be involved in transmission clusters compared to cases born outside the U.S., with the authors concluding that transmission occurs almost exclusively within the American-born population.337 However, in this study, nearly one-quarter of clusters involved both Canadian- and non-Canadian-born individuals, suggesting transmission likely occurs both across and within these populations. A 2014 systematic review of European TB found the percentage of cases in “mixed” clusters ranged from 0% to 34.2%;338 the extent to which this is occurring in BC will be revealed through genomic investigation. Understanding where and amongst whom transmission is occurring permits targeted contact tracing and cluster investigation efforts, improved resource allocation, and interventions tailored to local epidemiology. Here, it was found that while incidence was higher in metro areas, the odds of clustering were higher and cluster size was larger in rural and remote settings (Table 3-6, Table 3-7), suggesting local transmission dominates in low-density settings, while both local transmission and LTBI reactivation contribute to TB case counts in urban areas. Individual-level factors, including HIV, illicit drug use, alcohol misuse, or residing in a marginalized area, were all associated with increased odds of clustering, consistent with other studies.117,339,340 All of this information could be used to develop a risk score for an individual contributing to onward transmission, based on both clinical and demographic factors and a strain’s specific genotype and lineage. Such a score could be used to prioritize individuals for enhanced contact tracing and follow-up during therapy. Additionally, the observation that nearly 70% of non-Canadian-born persons have a unique genotype suggests that targeted LTBI screening is an important strategy for preventing the reactivation that is contributing to the bulk of TB diagnoses in BC. 79 The dataset included only a small number of MDR-TB cases, the majority of which occurred in non-Canadian-born individuals with East-Asian lineage isolates—a lineage known for its association with drug resistance.341 With one exception—a known family transmission—MDR-TB isolates did not cluster by MIRU-VNTR, indicating that transmission most likely occurred prior to arrival in Canada. As immigrant numbers continue to rise in BC, many arriving from regions with high MDR-TB rates, BC is at risk of increased MDR-TB as reported in other low-incidence settings.342 Thus, it is vital to have the molecular tools available to monitor the presence of drug-resistant strains and differentiate MDR-TB resulting through treatment failure from newly acquired MDR-TB infection. The present study does have some important limitations. As noted, although the discriminatory power of MIRU-VNTR is similar to that of restriction fragment length polymorphism (RFLP),343 it does not provide the necessary resolution to differentiate closely related isolates, particularly for non-Euro-American lineages.140,336 It has been suggested that Euro-American strains were over-represented during method development, leading to a bias in the discriminatory power towards this lineage.318 WGS can improve this resolution, which was subsequently undertaken and detailed in Chapter 8. Second, an individual’s country of birth may not accurately reflect their movement. While Mtb lineage often matches what one would expect based on birthplace, some individuals may have lived in other countries prior to arrival in Canada, and furthermore may travel after immigration. Additionally, some of the Canadian-born persons may have non-Canadian-born parents, potentially increasing their risk of TB infection through household exposures and/or travel to their parents’ birthplace. This may account for some of the mixed Canadian-/non-Canadian-born clusters. Unfortunately, this level of detail is not often included in most public health databases, precluding its analysis, but these scenarios are likely infrequent. What is clear is that persons born outside Canada is too broad a category and a more refined definition would benefit TB surveillance efforts. Long-time residents of Canada with social risk factors comprise a very different group compared to recent immigrants and should be viewed as a distinct group by TB programs. An investigation of this issue is described in Chapter 9, Section 9.3.1. 80 This study provides a benchmark against which BC can measure future progress and offers new insight into the molecular epidemiology of tuberculosis in the province. This knowledge can be used to support new policy and practice as BC moves towards the ultimate goal of TB elimination, whether it be LTBI screening and prophylaxis in the immigrant population or a risk score to stratify individuals’ risk of onward transmission. In a setting with declining TB incidence, contact network heterogeneity means that local pockets of transmission will exist, 344 and identifying these quickly is critical to elimination efforts. The finding around rural/remote transmission highlights these regions as hotspots for such pockets. It is recommended to conduct better training of rural clinicians around recognizing TB, improving access to screening and treatment services, and the introduction of mobile technologies to facilitate a virtual clinic model.345 Moreover, to limit the spread of infection a lower threshold for extensive contact tracing in these regions is recommended. In conclusion, it is clear that a multi-pronged approach that includes targeted screening, treatment, and contact tracing informed by molecular epidemiology will have the greatest impact on tuberculosis rates in BC. 81 Chapter 4: Universal Genotyping Reveals Province-Level Differences in the Molecular Epidemiology of Tuberculosis 4.1 Background Tuberculosis (TB) remains a major public health issue in Canada. Molecular techniques, such as 24-locus mycobacterial interspersed repetitive unit–variable-number tandem repeat (MIRU-VNTR) genotyping have improved understanding of TB epidemiology, and many jurisdictions are adopting routine genotyping of all Mycobacterium tuberculosis (Mtb) isolates.121,346 Within Canada, Ontario—the province with the largest number of TB cases6—was an early adopter of universal genotyping, using MIRU-VNTR to genotype the first culture-positive isolate for each case since mid-2007.307 Interpreting these data in the context of linked clinical and demographic information has facilitated both contact tracing and outbreak detection in the province.117 More recently, British Columbia (BC) retrospectively genotyped all first culture-positive isolates since 2005 (see Chapter 3), and implemented universal genotyping in 2015. Together, Ontario and BC represent a substantial burden of disease in Canada, accounting for more than 50% of the nation’s TB cases,6 with rates in both settings largely driven by reactivation of latent TB infection (LTBI) in persons born outside the country. Both provinces are popular destinations for immigrants, with the large multi-cultural cities of Toronto and Vancouver attracting many newcomers.347 Vancouver has a high proportion of immigrants from Asia, whereas Toronto is more diverse, and in addition to people from Asia, also has many immigrants from Africa, the Caribbean and Latin America.347 Furthermore, although Ontario and BC are thousands of kilometres apart, there is substantial interprovincial migration, with ~15,000 individuals reportedly migrating between Ontario and BC in 2016/17,348 often for economic reasons or job opportunities. Migrants frequently lack support networks and are at greater risk for homelessness and other factors associated with increased risk of TB reactivation or infection.349 This is particularly true in BC, where under-housed migrants from other provinces—some of whom are experiencing mental illness, addictions, and/or chronic health conditions349,350—are thought to be attracted to Vancouver by the temperate climate. 82 Each Canadian province/territory works independently towards TB prevention and care and contributes data to the national TB surveillance programs—The Canadian Tuberculosis Reporting System and the Canadian Tuberculosis Laboratory Surveillance System. There is currently no national-level TB molecular surveillance program; however, molecular genotyping data are shared informally between provinces and nationally. With both Ontario and BC now having complete MIRU-VNTR genotyping linked to case-level data dating back over a decade, there is a unique opportunity to compare the molecular epidemiology of TB between the two provinces to provide context to the genotypes observed within each region—thereby improving our understanding of genotypic clustering as it relates to local spread of TB, and investigating the frequency of interprovincial TB transmission. 4.2 Methods 4.2.1 Study setting and design Ontario and BC are the first and third most populous Canadian provinces, respectively, with 14.2 and 4.8 million inhabitants,348 and rank first and second for the highest population proportion of immigrants, at 28.5% for Ontario and 27.6% for BC.351 All Mycobacterium tuberculosis (Mtb) isolates are either identified in culture at the provincial reference laboratories—Public Health Ontario Laboratory (PHOL) and British Columbia Centre for Disease Control Public Health Laboratory (BCPHL), or submitted for reference testing from other laboratories. The study population included all culture-positive TB cases residing in Ontario or BC at TB treatment initiation, with a first Mtb sensu stricto isolate received from 2008 through 2014. Included were 3,314 Ontario and 1,602 BC isolates, representing 75.2% and 79.7% of all notified TB diagnoses during this time period in the respective provinces. For individuals with a reoccurrence during the study period indicative of relapse—successful completion of treatment and identical MIRU-VNTR results for both episodes (Ontario: n = 5, BC: n = 9), only data from the first episode was included. 83 4.2.2 Diagnosis and case information All TB cases diagnosed in Ontario are reported to the responsible public health unit, and in BC are reported to the British Columbia Centre for Disease Control (BCCDC), as well as local public health authorities. Case-level clinical and demographic data such as age, gender, birthplace, and disease site were extracted from the Integrated Public Health Information System (iPHIS) for each province. To assess genotyping in the context of urban/rural regions, community type was determined using Statistics Canada-defined health region Peer Groups (A–I),352 which were grouped into four higher-level categories: Metro (G), Urban, high-density (A, H), Urban, moderate-density (B), Rural/Remote (C–E, I). 4.2.3 Genotyping by 24-locus MIRU-VNTR Using standard methods,77 MIRU-VNTR genotyping was completed for 97.8% (3,314/3,388) of Ontario isolates and 99.8% (1,602/1,605) of BC’s. Isolates lacking an amplicon peak at any locus had MIRU-VNTR repeated with newly extracted DNA, and where there remained no peak at a single locus—excluding loci 2163 and 2165, which are known to be absent in some strains353—the locus was coded as missing data and the isolate included in the analyses. Major lineage was predicted using TB-Insight’s conformal Bayesian network (CBN) method.329 An intraprovincial cluster was defined as ≥2 isolates with an identical MIRU-VNTR pattern within a province, and where one or more isolates shared an identical genotype across the two provinces, this was defined this as an interprovincial cluster. Genotypic clusters within each major lineage were visualized using a Minimum Spanning Tree (MST) created in PHYLOViZ 2.0330 and coloured by province. A circular chord diagram was used to graphically represent the relationship between the number of isolates contributing to a genotype match between the provinces—interprovincial clusters were displayed according to the number of isolates contributing to an interprovincial genotype match: single (1 isolate), small (2–9 isolates), large (≥10 isolates). 84 4.2.4 Statistical analysis Clinical and demographic characteristics were compared between Ontario and BC using a Chi-square test for categorical variables (Fisher’s Exact test where appropriate), and a t-test for continuous variables. Intraprovincial clustering proportions were also compared using Chi-square. Multivariable logistic regression was used to examine factors associated with interprovincial clustering, calculating the odds ratio (OR), adjusted OR (aOR), and 95% confidence interval (CI). To calculate the number of clustered TB cases, i.e. those that were potentially attributable to local transmission, the “n – 1” method was used in which the first case of each cluster is assumed to have initiated the cluster and is subtracted from the total number of clustered isolates.88 A complete-case analysis strategy (excluded records with missing data: n = 109 [2.2%]) was used, with stepwise backward selection of variables following Akaike Information Criterion minimization. All statistical analyses were conducted using R statistical software (v3.4.1). 4.3 Results 4.3.1 Descriptive epidemiology The study population included a total of 4,916 cases (3,314 in Ontario and 1,602 in BC) with a diagnosis of culture-positive TB from 2008 through 2014. The median age was 46 in Ontario with an interquartile range (IQR) of 30–67—significantly lower than in BC (53 years, IQR: 37–72), p < 0.001. Case distribution by community type varied between the provinces (Table 4-1), with many Ontario cases residing in Metro areas (47.0%) and most BC cases in high-density urban areas (55.7%). Notably, BC had a higher proportion of cases residing in rural/remote regions (11.8% versus 4.1%). Country of birth was available for 97.5% of individuals, the majority of whom were born outside Canada (Table 4-1); however, the proportion varied significantly between Ontario (91.3%) and BC (73.5%). Furthermore, Ontario had a higher proportion of recent immigrants—those arriving within the last five years—(n = 1,024; 35.5%) compared to BC (n = 309; 27.8%). BC had a higher proportion of persons with respiratory disease (85.1%) versus Ontario (74.9%). 85 Table 4-1. Study population. Demographic and clinical characteristics of culture-positive cases 2008–2014, Ontario (n = 3,314) and British Columbia (n = 1,602). Characteristic Ontario British Columbia p-valueb n (%)a n (%)a Age, years <0.001 0–14 51 (1.5) 20 (1.2) 15–34 1001 (30.2) 339 (21.2) 35–54 952 (28.7) 470 (29.3) 55–74 747 (22.5) 425 (26.5) 75+ 563 (17.0) 348 (21.7) Genderc Male 1838 (55.5) 939 (58.6) 0.041 Community type <0.001 Metro 1556 (47.0) 449 (28.0) Urban, high-density 1132 (34.2) 893 (55.7) Urban, moderate-density 490 (14.8) 71 (4.4) Rural/Remote 136 (4.1) 189 (11.8) Birthplaced <0.001 Canada 284 (8.7) 412 (26.5) Non-Canadian-born continente <0.001 Asia 2282 (77.2) 1017 (89.0) Africa 382 (12.9) 50 (4.4) Europe 167 (5.6) 45 (3.9) Americas 120 (4.1) 24 (2.1) Oceania 5 (0.2) 7 (0.6) Time in Canadaf <0.001 < 5 years 1024 (35.5) 309 (27.8) ≥ 5 years 1860 (64.5) 801 (72.2) Disease Siteg <0.001 Respiratory 2256 (68.1) 1250 (78.0) Non-Respiratory 832 (25.1) 238 (14.9) Respiratory + Non-Respiratory 226 (6.8) 114 (7.1) aPercentages have been rounded and my not total 100%. bChi-square test. cData unavailable for 4 Ontario individuals. dData unavailable for 60 Ontario and 45 British Columbia individuals. eData unavailable for 14 Ontario and 2 British Columbia individuals. fData unavailable for 86 Ontario and 35 British Columbia individuals. g“Other respiratory” sites (e.g. pleura) were excluded. 86 4.3.2 TB isolates in BC are more likely to be clustered by MIRU-VNTR MIRU-VNTR genotyping grouped the Ontario Mtb isolates into 290 clusters, with a mean cluster size of four isolates (size range: 2–49), yielding a clustered proportion of 31.8% (Table 4-2). In BC, 134 clusters were identified, with an average cluster size of five isolates (size range: 2–68) and an overall clustered proportion of 40.5%—significantly higher than in Ontario (p < 0.001). Using the “n – 1” method,88 the number of infections potentially attributable to local transmission was 1,053 (23.0%) in Ontario and 649 (32.1%) in BC. In both provinces, more than half the clusters—56.7% in Ontario and 54.9% in BC—contained only two individuals, with few large clusters of ≥10 individuals in either province (Ontario: n = 11 [3.8%], BC: n = 10 [7.5%]). Differences in the clustered proportion between the two provinces was largely driven by clustering amongst Canadian-born persons (Ontario: n = 142 [50.0%], BC: n = 312 [75.7%]), as the clustered proportion was similar for persons born outside Canada (Ontario: n = 892 [30.0%], BC: n = 322 [28.1%]). 87 Table 4-2. Genotype cluster sizes. Genotype (24-locus MIRU-VNTR) results, including intraprovincial genotype clusteringa by size and frequency in Ontario and British Columbia, 2008–2014. Characteristic Ontario n (%)b British Columbia n (%)b Isolates Unique genotype 2261 (68.2) 953 (59.5) Clustered genotype 1053 (31.8) 649 (40.5) Clusters Cluster Size 2 isolates 164 (56.7) 73 (54.9) 3 isolates 56 (19.4) 23 (17.3) 4 isolates 20 (6.9) 5 (3.8) 5–9 isolates 38 (13.1) 22 (16.5) 10–29 isolates 9 (3.1) 7 (5.3) 30–49 isolates 2 (0.7) 2 (1.5) ≥50 isolates 0 (0.0) 1 (0.8) Abbreviation: MIRU-VNTR, mycobacterial interspersed repetitive-unit–variable-number tandem repeat. aClusters are defined as ≥2 individuals with Mycobacterium tuberculosis infection who share an identical genotype. bPercentages have been rounded and may not total 100%. 4.3.3 Interprovincial clustering occurs frequently between Ontario and BC In total, 3,461 distinct MIRU-VNTR patterns were observed across both provinces. Although only 175 of these patterns were detected in both Ontario and BC (Figure 4-1), 22.4% (1,102/4,916) of all study isolates had a genotype pattern detected in both provinces—595 (18.0%) Ontario isolates and 507 (31.6%) BC isolates. The majority of these interprovincially matched isolates were also clustered within their respective provinces—85.5% (509/595) in Ontario and 79.1% (401/507) in BC (Figure 4-2). 88 Figure 4-1. Genotypes shared between provinces. Venn diagram representing the number of unique and shared 24-locus MIRU-VNTR genotypes between Ontario and British Columbia, 2008–2014. Figure 4-2. Proportion of genotypic clustering. Intra- and interprovincial 24-locus MIRU-VNTR genotypic clustering, Ontario and British Columbia (2008–2014). Each coloured pie wedge represents the proportion of isolates within the province that have a genotype match in the other. For the group that does have an interprovincial match, the stacked bar graphs show the relative frequency of isolates that are clustered or unique within the respective province. 89 Multivariable logistic regression was used to investigate independent factors associated with interprovincial genotype matches (Table 4-3) and found increased odds of matching for BC isolates (aOR 2.1, 95%CI: 1.8–2.5), Canadian-born persons (aOR 2.5, 95%CI: 1.9–3.2), and those with a non-Euro-American lineage Mtb isolate (aOR range: 1.9–4.7). Individuals residing in a Metro area had 1.8 times the odds of their isolate belonging to an interprovincial cluster (95%CI: 1.2–2.5) compared to those residing in a rural/remote region. Restricting the sample to include only isolates contributing to an interprovincial genotypic cluster and comparing single versus multiple contributors to a cluster, very similar trends to the factors associated with overall interprovincial clustering were observed (Table 4-4). Furthermore, upon examination of cluster composition (Figure 4-3), it was found that 68 of the 175 interprovincial clusters were comprised solely of a single isolate detected in each province—80.9% were East-Asian, East-Asian African, or Indo-Oceanic clusters and 93.9% of these isolates were identified in persons born outside Canada, Figure 4-4. 90 Table 4-3. Multivariable logistic regression. Distribution, frequency, and logistic regression analysis of factors associated with interprovincial genotypic clustering of Mycobacterium tuberculosis isolates between Ontario and British Columbia 2008–2014 (n = 4,807). Interprovincial Genotype Match Interprovincial Genotype Match Characteristic Yes n (%) No n (%) Yes vs. No OR (95%CI) Yes vs. No aORa (95%CI) Total 1075 (22.4) 3732 (77.6) Age, years 0–14 13 (18.3) 58 (81.7) 0.8 (0.4–1.4) 0.6 (0.3–1.1) 15–34 303 (22.8) 1026 (77.2) Reference Reference 35–54 357 (25.4) 1049 (74.6) 1.2 (1.0–1.4) 1.1 (0.9–1.3) 55–74 251 (22.0) 889 (78.0) 1.0 (0.8–1.2) 0.9 (0.7–1.1) 75+ 151 (17.5) 710 (82.5) 0.7 (0.6–0.9) 0.6 (0.5–0.8) Gender Male 610 (22.6) 2094 (77.4) 1.0 (0.9–1.2) 1.0 (0.9–1.2) Female 465 (22.1) 1638 (77.9) Reference Reference Province Ontario 578 (17.8) 2672 (82.2) Reference Reference British Columbia 497 (31.9) 1060 (68.1) 2.2 (1.9–2.5) 2.1 (1.8–2.5) Community type Metro 458 (23.2) 1518 (76.8) 1.3 (1.0–1.8) 1.8 (1.2–2.5) Urban, high-density 470 (23.6) 1520 (76.4) 1.3 (1.0–1.8) 1.6 (1.1–2.2) Urban, moderate-density 92 (16.8) 455 (83.2) 0.9 (0.6–1.3) 1.4 (1.0–2.2) Rural/Remote 55 (18.7) 239 (81.3) Reference Reference Birthplace Canada 192 (27.6) 503 (72.4) 1.4 (1.2–1.7) 2.5 (1.9–3.2) Outside Canada 883 (21.5) 3229 (78.5) Reference Reference Lineage Euro-American 233 (14.4) 1387 (85.6) Reference Reference East-Asian 340 (35.1) 628 (64.9) 3.2 (2.7–3.9) 4.7 (3.7–5.8) East-African Indian 156 (17.3) 744 (82.7) 1.2 (1.0–1.6) 1.9 (1.5–2.4) Indo-Oceanic 346 (26.2) 973 (73.8) 2.1 (1.8–2.5) 3.1 (2.5–3.9) Abbreviations: aOR, adjusted odds ratio; CI, confidence interval; OR, odds ratio. aAdjusted for age, gender, province, community type, birthplace, lineage. 91 Table 4-4. Multivariable logistic regression according to size. Multivariable analysis of factors associated with single and multi (≥2 isolates) contributors to an interprovincial 24-MIRU-VNTR cluster, Ontario and British Columbia 2008–2014. Characteristic Multi vs. Single OR (95%CI) Multi vs. Single aORa (95%CI) Age, years 0–14 1.4 (0.3–6.3) 1.0 (0.2–4.8) 15–34 Reference Reference 35–54 1.5 (1.0–2.3) 1.4 (0.9–2.2) 55–74 1.2 (0.8–1.8) 1.2 (0.8–1.9) 75+ 1.0 (0.6–1.7) 1.3 (0.7–2.1) Gender Male 1.3 (0.9–1.7) 1.2 (0.8–1.6) Female Reference Reference Province Ontario Reference Reference British Columbia 0.6 (0.5–0.9) 0.6 (0.4–0.8) Community type Metro 1.9 (1.0–3.8) 2.7 (1.2–5.8) Urban, high-density 1.2 (0.6–2.4) 1.9 (0.9–4.1) Urban, moderate-density 1.7 (0.7–4.0) 2.2 (0.9–5.7) Rural/Remote Reference Reference Birthplace Canada 3.3 (1.9–6.0) 8 (3.8–16.6) Outside Canada Reference Reference Lineage Euro-American Reference Reference East-Asian 0.9 (0.6–1.4) 2.0 (1.2–3.5) East-African Indian 0.6 (0.4–1.0) 1.4 (0.8–2.6) Indo-Oceanic 1.0 (0.6–1.6) 2.4 (1.4–4.2) Abbreviations: CI, confidence interval; OR, odds ratio. aAdjusted for age, gender, province, community type, birthplace, lineage. 92 Figure 4-3. Interprovincial genotype matches. A circular chord diagram visualizing the number (indicated by tick marks) of interprovincial 24-locus MIRU-VNTR genotype matches between Ontario and British Columbia from 2008–2014, grouped by the number of isolates within each province sharing the matched genotype: single (1 isolate), small (2–9 isolates), large (≥10 isolates). Flow width indicates the number of genotypes. Figure 4-4. Single contributors to clusters. Proportion of single contributors to an interprovincial cluster by province and birthplace. 93 The 1,894 isolates that were intra- and/or interprovincially clustered were visualized using a minimum spanning tree (Figure 4-5), revealing 17 large clusters (≥10 persons) across the lineages, many of which were observed in both Ontario and BC. Recognizing that MIRU-VNTR overestimates recent transmission in non-Euro-American lineages,354 and that recent transmission is more likely to occur among Canadian-born individuals, these clusters were examined in the context of lineage and birthplace (Table 4-5). Clusters of non-Euro-American lineage isolates were observed in persons born outside Canada, and all but one of these clusters spanned both provinces, suggesting that rather than recent transmission, these clusters may reflect reactivation of strains acquired overseas. Clusters involving predominantly Canadian-born persons tended to occur exclusively within one province or the other and in different community types—Metro and high-density urban in Ontario, largely rural/remote in BC. However, seven isolates with genotypes matching two large BC outbreaks (BC002 and BC012)—one of which has been previously described173—appeared in Ontario. 94 Table 4-5. Large genotypic clusters. Characteristics of 24-locus MIRU–VNTR large clusters (≥10 individuals) by predominant birthplace, community type, and lineage: Ontario and British Columbia, 2008–2014. Ontario British Columbia Cluster ID Cluster Size Predominant Birthplaceab (%) Predominant Community Type (%) Cluster Size Predominant Birthplaceab (%) Predominant Community Type (%) Lineage Interprovincial Clusters Canadian-born ON059/BC002 4 Canada (100.0) urbanMD/Rural/Remote (75.0) 68 Canada (87.7) urbanHD (67.6) EAm ON065/BC012 3 Canada (50.0) urbanMD/Rural/Remote (66.7) 46 Canada (91.3) urbanHD (52.2) EAm Non-Canadian-born ON253/BC011 49 Philippines (93.8) Metro (59.2) 30 Philippines (96.6) urbanHD (56.7) IO ON267/BC021 41 Philippines (100.0) Metro (56.1) 20 Philippines (100.0) Metro/urbanHD (85.0) IO ON155/BC187 26 China (54.2) Metro (57.7) 15 China (78.6) urbanHD (60.0) EAs ON150/BC038 18 China (61.1) Metro (55.6) 10 China (100.0) Metro (60.0) EAs ON181/BC046 20 India (65.0) urbanHD (55.0) 9 India (77.8) urbanHD (88.9) EAI ON012/BC141 19 E. Africa (73.7) Metro (68.4) 5 E. Africa (80.0) urbanHD (60.0) EAI ON058/– 13 S. Asia (69.2) Metro (76.9) 1 S. Asia (100.0) Rural-Remote (100.0) EAs ON104/BC157 12 E. Africa/E. Asia (75.0) Metro/urbanHD (83.3) 4 E. Africa/E. Asia (75.0) Metro/urbanHD (100.0) EAs ON179/BC149 9 India (66.7) Metro/urbanHD (77.7) 10 India (90.0) urbanHD (70.0) EAI Intraprovincial Clusters Canadian-born ON219 15 Canada (73.3) Metro/urbanHD (86.7) 0 – – EAm ON22 14 Canada (76.9) Metro (64.3) 0 – – EAm BC001 0 – – 28 Canada (96.4) Rural/Remote (85.7) EAm BC003 0 – – 26 Canada (96.0) Rural/Remote (84.6) EAm BC008 0 – – 21 Canada (85.7) urbanHD (81.0) EAm Non-Canadian-born ON73 11 E. Africa (55.6) Metro/urbanHD (72.8) 0 – – EAI Abbreviations: EAm, Euro-American; EAs, East-Asian; EAI, East-Asian Indian; IO, Indo-Oceanic; urbanMD, Urban, moderate-density; urbanHD, Urban, moderate-density. aBirthplace was unknown for 10 individuals; percentage represents those with complete data. bPredominant birthplace country or region. 95 Figure 4-5. Population structure of Mycobacterium tuberculosis isolates shared between BC and Ontario. Minimum spanning tree analysis of 24-locus MIRU-VNTR of the 1,894 intra- and interprovincially clustered isolates with lineage indicated, Ontario and British Columbia (2008–2014). The size of each circle is proportional to the number of isolates. Classification of genotypes by province is visualized by colour coding. 96 4.4 Discussion This study represents the first comprehensive interprovincial comparison of MIRU-VNTR genotyping in Canada using >4,900 Mtb isolates collected in Ontario and BC over a seven-year period. This represents >50% of culture-positive TB cases diagnosed in Canada during this period, and provides new insights into the comparative epidemiology of TB in two of Canada’s largest provinces, as well as insight into possible interprovincial TB transmission. Although both provinces have large, diverse populations with many people born outside Canada, there were significant differences in the epidemiology and the bacterial population structure between the two provinces. Ontario had more unique MIRU-VNTR patterns, primarily identified within persons born outside Canada, and more cases occurring in large urban areas. Despite the high strain diversity, the clustered proportion differed significantly between Ontario and BC—similar to findings in a Western Canada study using restriction fragment length polymorphism (RFLP) genotyping in which clustering varied from 9% to 64% across the provinces studied.328 BC cases were more frequently clustered than those in Ontario, consistent with BC’s higher proportion of TB in Canadian-born persons, amongst whom local transmission is likely to drive TB rates. MIRU-VNTR does overestimate true recent transmission,354 so whether these differences in the clustered proportion are still present when whole genome sequencing rather than MIRU-VNTR is used remains to be seen. Encouragingly, most clusters identified with MIRU-VNTR were small, with only seven large outbreaks consistent with recent transmission—most of which have been previously described.74,117,173,305 Thus, despite different models of TB management and care between the provinces—with Ontario following a decentralized model and BC a largely centralized system—common practices and national guidelines such as the Canadian Tuberculosis Standards6 result in consistently effective public health responses in most cases. When genotypes present in both provinces were examined, it was found that most MIRU-VNTR matches were due to a single individual in either province, of whom the vast majority were born outside Canada. This is consistent with the notion that these represent LTBI reactivation,355 and although the possibility that these individuals had travelled between Ontario and BC cannot be 97 excluded, such a transmission scenario is likely rare. There appears to be little interprovincial transmission between Ontario and BC, and the seven cases detected are genotypic matches to two strains endemic to BC circulating within vulnerable populations with known risk factors, including under-housing.74,173 It is possible one or more of these Ontario residents had a travel history to or prior residence in BC, with social/behavioural risk factors linked to a higher risk of exposure and infection—something that has been observed in other cross-jurisdictional studies.356–358 Interestingly, Ontario’s large MIRU-VNTR clusters (ON219, ON22) circulating amongst under-housed individuals in a metropolitan area of Ontario117,305 were not found in BC, suggesting potential differences in the epidemiology or movements of the under-housed populations between the provinces. However, because TB case management in Canada occurs at the provincial/territorial level, sharing of case-level data across jurisdictions is challenging, and prevented the comparison of risk factor and epidemiological data that may have allowed for exploration of within-Canada transmission further. It was also assumed that identical MIRU-VNTR patterns amongst Canadian-born individuals with Euro-American lineage TB represented recent transmission. This observation is supported by recent work in the English Midlands,354 but whether this is the case across thousands of kilometres remains to be seen; it is possible these interprovincial clusters may represent a common strain circulating in Canada amongst vulnerable populations. Currently, there is no coordinated national molecular surveillance program for tuberculosis in Canada. Although the National Microbiology Laboratory (NML) does offer genotyping services, not all provinces/territories use the service, and instead perform genotyping at their provincial reference laboratory. MIRU-VNTR data, whether generated at NML or at the provincial level, are not routinely shared nationwide, precluding a nationwide molecular surveillance program of the type implemented in the United Kingdom, the Netherlands, and other comparable low-incidence settings.75,314 While our analyses suggest minimal TB transmission between BC and Ontario, these are two geographically distant provinces—a similar study using geographically closer jurisdictions may tell a different story. 98 A national molecular surveillance program is a complex undertaking, requiring coordinated and collaborative efforts by all provinces/territories for implementation, maintenance, support, and evaluation. Perhaps the largest challenge is acquiring funding to support a national program, particularly the necessary personnel required to carry out such an effort, as provincial public health budgets are already limited. Additional issues complicate the ability to access and analyze health data across provincial/territorial borders—data ownership, legal, ethical, and privacy concerns limit what jurisdictions may be willing or able to share, yet these clinical and epidemiological data are required for meaningful interpretation of the genotypic data.359 Interpretation of these data requires trained molecular epidemiologists with a regional- and national-level understanding of TB epidemiology, as well as a suitable information technology platform to link genotyping and administrative data. Laboratory information management systems (LIMS) facilitate the recording and sharing of information at a specimen-level; however, LIMS are not designed for research or surveillance efforts and genotyping data are rarely integrated with the provincial health systems capturing the clinical and epidemiological information for each case. Full integration of these data sources, even within provinces, requires significant resources for creating and curating databases, and routinely linking data. In Ontario, the OUT-TB Web online platform is used to communicate case-level genotyping data across the province and could provide a template for a national system.307 This platform could be expanded to communicate genotyping information at a national level to the appropriate TB program personnel, but careful planning and investment in public health IT infrastructure are needed to ensure a national surveillance program operates as intended and that both personnel and financial resources are available to take action when cross-jurisdictional transmission is detected. Despite minimal evidence of cross-jurisdictional transmission in the present study, it is believed that the comparison of TB molecular epidemiology between Ontario and BC did further our understanding of local transmission by providing more context to what has been observed in each province independently. Next steps could include expanding the analyses to other Canadian jurisdictions with complete or near-complete genotyping data, as well as incorporating whole genome sequencing data currently being generated in several Canadian settings. 99 Chapter 5: Genotyping and Whole-Genome Sequencing to Identify Tuberculosis Transmission to Pediatric Patients in British Columbia 5.1 Background In 2016, there were an estimated 1 million new cases of childhood tuberculosis (TB), causing 253,000 deaths globally.5 In low-incidence countries such as Canada, children <18 years have the lowest TB rates of any age group;84 however, pediatric cases are often difficult to diagnose and complex to manage. Presentations may be atypical, diagnostic yields are low, and pediatric cases are at greater risk of developing severe, disseminated, and potentially fatal disease without prompt treatment.360,361 Tuberculosis in young children is considered indicative of recent transmission. In high-resource regions, reverse contact tracing is usually performed in an attempt to identify and treat the source case to prevent further spread.362 Contact investigations initiated around pediatric TB cases are most likely to identify a source case in children <2 years, typically revealing an adult caregiver or household member.362–364 However, the epidemiology is not always clear. In a setting such as Canada, immigrant children are less likely to be epidemiologically linked to a known case, and they are often presumed to have acquired infection before immigration.87,362,365 Even when a putative source is identified, the molecular epidemiology may not always support a relationship between the Mycobacterium tuberculosis (Mtb) isolates of the child and presumed source. Genotyping has revealed instances in which a pediatric patient’s Mtb isolate has a different genotype from their assumed source case, thus refuting transmission,362,366,367 and discordant drug-susceptibility patterns have also been used to disprove transmission. Multidrug-resistant (MDR)-TB, particularly in children of immigrants, is believed to result from exposure to adult family members with MDR-TB,368,369 yet at least two studies have shown conflicting resistance phenotypes between pediatric cases and their presumed source.367,370 100 In contrast, concordant genotypes and susceptibility patterns do not necessarily mean that a specific individual was the source of a child’s TB infection. Genotyping methods such as mycobacterial interspersed repetitive unit–variable-number tandem repeat (MIRU-VNTR) overestimate clustering in certain lineages of TB that are common among individuals born in high-incidence countries, thus identical genotypes in low-incidence countries often suggest infection with a strain common to a particular ethnic community140,336 rather than recent person-to-person transmission—a finding that might influence public health follow-up. The linkages between cases can be further refined through whole-genome sequencing (WGS), which—when combined with epidemiological data—can better identify transmission chains.74 Using WGS, Mtb isolates with identical genotypes may be separated by enough genomic distance (>5 mutations) to rule out recent transmission from a putative source.149 To better understand the transmission dynamics of pediatric TB in a low-incidence setting, a retrospective analysis was carried out of all culture-positive TB cases in children <18 years diagnosed in British Columbia (BC), Canada from 2005 through 2014. Routine programmatic contact investigation data combined with MIRU-VNTR and WGS was used to identify source cases and to quantify the extent to which transmission within the province contributes to the overall burden of pediatric TB in BC. 5.2 Methods 5.2.1 Study setting and design The British Columbia Centre for Disease Control (BCCDC)’s Public Health Laboratory (BCPHL) receives all Mtb cultures for the province and performs routine phenotypic drug-susceptibility testing and MIRU-VNTR genotyping on all Mtb isolates. Patient care, surveillance, and TB prevention programs are led by Provincial TB Services at the BCCDC. All children <18 years diagnosed with active TB from 2005 through 2014 (n = 98) in BC were identified from the provincial surveillance registry and the study population was restricted to only those with a culture-positive diagnosis made in BC (n = 49). 101 5.2.2 Case data Individual-level clinical, demographic, and contact investigation data were obtained for all study participants through BCCDC’s Integrated Public Health Information System (iPHIS). Disease site was categorized as respiratory or non-respiratory.6 Information regarding each child’s parents’ country of origin was obtained through a combination of physician narrative and contact investigation records in iPHIS. Children were categorized by birthplace as non-Canadian-born (nCB) or Canadian-born (CB); the latter group was further subdivided into children born to non-Canadian-born parents (nCBP) or to Canadian-born parents (CBP). Ethnic community was defined by a nCB individual’s country of birth, or, for Canadian-born children, their parents’ country of birth. In the one case in which the parents were from different countries, the ethnic community of the parent born in a high TB incidence country (≥30 cases/100 000)6 was used. 5.2.3 Laboratory methods Mycobacterium tuberculosis isolates were obtained from specimens submitted to the BCPHL for routine testing. Phenotypic drug susceptibility testing results were available all isolates for first-line antibiotics—isoniazid (INH), rifampin (RIF), ethambutol, and streptomycin—with additional data for pyrazinamide in isolates resistant to INH and/or RIF. Isolates were revived from archived stocks, DNA extracted, and genotyped using 24-locus MIRU-VNTR genotyping as described in Chapter 3. All 49 (100%) culture-positive isolates were successfully genotyped and isolates whose MIRU-VNTR genotype matched one or more isolates in BC during the study period (see Chapter 3) were assigned a cluster identifier (MClustID). All clustered isolates—those from the pediatric cases (n = 24) and all isolates from adult cases in each cluster (n = 202)—were sequenced using 125 base pairs, paired-end reads on the Illumina HiSeq 2500 platform at the Michael Smith Genome Sciences Centre (Vancouver, BC), according to the following protocol. TB genomic DNA samples were quantified using Quant-iT assay, and rearrayed in 96-well plates according to input amounts, normalized to 100ng, 30ng, and 1-29ng in 62.5 µL respectively using JANUS automated workstation (PerkinElmer). The TB genomic DNA was fragmented by Covaris LE220 sonication in a 96 microTUBE Plate for 120 seconds using a “Duty factor” of 102 30% and “Intensity” of 200 cycles per burst at 450 Peak Incident Power. The paired-end sequencing library was prepared following the BC Cancer Agency’s Genome Sciences Centre’s PCR Enriched 96-well Low Input Small Gap gDNA Library Construction protocol on a Biomek FX robot (Beckman-Coulter, USA) using a customized NEB premix chemistry. Briefly, the DNA was size selected for a 300 bp peak using PCRClean DX beads (0.67:1 to 1:1 beads to sample ratio for upper and lower cut, respectively), and was subject to end-repair, and phosphorylation by T4 DNA polymerase and T4 polynucleotide kinase in a single reaction, followed by purification using PCRClean DX beads (1:1 bead to sample ratio) and 3’ A-tailing by Klenow fragment (3’ to 5’ exo minus). Adenylated libraries were ligated to paired-end adapters using NEB quick ligation premix with enhancer and then subsequently purified twice using PCRClean DX beads (1:1 bead: sample ratio). Adapter ligated templates were PCR-amplified with Phusion DNA Polymerase (Thermo Fisher Scientific Inc. USA) using Illumina’s PE indexed primer set, with the following cycle conditions: 98˚C for 60 seconds followed by 6 to 12 cycles of 98˚C for 15 seconds, 65˚C for 30 seconds and 72˚C for 30 seconds, and a final extension at 72˚C for 5 minutes. The number of PCR cycles was dependent on the input amount. The PCR products were purified twice using PCRClean DX beads (1:1 bead to sample ratio), and their average size and distributions were determined using Caliper LabChip GX High Sensitivity DNA Chip Assay (PerkinElmer, Inc. USA). Libraries were quantified using Quant-iT High Sensitivity Assay Kit (Invitrogen, CA). Libraries were pooled in equal molar ratio for sequencing on the Illumina HiSeq2500 platform using version 4 chemistry and 125 bp paired end with index reads. 5.2.4 Whole genome sequencing analysis The resulting FASTQ files were analyzed using a pipeline developed by Oxford University and Public Health England.267 Reads were aligned to the Mtb H37Rv reference genome (GenBank ID: NC000962.2), and after masking for low complexity regions an average of 92% of the reference genome was covered. Single nucleotide variants (SNVs) were identified across all mapped nonrepetitive sites. Concatenated SNVs were used to construct maximum-likelihood phylogenetic trees (RAxML 8.2.10;371 GTRGAMMA model and 200 bootstrap replicates), which were then viewed using the R statistical software (version 3.4.1). Lineage-defining 103 SNVs372 were used to classify each sequenced isolate into 1 of the 7 genetic Mtb lineages. FASTQ files for all genomes are available at the National Center for Biotechnology Information under BioProject PRJNA413593 and PRJNA49659. 5.2.5 Transmission classification WGS data were combined with individual-level clinical and epidemiological data, including symptom onset and diagnosis dates, disease site(s), and contact investigation information, to identify the most probable source case for each child’s infection. Locally acquired infections were defined as follows: (1) those pediatric cases whose Mtb isolate fell within 0–5 SNVs of another isolate from someone diagnosed in BC and for which there was epidemiological support, or (2) those pediatric cases without WGS data but for which there was irrefutable epidemiological evidence of transmission. TB infections acquired outside BC were defined as those pediatric cases whose Mtb isolates had either a unique MIRU-VNTR pattern or who, by WGS, were >5 SNVs away from another isolate in BC and who had a documented history of residing in or traveling to a high-incidence TB country. Although other sources for these cases are possible—for example, a clinically diagnosed case unknown to the child, an adult source infecting a child before the study initiation, an unidentified visitor from outside BC, an individual whose active, infectious TB disease spontaneously resolved, or a case that left the province before diagnosis—these scenarios are considerably less likely. Pediatric cases not meeting either definition for place of acquisition were classified as having an unknown source. 5.2.6 Statistical analysis Descriptive statistics were computed for demographic, clinical, and contact investigation data, both overall and stratified by birthplace. Unadjusted differences in characteristics between birthplaces were analyzed using the Fisher’s exact test (categorical data) or the Kruskal-Wallis rank-sum test (non-normal continuous data). In addition, univariable analysis was conducted for factors related to locally acquired infection (yes/no) using the Chi-square test or Fisher’s exact test, where appropriate. All analyses were executed in R (version 3.3.1). 104 5.3 Results 5.3.1 Demographics, clinical presentation, and epidemiology From 2005 through 2014, a total of 98 children were diagnosed with active TB in British Columbia; 49 (50.0%) children had at least one culture-positive isolate available at the BCPHL (Figure 5-1). The median age of the study population was 14 (interquartile range [IQR]: 6–16); however, the age distribution varied significantly (p = 0.023) between CB and nCB children (Table 5-1, Figure 5-2), with nCB children almost always >10 years of age. Figure 5-1. Pediatric TB study inclusion/exclusion criteria. Pediatric was defined as children <18 years of age. 105 Figure 5-2. Pediatric age distribution by birthplace. Abbreviations: Canadian-born parents (CBP); non-Canadian-born parents (nCBP). Of the 26 children born outside Canada, 21 (80.8%) were born in Asia (including East, South-Eastern, and South-Central Asia) and the remaining five (19.2%) were born in Africa. All were born in high-incidence countries. With respect to ethnic community—defined as a combined measure of the child’s and/or parents’ region of birth—the highest proportion (42.9%) of the 49 pediatric cases were from South-Eastern Asia. Only 7 of 49 children (14.3%) came from a family that had resided in Canada for multiple generations. Thirty-nine pediatric cases (79.6%) were diagnosed after symptomatic presentation to healthcare providers (Table 5-1). Six children (12.2%)—all Canadian-born (2 CBP, 4 nCBP)—were detected through contact investigations, and three (6.1%) nCB children were diagnosed as the result of immigration-related postlanding surveillance. Clinically, 38 (77.6%) children had respiratory involvement (Table 5-1), and four children were characterized as having cavitary disease based on chest radiography (median age: 15.8 years, range: 14.7–17.9). One child in the study was HIV positive. Phenotypic drug susceptibilities revealed that 45 (91.8%) isolates were susceptible to all first-line antituberculous medications. Isoniazid monoresistance was seen in three individuals (6.1%)—all South-East Asian nCB adolescents with Indo-Oceanic lineage strains. 106 Table 5-1. Demographic and clinical characteristics of culture-positive pediatric TB cases. British Columbia, 2005–2014 (n = 49).a Characteristic Overall Canadian-born (CBP) Canadian-born (nCBP) Non-Canadian-born p-valueb Totals n = 49 n = 7 n = 16 n = 26 Age, years Median (IQR) 14 (6–16) 4 (1–13) 7 (1–16) 15 (13–17) 0.023 Gender — n (%) Male 25 (51.0) 2 (8.0) 10 (40.0) 13 (52.0) 0.329 Female 24 (49.0) 5 (20.8) 6 (25.0) 13 (54.2) Ethnic communityc — n (%) Multi-generational Canadian 7 (14.3) 7 (100.0) – – – South-Eastern Asia 21 (42.9) – 7 (33.3) 14 (66.7) South-Central Asia 9 (18.4) – 4 (44.4) 5 (55.6) East Asia 5 (10.2) – 3 (60.0) 2 (40.0) Africa 7 (14.3) – 2 (28.6) 5 (71.4) Disease Site — n (%) Respiratory 29 (59.2) 3 (10.3) 10 (34.5) 16 (55.2) 0.471 Non-Respiratory 11 (22.4) 1 (9.1) 3 (27.3) 7 (63.6) Respiratory + Non-Respiratory 9 (18.4) 3 (33.3) 3 (33.3) 3 (33.3) Respiratoryd Smear — n (%) Positive 21 (53.8) 4 (19.0) 7 (33.3) 10 (47.6) 1.000 Cavitary Yes 4 (8.2) 1 (25.0) 1 (25.0) 2 (50.0) 0.620 No. Contacts Median (IQR) 5 (2–19) 17 (2–29) 5 (2–13) 5 (1–19) 0.819 Method of Detection — n (%) Symptoms 39 (79.6) 5 (12.8) 12 (30.8) 22 (56.4) 0.031 Contact Investigation 6 (12.2) 2 (33.3) 4 (66.7) 0 (0.0) Post-Landing Surveillance 3 (6.1) – – 3 (100.0) Incidental Finding 1 (2.0) 1 (100.0) 0 (0.0) 0 (0.0) Clusterede — n (%) Yes 24 (49.0) 7 (29.2) 7 (29.2) 10 (41.7) 0.011 No 25 (51.0) 0 (0.0) 9 (36.0) 16 (64.0) Abbreviations: CBP, Canadian-born parents; nCBP, non-Canadian-born parents; IQR, interquartile range. aPercentages have been rounded and may not total to 100%. bFisher’s exact test (categorical variables), and Kruskal-Wallis rank-sum test (non-normal continuous data). cEthnic community is derived from a combination of the region of birth for the pediatric case and parents of the child. dExcluded “other respiratory” sites e.g. pleura. eClustered = Yes where the isolate was identical by 24-locus MIRU-VNTR to another isolate in British Columbia (2005–2014). 107 The number of contacts varied considerably between individuals, ranging from 0 to 207, with 30 pediatric cases (61.2%) having fewer than 10 contacts (Figure 5-3). Extensive investigations (>50 contacts) were conducted around 3 acid-fast bacilli smear-positive children with respiratory TB. Contact investigation data suggested putative BC-resident source cases for 12 (24.5%) children; in four of these instances, the child was diagnosed before the adult source and served as the signal of an active infectious case in the community. Figure 5-3. Number of contacts. Relative frequency representing the percentage of pediatric cases by number of individuals identified during contact investigation. 5.3.2 Molecular and genomic epidemiology investigation of putative sources First, the 12 children with putative BC-resident sources identified through contact investigation were examined. In 11 instances, MIRU-VNTR and WGS supported the relationship between the child and assumed source. For the twelfth case, molecular data was not available for the adult TB contact; however, epidemiological evidence strongly corroborated the source of infection. For eight children (6 Canadian-born [nCBP], 1 Canadian-born [CBP], 1 non-Canadian-born), the source was an adult family member regularly residing in the same household as the child. For 4 children (3 Canadian-born [CBP], 1 Canadian-born [nCBP]), the source was a visitor to the household who resided elsewhere in BC or Canada. In one of these cases, contact investigation 108 had identified two plausible sources—a household member and a visitor; however, the combination of WGS results and epidemiological information suggested the visitor most likely transmitted to both the child and adult household member. One child did not meet either of the definitions for place of acquisition and was classified as having an unknown source. 5.3.3 Identification of infections acquired out of province The MIRU-VNTR revealed that 25 Mtb isolates (51.0%) had a unique genotype, suggesting that the infection was likely acquired outside of the province. Indeed, 16 (64.0%) of the children with unique MIRU-VNTR patterns were born in high-burden countries.333 Of the nine Canadian-born children with unique MIRU-VNTR patterns, all had parents born outside Canada and seven had a confirmed travel history compatible with acquiring infection overseas. One of the 25 cases was ultimately determined to be the result of transmission in BC from a family member visiting from elsewhere in Canada, leaving 24 cases with an unknown source outside BC. Also identified were 10 cases in which a pediatric Mtb isolate shared a MIRU-VNTR genotype with at least one other isolate from BC. In eight cases, the genomic distance between the pediatric case’s isolate and the nearest BC isolate with an identical genotype precluded transmission (81–170 SNVs). In one case, the child’s isolate was six SNVs away from an Mtb isolate from an adult born in the same country as the child and diagnosed in the previous year; however, there was no epidemiological link between the two, and a distance of six SNVs is thought to be incompatible with a transmission event occurring within a single year. In the absence of documented travel history, it was concluded that these nine children had been exposed to TB before emigration. The tenth case represented a non-Canadian-born sibling pair separated by a single SNV, for which epidemiological evidence pointed to infection before emigration from a common source. Ultimately, whether through MIRU-VNTR or WGS, it was found that 33 (67.3%) culture-positive pediatric TB cases diagnosed in BC likely did not arise from local transmission (Figure 5-4). Epidemiological data suggested that most of these children acquired TB before their arrival in BC (n = 23), or through travel to their parents’ birth country (n = 8). Two nCB children had 109 documented travel histories post arrival to Canada, making it unclear whether their infection was acquired before emigration or was travel-related. Figure 5-4. Pediatric tuberculosis investigation summary. Summary results of molecular epidemiological investigation of culture-positive pediatric tuberculosis (TB) cases, British Columbia (BC), 2005–2014. (A) Summarizes place of acquisition, and countries colored in blue correspond to travel history of individuals; (B) stratifies the birthplace of the pediatric case and source for those in which transmission occurred within BC. Canadian-born parents (CBP); non-Canadian-born parents (nCBP). 15 Acquired TB Within BC7 Canadian-born (CBP)7 Canadian-born (nCBP)1 non-Canadian-born33 Acquired TB Outside BC8 Canadian-born (nCBP)8 Travel History25 non-Canadian-born2 Travel History23 No Travel History15 Acquired TB WithinBC6 Canadian-born Source 6 Canadian-born(CBP) Children9 non-Canadian-bornSource1 Canadian-born(CBP) Child7 Canadian-born(nCBP) Children1 non-Canadian-bornChildAB*1 Canadian-born (nCBP) acquisition location unknown110 5.3.4 Identification of locally acquired infections Fifteen (30.6%) culture-positive pediatric TB diagnoses in the study period resulted from presumed local transmission; this includes the 12 cases described earlier, for whom contact investigation suggested a BC resident as the likely source, and three additional transmissions identified through WGS, with ≤5 SNVs between the pediatric case and one or more adult cases (Figure 5-5). Of the 15 locally acquired cases, seven children were born in Canada to CBP. Only one WGS-confirmed source was a Canadian-born household family member; instead, three sources were Canadian-born visitors to the home, and in two cases, although a specific source was not identified, the children were infected with strains known to circulate within their communities. One Canadian-born child (CBP), most likely acquired TB from a nCB source (Figure 5-5) who was not identified through reverse contact investigation. Four of these children belonged to large, previously documented MIRU-VNTR clusters involving largely Canadian-born individuals: MClust-001 (n = 56), MClust-003 (n = 39), and MClust-055 (n = 10) (Figure 5-5). Of the remaining eight cases—7 Canadian-born (nCBP) and 1 non-Canadian-born child—WGS (or epidemiology alone [n = 1]) suggested infection was acquired within BC from a nCB family member; seven regularly resided in the household. Two Canadian-born children (nCBP) belonged to large clusters comprising predominantly nCB individuals (MClust-011, MClust-187) (Figure 5-5). The small sample size precluded multivariable regression; however, descriptive statistics (Table 5-2) indicate that local acquisition was associated with birth in Canada to Canadian parents, age under five, and infection with the Euro-American Mtb lineage. Of the 49 cases, only one remained unclear at the conclusion of the investigation. The Canadian-born (nCBP) child had a unique MIRU-VNTR pattern suggesting acquisition overseas, but there was no documented travel history, nor did contact investigation suggest a putative source. 111 Figure 5-5. Pediatric analysis phylogenetic tree. Phylogenetic tree based on whole genome sequences of Mycobacterium tuberculosis isolated from all pediatric diagnoses resulting from WGS-confirmed transmission within BC (n = 14), and all adult isolates related by 24-locus MIRU-VNTR. Genotypic clusters are indicated by coloured bands. Pediatric (star) and adult (circle) cases are coloured where genomic epidemiology identified a clear source case (n = 12). Pediatric cases resulting from community transmission from an unknown source are indicated with black stars (n = 2). Grey stars indicate two pediatric cases who belong to a MIRU-VNTR cluster frequently seen in British Columbia (BC), for whom WGS indicated their infections were acquired outside BC. Bold tip labels indicate Canadian-born individuals; plain tip labels indicate non-Canadian-born individuals, and italicized tip labels indicate unknown birthplace. Canadian-born children with Canadian-born parents are annotated with “_C” and those with non-Canadian-born parents with “_N”. Internal branches are labelled by lineage: Euro-American (EAm), East-Asian (EAs), EAI (East-Asian Indian) and Indo-Oceanic (IO). 112 Table 5-2. Factors associated with locally acquired pediatric tuberculosis. British Columbia, 2005–2014 (n = 48)a. Characteristic Acquired Locally p-valueb Yes No Birthplace Canadian-born (CBP) 7 (100.0) 0 (0.0) <0.001 Canadian-born (nCBP) 7 (46.7) 8 (53.3) Non-Canadian-born 1 (3.8) 25 (96.2) Age, years <5 9 (81.8) 2 (18.2) <0.001 ≥5 6 (16.2) 31 (83.8) Gender Male 7 (28.0) 18 (72.0) 0.846 Female 8 (34.8) 15 (65.2) Travel Historyc Yes 2 (16.7) 10 (83.3) 0.292 No 13 (36.1) 23 (63.9) Lineage Euro-American 10 (83.3) 2 (16.7) <0.001 East-Asian 2 (25.0) 6 (75.0) East-African Indian 1 (10.0) 9 (90.0) Indo-Oceanic 2 (11.1) 16 (88.9) Abbreviations: CBP, Canadian-born parents; nCBP, non-Canadian-born parents. aSource unknown (n = 1). bChi-square test, (Fisher’s exact test where appropriate). cTravel to a high-incidence TB country. 113 5.3.5 Household transmission of multidrug resistant tuberculosis Multidrug resistance, defined as resistance to at least isoniazid and rifampin, was observed in one pediatric case with documented exposure to two adult TB cases, one with MDR-TB, and one with a pan-susceptible organism. The MIRU-VNTR genotyping placed this child and both adults into MClust-187; a cluster of cases (n = 16) involving an East-Asian lineage strain and predominantly nCB individuals with a median age of 66 (IQR: 47–87). Whole-genome sequencing analysis of MClust-187 revealed that the pediatric case was separated from the adult MDR-TB case by a single SNV; the adult contact with the pan-susceptible organism was 197 SNVs apart (Figure 5-5). Whole-genome sequencing revealed a second transmission pair in MClust-187—a household transmission between family members, both of whom harbored streptomycin-resistant organism—but neither individual was a pediatric case. With distances of 35–247 SNVs between them, the remaining 12 isolates in MClust-187 do not represent local transmission but rather a common region of birth. 5.4 Discussion In the present study, genotyping and genomics was used to provide the first accurate estimate of TB transmission to children in a low-incidence setting, where the majority of all TB diagnoses (73.7%) are thought to represent reactivation of infections acquired abroad. By coupling a genotyping database including all culture-positive TB isolates diagnosed in British Columbia, Canada (2005–2014) to whole genome sequencing of clustered isolates and including epidemiological information, we find that one-third of culture-confirmed pediatric TB cases acquired their infection within BC. This rate is approximately three times that observed when genomics was used to interrogate transmission in a predominantly adult population in a similar low-incidence setting,73 yet it is considerably lower than the pediatric transmission rate that would have been estimated based on MIRU-VNTR alone (49.0%). A lack of laboratory testing and low diagnostic yields in children meant that only half of the notified pediatric cases had an isolate available for genotyping. This limits the present study somewhat, in that only transmission for cases with a culture-positive specimen can be reliably 114 assessed. Because novel technologies are making genome sequencing from primary specimens a reality,373 future studies of pediatric cases may be able to examine genomic data from a higher proportion of cases where specimens are submitted. The findings of this study indicate that to more fully understand TB transmission and the molecular epidemiology of a population, culture confirmation should be pursued in all cases. The pediatric study TB population described here assorts into 3 distinct groups. Two thirds of the cases—largely nCB older adolescents—likely acquired their infection outside of BC, either before immigration or on a visit to their family’s country of origin. That this group tended to be older teenagers is in line with the findings of an American study reporting differing age distributions between American-born and immigrant children and attributing disease in the latter group to reactivation of latent TB infection (LTBI).368 The one third of TB cases likely to have acquired their infection in BC can be further divided into two groups: half of these cases were attributed to household transmission from a nCB family member, whereas the other half were community transmissions to Canadian-born children from Canadian-born adults, typically in the context of large outbreaks, two of which have been described previously.74,173 This latter group is notable—studies of pediatric TB cases in other Canadian provinces report 99–100% of children had nCB parents,87,365 but, in this BC-based study, 1 in 7 children diagnosed with TB had Canadian-born parents. This may reflect differing rates of local transmission between provinces; however, without WGS-based accurate estimates of transmission rates in each province, this hypothesis cannot be confirmed. Although it is often stated that children with active TB serve as a sentinel case indicative of ongoing community transmission, this appears only to be true in particular sub-populations. Here, the only observed community transmission was in children of Canadian-born parents, where genomics confirmed that six of seven cases were attributed to community sources. No community transmission was observed in children with nCB parents, suggesting that extensive reverse contact investigations may not be warranted in this group. However, it should be noted that the study was limited to the detection of active TB infection as a marker of transmission and that not all transmissions result in active disease. Age <5 years is also often associated with local 115 transmission, and, although this was indeed true here, with most children <5 exposed via a household contact, over one third of locally acquired cases were in older children. It was observed that 12 children had travel histories, and in at least eight of these cases the TB infection was likely acquired while on a trip to their parents’ country of birth. Tuberculosis attributable to travel is often difficult to capture and separate from risk before immigration in nCB adults. Significant resources are dedicated to screening immigrants before and upon arrival in Canada; however, in subsequent encounters with the healthcare system, we do not reliably collect travel history for these individuals and tend to attribute their TB diagnoses as LTBI reactivation. The data indicate that travel to high-incidence settings to visit family poses an infection risk to children, thus it may also contribute to active TB cases among adults who travel to their country of birth. Immigrants traveling to visit friends and relatives in their country of origin are recognized as having increased risks for TB, particularly for long stays.373 It is interesting to note that at least three children in the study who likely acquired their infection during travel visited for less than the three-month indicator for screening recommended in the Canadian Tuberculosis Standards.6 Improved education around the risks of travel, better documentation of travel histories, and more aggressive screening protocols may be warranted in individuals returning from high-risk settings involving community-based travel. The findings of this study are: (1) in agreement with other studies regarding the risks of travel for children365 as well as adults374,375 and (2) suggest that new recommendations around screening individuals with community-based travel to high-incidence settings may be warranted. The retrospective nature of this study meant that it was limited to epidemiological data recorded in BC’s provincial TB registry and, in cases where genomics suggested a source that had not been named in the initial investigation, it was not possible to follow up these leads. Furthermore, the study was limited to use only molecular data to infer potential source cases diagnosed within the study window, because MIRU-VNTR and WGS data were unavailable for isolates obtained before 2005. This complicates source ascertainment for those children diagnosed in the first few years of the study; however, in each case, the available epidemiological data were sufficient to infer a reasonable source. Prospective WGS of all new culture-positive cases, recently 116 implemented by certain state and national mycobacterial reference laboratories, should allow for more timely and focused contact investigations, particularly in the context of larger outbreaks, where genotyping might suggest many possible sources, and in certain clusters involving nCB persons, where a genotypic relationship is infrequently borne out upon WGS. Genomics is changing our understanding of TB transmission dynamics in low-incidence settings, and in the present study, its high resolution was used to more accurately estimate the proportion of pediatric TB attributable to local transmission. The study findings suggest that pediatric TB in BC is a mosaic and that factors including age, place of birth, and travel history must all be considered together when inferring a child’s likely exposure. Thus, preventing future pediatric TB cases will likely require a flexible system with varying interventions, in some instances enhanced travel-associated screening, and in others, looking outside the home for source cases. Only through a combination of interventions will we be able to fully address this important issue. 117 Chapter 6: Whole Genome Sequencing for Improved Understanding of Mycobacterium tuberculosis Transmission in a Remote Circumpolar Region 6.1 Background Canada’s tuberculosis (TB) rate has been decreasing overall, yet rates remain elevated in particular populations and regions. Recent outbreaks in two areas of Canada’s North—Nunavik and Nunavut—resulted in annual incidence rates higher than many low-income countries.6,82 However, this is not the case in all circumpolar settings, where public health efforts have contributed to declining TB rates. From 2006 through 2012, Yukon Territory (YT) reported a rate of 12.1 cases per 100,000 population. While this is over twice the national average of 4.8 cases/100,000; it is the lowest rate amongst Canada’s Northern territories (25.4/100,000 in the Northwest Territories, immediately east of YT, and 194.3/100,000 in Nunavut).13,82 Alaska, located west of YT, has seen a sharp decrease in cases over the last few decades, reporting an average incidence of 8.1/100,000 (2006–2012), with most cases concentrated in rural communities—frequently inaccessible by road.82,376 Thus, while northern remote settings are often viewed similarly by population and public health programs, it is clear that with respect to TB, there are significant differences across these regions, likely explained by a combination of the robustness of regional public health, access to appropriate housing, geography, intra-community movement, and the populations themselves.377 Understanding the unique epidemiology of TB in each region is therefore vital to delivering tailored interventions to drive rates in circumpolar settings closer to the World Health Organization’s elimination goals. Genotyping programs have provided significant insights into the molecular epidemiology of TB in many low-incidence countries, helping to detect outbreaks,342,378 and more recently, genome sequencing has dramatically improved our understanding of both clustering and TB transmission in communities worldwide.74,117,149 However, only two studies to date have used this genomic epidemiology approach to examine transmission in remote northern locations: one in Nunavik, Québec166—an Arctic region of Canada’s North, and a second in Greenland, which used 118 genomics to detect “hotspot cases” responsible for chains of transmission.379 To better understand patterns of TB transmission in Yukon Territory, Mycobacterium tuberculosis (Mtb) genomes were sequenced from all culture-positive TB diagnoses in YT over a ten-year period—the first genomic epidemiology study of TB in this region. Recognizing that in contrast to many other northern regions in Canada, year-round highway access and multiple airports facilitate travel between YT and its southern neighbour, British Columbia (BC), YT Mtb genomes were also examined in the context of Mtb genomes sequenced in BC during the same time period. This unique cross-border comparison is possible because the BC Centre for Disease Control (BCCDC) and the BCCDC Public Health Laboratory (BCPHL) are contracted by YT to provide TB services such as laboratory diagnostics and case management support, and both jurisdictions access a shared data repository, thus allowing us to identify chains of transmission within and across YT/BC borders, and to fully describe the genomic epidemiology of tuberculosis in this remote circumpolar region. 6.2 Methods 6.2.1 Study setting and design Yukon Territory is a sparsely populated (0.1 persons/km2)380 territory located in the most northwestern region of Canada, immediately north of British Columbia. All tuberculosis cases diagnosed in YT are reported to the Yukon Communicable Disease Control (YCDC), and those in BC to the BCCDC. Care and treatment of individuals diagnosed with TB is the responsibility of YCDC, in partnership with Yukon Government Community Nursing and includes contact investigations (CIs) for newly diagnosed cases. The BCPHL receives all Mtb isolates for both YT and BC, and conducts routine diagnostic testing, universal 24-locus mycobacterial interspersed repetitive unit–variable-number tandem repeat (MIRU-VNTR) genotyping, and whole genome sequencing on request. The study population (Figure 6-1) included all YT culture-positive TB cases from 2005 through 2014 (n = 32), which were compared to TB cases diagnosed in BC during the same time period (n = 2,292), for which the BC study population has been previously described (Chapter 3). 119 Figure 6-1. Study sample. Flow diagram summarizing the number of isolates from British Columbia (BC) and Yukon Territory (YT) belonging to a YT involved genomic cluster based on a five single nucleotide variants (SNVs) threshold. 6.2.2 Case-level information Case-level clinical and demographic data, as well as epidemiological data collected during routine contact investigations (CIs), for all TB cases from BC and YT were extracted from the Integrated Public Health Information System (iPHIS). To classify community type for BC cases into metro (>190,000), urban/rural (40,001–190,000), rural (10,001–40,000), and remote (≤10,000) groups, the population density of the geographic service area in which each case resided was used. YT community types were classified by home postal code, with the second digit '1' in the forward sortation area indicating urban/rural, and a '0' indicating a remote community. YT culture positive Mtb isolates(2005–2014)n = 32BC culture positive Mtb isolates(2005–2014)n = 2,292Genotype comparison of BC and YTculture positive Mtb isolates (2005–2014)n = 2,324WGS completed for culture positive Mtbisolates (2005–2014)n = 32 (YT)n = 1,282* (BC)Mtb isolates belonging to a YTinvolved genomic cluster†n = 28 (YT)n = 101 (BC)Mtb isolates NOT belonging to a YTinvolved genomic cluster†n = 4 (YT)n = 1,181 (BC)*Included were two isolates for which genotyping results were unavailable.†Genomic cluster threshold of 5 SNVs120 6.2.3 Laboratory methods All Mtb isolates were obtained from specimens submitted to the BCPHL for routine testing. Isolates were revived from archived frozen stocks, DNA was extracted, and 24-locus MIRU-VNTR genotyping was performed as previously described (Chapter 3). Isolates lacking an amplicon peak at any locus were repeated with newly extracted DNA, and where there remained no peak at a single locus, the locus was coded as missing data and included in the analyses. All 32 culture-positive isolates of 38 notified cases in YT during the study period were successfully genotyped. These results were compared to genotypes of all culture-positive Mtb isolates from BC over the same period (Chapter 3). Whole genome sequencing (WGS) was completed for all 32 YT isolates as well as 1,284 BC isolates—including all those genotypically clustered by MIRU-VNTR to a YT isolate. WGS was completed using 125 bp paired-end reads on the Illumina HiSeq 2500 platform at Canada’s Michael Smith Genome Sciences Centre (Vancouver, BC). 6.2.4 WGS analysis The bioinformatics pipeline developed by Oxford University and Public Health England was used to analyze the resulting FASTQ files.214 Reads were aligned to the Mtb H37Rv reference genome (GenBank ID: NC000962.2), and after masking for low complexity regions an average of 92% of the reference genome was covered. Single nucleotide variants (SNVs) were identified across all mapped non-repetitive sites. Genomic clusters were defined independently of MIRU-VNTR clusters and a unique identifier (WClustID) was assigned where isolates differed by ≤5 SNVs—a threshold reflecting recent local transmission.149 Concatenated SNVs combined with epidemiological data collected through routine CIs and consultation with YCDC public health authorities were used to generate temporal transmission networks. Major lineage was predicted for each sequenced isolate based on lineage-defining SNVs.372 FASTQ files for all genomes are available at NCBI under BioProject PRJNA413593 and PRJNA49659. 121 6.2.5 Statistical Analysis Descriptive statistics were calculated for basic demographic and clinical information across two categories: (i) all cases diagnosed within YT, and (ii) BC cases with an Mtb isolate ≤5 SNVs to a YT case and thereby classified as “Related” (BCR). Univariable analysis used the t-test for comparisons of mean age, and categorical variables were compared using Chi-square or Fisher’s exact test where appropriate. The frequency for which a MIRU-VNTR pattern was observed within the YT and/or BCR populations was described, and to place MIRU-VNTR genotypes in the wider context of BC as a whole, genotypes were also compared to BC isolates not closely related to YT isolates based on genomic distance thresholds (>5 SNVs). These were classified as “Not Related” (BCNR). A dendrogram based on 24-locus MIRU-VNTR genotyping patterns was generated using the categorical (Hamming) distance and UPGMA (unweighted pair group method with arithmetic mean) algorithm. All statistical analyses were done in R v3.4.1. 6.3 Results 6.3.1 MIRU-VNTR and WGS provide different estimates of clustering From 2005 through 2014, 32 individuals were diagnosed with culture-positive TB in Yukon Territory. MIRU-VNTR genotyping grouped 21 of these cases into three clusters (3–13 YT isolates/cluster), yielding a clustered proportion of 65.6% within the territory. One YT isolate had an untypable locus yet matched a cluster unique to YT for the other 23 typable loci. Six YT isolates had MIRU-VNTR patterns that were unique amongst the YT population yet clustered with isolates in BC, bringing the total number of MIRU-VNTR clusters across both jurisdictions containing at least one YT case to nine (Figure 6-2). Four YT isolates remained unclustered after comparison with all BC isolates; however, all four were within one or two loci of a YT and/or BC genotype cluster. 122 Figure 6-2. Population structure of Mycobacterium tuberculosis in Yukon Territory. Dendrogram based on 24-locus MIRU-VNTR genotypes of Mycobacterium tuberculosis isolates collected in Yukon Territory (YT) from 2005 through 2014. MIRU-VNTR clusters (≥2 isolates) were assigned a unique MClustID. The number of times each MIRU-VNTR pattern was observed in the YT, BCR—(within 0–5 SNVs of a YT isolate), and BCNR—BC (>5 SNVs to a YT isolate) populations are indicated in the right-hand side columns. Coloured squares represent the whole genome sequencing cluster, and isolates >5 SNVs from isolates in YT or British Columbia (BC) were considered not genomically clustered (NC). WGS clusters (≥2 isolates within 0–5 SNVs) were assigned a unique WClustID independent of MIRU-VNTR. Lineage is indicated at the root. Abbreviations: EAm, Euro-American, IO, Indo-Oceanic. Order of loci: MIRU 04, MIRU 26, MIRU 40, MIRU 10, MIRU 16, MIRU 31, 424, 577, 2165, 2401, 3690, 4156, 2163, 1955, 4052, MIRU 02, MIRU 23, MIRU 39, MIRU 20, MIRU 24, MIRU 27, 2347, 2461, 3171. 123 Genomics provided a higher resolution view of clusters suggestive of recent transmission, merging several MIRU-VNTR clusters that differed by a single locus or had an untypable locus into single groups supported by contact investigation data, and in other cases revealing that MIRU-VNTR clustered isolates, such as those belonging to MClust-023, were not truly clustered in a way that would suggest recent local transmission (Figure 6-2). Using a five SNV threshold, six genomic clusters were identified with at least one YT case, involving a total of 28 YT and 101 BCR isolates and ranging from two to 59 isolates (Figure 6-3). Another YT isolate was within 20 SNVs of a genomic cluster, while the remaining three isolates were >200 SNVs away from any other YT isolate. By WGS, the clustered proportion was 28/32 (87.5%) when YT isolates were considered alongside BC isolates, and 25/32 (78.1%) considering only isolates among YT residents. With the exception of two Indo-Oceanic lineage isolates, all other YT isolates (94.1%) belonged to the Euro-American lineage. 124 Figure 6-3. Yukon Territory Mycobacterium tuberculosis isolates in the context of related BC isolates. Minimum-spanning tree based on whole genome sequences of Mycobacterium tuberculosis (Mtb) isolates from the Yukon Territory (YT), Canada study population (n = 32) and closely related (five single nucleotide variants [SNVs] threshold) isolates from British Columbia (BC) (n = 101). The size of each circle is proportional to the number of isolates, and circles are coloured in blue to represent the YT study population and grey for the BC population. Unique cluster identifiers (WClustID) are indicated for isolates in genomic clusters. The number of SNVs between isolates with >5-SNVs are indicated along the connecting branches. WClust-1WClust-19WClust-9WClust-7WClust-80WClust-4125 6.3.2 Genomically related cases across jurisdictions are similar clinically Comparing all YT cases to the genomically related BCR cases (n = 101), characteristics were found to be similar across both populations, including the mean age of 45.8 years (standard deviation [SD] ± 16.7) and 46.8 years (SD ± 11.9) for YT and BCR individuals, respectively. Both groups were predominantly Canadian-born, with 93.8% of the YT study population and 88.9% of BCR persons born in Canada (Table 6-1). The proportion of individuals with a clinical presentation associated with TB transmission was high in the YT and BCR populations, with respiratory TB diagnosed in 90.6% of YT and 89.1% of BCR individuals. Likewise, the smear-positive TB proportion was high—greater than 82% in YT and BCR persons. Of note, the proportion of individuals with cavitary TB was over 1.5× higher in the YT population compared to BCR individuals, with cavitary disease in 37.5% (12/29) of YT persons (p = 0.099). With respect to risk factors for transmission,381 the majority of individuals (YT: 71.9%, BCR: 61.5%) reported ≥1 risk factor (HIV, illicit drug use or alcohol misuse). Reflecting the differing demographics between the two settings, the majority of YT individuals resided in remote (84.4%) regions, compared to those in BCR where the majority resided in metro areas (82.2%). 126 Table 6-1. Characteristics of Yukon study population. Demographic and clinical characteristics of culture positive cases across Yukon and genomically related cases in British Columbia, Canada, 2005–2014.a No. Cases (%) Characteristic YT BCR p-valueb Totals n = 32 n = 101 Age, years 0–24 3 (9.4) 1 (1.0) 0.086 25–44 10 (31.2) 40 (39.6) 45–64 14 (43.8) 50 (49.5) 65+ 5 (15.6) 10 (9.9) Gender Male 21 (65.6) 69 (68.3) 0.777 Community Metro 0 (0.0) 83 (82.2) <0.001 Urban/Rural 5 (15.6) 7 (6.9) Rural 0 (0.0) 8 (7.9) Remote 27 (84.4) 3 (3.0) Birthplacec Canada 30 (93.8) 88 (88.9) 0.734 Disease Site Respiratory 29 (90.6) 82 (81.2) 0.344 Non-Respiratory 3 (9.4) 11 (10.9) Respiratory + Non-Respiratory 0 (0.0) 8 (7.9) Respiratoryd smear Positive 23 (82.1) 76 (83.5) 1.000 Cavitary disease Yes 12 (37.5) 23 (22.8) 0.099 Risk Factorse None 9 (28.1) 30 (38.0) 0.325 ≥1 23 (71.9) 49 (62.0) Abbreviations: BCR, British Columbia Related (Mycobacterium tuberculosis isolates ≤5 SNVs to YT study population); SNVs, single nucleotide variants; YT, Yukon Territory. aPercentages have been rounded and may not total to 100%. bChi-square test, (Fisher’s exact test where appropriate). cData unavailable n = 2 (BCR). dExcluded “other respiratory” sites e.g. pleura. eRisk Factors = HIV, illicit drug use, or alcohol misuse; data unavailable for 1 or more risk factor in BCR population (n = 22). 127 6.3.3 Transmission reconstruction To characterize person-to-person spread of TB within YT, temporal transmission networks were constructed using WGS results combined with epidemiological data for the three genomic clusters with sustained transmission between YT persons—WClust-1, WClust-9, and WClust-19 (Figure 6-4). Although Mtb isolate YT13 is above the five SNV threshold set for recent transmission, it is within 18 SNVs of WClust-19—a cluster genotypically and genomically unique to the YT population—and was therefore included in the reconstruction figure. This case likely represents reactivation of a previously acquired infection with a strain circulating within YT. For WClust-1, a large cluster with discrete minimum spanning tree branches in both YT and BC, only the branch of YT isolates was included, together with the two closely related BC isolates (Figure 6-3). 128 Figure 6-4. Yukon WGS transmission reconstructions. Transmission networks of three Mycobacterium tuberculosis genomic clusters (based on a five single nucleotide variants [SNVs] threshold) representing transmission in Yukon Territory (YT), Canada (2005–2014). Blue circles represent YT isolates and grey British Columbia (BC) isolates. A red outline around a circle represents an individual who is acid fast bacillus smear-positive with cavitary TB disease. Solid lines indicate strong epidemiological linkages, and dashed lines indicate weak epidemiological linkages. SNVs acquired over time are represented by dots between isolates. The pink dot represents the presence of a minority-variant which becomes fixed in all isolates in the subsequent transmission chain. 129 Each of the three clusters differ slightly. WClust-19 is the only cluster exclusively comprising YT individuals, whereas WClust-1 and WClust-9 had one or more BC persons with related isolates. Within WClust-1 the BC cases may have acquired TB from a YT individual, whereas in WClust-9 a BC individual likely transmitted TB to a number of BC and YT cases. SNV distances ranged within clusters; however, WClust-19 saw no genomic variation in the transmission chain stemming from YT8, despite up to six years between disease acquisition and diagnosis. WClust-9 has four BC isolates 0–5 SNVs from those in YT (Figure 6-4). However, with the exception of BC2 there are no known epidemiological connections between these cases that would suggest a common source not identified through CIs. Interestingly, all three transmission clusters each have at least one individual acting as the source of three or more active culture-positive cases. WClust-1 represents the largest YT cluster. CIs revealed that many of the individuals were social contacts of one another, with at least two individuals suspected of giving rise to multiple secondary cases. Here, genomics identified a minority variant (at the SNV site, 15% of reads had adenine [A] and 85% were cytosine [C]) in the sample from YT18, whereas in samples from subsequent cases, the minority SNV was fully fixed, confirming this individual as the most likely source for the cases that followed (Figure 6-5). Genomic data also confirmed the inclusion of three Yukon (YT23, YT25 and YT27) and two BC isolates (BC31 and BC49) in this cluster, despite no apparent epidemiological linkages to each other or other cluster members. While each of the three genomic clusters had unique features, all had at least one individual source of multiple culture-positive secondary cases, and all spanned several years, with some individuals progressing rapidly to active disease, and others reactivating after a long period of latency. 130 Figure 6-5. Transmission clusters—SNV alignments. Multiple sequence alignments of concatenated single nucleotide variants (SNVs) for three Mycobacterium tuberculosis genomic clusters (based on a five SNVs threshold) representing sustained transmission in Yukon Territory, Canada (2005–2014). 6.4 Discussion The genomic epidemiology of tuberculosis in northwestern Canada over a ten-year period was described, finding that persons diagnosed with TB were largely Canadian-born with nearly all cases attributable to local transmission, consistent with the epidemiology of TB elsewhere in Canada’s North.6,166 Genomic data, combined with detailed epidemiological data, allowed for the reconstruction of likely transmission routes among the three large clusters. It was found that, as is true for a number of infectious diseases, a small number of individuals account for a disproportionate number of secondary cases—the phenomenon of “super-spreaders”.382 Understanding the risk 131 factors and epidemiological characteristics driving super-spreading in a community is important for better prioritizing TB prevention and care programs. In the YT study population, the proportion of individuals with clinical risk factors frequently associated with transmission, such as cavitary disease and smear positivity,381 was quite high, and anecdotal evidence from the local public health team suggested that delays in diagnosis might have also contributed to transmission. A recent publication4 discussed the various drivers of TB transmission outside clinical risk factors, including diagnostic delays, which increase the potential for disease progression and transmission,54,383 particularly amongst highly mobile, socially connected, and infectious individuals. Given the shared border between Yukon Territory and BC, transmission across jurisdictions was also examined. Including genomically related BC isolates increased the estimate of clustering for YT isolates, suggesting that estimates derived from provincial or territory data alone likely underestimate transmission rates in relation to remote settings. Cross-border transmission appears to occur in both directions—in several cases YT residents likely transmitted to BC residents via social/community connections with YT residents reporting travel/residential histories in both northern BC communities and larger metropolitan regions. Additionally, three YT cases had isolates that clustered only with BC isolates and likely acquired their infections within BC, while a BC source was linked to six YT cases in WClust-9. Given the low genomic variation between cases, with most cases differing by 0–1 SNVs, the cluster reconstructions were only possible thanks to the detailed epidemiological information collected by the local public health team. Such minimal variation across multiple hosts over many years is not uncommon, and has been previously described in outbreaks elsewhere in Canada.117 This observation reinforces the need for comprehensive contact investigation data coupled to genomics to fully understand regional epidemiology, though it is important to note that because genomic studies currently require Mtb culture, culture-negative TB cases are excluded from reconstructions. These cases are less likely to contribute to transmission due to low bacterial loads but cannot be completely excluded. TB diagnoses prior to the study period are also not captured here. 132 Understanding TB transmission dynamics is key to the design and delivery of effective evidence-based interventions to prevent the continuing spread of TB. Here, it was found that WGS combined with detailed CIs information allowed for a more refined picture of transmission than either method alone, or with the use of MIRU-VNTR. The implementation of routine genotyping and WGS is recommended with linkage of these results to epidemiological and CIs data. Knowledge of the genomic connections between isolates in YT and BC will support TB programs and improve communication and understanding of transmission across jurisdictional boundaries. 133 Chapter 7: Comparison of Traditional Field Epidemiology and Whole Genome Sequencing to Understand Tuberculosis Transmission in a Remote Setting 7.1 Background Tuberculosis (TB) remains an important public health concern in Canada, particularly in northern rural and remote areas where endemic spread of TB is commonplace.166 Understanding patterns of transmission in these settings is an integral part of developing evidence-based prevention and care strategies and prioritizing public health resources—this includes understanding the burden of disease resulting from recent local transmission versus reactivation of historic latent TB infection (LTBI), as well as understanding the nature of recent transmission. This latter point is critical for improving TB services in a region—understanding the clinical, demographic, and/or epidemiological factors driving TB transmission is vital to developing informed prevention programs, screening activities, and contact investigations (CIs) and ultimately preventing the continued spread of TB. Field-based epidemiological investigation is used to identify both infected contacts, secondary active cases, and possible sources of a given case, and for decades was the only means to detect transmission.384 In recent years, a combination of field and molecular epidemiology has been used in many settings—contact data collected through interviews of recently diagnosed individuals may reveal the potential links between cases, while genotyping techniques identify related Mycobacterium tuberculosis (Mtb) isolates and can confirm or refute a potential transmission event. Now, several studies have shown that whole genome sequencing (WGS) yields more accurate transmission reconstructions than the approaches based on genotypic data.74,149,158 In British Columbia (BC), Canada, WGS was used retrospectively to better understand a large TB outbreak74 and in real-time as part of the management of a second large outbreak in BC,173,174 and it has now become routine practice in the UK to use WGS to identify clusters of related cases for public health follow-up. 134 Despite global interest in WGS as a tool for understanding TB epidemiology and a continuously expanding dataset of publicly available Mtb genomes, there are gaps in our understanding of how useful this new technique is. There are technical questions around how consistent Mtb mutation rates are, particularly during latent infection versus active disease385,386 and from human host to human host,147,300 as well as around how to identify transmission-informative variants in the many repetitive elements within the Mtb genome.387,388 There are also questions surrounding its utility. In well-resourced rural and remote settings, where detailed contact tracing and interview data are often available for each case, it is not known whether WGS offers any benefit over the current standard of care—interpreting genotyping data in the context of this rich field epidemiological data—and there have only been limited comparisons of how useful the molecular data alone is, whether genotypic or genomic.166,389 Furthermore, there has been no qualitative feedback data from frontline public health personnel describing if/how molecular data improved their ability to understand a cluster of cases in a remote setting. The Yukon Territory (YT), located in Canada’s northwest, has a higher TB incidence (12.1 per 100,000) than the Canadian average (4.9 per 100,000), yet lower than other northern Canadian settings.82,84 As described in Chapter 6, the majority of YT residents diagnosed with TB are Canadian-born (93.8%) and live in remote regions (84.4%). All YT TB cases are managed by a small team of public health professionals, many of whom have deep and long-standing ties to the territory; this strong tradition of engagement between public health, community nurses, and YT’s communities means the local public health unit has uniquely detailed insights into the social networks underlying YT’s TB clusters. This, coupled to a small, remote population with frequent travel to BC, yet little in- or out-migration, makes this an ideal population in which to explore the utility of genomic data in enhancing contact investigations. Here, the utility of whole genome sequencing versus 24-locus mycobacterial interspersed repetitive unit–variable-number tandem repeat (MIRU-VNTR) coupled to robust traditional field epidemiology for identifying transmission, outbreaks, and reactivation of LTBI was determined. Two independent teams—one working with MIRU-VNTR and detailed contact investigation data and the other working with WGS data and basic clinical and epidemiological information—each reconstructed the most likely transmission pathways for every culture-positive TB case diagnosed in YT from 2005–135 2014. The teams then met to jointly infer the most plausible transmission network, given both the social contact investigation and WGS data. Here, the results of these reconstructions are presented, as well as qualitative user feedback on the utility of genotyping and genomics in a remote setting with a comprehensive TB contact investigation program. 7.2 Methods 7.2.1 Study setting and design The study took place in Yukon Territory (YT), Canada, a remote arctic/sub-arctic territory with a population of approximately 38,400 in 2017, spread over an area of more than 470,000 square kilometers.380,390 The study population included all 32 persons diagnosed in YT with culture-confirmed TB from 2005 through 2014 (84.2% of all 38 diagnoses). Yukon Communicable Disease Control (YCDC), in partnership with Yukon Government Community Nursing, is responsible for patient care and treatment, with contracted TB services including laboratory diagnostics, case management support, and access to a shared data system provided by the British Columbia Centre for Disease Control (BCCDC) and the BCCDC Public Health Laboratory (BCPHL). 7.2.2 Bacterial culture, genotyping and whole genome sequencing All Mycobacterium tuberculosis (Mtb) isolates were obtained from specimens submitted to BCPHL for routine clinical testing of tuberculosis. Mtb isolates were cultured, DNA extracted, and 24-locus MIRU-VNTR genotyping was carried out using standard methods.77 All samples were sequenced on the Illumina HiSeq 2500 platform (Illumina, San Diego, USA) at the Michael Smith British Columbia Genome Sciences Centre (Vancouver, Canada) to produce 125 bp paired-end reads, which were mapped to the H37Rv reference genome (GenBank ID: NC000962.2) using the Public Health England/Oxford University bioinformatics pipeline.267 136 7.2.3 Source identification by field and molecular epidemiology The first team (field-based) comprised YCDC nursing staff and program managers responsible for treatment and care of all persons diagnosed with TB and their contacts in the territory. They reviewed detailed notes from CIs for each individual in the study, and were provided with the MIRU-VNTR cluster each isolate belonged to, along with a general description of these clusters across BC and YT (e.g. size, geographic distribution, basic demographics) (Figure 7-1). The team was provided with a structured spreadsheet and was asked to identify each case’s most likely source from the following options: a specific individual within YT; an unknown individual within YT; acquisition from an unknown individual through travel outside YT; or reactivation of LTBI acquired prior to the study period. Deliberations took into consideration MIRU-VNTR data, along with each TB case’s prior contact history, symptom onset date, tuberculin skin test (TST) records, and transmission risk factors including acid-fast bacillus (AFB) smear status and presence of cavitary disease. Respondents were also asked to provide a confidence score to each presumed source: 0 – not at all confident, 1 – somewhat confident, 2 – very confident, 3 – certain. MClust-008 This cluster belongs to the Euro-American lineage, and the 24-locus MIRU-VNTR pattern has been seen 40 times in British Columbia from 2005 through 2014 in the following BC health service delivery area(s): South Vancouver Island, Vancouver, Fraser East, Fraser North, Fraser South, Northwest The overall demographics of this MIRU-VNTR cluster (BC & YT): 62% male, median age (years) = 45 (IQR: 35–55) and 95% Canadian-born. Figure 7-1. MIRU-VNTR cluster summary example. 137 7.2.4 Source identification by genomic epidemiology The second team (genomic-based), comprising TB genomics experts from BCCDC, had access to WGS data for each YT isolate (see Chapter 6), as well as WGS data from all MIRU-VNTR clustered Mtb isolates from cases diagnosed in BC as part of the ten-year retrospective study (Chapter 3), including those that matched at least 23/24 MIRU-VNTR loci with a YT isolate. Genomic clusters were defined using a threshold of five single nucleotide variants (SNVs)149 and were assigned a unique identifier (WClustID). Using WGS data but no field epidemiological information, this team independently constructed putative transmission networks from the genome sequences of all YT study isolates (n = 32) and any BC isolates within five SNVs of a YT isolate (n = 101). A minimum-spanning tree (MST) was generated and coloured by MIRU-VNTR cluster ID (MClustID), with labels indicating the genomic WClustID. The team subsequently refined each network with basic epidemiological data, including diagnosis date, place of residence, acid-fact bacilli smear status, chest radiology results and risk factors (HIV, illicit drug use, alcohol misuse) from the integrated Public Health Information System (iPHIS), but did not have access to any contact investigation or social network information. The team identified each case’s most likely source from the above options. A confidence score was assigned to each source identified as described above. 7.2.5 Source identification consensus At a joint, in-person meeting with both teams, each YT study case was reviewed and a consensus reached regarding the most plausible source, given the combination of WGS and field epidemiological data. During this meeting, informal training and background information regarding the interpretation and limitations of genotyping and WGS data were provided through a PowerPoint presentation and discussion of the genotyping and genomic data for each case. 138 7.2.6 Qualitative assessment To examine the YCDC’s team knowledge, attitudes, and practices around genotyping and genomic services, an online, multiple-choice survey was conducted both before and after the in-person consensus meeting (see Appendix Table A-1, Appendix Table A-2 for questions). At the conclusion of the consensus meeting, a semi-structured group interview was conducted with the TB prevention and care team who completed the field epidemiology-based source identification—three nurses, a program manager (also a nurse) and Yukon’s Chief Medical Officer of Health. The interview’s objective was to collect qualitative feedback on the usefulness of molecular and genomic data for investigation of TB cases, the potential added value of MIRU-VNTR and WGS, and how this information could be used prospectively (see Appendix Table A-3 for questions). The interview questions served as prompts to structure the conversation, but all persons were free to comment, at any depth. The interview was recorded, and manually reviewed. Using a thematic analysis method,391 statements were coded and categorized according to identified common themes. 7.2.7 Statistical methods The insights into TB transmission provided by field epidemiology/genotyping and WGS were compared by analyzing the outcomes of each investigation at three levels of resolution—individual (i.e. did the source identified by either team match the source determined at the consensus meeting), population-level (i.e. was the case ascribed to the correct transmission cluster—defined genomically) and probable location of TB acquisition (YT, BC, other province/territory, or outside Canada). All statistical analyses were completed using R (v3.4.1). Agreement between results for identified source from the field- and genomic-based investigations was measured using Cohen’s kappa. Kappa values of <0.2, 0.21–0.40, 0.41–0.60, 0.61–0.80, 0.81–1.00 indicate poor, fair, moderate, good, and very good agreement, respectively.392 Fisher’s exact test was used for comparisons of proportions. Correlations between qualitative variables—level of certainty assigned to source identification, were assessed using Spearman’s rho. 139 7.3 Results Detailed clinical and epidemiological information, social contact data, 24-locus MIRU-VNTR genotypes, and whole genome sequences were available for all 32 (100%) of the YT study isolates diagnosed from 2005 through 2014. Typically, 1–2 cases were diagnosed each quarter, with the epidemiological curve (Figure 7-2) showing a notable peak corresponding to an increase in cases matched to one of the three circulating YT-specific Mtb strains, as defined by WGS. Figure 7-2. Yukon cases over time. Number of tuberculosis cases by year-quarter of diagnosis over a ten-year period in Yukon, Canada. Each circle represents a single case, and colours distinguish the three large clusters identified by a combination of whole genome sequencing and traditional epidemiology. NC (Not Clustered) represents persons with Mycobacterium tuberculosis strains unique in Yukon. 7.3.1 Good agreement around clusters and location of TB exposure between methods Both the CIs+MIRU-VNTR analysis and WGS identified three large clusters (Figure 7-3); however, 21/32 (65%) individuals were assigned to one of these three clusters by the team using genotypic data, while WGS placed 25 individuals into these large clusters. Three of the four discordant cases represented scenarios in which the MIRU-VNTR pattern differed from the larger clusters’ patterns by a single locus—these were reported as genotypically unique MIRU-VNTR isolates in YT by the laboratory, and led the team to conclude that despite the fact the two of the three discordant cases had epidemiological linkages to known YT cases, these individuals had either acquired TB from an unknown individual in BC (n = 1) or another province/territory 140 (n = 2). The fourth discordant case had a MIRU-VNTR pattern common to both YT and BC, and while WGS placed this individual with a genomically distinct YT cluster within this MIRU-VNTR group (WClust-1 in Figure 6-3), the field team classified this case as having a BC source based on epidemiological information. Of the remaining seven cases, both teams agreed that two cases were the result of reactivation of LTBI acquired outside Canada; both were persons born outside Canada and involving unique MIRU-VNTR genotypes within YT. For the five other cases, three were genomically clustered with BC isolates (≤5 SNVs), one was 26 SNVs from a BC genomic cluster, and one was 18 SNVs from a cluster observed only in YT. The field team classified three of these individuals as having acquired TB from an unknown BC source, thereby agreeing with the genomic assignment. The remaining two were hypothesized by the field team to have acquired their infection within Canada but not YT or BC, which was not supported by the WGS results. 141 Figure 7-3. Whole genome sequencing-based population structure of Yukon Territory Mycobacterium tuberculosis isolates. Minimum-spanning tree based on whole genome sequences of Mycobacterium tuberculosis (Mtb) isolates from the Yukon Territory (YT), Canada study population (n = 32). The size of each circle is proportional to the number of isolates, and circles are coloured to represent the MIRU-VNTR cluster (MClust). Isolates not matching identically at all 24 MIRU-VNTR loci were considered not clustered (NC). Whole genome sequence cluster identifiers (WClustID) are indicated for isolates clustering using a five SNV threshold. The number of SNVs between isolates with >5-SNVs are indicated along the connecting branches. Ultimately, the two teams agreed on 26/32 (81%) locations of acquisition (Table 7-1), with a Cohen's kappa of 0.68 (p < 0.001). Concordance was highest amongst individuals belonging to large YT clusters and persons born outside Canada. Qualitative feedback collected at the consensus meeting indicated multiple reasons for conflicting assessments. Unique MIRU-VNTR patterns were cited as a frequent cause—both scenarios in which an isolate’s MIRU-VNTR pattern was a single-locus mismatch to an existing cluster, but reported as unique (e.g. WClust-9, Figure 7-3) and in which an isolate’s MIRU-VNTR pattern was unique to YT but identical to a strain circulating in BC—as was a lack of epidemiological linkages to another YT case. 142 Examining each method of investigation within the full context of all available data, the genomic-based method had a higher agreement than the field-based approach for identifying connections at a high-level (i.e. provincial/territorial or cluster-level)—30 of 32 (94%, p = 0.148) YT cases and all 25 (100%, p = 0.110) cases associated with large clusters were correctly categorized (Table 7-2). The two discordant genomic-based assignments were the result of low genomic diversity between isolates, and single-locus mismatches to YT MIRU-VNTR patterns leading the team using genomics to assume BC sources. Table 7-1. Location of TB Acquisition. For each Yukon Territory (YT) Mycobacterium tuberculosis isolate (n = 32), a pairwise comparison of the two methods used to identify location of tuberculosis acquisition is shown. The four possible categories for location provided to the YT field nurses and BC Centre for Disease Control genomic epidemiologists included YT, British Columbia (BC), Other Province/Territory, and outside Canada. Genomic Epidemiology Field Epidemiology Totals YT BC Other Prov./Territory Outside Canada YT 17 1 2 0 20 BC 0 7 3 0 10 Other Prov./Territory 0 0 0 0 0 Outside Canada 0 0 0 2 2 Totals 17 8 5 2 32 Table 7-2. High level concordance between methods. Comparison of each method of investigation against the final assignments of tuberculosis source at two levels — province/territory and large cluster. Prov./Territory-Level Cluster-Level Method of Investigation Concordant n (%) Discordant n (%) Totals Concordant n (%) Discordant n (%) Totalsa Field-based 25 (78) 7 (22) 32 21 (84) 4 (16) 25 Genomic-based 30 (94) 2 (6) 32 25 (100) 0 (0) 25 aExcluded individuals not linked by both field- and genomic-based methods to a large cluster (n = 7). 143 7.3.2 Low genomic variability within clusters limited of an exact source Next, the concordance between each team’s identification of a specific source was examined for the subset of cases that acquired TB within YT (n = 23). The two teams agreed on a likely source in 13 (57%) instances; of the nine discrepant results, all but one belonged to the largest cluster of cases, WClust-1 (Table 7-3). When source case assignments were compared during the in-person consensus meeting, discussion revealed that the team using CIs+MIRU-VNTR data struggled with the complex social network of this cluster, with many connections between individuals, while the team using WGS data were challenged by the minimal genomic diversity between YT isolates (0–4 SNVs). Although the presence of a minority variant in one WClust-1 case (see Chapter 6) divided the cluster into two genomically linked sub-clusters, facilitating source identification at the consensus meeting. While there was no strong agreement between the team’s source case assignments, the field-based methods did accurately link individuals to the correct WClust-1 genomic sub-cluster for 11 of 13 persons (85%) with only one individual linked to the incorrect genomic sub-cluster, and a second individual thought to have acquired their infection in BC due to an absence of clear epidemiological connections. Table 7-3. Concordance between methods at a case-level. Concordance/discordance between methods of investigation—field- and genomic-based epidemiology—for tuberculosis source case identification, overall and by large cluster. Characteristic Concordant n (%) Discordant n (%) Totalsa Overall 13 (57) 10 (43) 23 Large Cluster WClust-1 5 (38) 8 (62) 13 WClust-9 3 (60) 2 (40) 5 WClust-19 5 (100) 0 (0) 5 aExcluded individuals not assigned a Yukon source by field- and/or genomic-based methods (n = 12). 144 7.3.3 Confidence in correct source identification varied between teams Next, both teams’ level of confidence was examined for each inferred source case/location—not at all, somewhat, very confident or certain. Comparing the confidence category assigned to each inferred source revealed no correlation (p = 0.365) between the two teams. The team using genotyping and contact data on average reported higher levels of certainty in their source ascertainment (p = 0.007) (Figure 7-4). Differences were also noted within and across the large clusters (Figure 7-5). The largest genomic cluster (WClust-1) had the widest distribution of confidence in source case ascertainment; conversely, participants reported higher confidence in inferred sources in the smaller clusters (WClust-9; WClust-19), particularly the team using CIs+MIRU-VNTR data, who reported "very confident" or "certain" for all source cases identified. Figure 7-4. Certainty assigned to identified sources. Relationship between degree of certainty assigned to each source case/location identified by field- and genomic-based methods. Link widths are proportional to the number of cases, which are indicated in margins. 145 Figure 7-5. Frequency of certainty categories assigned for each source identified, divided by cluster. During the in-person consensus meeting, the genomic, clinical, and contact investigation data were combined and discussed at length to determine the most plausible source for each individual. Overall, YT-resident sources were assigned to 23 individuals, and the degree of certainty assigned to each source (field-based: n =20; genomic-based: n = 22) identified during the independent investigations was examined. It was found that where there was a high level of confidence (i.e. "very confident" or "certain") the correct source had been identified in 100% of cases using either investigation method (field-based: n = 12; genomic-based: n = 5), Table 7-4. Where there was a lower level of certainty for the identified source, only five of eight (62%, field-based), and 10 of 17 (59%, genomic-based) correctly identified the source (p > 0.05). Table 7-4. Accuracy of source case identification. Frequency of “correct” tuberculosis source case identification by levels of confidence for each investigation method. Level of Confidence Field-baseda n (%) Genomic-basedb n (%) Correct Source Incorrect Source Correct Source Incorrect Source Very Confident/Certain 12 (100) 0 (0) 5 (100) 0 (0) Somewhat/Not at All Confident 5 (62) 3 (38) 10 (59) 7 (41) aExcluded individuals not assigned a Yukon source by field-based methods (n = 12). bExcluded individuals not assigned a Yukon source by both field- and genomic-based methods (n = 10). 146 7.3.4 Preference for genomics over genotyping Each member of the YCDC TB program team (n = 4) completed an online survey at the outset of the study in which they were asked about their role in TB prevention and care and their knowledge of genotyping methods and use in TB investigations. All respondents were engaged in direct care and treatment of persons with TB, including the collection of personal information, supervising daily medication doses, CIs, and program oversight, and three of four spend an average of >60% of their week on TB-related activities. All respondents have a background in nursing with approximately 13 to 35 years of experience—most of which in rural and remote communities. Three of the four team members had heard of MIRU-VNTR prior to this study, through presentations, conferences, and/or journal articles. Only one respondent reported using MIRU-VNTR information in their daily work. None indicated that they had received formal training in the use and interpretation of MIRU-VNTR in TB investigations, although three of four were aware that MIRU-VNTR data were available for their cases. No respondents reported complete confidence in using MIRU-VNTR data for their investigations, and none had used MIRU-VNTR to inform their TB investigations prior to this study. At the conclusion of the in-person consensus meeting, a semi-structured group interview was conducted to collect qualitative feedback regarding the use of molecular/genomic data in this setting. Two main themes emerged as detailed on the next page. 147 Key themes from a semi-structured group interview with the field team regarding the use of tuberculosis molecular/genomic data. 1. The accuracy of genomics over genotyping. The team reported that MIRU-VNTR genotyping data conflicted with known epidemiological connections in a number of instances, whereas genomics identified clusters more closely aligned with the epidemiological data, and provided some novel insight. “I’m liking MIRU a little less” “The MIRU can be helpful or not helpful” “To have had the WGS data, would have saved many hours of discussion—would have helped to focus the discussion by narrowing the list of potential sources” 2. Program assessment. Participants acknowledged that genomic epidemiology provided new insights into transmission patterns and saw WGS as a way to assess the effectiveness of treatment and prevention programs, including screening and prophylaxis. “Many of these confirmed our suspicions” “It was nice to know this was a reactivation and not a contact of a missed source” “Small case load means few people working on TB, and we need to focus limited resources on the highest risk contacts.” “…prophylaxis could have prevented the cluster” 148 Overall, participants viewed genomic epidemiology as a useful tool to streamline investigations, particularly in differentiating LTBI reactivation from recent transmission, but not essential to their current practices, instead noting it would most be useful for program assessment. The team found that WGS results were useful for confirming probable source cases and ruling out local transmission. MIRU-VNTR data was cited as a source of frustration where it did not align with the epidemiology. Improved communication around how to interpret closely related MIRU-VNTR patterns, as well as the limitations of genotyping, was strongly recommended. In a post-meeting follow-up online survey (Appendix Table A-3), respondents reiterated the themes from the in-person group interview by highlighting their preference for WGS over MIRU-VNTR, with qualitative feedback such as “WGS provides a clearer picture than MIRU-VNTR of what is happening in terms of transmission.” Additionally, respondents noted that WGS highlighted some gaps in knowledge or what may have been missed during contact tracing, supporting the idea of using WGS towards program assessment. When asked if they felt more confident using WGS data following this study, all stated that they were considerably more confident and would like to have genomic data for all cases. The team also indicated they would be open to further training in the interpretation of genomic data, with in-person training preferred over an instruction manual or instructional videos. 149 7.4 Discussion In this study, both the added value of using genomic epidemiology in settings with rich field epidemiological data, as well as the knowledge, attitudes and practices around the use of molecular and genomic data for TB case investigations was examined. Comparing the traditional approach of inferring TB transmission from genotyping and contact investigation data to the use of genomics with limited case-level data revealed that WGS more accurately identified connections between cases at a high level, such as cluster membership, but that the data from CIs was integral to identifying source cases at an individual-level, particularly within large clusters. In certain settings, genotyping by 24-locus MIRU-VNTR has been reported as having high discriminatory power and good concordance with known epidemiological linkages.118,306,393 However, technical issues with particular loci, rendering them untypable, can make cluster assignment challenging—these patterns are often assigned their own unique identifier, and obscure the potential linkage between isolates.76,108,394 Mtb isolates with single-locus mismatches have been shown to be linked by both epidemiology and genomics.112,149,306,354,355 In this study, these “falsely unique” MIRU-VNTR patterns complicated the interpretation of contact investigation data, with the true nature of clustering only revealed through the higher-resolution genomic approach. Given that WGS may not be available to all TB programs, it is recommended that laboratories reporting MIRU-VNTR data include information not just on identical patterns, but also closely related patterns that might suggest a larger cluster. The survey results also indicated that there is a substantial gap in training end-users to interpret genotyping data, suggesting that laboratories might consider including some interpretive commentary on their genotyping reports beyond simply a pattern and a cluster identifier. As expected, genomic data coupled to basic clinical and epidemiological data was able to identify clusters and infer some potential sources, but it was only when it was combined with extensive contact investigation data that a more comprehensive picture of TB transmission began to emerge. This highlights the importance of engaging both the laboratory and public health nursing and epidemiology staff in the joint interpretation of genomic epidemiology data. In remote northern settings with extensive person-to-person transmission,166,379 the minimal 150 genomic variation observed means that data from CIs is integral to understanding local epidemiology, and that enhanced investigation questionnaires, as recently used in a United Kingdom study, can establish epidemiological connections between individuals that would have otherwise not been linked.118 During discussions with the YCDC public health team, they noted that had WGS data been available during CIs, more focused questioning likely would have uncovered some missed connections and would have helped to confirm/refute tenuous linkages, saving time and resources. Discussions also revealed a strong preference for WGS over MIRU-VNTR to support CIs, and identified program assessment as an important secondary use for WGS data. This study also alluded to the importance of sharing molecular epidemiology data across jurisdictional boundaries. Genotyping results are routinely reported at the provincial/territory level, but information on the presence of a pattern in another jurisdiction may not always be provided. Here, the YCDC team did not have access to molecular data from BC cases prior to this study, and during routine CIs were unaware that several cases with MIRU-VNTR patterns unique to YT were actually members of genotypic clusters comprising multiple BC cases. A major strength of the present study was the availability of a small, well-characterized population, particularly with the long service of several of the nurses involved in the study who have considerable experience within the community. A limitation of the comparison between investigation methods was that the team using data from CIs could make connections between culture-positive and -negative cases; however, the molecular and genomic analyses were limited to culture-positive cases and may have resulted in missed linkages between individuals. This study highlights the need to better integrate laboratory, clinical, and epidemiological data to more comprehensively describe TB epidemiology in a given setting, including using higher-resolution genomic approaches where possible, providing better interpretation of MIRU-VNTR data when WGS is not available, and bringing individuals together for collaborative discussion of cases and clusters. Through a molecular-informed, enhanced contact investigation approach, it is believed that TB programs might better focus their resources and avoid missed opportunities 151 for intervention, thereby limiting new transmissions. For this to occur, communication is key. Given the dynamic and complex nature of genomic and contact investigation data, regular review of cases through in-person meetings and training in interpretation is recommended. Genomics also has the potential to aid in TB program evaluation, and as the technique becomes more commonplace, TB laboratories and prevention and care programs must work together to jointly assess the impact of this emerging epidemiological approach. 152 Chapter 8: Whole Genome Sequencing as a Tool to Understand and Quantify Active Tuberculosis Arising from Local Transmission 8.1 Background While TB in low-incidence settings such as Canada is thought to largely result from reactivation of latent TB infection (LTBI) in migrants to Canada—67% of diagnoses occur in non-Canadian-born (nCB) persons6—quantifying the actual contribution of this LTBI reactivation versus locally acquired infection has been difficult. Genotyping methods, such as 24-locus mycobacterial interspersed repetitive unit–variable-number tandem repeat (MIRU-VNTR), have provided some insight by identifying unique strains presumed to represent LTBI reactivation.101,342,395 However, the increasingly frequent application of whole genome sequencing (WGS) to Mycobacterium tuberculosis (Mtb) isolates has called into question genotyping’s utility for identifying local transmission,336,354,396–398 particularly amongst non-Euro-American lineage Mtb strains, where the method over-estimates clustering.318 Beyond improved resolution for defining clusters, genomics also permits the inference of both the timing and direction of person-to-person spread.74,144,149,158,174 British Columbia (BC) has committed to reducing TB incidence rates by 50% by the year 2022 as part of its Provincial TB Strategy.14 Reducing local transmission through targeted, evidence-based interventions has been identified as an important objective within the strategy. This necessitates first determining the proportion of active TB disease due to local, i.e. BC-based, transmission. Then filling knowledge gaps in our understanding of this transmission, particularly with respect to describing general characteristics of transmission networks, identifying the drivers of large outbreaks, as well as enumerating the clinical, demographic, and epidemiological risk factors associated with persons who give rise to multiple secondary cases (transmitters or super-spreaders) versus non-transmitters. Here, the genomic epidemiology of TB transmission in BC over a ten-year period is described, as a first step in developing bespoke interventions aimed at reducing local transmission within the province. 153 8.2 Methods 8.2.1 Study population Mtb specimens and referred-in cultures for the province are received by the British Columbia Centre for Disease Control (BCCDC)’s Public Health Laboratory (BCPHL), which oversees routine diagnostics, phenotypic drug sensitivity testing, and 24-locus MIRU-VNTR genotyping. TB prevention programs, routine surveillance, and patient care are led by the BCCDC’s Provincial TB Services program. The study population included all persons with culture-confirmed TB residing in BC whose first Mtb isolate was received by the BCPHL from 2005 through 2014, representing 79.5% of TB cases reported to TB Services during this period. 8.2.2 Case data Individual-level clinical and demographic data variables were extracted from BCCDC’s electronic medical registry, Integrated Public Health Information System (iPHIS). To support transmission analyses, further case information was obtained as needed through chart review of physician narratives and contact investigation records in iPHIS. For demographic analyses, community type was determined using the population density of the geographic service area in which each case resided—metro (>190,000), urban/rural (40,001–190,000), rural (10,001–40,000), and remote (≤10,000). Census dissemination areas (DA), based on postal codes for each case, were linked to the 2006 Canadian Marginalization Index (CAN-Marg)316 to determine the deprivation index quintile representing the relative socioeconomic disadvantage of a DA compared to the rest of Canada, (quintile 1: least deprived, quintile 5: most deprived). To protect privacy, some data is suppressed at the DA level by Statistics Canada, resulting in a small number of unlinked records for individual deprivation indices. 8.2.3 Laboratory analysis 2,303 Mtb isolates were revived from BCPHL’s archival stocks, DNA was extracted, and genotyped using 24-locus MIRU-VNTR genotyping as previously described (Chapter 3). Phenotypic drug susceptibility testing (DST) results for first-line antibiotics—isoniazid (INH), rifampin (RIF), ethambutol (ETB), and streptomycin (SM)—with additional data for 154 pyrazinamide (PZA) in multi-drug resistant isolates, were available for each isolate through routine testing on the BACTEC MGIT 460 or 960 (Becton-Dickinson, Sparks, MD) interpreted according to Clinical and Laboratory Standards Institute (CLSI) recommendations.399 Multi-drug-resistant (MDR) tuberculosis was defined as an isolate resistant to INH and RIF. Assuming that isolates with a unique MIRU-VNTR genotype would also be unrelated to another BC isolate by WGS, the subset of genotypically clustered isolates representing possible local transmission were sequenced. The 974 MIRU-VNTR-clustered isolates were sequenced using 125 bp paired-end reads on the Illumina HiSeq 2500 platform at the Michael Smith Genome Sciences Centre (Vancouver, BC). An additional 247 Mtb isolates of special interest were also sequenced. These genotypically unique isolates were selected by meeting any or all of the following criteria: (i) isolates from Canadian-born persons (n = 105), (ii) isolates matching at 23 of 24 MIRU-VNTR loci to a genotypic cluster likely to represent local transmission (n = 37), (iii) isolates from a culture-positive TB reoccurrence either during the 2005–2014 study period or beginning within the five years prior to the study period (n = 51), and (iv) isolates phenotypically resistant to one or more first-line antibiotic (n = 118). 8.2.4 WGS analysis and genomic clustering The resulting FASTQ files were analyzed using a pipeline developed by Oxford University and Public Health England.267 Reads were aligned to the Mtb H37Rv reference genome (GenBank ID: NC000962.2), and after masking for low complexity regions an average of 92% of the reference genome was covered. Single nucleotide variants (SNVs) were identified across all mapped non-repetitive sites and concatenated SNVs were used to construct a maximum-likelihood phylogenetic tree in RAxML 8.2.10371, using the GTRGAMMA model and 200 bootstrap replicates. Major lineage was predicted for each isolate, first by using TB-Insight’s329 CBN method with MIRU-VNTR data as input, and second by lineage-specific SNVs372 for those isolates that were sequenced. FASTQ files for all genomes are available at NCBI under BioProject PRJNA413593 and PRJNA49659. While several studies73,149,158,165,166 have used SNV thresholds—typically 5–12 SNVs—as a bound for recent transmission, thresholds are sensitive to SNV filtering approaches, and a hard 155 cut-off of five or 12 SNVs might exclude some cases of interest, for example, reactivation cases of a strain endemic to BC. Thus, it was decided to initially define locally acquired infections as those in genomic clusters within 0–20 SNVs of another study isolate, with further refinement of the threshold based on clinical and demographic analysis. In a sensitivity analysis, clustering estimates were recalculated using thresholds of both 5 and 12 SNVs. 8.2.5 Transmission across population groups All genomic clusters containing both CB and nCB persons were analyzed to identify possible transmission between individuals in these population groups. A combination of genomic and epidemiological information obtained from case-record review was used to determine the most likely source of an individual’s infection. A diagram was constructed to depict the number and direction of transmission events between CB and nCB persons. The strength of the epidemiological linkage is indicated as: (1) household contacts, (2) known—one or both individuals named the other and do not reside in the same household, (3) probable—individuals did not name one another yet share epidemiological characteristics along with overlapping geographical locations during the likely infectious period of the source, and (4) unknown—individuals do not have clear connections to one another. 8.2.6 Tuberculosis reoccurrences Mtb isolates from individuals diagnosed with TB from 2005 through 2014, and who either had a second episode of culture-confirmed TB within the study period or had a previous TB episode of culture-confirmed TB in 2000–2004, were analyzed to differentiate between relapse (second episode >6 months after last treatment) and exogenous reinfection. SNV profiles for each isolate were examined within the context of the larger study population, in addition to case-level characteristics of each individual obtained through case record review, including first episode treatment status, travel to endemic country of origin between episodes, and health-related risk factors such as HIV and diabetes. Descriptive statistics were calculated for cases determined to be either relapse or reinfection. 156 8.2.7 Characterization of large clusters and transmission reconstruction To characterize ongoing endemic transmission representing the greatest potential for public health intervention, the epidemiological characteristics of genomic clusters (20-SNV threshold) with ≥10 isolates were described. Additionally, transmission pathways were reconstructed for one of the largest clusters not previously described in the literature. To do this SNV profiles were analyzed within the context of case-level clinical, demographic and contact investigation information gained through detailed case-record review. An initial network was constructed using concatenated SNVs which was then refined with epidemiological data to represent the most likely transmission pathway. Where transmission likely occurred prior to the study period a node was placed in the network to indicate such an event. Epidemiological linkages were classified by the strength of evidence connecting cases. A linkage was categorized as “known” if either the source or contact named the other. “Probable” linkages represented individuals that spent time in the same locale (e.g. shelter) which overlapped the likely infectious period of the source and shared unique characteristics within the cluster (e.g. same nCB country of birth within a CB cluster) or social/behavioural characteristics (e.g. substance use), and where neither identified the other as a contact. Where none of these criteria applied the epidemiological association between genomically linked isolates was classified as “unknown”. 8.2.8 Statistical analysis A genotypic cluster was defined as ≥2 individuals with identical MIRU-VNTR results and a unique identifier (MClustID) was assigned to each cluster. A unique WGS cluster identifier (WClustID) was given to isolates which were within 20 SNVs of another individual’s isolate within the study. Regression models were constructed under the assumption that isolates that were not selected for sequencing would be >20 SNVs from other study isolates and therefore were included as genomically unique cases. A multivariable logistic regression model was constructed to estimate the odds ratio (OR), adjusted odds ratio (aOR) and 95% confidence interval (CI) for the distribution of cases by cluster status (clustered/non-clustered) according to birthplace and other clinical and demographic variables using backward elimination of factors identified in univariable analysis (p < 0.20), and Akaike’s Information Criterion (AIC) minimisation.331 A similar model was constructed to examine the characteristics where three 157 different SNV thresholds for clustering were used (5, 12 and 20 SNVs). Given that the substance use variables had >5% missing values, Little's test332 was used to determine whether data was missing completely at random (MCAR). A significant result (p < 0.001) indicated the missing values were not MCAR, and a complete-case analysis was used for the logistic regression models. Cases excluded from the analysis due missing data were compared to those remaining in the analytic sample using Chi-square tests to assess potential bias due to missing data. Descriptive statistics were calculated to characterize all large (≥10 isolates) genomic clusters according to birthplace, age, gender, substance use, housing status, community type and number of cases with known epidemiological linkages to others in the cluster. Under-housed was defined as an individual with no fixed address or someone living in a homeless shelter, group home, or residing in single-room occupancy (SRO) housing. All statistical analyses were executed in R (v3.4.1). 8.3 Results From 2005 through 2014, 2,309 Mtb isolates were received at BCPHL for routine testing. These represent the first isolate of each culture-positive TB case (excluding subsequent isolates from a relapse) and 79.2% of all TB diagnoses in BC during this time period. Six isolates were unavailable and excluded from the study, leaving a total study sample of 2,303 Mtb isolates (Figure 8-1). The characteristics of the study population are described in Table 8-1. Briefly, 30.7% of the study population were aged 35–54, with higher proportions of males (58.2%), individuals residing in a metro area (76.5%), and individuals born outside Canada (73.6%). The majority of nCB individuals had immigrated ≥5 years prior to diagnosis (71.5%), and 87.5% of nCB were born in Asia (87.5%). Clinically, 84.1% of the study cohort had respiratory disease, and 62.1% were smear-positive. Chest radiographs identified cavitary TB in 13.9% of individuals. HIV-positivity was reported for 4.5% of the study population, and the proportion of substance use was recorded as 8.8% for illicit drug use and 9.6% alcohol misuse. With respect to marginalization, 43.9% of individuals resided in the most deprived quintiles (≥4 on the material deprivation index). 158 Figure 8-1. Study sample inclusion/exclusion criteria. Number of Mycobacterium tuberculosis (Mtb) isolates at each stage of the study sample selection. Abbreviations: WGS, whole genome sequencing; MIRU-VNTR, Mycobacterial Interspersed Repetitive Units–Variable Number Tandem Repeats. 159 Table 8-1. Characteristics of the study sample. Demographic and clinical characteristics of culture positive TB cases, British Columbia 2005–2014 (n = 2,303)a. Characteristic No. Cases (%) Age, years 0–14 32 (1.4) 15–34 504 (21.9) 35–54 708 (30.7) 55–74 588 (25.5) 75+ 471 (20.5) Genderb Male 1339 (58.2) Community type Metro 1762 (76.5) Urban/Rural 334 (14.5) Rural 174 (7.6) Remote 33 (1.4) Birthplacec Canada 592 (26.4) Non-Canadian-born continentd Asia 1444 (87.5) Africa 80 (4.8) Europe 69 (4.2) Americas 46 (2.8) Oceania 11 (0.7) Time in Canadae < 5 years 458 (28.5) ≥ 5 years 1148 (71.5) Disease Site Respiratory 1776 (77.1) Non-Respiratory 366 (15.9) Respiratory + Non-Respiratory 161 (7.0) Respiratoryf smear Positive 1159 (62.1) Cavitary disease Yes 319 (13.9) Drug susceptibility MDR 19 (0.8) INH-R (non-MDR) 174 (7.6) 160 Table 8-1 Continued from previous page Characteristic No. Cases (%) HIV Positive 104 (4.5) Negative 1810 (78.6) Unknown 389 (16.9) Illicit drug use Yes 203 (8.8) No 1616 (70.2) Unknown 484 (21.0) Alcohol misuse Yes 220 (9.6) No 1622 (70.4) Unknown 461 (20.0) Material deprivationg Quintile 1 (least) 275 (12.6) Quintile 2 420 (19.2) Quintile 3 532 (24.3) Quintile 4 530 (24.2) Quintile 5 (most) 429 (19.7) Abbreviations: HIV, human immunodeficiency virus; INH-R, isoniazid resistant; MDR, multi-drug resistant tuberculosis (resistant to isoniazid and rifampin). aPercentages have been rounded and may not total 100%. bOne transgender/gender-unknown individual excluded from analysis. cData unavailable in 57 cases. dData unavailable in 4 cases. eData unavailable in 48 cases. f“Other respiratory” sites (e.g. pleura) were excluded. gData unavailable in 116 cases. 161 8.3.1 Whole genome sequencing reduces the local transmission estimate The retrospective 24-locus MIRU-VNTR genotyping of 2,303 Mtb isolates representing >99% of all culture-positive TB cases diagnosed in BC between 2005–2014 was described in Chapter 3, in which 42.4% of isolates were clustered and potentially representative of recent transmission. By WGS (Figure 8-2), only 594 (25.8%) were clustered at a 20-SNV threshold, substantially lowering the estimate of local transmission within BC. The reduction of the clustered proportion was largely due to nCB isolates—only 24.2% of MIRU-VNTR clustered isolates from nCB persons were genomically clustered, in contrast to 94.5% of isolates from CB persons. Study isolates belonged to 93 distinct genomic clusters, ranging in size from 2–72 isolates (Figure 8-3). While 57.0% of clusters consisted of just two persons, individuals in large clusters (≥10 cases/cluster) accounted for 61.4% (365/594) of all clustered cases. Using the “n – 1” method,88 in which the first isolate in each genomic cluster is not counted, the proportion of BC’s TB cases resulting from local transmission was estimated at 21.8%. The clustered proportion remained stable when the SNV thresholds for clustering were reduced from 20 SNVs to 12 and 5 SNVs. Thirty-three cases were no longer considered clustered at the 12-SNV threshold, reducing the clustered proportion from 25.8% to 24.4% (561/2,303), while further reduction of the threshold to 5 SNVs left 511 (22.2%) isolates clustered. Plotting the minimum pairwise SNV distance for each sequenced isolate within the study period—the majority of which were genotypically clustered—revealed that 74.4% (406/546) of isolates obtained from Canadian-born persons fell within 0–5 SNVs of another study isolate. In contrast, only 15.6% (n = 96/617) of isolates from nCB persons were similarly clustered. The majority (68.7%) of nCB persons had isolates >50 SNVs from the nearest isolate in BC (Figure 8-4). 162 Figure 8-2. Maximum-likelihood phylogenetic tree of study isolates. The tree was constructed from concatenated single nucleotide variant alignments derived from whole genome sequencing of Mycobacterium tuberculosis isolates (n = 1,221). Isolates from Canadian-born persons are indicated in red ( ▬ ). Multi-drug resistant isolates—those resistant to isoniazid and rifampin—are indicated by a circle ( ● ). The outer ring indicates the lineage. 163 Figure 8-3. Genomic cluster sizes. Number of genomic clusters by size, and the proportion of all clustered isolates represented at each cluster size. A threshold of 20 single nucleotide variants was used to define a cluster (n = 93), British Columbia, 2005–2014. Figure 8-4. Pairwise SNV distances between study isolates. Minimum pairwise single nucleotide variant (SNV) distance between isolates, coloured according to birthplace—red for Canadian-born, blue for non-Canadian-born, and grey for persons with an unknown birthplace. Of the 1,221 isolates that were sequenced, 24 isolates received prior to 2005, and 11 isolates representing a TB relapse were excluded from this figure. 164 8.3.2 Transmitted Mtb isolates belong largely to the Euro-American lineage Clustering proportions varied significantly across lineages—54.0% of Euro-American lineage strains were genomically clustered, in contrast to 12.6% of East-Asian, 4.5% of Indo-Oceanic and 7.9% of East-African Indian lineage isolates (p < 0.001). Restricting the analysis to consider only genomically clustered isolates, the Euro-American lineage dominated with 79.6% of clustered isolates belonging to this lineage (p < 0.001). All but one of BC’s 11 large genomic clusters (11–72 isolates/cluster) belonged to the Euro-American lineage and were comprised of predominantly CB persons. The single non-Euro-American cluster, WClust-6, belongs to the East-Asian lineage and the majority of cases—88.9% (16/18)—were Canadian-born persons, with nine (50%) known to have connections to a highly marginalized urban area. A maximum-likelihood tree (Figure 8-2) reveals the population structure of BC’s Mtb isolates. Isolates from CB persons were concentrated in the Euro-American Mtb lineage (480 of 546 isolates, 87.9%). Of the 85 CB persons with a non-Euro-American isolate and excluding those belonging to the known transmission cluster WClust-6, it was determined that the Mtb lineage aligned with the travel history, ethnic community, or known contact to a nCB case for 48/69 (69.6%) individuals. For the remaining 21 (30.4%) individuals, there was no documented connection to the geographic region or ethnic community expected according to the Mtb lineage of their isolate. Of note, 61.9% (13/21) of these cases with an unexplained non-Euro-American lineage isolate resided in the Greater Vancouver Region—an ethnically diverse metropolitan area of BC. 8.3.3 Risk factors for local transmission Due to missing values for certain risk factor variables, 537 records were excluded from logistic regression analysis, resulting in an analytic sample of 1,766 cases. Missing data was more often associated with particular regions of BC, with rural Health Service Delivery Areas (HSDAs) having significantly higher proportions of missing data for substance use variables (illicit drug use: 35.4% [96/271], alcohol misuse: 30.3% [82/271]), as compared to urban-based HSDAs, for which 19.1% (388/2,032) of illicit drug use and 18.7% (379/2,032) of alcohol misuse fields had 165 missing values (p < 0.001). Because data missingness was not likely associated with the variables themselves or other case characteristics and was instead related to data entry, the decision was not to impute missing values and instead use a complete-case analysis for logistic regression. Given that the demographics of individuals in rural HSDAs differ from those in urban regions, cases excluded from the analytical sample were more likely to be older (p < 0.001), male (p = 0.034), CB (p < 0.001), and have exclusively respiratory TB (p = 0.005). Using a 20-SNV threshold, the proportion of culture-confirmed TB arising from local transmission was calculated as 76.7% (454/592) for CB persons and 7.7% (127/1654) for nCB individuals, and with the complete-case logistic regression analysis CB persons were found to have 19.7 (95%CI: 13.9–28.0) times the odds of belonging to a genomic cluster as compared to non-Canadian-born persons, after adjustment for age, gender, anatomical disease site, and substance use (Table 8-2). Other significant risk factors for clustering indicative of local transmission included respiratory disease (aOR 2.4, 95%CI: 1.5–4.0), illicit drug use (aOR 3.8, 95%CI: 2.0–7.2) and alcohol misuse (aOR 5.0, 95%CI: 2.8–8.9). In a sensitivity analysis examining factors associated with clustering, when the multivariable regression model was fitted with lower SNV clustering thresholds (12 and five SNVs), estimated odds ratios did not change appreciably (Table 8-3). 166 Table 8-2. Genomic clustering logistic regression. Distribution and multivariable analysis of factors associated with genomically clustered Mycobacterium tuberculosis isolates (≤20 SNVs) and unique isolates (>20 SNVs), British Columbia 2005–2014 (n = 1,766). Characteristic Clustered n (%) Unique n (%) Clustered vs. Unique OR (95%CI) Clustered vs. Unique aORa (95%CI) Age, years 0–14 11 (37.9) 18 (62.1) 1.7 (0.8–3.8) 1.0 (0.4–2.8) 15–34 104 (26.0) 296 (74.0) Reference Reference 35–54 207 (36.3) 364 (63.7) 1.6 (1.2–2.1) 1.2 (0.8–1.8) 55–74 93 (19.8) 377 (80.2) 0.7 (0.5–1.0) 0.5 (0.3–0.8) 75+ 14 (4.7) 282 (95.3) 0.1 (0.1–0.3) 0.2 (0.1–0.3) Gender Male 278 (27.6) 729 (72.4) 1.5 (1.2–1.9) 1.1 (0.8–1.5) Female 151 (19.9) 608 (80.1) Reference Reference Birthplace Canada 321 (77.5) 93 (22.5) 39.8 (29.4–53.8) 19.7 (13.9–28.0) Outside Canada 108 (8.0) 1244 (92.0) Reference Reference Disease Site Resp. 370 (27.5) 976 (72.5) 3.0 (2.1–4.4) 2.4 (1.5–4.0) Non-Resp. 34 (11.2) 269 (88.8) Reference Reference Resp. + Non-Resp. 25 (21.4) 92 (78.6) 2.1 (1.2–3.8) 1.8 (0.8–3.9) Illicit drug use 157 (90.8) 16 (9.2) 47.7 (28.0–81.0) 3.8 (2.0–7.2) Alcohol misuse 154 (84.6) 28 (15.4) 26.2 (17.1–40.0) 5.0 (2.8–8.9) Abbreviations: SNVs, single nucleotide variants; OR, odds ratio; CI, confidence interval; aOR, adjusted odds ratio; Resp., respiratory; Non-Resp., non-respiratory. aAdjusted for age, gender, birthplace, disease site, illicit drug use and alcohol misuse. 167 Table 8-3. Logistic regression for risk factors for genomic clustering—various thresholds. Multivariable analysis of factors associated with clustered Mycobacterium tuberculosis isolates using three different upper thresholds for clustering, 20 SNVs, 12 SNVs, and 5 SNVs versus unique isolates (>20 SNVs or unique MIRU-VNTR for isolates not sequenced), British Columbia 2005–2014 (n = 1,766). Clustered vs. Unique aOR (95%CI)a Characteristic 20 SNV 12 SNV 5 SNV Age, years 0–14 1.0 (0.4–2.8) 1.2 (0.4–3.3) 1.6 (0.6–4.5) 15–34 Reference Reference Reference 35–54 1.2 (0.8–1.8) 1.2 (0.8–1.9) 1.2 (0.8–1.9) 55–74 0.5 (0.3–0.8) 0.5 (0.3–0.9) 0.5 (0.3–0.9) 75+ 0.2 (0.1–0.3) 0.1 (0.1–0.3) 0.1 (0.1–0.3) Gender Male 1.1 (0.8–1.5) 1.2 (0.8–1.7) 1.2 (0.9–1.8) Female Reference Reference Reference Birthplace Canada 19.7 (13.9–28.0) 19.8 (13.9–28.2) 16.3 (11.5–23.3) Outside Canada Reference Reference Reference Disease Site Resp. 2.4 (1.5–4.0) 2.9 (1.7–4.9) 3.3 (1.9–5.7) Non-Resp. Reference Reference Reference Resp. + Non-Resp. 1.8 (0.8–3.9) 2.4 (1.0–5.3) 2.3 (1.0–5.3) Illicit drug use 3.8 (2.0–7.2) 4.4 (2.3–8.4) 3.3 (1.9–5.8) Alcohol misuse 5.0 (2.8–8.9) 4.3 (2.5–7.7) 4.0 (2.4–6.8) Abbreviations: SNVs, single nucleotide variants; OR, odds ratio; CI, confidence interval; aOR, adjusted odds ratio; Resp., respiratory; Non-Resp., non-respiratory. aAdjusted for age, gender, birthplace, disease site, illicit drug use and alcohol misuse. 168 8.3.4 Transmission occurs in both directions between Canadian-born and non-Canadian-born persons Of the 93 genomic clusters in this study, 24 (25.8%) consisted entirely of CB persons, 26 (28.0%) entirely of nCB persons, 37 (39.8%) were heterogenous clusters with both CB and nCB persons, and six (6.5%) were unclassified, as they had a single individual of unknown birthplace amongst all CB or all nCB persons (Figure 8-5). Of these heterogenous clusters, 12 were predominantly CB with a median cluster size of 17 persons (interquartile range [IQR]: 10–51), eight were predominantly (>50%) nCB with a median of three persons/cluster (IQR: 3–4), and 17 were evenly split with 50% CB and 50% nCB persons. All but one of these evenly split clusters consisted of just two individuals—the remaining cluster was comprised of six individuals. Transmission reconstruction within the heterogenous clusters, using a combination of WGS, epidemiological data, and review of case notes, suggested that transmission occurred in both directions. In 30 instances, a CB person was the most likely source of a nCB person’s infection. Transmissions originating from nCB persons led to 31 TB diagnoses in CB individuals, although it should be noted that in seven of these transmission events, the nCB individuals transmitted a locally circulating strain they likely acquired from a CB person (Figure 8-6). The inferred setting of TB acquisition differed depending on the direction of transmission, with all but one of the 30 CB to nCB transmissions occurring within the community and 26 of these involving strains linked to large (≥10 cases) clusters. nCB to CB transmissions occurred from both household sources (29.0%) and the community (71.0%), and 77.4% involved strains belonging to small clusters (<10 cases/cluster), with 15 of these nCB to CB transmission events being two-person clusters. 169 Figure 8-5. Genomic cluster sizes and birthplace composition. Pie charts representing all Mycobacterium tuberculosis genomic clusters (n = 93) based on a 20-single nucleotide variant threshold, British Columbia, 2005–2014. Categorized by clusters with a heterogenous birthplace, homogenous birthplace or clusters comprised solely of those born inside/outside Canada in addition to ≥1 case of unknown birthplace. Pie chart areas are scaled relative to the number of cases and the unique WClustID for genomic clusters are indicated above each pie. 170 Figure 8-6. Mixed cluster transmission. Diagram depicting the number and direction of tuberculosis transmissions between Canadian-born and non-Canadian-born persons, according to genomic epidemiological investigations, British Columbia, 2005–2014. Transmissions are grouped by birthplace of initial source. Seven persons of unknown birthplace found within large clusters (≥10 persons) were not coloured separately. Circle size is proportional to the number of cases, and strength of the epidemiological evidence supporting linkages is indicated by different arrow type. Three small (2–3 persons) clusters were deemed not to represent local transmission and were excluded. Canadian-born non-Canadian-bornCanadaOutside Canada knownprobableBirthplace Epidemiological Linkageunknown11030Cluster Sizehousehold contacts171 8.3.5 TB relapse vs reinfection Accurate quantification of transmission also requires differentiating between TB relapse and exogenous reinfection; additionally, the true rate of relapse within a population is of interest to TB prevention and care programs, but is challenging to accurately measure with any technique besides WGS.400 Thirty-nine individuals diagnosed with TB during the study period had either a previous culture-positive TB episode in 2000–2004 or a second episode—and in one instance a third episode—of culture-confirmed TB within the study period, giving a total of 40 recurrence events. The genomic data for each episode’s isolate were examined relative to each other and within the context of the complete study cohort, and case notes for each individual were reviewed. It was determined that 32 (80%) recurrent episodes likely represented a relapse, while eight (20%) were new infections. Individuals deemed to have relapsed had Mtb isolates with a median of 1 SNV (IQR: 0–3) between episodes, whereas those with a new infection had a median of 205 SNVs (IQR: 36–503) (Figure 8-7). In the absence of WGS results for three reoccurrences, MIRU-VNTR genotyping was used instead to classify these episodes as a likely relapse (n = 2) or reinfection (n = 1). Figure 8-7. Recurrent tuberculosis characteristics. Differences in single nucleotide variants (SNVs) and time between episodes according to reoccurrence type, where the initial culture positive occurred Jan 1, 2000 or later and subsequent episode(s) during the study period (2005–2014), British Columbia, Canada. (A) Bar plot representing the number of single nucleotide variants (SNVs) observed between Mycobacterium tuberculosis isolates. (B) Boxplot representing the median and interquartile range for the number of years between episodes. 172 When the genomic profile of each recurrence isolate was examined against the background of the complete study cohort and episode timelines, it was not possible to establish a SNV cut-point to definitively distinguish between relapse and reinfection. Two cases had 10 SNVs between episodes, and, when viewed in the context of clinical, epidemiological, and physician narrative data, one was deemed to have relapsed while the other was likely infected with a new strain. Two cases had 20 SNVs between episodes, and similar to the individuals with 10 SNVs the data supported one as a relapse and one as a reinfection. Of those individuals with a TB relapse, resistance to one or more first-line antibiotics was observed in the original isolate from seven individuals, one of which acquired resistance to an additional antibiotic by the time of the second episode. Six other isolates originally pan-sensitive, developed resistance to a first-line antibiotic by the time of relapse. It should be noted that nine of the relapse cases harboured strains common to their area of residence, thus it is possible that these individuals had been re-infected with a nearly identical strain to their original isolate. However, comorbidities and complications around treatment adherence for these individuals during their first episode suggested that relapse rather than re-infection was most likely. Among the eight individuals with a reinfection, three were CB, with both episodes involving strains known to circulate in their communities, and all three had substance use as a risk factor for TB infection. The remaining five were nCB persons and their isolates were unique within the BC collection, indicating that these individuals likely acquired their infections outside BC. Three of these individuals were known to have travelled to their high-incidence TB birth country between episodes. 173 8.3.6 Characterization of large genomic clusters To understand the patterns driving local transmission beyond the household contact level, the characteristics of large (≥10 persons) genomic clusters were examined and found that all were comprised of predominantly CB persons, with 8 of 11 large clusters concentrated in metro regions (Table 8-4). The median age of individuals varied from 40 to 58 years across the clusters (p = 0.001), with the youngest cluster (WClust-5) also representing the single cluster comprising more females than males (57.1% versus 42.9%). While the proportion of individuals with one or more risk factor for TB (HIV, illicit drug use or alcohol misuse) varied between clusters, 76.8% (255 of 332 with known risk factor data) of persons in a large genomic cluster had at least one risk factor. With the exception of the two smallest clusters, under-housed persons represented >25% of individuals in any large cluster. Clusters with individuals residing in predominantly rural areas had the lowest proportion of under-housed persons. With respect to epidemiological linkages between individuals within the same genomic cluster, the number of persons with known connections (i.e. named contacts) varied substantially (0.0–83.9%), with the three largest clusters (WClust-3, WClust-2, WClust-5) having the largest proportion of cases with known epidemiological linkages. WClust-5 was particularly well-characterized, with 47 of 56 persons (83.9%) connected to another case within the cluster. Among the nine persons not linked to another case, all had TB risk factors, two were under-housed and five were noted as contacts of active TB cases in the case narratives; however, the names of these individuals were not specified. Eight of the nine were diagnosed in the geographic region in which WClust-5 is concentrated and, while the remaining individual was diagnosed outside this region, they previously resided there. 174 Table 8-4. Large genomic clusters. Characteristics of genomic clusters (20 single nucleotide variant threshold) comprised of ≥ 10 individuals, British Columbia, 2005–2014. Cluster ID Cluster Size Canadian-borna n (%) Median Age (IQR) years Gender M:F Risk Factorsb n (%) Under-housedc n (%) Predominant Community Type (%) Known epi-linksd n (%) WClust-3 72 61 (88.4) 50 (43–57) 11.0 49 (74.2) 47 (65.3) Metro (76.4) 29 (40.3) WClust-2 56 54 (96.4) 46 (36–54) 1.4 36 (78.3) 15 (26.8) Rural (51.8) 37 (66.1) WClust-5 56 54 (96.4) 40 (29–48) 0.8 42 (80.8) 22 (33.9) Rural (75.0) 47 (83.9) WClust-4 49 40 (85.1) 49 (39–54) 3.9 33 (71.7) 23 (46.9) Metro (79.6) 9 (18.4) WClust-1 46 43 (93.5) 44 (35–54) 1.6 37 (84.1) 31 (67.4) Metro (87.0) 16 (34.8) WClust-6 18 16 (88.9) 42 (33–60) 1.8e 11 (64.7) 8 (44.4) Metro (83.3) 2 (11.1) WClust-7 16 13 (86.7) 50 (48–56) 3.0 13 (86.7) 11 (68.8) Metro (93.8) 6 (37.5) WClust-8 15 14 (93.3) 44 (40–54) 2.0 12 (85.7) 7 (46.7) Metro (80.0) 2 (13.3) WClust-9 14 14 (100.0) 48 (44–58) 1.0 10 (83.3) 10 (71.4) Metro (57.1) 0 (0.0) WClust-11 12 12 (100.0) 58 (44–70) 3.0 6 (66.7) 1 (8.3) Rural (83.3) 3 (25.0) WClust-12 11 8 (72.7) 51 (38–58) 2.7 6 (54.5) 1 (9.1) Metro (72.7) 4 (36.4) aData not available for 7 individuals, percentage represents those with complete data. bOne or more risk factors (HIV, illicit drug use, or alcohol misuse); data unavailable (n = 33), percentage represents those with complete data. cPersons with no fixed address or living in a shelter, group home, or single-room occupancy housing. dKnown epi-links (epidemiological linkages) represent individuals that were named contacts of another within the cluster. eOne transgender/gender-unknown individual excluded from analysis. 175 Previously, genomic reconstructions of two of the largest clusters were described in the literature—these were pilot studies that enabled BCCDC TB Services to obtain funding for the present dissertation work. Here, the timeline (Figure 8-8) and likely transmission pathways for another of the largest genomic clusters (WClust-2) was reconstructed (Figure 8-9). This cluster comprises individuals residing in eight BC communities spanning a maximum distance of 1000km, with cases largely concentrated in the northern region of the province. Many cases resided in rural (51.8%) or remote (5.4%) areas, with 35.7% of cases residing in urban/rural areas, and 7.1% residing in a metro area. It was found that 53.6% (30/56) of isolates had zero SNVs when compared to at least one other isolate and 89.3% (50/56) were within five SNVs of another isolate. However; when examining the individual SNV profiles, it appears that TB in at least 18 individuals was likely the result of initial transmission prior to the study period. In one instance, both the epidemiological and genomic evidence support this—a case who relapsed within the study period and who had their first episode several years prior was identified as the source of at least one known transmission and one possible transmission event during this first episode. Among the 56 cases, 37 individuals identified one another within WClust-2 as a contact—this is not surprising, given the rural and remote nature of the population; however, only 12 of these named individuals were plausible source cases, as revealed by the SNV profiles. The remaining six individuals either acquired their infection from a different, unnamed individual in the cluster or they represent reactivation of LTBI acquired before the study period. In five cases, individuals within WClust-2 named contacts not belonging to WClust-2 who instead harboured genomically distant isolates from strains circulating in the same region. One individual thought to be part of WClust-2 was diagnosed on autopsy (indicated as case “x” in Figure 8-9) and an isolate was not available for genotyping or WGS. This individual had respiratory TB and was believed to be highly infectious—epidemiological evidence supports this individual as the likely source for three subsequent culture-confirmed cases. Also of interest in this cluster is the high number of individuals that represent super-spreaders—transmitting to multiple persons that went on to develop culture-confirmed TB. Of note, isolate #21 represents 176 an individual that presented to healthcare on multiple occasions with signs and symptoms of tuberculosis, and was hospitalized for several days without consideration of TB as a cause of their respiratory infection. This individual was not diagnosed until five months later and was potentially the source for at least four secondary cases, and ultimately a transmission chain involving seven culture-confirmed cases. Figure 8-8. Timeline of case diagnosis. Number of tuberculosis cases organized by year and quarter of diagnosis over a 10-year period in British Columbia, Canada. Each circle represents a single case, and are coloured by community (A – H) in which the individuals reside. The subscript numbers “1” and “2” indicate the first and second episode for the relapse case. 177 Figure 8-9. Characterization of WClust-2. (A) Phylogenetic tree. (B) Putative transmission pathways. Each node represents a single culture-positive isolate (with the exception of case “x”, diagnosed on autopsy and for which no Mtb isolate or genetic material was available). Nodes labelled with “?” represent putative source cases outside the study period. Arrows indicate a transmission event, with the arrow type indicating the strength of the epidemiological connection between individuals. The number of single nucleotide variants (SNVs) between isolates are indicated along the arrows as small circles—grey for fixed SNVs and pink indicating the presence of a minority variant. One case, enclosed in a grey box, represents a relapse where the second episode fell within the study period, thus both isolates were included in the reconstruction. *Excludes “other respiratory” sites. (C) Network of named contacts. Medium dark grey circles represent persons with culture-positive isolates genomically distant from WClust-2. Small grey circles represent persons named contacts of individuals in WClust-2 in which culture-positive TB was not diagnosed. The subscript numbers “1” and “2” indicate the first and second episode for the relapse case. 178 8.4 Discussion In this ten-year retrospective study, 2,303 Mtb isolates were successfully genotyped and the genomes of 1,221 were sequenced—including all 974 genotypically clustered and 247 special interest isolates. WGS indicated that the proportion of culture-confirmed TB cases arising from local transmission in BC is 21.8% using the “n – 1” method88 and a 20-SNV threshold. This is a much more accurate quantification versus previous estimates made using MIRU-VNTR data alone. The most important risk factor for belonging to a genomic cluster was Canadian birth. In contrast, the majority (75.8%) of genotypically clustered nCB persons were no longer clustered after genome sequencing. This is an important observation, revealing the shortcomings of MIRU-VNTR as a tool for understanding transmission and identifying outbreaks.121,401 Many of these predominantly nCB genotypic clusters instead likely represent an Mtb genotype common to individuals who have emigrated from the same region of the world—an observation that has been reported in other low-incidence TB settings.336,354 The ability to distinguish between LTBI reactivation and local transmission is key to developing evidence-based TB prevention strategies, and the quantification of cases attributable to local versus overseas acquisition allows TB prevention programs to appropriately direct their interventions, whether they be enhanced contact investigations to prevent local transmission or better screening and LTBI treatment programs for migrants. Beyond Canadian birth, illicit drug use and alcohol misuse were identified as significant risk factors for clustering. Indeed, most individuals in large genomic clusters had one or more of these risk factors, and often reported a lack of adequate housing, highlighting the need for TB prevention programs to focus on vulnerable populations to curb ongoing local spread of TB. While transmission does occur between CB and nCB populations, it is not very common. Not surprisingly, transmission from nCB persons most often represented single transmission events within households—a number of which were nCB adult family members transmitting to CB children, as described in Chapter 5. Although stories in the media often state that immigrants pose a TB risk to the population of the country to which they are immigrating,402,403 the results 179 reported here support the findings of other studies which have repeatedly shown that nCB persons do not pose a substantial TB risk to CB persons, as transmission from these individuals is rare.404,405 Clustering rates varied significantly between lineages, with the highest level of clustering amongst Euro-American isolates. This is in due in part to the strong phylogeography of TB and Canada’s history—early European settlers introduced this lineage to the population.92 Additionally, studies have indicated that outbreaks occur more often with Euro-American and East-Asian lineage strains as a result of phenotypic differences in the virulence of these lineages compared to the others.44,45 Indeed, the only other lineage representing a large genomic cluster within BC was the East-Asian lineage (WClust-6). This cluster was comprised of predominantly CB persons, including the earliest cases, suggesting that this lineage was likely introduced to BC and was circulating locally prior to the study period. With respect to TB reoccurrences, WGS can help to distinguish between a relapse or reinfection, more so than MIRU-VNTR; however, where individuals are exposed to a common strain circulating in their community after treatment of their initial episode, it may not always be possible to differentiate between a relapse or new infection. In this study, there were several cases for which the presumed relapse isolate was 0–2 SNVs from another individual’s isolate and could have represented reinfection, although in these instances, it was determined that these were most likely relapses due to comorbidities and treatment compliance issues increasing the likelihood of treatment failure. Reconstructing the putative transmission pathways in a large genomic cluster revealed that contrary to the assumption of a single outbreak, WClust-2 represents multiple discrete chains of transmission with strains diverged from a shared common ancestor circulating in the northern region of BC. Reactivation of a previously acquired infection in a single individual is frequently the seed for a new localized outbreak, with super-spreaders—often individuals that for various reasons406 experience a diagnostic delay and thus increase the risk of TB spread6,406—driving much of the transmission in these large clusters. While many features, such as super-spreading 180 and transmission amongst marginalized populations, are shared across the large clusters, each cluster differed slightly based on the community, sub-population, and geography of the region. In contrast to WClust-2, the large clusters WClust-3 and WClust-5, which have been detailed previously,74,173 were highly concentrated in particular populations and regions, with one linked to specific homeless shelters and the other illicit drug use. The findings presented here highlight the importance of conducting population-based retrospective studies to build a knowledge base of genomic clusters and provide context for isolates that are prospectively sequenced. While this work was retrospective, access to genomic data at the time of investigation is key to quick and accurate identification of significant clusters as well as those individuals within a cluster contributing the most to onward transmission. Epidemiological information alone is insufficient—within several clusters, individuals named contacts who, after sequencing, were determined to belong to different genomic clusters. Likewise, genome sequencing alone is often insufficient to fully derive transmission networks. While this large, retrospective study over a ten-year period represents a comprehensive investigation of the genomic epidemiology of TB in BC, there are several limitations to note. First, not all 2,303 genotyped isolates were sequenced, only those which that were assumed to have been locally transmitted based on MIRU-VNTR data or other epidemiological information. Thus, it is possible that this sequencing strategy may have missed isolates that were indeed part of a local transmission cluster, just as it missed culture-negative cases or those for which no genetic material was available. The study was also limited by the retrospective nature of the epidemiological data collected, with no ability to ask additional questions of individuals that were prompted by the genomic findings, such as connections to a particular location/region. Genomics-guided investigations, with questions tailored to a specific cluster, may have uncovered more epidemiological linkages between individuals than classic contact investigation techniques, particularly when the transmission event leading to disease occurred several years prior—contact investigations are generally focused on more recent exposures, and failure to ask about earlier potential sources of 181 TB may lead to missed connections within the transmission network.6,68 Accurate source case identification is also hampered by Mtb’s long latency period, and even with a ten-year study window, there were still challenges in identifying source cases. In conclusion, nearly 20 years after the World Health Organization declared TB a global health emergency, the disease still affects 10 million people per year and was responsible for 1.7 million deaths in 2016.5 Innovative new tools are necessary to both interrupt current transmission and reduce the pool of infected individuals who might reactivate in the future, seeding an outbreak. This study demonstrated the value of WGS for large population-based studies, providing a benchmark transmission estimate against which future progress towards elimination within BC’s TB program can be compared. It is clear that WGS offers significant value over traditional genotyping, and is able to address both population surveillance and case investigation needs. Routine use of WGS will significantly improve our understanding of TB transmission, and provide the evidence necessary to develop more effective care and treatment strategies as we move towards TB elimination goals. 182 Chapter 9: Conclusion 9.1 Summary of findings The overarching aim of this dissertation was to describe the molecular and genomic epidemiology of TB in British Columbia with a view to increase our overall understanding of local TB transmission and identify risk factors related to person-to-person spread with a particular focus on large clusters. In doing so, the findings presented here have added to the evidence base for public health follow-up of TB cases. A summary of the major findings of the dissertation are listed below. In Chapter 2, a comparison of the previously established on-request and new universal genotyping program revealed that where requestors specified an individual or outbreak comparator, they had only slightly better than a 50% chance of correctly matching the individual or strain. Furthermore, few genotypic clusters had all isolates requested—including several large clusters of public health concern. The findings underscore that the social networks of persons with TB are complex and incompletely understood, with unknown epidemiological linkages between individuals. They also demonstrate that a universal approach is superior to a limited, on-request genotyping program, and suggest that equipping TB personnel with molecular epidemiology clustering from a universal genotyping program will allow TB programs to better focus and prioritize investigations. Extending universal genotyping to a ten-year period, Chapter 3 details a comprehensive analysis of the molecular epidemiology of TB in BC. More than 1,500 distinct Mtb genotypes were detected across four major Mtb lineages, reflecting BC’s diverse population. Based on genotype clustering, it was estimated that over one-third of BC’s TB cases were potentially the result of local transmission. Large clusters of predominantly Canadian-born persons were identified and analyses of clinical, epidemiological, and demographic characteristics revealed these to be discrete populations with distinct disease and risk factor profiles, likely representing true local transmission, and ideal groups to which to target specific interventions. 183 To place the findings of the previous chapter in the larger context of TB molecular epidemiology elsewhere in Canada, an analysis was undertaken in which the genotyping data and selected clinical and demographic data were compared across BC and Ontario (Chapter 4). Among 4,916 isolates, there were 3,461 unique genotypes. Of these 175, were common to both BC and Ontario, which represented just 18.0% of Ontario’s and 31.6% of BC’s isolates. Interestingly, several large genotype clusters known to represent transmission among predominantly Canadian-born persons within each respective province were not observed in the other province. Genotypes common to both provinces were largely associated with persons born outside Canada and representing infections acquired elsewhere. These findings underscore the importance of understanding regional epidemiology in order to interpret interprovincial genotypic clustering and indicate that interprovincial transmission events, at least in geographically distant, large, immigrant-receiving provinces, are uncommon. Through a combination of genotype and genomic analyses, in Chapter 5 it was determined that nearly a third of pediatric TB infections in BC were the result of local transmission from an adult—most often a non-Canadian-born family member for children of immigrant parents or an adult contact from the community for Canadian-born children of Canadian-born parents. Importantly, travel to the high-burden TB countries of their parent’s birth was the source of infection for at least 50% of children born in Canada to immigrant parents. These findings reveal the role that age, birthplace, and parents’ birthplace play in determining a child’s likely route of TB exposure, and stand in contrast to the assumption that most pediatric TB cases arise from household transmission. Building on the knowledge gained through the interprovincial comparison of Mtb genotypes, the next analysis used genomics to explore TB transmission within Yukon Territory (YT) and between YT and BC. Chapter 6 details the TB transmission dynamics within YT and characterizes three large clusters, each with a super-spreader—an individual who was likely the source for multiple secondary cases. The finding that transmission also occurs between YT and BC residents highlights the need for routine molecular-based surveillance and communication across jurisdictions with known frequent cross-border movement. 184 Taking advantage of the long-standing relationship with the community and extensive knowledge YT nurses have of the small, well-defined population of persons with TB, a mixed methods study was conducted to both compare traditional field epidemiology to molecular-based approaches, and understand the value added by using molecular and genomic methods in a remote setting with well-described epidemiology (Chapter 7). Both field epidemiology and molecular methods correctly assigned individuals to specific clusters; however, neither method on its own proved capable of regularly identifying source cases and specific transmission events. Together, genomics and detailed field epidemiology yielded the highest-resolution insights into transmission and could be used to guide future contact investigations. Participant feedback indicated that receiving WGS data was preferable to genotyping results, and that in-person training in how to interpret WGS results was desired by the YT TB prevention team. Chapter 8 describes the genomic epidemiology of tuberculosis in BC using WGS data of Mtb isolates, linked to key clinical, epidemiological, and demographic information. Genomics refined the previous estimate of recent transmission rates in BC, revealing that approximately 21.8% of TB infections were locally acquired. Genomics-based investigation of the largest clusters—all of which predominantly comprised Canadian-born persons—identified distinct sub-populations and risk factors for transmission. Although each of these clusters had some unique characteristics, common factors associated with belonging to a large genomically-defined cluster were substance use and Canadian birth. Notably, Canadian-born persons had considerably higher odds of belonging to a cluster (aOR 19.7, 95%CI: 13.9–28.0). In contrast, among the small proportion (7.7%) of persons born outside Canada that acquired TB locally, no large genomic clusters were seen. Reconstruction of putative transmission pathways for a large cluster revealed several smaller sub-clusters associated with specific communities and genomic variation—indicating this strain has likely been circulating in BC’s northern region prior to the study period. The transmission patterns observed in this cluster stand in contrast to the two large BC clusters described previously in the literature74,173—outbreaks concentrated in a specific location/region with key super-spreading individuals—and provide additional insight into the drivers of transmission within BC. 185 To summarize, the use of molecular-based methods, particularly whole genome sequencing, in combination with detailed epidemiological data has revealed deeper insights into BC’s Mtb population structure and transmission dynamics than has ever before been possible. The quantification of the proportion of TB attributable to local transmission, and the identification of specific risk factors that play an important role in the ongoing person-to-person spread of TB within the province, will ultimately inform improved monitoring, surveillance, and resource allocation and inform the development of new prevention strategies. 9.2 Unique contributions, implications and impact The series of studies comprising this dissertation have contributed greatly to our understanding of the genomic epidemiology of tuberculosis in BC and together they represent the largest genotyping and genomic investigation of TB ever undertaken in North America. Nearly all culture-positive TB cases diagnosed in BC from 2005 through 2014 (n = 2,290) were genotyped using 24-locus MIRU-VNTR, and 1,221 of these isolates were submitted for WGS—including all genotypically clustered isolates (n = 974). Through this analysis, it was possible to more reliably distinguish between cases arising from LTBI reactivation versus local transmission, and has provided a better estimate of transmission than has ever been possible. Analysis of the ten-year BC cohort revealed that the likelihood of clustering, particularly in large clusters (≥10 persons), was higher in rural settings compared to metropolitan areas. Previous studies of TB clustering elsewhere have largely focused on urban areas, and thus this finding’s implications are critical to appropriately directing TB prevention and care resources to these often underserved communities and to educating clinicians and public health personnel in these areas to better recognize TB cases in order to rapidly diagnose and treat. An equally important finding was the determination that at least half of pediatric TB cases born in Canada to immigrant parents most likely acquired their infection through travel to the high-burden TB countries of their parent’s birth. This has implications for pediatric TB investigations, but more importantly extends to policy around individuals born outside Canada, who are subject to TB screening during immigration but not upon returning to Canada after travelling to their country 186 of origin. The data suggest that new recommendations around screening individuals with recent community-based travel to high-incidence settings may be warranted. While several genomics studies of outbreaks in the remote regions of Canada’s North have been conducted,166,167 the Yukon studies presented in this dissertation are a first. The genomic epidemiology of YT revealed considerably different TB transmission patterns relative to other northern regions, likely due to the different history, community structure, and demographics of YT versus other northern territories. Also, in part due to frequent travel between BC and YT, which, unlike other northern settings, has year-round road access. WGS provided definitive proof that transmission does indeed occur between YT and BC residents and highlights the need for routine molecular-based surveillance and communication across these two jurisdictions. The genomic epidemiology approach used in these studies allowed for the identification of linkages that traditional contact investigations failed to detect. This has provided important data for understanding the sub-populations and risk factors associated with TB transmission in BC and YT. Particularly informative was the large cluster reconstruction using WGS, which has the ability to determine the direction of transmission—not possible with classic genotyping techniques. Genomics identified clusters and revealed many individual transmission events within each cluster, giving an indication of the role that super-spreaders play in transmission and a deeper understanding of the characteristics common to persons who spread TB to multiple other individuals. These insights will have a direct impact on TB prevention policies and guidelines, and lead to the development of improved contact investigation methods supported by genomics-based approaches. Findings from this dissertation add to the growing body of literature investigating the use of WGS for diagnostics and clinical practice. At this time much of the focus remains on the use of WGS for research purposes or in individual outbreak investigations, and few mycobacteriology laboratories in the world have rolled out WGS for all culture-positive TB isolates. The large, comprehensive retrospective population-based study described herein serves as a demonstration of the powerful insights attainable through universal WGS and will provide critical guide as BC 187 considers implementing routine WGS. If the province implements WGS for routine use, these data suggest that there will be subsets of culture-positive TB cases in which WGS is likely to provide actionable insights for contact investigation and outbreak management. The data also provide a retrospective data bank against which isolates sequenced in the future can be mapped—given TB’s potentially long latent period, having this historical database to back-compare against is important, and that is something that to the best of my knowledge no other public health agency currently has. 9.3 Strengths and limitations This dissertation’s scope—no other study has used genotyping and genomics to look at as large and complete a retrospective TB dataset as this, its uniqueness—few jurisdictions have access to linked clinical, epidemiological, and demographic data as well as the capacity for WGS, and the extensibility of the findings to other TB programs in low-incidence settings all represent major strengths of this work. Sampling 99.3% of culture-positive Mtb isolates over a ten-year period reduced the potential for bias and provided validity to the conclusions. The comparison of Mtb genotypes with Ontario data placed the BC results in the larger Canadian context and confirmed the molecular epidemiology study conclusions in Chapter 3. The ability to work with smaller datasets with detailed epidemiological case-level information, such as the pediatric and Yukon Territory cohorts, permitted the testing of the approaches to transmission reconstruction, facilitating the later work on the large WGS study (Chapter 8), while also providing interesting and relevant results. Additionally, all eight research studies included in this dissertation received input from clinical, field-based TB program officers—these co-authors critically revised each manuscript and provided important insight into how the findings could impact TB policy and practice. Nevertheless, there are several limitations that should be noted, including the important point that much of the case-level data used throughout this dissertation were collected for purposes other than to test specific research hypotheses; for example, many data fields were collected for broad, population-level surveillance activities, rather than individual transmission inference and contact investigation. Furthermore, all studies were retrospective in nature—meaning the 188 genotyping and WGS results were not available at the time of investigation and there was no opportunity to confirm molecular-based linkages that had not already been captured through the original case interviews. An inherent limitation of all TB genotyping and genomics studies is the current requirement for large volumes of input DNA. This means that genotyping and sequencing must begin with an Mtb culture, which restricts the study population to only culture-confirmed TB cases. This may result in missed connections in a transmission chain. However, individuals with culture-negative TB (~20% in BC) are generally considered non-infectious and are unlikely to contribute to the spread of the disease.407 Other possible reasons for missing links within chains of transmission are instances in which an infectious individual left the province or died prior to diagnosis. It is difficult to estimate how often this may have occurred, but the contribution of these missed cases should be considered in future studies, particularly when investigating TB transmission amongst Canadian-born, street-involved individuals. Given the current opioid overdose crisis in BC and the association of TB and substance use, it is reasonable to expect that amongst the province’s annual opioid deaths—over 1,400 in 2017408—there may be some active TB cases. Under the BC Coroners Act, coroners have the authority to authorize a post-mortem examination if the coroner deems it necessary for the purposes of the investigation. From 2015–2017, a post-mortem examination was conducted in under a quarter of suspected illicit drug overdose deaths (Andrew Tu, BC Office of the Chief Coroner, personal communication). While WGS has provided significant insights into TB transmission, there remain several challenges in using WGS data as part of routine TB prevention and care, even in a well-resourced setting such as BC. First, the costs of large-scale, routine WGS remain considerable, despite the significant reductions in sequencing prices seen over the past decade. Culture and sample preparation still incur substantial reagent and technical personnel costs, and sequencing economy is best achieved when batching hundreds of isolates per run—sequencing BC’s infrequent TB cases as they arise is lower-throughput and higher cost. To mitigate these costs, WGS for this dissertation focused only on the subset of genotypically identical isolates, along with isolates closely related to known transmission clusters by MIRU-VNTR, e.g. one- or two-189 locus mismatches. Although the sequenced isolates likely captured nearly all the local transmission events, it is possible that a small number of transmitted cases were not sequenced due to larger-than-expected differences in genotype results. Second, 10% of isolates were genomically indistinguishable from one or more isolates in the study population, and in some cases, even the addition of contact tracing data did not suggest specific individual transmission events within these clusters. Lastly, interpretation of WGS results is complicated by the currently available laboratory methods. Sequence heterogeneity may be introduced by laboratory culture of isolates—an issue that will be resolved once we can reliably sequence directly from specimens373—or may occur naturally as a result of within-host microevolution.162,409 Both issues could lead to erroneously excluding individuals from a transmission chain. While it is quite possible these issues may have occurred in this study, the large number of individuals with no genomic variation over a number of years and multiple hosts, some with long infectious periods, indicates that this may not be a widespread phenomenon. In the early chapters of this dissertation, a number of limitations were raised which were subsequently addressed in the ensuing studies, particularly with respect to the overestimates of clustering using MIRU-VNTR genotyping, which WGS largely resolved. However, the concerns raised around classifying all persons born outside Canada as a single group—described in Chapter 3—had been left unanswered. Therefore, a small analysis to investigate this potential limitation was undertaken, the findings of which are detailed in the next section. 190 9.3.1 Homogeneous Group or a Multicultural Mosaic? The Challenge with Reporting Birth Outside Canada as a Tuberculosis Risk Background Birth in a country with high tuberculosis (TB) incidence—greater than 30 TB cases per 100,000 population6—or very high incidence (≥200 cases/100,000) is a major risk factor for TB disease in many low- (<10 cases/100,000 population)410 and medium-incidence (11–29 cases/100,000) settings, where TB rates are largely driven by reactivation of latent tuberculosis infection (LTBI) acquired in an individual’s country of birth.410 Thus, population-level TB surveillance programs and research studies typically collect birth country for each individual. However, these data are often reported in aggregate—all persons born outside Canada are reported as a single group, without stratification based on birth country or age at immigration. In these studies, a person who immigrated from a low-incidence country as an infant and an adult recently arrived from a high-incidence country would be similarly classified, despite likely having different TB exposure histories. Like many low-incidence countries, Canada is multicultural, welcoming over 250,000 permanent residents from ~200 countries in 2015.411 Migrants arrive as both temporary and permanent residents and include refugees, students, and skilled workers.412 More than 60% of new permanent residents come from just 10 countries, where TB incidence ranges from low (USA, France, UK) to medium (Iran, Syria) to high (China) and very high (Philippines, India, China, Pakistan, Nigeria).411 Reporting these populations under a single label—typically “foreign-born”—limits our understanding of local TB epidemiology, and may stigmatize immigrants, particularly around public understanding of TB transmission within Canada and risk to the population. Here, it is examined how characterizing migrants based on TB incidence in their birthplace improves our understanding of regional epidemiology and transmission. 191 Methods Two previously described cohorts were used— the Chapter 3 study population and cohort detailed by Ronald et al. (2018), to examine how stratifying persons born outside Canada according to TB rates in their country of birth affects the understanding of TB trends. With the first cohort, individual-level clinical and demographic data was extracted from the British Columbia (BC) Provincial TB registry413 and linked these to genotypic data representing 99.3% of all culture-positive TB cases in BC, 2005–2014, as previously described (Chapter 3). Separately, in a second cohort, records from the BC Provincial TB Registry413 were linked to data from the Immigration, Refugees and Citizenship Canada Permanent Residents database and Population Data BC health administrative databases414,415 to calculate TB incidence rates and time from immigration to active TB as described previously.416 Birthplace TB incidence rates were derived from yearly country-level incidence data (all forms of TB/100,000 population).79 For the genotypic analysis, the cohort was divided into two groups—individuals from high-to-very-high-incidence settings and those from medium-to-low-incidence settings. For the population-based TB rate analyses, three groups were used: very high incidence, high incidence and medium-to-low incidence. TB rates according to years following immigration and case counts by TB incidence in country of birth were represented graphically, with the time trends in case counts evaluated by linear regression. Demographics and genotypic clustering of individuals between groups was compared using summary statistics including median, interquartile range (IQR) and t-test, where appropriate. Analyses were conducted in R (v3.4.1). Results Amongst 2005–2014 culture-confirmed TB diagnoses (n = 2,290), the number of cases was lowest in persons born outside Canada in regions with medium-to-low TB incidence (Figure 9-1). While case counts were stable in this group (p = 0.522) and the Canadian-born (p = 0.636) after 2010, they increased in persons from high-incidence countries (p = 0.005). TB incidence rates among permanent residents arriving in BC between 2005–2012 reflect a similar pattern—the overall rate in persons from very high-incidence countries (53.6/100,000 person-years) was 192 >20-fold higher than in those born in a low-incidence country (2.6/100,000 person-years). Furthermore, there is a clear trend whereby TB incidence rates after arrival in BC are highest among residents from very high TB incidence countries (Figure 9-2). Age at time of diagnosis, and years between arrival and TB diagnosis also vary substantially amongst those born outside Canada, from a median age-at-diagnosis of 53 years (IQR: 35–73) and 12 years (IQR: 3–21) since arrival for persons born in high-incidence countries, to 66 years (IQR: 48–78) and 36 years (IQR: 14–50) for those born in medium-to-low-incidence countries (p < 0.001). Figure 9-1. Trends in active tuberculosis diagnoses in British Columbia (BC), Canada. Number of culture-confirmed cases over a ten-year period categorized by birthplace: Outside Canada (H—high-incidence countries, M/L— medium to low-incidence countries), Canada. 193 Figure 9-2. Tuberculosis incidence rates in British Columbia (BC) for persons born outside of Canada. Plot represents TB cases who immigrated to BC from 2005 through 2012, stratified by country of birth incidence (very high, high, and medium to low-incidence) and number of years in BC. TB Transmission Within BC Varies According to Birthplace Next, genotypic clustering was examined in the linked cohort. Of the 1,641 persons born outside Canada in the dataset of 2,290 genotyped isolates, 69.8% (n = 1,146) grew a TB isolate with a unique genotype, suggestive of LTBI reactivation. 1,071 (93.5%) of these 1,146 cases emigrated from high-incidence settings. Examining persons born outside Canada in the study, two distinct groups were observed. First, in large clusters (≥10 cases) consisting of predominantly Canadian-born persons and representing local transmission, persons born outside Canada represented 25/322 (7.9%) of diagnoses. Amongst these individuals, the median time from immigration was 40 years (IQR: 25–49), and of the 24 people whose specific country of birth was known, only 10 (41.7%) emigrated from high-incidence TB countries (Figure 9-3). Seven of the 25 (28.0%) individuals reported risk factors similar to those seen in Canadian-born cluster members, including HIV and substance-use. In contrast, among large genotypic clusters consisting of predominantly persons born outside Canada from similar regions of the world—clusters that epidemiologic field work suggests largely arose from LTBI reactivation—the median time from 194 immigration was 12 years (IQR: 3–18), all 119 (100%) people emigrated from high-incidence TB countries, and none reported HIV or substance-use as risk factors. Figure 9-3. Number of tuberculosis cases for each large (≥10 persons) genotypic cluster. Large clusters in British Columbia, Canada (2005–2015) by cluster type: (A) predominantly Canadian-born and presumed to represent local transmission; (B) predominantly persons born outside Canada and presumed to largely represent reactivation of LTBI. Coloured to indicate persons born outside Canada (H—high-incidence countries, M/L— medium to low-incidence countries), or Canadian-born. Discussion Across Canada, 70% of new TB diagnoses occur in persons born outside Canada. These individuals come from all nine epidemiological regions defined by the World Health Organization, though each province has its own unique demographics.13 Between the years 2005 and 2012, there were 337,492 new Canadian permanent residents arriving in BC—one of the largest immigrant receiving provinces in Canada, with the majority (78.3%) coming from high TB incidence countries. The TB incidence rate in BC was 22.1 per 100,000 person-years in this population during this period.416 The differences observed in absolute numbers and rate, as well 195 as time since immigration to active disease, and risk factors such as substance-use, underscore the notion that persons born outside Canada are not a homogeneous group when it comes to local transmission or risk factors. Stratifying Individuals by Country of Birth Reveals Different Trends in TB Epidemiology TB programs regularly report case counts and incidence rates in Canadian-born non-Indigenous, Canadian-born Indigenous (First Nations, Inuit, and Métis), and “foreign-born” populations. While this may be sufficient for high-level reporting and surveillance purposes, case investigations, program evaluations, and research should examine TB trends at a more granular level to obtain an accurate picture of TB epidemiology, including individual-level risk factors and estimates of recent, local transmission. As found in the study, splitting persons born outside of Canada into two groups—people born in high-incidence countries and people born in medium to low-incidence countries—yields a very different picture of TB epidemiology as it relates to foreign birth. Younger recent migrants from high-incidence countries largely represent LTBI reactivation, whereas long-time residents of Canada from lower incidence regions acquire TB locally. However, it should be noted that this study was limited to a single, albeit large and diverse Canadian province, and the results may not be generalizable to all low-incidence settings. Recognizing the diversity within migrants and tailoring interventions to those at the highest risk for TB is clearly important; however, there is a fine balance between describing higher “risk” groups and developing targeted interventions while not reinforcing stigma. Stigma and concerns about immigration status amongst newcomers to Canada may delay individuals from seeking treatment.410 Furthermore, the media’s portrayal of migrants as a potential public health risk contributes to this stigma.402,403 In reality, data from molecular epidemiology studies show that immigrants to low-incidence TB countries who are subsequently diagnosed with TB rarely transmit TB outside of their immediate family.404,405,417 Identification and quantification of risk factors beyond simply “foreign-born”, particularly in the context of TB molecular data will allow for a more clear understanding of TB transmission. 196 Conclusion TB surveillance data and population research provides the evidence upon which many programs and policies are based.418 Research examining public health interventions for populations have found that targeted strategies tailored to smaller, better defined subgroups are often more successful.419 Change will require substantial evidence to define the optimal strategy to eliminate TB, but a first step towards this end is for TB surveillance programs and population studies to recognize the diversity amongst people with TB and to analyze data with the understanding that people born abroad represent a culturally, socially, and ethnically diverse population. Disclaimer Statement All inferences, opinions, and conclusions drawn in this study are those of the author, and do not reflect the opinions or policies of the Data Steward(s). 197 9.4 Knowledge translation Studies suggest that it often takes many years before research findings are reflected in practice or translated into improved health outcomes for patients.420 To meet TB elimination goals, it is clear that we will need to reduce the time lag between research and implementation. During all stages of planning and conducting the research presented herein, knowledge translation and dissemination strategies were applied. The new knowledge generated from these projects is not only applicable to TB programs in BC, but also those across Canada and in other low-incidence settings. Accordingly, I have presented my research findings on 20 different occasions at local, provincial, and international meetings and conferences. Recently, results of this dissertation have been presented in a meeting with BC’s Tuberculosis Strategic Plan Implementation Committee. Public-facing communication was achieved through the media, which included an online publication for World TB Day (2018) and a podcast interview. Full details can be seen in Appendix B. To date, three peer-reviewed original research articles have been published as a result of my work, with five more publications expected. I have also published a review and one commentary paper. To translate key findings from the genotyping study to BC’s TB program stakeholders, which required conversion of highly technical data into an intuitive and interpretable format, I prepared a genotyping summary report (Appendix C ), the content of which I presented at BC’s TB Clinical Leadership Meeting. This report has been disseminated to various laboratory and clinical stakeholders for use as training and reference material, and is now publicly available on the BCCDC website. Similar to the genotyping report, two documents based on the results of the genomic epidemiology studies are currently under preparation, one for BC and another for YT. Additionally, a training session for the use of genomic data to guide contact investigations will be held for staff at the Yukon Communicable Disease Control based on the recommendations presented in Chapter 7. 198 9.5 Future research and recommendations The data obtained from genotyping and WGS of Mtb isolates in British Columbia (2005–2014) as part of this dissertation has provided significant insights to the population structure and transmission dynamics of Mtb in BC, providing an accurate baseline estimate of recent transmission rates, against which progress towards elimination can be measured. The genomic database created for these studies will form the basis for multiple future projects, and provide an important retrospective database against which prospective sequenced isolates can be compared. 9.5.1 Prospective provincial MIRU-VNTR genotyping Prior to 2015, genotyping was only done on request from a clinician, generally in support of outbreak investigations and contact tracing efforts. The study results described in Chapter 2 made the added value of universal genotyping clear, and as of 2015, TB genotyping has been part of routine laboratory operations. Future implementation-related work in this area includes: i) developing a means to automatically link case- and cluster-level information to genotyping results, allowing for epidemiologically-informed interpretation of the genotype data; ii) developing a reporting process that effectively communicates genotyping results in a timely manner to all stakeholders; and iii) incorporating genotypic data into BC’s annual TB report. 9.5.2 Standardization of WGS bioinformatics pipelines As WGS moves from the microbial research laboratory into routine clinical and public health practice, regulatory bodies will require standardized, clinically validated protocols for both WGS laboratory methods and interpretation of results. Currently, Public Health England is leading the way in laboratory accreditation of WGS for TB diagnosis and surveillance, and has developed and validated a bioinformatics pipeline for downstream analysis, which was used for the studies presented in this dissertation. However, many other research and public health laboratories have independently developed software pipelines and there is now a need for standardization of these computational approaches. Recently, a group of individuals involved world-wide in Mtb sequencing and analysis came together to jointly author a position paper on the minimum standards for a TB WGS bioinformatics pipeline—information necessary for both the reliable 199 interpretation of genome data and cross-laboratory comparison of data. Dr. Gardy and myself have been included in this group, and we are contributing our expertise in the form of recommendations around standardized reporting and visualization of results, both clinically and in the research community. 9.5.3 WGS as a tool for TB prevention This work has provided a substantial database of over 1,500 Mtb genome sequences, all of which have been made publicly available through NCBI, as well as a list of insights into TB transmission that will be beneficial as BC plans for the implementation of prospective, real-time WGS. Yet, a number of challenges remain. First, laboratory results, including genomic data, and case-level information—key to interpreting the genomic data—are housed separately within databases that are not linked. Routine data linkage and ongoing curation of case- and isolate-level data requires significant personnel effort and the ability to link data across two discrete IT systems. Second, interpretation of WGS data in the context of epidemiological data is a manual process, requiring the time and expertise of a staff member able to understand both genomic data and the clinical and epidemiological complexities of TB. Ideally, an individual dedicated to this would work on TB genomic data, interpreting routine results and monitoring outbreaks and emerging strains of public health concern, such as drug-resistant strains. Lastly, communication of a novel type of complex laboratory data, such as genome sequence, is challenging, as many stakeholders are unfamiliar with genomic data and its interpretation. Work has begun to determine the most effective means of reporting WGS clustering data such that it is acceptable and interpretable to end-users and is able to support TB prevention programs and policies. While the ten-year genomics studies presented herein have provided a broad overview of person-to-person spread of TB within the province, the next steps will be to examine transmission at a deeper level. Future studies will focus on developing models using epidemiologically-informed genomic data that will to improve our ability to predict onward transmission at a case- and outbreak-level. Understanding the characteristics of individuals more likely to spread TB and being able to predict whether or not a cluster may go on to become a large outbreak are both 200 important future analyses that will support the development of evidence-based strategies for public health intervention. 9.6 Final Conclusions The overall aim of this dissertation was to describe the molecular and genomic epidemiology of TB in British Columbia to improve our knowledge and understanding of tuberculosis transmission. While genotyping revealed considerable strain diversity—indicative of infections acquired outside BC—WGS provided a more accurate picture of TB dynamics in the province and provided unquestionable evidence of ongoing transmission within BC. Characterization of these transmission clusters provided new insights into sub-populations involved in person-to-person spread of TB, and the WGS data provided a more accurate estimate of LTBI reactivation in persons born outside Canada—key to obtaining funding for and efficiently delivering immigrant screening programs. In conclusion, the depth and breadth of the research presented herein, ranging from metropolitan centers to remote northern regions and including both adult and pediatric cases over a decade, means that the study outcomes are likely to be applicable to a variety of settings. 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(Select all that apply) • Supervising daily patient medication doses • Collecting contact information directly from patients • Writing notes on each patient encounter • Entering information into Panorama • Case management • Outbreak investigation • General program oversight • None of the above 2. What are your working hours? • Full time • Part time • Other (please specify) 3. Approximately how long have you worked in the area of TB? • < 1 year • 1–5 years • > 5 years 4. Do your daily activities include anything other than TB? • Yes • No 5. What proportion of your average week do you spend on TB-related work? • 0–19% • 20–39% • 40–59% • 60–79% • 80–100% B. TB Genotyping Knowledge 6. Prior to this study had you heard of TB genotyping (i.e. MIRU-VNTR/spoligotyping/RFLP)? • Yes • No 7. Which genotyping methods have you heard of? (Select all that apply) • RFLP/fingerprinting • MIRU-VNTR • spoligotyping Continued on next page 239 Appendix Table A-1 Continued from previous page Questions 8. Through what means have you been exposed to information about genotyping? (Select all that apply) • daily work • presentations • conferences • journal articles • co-workers • reports • other 9. Have you ever received formal training (e.g. course, workshop, user guide) in the use of genotyping data for investigations? (e.g. confirm/refute transmission, guide contact tracing) • Yes • No 10. Prior to this study were you aware that genotyping data (MIRU-VNTR) was available for your TB patients? • Yes • No C. Current Process 11. Prior to this study, have you used MIRU-VNTR genotyping data in your investigations? (e.g. confirm/refute transmission, guide contact tracing) • Yes • No 12. Prior to this study, what have you used MIRU-VNTR genotyping data for? (Select all that apply) • Confirm clusters and links between cases • Refute clusters and links between cases • Identify unknown links between cases • Justify extending contact investigation • Investigate potential false positive TB diagnosis • Don't know • Other (please specify) 13. Prior to this study, how often do you use MIRU-VNTR genotyping data in your case management or outbreak investigation? • Never • For few cases • For about half of cases • For many cases • For every case 14. How confident are you in using MIRU-VNTR genotyping data in your investigations? (e.g. confirm/refute transmission, guide contact tracing) • Novice – not at all confident • Average – somewhat confident • Expert – completely confident 240 Appendix Table A-2. Online survey questions administered post-meeting. Questions A. Future Processes 1. How often would you like to use MIRU-VNTR genotyping data in your TB case management or outbreak investigations? • Never • For few cases • For about half of cases • For many cases • For every case 2. After completion of this study do you feel more confident using MIRU-VNTR genotyping data in your investigations? • Not at all • Somewhat • Considerably 3. How often would you like to use whole genome sequencing (WGS) data in your case management or outbreak investigation? • Never • For few cases • For about half of cases • For many cases • For every case 4. After completion of this study do you feel more confident using whole genome sequencing (WGS) data in your investigations? • Not at all • Somewhat • Considerably 5. Would you like to receive training in interpretation of TB genotyping and genome sequencing? • Yes • Maybe • No 6. What format would you prefer for training? (Select all that apply) • In-person workshops • User guides • Online videos • Other 7. Reason you do not want training? • Not part of my job • Not valuable • No time • Other 8. Do you have any comments about the survey or the study that you would like to make? We value all input. 241 Appendix Table A-3. Interview questions used to guide discussion. Category Questions Impact • Overall, do you think that genotyping and/or genome sequencing data improved your understanding of transmission dynamics in Yukon? • Do you think that you would have changed your approach to any investigations if you had genotyping and/or genome sequencing data at the time of investigation? • What proportion of cases do you feel that genotyping data and/or genome sequencing data would be helpful? • Overall, do you think that there is added value in genotyping and/or genome sequencing Yukon’s TB isolates? Future Processes • How should the BCCDC Public Health Laboratory, clinicians, and contact investigation teams communicate genotyping and/or genome sequencing information to YCDC? o Data: raw data or analyzed/interpreted data o Format: case-level reports, regular summary reports, regular teleconferences, phone calls as needed • After reviewing the genotype cluster descriptions for Yukon’s MIRU-VNTR clusters we’d like to get your feedback. o Did you find the cluster descriptions useful? (Useful / Not very useful / Useless) o What other information would you like to see in the cluster descriptions? o Other feedback? 242 Presentations B.1 Oral presentations 1. Whole Genome Sequencing as a Tool to Quantify Local Tuberculosis Transmission in British Columbia, Canada. 39th Annual Congress of European Society of Mycobacteriology. Dresden, GERMANY, 3 July 2018. 2. Knowledge Translation: Molecular Epidemiology of Tuberculosis in British Columbia. BC Tuberculosis Clinical Leadership Meeting. Vancouver, CANADA. 28 May 2018. 3. Whole Genome Sequencing of Mycobacterium tuberculosis Identifies Transmission to Pediatric Patients in British Columbia Canada, 2005–2014. 22nd Annual Conference of the Union—North America Region. Chicago, IL, USA. 2 March 2018. 4. Molecular Epidemiology of Tuberculosis in British Columbia. Faculty of Medicine Trainees Roundtable. Vancouver, CANADA. 26 October 2017. 5. A Comparison of Knowledge Gained Through Universal Tuberculosis Genotyping Over the Previous On-Request Program. BCCDC Research Week Symposium. Vancouver, CANADA. 26 October 2017. 6. Genotyping and Whole Genome Sequencing to Identify Tuberculosis Transmission Related to Pediatric Patients in British Columbia, Canada, 2005–2014. 48th Union World Conference on Lung Health. Guadalajara, MEXICO. 12 October 2017. 7. Findings from Whole Genome Sequencing of Tuberculosis in a Geographically Large Canadian Province with a Diverse Population. 38th Congress of European Society of Mycobacteriology. Šibenik, CROATIA. 28 July 2017. 8. Whole Genome Sequencing for Diagnosis and Surveillance of Tuberculosis in British Columbia. ASM Microbe 2017. New Orleans, LA, USA, 5 June 2017. 9. Molecular Epidemiology of Tuberculosis in British Columbia—a 10-year Retrospective Study. British Columbia Centre for Disease Control Grand Rounds, Vancouver, BC, CANADA, 2 May 2017. 10. Molecular Epidemiology of Tuberculosis in British Columbia and Yukon—A 10-year Retrospective Study, Whitehorse General Hospital Grand Rounds. Whitehorse, YT, CANADA. 27 September 2016. 11. Tuberculosis—Molecular Epidemiology. TB Molecular Epidemiology Stakeholders' Meeting. Vancouver, BC, CANADA. 18 April 2016. 243 B.2 Poster Presentations 1. Whole Genome Sequencing Provides New Insights to Tuberculosis Transmission Over a 10-year Period in British Columbia, Canada. AMMI Canada—CACMID Annual Conference. Vancouver, BC, CANADA. 3 May 2018. 2. Whole Genome Sequencing of Mycobacterium tuberculosis Identifies Transmission to Pediatric Patients in British Columbia Canada, 2005–2014. 22nd Annual Conference of The Union - North America Region. Chicago, IL, USA. 3 March 2018. 3. Tuberculosis Transmission in British Columbia, Canada: Insights from Whole Genome Sequencing. Applied Bioinformatics and Public Health Microbiology. Hinxton, UK. 18 May 2017. 4. Molecular Epidemiology of Tuberculosis in British Columbia, Canada – A 10-year Retrospective Study. 21st Annual Conference of The Union - North America Region. Vancouver, BC, CANADA. 24 February 2017. 5. Improved Understanding of Tuberculosis Transmission in British Columbia Using Whole Genome Sequencing. Faculty of Medicine, UBC—Genes and Environment Workshop. Vancouver, BC, CANADA. 26 September 2016. 6. Improved Understanding of Tuberculosis Transmission in British Columbia Using Whole Genome Sequencing. 37th Congress of European Society of Mycobacteriology. Catania, ITALY. 4-5 July 2016. 7. First Steps to Understanding Tuberculosis Transmission in British Columbia: Insights from 24-locus MIRU-VNTR Genotyping. 20th Annual Conference of The Union—North America Region. Denver, CO, USA. 26 February 2016. 8. Progress Update—A Retrospective Study Utilizing Whole Genome Sequencing to Understand Tuberculosis Transmission in British Columbia. Applied Bioinformatics and Public Health Microbiology. Cambridge, UK. 6 May 2015. 9. Progress Update—A Retrospective Study Utilizing Whole Genome Sequencing to Understand Tuberculosis Transmission in British Columbia. 19th Annual Conference of The Union—North America Region. Vancouver, BC, CANADA. 26 February 2015. B.3 Media 1. The biomedical science behind tuberculosis treatments [Internet]. Institute of Biomedical Science. [cited 2018 Aug 8]. Available from: https://www.ibms.org/resources/news/the-biomedical-science-behind-tuberculosis-treatments/ 2. The Poison Terminator – A killer bacteria that won’t go away [Internet]. [cited 2018 Aug 8]. Available from: https://radiopublic.com/the-poison-terminator-8QrMnr/ep/s1!28c45 244 Genotyping Summary Report Copy of: Tuberculosis Genotyping in British Columbia 10-year Retrospective Study Report 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275
Thesis/Dissertation
eng
Population and Public Health
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Unravelling the thread of transmission : pairing traditional epidemiology with genomics to understand tuberculosis transmission dynamics
Text