Medicine, Faculty of
Population and Public Health (SPPH), School of
DSpace
UBCV
Clementi, Emilia Marialuisa
Master of Science - MSc
University of British Columbia
Background Hepatitis C virus (HCV) infection affects diverse populations such as people who inject drugs (PWID), 'baby boomers' (BB’s), gay, bisexual, and other men who have sex with men (gbMSM), and immigrants from HCV endemic regions. Assessing patterns of shared characteristics, the burden of syndemics, and the likelihood of treatment uptake among subpopulations may facilitate targeted program planning. Methods The BC Hepatitis Testers Cohort includes all HCV cases identified in BC from 1990 to 2015 followed up to 2018 and linked with data on medical visits, emergency department visits, hospitalizations, cancers, prescription drugs, and deaths. Latent Class Analysis was used to group people diagnosed with HCV according to shared characteristics previously shown to be related to HCV acquisition, transmission, retention in care and/or treatment uptake. Models were fitted step-wise, with the best fitting model chosen based on fit statistics, epidemiological meaningfulness, and maximisation of posterior probability for class assignment. Latent class groups were named based on defining characteristics and analysed for their association with treatment uptake (based on first treatment course). Results The best fitting latent class model had six groups, with names and characteristics as follows: Younger PWID (n=11,563): people born >1974, mental illness, material deprivation, recent injection drug use (IDU). Older PWID (n=15,266): past IDU, HIV, HBV, TB coinfections, alcohol misuse. gbMSM (n=12,698): gbMSM, material privilege, no liver disease. People of Asian Backgrounds (n=4,718): East/South Asians, people born <1945, social privilege, no alcohol misuse/mental illness. Rural BB (n=20,401): rural dwellers, BBs, heterosexuals, no HIV. Urban socially deprived BB (n=12,698): urbanites, liver disease, social deprivation, no IDU. Compared to Younger PWID, groups with higher odds of treatment uptake were People of Asian Backgrounds and Rural BB. Compared to all other groups, gbMSM and Younger PWID had lower likelihoods of treatment uptake. Conclusion There are differences in HCV treatment uptake among the multivariable patient profiles, suggesting that the co-occurrence of multiple factors influences likelihood of receiving treatment. Further investigation of treatment uptake patterns related to multivariable patient profiles may help with program planning or addressing areas where services do not align with the particular patient populations.
CLASSIFYING PEOPLE LIVING WITH HEPATITIS C VIRUS USING A POPULATION-LEVEL LATENT CLASS ANALYSIS TO UNDERSTAND ASSOCIATION WITH TREATMENT UPTAKE AND OPTIMIZE INTEGRATION OF HEALTH SERVICES by EMILIA MARIALUISA CLEMENTI BA (Honours), Connecticut College, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIRMENTS FOR THE DEGREE OF MASTER OF SCIENCE in The Faculty of Graduate Studies and Postdoctoral Studies (Population and Public Health) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2020 © Emilia Marialuisa Clementi, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled: “Classifying people living with hepatitis C virus using a population-level latent class analysis to understand association with treatment uptake and optimize integration of health services” submitted by Emilia Clementi in partial fulfillment of the requirements of the degree of Master of Science in Population and Public Health Examining Committee: Naveed Janjua, Professor, School of Population and Public Health, UBC Supervisor Michael Otterstatter, Professor, School of Population and Public Health, UBC Supervisory Committee Member Jane Buxton, Professor, School of Population and Public Health, UBC Supervisory Committee Member Michael Law, Professor, School of Population and Public Health, UBC Additional Examiner iii Abstract Background Hepatitis C virus (HCV) infection affects diverse populations such as people who inject drugs (PWID), 'baby boomers' (BB’s), gay, bisexual, and other men who have sex with men (gbMSM), and immigrants from HCV endemic regions. Assessing patterns of shared characteristics, the burden of syndemics, and the likelihood of treatment uptake among subpopulations may facilitate targeted program planning. Methods The BC Hepatitis Testers Cohort includes all HCV cases identified in BC from 1990 to 2015 followed up to 2018 and linked with data on medical visits, emergency department visits, hospitalizations, cancers, prescription drugs, and deaths. Latent Class Analysis was used to group people diagnosed with HCV according to shared characteristics previously shown to be related to HCV acquisition, transmission, retention in care and/or treatment uptake. Models were fitted step-wise, with the best fitting model chosen based on fit statistics, epidemiological meaningfulness, and maximisation of posterior probability for class assignment. Latent class groups were named based on defining characteristics and analysed for their association with treatment uptake (based on first treatment course). Results The best fitting latent class model had six groups, with names and characteristics as follows: Younger PWID (n=11,563): people born >1974, mental illness, material deprivation, recent injection drug use (IDU). Older PWID (n=15,266): past IDU, HIV, HBV, TB coinfections, alcohol misuse. gbMSM (n=12,698): gbMSM, material privilege, no liver disease. People of Asian Backgrounds (n=4,718): East/South Asians, people born <1945, social privilege, no alcohol misuse/mental illness. Rural BB (n=20,401): rural dwellers, BBs, heterosexuals, no HIV. Urban socially deprived BB (n=12,698): urbanites, liver disease, social deprivation, no IDU. iv Compared to Younger PWID, groups with higher odds of treatment uptake were People of Asian Backgrounds and Rural BB. Compared to all other groups, gbMSM and Younger PWID had lower likelihoods of treatment uptake. Conclusion There are differences in HCV treatment uptake among the multivariable patient profiles, suggesting that the co-occurrence of multiple factors influences likelihood of receiving treatment. Further investigation of treatment uptake patterns related to multivariable patient profiles may help with program planning or addressing areas where services do not align with the particular patient populations. v Lay Summary Significant gaps in the HCV care cascade, from screening to diagnosis to treatment are major impediments to hepatitis C virus (HCV) elimination. HCV affects diverse and often overlapping populations, and assessing patterns of shared characteristics among subpopulations is important for optimal design of a suite of HCV services and for decision making at point-of-care. Latent Class Analysis (LCA) is a statistical grouping approach that creates clusters of similar people. This study used LCA to develop multifaceted profiles of people affected by HCV based on their sociodemographic and clinical characteristics. It also examined indicators of treatment uptake and the association between treatment uptake and these HCV patient profiles. LCA can be a valuable tool to inform tailored program planning efforts that reduce care-cascade gaps and increase service delivery efficiency. Services that target groups with decreased treatment uptake or access should be explored and implemented with the goal of eliminating HCV as a public health threat. vi Preface The work contained in this thesis was fully conducted by Emilia Clementi, under the supervision of Dr. Naveed Janjua, with assistance from members of the thesis committee (Drs. Jane Buxton and Michael Otterstatter). The data used in this study came from the British Columbia Hepatitis Testers Cohort (BC-HTC) at the British Columbia Centre for Disease Control (BCCDC), led by Dr. Naveed Janjua. Members of the BC-HTC team offered valuable insights and greatly facilitated in the preparation of the data for this study. The BC-HTC team included Dr. Sofia Bartlett, Amanda Yu, Stanley Wong, Dr. Margo Pearce, Dahn Jeong, James Wilton, Maria Alvarez, Dr. Prince Adu, Dr. Mawuena Binka, Dr. Younathan Abdia, Dr. Zahid Butt, Dr. Hasina Samji, Dr. Terri Buller-Taylor and Dr. Mel Kradjen. Work from Chapter 2 was presented at the Canadian Liver Meeting and accepted to the International Liver Conference, Western Canadian Universities Big Data Health Conference, and the Canadian Conference on HIV/AIDS. This study was part of a larger project that was granted ethics approval by the University of British Columbia Research Ethics Board: H14-01649 (“Assessment of the Burden of Hepatitis C to Support Appropriate Prevention, Care and Treatment Using a Comprehensive Dataset of HCV and HIV Testers in British Columbia”). vii Table of Contents Abstract ....................................................................................................................................... iii Lay Summary ............................................................................................................................... v Preface .......................................................................................................................................... vi Table of Contents........................................................................................................................ vii List of Tables................................................................................................................................ ix List of Figures ............................................................................................................................... x List of Abbreviations .................................................................................................................. xi Acknowledgements.................................................................................................................... xiii Dedication .................................................................................................................................. xiv 1 Introduction and Review of Literature…....................................................................... 1 1.1 Background................................................................................................................... 1 1.2 Research Objectives.................................................................................................... 13 1.3 Thesis Outline............................................................................................................. 13 1.4 Disclaimer Statement ................................................................................................. 14 2 Syndemic Profiles of People Living with HCV using Population-Level LCA........... 15 2.1 Introduction................................................................................................................. 15 2.2 Methods ...................................................................................................................... 17 2.3 Results......................................................................................................................... 20 2.4 Discussion................................................................................................................... 28 2.5 Conclusion.................................................................................................................. 33 viii 3 Factors Associated with Treatment Uptake................................................................. 34 3.1 Introduction................................................................................................................. 34 3.2 Methods ...................................................................................................................... 36 3.3 Results......................................................................................................................... 39 3.4 Discussion................................................................................................................... 46 3.5 Conclusion.................................................................................................................. 52 4 Conclusions ..................................................................................................................... 53 4.1 Summary of Findings ................................................................................................. 53 4.2 Recommendations ...................................................................................................... 56 4.3 Final Conclusions ...................................................................................................... 59 Bibliography ............................................................................................................................... 60 Appendices .................................................................................................................................. 72 Appendix 1........................................................................................................................ 72 Appendix 2........................................................................................................................ 73 Appendix 3........................................................................................................................ 74 Appendix 4........................................................................................................................ 78 Appendix 5........................................................................................................................ 80 Appendix 6........................................................................................................................ 81 Appendix 7........................................................................................................................ 82 Appendix 8........................................................................................................................ 82 Appendix 9........................................................................................................................ 83 Appendix 10...................................................................................................................... 84 Appendix 11...................................................................................................................... 86List of Tables Table 2.1 Comparison of fit statistics for LCA models built with 1 to 10 classes for people affected by HCV in British Columbia between 1990-2018...........................................................21 Table 2.2 Patient characteristics within the six patient profiles by sociodemographic and clinical indicators........................................................................................................................................24 Table 2.3 Frequencies of DAA prescriptions by prescriber type and latent class........................26 Table 2.4 Average posterior probabilities of belonging to each class..........................................27 Table 2.5 Sensitivity analysis; patient characteristics within the five-class model......................28 Table 3.1 Characteristics of those included, excluded, and treated in the study population......................................................................................................................................40 Table 3.2 Hypothesis tests for final model Part 1 MLR...............................................................41 Table 3.3 Frequency of treatment outcomes by latent class.........................................................43 Table 3.4 Patient characteristics within the six patient profiles by sociodemographic and clinical indicators, restricted to study population.......................................................................................47 x List of Figures Figure 1.1 Timeline of HCV in British Columbia..........................................................................3 Figure 1.2 Diagram Comparing LCA, PCA, and Factor Analysis...............................................12 Figure 2.1 Latent Class Analysis goodness of fit statistics...........................................................22 Figure 2.2 Distribution of posterior probabilities for the six-class model....................................22 Figure 2.3 Characteristics of six latent classes of people living with HCV in BC.......................23 Figure 3.1 Inclusion flowchart for logistic regression analyses ...................................................37 Figure 3.2 Part 1; adjusted odds of treatment uptake among indicator variables.........................42 Figure 3.3 Part 2; adjusted odds of treatment uptake of interferon and DAA treatments among latent classes compared to Younger PWID....................................................................................44 Figure 3.4 Part 2 Sensitivity analysis including unknown RNA; adjusted odds of treatment uptake of interferon and DAA treatments among latent classes compared to Younger PWID.....45 xi List of Abbreviations AIC Akaike Information Criterion AOR Adjusted odds ratio BB Baby boomer (born 1945-1964) BC British Columbia BCCDC British Columbia Centre for Disease Control BC-HTC British Columbia Hepatitis Testers Cohort BIC Bayesian Information Criterion CI Confidence interval DAA Direct-acting antiviral DAD Discharge abstract database D.F. Degrees of freedom DIN/PIN Drug identification number/product identification number gbMSM Gay/bisexual men who have sex with men HBV Hepatitis B virus HCC Hepatocellular carcinoma HCV Hepatitis C virus HIV/AIDS Human immunodeficiency virus/acquired immunodeficiency syndrome ICD International Statistical Classification of Diseases and Related Health Problems IDU Injection Drug Use LCA Latent class analysis LR Likelihood ratio mCAR Missing Completely at Random MSP Medical services plan OAT Opioid agonist therapy OR Odds ratio PHN Personal Health Number P.P. Posterior probability PrEP Pre-Exposure Prophylaxis PWID People who inject drugs xii RNA Ribonucleic acid SD Standard deviation SDBB Socially deprived baby boomer SVR Sustained virologic response U.S. United States xiii Acknowledgements First, I would like to thank my graduate supervisor, Dr. Naveed Janjua for providing me with the opportunity to work with the BCHTC team. Without his expertise and the resources at the BCCDC, this study would not have been possible. In addition, I am grateful for all the support I have received from the BC-HTC team. I would also like to thank Drs. Jane Buxton and Michael Otterstatter for their helpful insights, feedback, and encouragement throughout this process. Thank you to all the wonderful people who have supported me throughout this journey. I would like to thank my family for their support and patience. I would also like to thank all the faculty, staff, and students I have met at the School of Population and Public Health and in British Columbia who have enriched my learning experience. xiv Dedication To my parents for their boundless support1 1 Introduction and Review of Literature 1.1 Background 1.1.1 What is hepatitis C virus (HCV)? Hepatitis C virus (HCV) is a global public health concern that affects more than 71.1 million people around the world.(1) It is a bloodborne pathogen that affects the liver and is spread via contact with infected human blood and other body fluids that are visibly contaminated with blood. Of those infected, up to 25% spontaneously clear their infection, and the remainder becoming chronically infected.(2) Chronic HCV is one of the leading causes of chronic liver disease, fibrosis, cirrhosis, and hepatocellular carcinoma. Liver fibrosis or liver scarring is the formation of excess connective tissue in the liver, which usually occurs in response to injury. Fibrosis is classified into stages 0 to 4 on liver biopsy using the METAVIR scoring system: (F0—no fibrosis, F1—portal fibrosis, F2—periportal fibrosis, F3—bridging fibrosis, F4—cirrhosis). Fibrosis is classified into F0-F4.(3) Fibrosis leads to cirrhosis, or liver malfunction, which may culminate in liver cancer or hepatocellular carcinoma. Each year, chronic HCV accounts for 399,000 deaths globally. In 2016, the 69th World Health Assembly passed a resolution to eliminate hepatitis infection by 2030 as a public health threat. Reaching this target will require diagnosis of 90% of people living with HCV and treating 80% of these people, along with radically reducing new infections.(4) In order to achieve these goals, countries like Canada need to develop targeted policies to combat the spread of HCV infection.(1) Based on 2018/2019 treatment uptake data, Canada is on track to reach HCV elimination by 2030, but it is important to sustain this level of treatment in order maintain this trajectory.(5) There is a relatively large burden of disease in Canada with recent estimates suggesting that between 136,000–246,000 Canadians are living with HCV.(1) Only about 1 in 4 people have symptoms when first infected; most feel well and are unaware of the disease. Because the liver can continue to function for a long time after it is damaged, many do not have severe adverse effects like cirrhosis until 10 to 30 years after infection.(6) In 2011, an estimated 44% of Canadians living with chronic HCV were unaware of their status.(7) In all of Canada, BC has 2 among the highest rates of HCV infection at 44.9 cases per 100,000 in 2014.(8) From 1990 to 2018, 53,441 people diagnosed with HCV were living in BC.(9) Yet, only 54.4% of those eligible for treatment (alive and confirmatory tested as HCV RNA positive) have been treated.(9) In BC, an annual average of 3,900 people need to be treated between 2020 and 2030 in order to reach elimination by 2030.(5) There are various genotypes of HCV, which may help determine what treatment regimen is prescribed, though the advent of pangeotypic treatments have greatly simplified treatment. In BC genotype 1 accounts for 61.4% of all HCV infections. Genotype 3 has a prevalence of 24.3%, followed by 2 has prevalence of about 13%. Only 1.1% had genotypes 4, 5, or 6.(10) 1.1.2 Transmission of HCV and syndemic risk groups Parenteral or injection transmission remains the predominant route of HCV transmission and PWID represent the largest risk group in developed countries. PWID accounted for 83% of all incident infections in Canada in 2007.(11) The prevalence of HCV infection in PWID is around 53% in Canada.(13) Yet, some research has found up to a 70% prevalence.(14) In BC, there were an estimated 41,358 people who had injected drugs between 2013 and 2015, representing 1.2% of BC’s population.(15) Thus, PWID are the main risk group for HCV transmission in BC. HCV was discovered in 1989 and was linked to what was previously known as “non-A, non-B hepatitis”. In 1990, the first HCV blood screening test was developed, and Héma-Québec and Canadian Blood Services began to screen blood donations for the virus.(16) By 1992, HCV was effectively eliminated from Canadian blood supplies via a more sensitive screening test. Before this time, there was a high risk of becoming infected with HCV through blood transfusions or organ transplantations, which resulted in many baby boomers and older people becoming infected (Figure 1.1).(17,18) Despite these increased screening and safety protocols that largely reduced nosocomial transmission, it still occurs today through organ donations although risk is extremely low.(19) Moreover, nosocomial transmission of HCV in dialysis units continues to be a health concern globally due to poor adherence to infection control procedures. There has been evidence of novel HCV infections for dialysis patients who received dialysis from the same machine as someone living with HCV.(20) Other nosocomial transmissions include multi-dose vials where there is an increased risk of contamination of the vial and errors in the administration 3 of substances by health care workers.(21) Needle stick injury also presents an occupational hazard for healthcare workers after exposure to HCV-infected blood. (22) HCV can also be transmitted vertically, from mother to baby in about 5% of births.(23) Chronically infected HCV mothers have a 1 in 20 chance (higher if HIVco-infected) of transmitting infection to their babies, constituting the primary transmission route among infants and children.(24) Figure 1.1: Timeline of HCV in British Columbia [adapted from an infographic by Dr. Sofia Bartlett, 2019 of the BC Hepatitis Testers Cohort with information from CATIE]. (16) 1989199019922002Hep C discoveredHepatitis C virus (HCV) is discovered and quickly established as the major cause of what was previously known as "non-A, non-B hepatitis".HCV screening test is developed. Blood banks across Canada begin screening blood donations.First hep C test developedA more sensitive screening test is developed and used to screen blood donations for HCV, effectively eliminating transmission through the blood supply in Canada. More sensitive hep C screening testHealth Canada approves the first combination of alpha interferon and ribavirin for the treatment of HCV. This treatment involves weekly injections with a poor cure rate.Health Canada approves pegylated interferon for HCV treatmentCombining pegylated interferon with ribavirin increases the cure rate to 60%, however side effects can be debilitating and treatment lasts up to 48 weeks.1999First hep C treatment approved by Health CanadaThe U.S. Centers for Disease Control and Prevention recommends that all Baby Boomers (born 1945-1965) get tested for HCV.All 'Baby Boomers' recommended for hep C testing2012Simeprevir, the 1st of a new generation of oral HCV medications known as Direct Acting Antivirals (DAAs) is available in BC, but only people with Fibrosis Stage F2 or above are eligible to receive it. DAAs are listed by PharmaCare as limited coverage drugs, which can only be accessed through a Special Authority request,BC PharmaCare approves 1st 'DAA' 2014 BC PharmaCare approves 2 new DAAsDAAs sofosbuvir and ledipasvir/sofosbuvir, which improve HCV treatment cure rates to above 90%, are approved by B.C. PharmaCare, but only people with Fibrosis Stage F2 or above are eligible to receive these treatments.2015201669th World Health Assembly unanimously adopted the Global Health Sector Strategy on Viral Hepatitis, signalling the greatest global commitment on viral hepatitis ever. Canada’s Minister of Health reaffirms the commitment to end hepatitis by 2030.WHO releases global viral hep elimination targets 2018 Hep C treatment eligibility restrictions lifted in BC for allAll people living with hep C in BC are eligible for treatment with DAAs.4 Immigrants and refugees (collectively referred to here as migrants) also have a higher burden of HCV infection mainly acquired through unsafe medical practices in their countries of origin.(25) In a systematic review by Greenaway et al. (2015), it was demonstrated that immigrants from Asia, Sub-Saharan Africa, Egypt, and Eastern Europe had a higher risk of HCV prevalence. Also, the majority of migrants that come to Canada (≥70%) are from areas with intermediate and high HCV seroprevalence, and they were estimated to have accounted for 21% (n=51,560) of all HCV infections in Canada in 2007.(11,26) Despite this, Canada does not conduct HCV (or even hepatitis B [HBV]) screening of migrants who were born in endemic areas.(26) Furthermore, migrants generally lack traditional HCV risk behaviors as they were most likely infected through contaminated blood products, unsterile medicinal procedures or medical injections in their countries of origin. Therefore, many migrants are not screened for HCV and fall through the cracks of the health care system. Because they remain unidentified by the system, they are often left untreated. Moreover, migrants may need to overcome additional barriers such as lack of knowledge of where to get tested for HCV, lack of primary care physicians, or language barriers among others. They may also face providers who are reluctant to test for chronic conditions in patients that they may never see again.(25) Thus, it is important to promote HCV services or programs that are targeted towards this risk group. Finally, sexual transmission of HCV has been well documented, especially amongst HIV-positive gay, bisexual, and other men who have sex with men (gbMSM). In fact, a 20-year prospective longitudinal cohort study by Van de Laar et al. (2007) found that the incidence of HCV among HIV-positive MSM was increasing with strong evidence of the presence of gbMSM-specific sexual transmission networks.(27) More recently, Lockart et al. (2019) confirmed these findings despite the increased uptake of DAA therapy. They also found that even HIV-negative gbMSM had an equally high overall incidence of HCV as HIV-positive gbMSM if they accessed pre-exposure prophylaxis (PrEP). These studies support the hypothesis that behavioral factors such as high-risk sexual behaviors and sexualized drug use are driving HCV epidemics amongst this sub-population.(28) Also, Ingiliz et al. (2017) showed that reinfection was a particular challenge among gbMSM living with HIV, with a reinfection rate of 25% after 2 years of follow-up.(29) They concluded that specific intervention strategies should be implemented to prevent HCV incidence and reinfection in this particular high-risk group and regular screening and testing for HCV should be done. 5 In conclusion, the source of HCV infection or route of transmission varies greatly depending on the time of HCV acquisition and the syndemic, socio-demographic or clinical characteristics of the patient. A syndemic is the combination of co-occurring diseases and the sociological or large-scale social forces that enable them.(30) 1.1.3 Timeline of HCV treatment The first HCV treatment was approved by Health Canada in 1999 (Figure 1.1).(16) This treatment consisted of a combination of alpha interferon and ribavirin and involved weekly injections.(16) The interferon combination therapy had a sustained virological response (SVR) or cure rate of around 40-60%.(31),(10) SVR was typically measured around 6-12 months (length of time depended on HCV genotype) after the completion of treatment, which is deemed successful if HCV RNA is undetectable.(32) While 25% of those with acute HCV spontaneously clear the infection within six months of exposure, the remainder lives with chronic HCV infection if left untreated.(33)Three years later, in 2002, pegylated interferon combined with ribavirin became a viable treatment option that was publicly funded in BC for patients with advanced liver fibrosis.(34) Unfortunately, the pegylated interferon treatments still consisted of regular injections and had long treatment durations around 48 weeks.(35) These therapies also had unpleasant side effects such as flu-like symptoms (nausea, diarrhea, weight loss), fatigue associated with hematologic abnormalities such as anemia, and depression and irritability.(36) Furthermore, these treatments were generally only available to people with liver damage (as documented by abnormal liver function tests) and were largely inaccessible to certain groups of people who were deemed less likely to follow through with the treatment regimen or more likely to become re-infected.(37) Near the end of 2013, new HCV medications known as Direct Acting Antivirals (DAAs) were approved for use in adults by Health Canada.(37) DAAs were a revolutionary treatment solution because they achieved high SVR rates of >95% (38) and required shorter treatment duration of around 12 weeks with fewer side effects.(39) Health Canada defines DAAs as containing simeprevir, sofosburvir, ledipasvir, ombitasvir/paritaprevir/ritonavir, daclatasvir, velpatasvir, voxilaprevir, grazoprevir, elbasvir, glecaprevir, and pibrentasvir.(40) These drugs have offered the potential for population-level elimination.(41) They are taken once a day, orally, over the 6 course of 8 to 24 weeks based on HCV genotype. Their simplicity of administration and effectiveness should help overcome patient, provider, and other barriers towards treating HCV. PharmaCare is part of the public insurance for BC residents that reimburses the costs of authorized prescription drugs, pharmacy services, and medical supplies.(42) A patient’s coverage is based on the details of their PharmaCare plan, and they must have their physicians submit Special Authority requests in order to be approved for coverage for HCV treatments.(43) On March 23, 2015, PharmaCare began public coverage for DAAs (sofosbuvir and ledipasvir-sofosbuvir) under the limited coverage drug program.(16) However, they were only approved for people with advanced liver disease (i.e. liver fibrosis stage F2, an early stage of liver scarring, or greater as detected by fibroscan) and re-treatment requests were not permitted.(44) Recently, the pan-Canadian Pharmaceutical Alliance has formed a new pricing agreement with pharmaceutical companies so that DAAs are more affordable for public health plans in Canada.(45) Beginning in 2018, PharmaCare provides coverage for any British Columbian living with chronic hepatitis C, regardless of the type or severity of their disease (Figure 1.1).(45) 1.1.4 Treatment access and outcomes Populations that have traditionally experienced more difficulty accessing these therapies include marginalized populations, i.e. people who inject drugs (PWID), Indigenous people, people experiencing homelessness or unstable housing, and people living with HIV. Poor access includes issues related to the 5 A’s, 1) Approachability; 2) Acceptability; 3) Availability and accommodation; 4) Affordability; 5) Appropriateness.(46) HCV has been known to disproportionately affect PWID, and even though they accounted for the majority of all new infections, this group had the lowest uptake of treatment.(47) During the interferon era, when drug treatment courses were long, not well tolerated, and patients needed considerable support, many PWID did not uptake treatment despite evidence of comparable effectiveness and adherence to non-PWID.(48) There are some jurisdictions in the United States (U.S.) and Europe with restrictions for the reimbursement of HCV therapy for people with recent illicit drug or alcohol use or who are receiving opioid agonist therapy (OAT) irrespective of liver disease stage.(49,50) Mehta et al. (2008) showed that healthcare providers were less likely to discuss treatment options with PWID, and only 19% of PWID had any discussion of HCV 7 treatment with a provider.(51) Yet, it was not clear whether injection drug use itself impeded HCV treatment referral or if it was a marker for other, associated factors like unstable lifestyle.(51) To make matters worse, because re-treatment requests are not accepted in some countries, it is likely that PWID who continue to inject drugs have a high chance of re-infection and a low chance of re-treatment. Services that target PWID and address their needs are critical. Lazarus et al. (2018) demonstrated that there are important differences within the population of PWID, warranting a public health strategy that implements different types of service delivery models for PWID. For example, some PWID are younger while others are older, some are incarcerated and others are in the community, and some are receiving OAT and others are not. Addressing these differences within subpopulations is important in order to deliver the most appropriate, individualized care.(25) Micro-elimination of HCV is a strategy to target efforts to specific segments of the population or syndemic groups to incrementally achieve national elimination. These approaches target populations that are at a higher risk of transmitting HCV and could allow for “treatment as prevention”.(52) Modeling studies of HCV transmission have suggested that implementing these strategies on a large-scale could halt the spread of HCV, especially among PWID.(53,54) Lower rates of re-infection that have been observed to date may assure providers who are still reluctant to provide HCV treatment to PWID.(55) With the introduction of DAAs, HCV treatment referral became more accessible to marginalized groups such as PWID as adherence to these treatments was more feasible due to fewer side-effects and oral administration. Also, treatment initiation rates increased more than threefold between 2013 and 2015.(9) Grebley et al. (2018) found an equally satisfactory response rate in PWID compared to non-PWID to DAAs (sofosbuvir and velpatasvir).(56) Petersen et al. (2016) found that illicit drug use was only a risk factor for non-adherence in longer, 12-week treatment courses, but there was no significant difference in treatment adherence between people who use drugs and those who do not for shorter, 6-week long treatment courses.(57) However, real-world studies have shown lower SVR among PWID mainly related to loss to follow-up.(58–60) Furthermore, Nitulescu et al. (2019) demonstrated a high SVR among those receiving antiretrovirals (for HIV) and active PWID, equal to that of people who do not use drugs.(61),(62) 8 Janjua et al. (2017) showed that HIV-HCV co-infected individuals were more likely to initiate DAA treatments than interferon therapies.(37) Another patient characteristic that affects treatment access and outcomes is ethnicity. Many studies have tried to understand the role that it plays in determining HCV outcomes. A U.S. study by Muir et al. (2010) found poorer efficacy of HCV treatment among African American and Hispanic minority groups compared to whites and Asian Americans. While the number of Asian patients was small (n=51), the SVR rate for this minority group was the highest at 59%, and the second highest SVR rate was for whites at 44%.(63) A study by Hepburn et al. (2004) represented similar results with the highest SVR rate among Asians (61%) followed by whites (39%), Hispanics (23%), and African Americans (14%).(64) Dev et al. (2002) found 59% SVR rates among Southeast Asians compared to 34% among people of white ethnicity.(65) Thus, these studies support the concept that race and ethnicity are important determinants of treatment outcome in people living with HCV during the interferon era. However, a U.S. randomized control trial (RCT) performed with DAA’s showed no difference in SVR rates between black and non-black ethnicities.(66) Age is also an important factor when considering HCV treatment outcomes. During the interferon era, many studies documented lower SVR rates among older people compared to younger who have initiated HCV treatments. In an RCT by Poynard et al. (2000), people older than 40 had lower SVR rates than people younger than 40 for interferon based treatments. (67) Antonucci et al. (2007) also found that age over 40 years was an independent predictor of reduced SVR.(68) However, Luetkemeyert et al. (2016) and Ippolito et al. (2017) examined SVR rates in the DAA era with sample sizes of n=151 and n=2612 respectively and found insignificant differences between people older or younger than 50 years.(66),(69) Patients’ co-morbidities and chronic conditions also determine HCV treatment outcomes. HCV is a main cause of cirrhosis and liver cancer or hepatocellular carcinoma.(70) Liver transplant used to be the only HCV treatment option for those with decompensated cirrhosis, but now DAAs are approved to effectively treat these patients for HCV.(71) In a recent study by Kwo et al. (2019), history of liver transplant and cirrhosis were both positive indicators of treatment uptake.(72) People with more severe liver disease were more likely to uptake or be eligible for 9 treatment as international guidelines in 2014 recommend treatment prioritization for moderate to severe liver disease stages due to the individual’s increased risk of liver disease progression.(73) Non-liver related chronic conditions have also been shown to be related to HCV treatment. For example, diabetes and obesity are related to worse outcomes and advanced liver disease for people living with HCV.(74) In conclusion, traditional restrictions against insurance reimbursement based on liver fibrosis level that prevented many individuals from accessing treatment were lifted in BC in 2018. Now that there are no restrictions to reimbursement for HCV treatment, related to substance use, HIV therapy, or other related behaviours, there should be fewer barriers preventing people from receiving treatment. However, it is important to note that many barriers continue to exist. For example, many providers are not-authorized to prescribe HCV treatment as PharmaCare’s process for receiving the qualification to prescribe is unclear. Many people affected by HCV must still be referred to a liver specialist to initiate treatment. 1.1.5 Importance of providers With providers being the primary enablers of treatment access, studying the factors related to their prescription of treatments is pinnacle. Judgements about who should be eligible to receive treatment, given the high cost/difficulty of retreatment to the health care system, may influence treatment decisions.(75) However, in more recent years, since the introduction of DAAs in 2015, many more people living with HCV have access to treatment (Figure 1.1). Wagner et al. (2012) showed that providers’ decisions to recommend HCV treatment are influenced by patient factors including race and psychosocial treatment readiness, in addition to providers’ treatment philosophy and characteristics of their practice.(76) They found that patients with HIV who were offered treatment were more likely to have more robust immune systems (CD4 counts above 200 cells/mm3) and lower HIV viral loads, greater HCV knowledge, be receiving depression treatment if depressed, and not being Black or Hispanic. The providers who offered treatment to more patients were more likely to be female, have worked at the practice longer, saw fewer patients on a weekly basis, and have lower thresholds for indicators of patient readiness for treatment. Furthermore, provider decisions to offer treatment were not correlated to measures of HCV disease including HCV genotype and HCV RNA. Taylor et al. (2002) and Fultz et al. 10 (2003) have also demonstrated that patient’s mental health, substance use, and adherence to clinical appointments and antiretroviral therapy were associated with HCV treatment eligibly.(77),(78) Also, a meta-analysis by McGowan et al. (2012) has showed that providers’ preconceived concerns regarding poor patient adherence and patient risk of reinfection has led them to not prescribe treatment.(79) In British Columbia, “patients who are at high risk for noncompliance,” at the physician’s discretion, were ineligible for treatment in 2016.(80) Therefore, providers’ perception of patient readiness plays a large role in their decision to offer HCV treatment. Moreover, referral to a specialist is often necessary to initiate treatment. According to BC PharmaCare in 2018, “hepatologists, gastroenterologists, infectious disease specialists, and other physicians experienced with treating [chronic Hepatitis C] can apply for coverage of the new direct-acting antiviral (DAA) on behalf of their adult patients with [chronic Hepatitis C] infection.” (81) However, the statement “physicians experienced with treating chronic hepatitis C” is not well defined and may have prevented many providers from gaining the ability to prescribe HCV treatment. There is no clear educational process for becoming an eligible prescriber, which may have barred many non-liver specialists from helping treat HCV infection in their own patients. This barrier has also prevented some specialists from prescribing, as Chan et al. (2018) found that most non-prescribing infectious disease physicians in Canada do not feel sufficiently trained to prescribe DAAs, especially to patients with illicit substance use issues.(82) Thus, many non-liver specialists, such as general practitioners and family doctors, must refer their patients to a specialist prescriber, such as gastroenterologists or hepatologists, who are often concentrated in urban centres. In fact, Marshall et al. (2016) found that prescriber type was restricted to specialists in up to 42% of all Canadian provinces/territories.(80) This extra step in receiving treatment may present an excessive and insurmountable barrier for many patients, which causes them to drop out of the care-cascade. Allowing more general practitioners to prescribe, including addiction medicine physicians, who have long-term relationships with and are proximally located to these patients could allow for HCV elimination. 11 1.1.6 Latent Class Analysis for program planning The population of people living with HCV in BC is extremely diverse, with variable modes of infection acquisition, distinct co-morbidities, access to prescribers, and needs.(83) Therefore, a “one-size-fits-all” approach to eliminating HCV is inappropriate. By dividing the BC population into subgroups, we can more effectively target groups of individuals who share various needs through more tailored program planning efforts like micro-elimination projects.(25) Latent Class Analysis (LCA) is a statistical clustering technique that identifies unobserved subgroups in a population by a set of selected indicators.(84) It allows the population to be segmented into a number of exclusive and exhaustive subsets called latent classes. Because LCA is based upon a statistical model, maximum likelihood estimates can be implemented to classify individuals into their appropriate subgroup or class based on posterior probability estimates (i.e. the likelihood that they belong to that assigned class).(85) Also, empirical fit statistics like BIC, AIC, and G-squared are provided to help determine the appropriate number of latent classes. LCA offers many advantages over standard clustering methods (e.g. k-means cluster analysis), principal component analysis (PCA), and factor analysis. First, LCA is a “finite mixture model”, meaning that it derives its clusters using a probabilistic model that describes the distribution of the particular dataset.(86) Instead of determining clusters using an arbitrary distance measure (nearest distance), LCA first describes the distribution of the data and then clusters based on the probability of class membership. In other words, standard cluster analysis involves grouping individuals by looking at all possible pairs of individuals and linking those with the smallest distance, which often requires a smaller dataset or a random sample of a large dataset. LCA, on the other hand, can handle large datasets. Moreover, LCA handles missing data in a logical way by grouping based on available data, using a missing at random assumption. Also, it is a non-parametric test which does not assume linearity assumptions or a normal distribution in the data set.(87) The main difference between principal component analysis (PCA) and LCA is that PCA involves the reduction of multiple variables into one “component” variable that cannot be tested for accuracy (Figure 1.2). LCA on the other hand, implements indicators to group individuals and create a new “latent” variable. Additionally, PCA requires many more assumptions such as linearity, while LCA mainly requires an independence assumption. However, these two methods have a high level of agreement and consistency.(88) Moreover, as opposed to factor analysis, 12 LCA groups similar people together whereas factor analysis groups variables along a continuum. In other words, factor analysis is concerned with the structure of variables and their correlations whereas LCA is more concerned with the structure of individuals. Furthermore, factor analysis works with continuous variables whereas LCA uses categorical.(89) In conclusion, LCA is more “person-centered” vs. “variable-centered” because it doesn’t rely on arbitrary cut-offs for classification.(84) Using LCA to create syndemic groups of people affected by HCV and exploring differences in treatment uptake by population subgroups will allow for a greater understanding of which healthcare gaps exist in BC, with the goal of promoting more timely screening and treatment access. Thus far, there have been no population-level studies that have used LCA to explore the differences between population subgroups and HCV treatment prescribing behavior. Figure 1.2: Diagram Comparing LCA, PCA, and Factor Analysis. (90,91) 13 1.2 Research Objectives In keeping with the World Health Organization’s (WHO) mandate to eliminate HCV, I aim to provide real-world evidence on treatment uptake among multi-faceted patient profiles developed by LCA to contribute to this goal. There is currently a lack of real-world evidence on the relationship between patient subgroups and treatment uptake. This analysis aims to use data from the British Columbia Hepatitis Testers Cohort (BC-HTC) to describe the patient and provider level characteristics associated with HCV treatment initiation in order to identify potential strategies to further enhance HCV treatment uptake. Unveiling any structural disparities in the British Columbian health care system that may be disabling HCV treatment access for marginalized groups may also assist in improving their access to health care overall. 1.2.1 Profiles of different types of HCV patients 1. To develop multifaceted patient profiles based on their sociodemographic and clinical characteristics. 1.2.2 Relationship between patient characteristics and treatment uptake 1. To understand variables associated with treatment uptake. 2. To assess the relationship between multifaceted patient profiles and treatment uptake. 1.3 Thesis Outline This thesis is structured in four chapters. This first chapter provided a review of the literature and an overview of HCV in British Columbia. It covered the characteristics of the affected population, behaviors of treatment prescribers, current policies and regulations surrounding its treatment, and delivers an outline of the thesis. The second chapter lays out the multi-faceted patient profiles that were determined through latent class analysis. The third chapter explores the relationship between the patient profiles and treatment uptake through a multivariable logistic regression. The fourth chapter and final chapter summarizes the findings from this thesis, and further discusses the implications of the results. 14 1.4 Disclaimer Statement All inferences, opinions, and conclusions drawn in this thesis are those of the author and do not reflect the opinions or policies of the Data Steward(s). 15 2 Syndemic profiles of people living with HCV using Latent Class Analysis 2.1 Introduction As discussed in Chapter 1, HCV affects diverse and often overlapping populations, such as people who inject drugs (PWID), 'baby boomers', gbMSM, and immigrants from endemic regions. Chapter 2 will assess patterns of shared socio-demographic and clinical characteristics among subpopulations in BC in order to inform more targeted program planning of HCV services as well as decision making at the point of care. HCV has been demonstrated to act synergistically with other chronic infections such as HIV and hepatitis B virus (HBV). Many marginalized groups, such as people who inject drugs (PWID), people experiencing unstable housing, and people living with comorbidities have higher risk of HCV infection and have faced the greatest barriers in treatment access.(92) A syndemic is the combination of co-occurring diseases and the large-scale social forces that enable them.(93) Epidemics can co-exist in particular physical settings due to detrimental social conditions or risk environments. For instance, the risk of acquiring HCV is greater among people living with HIV who inject drugs and have mental illness (than people without these sub-characterizations).(83) Currently, a major limitation of the application of syndemic theory is a focus on individuals or discrete variables rather than populations or combinations of factors.(83,94) Measuring isolated variables against an outcome does not capture the social environment that may enhance risks or strengthen associations.(95) Chapter 2 seeks to address this drawback by quantifying the burden and distribution of the syndemics of deprivation, substance use, and infectious diseases at the population-level.(30) Latent Class Analysis (LCA), a multivariate segmentation analysis, can quantify the composite effect of multiple variables, thus allowing for the scientific characterization and quantification of syndemics and their associated outcomes. LCA identifies an unobserved, latent variable that can reveal important distinctions between subgroups of the population.(89) In Chapter 2, we conduct a population-level LCA with socio-demographic and clinical indicators to determine distinct subgroups of people diagnosed with HCV using the BC Hepatitis Testers 16 Cohort (BC-HTC).(10) We hypothesized that subgroups would have important differences in HCV infection acquisition and involvement in the HCV care cascade. Then, we describe the make-up of these classes to inform tailored program planning efforts that reduce care-cascade gaps and increase service delivery efficiency. 2.1.1 Measures of LCA As discussed in Chapter 1, LCA is a statistical method for classifying groups based on an unobserved or unmeasured ‘latent’ variable that better explains differences between people than the observed or measured variables. Based on LCA fit statistics, a population is divided into a certain number of subgroups or latent classes.(84) These ‘latent classes’ can then be studied to reveal “hidden” subgroups and characterize the heterogeneity in a population. LCA may be able to account for some of the uncertainty in using linked administrative data. By measuring an unobserved variable, LCA may better explain differences among groups of people than variables derived using administrative data algorithms. For example, variables for deprivation, sexual orientation, injection drug use (IDU), or alcohol misuse that are not directly measured and are instead derived from administrative codes may be better accounted for by LCA. 2.1.2 Past LCA studies Previous studies have used LCA to identify symptom clusters or patterns of drug use/polydrug use (95,96) the phylogenetic clustering of HCV (97,98) risky sexual behaviors (99), patterns of HCV service provision in the U.S.(100), and people living with HCV in Switzerland (101) among other types of multi-faceted profiles on cohorts with small sample sizes. I searched Google Scholar and PubMed for journal articles published with terms “latent class analysis”, “hepatitis C”, and “Canada” since 2015 until December 15, 2019, and there appeared to be no published studies that use a population-level cohort to examine HCV patient profiles in Canada. There have been several studies that have described syndemics of HIV, HCV, HBV for specific population groups and among a general population.(83,94) There has been no use of LCA on an entire population of HCV diagnosed individuals, and no other study has conducted a syndemic analysis by as many combinations of characteristics. 17 2.1.3 Contributions of this study To the best of my knowledge, this is the first study using data from one of the largest population-based cohort studies in the world, including all HCV diagnosed individuals within a province, to characterize classes of people with shared characteristics.(102) This study is also the first to quantify the syndemic diseases and conditions associated with HCV infection at a population-level. 2.2 Methods 2.2.1 Study context British Columbia is a province of Canada with 5 million inhabitants.(103) Under the Medicare Protection Act, enrolment with the publicly funded Medical Services Plan that acts as a single-payer system is mandatory for all eligible residents and their dependents.(104) It covers medically necessary services provided by fee for service practitioners including general practices and private laboratories. 2.2.2 Data sources Data were used from the BC-HTC at the BC Centre for Disease Control (BCCDC), which is a dynamic cohort including all individuals (~1.7 million) who ever tested for HCV or HIV, or diagnosed with HBV, HCV, HIV or active tuberculosis in BC between 1990 and 2015, linked with data through personal health numbers (PHN) on medical visits, hospitalizations, emergency department visits, cancers, prescription drugs, and deaths. Provider information was obtained by determining the initiating provider in patients’ first treatment course. Further information about the BC-HTC, including methodology and data linkages has previously been published.(10,105) Data linkage to establish the BC-HTC was performed with support from the BCCDC's public health mandate. This study was reviewed and approved by the Behavioral Research Ethics Board at the University of British Columbia (H14-01649). 18 2.2.3 Analytic sample The analytic sample included all individuals in the BC-HTC diagnosed with HCV, i.e. with at least one positive HCV test (antibody, HCV RNA, or genotype), dispensation of at least one HCV specific treatment, or those reported as an HCV case to a public health authority (Appendix 1). The entire study period in the cohort data set was considered, from 1990-2015, with information about prescriptions and vital statistics followed up to 2018. Time-dependent variables were anchored at death date or on the end of the study period (12/31/15) in order to allow for a future logistic regression analysis of the relationship between latent classes and treatment uptake in Chapter 3 (Appendix 2, 3). 2.2.4 LCA Variables LCA was used to examine the structure of underlying patterns of socioeconomic and clinical characteristics of people with a positive HCV diagnosis (including patients who died or spontaneously cleared) to understand potential variations in the population of those affected by HCV. LCA models were fitted to the joint distribution of the indicators that were previously found to have epidemiologically or statistically relevant relationships with HCV-related outcomes.(37,61,77,106–109) LCA assumes that variables with missing data are missing completely at random and are handled with a full-information maximum likelihood technique.(110) Information on how variables were assessed and/or defined is available in Appendix 1-3. Variables that were used to build the LCA model were determined to be: 1. Birth cohort (<1945, 1945-1964, 1965-1974, >1974) 2. Sex (male, female) 3. Ethnicity (East Asian, South Asian, other BC residents [which is comprised of white ethnicity and other], and unknown) 4. Coinfections: HIV, HBV, TB 5. Sexual orientation (gbMSM, heterosexual) 19 6. Urbanicity (urban vs. rural residence) 7. Social and material deprivation (1-5, where 5 is most deprived) 8. History of injection drug use (IDU) (never vs. past vs. recent 2012-2015) 9. History of opioid agonist therapy (OAT) (never vs. past vs recent 2015-2018) 10. Alcohol misuse 11. Major mental health diagnosis 12. Liver disease (cirrhosis, hepatocellular carcinoma, previous liver transplant) 2.2.5 LCA model building Multiple models were estimated with varying numbers of classes in SAS 9.4©, using PROC LCA.(111),(112) This analysis was conducted in a step-wise manner, adding classes sequentially until the model was ‘overfit’, meaning the model contained more parameters than could be justified by the data. The fit of each model was assessed using various fit statistics (assuming sufficient sample size): Bayesian information criterion (BIC), Akaike information criterion (AIC), adjusted BIC (aBIC), adjusted AIC (aAIC), degrees of freedom (d.f.), log-likelihood ratio (LR), G-squared, posterior probabilities (p.p.), in addition to entropy and epidemiological meaningfulness of class structure. These fit statistics summarized how close the estimated expected frequencies of the model were to the observed frequencies. Lower LR statistics indicated that the model could account for the observed relationships in the data. The p.p. measured the proportion of participants in each model who had a >70% chance that they were assigned to the ‘correct’ group. We sought a model that was both simple, accounted for the data, and was substantively meaningful. No a priori assumptions about the number of latent classes and their comparative fit were made. The most parsimonious model (the one with the highest percentage of p.p. and smallest number of latent classes) that fit the data and still captured epidemiologically relevant distinctions among participants was chosen as the final model. An epidemiologically relevant name was given to each of the classes based on the percentage of the variables that most defined that group both within and across classes. Calculations were performed to determine these defining characteristics based on a percentage difference greater than 100% between one class’s most defining characteristic and the class with the second highest proportion of that variable. All analyses were descriptive. 20 2.3 Results 2.3.1 Characteristics of the subset of cohort data A total of 73,665 individuals were included in the LCA. The majority of study participants were baby boomers (n=43,342; 58.8%), male (n=47,861; 65.0%), other BC residents (n=67,257; 91.3%), heterosexual (n=42,694; 89.2%), never PWID (n=46,319; 62.9%), in the most deprived, 5th quintile for material and social deprivation (22,119 & 16,268; 39.1% & 28.7% respectively), were urban dwellers (n=49,928; 87.3%), had no liver-related chronic conditions (n=65,314; 88.7%), negative/unknown for HIV, HBV, and TB (93.3%, 92.3%, and 99.3% respectively), no major mental health illness (n=48,897; 66.4%), and no alcohol misuse (n=50,774; 68.9%). Values that were missing were related to area of residence (maximum 3.0% missing), i.e. variables that depended on postal codes (urban/rural flag, health authority, social/material deprivation) (Appendix 3). 2.3.2 LCA model After comparison of fit statistics, a model with six classes was found to be best fit (Table 2.1) (Figure 2.1, 2.2). However, as seen in Table 2.1 and Figure 2.1, the four through ten class models were fairly similar in terms of fit statistics. This relative similarity in fit statistics made it difficult to choose between models, as the theoretical basis for comparing these models is still an area very much under development. However, there were clearer differences in p.p. and entropy. The seven through ten class models have fit statistics that were not much lower than those of the six-class model, leading us to conclude that it was not worth adding the complexity of additional classes. 21 Table 2.1. Comparison of fit statistics for LCA models built with 1 to 10 classes for people affected by HCV in British Columbia between 1990-2018. [mCAR G-squared value 100757, d.f. 5598713] No. of classes d.f. G-squared log-LR AIC BIC cAIC aBIC mCAR-log-LR Entropy % with p.p. ≥70% 1 5183969 164305 -703607 164365 164641 164671 164545 -621454 1.00 100.0 2 5183938 104683 -673796 104805 105366 105427 105173 -621454 0.82 69.4 3 5183907 92770 -667839 92954 93801 93893 93509 -621454 0.75 86.7 4 5183876 85823 -664366 86069 87201 87324 86810 -621454 0.64 68.3 5 5183845 82151 -662530 82459 83877 84031 83388 -621454 0.71 77.0 6 5183814 74964 -658936 75334 77037 77222 76449 -621454 0.68 64.0 7 5183783 71648 -657279 72080 74069 74285 73383 -621454 0.68 61.3 8 5183752 69139 -656024 69633 71907 72154 71122 -621454 0.66 59.6 9 5183721 67948 -655428 68504 71063 71341 70180 -621454 0.65 54.6 10 5183690 66321 -654615 66939 69784 70093 68802 -621454 0.67 53.8 Abbreviations: Degrees of freedom (d.f.), likelihood ratio (LR), The Bayesian Information Criterion (BIC), Akaike Information Criterion (AIC), adjusted BIC (aBIC), adjusted AIC (aAIC), missing Completely At Random (mCAR). 22 Figure 2.1: Latent class analysis goodness of fit statistics Figure 2.2: Distribution of posterior probabilities for the six-class model 01000002000003000004000005000006000007000008000009000001 2 3 4 5 6 7 8 9 10Number of ClassescAICBICAICG-squaredlog-likelihoodposterior probability 23 Figure 2.3 Characteristics of the six latent classes of people living with hepatitis C in British Columbia Bar color Birth Cohort Ethnicity Urbanicity Social index Wealth index PWID OAT HIV gbMSM Liver disease Mental illness Alcohol misuse Treated Prescriber Type <1945 East Asian Urban 1- (most privileged) 1- (most privileged) Never Never Yes Yes Yes Yes Yes Yes Gastroenterology 1945-1964 South Asian Rural 2 2 Recent Recent No No No No No No Infectious Disease 1965-1974 other BC residents 3 3 Past Past General Practice >1974 unknown 4 4 Internal Medicine 5-(most deprived) 5-(most deprived) 24 Table 2.2: Patient characteristics within the six patient profiles by sociodemographic and clinical indicators Younger PWID Urban Socially Deprived Baby Boomers gbMSM Rural Baby Boomers People of Asian backgrounds Older PWID No. individuals, % 11563 15.7 12698 17.2 9019 12.2 20401 27.7 4718 6.4 15266 20.7 Sex male 6341 54.8 8625 67.9 6295 69.8 13733 67.3 2448 51.9 10419 68.25 Birth cohort <1945 0 0.0 3019 23.8 4 0.0 1826 9.0 1237 26.2 546 3.58 1945-1964 2360 20.4 9591 75.5 1864 20.7 16215 79.5 2023 42.9 11289 73.95 1965-1974 3697 32.0 88 0.7 4206 46.6 1907 9.4 635 13.5 3372 22.09 >1974 5506 47.6 0 0.0 2945 32.7 453 2.2 823 17.4 59 0.39 Ethnicity East Asian 67 0.6 152 1.2 123 1.4 8 0.0 2244 47.6 116 0.76 South Asian 199 1.7 14 0.1 91 1.0 22 0.1 2102 44.6 139 0.91 other BC residents 11178 96.7 12419 97.8 8636 95.8 20302 99.5 170 3.6 14953 98.0 unknown 197 1.7 161 1.3 258 2.9 97 0.5 307 6.5 111 0.7 HBV positive 834 7.2 786 6.2 248 2.8 729 3.6 519 11.0 2535 16.61 HIV positive 1155 10.0 515 4.1 650 7.2 69 0.3 23 0.5 2539 16.63 TB positive 60 0.5 98 0.8 4 0.0 11 0.1 61 1.3 321 2.1 gbMSM yes 671 10.6 1817 21.1 1636 26.0 168 1.2 314 12.8 473 4.54 Residence rural 798 6.9 113 0.9 269 3.3 6379 32.0 99 2.2 1164 7.65 PWID never 319 2.8 12698 100.0 8237 91.3 19704 96.6 4707 99.8 654 4.28 recent 7723 66.8 0 0.0 0 0.0 99 0.5 9 0.2 2645 17.33 past 3521 30.5 0 0.0 782 8.7 598 2.9 2 0.0 11967 78.39 OAT never 1616 14.0 12575 99.0 5470 60.7 19727 96.7 4580 97.1 9676 63.38 recent 8500 73.5 9 0.1 2051 22.7 265 1.3 59 1.3 864 5.66 past 1447 12.5 114 0.9 1498 16.6 409 2.0 79 1.7 4726 30.96 Alcohol misuse 5511 47.7 3362 26.5 367 4.1 3199 15.7 131 2.8 10321 67.61 Mental health diagnosis 7204 62.3 3553 28.0 1127 12.5 3097 15.2 352 7.5 9435 61.8 Liver disease 99 0.9 2745 21.6 0 0.0 2195 10.8 500 10.6 2812 18.42 Social deprivation 1 (privileged) 970 8.5 178 1.4 813 9.9 2874 14.5 2000 43.8 931 6.17 2 1244 10.9 284 2.3 823 10.0 3925 19.8 1020 22.3 1136 7.53 3 1202 10.6 233 1.9 741 9.0 5947 30.0 647 14.2 1701 11.28 4 2054 18.0 1569 12.7 1610 19.6 6692 33.8 554 12.1 3224 21.38 5 (deprived) 5918 52.0 10117 81.7 4238 51.5 372 1.9 349 7.6 8088 53.63 Material deprivation 1 (privileged) 1639 14.4 2197 17.7 1714 20.8 2483 12.5 395 8.6 1790 11.87 2 1331 11.7 1245 10.1 833 10.1 4993 25.2 633 13.9 2025 13.43 3 1665 14.6 1623 13.1 1035 12.6 5247 26.5 1015 22.2 2306 15.29 4 2452 21.5 2882 23.3 1864 22.7 4263 21.5 1283 28.1 3384 22.44 5 (deprived) 4301 37.8 4434 35.8 2779 33.8 2824 14.3 1244 27.2 5575 36.97 25 2.3.3 Patient profiles (latent classes) LCA identified six latent classes that were given epidemiologically relevant names based on their distribution of defining characteristics or variables (Table 2.2): “Younger PWID”, “Older PWID”, “Urban socially deprived baby boomers (Urban SDBB)”, “gbMSM”, “Rural baby boomers (Rural BB)”, and “People of Asian backgrounds” (names of classes are italicized throughout) (Figure 2.3). Younger PWID consisted mostly of people born after 1965 that had recent injection drug and OAT use, mental health diagnoses, some HIV/HBV coinfections, and were materially deprived. In comparison, Older PWID included mostly people born before 1964 with past injection drug and OAT use, alcohol misuse, mental health diagnoses, liver disease, material deprivation, and the highest proportion of coinfections across the six classes. Urban SDBB were urban baby boomers (born between 1945-1964) that never injected drugs or engaged in OAT, had liver disease, and were almost all socially deprived. The gbMSM class had the highest proportion gbMSM across the six classes, were younger, mostly male, third highest proportion of HIV infection, no liver disease, highest proportion of top quintile material privilege but were also mostly socially deprived. Rural BB class included many rural, heterosexual baby boomers with no previous history of IDU, and were both materially and socially privileged. People of Asian Backgrounds included people with East and South Asian background that lived in urban areas, were older, socially privileged, and had some liver disease but most had no history of IDU, alcohol misuse, or mental health diagnoses (Table 2.2). The top DAA prescriber differed by latent class. Gastroenterologists were the top prescribers for People of Asian Backgrounds (62.2% of all DAA prescriptions), Urban SDBB (44.1%), and Rural BB (41.8%). While general practitioners were the top prescribers for Younger PWID (35.0%) and infectious disease physicians were the top prescribers for Older PWID (29.9%) and gbMSM (32.7%) (Table 2.3). 26 Table 2.3 Frequencies of DAA prescriptions (up to end of 2018) by prescriber type and latent class. Younger PWID Urban SDBB gbMSM Rural BB People of Asian Background Older PWID Gastro-enterologist 282 (17%) 634 (44%) 272 (30%) 1268 (42%) 304 (62%) 378 (27%) Infectious Disease 499 (30%) 319 (22%) 301 (33%) 681 (22%) 79 (16%) 427 (30%) General Practitioner 577 (35%) 154 (11%) 157 (17%) 154 (5%) 24 (5%) 286 (20%) Internist 267 (16%) 314 (22%) 183 (20%) 884 (29%) 79 (16%) 312 (22%) Other specialty 26 (2%) 18 (1%) 9 (1%) 50 (2%) 3 (1%) 23 (2%) 2.3.4 Class membership fit statistics The median probability and standard deviation (SD) of class membership of the six classes are as follows: Younger PWID (93%, 0.17), Urban SDBB (70%, 0.16), gbMSM (73%, 0.19), Rural BB (74%, 0.18), People of Asian Backgrounds (95%, 0.15), and Older PWID (88%, 0.16) (Table 2.1). The group with the lowest median probability of belonging to their assigned class, Urban SDBB, had the second highest probability of belonging to gbMSM. In other words, there is a 30% chance that individuals assigned to the Urban SDBB class may have been better fit in gbMSM or another class (Table 2.4). There is a clear distributional split of gbMSM between gbMSM and Urban SDBB classes. 1,817 gbMSM are in Urban SDBB (21% of the class) and 1,636 are in gbMSM class (26% of the class). Urban SDBB had proportionally fewer gbMSM and contained fewer attributes that have been previously shown to be related to gbMSM populations in BC such as drug use and HIV co-infection.(113) Urban SDBB had 0% PWID, 1% OAT, and only 4% HIV coinfection while the gbMSM class had 9% PWID, and 39% OAT, with 7% also living with HIV. LCA seems to have split these groups according to age as well, where 99% Urban SDBB were born before 1964 and 80% gbMSM were born after 1964. The naming process is relatively subjective as there is no established method of naming in LCA. 27 Table 2.4 Average posterior probabilities of belonging to each latent class. [Values in red indicate highest average p.p.] class 1 2 3 4 5 6 1 Young PWID 0.846 0.000 0.040 0.006 0.000 0.107 2 Urban SDBB 0.002 0.731 0.120 0.094 0.014 0.039 3 gbMSM 0.046 0.057 0.719 0.114 0.027 0.038 4 Rural BB 0.004 0.131 0.083 0.737 0.023 0.023 5 People of Asian Background 0.002 0.037 0.047 0.027 0.884 0.003 6 Older PWID 0.103 0.008 0.036 0.029 0.000 0.823 2.3.5 Sensitivity analysis A sensitivity analysis was performed to ensure that a six-class model was more epidemiologically relevant than a five-class model; despite the fact that the five-class model had higher posterior probability average than the six-class model (77% compared to 64%) (Table 2.1). The five-class model produced two of the same subgroups as the six-class model, combined four of the six classes into two, and developed a novel class. The five classes were identified as Young PWID, Older PWID, a gbMSM & Urban SDBB class that I named ‘gbMSM’, a Rural BB & People of Asian Backgrounds class that I named ‘Older non-PWID’, and a new class of people who engaged in OAT ever, which I named ‘Ever OAT’. This Ever OAT class was mostly made up of males born after 1964, with no coinfections, no liver disease, little mental health or alcohol use issues, and who were recently or previously on OAT. While only half of this class indicated no previous IDU, 91% had engaged in recent or past OAT (Table 2.5). 28 Table 2.5 Sensitivity Analysis: Patient characteristics within the five-class model by sociodemographic and clinical indicators Younger PWID gbMSM OAT Ever Older non-PWID Older PWID Variable: number % number % number % number % number % Total individuals 9,114 12.4 12,861 17.5 7,717 10.5 28,292 38.4 15,681 21.3 sex male 4722 51.81 8814 68.53 5432 70.39 18090 63.94 10803 68.89 birth cohort <1945 0 0 2109 16.4 6 0.08 3977 14.06 540 3.44 1945-1964 1879 20.62 8321 64.7 2657 34.43 18508 65.42 11977 76.38 1965-1974 3063 33.61 1677 13.04 2486 32.21 3555 12.57 3124 19.92 >1974 4172 45.78 754 5.86 2568 33.28 2252 7.96 40 0.26 ethnicity East Asian 50 0.55 320 2.49 92 1.19 2156 7.62 92 0.59 South Asian 135 1.48 11 0.09 213 2.76 2096 7.41 112 0.71 Other BC Residents 8838 96.97 12373 96.21 7322 94.88 23707 83.79 15418 98.33 HBV positive 918 10.07 645 5.02 188 2.44 1422 5.03 2478 15.8 HIV positive 1251 13.73 792 6.16 214 2.77 284 1 2410 15.37 TB positive 73 0.8 54 0.42 4 0.05 92 0.33 332 2.12 gbMSM yes 700 14.82 2341 26.56 409 7.53 1233 6.82 396 3.67 Residence rural 449 4.93 5 0.04 769 10.35 6193 22.69 1406 8.99 PWID never 22 0.24 12811 99.61 3831 49.64 28113 99.37 1542 9.83 recent 6734 73.89 0 0 1445 18.72 0 0 2297 14.65 past 2358 25.87 50 0.39 2441 31.63 179 0.63 11842 75.52 OAT never 1624 17.82 12646 98.33 668 8.66 27984 98.91 10722 68.38 recent 6466 70.95 26 0.2 4531 58.71 31 0.11 694 4.43 past 1024 11.24 189 1.47 2518 32.63 277 0.98 4265 27.2 alcohol misuse yes 5643 61.92 2510 19.52 426 5.52 3525 12.46 10787 68.79 mental health diagnosis yes 7188 78.87 2847 22.14 916 11.87 4020 14.21 9797 62.48 liver disease yes 170 1.87 1663 12.93 0 0 3404 12.03 3114 19.86 social deprivation 1 650 7.25 0 0 808 10.96 5374 19.93 934 6.03 2 864 9.64 6 0.05 895 12.14 5440 20.17 1227 7.92 3 778 8.68 13 0.1 1061 14.4 6731 24.96 1888 12.18 4 1441 16.08 365 2.88 1633 22.16 8658 32.11 3606 23.27 5 (most deprived) 5230 58.35 12276 96.97 2973 40.34 762 2.83 7841 50.6 material deprivation 1 1518 16.94 2455 19.39 901 12.23 3554 13.18 1790 11.55 2 967 10.79 1331 10.51 935 12.69 5618 20.83 2209 14.26 3 1103 12.31 1633 12.9 1373 18.63 6264 23.23 2518 16.25 4 1801 20.09 2888 22.81 1858 25.21 6144 22.79 3437 22.18 5 (most deprived) 3574 39.88 4353 34.38 2303 31.25 5385 19.97 5542 35.76 28 2.4 Discussion 2.4.1 Main findings LCA divided the cohort into six classes that mirror previously-defined syndemic risk groups for HCV, which were named Younger PWID, Older PWID, gbMSM, Urban SDBB, Rural BB, and People of Asian Backgrounds.(101) The top DAA prescribers differed in specialty for each group. The groups had unique socio-demographic and clinical characteristics, which suggests differences in needs for particular health services. Integration of HCV related services with those that these groups already access could be a beneficial program planning tactic. 2.4.2 PWID Since HCV is a blood-borne pathogen, people who share needles and other drug use paraphernalia are at an increased risk of acquiring HCV infection. Therefore, it is no surprise that the two PWID classes comprised almost 40% of the entire cohort. LCA divided this at-risk population primarily by age. Younger PWID had highest proportion of younger people with more recent OAT and IDU, without liver disease. By contrast, Older PWID were mostly baby boomers who used injection drugs and OAT in the past, had liver disease, and had the highest proportion of coinfections. This syndemic characterization of PWID in BC is consistent with previous knowledge in that older PWID are more likely to have HIV and HCV compared to younger PWID.(114,115) However, it may be a new finding that Older PWID in BC have a lower frequency of recent OAT use, at 6% vs. 74% in Younger PWID. A possible explanation could be that Older PWID who passed away before 2013 would be encoded as past OAT, and there were many more deaths in this group compared to Younger PWID. Common between these PWID classes were substantial mental health diagnoses and alcohol misuse. Based on their differences in age, recency of OAT and IDU, and co-morbidities these two classes may have different needs. For instance, Older PWID may require access to infectious disease specialists, gastroenterologists, or hepatologists due to their positive HIV/HBV status, and management of chronic liver disease. In fact, the top prescriber of DAAs for this group was infectious disease physicians. 29 Younger PWID can benefit from HCV care from generalists such as nurse practitioners or family physicians, as treatment of their HCV may be less complicated due to the absence of liver disease or lack of comorbidities. Therefore, while both groups likely require mental health, addiction services, support from social workers or case managers, and stable housing, perhaps services for Older PWID would be age-appropriate with more specialists in-house compared to Younger PWID. Future research by and with older and younger PWID is warranted to understand the best ways to reach these groups, and what their health priorities are. 2.4.3 gbMSM The gbMSM class had the highest proportion of gbMSM and the highest amount of HIV co-infections aside from the PWID classes and were also younger, had no liver disease, and were largely socially deprived. Their amplified social deprivation could be due to the definition provided by the Quebec Index, which classifies single-households as socially-deprived.(116) A vibrant, populated area of downtown Vancouver is home to Western Canada’s largest LGBTQ community and has many expensive single-bedroom apartments.(117) Therefore, it may be inaccurate to describe people who live in this area as deprived. This group of people may be experiencing syndemic conditions within higher risk environments where patterns of sexual risk contribute to risk of acquiring ulcerative STIs and the potential for HCV infection via condomless sex.(28) With the increase in uptake of pre-exposure prophylaxis (PrEP) to prevent HIV infections, there has been an increase in rates of syphilis leading concerns about increased HCV incidence in HIV-negative gbMSM.(118,119) It has been previously demonstrated that gbMSM are at increased risk of stigma and discrimination, and have a higher incidence of suicide and mental health conditions such as anxiety and depression.(120) In fact, 13% of gbMSM had a major mental health diagnosis, which is likely an underestimation. Therefore, health services that include the combination of mental health professionals, infectious disease physicians, and sexual health resources may be beneficial. 2.4.4 Baby boomers (BB’s) One of the largest risk groups for HCV in BC is baby boomers, many of whom may have been exposed to HCV many years ago.(33) The LCA divided this group into two classes, Urban 30 SDBB and Rural BB, by area of residence, sexuality, and indices of deprivation. Urban SDBB class had the highest proportion of urban dwellers and socially deprived people among all the classes. Urban SDBB also had the second highest proportion of gbMSM, only 5% less than the gbMSM class, yet these Urban SDBB gbMSM were much older than those in the gbMSM class. Conversely, Rural BB’s had the most rural, fairly privileged, heterosexual people. Shared between these baby boomer classes was considerable liver disease, some alcohol use and mental health diagnoses, and no IDU or OAT. The liver disease variable contained measures for cirrhosis, hepatocellular carcinoma (HCC), and liver transplant. Among Urban SDBB flagged with the liver disease flag (n=2745, 22%), 92.5% had cirrhosis, 23.0% had HCC, and 5.1% had liver transplants, and among Rural BB with liver disease (n=2195, 11%), 91.9% had cirrhosis, 24.4% had HCC, and 7.2% had liver transplants. Also, while death was not a variable in the LCA, these classes had some of the highest proportions of people who had died (41% of Urban SDBB & 23% of Rural BB had died). Therefore, while services should be tailored to each class based on their differences in areas of residence, sexuality, and privilege, they both require urgent treatment of those who are still alive. Rural BB may need more liver specialists co-located with alcohol cessation programs in rural areas near their homes, while Urban SDBB need access to services in urban centres and may benefit from HCV services located in social or supportive housing. Distance from services is a significant inequality for people living in rural or underserved areas. Far distances from providers may represent complex barriers that may be worse or even insurmountable for deprived populations. Some practical ways to overcome these disparities that place responsibility on the health care system could be telemedicine and streamlined clinical training.(121) 2.4.5 Immigrants from endemic regions Immigrants who come from HCV endemic regions (high HCV prevalence) and/or from resource-limited areas with unsafe medical procedures are at higher risk of HCV infection.(26) The People of Asian Backgrounds class was made up of East and South Asians that were older, lived in urban areas, were socially privileged, and had liver disease but did not have a history of IDU, alcohol misuse, or mental health conditions. This class may benefit from culturally appropriate services located in urban areas.(26) Among People of Asian Backgrounds with liver disease (n=500, 11%), 84.0% had cirrhosis, 38.4% had HCC, and 4.8% had liver transplants. This high 31 proportion of HCC may indicate the need for oncologists to be co-located or able to prescribe HCV treatments. 2.4.6 Sensitivity analysis The six-class model was more informative than the five-class model because it was more epidemiologically relevant. First, after looking at the clustering of classes by dissemination areas, it was clear that People of Asian Backgrounds were located in areas that were distinct from Rural BB.(122) This geographical distinction is particularly relevant from a public health planning perspective because the services would need to be in areas where these classes do not have much overlap. Therefore, these groups should be separate, as in the six-class model, rather than combined. Second, the “Ever OAT” class separated individuals who were ever on OAT from the PWID classes, as opposed to the six-class model that puts them in the Younger PWID and Older PWID classes. From previous research, we know that the PWID flag underestimates the number of people who have ever injected drugs.(123) Perhaps due to stigma, there is underreporting of IDU. Yet, individuals who are HCV positive and on OAT most likely became infected by injecting. Therefore, while separating OAT from PWID may have allowed for a marginally better fit (13 point higher average posterior probability), it does not make epidemiologic sense to separate these groups of people as they may require the same services.(123,124) 2.4.7 Strengths and limitations Chapter 2’s study has several notable strengths; membership probabilities of all latent classes were high, and the model was able to create mutually exclusive and exhaustive heterogeneous classes. Moreover, there was a very small proportion of missing values. Variables with missing data were urbanicity (1.96% missing) and social/material wealth indices (3.0% missing). The use of LCA in this study was an important strength. Instead of emphasizing the variables such as in typical regression analysis, LCA places the emphasis on the population, elucidating differences between people. LCA also allows for the reduction of the number of variables for analysis, thus allowing a future examination of the true effect of class membership (a composite of many different variables) on an outcome such as HCV treatment uptake in Chapter 3. This method 32 avoids errors like misidentification of effect modification or missing confounding variables and allows for the true measure strengths of association. It also may compensate for some assumptions made in the imputation of variables from administrative data sources. Furthermore, LCA offers several advantages over other types of statistical grouping methods such as cluster and factor analysis, because it groups similar people together whereas factor analysis groups variables along a continuum. In other words, factor analysis is concerned with the structure of variables and their correlations whereas LCA is more concerned with the structure of individuals.(89) Therefore, LCA is more “person-centered” vs. “variable-centered” because it doesn’t rely on arbitrary cut-offs for classification.(84) Several limitations have been identified in this study. This data only reflects population-level subgroups in BC included in the BC-HTC, and may not be as generalizable outside of BC. However, this method of subgrouping the population could be applied to any population, and it is likely relevant in other Canadian provinces due to similar publicly-funded health care. Another limitation was that the study sample included people who died during the study period; thus, the present-day population may differ slightly in terms of the distribution of certain characteristics. However, it is likely that these syndemic groups are still relevant today due to persisting social and clinical variables that increase risk of HCV infection. Also, it was important to include people who died because many may have died prematurely due to the opioid crisis and thus were important in our analysis. In fact, 20.5% of deaths among people affected by HCV in BC were related to injection drug use.(125) There may have been sampling bias in the use of derived variables like PWID, OAT, mental illness, alcohol misuse, and liver disease because they rely on diagnostic codes (Table S1-3). Using administrative diagnostic codes to determine these variables may underestimate the prevalence of those with less connection to health care services. This method may also misclassify some cases as a result of the sensitivity and specificity of the measures implemented. However, LCA may overcome some of this bias through its focus on underlying patterns. Also, the decision on which variables to include in the LCA was subjective, but it was made a priori to reduce design bias. Finally, time-varying variables were anchored on the end of the study period or death to obtain individuals’ most recent information in order to allow for future comparisons of outcomes, like treatment uptake in Chapter 3. Certain variables may have differed in the time between a person’s HCV diagnosis date and the end of the study period. In this way, latent classes may not represent characteristics of people affected by HCV at 33 the time of their HCV diagnosis, but rather portray their most current information. Among those who have not received treatment, this anchor point may yield an increase in the negative effects of HCV such as liver disease, which may have been less advanced at time of diagnosis. Nevertheless, the use of older data (at time of diagnosis) in the LCA would have precluded an appropriate analysis of DAA treatment outcomes among classes in Chapter 3. 2.5 Conclusion In this study, we were able to empirically characterize syndemic groups of people affected by HCV that may be missed with single-variable methods. We have provided a framework to generate quantitative data to guide the deployment and integration of services to improve HCV treatment uptake. This information can help identify key differences in potential transmission risks between groups that may otherwise be concealed, determine who should be screened for HCV, and what services should be co-located with HCV treatment and vice versa (i.e. HCV treatment with what services). Through this methodical and resource-effective strategy, HCV could be eliminated by 2030 per the World Health Organization’s mandate.(4) 34 3 Factors associated with treatment uptake 3.1 Introduction As expressed in Chapter 1, there is a relatively large burden of HCV in Canada with recent estimates suggesting that between 136,000–246,000 people living with HCV.(105) British Columbia (BC), has among the highest rates of HCV infection at 44.9 cases per 100,000.(126) From 1990 to 2018, 53,441 people diagnosed with HCV were alive in BC.(127) Yet, only 54.4% of those known to be eligible for treatment (HCV RNA-positive test results) have been treated.(9) In the absence of an HCV vaccine, treatment is one of the most effective methods of elimination.(128) However, many people have yet to receive treatment for a multitude of reasons including individual level, provider level and system level barriers. Previously, older, interferon-based treatments had long treatment courses (24-48 weeks) with regular injections, many unpleasant side effects, such as flu-like symptoms and depression, and cure rates at around 40% depending on HCV genotype.(36) Interferon treatment regimens were determined by patients’ HCV genotype. For example, patients with genotype 4 respond poorly to conventional interferon monotherapy and better with combined interferon and ribavirin therapies, while patients with genotype 2 obtain high SVR after 14 weeks of treatment with convention interferon monotherapy. (129,130) Interferon therapies were less accessible to certain groups of people who physicians thought were less likely to follow through with the treatment regimen or more likely to become re-infected, such as PWID and people co-infected with HIV, and issues of tolerability (due to severe side-effects) were also relevant for people with comorbidities like cirrhosis, among others. (36) Starting in 2014, direct-acting antiviral (DAA) therapies became available in BC, which were substantially more tolerable and effective at treating HCV, thus providing the potential for HCV elimination. Before March of 2018, public coverage for DAAs was restricted to people with fibrosis scores of F2 or higher in order to prioritize people with advanced liver disease in order to prevent adverse outcomes like cancer.(16) Liver fibrosis is the formation of excess connective tissue in the liver, and advanced fibrosis is marked with higher fibrosis scores, while mild with lower.(3) After March of 2018, innovative treatments became indiscriminately covered by public health plans in Canada and allowed for wider access to HCV treatment for all people, 35 including those with other comorbidities. However, many people are still not being treated.(39,81) Barriers during the interferon era are different from barriers that exist now. Currently, there is a knowledge gap on what barriers exist for individuals, providers, and within the system. Furthermore, as seen in Chapter 2, different groups of people with shared characteristics are being treated by different types of physicians. These results point to the fact that people affected by HCV may require different suites of services. Exploring the association between shared socio-demographic characteristics of patients and treatment uptake will allow for a greater understanding of where these barriers exist in BC. Using a population data set of all individuals diagnosed HCV positive in BC, the BC Hepatitis Testers Cohort (BC-HTC), Chapter 3 will examine HCV treatment uptake by six real population subgroups that were predetermined. These six subgroups were categorized using a statistical segmentation procedure, Latent Class Analysis (LCA), in the previous chapter and include Younger PWID, Urban Socially-Deprived Baby Boomers (Urban SDBB), gbMSM, Rural Baby Boomers (Rural BB), People of Asian Backgrounds, and Older PWID (names italicized throughout). Thus far there have been no population-level studies that have explored the relationship between population subgroups (latent classes) and HCV treatment uptake. Population-level studies can be more generalizable, as they are representative of the public and have the ability to reveal system level barriers. 3.1.1 Objectives The two objectives of this study were to use multivariable logistic regression (MLR) to (1) understand indicators of treatment uptake (Part 1) (2) determine the relationship between six predetermined population subgroups (latent classes) and treatment uptake (Part 2) 36 3.2 Methods 3.2.1 Study Design Data was used from the BC-HTC, which is a dynamic cohort described in Chapter 2, including all individuals (~1.7 million) who ever tested for HCV or HIV, or diagnosed with HBV, HCV, HIV or active TB in BC between 1990 and 2015, linked with data through personal health numbers on medical visits, hospitalizations, emergency department visits, cancers, prescription drugs, and deaths (Appendix 1-3).(10,105) Prescriptions and vital statistics data for these analyses were available until 12/31/2018. Time-dependent variables were anchored on the end of the cohort (12/31/15) (Appendix 2, 3). We performed two analyses: Part 1 examines individual indicators for treatment uptake using multivariable logistic regression. Part 2 is a multinomial logistic regression examining association between latent classes and DAA or interferon treatment uptake vs. no treatment. Data linkage to establish the BC-HTC was performed with support from the BCCDC's public health mandate. This study was reviewed and approved by the Behavioral Research Ethics Board at the University of British Columbia (H14-01649). 3.2.2 Study population As revealed in Chapter 2, people who were diagnosed with HCV were included in the LCA analysis (Appendix 1). A subset of these individuals was included for this analysis. Those that were found to be RNA positive (through PCR confirmatory testing), did not spontaneously clear their infection (i.e. had no viral load (RNA negative) without any treatment or pre-treatment, and were alive (did not die within the study period) were included in the study population (Figure 3.1). The outcome variable was treatment uptake based on first treatment course, and all treatment-types were considered, interferon and DAA. First treatment course was selected and analysed to understand likelihood of treatment access. Many populations with co-infections and other comorbidities were kept from interferon treatments due to physicians’ concerns about their adherence to expensive treatments. Thus, in general, people with fewer disadvantages were able to access interferon therapies. However, because interferons had low cure rates, many people had to subsequently initiate DAAs to achieve SVR. Thus, it is important to consider first treatment course in order to avoid conflating issues of treatment effectiveness with treatment access. 37 Figure 3.1 Inclusion flowchart of study population for logistic regression analyses. 2015 British Columbia Hepatitis Testers Cohort n=1.7 million Diagnosed with HCV per study definition (Appendix 1) [Analytic sample for LCA] n=73,665 n=12,725; RNA negative pre-treatment or with no treatment (spontaneously cleared infection) n=9,202; RNA unknown n=589 excluded, missing data [Health Authority 1.93% missing, residence 1.96% missing, & social/material deprivation quintiles 3% missing] n=8,882 treated with interferons n=8,559 treated with DAAs Treated n=17,441 Analytic sample for Part 1 MLR, n=30,925 n=13,484 not treated RNA Positive [Analytic sample for Part 2] n=31,514 n=20,224; deceased, excluded 38 3.2.3 Part 1 study design: indicators of treatment uptake We constructed multivariable logistic regression models for treatment receipt including 18 categorical variables to identify the most important indicators of treatment uptake. Results are presented as adjusted odd ratios (AOR) of treatment uptake with 95% confidence intervals (CI). The binary outcome variable, treatment uptake (yes/no), was determined by assessing if participants had a prescription of either DAA or interferon treatment recorded in the PharmaNet. If individuals had multiple treatment courses, their first treatment course was chosen. The 18 categorical variables that were analysed for their relationship with treatment uptake are listed below. Detailed definitions of each variable is provided in Appendix 2,3. - Birth cohort (born <1945, 1945-1964, 1965-1974, >1974) - Sex (male, female) - Ethnicity (East Asian, South Asian, other BC residents, unknown) - Coinfections: HIV, HBV, TB - Sexual orientation (gbMSM, heterosexual) - Urbanicity (urban vs. rural residence) - Social and material deprivation (1-5, where 5 is most deprived) - History of injection drug use (IDU) (never vs. past vs. recent 2012-2015) - History of opioid agonist therapy (OAT) (never vs past vs recent 2015-2018) - Alcohol misuse - Major mental health diagnosis - Liver disease (cirrhosis, hepatocellular carcinoma, previous liver transplant) - Number of non-liver related chronic conditions (all of those included in chronic disease registry) (4 levels [1,2,3, ≥4]) - Health authority (HA) (Interior, Fraser, Vancouver Coastal, Island Health, Northern). - HCV diagnosis year (‘90- ‘94, ‘95- ‘99, ‘00-‘04, ‘05-‘09, ‘10-‘18) 3.2.4 Part 2 study design: LCA and treatment uptake In Part 2, a logistic regression analysis was performed to understand the relationship between latent classes and treatment uptake in both a bivariable analysis and multivariable analysis 39 adjusted for HCV diagnosis year. Results were presented as odds ratios (OR) and AOR with 95% CI’s. Compared to Part 1, a slightly different treatment uptake outcome variable was used. This treatment uptake variable had three levels: DAA treatment, interferon treatment, or no treatment (based on first treatment course, one per patient). Thus, a multinomial logistic regression was used (with non-ordinal outcome levels). The explanatory variables were patient profiles, as determined through LCA described in Chapter 2, and categorical HCV diagnosis year (same as Part 1). HCV diagnosis year was the only variable that was adjusted for in order to avoid re-adjusting for variables that were used in LCA to build the patient profiles. HA was not adjusted for as it is a bureaucratic measure of location. 3.3 Results 3.3.1 Characteristics of the subset of cohort data Between January 1st, 1990 and December 31, 2018, 73,665 individuals were diagnosed HCV positive. Of these, 20,224 (28%) died and were excluded. Of those alive, 12,725 (24%) spontaneously cleared their infection (i.e. had a negative RNA test after a positive diagnosis, pre-treatment or with no treatment within study period). Since people who clear their infection spontaneously do not need treatment, and people who died may have not had the chance to get treated, these groups were excluded from this analysis. Finally of those alive, 9,202 (17%) people had unknown RNA tests and were excluded in the primary analysis. Therefore, the final analytic sample included a total of 31,514 individuals who had a positive RNA test. This analytic sample was 42% of all those diagnosed with HCV (Figure 3.1). Details of those included, excluded, and treated in the final analytic sample can be found in Table 3.1. In Part 1’s adjusted MLR to understand indicators of treatment uptake, those that were missing any explanatory variables were excluded. Two sensitivity analyses were performed, one that included people who had unknown RNA tests (i.e. were not confirmatory tested) and one that included those who died in order to understand the differences in outcome measures and strengths of association.40 Table 3.1 Characteristics of those included, excluded, and treated in the study population Variable Included Excluded Treated n=31,514 n=42,151 n=17,441 number % number % number % Birth Cohort <1945 1309 4.2 5323 12.6 697 4.0 1945-1964 18949 60.1 24393 57.9 12030 69.0 1965-1974 6508 20.7 7397 17.6 2981 17.1 >1974 4748 15.1 5038 12.0 1733 9.9 Sex male 20652 65.5 27209 64.6 11374 65.2 female 10862 34.5 14936 35.4 6067 34.8 Ethnicity East Asian 1209 3.8 1501 3.6 796 4.6 South Asian 1337 4.2 1230 2.9 863 5.0 other BC residents 28661 90.9 38997 92.5 15622 89.6 unknown 307 1.0 423 1.0 160 0.9 HBV positive 1731 5.5 3920 9.3 1060 6.1 negative 29783 94.5 38231 90.7 16381 93.9 HIV positive 1745 5.5 3206 7.6 1124 6.4 negative 29769 94.5 38945 92.4 16317 93.6 TB positive 159 0.5 396 0.9 80 0.5 negative 31355 99.5 41755 99.1 17361 99.5 Sexual Orientation gbMSM 2435 11.8 2644 9.7 1406 12.4 heterosexual 18189 88.1 24505 90.1 9964 87.6 Residence urban 27015 86.6 36328 88.6 14803 85.0 rural 4190 13.4 4632 11.3 2610 15.0 PWID never 20143 63.9 26176 62.1 12241 70.2 recent 5486 17.4 4990 11.8 2288 13.1 past 5885 18.7 10985 26.1 2912 16.7 OAT never 22241 70.6 31403 74.5 13436 77.0 recent 6953 22.1 4795 11.4 3032 17.4 past 2320 7.4 5953 14.1 973 5.6 Alcohol misuse yes 8691 27.6 14200 33.7 4337 24.9 no 22823 72.4 27951 66.3 13104 75.1 Mental illness yes 10714 34.0 14054 33.3 5719 32.8 no 20800 66.0 28097 66.7 11722 67.2 Liver disease yes 1543 4.9 6808 16.2 1191 6.8 no 29971 95.1 35343 83.9 16250 93.2 Social deprivation quintile 1- most privileged 3726 12.1 4040 10.0 2296 13.3 2 3893 12.6 4539 11.2 2367 13.7 3 4762 15.4 5709 14.1 2872 16.6 4 6700 21.7 9003 22.2 3865 22.4 5- most deprived 11844 38.3 17238 42.5 5860 34.0 Material deprivation quintile 1-most privileged 4581 14.8 5637 13.9 2660 15.4 2 4927 15.9 6133 15.1 2957 17.1 3 5768 18.7 7123 17.6 3434 19.9 4 6978 22.6 9150 22.6 3925 22.7 5- most deprived 8671 28.0 12486 30.8 4284 24.8 41 3.3.2 Part 1: indicators of treatment uptake The indicators that were found to be especially important based on high Wald-chi square values and low p-values were age, non-liver disease, HIV, and liver disease (Table 3.2). Model fit statistics are reported in Appendix 5. Table 3.2 Hypothesis test for final model Part 1 MLR [value in red indicates insignificance, p >0.05] The unadjusted, bivariable logistic regression analysis with all variables in the final model showed that presence of liver disease was most predictive of treatment uptake, followed by East Asian ethnicity (Appendix 6). The adjusted MLR of the final model, involved 30,925 individuals (excluded 589 individuals because they were missing explanatory variables) and showed that the most important indicators of treatment uptake were non-liver and liver disease, baby boomer birth cohort (1945-1964), and HIV (Figure 3.2). Odds of treatment uptake were overall slightly attenuated in the adjusted model compared to the unadjusted, although a few AOR’s were greater than unadjusted (Appendix 6).Effect Df Wald Chi-Square Pr > ChiSq Age 3 452.5 <.0001 Non-liver disease 3 226.7 <.0001 HIV 1 164.3 <.0001 Liver disease 1 156.8 <.0001 PWID 2 133.3 <.0001 Social deprivation 4 113.2 <.0001 HCV diagnosis year 5 102.8 <.0001 OAT 2 75.8 <.0001 Ethnicity 4 73.8 <.0001 Material deprivation 4 54.9 <.0001 Alcohol misuse 1 42.7 <.0001 Mental illness 1 35.9 <.0001 Health Authority 4 32.5 <.0001 gbMSM 1 11.4 0.0007 HBV 1 6.0 0.0142 Urbanicity 1 4.5 0.0332 TB 1 4.0 0.0445 Sex 1 1.3 0.2514 42 Figure 3.2: Part 1; adjusted odds ratios of treatment uptake by indicator variables (log scale) *For social/material indices, 1 indicates most privileged and 5 most deprived. 1.030.871.881.201.451.611.051.191.181.152.080.710.820.850.660.640.712.251.411.501.561.271.231.241.161.431.421.301.211.091.110.991.201.010.810.830.700.680.550.5 1 2male vs femalebirth cohort >1945 vs <1974birth cohort 1945-1964 vs <1974birth cohort 1965-1974 vs <1974East Asian vs. Other BC ResidentsSouth Asian vs Other BC Residentsunknown vs. Other BC Residentsmental illness (yes vs. no)gbMSM vs. heterosexualHBV positive vs notHIV positive vs. notTB positive vs notalcohol misuse (yes vs. no)OAT recent vs neverOAT past vs neverPWID recent vs neverPWID past vs neverliver disease (yes vs. no)2 non-liver conditions vs 13 non-liver conditions vs 14 non-liver conditions vs 1material index 1 vs 5material index 2 vs 5material index 3 vs 5material index 4 vs 5social index 1 vs 5social index 2 vs 5social index 3 vs 5social index 4 vs 5rural vs urbanInterior vs Fraser HAVancouver Coastal vs Fraser HAIsland vs Fraser HANorthern vs Fraser HAHCV diagnosed '95-'99 vs '90-'94 HCV diagnosed '00-'04 vs '90-'94 HCV diagnosed '05-'09 vs '90-'94 HCV diagnosed '10-'14 vs '90-'94 HCV diagnosed '15-'18 vs '90-'94 43 3.3.3 Part 2: latent classes and treatment uptake Among those within the analytic sample, the number and percentage of those treated with interferon therapy, DAAs, and not treated (respectively) by latent class are laid out in Table 3.3. Younger PWID, gbMSM, and Older PWID have the highest frequency of not being treated. Table 3.3 Frequency (and row percent) of treatment outcomes by latent class Latent Class Treated with IFN Treated with DAAs Not Treated Younger PWID 1003 (15.8%) 1587 (25.0%) 3763 (59.2%) Urban SDBB 1449 (32.2%) 1344 (29.9%) 1709 (38.0%) gbMSM 857 (20.8%) 904 (21.9%) 2366 (57.3%) Rural BB 3340 (35.1%) 2937 (30.86%) 3241 (34.1%) People of Asian Backgrounds 1025 (45.6%) 477 (21.2%) 748 (33.2%) Older PWID 1208 (25.4%) 1310 (27.5%) 2246 (47.2%) After adjusting for HCV diagnosis year, the AOR of interferon (IFN) treatment uptake compared to Younger PWID was highest in People of Asian Backgrounds 5.45 (95% CI:4.82-6.15), lowest in gbMSM 1.37 (95% CI:1.23-1.52) (Figure 3.3). The AOR of DAA treatment uptake compared to Younger PWID was highest in Rural BB 2.18 (95% CI: 2.02-2.36) and lowest in gbMSM 0.92 (95% CI: 0.83-1.01). Therefore, in the adjusted analysis, all groups had a significantly higher odds of interferon treatment uptake compared to Younger PWID, and all but gbMSM had significantly higher odds of DAA treatment uptake (compared to Younger PWID) (Figure 3.3). Unadjusted odds are shown in Appendix 7. 44 Figure 3.3 Part 2; adjusted odds of treatment uptake of interferon and DAA treatments among latent classes compared to Younger PWID 3.3.4 Sensitivity analysis including those without RNA test A sensitivity analysis was performed that included people who did not have confirmatory RNA testing. Including people who may have been lost to follow up allows for a more holistic analysis of treatment access. This analysis added 9,202 people to the study population for a total of 40,716 people (1,456 excluded Part 1 sensitivity analysis because of missing explanatory variables). The AORs for this sensitivity analysis were relatively similar to the original model for Part 1 and Part 2 (Appendix 8, 10). For Part 1, a difference was noted with the HCV diagnosis year variable; the association changed from having a significantly lower odds of treatment uptake compared to the reference group (being diagnosed in 1990-1994) to higher odds for many diagnosis intervals in the sensitivity model (Appendix 6,10). Also, being on OAT recently (vs. 2.761.373.455.451.561.900.922.181.531.400.2 1 5Latent Class (ref=Younger PWID)Urban SDBB (IFN)gbMSM (IFN)Rural BB (IFN)People of Asian Backgrounds (IFN)Older PWID (IFN)Urban SDBB (DAA)gbMSM (DAA)Rural BB (DAA)People of Asian Backgrounds (DAA)Older PWID (DAA)Odds Ratios; 95% CI (log scale)45 never) in the sensitivity analysis showed significantly higher AOR of treatment uptake, while the original showed lower AOR. Figure 3.4 Part 2 Sensitivity analysis including those with unknown RNA test; adjusted odds of treatment uptake of interferon and DAA treatments among latent classes compared to Younger PWID For Part 2, a major difference was noted with the AOR for gbMSM and People of Asian Backgrounds. The AOR for gbMSM compared to Younger PWID changed from having significantly greater odds of IFN treatment uptake to significantly lower odds [from 1.37 (95% CI: 1.23-1.52) to 0.8 (95% CI: 0.72-0.88)], and for DAA treatment, the AOR changed from an insignificant difference to significantly lower odds [from 0.92 (95% CI: 0.83-1.01)] (Figure 3.3, Figure 3.4). The AOR of DAA treatment uptake for People of Asian Backgrounds changed from 1.750.802.103.141.231.320.591.450.991.170.2 1 5Latent Class (ref=Younger PWID)Urban SDBB (IFN)gbMSM (IFN)Rural BB (IFN)People of Asian Backgrounds (IFN)Older PWID (IFN)Urban SDBB (DAA)gbMSM (DAA)Rural BB (DAA)People of Asian Backgrounds (DAA)Older PWID (DAA)Odds Ratios; 95% CI (log scale)46 being significantly greater than Younger PWID to insignificant [1.53 (95% CI: 1.23-1.52) to 0.99 (95% CI: 0.88-1.12)] (Figure 3.3, Figure 3.4). 3.3.5 Sensitivity analysis including deceased The odds of treatment uptake may have been affected by sampling bias, as people who died were not included in the analysis Another MLR was run which included those who died to explore this bias and is laid out in Appendix 4. 3.4 Discussion 3.4.1 Summary of key findings and their implications This study demonstrated significant variability in treatment uptake both among individual indicators and among latent classes. Certain subgroups of the population have lower treatment uptake compared to others, suggesting that program planning efforts need to target these groups to facilitate treatment uptake. Observed differences in HCV treatment uptake between the classes suggests that the co-occurrence of multiple factors, rather than factors in isolation, may uniquely influence uptake likelihood. Indicators such as alcohol misuse and social deprivation had decreased odds of treatment uptake compared to no alcohol misuse and social privilege, suggesting that indicators of deprivation are related to lower treatment uptake outcomes. People of Asian Backgrounds had the greatest odds of interferon treatment, and Rural BB had greatest odds of DAA treatment uptake compared to all other syndemic groups, in unadjusted, adjusted, and sensitivity analyses (Figure 3.3, Figure 3.4, and Appendix 6). This finding aligns with the analysis in Part 1, which found liver disease, baby boomer birth cohort, and East and South Asian ethnicity to be among the most important indicators (Figure 3.2). 47 Table 3.4 Patient characteristics within the six patient profiles by sociodemographic and clinical indicators, restricted to study population Younger PWID Urban Socially Deprived Baby Boomers gbMSM Rural Baby Boomers People of Asian backgrounds Older PWID No. individuals, % 6,353 20.2% 4,502 14.3% 4,127 13.1% 9,518 30.2% 2,250 7.1% 4,764 15.1% Sex male 3832 60.3% 3055 67.9% 2910 70.5% 6380 67.0% 1156 51.4% 3319 69.7% Birth cohort <1945 0 0.0% 413 9.2% 3 0.1% 434 4.6% 396 17.6% 63 1.3% 1945-1964 1397 22.0% 4056 90.1% 923 22.4% 7985 83.9% 1124 50.0% 3464 72.7% 1965-1974 2188 34.4% 33 0.7% 1844 44.7% 895 9.4% 325 14.4% 1223 25.7% >1974 2768 43.6% 0 0.0% 1357 32.9% 204 2.1% 405 18.0% 14 0.3% Ethnicity East Asian 43 0.7% 51 1.1% 70 1.7% 4 0.0% 996 44.3% 45 0.9% South Asian 116 1.8% 4 0.1% 53 1.3% 17 0.2% 1095 48.7% 52 1.1% other BC residents 6131 96.5% 4417 98.1% 3929 95.2% 9461 99.4% 71 3.2% 4652 97.7% unknown 63 1.0% 30 0.7% 75 1.8% 36 0.4% 88 3.9% 15 0.3% HBV1 positive 426 6.7% 204 4.5% 90 2.2% 245 2.6% 182 8.1% 584 12.3% HIV1 positive 675 10.6% 162 3.6% 324 7.9% 27 0.3% 8 0.4% 549 11.5% TB1 positive 33 0.5% 23 0.5% 3 0.1% 6 0.1% 27 1.2% 67 1.4% gbMSM yes 422 11.0% 733 24.0% 811 27.9% 91 1.4% 162 14.0% 216 6.5% Residence2 rural 425 6.7% 36 0.8% 142 3.6% 3094 32.8% 57 2.6% 436 9.2% PWID3 never 166 2.6% 4502 100.0% 3797 92.0% 19704 96.6% 2244 99.7% 228 4.8% recent 4294 67.6% 0 0.0% 0 0.0% 9206 96.7% 6 0.3% 1136 23.9% past 1893 29.8% 0 0.0% 330 8.0% 50 0.5% 2 0.0% 3400 71.4% OAT3 never 786 12.4% 4483 99.6% 2357 57.1% 9195 96.6% 2180 96.9% 3240 68.0% recent 5009 78.8% 5 0.1% 1232 29.9% 168 1.8% 42 1.9% 497 10.4% past 558 8.8% 14 0.3% 538 13.0% 155 1.6% 28 1.2% 1027 21.6% Alcohol misuse3 2987 47.0% 934 20.8% 196 4.8% 1287 13.5% 51 2.3% 3236 67.9% Mental health diagnosis3 3943 62.1% 1340 29.8% 598 14.5% 1492 15.7% 168 7.5% 3173 66.6% Liver disease3,4 52 0.8% 413 9.2% 0 0.0% 522 5.5% 115 5.1% 441 9.3% Social deprivation2,5 1 (privileged) 533 8.5% 37 0.8% 391 10.0% 1415 15.1% 1018 46.2% 332 7.0% 2 691 11.0% 70 1.6% 407 10.4% 1820 19.4% 516 23.4% 389 8.3% 3 656 10.5% 49 1.1% 357 9.1% 2816 30.1% 297 13.5% 587 12.5% 4 1108 17.7% 491 11.0% 748 19.0% 3112 33.2% 229 10.4% 1012 21.5% 5 (deprived) 3273 52.3% 3801 85.5% 2027 51.6% 204 2.2% 143 6.5% 2396 50.8% Material deprivation2,5 1 (privileged) 880 14.1% 849 19.1% 844 21.5% 1246 13.3% 175 7.9% 587 12.5% 2 725 11.6% 448 10.1% 384 9.8% 2437 26.0% 297 13.5% 636 13.5% 3 910 14.5% 636 14.3% 527 13.4% 2439 26.0% 480 21.8% 776 16.5% 4 1397 22.3% 1007 22.6% 870 22.1% 1976 21.1% 645 29.3% 1083 23.0% 5 (deprived) 2349 37.5% 1508 33.9% 1305 33.2% 1269 13.6% 606 27.5% 1634 34.7% Deceased yes 1322 11.4% 5211 41.0% 878 9.7% 4682 23.0% 768 16.3% 7363 48.2% no 10241 88.6% 7487 59.0% 8141 90.3% 15719 77.0% 3950 83.7% 7903 51.8% 1 Comorbid infections based on “ever” positive, proportion is in comparison to negative which includes unknown. 2 Missing: Health Authority, residence, & social/material deprivation quintiles (1.93%, 1.96%, 3% respectively). 3 Based on hospital diagnosis codes (Appendix 2). 4 Includes: cirrhosis, liver transplant, and hepatocellular carcinoma 5 Based on the Québec Index of Material and Social Deprivation (116)48 3.4.2 Factors associated with treatment uptake The variables in Part 1 analysis that had higher odds of receiving HCV treatment were liver disease, HIV diagnosis, birth cohort 1945-1964 (baby boomers), East/South Asian ethnicity, gbMSM, no alcohol misuse, social privilege, and never OAT (Figure 3.2). Combination of these factors that predicted various classes were also associated with higher treatment uptake. For example, People of Asian Backgrounds had the most East and South Asians, many baby boomers, were the most socially privileged, had some liver disease, some gbMSM, and almost no alcohol misuse or OAT. Rural BB had the most baby boomers, some liver disease, and almost no OAT. Urban SDBB had baby boomers, some with HIV, some gbMSM, the most liver disease, and no OAT (Table 2.1). These classes were the top three groups to be treated with DAAs or interferon therapies (Figure 3.3). There were groups who were not treated during the interferon era such as people with cirrhosis, HIV co-infection, and PWID due to concerns related to side-effects and adherence.(37) DAAs were expected to increase treatment uptake among these groups as they had few side effects and access to treatments was expanded. Similar to an earlier analysis, we found that treatment uptake was high among people with liver disease (i.e. cirrhosis, liver transplant, hepatocellular carcinoma), and people diagnosed with HIV. However, we also found that indicators related to substance use, being on OAT and alcohol misuse, were associated with lower odds of treatment uptake, which may have been the result of barriers especially in the interferon era.(51) However these barriers may be less relevant for individuals treated with DAAs, as demonstrated by Gore at al. (2016) in an RCT.(75) Also, may studies in real-world settings have shown that PWID or people on OAT have SVR rates that are equal to people who do not use substances.(58,59) Future analyses should explore the idea of treatment era by re-running the Part-1 model with the three-level treatment variable used in Part 2 (this idea is expanded upon in Chapter 4). 3.4.3 Factors associated with lower treatment uptake Variables in the Part 1 MLR analysis that were the least indicative of treatment uptake were being born before 1945, being diagnosed with HCV more recently (after 2005), and being the most socially/materially deprived (Figure 3.2). Latent classes with lower odds of treatment 49 uptake had more of these characteristics and had fewer that were mentioned in 3.4.2. Younger PWID had almost no liver disease, the most material deprivation, some alcohol misuse, some social deprivation, were mostly heterosexual, and were likely diagnosed with HCV in recent years due to younger age. The gbMSM class had absolutely no liver disease, some social deprivation, and alcohol misuse. Older PWID was characterized by the most alcohol misuse, many heterosexual individuals, some social and material deprivation, and some born before 1945 (Table 3.4). These three classes had the lowest odds of treatment uptake with DAAs or interferon therapies (Figure 3.3). Individuals of lower socioeconomic status (SES) face barriers in HCV treatment access, especially before treatments became publicly funded in BC.(37) Even now that treatments are free for eligible patients, it has been well documented that people of lower SES are less integrated into the health care system and thus may have lesser treatment uptake.(131) Not having HIV or liver disease diagnosis may point to decreased contact with specialists who were more able to prescribe HCV treatments compared to GP’s.(50) Because HIV is more prevalent among gbMSM, perhaps some heterosexuals had less contact with specialists. Finally, people born before 1945 may have other non-liver related complications that prevent them from receiving treatment, especially before the advent of DAA therapies.(132) 3.4.4 Sensitivity analysis including who are not RNA tested RNA testing is a pre-requisite for treatment. People without RNA testing often have limited access to health care and increased loss to follow up compared to people with RNA testing. In this way, they are a ‘hardly-reached’ group with need for additional support to progress through the care-cascade. (133) A sensitivity analysis was performed to understand the effect of including this group. For Part 1, the AOR’s were very similar between the sensitivity analysis and the original model. The increase in AOR of treatment for all people HCV diagnosed >1995 vs. <1995 and for people on OAT recently vs. never were the only major differences. Perhaps because many people diagnosed in earlier years had less progression through the care-cascade due to limited viable treatment options and general lack of knowledge or support related to HCV services, there may 50 have been more people with unknown RNA test. Adding these people back into the model had the effect of decreasing the odds of treatment uptake in earlier years compared to recent. Additionally, perhaps people recently on OAT had more recent connection to the health care system, during the time that newer, viable treatment options became available, thus allowing for fewer losses to follow-up compared to never or past OAT. For Part 2, latent classes with more ‘hardly-reached’ people had the biggest difference in odds of treatment uptake compared to the original model. As seen in previous studies, PWID are more highly engaged with the system and have more RNA testing, although they are treated less. (9,109) It follows that the Younger PWID latent class had the lowest number and proportion of people with no RNA test. Because it was the reference group, all AOR’s decreased. gbMSM had the highest proportion of people with no RNA test, resulting in a large decrease in odds of DAA and interferon treatment uptake compared to Younger PWID. All groups with higher number of people diagnosed in earlier years also had large decreases in odds of treatment uptake. 3.4.5 Sensitivity analysis included those who died It may seem that individuals with earlier HCV diagnoses had higher odds of treatment uptake because there was more time for them to be treated and could be related to disease severity. However, Bartlett et al. (2018) showed that people diagnosed after 2013 took much less time to be treated than people diagnosed before, refuting the concept that more recent diagnoses contraindicates treatment uptake.(9) Instead, because the analysis excluded those who died and more people who were not treated in earlier years died (perhaps due to HCV-related complications), there could be bias. For example, there could be reverse causation, as people diagnosed in earlier years may still be alive because they were treated. A sensitivity analysis was performed to address this potential limitation (Appendix 4). The major difference for Part 1 sensitivity analysis was with liver disease variable, which went from AOR of 2.25 to 0.82 (Appendix 11). There was not a large difference between the Part 2 original model and this sensitivity analysis; however, the AOR for Older PWID went from having significantly greater odds of DAA treatment uptake to lower odds compared to Younger PWID (Appendix 4). 51 3.4.6 Strengths and Limitations To my knowledge, this is the first study to explore the relationship between HCV treatment uptake and syndemic groups in a population-level cohort of data using LCA. Previous research has either examined the effect of individual variables on treatment uptake (rather than their combined-effects) or used LCA in a smaller cohort of data.(51,101) This study can help answer questions regarding which factors are related to increased or decreased treatment uptake in certain population-groups in BC. It can also inform the layout of co-occurring services in order to improve HCV treatment uptake by implementing a “precision medicine” approach. Precision medicine is a medical model that customizes and tailors healthcare services for individual patients.(134) Scaling up precision medicine to the population level can greatly benefit the efficacy of public health programs. In the past, many health interventions have targeted people based on one particular characteristic or disease.(135) These programs could be stigmatizing for individuals who do not wish to be defined by their condition. There are important distinctions among people that share a condition. For example, people living with hepatitis C are different ages, ethnicities, and socioeconomic statuses, among many other things. Some became infected from blood transfusions while others from injecting drugs. Some have coinfections and some do not. They interact with the healthcare system in diverse ways. Standardized services may not be the most effective for improving health; while they are equal, they are not equitable. We need to better address the multifaceted nature of populations. This LCA classification of our local patient population allows us to deliver more targeted healthcare messages, which could better engage people with the healthcare system. Population-level precision medicine would be a more efficient allocation of resources that would prevent emergencies and save money, which could then be used to expand facilities in underserved areas. Several limitations have been identified in this project. First of all, this data only reflects trends in BC and thus cannot be generalizable beyond, though it is likely relevant in other Canadian provinces. Also, the analysis depends on a retrospective analysis, which precludes causal inferences. Using administrative diagnostic codes to determine these variables may have bias towards underestimating prevalence among those less engaged in, or able to access healthcare, and potentially misclassifying some cases due to sensitivity and specificity of the measures used. Also, the treatment uptake variable only measured those who were initiated in care, instead of 52 those who were retained. Thus, it does not represent those that completed treatment, as completion was not the main focus of this study. Finally, because the time-varying demographic variables could not be anchored on the date of the outcome (being treated or not), they were anchored on the most recent date (end of the cohort). Future work could explore the use of longitudinal analyses that explore time to treatment uptake, which could capture multiple treatment courses per individual, or it could examine time to SVR to measure successful treatment completion. 3.5 Conclusions These findings address an information void regarding HCV treatment patterns within syndemic subgroups of the population. Populations groups such as People of Asian Backgrounds and Rural Baby Boomers had higher treatment uptake while Urban SDBB and Younger PWID were less likely to receive treatment. People with non-liver and liver related diseases, HIV, and higher socioeconomic status were also more likely to receive treatment. These results highlight important disparities in treatment uptake including people from lower SES and those with substance use disorders. Although, DAAs were expected to reduce barriers for people with substance use disorders, our results indicate that these barriers still remain and efforts are needed to increase uptake of treatment for marginalized groups, otherwise, progress towards WHO elimination goals will be hampered. 53 4 Conclusions 4.1 Summary of Findings 4.1.1 Introduction The advent of newer, more effective HCV treatments, direct-acting antivirals (DAAs), in 2015 allowed many more people living with HCV to be treated, especially those with advanced liver fibrosis who were unable to tolerate previous treatment regimens due to severe side-effects. The DAAs are associated with fewer side-effects and provide the opportunity for widespread cure.(39,81) In 2018, all HCV treatment restrictions were lifted in BC, and all people living with HCV were eligible for treatment with DAAs. However, only 54.4% of individuals in BC-HTC known to be eligible for treatment (i.e. those with HCV RNA-positive test results) in 2018 had been treated.(9) In order to reach the 2030 WHO HCV elimination goals, throughout the world there needs to be an 80% decrease in incidence of HCV, 90% of people living with HCV need to be diagnosed, and 80% of those diagnosed need to initiate DAA treatment.(136) Significant gaps in the HCV care cascade, from screening to diagnosis to treatment are major impediments to HCV elimination.(136) HCV affects diverse and often overlapping populations, and assessing patterns of shared socio-demographic and clinical characteristics among subpopulations is important for the optimal design of a suite of HCV services and decision making at point-of-care. Latent Class Analysis has been demonstrated to be a valuable tool to inform tailored program planning efforts that reduce care-cascade gaps and increase service delivery efficiency. The pathway to hepatitis C elimination requires the collaboration of different health services such as mental health and addiction, community clinics, pharmacies, and primary care clinics among others.(136) After analyzing the results of modeling studies that project HCV elimination goals, we can see that Canada has had much success immediately following the advent of DAAs.(9,136) However, this success should be interpreted with caution, as the individuals who were treated earlier-on were perhaps easier to reach. These “frequently-reached” people include those who failed SVR with interferon treatments, those who were initially deterred from HCV therapy due to fear of severe side-effects associated with interferon-based therapy, and those 54 with newer infections that were “warehoused” or waiting for the release of more effective and tolerable treatments.(16) Therefore, there was a group of people essentially at health care providers’ door-step ready to be prescribed and treated. In 2018, there was a 35% increase in the proportion of people who initiated treatment from 2012.(9) This proportional increase needs to be sustained in order to eliminate HCV in Canada by 2030.(5,136) In BC, an annual average of 3,900 people need to be treated between 2020 and 2030 in order to reach elimination.(5) Hypothetically, each year that people are treated, there are fewer that require treatment. The people that remain untreated will be those that have less contact with the health care system or are part of a “hardly-reached” group. My thesis aimed to address several objectives: (1) to develop multifaceted HCV patient profiles and population subgroups based on their sociodemographic and clinical characteristics, (2) to understand indicators of HCV treatment uptake, (3) to assess the relationship between the multifaceted patient subgroups and treatment uptake. To examine these objectives, I performed two studies using administrative data from the BC Hepatitis Testers Cohort: the first implemented Latent Class Analysis to address objective (1) and the second used multivariable logistic regression to address objectives (2) and (3). 4.1.2 LCA Main Findings In Chapter 2, I used LCA to develop patient profiles of people living with HCV in BC. After reviewing the literature, I found that no other published study has defined clusters of people affected by HCV on a population-level scale. Chapter 2 aimed to understand which population-subgroups in British Columbia are affected by HCV infection and what their makeup was. Understanding the shared characteristics of people affected by HCV and where they are co-located can provide valuable information for policy-makers to optimize service delivery. The LCA classified individuals into six unique subpopulations in BC that mirror previously defined syndemic risk groups for HCV.(137) These included Older PWID, Younger PWID, gbMSM, Urban Socially Deprived Baby Boomers, Rural Baby Boomers, and People of Asian Backgrounds. While some people may have lower posterior probabilities of belonging to their group (and thus may have some probability of fitting in another group), the majority of 55 individuals had good fit statistics for their assigned groups. With regard to service alignment, people that may have lower probabilities of fitting in one specific group can still be targeted if services for each group are provided. By providing a suite of services, we can let individuals decide which group they most align with. Better connection with individualized or directed health care services programs may offer more opportunities for HCV treatment enrollment. Thus, health care services should be designed in ways that are specific for each syndemic group so that individuals feel more of a connection with their healthcare delivery and are getting all the services they need in one place. 4.1.3 Logistic Regression Analyses Findings In Chapter 3, I examined patterns of treatment uptake, both among individual indictor variables and among latent classes. While there have been previous studies that have analyzed indicators of treatment uptake among multivariable models, to my knowledge, this is first study to examine treatment uptake among population-level latent classes of HCV- affected peoples. With the HCV elimination goals in mind, it is important to know which groups have lower treatment uptake in order to provide more accessible, acceptable, accommodating, and affordable services or efforts for these groups. History of liver disease, chronic diseases outside of the liver, HIV infection, East Asian ethnicity, and higher socioeconomic status were all associated with increased odds of treatment uptake. These findings were reflected by the latent class logistic regression analysis that showed that latent classes or groups with higher proportions of people with these sociodemographic and clinical characteristics were more likely to have initiated HCV treatment. People of Asian Backgrounds were most likely to be treated with interferon-based therapies and Rural BB had the greatest odds of initiating treatment with DAAs. Younger PWID were consistently less likely to be treated, suggesting the need for more accessible services for members of this sub-population. The sensitivity analyses showed that gbMSM had many individuals who experienced gaps in the service delivery across the HCV care cascade. Many people in this group did not receive an RNA test after receiving a positive antibody test, thus inhibiting their ability to be treated. It is 56 important to offer testing services that are convenient to members of this group in order to not have loss to follow-up. In summary, treatment uptake varied by syndemic group, and these groups had varying co-morbidities, places of residence, and other healthcare needs. 4.2 Recommendations 4.2.1 Recommendations for policy and practice Tailored services and precision medicine My findings suggest that HCV services should not be uniform, but rather tailored to meet certain patient groups’ shared needs. People living with HCV have varied levels of material and social poverty, access to health care, mental and physical well-being, age-distribution, and places of residence among other factors. My findings suggest that specific services relevant to certain subgroups should be co-located in order to streamline testing and treatment for hardly-reached groups in in locations that are proximal and convenient. This is consistent with previous studies.(137) Identifying the difference between HCV transmission and HCV disease When considering the elimination of HCV, it is important to point out the distinction between acute HCV infections vs. chronic HCV disease due to long term consequences of infection. Effective DAA treatments allow for the elimination of HCV as a chronic disease, as they are able to improve health outcomes for many people with liver damage and scarring. However, treatment of acute HCV infection does not necessarily eliminate the possibility of transmission, as risk-behaviours can continue to propagate spread. Unless all people living with HCV are treated simultaneously, which is not necessarily feasible, there will continue to be transmission of the virus. Furthermore, many people living with HCV have other health and wellness concerns that are as, if not more, important to treat than their HCV infection. It is only after we address the root of the problem that causes perpetuation of HCV, that HCV elimination can be reached. Moreover, treating people for HCV without ensuring stable housing, food supplies, or other necessary resources may not solve their most pressing concerns. In these situations, it is 57 important to understand the hierarchy of needs and address them sequentially. For example, helping patients manage injection drug-related addiction may be a priority to prevent future infection, and HCV treatment could be offered concurrently. The attitudes and decisions of health care providers affect patients’ access to HCV services. Providers play an essential role in offering patient-centered care by asking patients what they need and involving them discussions around research gaps. The burden and responsibility of HCV treatment and adherence should not lie solely on the patients, but instead on the policy-makers, public health workers, and providers, who play a huge role in allowing accessibility. In this way, perhaps patients should not be thought-of as hard-to-reach, because they may be hardly-reached.(133) In summary, my study shows that there are distinct syndemic groups of people living with HCV in BC, who have varied levels of treatment uptake, implicating the benefit of more targeted provision of services. 4.2.2 Recommendations for research Longitudinal analysis of HCV outcomes over time Assessing the movement of people affected by HCV over time and through the care cascade, from infection to perhaps cure or reinfection, could provide more detailed information about treatment access and barriers to HCV elimination. A future analysis could re-run the Part 1, indicators of treatment uptake analysis to include multiple treatment courses per individual, with a more detailed outcome variable that delineates type of treatment (DAA or interferon). This analysis could be a time-to-event analysis (i.e. cox-proportional hazard) that takes into account the amount of time is involved between diagnosis and treatment (interesting information that was not included in this thesis) in order to address disparities in timeliness of treatment access for certain people or syndemic groups. Furthermore, SVR could be measured in conjunction with treatment uptake to understand not only who is initiated, but also adherence and cure. LCA in different provinces or countries 58 It would be useful to reproduce this study in other provinces in Canada or other parts of the world to see if similar or different syndemic groups appear. In a smaller-scale LCA performed in Switzerland, they found five groups named “PWID”, “Swiss Employees”, “Foreign Employees”, “Retirees”, and “Male-drinkers”.(101) While this study used different variables for grouping, it had overlaps with my findings. For example, we both found that PWID had the lowest frequency of treatment uptake and people with “foreign” backgrounds had the highest frequency of treatment uptake. Reproducing an LCA among people living with HCV in Canada may further strengthen the generalizability of the results to countries with similar settings and population profiles. Geographic Information System Mapping Geographic Information System (GIS) mapping of syndemic groups affected by HCV may provide further valuable information on the exact area that services should be rolled-out. A study that mapped the latent class groups of this study across BC census dissemination areas has recently been conducted and presented at the 2020 Canadian Liver Meeting by me and Butt ZA et al.(122) The latent classes were found in geographically distinct areas. Urban SDBB and Younger PWID were concentrated around urban centres such as Vancouver city. gbMSM were mostly located around West End of Vancouver City, and Prince George. Rural Baby Boomers were the predominant group in Northern BC and in more sparsely populated areas of BC. People of Asian backgrounds were concentrated in Burnaby, Richmond, Surrey, and South Vancouver with very low proportions in other areas. Finally, a higher proportion of Older PWID were clustered in North/West Vancouver, Coquitlam, Port Alberni, and Kelowna.(122) Application of LCA to other health conditions Latent Class Analysis could be applied to other diseases to understand the multi-faceted nature of many health conditions. This could offer a paradigm shift in the way that public health is understood, by applying ideas of personalization from precision medicine to the whole population. 59 4.3 Final Conclusions In this study, LCA classified individuals into six unique subpopulations in BC including Older PWID, Younger PWID, gbMSM, Urban Socially Deprived Baby Boomers, Rural Baby Boomers, and People of Asian Backgrounds that had varying levels of treatment access and were prescribed by different provider-types. Classification of populations into distinct groups can offer both a cost-effective strategy for the elimination of HCV and more individualized care for patients.(138) Statistical grouping methods can be valuable but should not be rolled-out without input from the populations they are meant to serve. It is important to involve patients in decisions regarding their own health care and listen to their needs. 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Montreal, Quebec; 2019 [cited 2020 Mar 11]. Available from: canhepc.ca/sites/default/ files/media/documents/blueprint_hcv_2019_05.pdf 137. Krajden M, Cook D, Janjua NZ. Contextualizing Canada’s hepatitis C virus epidemic. Can Liver J [Internet]. 2018 Dec 1 [cited 2020 Mar 12]; Available from: https://canlivj.utpjournals.press/doi/abs/10.3138/canlivj.2018-0011 138. Kracht PAM, Arends JE, van Erpecum KJ, Urbanus A, Willemse JA, Hoepelman AIM, et al. Strategies for achieving viral hepatitis C micro-elimination in the Netherlands. Hepatol Med Policy [Internet]. 2018 Sep 29 [cited 2020 Mar 13];3. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6162944/ 72 Appendices Appendix 1 HCV Diagnosed Definition 1. HCV Diagnosed† On or before December 31 of the year assessed: • a confirmed anti-HCV positive test OR • a confirmed HCV RNA positive test OR • an HCV genotype result OR • dispensation of at least one HCV specific treatment (interferon-based or direct-acting antivirals) OR • a confirmed public health HCV case report 2. HCV RNA Tested • At least one HCV RNA (PCR) test on record on or before December 31 of the year assessed 3. HCV RNA Positive • At least one HCV positive RNA (PCR) test on record on or before December 31 of the year assessed. OR • an HCV genotype result on or before December 31 of the year assessed OR • dispensation of interferon-based or direct-acting antivirals • It excludes individuals with no dispensation of interferon-based or direct-acting antiviral treatment whose last HCV RNA test on record is negative (these individuals are considered to have spontaneously cleared and thus, are not actively infected/non-viraemic) 4. Initiated HCV Treatment • Dispensation of antivirals specific to HCV treatment (interferon-based or direct-acting antivirals) ‡, on or before December 31 of the year assessed. †Earliest available laboratory testing data (1992), reportable disease data (1990), and drug dispensation data (2000) ‡ PharmaNet DIN/PIN: 2239730, 2241159, 2246026, 2246027, 2246028, 2246029, 2246030, 2248077, 2248078, 2253410, 2253429, 2254581, 2254603, 2254638, 2254646, 2370816, 2371448, 2371456, 2371464, 2371472, 2371553, 2416441, 2418355, 2425890, 2425904, 2432226, 2436027, 2436396, 2436418, 2436426, 2439212, 2444747, 2444755, 2447711, 2451131, 2452294, 2456370, 2467542, 2467550 Abbreviations: HCV, hepatitis C virus; RNA, ribonucleic acid, PCR; polymerase chain reaction, SVR; sustained virological response, DINPIN; Drug Information Number/Product Identification Number. 73 Appendix 2 Criteria and data sources for the BC Hepatitis Testers Cohort (BC-HTC) Criteria for Inclusion in BC-HTC All individuals: • tested at the centralized provincial laboratory for HCV or HIV OR • reported by BC public health as a confirmed case of HCV OR • reported in BC enhanced surveillance system as a confirmed case of HIV or AIDS (all reports) OR • reported by BC public health as a confirmed case of HBV OR • included in BC Enhanced Strain Surveillance System (EHSSS) as an acute HBV or HCV case • All individuals meeting at least one the above criteria were linked internally across all their tests and case reports. Those with a valid personal health number (PHN) were then sent for deterministic linkage with province-wide Cancer and Ministry of Health (MoH) datasets Provincial Communicable Disease Data Sources: Data Date Ranges: BC-PHMRL HIV laboratory testing datasets (tests: ELISA, Western blot, NAAT, p24, culture) 1988–2015 BC-PHMRL HCV laboratory tests datasets (tests: antibody, HCV RNA, genotyping) 1992–2018 HIV/AIDS Information System (HAISYS) (public health HIV/AIDS case reports) 1980–2015 Integrated Public Health information System (iPHIS) (public health case reports of HCV, HBV, and TB) 1990–2015 Enhanced Strain Surveillance System (EHSSS) (risk factor data on a subset of acute HCV and acute HBV cases) 2000–2013 Cancer and MoH Administrative Data Sources: Data Date Ranges: Client Roster (CR) (Registry of enrollment in the universal public health insurance plan including residential history) 1 1990–2016 BC Cancer Registry (BCCR) (primary tumour registry, excludes metastatic cancers) 2 1970–2016 Discharge Abstracts Dataset (DAD) (hospitalization records) 3 1985–2015 Medical Services Plan (MSP) (physician diagnostic and billing data) 4 1990–2015 PharmaCare/PharmaNet (Pharma) (prescription drug dispensations) 5, 6 1985–2018 BC Vital Statistics (VS) (deaths registry) 7 1985–2018 National Ambulatory Care Reporting System (NACRS) (emergency departments) 8 2012–2015 Chronic Disease Registry 9 1992–2015 The final BC-HTC comprises all individuals successfully linked on PHN to the MoH Client Roster (a registry of all BC residents enrolled in the publicly-funded universal healthcare system) Abbreviations: HCV, Hepatitis C Virus; HBV, Hepatitis B Virus; HIV/AIDS, Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome; BC-PHMRL, BC Public Health Microbiology and Reference Laboratory; RNA, Ribonucleic Acid; PCR, Polymerase Chain Reaction. 74 Appendix 3 Definitions for co-variables in the BC-HTC administrative code definitions Ethnicity Ethnicity classification was based on Onomap10,11 name recognition software and grouped as South Asian (Pakistani, Indians, Bangladeshis, Nepalese, Sri Lankans), East Asian (Chinese, Filipinos, Japanese, Korean, South East Asians), and Other BC Residents (White, Black, Central Asian, Latin American, Pacific Islander, West Asian individuals). Gay, bisexual, and other men who have sex with men (gbMSM) gbMSM was defined as men who reported a male sexual partner, including those who reported both male and female sexual partners, or had a history of STBBI testing and/or diagnoses, lack of drug or alcohol misuse diagnoses, history of visit to an identified gay and bisexual men’s clinic, and residence in MSM-dense neighborhoods.12 Material and Social Deprivation Quintiles (Ever) The Québec Index of Material and Social Deprivation 13 was calculated based on 6-digit postal code of individuals’ residence. The deprivation index combines six indicators related to health and welfare that represent material or social deprivation and are available by enumeration area in Canadian census data: 1) proportion of persons without high-school diploma 2) ratio of employment to population 3) average income 4) proportion of persons separated, divorced, widowed 5) the proportion of single-parent families 6) proportion of people living alone. Urbanicity (urban/rural) Postal codes were assigned an ‘Urban’ flag (on a year basis) if they belonged to a population centre with a population of at least 1,000 and a density of 400 or more persons per square kilometre. Starting in 2011, the term 'population centre' (POPCTR) replaced the term 'urban area' (UA). Otherwise, postal codes were assigned ‘Rural’ flag if they were not missing geocoding information. People who injection drugs (PWID)/ injection drug use (IDU)14 Injection drug use was defined at the occurrence of at least 1 MSP, 1 hospitalization, or 1 PharmaNet code for major drug-related diagnoses involving addiction, dependence, and drug-induced mental disorders; illicit drug use most likely to be injecting (e.g. excluding cannabis), or illicit use of prescribed drugs including: hallucinogens, barbiturates/tranquillizers, sedatives, hypnotics, anxiolytics, opioids, cocaine, amphetamine, volatile solvents; or discharge to drug rehabilitation, counselling, and surveillance, or methadone/buprenorphine substitution treatment (11 ≤ diagnosis age ≤ 65). Recent PWID was IDU between 2012 and 2015, while past was IDU before 2012. Physician Billing Data: MSP ICD-9 diagnostic codes: starting with 292, 3040, 3042, 3044, 3046-9, 3054-7, 3059, 6483, 9650, 9658, 9663-64, 9670, 9684, 9685, 9697-99, 9700-01, 9708-09, E8500, or exact codes V6542 or fee item = 39 Hospitalization Data: DAD1/ICD-9-CM: starting with 292, 3040-42, 3044-49, 3054-7, 3059, 6483, 7960, 9621, 9650, 9658, 9663-64, 9670, 9684-85, 9694, 9696-99, 970, 9700-01, 9709-09, E8500, V6542; NACRS/DAD2/ICD-10-CA: starting with F11, F13-5, F19, or exact codes R781-82, T387, T400-T406, T409, T412, T423-28, T436-439, T507; NACRS (ICD10 complaint codes): 751, 753. 75 PharmaNet DIN/PIN*: 2242963-64, 2295695, 2295709, 22123346-49, 99792-93, 66999990-66999993, 66999997-99, 67000000-4, 999776, 2408090, 2408104, 2424851, 2424878. Opioid Agonist Therapy (OAT) Opioid agonist therapy was defined as at least 1 PharmaNet code involving methadone or buprenorphine treatment PharmaNet DIN/PIN*: 2242963-5, 999792-3, 999776, 2295695, 2295709, 2408090, 2408104, 2424851, 2424878, 2453908, 2453916, 2468085, 2468093, 2474921, 22123346-9, 66999990-3, 66999997-9, 67000000-12 Alcohol Misuse Alcohol misuse was defined at the first occurrence of 2 MSP or 1 hospitalization codes for major alcohol-related diagnoses including alcoholic mental disorders and dependence/abuse syndromes; alcoholic polyneuropathy, myopathy, cardiomyopathy; pseudo Cushing’s syndrome; or discharge to alcohol rehabilitation, counselling, or surveillance Physician Billing Data: MSP ICD-9 diagnostic codes: starting with 291, 303, 3050, 3575, 4255 Hospitalization Data: DAD1/ICD-9-CM: starting with 291, 303, 3050, 3575, 4255; NACRS/DAD2/ICD-10-CA: starting with F10, E244, G312, G621, G721, I426, Z502, Z714 Major mental health illness Major mental illness was defined at the first occurrence of 1 hospitalization diagnostic code OR 2 MSP diagnostic codes from a psychiatrist visit for schizophrenic, bipolar, delusional, nonorganic psychotic, adjustment, anxiety, dissociative, personality and major depressive disorders. Physician Billing Data: MSP ICD-9 diagnostic codes: starting with 295-298, 300-301, 308-309, 311 or exact code 50B AND claim specialty = 3 Hospitalization Data: DAD1/ICD-9-CM: starting with 295-298, 300-301, 308-309, 311; NACRS/DAD2/ICD-10-CA: starting with F20-F25, F28-F34, F38-F45, F48, F60-F61 Hepatitis B virus (HBV) HBV coinfection was defined at the first occurrence of a case of HBV recorded in iPHIS or PANORAMA; if none recorded, then the first occurrence of 2 MSP (2 physician visits or 2 fee items), 1 hospitalization code or 1 PharmaNet code involving acute or chronic hepatitis B diagnoses with or without hepatic coma and with or without delta agents; inactive carriers; or hepatitis B-specific treatment among those diagnosed at age 18 months or older. Physician Billing Data: MSP ICD-9 diagnostic codes: starting with 702, 703, V0261; Fee item codes: 90675, 90690, 90831, 91765. Hospitalization Data: DAD1/ICD-9-CM: starting with 702, 703, V0261; DAD2/ICD-10-CA: starting with B16, B180, B181, Z2250. PharmaNet Data: DINPIN numbers: 02239193, 02247128, 02247823, 02282224, 02288389 Human immunodeficiency virus (HIV) co-infection HIV coinfection was defined at the first occurrence of 3 MSP or 1 hospitalization for HIV or a positive HIV serologic test, HAISYS or BC Vital Statistics indication (cause of death). Physician Billing Data: MSP ICD-9 diagnostic codes: starting with 042, 043, 044, 7953, 7958, 76 79571, V08. Hospitalization Data: DAD1/ICD-9-CM: starting with 042, 043, 044, 7953, 7958, 79571, V08; NACRS/DAD2/ICD-10-CA: starting with B20-B24, B9735, F024, O987, R75, Z21. Liver cirrhosis Liver cirrhosis was defined as the occurrence of either 1 physician visit or 1 hospitalization code relevant to decompensated cirrhosis and compensated cirrhosis including esophageal varices with or without bleeding, spontaneous bacterial peritonitis, hepatic encephalopathy, hepatorenal syndrome, ascites, and portal hypertension, chronic hepatitis failure (alcoholic/non-alcoholic), hepatic coma, unspecified hepatic failure, unspecified cirrhosis of the liver or previous cause of chronic liver disease. Physician Billing Data: MSP ICD-9 diagnostic codes: starting with 4562; exact codes 4560, 4561, 56723, 5722, 5724, 5712, 5728, 5715, 7895, 07044, 5713. Hospitalization Data: NACRS/DAD1/ICD-9-CM diagnostic codes: starting with 4562; exact codes 4560, 4561, 56723, 5722, 5723, 5724, 5712, 5728, 5715, 7895, 07044, 5713. DAD2/ICD-10-CA diagnostic codes: starting with K703; exact codes: I850, K652, K721, K729, K766, K767, K7460, K7469, R18, I98.20, I98.3, K704. *Abbreviations: DAD=Discharge Abstract Database (hospitalization); DIN = Drug Identification Number; MSP=Medical Services Plan (medical visits); PIN = Product Information Number; NACRS=National Ambulatory Care Reporting System Liver Transplant Liver transplant was defined as the occurrence of one hospitalization code related to liver transplant (diagnosis, procedure, failure, rejection and/or, infection) Hospitalization Data: DAD1,3/ICD-9-CM diagnostic codes: starting with 624; exact codes: 99682, V427. DAD2/ICD-10-CA diagnostic codes: starting with T864, 1OA85; exact codes: Z944. Hepatocellular Carcinoma (HCC) Hepatocellular carcinoma was defined as 1 topography code related to the liver and 1 histology code related to hepatocellular carcinoma (NOS, fibromellar, scirrhous, spindle cell variant, clear cell type, or pleomorphic type). Hospitalization Data: ICD-0-3 exact code: C22.0; SEER site/histology exact codes: 81703-81753. Non-Liver Chronic Disease/Conditions All non-liver chronic conditions were based off of the definitions provided by the BC Ministry of Health Chronic Disease Registry.9 For the purposes of this study, we included non-mental health and non-liver related chronic diseases and quantified how many each individual had (1,2,3, ≥4) based on prevalence or ever having the condition. The diseases that were included were: diabetes, chronic kidney disease, rheumatoid arthritis, end-stage renal disease, ischemic heart disease, hemorrhagic stroke, hospitalized stroke, hospitalized transient ischemic attack, ischemic stroke, major adverse cardiovascular event, hypertension, dialysis, obesity, peripheral vascular disease, Alzheimer’s/dementia, acute myocardial infarction, angina, asthma, coronary artery bypass grafting, chronic obstructive pulmonary disease, coronary angiogram, epilepsy, heart failure, multiple sclerosis, osteoporosis fracture, osteoarthritis, osteoporosis, parkinsonism, percutaneous transluminal coronary angioplasty, and transplant. 77 Appendix 1-3 References 1. British Columbia Ministry of Health [creator]. Client Roster (Client Registry System/Enterprise Master Patient Index). British Columbia Ministry of Health [publisher]. Data Extract. MOH (2013). 2016. https://www2.gov.bc.ca/gov/content/health/conducting-health-research-evaluation/data-access-health-data-central/health-authorities 2. BC Cancer [creator]. BC Cancer Registry. BC Cancer [publisher]. Data Extract. BC Cancer (2013). 2017. http://www.bccancer.bc.ca/health-professionals/professional-resources/bc-cancer-registry/request-registry-data 3. British Columbia Ministry of Health [creator]. Discharge Abstract Database (Hospital Separations). British Columbia Ministry of Health [publisher]. Data Extract. MOH (2013). 2016. https://www2.gov.bc.ca/gov/content/health/conducting-health-research-evaluation/data-access-health-data-central/health-authorities 4. British Columbia Ministry of Health [creator]. Medical Services Plan (MSP) Payment Information File. British Columbia Ministry of Health [publisher]. Data Extract. MOH (2013). 2016. https://www2.gov.bc.ca/gov/content/health/conducting-health-research-evaluation/data-access-health-data-central/health-authorities 5. British Columbia Ministry of Health [creator]. PharmaCare. British Columbia Ministry of Health [publisher]. Data Extract. MOH (2013). 2019. https://www2.gov.bc.ca/gov/content/health/conducting-health-research-evaluation/data-access-health-data-central/health-authorities 6. British Columbia Ministry of Health [creator]. PharmaNet. British Columbia Ministry of Health [publisher]. Data Extract. MOH (2013). 2019. https://www2.gov.bc.ca/gov/content/health/conducting-health-research-evaluation/data-access-health-data-central/health-authorities 7. BC Vital Statistics Agency [creator]. Vital Statistics Deaths. BC Vital Statistics Agency [publisher]. Data Extract. BC Vital Statistics Agency (2014). 2019. https://www2.gov.bc.ca/gov/content/health/conducting-health-research-evaluation/data-access-health-data-central/health-authorities 8. British Columbia Ministry of Health [creator]. National Ambulatory Care Reporting System. British Columbia Ministry of Health [publisher]. Data Extract. MOH (2017). 2017. https://www2.gov.bc.ca/gov/content/health/conducting-health-research-evaluation/data-access-health-data-central/health-authorities 9. British Columbia Ministry of Health [creator]. Chronic Disease Registry. British Columbia Ministry of Health [publisher]. Data Extract. MOH (2017). 2017. https://www2.gov.bc.ca/gov/content/health/conducting-health-research-evaluation/data-access-health-data-central/health-authorities 10. Ryan R, Vernon S, Lawrence G, Wilson S. Use of name recognition software, census data and multiple imputation to predict missing data on ethnicity: application to cancer registry records. BMC Med Inform Decis Mak 2012; 12: 3. 11. Lakha F, Gorman DR, Mateos P. Name analysis to classify populations by ethnicity in public health: validation of Onomap in Scotland. Public Health 2011; 125(10): 688-96. 12. Salway T, Butt Z, Wong S, Balshaw R, Rossi C, Wong J, Yu A, Alvarez M, Grennan T Gilbert M, Krajden M, Janjua N. MSM predictive modeling within a large, linked database of laboratory, surveillance, and administrative healthcare records. Joint Meeting of the 23rd International Society for Sexually Transmitted Disease Research and 20th International Union against Sexually Transmitted Infections; 2019 Jul 14-17; Vancouver, Canada 13. Pampalon R, Hamel D, Gamache P, Raymond G. A deprivation index for health planning in Canada. Chronic Dis Can 2009; 29(4): 178-91. 14. Janjua NZ, Islam N, Kuo M, et al. Identifying injection drug use and estimating population size of people who inject drugs using healthcare administrative datasets. International Journal of Drug Policy 2018; 55: 31-9. 78 Appendix 4 Sensitivity Analysis Including Deceased A total of 41,521 individuals were included in this sensitivity analysis (691 excluded in Part 1’s sensitivity analysis because missing explanatory variables). The latent classes had differing proportions of people who passed away, and those with higher proportions had the greatest change in odds of treatment uptake compared to the original model (Table 3.4). Older PWID and Urban SDBB had the highest proportions of people who died at 48.2% and 41.0%, respectively (Table 3.4). A major difference between the original model and this sensitivity analysis was the AOR for Older PWID; it changed from having significantly greater odds of DAA treatment uptake to significantly lower odds compared to Younger PWID [from 1.40 (95% CI: 1.28-1.54) to 0.82 (95% CI: 0.75-0.89)] (Figure 3.3, Figure Appendix 4). Generally, all AOR of treatment uptake were attenuated in the sensitivity analysis compared to the original analyses for Part 1 and 2 (Appendix 9, 6). A major difference for the Part 1 analysis was observed with the liver disease variable. Liver disease (vs. no liver disease) went from an AOR of 2.25 (95% CI: 1.98, 2.55) in the original model to 0.82 (95% CI: 0.77, 0.87) in this sensitivity analysis (Appendix 6, 11). This difference may have arisen because those who had liver disease and died may have died because they did not receive treatment. Therefore, by excluding those who died in the original analytic sample, it included only those who had liver disease that survived (potentially because they received treatment) thus causing the results to show a positive association between liver disease and treatment uptake. Consequently, this association should be examined further in future analyses. For Part 2, a key difference was noted with Older PWID; slightly lower odds of DAA treatment uptake were observed compared to Younger PWID (vs. higher odds in original analysis). This variation may have been due to the fact that many more Older PWID died than Younger PWID. Including those that died (who may have not had the opportunity to be treated) may have caused the overall odds of treatment uptake for Older PWID to be lower than Younger PWID (a class with fewer deaths—thus fewer people missing out on opportunity for treatment) (Figure Appendix 4). Because this was the only major difference from the original model, Part 2 was deemed robust. 79 Figure Appendix 4 Sensitivity analysis including deceased; adjusted odds of treatment uptake of interferon and DAA treatments by latent class in comparison to Younger PWID (multinomial logistic regression model) 2.061.372.934.541.111.180.931.731.280.820.2 1 5Latent Class (ref=Younger PWID)Urban SDBB (IFN)gbMSM (IFN)Rural BB (IFN)People of Asian Backgrounds (IFN)Older PWID (IFN)Urban SDBB (DAA)gbMSM (DAA)Rural BB (DAA)People of Asian Backgrounds (DAA)Older PWID (DAA)Odds Ratios; 95% CI (log scale)80 Appendix 5 Part 1 MLR final model fit statistics Criterion Intercept Only Intercept and Covariates AIC 42454.29 39521.21 SC 42462.63 39863.13 -2 Log L 42452.292 39439.21 R-Square 0.0928 Max-rescaled R-Square 0.1243 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 3013.079 40 <.0001 Score 2895.182 40 <.0001 Wald 2645.444 40 <.0001 81 Appendix 6 Adjusted and unadjusted odds of treatment uptake from Part 1 analysis Variable Variable levels Adjusted odds lower upper Unadjusted odds lower upper Sex male 2.11 1.72 2.63 1.27 1.22 1.31 female 2.18 1.77 2.72 1.23 1.19 1.26 Birth cohort <1945 1.57 1.25 2.01 1.14 1.02 1.27 1945-1964 3.42 2.79 4.28 1.74 1.69 1.79 1965-1974 2.17 1.76 2.71 0.85 0.81 0.89 >1974 1.81 1.47 2.27 0.57 0.54 0.61 Ethnicity East Asian 2.51 2.02 3.20 1.93 1.71 2.17 South Asian 2.79 2.24 3.55 1.82 1.63 2.04 Other BC Residents 2.05 1.45 3.03 1.20 1.17 1.23 unknown 1.82 1.35 2.53 1.09 0.87 1.36 HBV HBV positive 2.29 1.85 2.91 1.58 1.43 1.74 HBV negative 2.00 1.64 2.49 1.22 1.19 1.25 HIV HIV positive 3.09 2.50 3.93 1.81 1.64 2.00 HIV negative 1.49 1.21 1.85 1.21 1.19 1.24 TB TB positive 1.81 1.29 2.64 1.01 0.74 1.38 TB negative 2.54 2.22 2.93 1.24 1.21 1.27 gbMSM gbMSM 2.33 1.88 2.95 1.37 1.26 1.48 heterosexual 1.97 1.61 2.45 1.23 1.20 1.26 PWID never 2.79 2.27 3.50 1.55 1.51 1.59 recent 1.79 1.45 2.24 0.72 0.68 0.76 past 1.98 1.60 2.47 0.98 0.93 1.03 OAT never 2.59 2.12 3.24 1.53 1.49 1.57 recent 2.22 1.80 2.78 0.77 0.74 0.81 past 1.71 1.38 2.17 0.72 0.67 0.78 Alcohol misuse yes 1.94 1.58 2.42 1.00 0.96 1.04 no 2.37 1.93 2.96 1.35 1.31 1.38 Mental health diagnosis yes 2.34 1.90 2.92 1.15 1.10 1.19 no 1.97 1.60 2.45 1.29 1.26 1.33 Social quintile 1 (most privileged) 2.43 1.97 3.07 1.61 1.50 1.72 2 2.41 1.95 3.04 1.55 1.45 1.65 3 2.21 1.79 2.78 1.52 1.43 1.61 4 2.06 1.67 2.58 1.36 1.30 1.43 5 (least) 1.70 1.38 2.12 0.98 0.94 1.02 Material quintile 1 (most privileged) 2.31 1.87 2.92 1.38 1.31 1.47 2 2.25 1.83 2.83 1.50 1.42 1.59 3 2.26 1.83 2.83 1.47 1.40 1.55 4 2.11 1.71 2.64 1.29 1.23 1.35 5 (least) 1.83 1.48 2.28 0.98 0.94 1.02 Liver disease yes 3.21 2.57 4.14 3.38 3.00 3.81 no 1.43 1.17 1.76 1.18 1.16 1.21 HA Interior 2.25 1.82 2.83 1.35 1.28 1.42 Fraser 2.03 1.65 2.53 1.28 1.23 1.33 Vancouver Coastal 2.01 1.64 2.51 1.12 1.07 1.17 Island 2.42 1.96 3.05 1.40 1.33 1.47 Northern 2.04 1.63 2.59 1.17 1.07 1.27 Number of non-liver chronic conditions 1 1.59 1.29 1.98 0.92 0.89 0.95 2 2.24 1.82 2.81 1.46 1.40 1.53 3 2.38 1.93 3.00 1.67 1.56 1.78 4 or more 2.48 2.02 3.12 1.80 1.70 1.91 HCV diagnosis year 1990-1994 2.87 2.31 3.67 1.86 1.69 2.05 1995-1999 2.32 1.89 2.90 1.41 1.35 1.47 2000-2004 2.37 1.93 2.97 1.41 1.35 1.47 2005-2009 2.00 1.63 2.51 1.13 1.07 1.18 2010-2014 1.94 1.57 2.44 1.05 0.99 1.11 2015-2018 1.58 1.26 2.00 0.64 0.59 0.70 Urbanicity urban 2.06 1.68 2.56 1.21 1.18 1.24 rural 2.23 1.81 2.81 1.65 1.55 1.76 82 Appendix 7 Multivariable and bivariable odds of treatment uptake among latent classes Effect (vs. Younger PWID) for interferon (IFN) and direct-acting antiviral therapy (DAA) Adjusted Point Estimate Confidence Interval Unadjusted Point Estimate Confidence Interval 25% 75% 25% 75% Urban SDBB (IFN) 2.76 2.49 3.05 3.18 2.88 3.51 gbMSM (IFN) 1.37 1.23 1.52 1.36 1.22 1.51 Rural BB (IFN) 3.45 3.17 3.76 3.87 3.55 4.21 People of Asian Backgrounds (IFN) 5.45 4.82 6.15 5.14 4.57 5.78 Older PWID (IFN) 1.56 1.41 1.73 2.02 1.83 2.23 Urban SDBB (DAA) 1.90 1.73 2.08 1.87 1.70 2.05 gbMSM (DAA) 0.92 0.83 1.01 0.91 0.82 1.00 Rural BB (DAA) 2.18 2.02 2.36 2.15 1.99 2.32 People of Asian Backgrounds (DAA) 1.53 1.34 1.74 1.51 1.33 1.72 Older PWID (DAA) 1.40 1.28 1.54 1.38 1.26 1.51 Appendix 8 Sensitivity analysis including no RNA test; multivariable and bivariable odds of treatment uptake among latent classes Effect (vs. Younger PWID) Adjusted Point Estimate Confidence Interval Unadjusted Point Estimate Confidence Interval 25% 75% 25% 75% Urban SDBB (IFN) 1.75 1.60 1.93 2.06 1.88 2.26 gbMSM (IFN) 0.80 0.72 0.88 0.87 0.79 0.96 Rural BB (IFN) 2.10 1.94 2.28 2.44 2.25 2.64 People of Asian Backgrounds (IFN) 3.14 2.82 3.50 3.42 3.08 3.81 Older PWID (IFN) 1.23 1.12 1.35 1.48 1.35 1.63 Urban SDBB (DAA) 1.32 1.21 1.44 1.21 1.11 1.31 gbMSM (DAA) 0.59 0.54 0.65 0.58 0.53 0.63 Rural BB (DAA) 1.45 1.35 1.57 1.35 1.26 1.45 People of Asian Backgrounds (DAA) 0.99 0.88 1.12 1.01 0.89 1.13 Older PWID (DAA) 1.17 1.07 1.28 1.02 0.93 1.11 83 Appendix 9 Sensitivity analysis including those who died; odds of treatment uptake (controlling for year of HCV diagnosis) and Unadjusted estimates Effect (vs. Younger PWID) Adjusted Point Estimate Confidence Interval Unadjusted Point Estimate Confidence Interval 25% 75% 25% 75% Urban SDBB (IFN) 2.06 1.89 2.24 2.40 2.21 2.62 gbMSM (IFN) 1.37 1.24 1.52 1.37 1.24 1.51 Rural BB (IFN) 2.93 2.70 3.17 3.29 3.04 3.55 People of Asian Backgrounds (IFN) 4.54 4.07 5.07 4.45 4.00 4.95 Older PWID (IFN) 1.11 1.02 1.21 1.42 1.30 1.55 Urban SDBB (DAA) 1.18 1.09 1.29 1.11 1.02 1.21 gbMSM (DAA) 0.93 0.85 1.02 0.93 0.84 1.02 Rural BB (DAA) 1.73 1.60 1.86 1.65 1.53 1.77 People of Asian Backgrounds (DAA) 1.28 1.13 1.44 1.27 1.13 1.44 Older PWID (DAA) 0.82 0.75 0.89 0.75 0.69 0.81 84 Appendix 10.1 Sensitivity analysis including those without RNA test; multivariable and bivariable odds of treatment uptake Variable Variable levels Adjusted odds lower upper Unadjusted odds lower upper Sex male 1.54 1.27 1.86 0.78 0.75 0.81 female 1.62 1.34 1.96 0.73 0.72 0.75 Birth cohort <1945 1.12 0.91 1.38 0.63 0.57 0.69 1945-1964 2.65 2.19 3.20 1.00 0.98 1.03 1965-1974 1.62 1.34 1.97 0.51 0.49 0.54 >1974 1.29 1.06 1.57 0.40 0.38 0.42 Ethnicity East Asian 1.77 1.45 2.16 0.97 0.88 1.07 South Asian 2.23 1.81 2.73 1.23 1.12 1.36 Other BC Residents 1.28 1.08 1.53 0.73 0.71 0.74 unknown 1.27 0.96 1.67 0.83 0.76 0.90 HBV HBV positive 1.55 1.27 1.90 0.74 0.73 0.76 HBV negative 1.60 1.33 1.93 1.25 1.15 1.37 HIV HIV positive 2.33 1.90 2.85 0.73 0.71 0.74 HIV negative 1.07 0.88 1.29 0.67 0.50 0.89 TB TB positive 1.33 0.97 1.83 0.75 0.74 0.76 TB negative 1.87 1.65 2.11 0.84 0.78 0.90 Sexual orientation gbMSM 1.77 1.45 2.17 0.74 0.73 0.76 heterosexual 1.41 1.17 1.69 0.83 0.81 0.85 PWID never 1.93 1.59 2.34 0.61 0.58 0.64 recent 1.46 1.20 1.77 0.61 0.59 0.64 past 1.40 1.15 1.70 0.82 0.80 0.84 OAT never 1.68 1.39 2.03 0.66 0.63 0.69 recent 2.04 1.68 2.48 0.41 0.38 0.45 past 1.15 0.94 1.41 0.70 0.68 0.73 Alcohol misuse yes 1.47 1.22 1.78 0.77 0.75 0.78 no 1.69 1.40 2.05 0.82 0.79 0.85 Mental health diagnosis yes 1.79 1.48 2.17 0.72 0.70 0.74 no 1.39 1.15 1.68 0.98 0.92 1.03 Social quintile 1 (most privileged) 1.82 1.49 2.21 0.93 0.88 0.99 2 1.76 1.45 2.15 0.92 0.87 0.97 3 1.65 1.36 2.01 0.80 0.77 0.84 4 1.47 1.21 1.79 0.64 0.62 0.66 5 (least) 1.26 1.04 1.52 0.85 0.80 0.89 Material quintile 1 (most privileged) 1.69 1.39 2.06 0.90 0.86 0.95 2 1.67 1.37 2.02 0.90 0.86 0.94 3 1.67 1.38 2.03 0.79 0.76 0.82 4 1.55 1.28 1.88 0.63 0.61 0.66 5 (least) 1.34 1.11 1.63 2.28 2.06 2.53 Liver disease yes 2.48 2.01 3.06 0.71 0.70 0.73 no 1.00 0.84 1.20 0.81 0.77 0.84 HA Interior 1.63 1.34 1.99 0.78 0.75 0.81 Fraser 1.49 1.23 1.81 0.72 0.70 0.75 Vancouver Coastal 1.51 1.25 1.83 0.90 0.87 0.95 Island 1.90 1.56 2.31 0.66 0.61 0.71 Northern 1.40 1.14 1.71 0.50 0.48 0.51 Number of non-liver chronic conditions 1 0.97 0.80 1.18 0.97 0.94 1.01 2 1.64 1.35 1.99 1.20 1.14 1.28 3 1.93 1.59 2.36 1.27 1.20 1.33 4 or more 2.01 1.66 2.44 0.78 0.72 0.84 HCV diagnosis year 1990-1994 1.44 1.18 1.77 0.69 0.66 0.71 1995-1999 1.38 1.14 1.67 0.87 0.83 0.90 2000-2004 1.82 1.50 2.21 0.80 0.77 0.84 2005-2009 1.78 1.47 2.17 0.79 0.75 0.83 2010-2014 1.85 1.52 2.25 0.46 0.42 0.49 2015-2018 1.29 1.05 1.59 0.76 0.74 0.77 Urbanicity urban 1.48 1.23 1.78 1.01 0.96 1.07 rural 1.69 1.39 2.05 0.78 0.75 0.81 85 Appendix 10.2 Sensitivity analysis including those without RNA test; MLR odds ratios (log scale) 1.050.872.051.261.381.740.991.291.260.972.180.710.871.220.680.760.732.471.691.992.071.261.241.241.151.451.401.311.171.141.101.021.270.940.951.261.241.280.900.2 1 5male vs femalebirth cohort >1945 vs <1974birth cohort 1945-1964 vs <1974birth cohort 1965-1974 vs <1974East Asian vs. Other BC ResidentsSouth Asian vs Other BC Residentsunknown vs. Other BC Residentsmental illness (yes vs. no)gbMSM vs. heterosexualHBV positive vs notHIV positive vs. notTB positive vs notalcohol misuse (yes vs. no)OAT recent vs neverOAT past vs neverPWID recent vs neverPWID past vs neverliver disease (yes vs. no)2 non-liver conditions vs 13 non-liver conditions vs 14 non-liver conditions vs 1material index 1 vs 5material index 2 vs 5material index 3 vs 5material index 4 vs 5social index 1 vs 5social index 2 vs 5social index 3 vs 5social index 4 vs 5rural vs urbanInterior vs Fraser HAVancouver Coastal vs Fraser HAIsland vs Fraser HANorthern vs Fraser HAHCV diagnosed '95-'99 vs '90-'94 HCV diagnosed '00-'04 vs '90-'94 HCV diagnosed '05-'09 vs '90-'94 HCV diagnosed '10-'14 vs '90-'94 HCV diagnosed '15-'18 vs '90-'94 86 Appendix 11.1 Sensitivity analysis including those who died; multivariable and bivariable odds of treatment uptake Variable Variable levels Adjusted odds lower upper Unadjusted odds lower upper Sex male 0.34 0.16 0.74 0.91 0.89 0.93 female 0.37 0.17 0.80 0.97 0.94 1.00 Birth cohort <1945 0.20 0.09 0.43 0.54 0.50 0.58 1945-1964 0.58 0.27 1.26 1.19 1.16 1.22 1965-1974 0.42 0.20 0.91 0.72 0.69 0.76 >1974 0.33 0.16 0.72 0.53 0.50 0.56 Ethnicity East Asian 0.43 0.20 0.93 1.44 1.30 1.60 South Asian 0.47 0.22 1.02 1.48 1.34 1.64 Other BC Residents 0.28 0.13 0.60 0.90 0.88 0.92 unknown 0.32 0.14 0.71 0.93 0.76 1.14 HBV HBV positive 0.36 0.17 0.77 0.89 0.82 0.96 HBV negative 0.36 0.17 0.77 0.93 0.91 0.95 HIV HIV positive 0.41 0.19 0.88 0.90 0.83 0.97 HIV negative 0.31 0.15 0.67 0.93 0.91 0.95 TB TB positive 0.27 0.12 0.60 0.50 0.39 0.65 TB negative 0.47 0.22 1.00 0.93 0.92 0.95 Sexual orientation gbMSM 0.40 0.19 0.87 1.08 1.01 1.16 heterosexual 0.32 0.15 0.68 0.92 0.90 0.94 PWID never 0.48 0.22 1.04 1.17 1.15 1.20 recent 0.32 0.15 0.69 0.64 0.62 0.67 past 0.30 0.14 0.64 1.17 1.15 1.20 OAT never 0.42 0.19 0.90 1.08 1.06 1.11 recent 0.45 0.21 0.98 0.74 0.71 0.78 past 0.24 0.11 0.52 0.46 0.43 0.49 Alcohol misuse yes 0.31 0.14 0.66 0.68 0.66 0.71 no 0.42 0.19 0.89 1.07 1.04 1.09 Mental health diagnosis yes 0.38 0.18 0.83 0.83 0.81 0.86 no 0.33 0.15 0.72 0.99 0.96 1.01 Social quintile 1 (most privileged) 0.41 0.19 0.88 1.26 1.19 1.34 2 0.40 0.18 0.86 1.19 1.13 1.26 3 0.38 0.18 0.82 1.17 1.11 1.23 4 0.33 0.16 0.72 0.98 0.94 1.03 5 (least) 0.28 0.13 0.60 0.73 0.71 0.75 Material quintile 1 (most privileged) 0.40 0.19 0.86 1.06 1.01 1.12 2 0.37 0.17 0.80 1.10 1.05 1.16 3 0.37 0.17 0.80 1.08 1.03 1.13 4 0.35 0.16 0.75 0.96 0.92 1.00 5 (least) 0.30 0.14 0.65 0.74 0.71 0.76 Liver disease yes 0.32 0.15 0.70 0.83 0.79 0.88 no 0.40 0.18 0.85 0.95 0.93 0.96 HA Interior 0.38 0.18 0.82 0.99 0.94 1.03 Fraser 0.35 0.16 0.75 1.00 0.97 1.04 Vancouver Coastal 0.33 0.15 0.72 0.82 0.79 0.85 Island 0.39 0.18 0.85 1.01 0.96 1.05 Northern 0.34 0.16 0.72 0.86 0.79 0.92 Number of non-liver chronic conditions 1 0.29 0.13 0.62 0.78 0.76 0.80 2 0.40 0.19 0.86 1.13 1.09 1.18 3 0.40 0.18 0.85 1.12 1.06 1.18 4 or more 0.36 0.17 0.77 0.93 0.89 0.97 HCV diagnosis year 1990-1994 0.46 0.21 0.99 1.10 1.02 1.19 1995-1999 0.39 0.18 0.84 0.96 0.92 0.99 2000-2004 0.40 0.19 0.86 1.04 1.00 1.08 2005-2009 0.33 0.15 0.72 0.87 0.83 0.91 2010-2014 0.33 0.15 0.70 0.85 0.81 0.90 2015-2018 0.27 0.12 0.58 0.61 0.56 0.66 Urbanicity urban 0.64 0.54 0.76 1.21 1.14 1.27 rural 0.69 0.58 0.82 0.91 0.89 0.93 87 Appendix 11.2 Sensitivity analysis including those who died; MLR odds ratios (log scale) 1.090.591.751.261.531.681.141.151.271.001.300.570.741.090.580.660.620.821.391.371.251.321.231.221.161.471.421.371.201.071.080.951.130.960.850.870.720.710.580.2 1 5male vs femalebirth cohort >1945 vs <1974birth cohort 1945-1964 vs <1974birth cohort 1965-1974 vs <1974East Asian vs Other BC ResidentsSouth Asian vs Other BC Residentsunknown vs Other BC Residentsmental illness (yes vs no)gbMSM vs heterosexualHBV positive vs notHIV positive vs notTB positive vs notalcohol misuse (yes vs no)OAT recent vs neverOAT past vs neverPWID recent vs neverPWID past vs neverliver disease (yes vs no)2 non-liver conditions vs 13 non-liver conditions vs 14 non-liver conditions vs 1material index 1 vs 5material index 2 vs 5material index 3 vs 5material index 4 vs 5social index 1 vs 5social index 2 vs 5social index 3 vs 5social index 4 vs 5rural vs urbanInterior vs Fraser HAVancouver Coastal vs Fraser HAIsland vs Fraser HANorthern vs Fraser HAHCV diagnosed '95-'99 vs '90-'94 HCV diagnosed '00-'04 vs '90-'94 HCV diagnosed '05-'09 vs '90-'94 HCV diagnosed '10-'14 vs '90-'94 HCV diagnosed '15-'18 vs '90-'94
Thesis/Dissertation
eng
Population and Public Health
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Classifying people living with hepatitis C virus using a population-level latent class analysis to understand association with treatment uptake and optimize integration of health services
Text