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All-cause and cause-specific mortality risks in granulomatosis with polyangiitis Tan, Ju Ann 2017

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ALL-CAUSE AND CAUSE-SPECIFIC MORTALITY RISKS IN GRANULOMATOSIS WITH POLYANGIITIS  by  Ju Ann Tan  MBBS, University of Malaya, Malaysia, 2003  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Experimental Medicine)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)   August 2017  © Ju Ann Tan, 2017   ii Abstract Background Granulomatosis with polyangiitis (GPA) is a form of ANCA-associated vasculitis (AAV), a heterogenous group of small vessel vasculitides associated with anti-neutrophil cytoplasmic antibodies (ANCA). There is still significant disease mortality despite advances in treatment of GPA. Furthermore, longitudinal data on secular trends in GPA mortality are scarce and most are from selected populations. We aim to determine all-cause and cause-specific mortality risks in GPA patients compared to the general population, as well as to estimate the difference in mortality risks in GPA patients between 1997-2004 and 2005-2012.  Methods Chapter 2 was a systematic review and meta-analysis of observational studies in GPA. An extensive literature search was performed on the EMbase and Medline databases. Data was extracted for a pooled meta-analysis of standardized mortality ratios (SMR). Heterogeneity between studies was assessed using I2. Chapter 3 was a matched cohort population-based study using an administrative health database, comparing incident GPA cases and non-GPA individuals randomly selected from the general population. Primary outcome was death during the follow up period, 1997-2012. Cohorts were subdivided to early cohort (1997-2004) and late cohort (2005-2012). Hazard ratios (HR) were estimated using Cox proportional hazard regression models.  Results From the meta-analysis, we found a 2.7-fold increased risk of death in AAV patients when compared to the general population. Subgroup analyses showed that   iii mortality risks were higher in older cohorts with a trend towards improvement over time (i.e., midpoint of enrolment periods 1980-1993 and 1994-1999, vs. 2000-2005). From the matched cohort study, 370 GPA patients and 3,700 non-GPA individuals were included, with 68 and 310 observed deaths, respectively. Overall, the age, sex and entry time-adjusted all-cause mortality HR in the GPA cohort was 3.12 (95%CI 2.35-4.14). There was excess mortality from CVD causes, but not cancer, in the GPA cohort. All-cause mortality significantly improved between the early and late GPA cohorts (HR 5.61 vs. 2.33, respectively; p = 0.017). Conclusion  There was a 3-fold increase in all-cause mortality risks in GPA patients, with excess mortality from CVD causes. There was a significant improvement in all-cause mortality risks over time but remained elevated compared to the general population.   iv Lay Summary Granulomatosis with polyangiitis (GPA) is an inflammatory disease of the blood vessels associated with ANCA autoantibody. In the past without effective treatment, 80% will die within a year. Significant advances in the medical treatment and care of these patients have greatly improved their survival chances. Therefore, the primary purpose of my research is to determine the current risk of death in patients with GPA and its time trends. My research has shown that GPA patients are at 3.1 times greater risk of dying compared to the general population, ie those without the disease. On a positive note, this mortality risk has reduced over time. Cardiovascular disease appears to be a major contributor to premature deaths in GPA patients. With this knowledge, we would be able to identify mortality risk factors and to develop strategies to mitigate these risks, in the hope that we will further improve patient survival.    v Preface Sections of this thesis are multi-authored. One has been accepted for publication and another has been submitted for publication in a peer-review journal. Contributions of co-authors are detailed below.  Chapter 1. Ju Ann Tan was responsible for the design, literature review and writing of chapter 1. J. Antonio Avina-Zubieta provided supervision and review of the contents in Chapter 1.  Chapter 2. The work in this chapter was published as “Mortality in ANCA-associated vasculitis: a meta-analysis of observational studies” in Annals of the Rheumatic Diseases (Published Online First: 03 May 2017. doi: 10.1136/annrheumdis-2016-210942). Ju Ann Tan was responsible for the study design, literature review, data extraction, statistical analysis and preparation of manuscript. Natasha Dehghan was responsible for the study design, literature review, data extraction and preparation of manuscript. Wenjia Chen was responsible for advanced statistical analysis and preparation of manuscript. Hui Xie was responsible for the study design and preparation of manuscript. John Esdaile was responsible for the study design and preparation of manuscript. J. Antonio Avina-Zubieta was responsible for the study design, literature review, statistical analysis and preparation of manuscript.   Chapter 3. The work in this chapter has been submitted to a peer-review journal for publication as “All-Cause and Cause-specific Mortality Risks in Granulomatosis with   vi Polyangiitis: A population-based study”. Ju Ann Tan was responsible for the study design, literature review, statistical analysis, interpretation of results and preparation of manuscript. Hyon K.Choi, Hui Xie and John Esdaile were responsible for the study design, interpretation of results and critical review of manuscript. Eric C. Sayre was responsible for statistical analysis and interpretation of results. J. Antonio Avina-Zubieta was responsible for the study design, interpretation of results, preparation of manuscript and critical review of manuscript.  Chapter 4. Ju Ann Tan was responsible for the design, interpretation of results and writing of Chapter 4. J. Antonio Avina-Zubieta provided supervision and review of the contents in Chapter 4.  Ethics approval for this study was granted by the University of British Columbia’s Behavioural Research Ethics Board (certificate number H07-03088).    vii Table of Contents  Abstract .............................................................................................................................. ii Lay Summary .................................................................................................................... iv Preface ................................................................................................................................ v Table of Contents ............................................................................................................ vii List of Tables ..................................................................................................................... x List of Figures ................................................................................................................... xi List of Abbreviations .......................................................................................................xii Acknowledgements......................................................................................................... xiv Dedication ........................................................................................................................ xvi  Introduction ......................................................................................................... 1 Chapter 1:1.1 Overview of Thesis Project and Chapters ...............................................................................1 1.2 Granulomatosis with Polyangiitis ..........................................................................................2 1.2.1 Definition ................................................................................................................................. 2 1.3 Epidemiology and Socioeconomic Impact of GPA ...................................................................3 1.4 Survival and Mortality in Granulomatosis with Polyangiitis ....................................................5 1.5 Causes of Death in Granulomatosis with Polyangiitis ........................................................... 12 1.5.1 Cardiovascular Morbidity and Mortality in Granulomatosis with Polyangiitis ...................... 12 1.5.2 Infection Morbidity and Mortality in Granulomatosis with Polyangiitis ............................... 13   viii 1.5.3 Cancer Morbidity and Mortality in Granulomatosis with Polyangiitis .................................. 13 1.5.4 Renal Morbidity and Mortality in Granulomatosis with Polyangiitis .................................... 14 1.6 Limitations of Current Mortality Data .................................................................................. 14 1.7 Working with Administrative Data ...................................................................................... 16 1.8 Hypotheses and Aims of Studies .......................................................................................... 18  Mortality in ANCA-associated Vasculitis: A Meta-analysis of Observational StudiesChapter 2: ........................................................................................................................................... 19 2.1 Background ........................................................................................................................ 19 2.2 Methods ............................................................................................................................. 20 2.2.1 Search strategies ................................................................................................................... 20 2.2.2 Data extraction ...................................................................................................................... 21 2.2.3 Quality scores of included studies ......................................................................................... 22 2.2.4 Statistical analysis .................................................................................................................. 23 2.2.5 Assessment of publication bias/small-study effect ............................................................... 24 2.3 Results ............................................................................................................................... 24 2.4 Discussion .......................................................................................................................... 34  All-cause and Cause-specific Mortality in Granulomatosis with Polyangiitis : A Chapter 3:Population-based study ....................................................................................................... 39 3.1 Background ........................................................................................................................ 39 3.2 Methods ............................................................................................................................. 40 3.2.1 Data sources .......................................................................................................................... 40 3.2.2 Study design and cohort definition ....................................................................................... 40 3.2.3 Assessment of outcome ........................................................................................................ 42   ix 3.2.4 Assessment of covariates ...................................................................................................... 42 3.2.5 Statistical analysis .................................................................................................................. 43 3.3 Results ............................................................................................................................... 44 3.4 Discussion .......................................................................................................................... 55  Discussion ........................................................................................................... 60 Chapter 4:4.1 Summary of Key Findings .................................................................................................... 60 4.2 Implications of Key Findings ................................................................................................ 61 4.3 Strengths and Limitations ................................................................................................... 63 4.4 Future Directions ................................................................................................................ 65 4.5 Conclusion .......................................................................................................................... 65 Bibliography ..................................................................................................................... 67 Appendices ...................................................................................................................... 75 Appendix A .................................................................................................................................. 75 Appendix B .................................................................................................................................. 79    x List of Tables   Table 1 : Summary of survival rates and mortality risks in granulomatosis with polyangiitis (GPA) .............................................................................................................................................. 7 Table 2 : Summary of studies included in meta-analysis ......................................................... 27 Table 3: Overall mortality and sensitivity analyses for the 10 studies (14 unique cohorts) in patients with AAV ........................................................................................................................ 31 Table 4: Sensitivity analysis using the jack-knife approach ......................................................... 32 Table 5: Baseline characteristics of GPA and non-GPA cohort in the early (1997-2004) and late (2005-2012) cohorts ...................................................................................................................... 47 Table 6: Summary of all-cause and cause-specific mortality risks in GPA and non-GPA cohorts (stratified by overall, early and late cohorts) ................................................................................ 52 Table 7: All-cause and cause-specific mortality risks in GPA from time of diagnosis ................ 54       xi List of Figures Figure 1 : Increasing annual incidence rates reported from several population based studies (adapted from Gibelin12) ................................................................................................................. 4 Figure 2 : Flow chart of study selection from literature search .................................................... 26 Figure 3: Meta-analysis of ten studies on all-cause mortality in patients with ANCA-associated vasculitis. ...................................................................................................................................... 30 Figure 4: Funnel plot of 14 cohorts evaluating publication bias of mortality studies in ANCA associated vasculitis. ..................................................................................................................... 33 Figure 5:  Survival from all-cause, cardiovascular disease and cancer deaths in GPA and non-GPA cohorts .................................................................................................................................. 49    xii List of Abbreviations AAV   ANCA-associated vasculitis ACR   American College of Rheumatology ANCA   Anti-neutrophil cytoplasmic antibodies BC   British Columbia  CHCC   Chapel Hill Consensus Conference CI   Confidence interval Cox PH  Cox proportional hazard CVD   Cardiovascular diseases EGPA   Eosinophilic granulomatosis with polyangiitis ESRF   End stage renal failure EUVAS  European Vasculitis Study Group GPA   Granulomatosis with polyangiitis HR   Hazard ratio ICD   International classification of disease MPA   Microscopic polyangiitis MPO   Myeloperoxidase MSP   Medical Services Plan OR   Odds ratio PAN    Polyarteritis nodosa PR3   Proteinase-3 PY   Person-years RLV   Renal limited vasculitis   xiii RR   Relative risk SD   Standard deviation  SIR   Standardized incidence ratio SLE   Systemic lupus erythematosus SMR   Standardized mortality ratio    xiv Acknowledgements The work in this thesis would not have been completed without my supervisor, Dr J. Antonio Avina-Zubieta who took a leap of faith on me. He provided clarity, vision and most of all, his generous support which allowed me to learn and grow throughout this journey. I am also grateful to my other supervisory committee members, Drs Hui Xie, Hyon K. Choi and John Esdaile for your time, expertise, invaluable insight and guidance towards making this work of the highest academic quality. I would also like to acknowledge Eric C. Sayre for his intuitive help and guidance in all things statistical. He was generous with his advice and understood the learning challenges involved.  I would like to extend my gratitude to the people at Arthritis Research Canada (ARC) who has welcomed me into their community when I was new to the country, and this includes the other primary investigators, research assistants and secretarial staff. The working environment at ARC was supportive and I was encouraged at every step of my academic endeavour. I would like to acknowledge my fellow graduate students, Ling Yi Li (Gloria), Natalie McCormic and Timothy Schmidt, who not only extended a hand in friendship but shared assistance and knowledge during times of need. I further acknowledge the Australian Rheumatology Association, Arthritis Australia and Roche Products Pty Ltd for providing a full year scholarship and thus the opportunity to embark on a post-graduate education overseas. This has lead to an improvement of my clinical research skills and knowledge, which in turn will allow me to   xv actively contribute to medical and epidemiological research in my home country of Australia.  Finally, I would like to acknowledge my parents, Kee Beng and Mei Wen, who have been nothing but exemplary and my pillars of strength. To my sister, Ju Nee, you keep me going when the going gets tough. I hope I have made all of you proud.    xvi Dedication   To my husband, Roshan and my daughter, Arianna, my achievements in life are yours and would not have been possible without your unwavering love and support.   1  Introduction Chapter 1: 1.1 Overview of Thesis Project and Chapters Primary systemic vasculitis is a rare necrotizing inflammatory condition of the blood vessels. Significant progress has been made in the last two decades in our understanding of the disease pathogenesis and, more importantly, in treating these patients effectively. Of all the primary systemic vasculitides, anti-neutrophil cytoplasmic antibodies (ANCA) associated vasculitis (AAV) has been most extensively studied. However, many aspects of this condition remain uncertain. There is still significant disease mortality despite advances in treatment. Current reported mortality data are subjected to biases and limitations inherent in the study of rare diseases. A population-based administrative database such as the Population Data BC would provide the platform needed to study the epidemiology and mortality trends of AAV in the Canadian population. Chapter 1 of this thesis is an introduction to granulomatosis with polyangiitis (GPA) as the most common form of AAV and reviews the literature on GPA epidemiology and mortality. This project’s hypotheses and aims are presented in the concluding section of this chapter. The following 3 chapters are original works. Chapter 2 is a systematic review and meta-analysis of mortality risk in AAV. Chapter 3 evaluates the all-cause and cause-specific mortality risk of GPA patients compared to the general population of British Columbia, and determines mortality trends over time. Chapter 4 is the concluding chapter, summarizing key results from chapters 2 and 3,   2 with an in-depth discussion of key implications, study strengths and limitations, and future directions.   1.2 Granulomatosis with Polyangiitis  1.2.1 Definition The vasculitides are a heterogeneous group of rare conditions that can occur as a primary event or secondary to other disorders, such as autoimmune diseases (eg. rheumatoid arthritis, lupus), infections (eg hepatitis B, HIV), drug-induced (eg. cocaine) or as a paraneoplastic phenomenon. Inflammation of the blood vessel walls is the predominant pathological process, hence the descriptive term “vasculitis” (or vasculitides in its plural form).  ANCA-associated vasculitis (AAV) is defined as “Necrotizing vasculitis, with few or no immune deposits, predominantly affecting small vessels (i.e., capillaries, venules, arterioles, and small arteries), associated with myeloperoxidase (MPO) ANCA or proteinase 3 (PR3) ANCA”.1 Under the umbrella of AAV, patients can be further classified to granulomatosis with polyangiitis (GPA, previously known as Wegener’s Granulomatosis), microscopic polyangiitis (MPA) and eosinophilic granulomatosis with polyangiitis (EGPA, previously known as Churg-Strauss Syndrome). For research purposes, the most commonly used classification or nomenclature systems are the American College of Rheumatology (ACR) 1990 Classification Criteria2 or the 2012 Chapel Hill Consensus Conference (CHCC) Nomenclature of Vasculitides.1 The distinction is made based on predominantly histopathological findings.   3  1.3 Epidemiology and Socioeconomic Impact of GPA GPA was first described by Wegener in 1939.3 GPA is more common in men than women and it usually occurs between 64 to 75 years of age. The 1990 ACR criteria did not include MPA, resulting in poor discrimination between GPA and MPA in historical data. MPA was later distinguished in the CHCC nomenclature but GPA is still the most common form of AAV diagnosed.4 It affects approximately 8 to 10 new individuals per million population with some variation seen depending on geographical locations.5 A North American study estimated the incidence of GPA at 8.6 per million population.6 The Canadian data is likely to be comparable although there has been only one Canadian report studying the incidence of renal-dominant AAV.7 Their total number of subjects was 33 renal biopsy-proven cases and urban incidence was reported as 6.1 cases per million versus rural incidence of 16.5 cases per million. In our British Columbia data set, the incidence of GPA in 2012 was 9.5 per million population (unpublished data).  Estimated prevalence has increased from 28.8 per million in 1990 to 64.8 per million in 2005.8 This could potentially be due to improved survival but there were studies suggesting there was increased disease incidence as well.9-11 See Figure 1.0. The increasing incidence has been attributed to increased clinical awareness, wider availability of ANCA testing, progress in disease classification and perhaps new environmental factors.12 Commercial kits for detecting ANCA became widely available in the 1990s.13     4 Figure 1 : Increasing annual incidence rates reported from several population based studies (adapted from Gibelin12)  References: Sweden9, Norway10, United Kingdom14 ,Australia15, Finland11, Germany16    The majority of epidemiology data in GPA came from the European or Caucasian population. Limited studies from Japan and China have observed that GPA is less common than MPA in their populations.17-19 In New Zealand, the incidence rate among those with European ancestry was twice the incidence of those identifying themselves as Maoris or Asians.20 This predominance in Caucasians could be a result of the interplay between environmental exposures and ethno-genetics. There has also been some speculation about the influence of seasonality (i.e., higher onset in winter)21 and latitude variations on disease incidence (ie higher incidences in countries further from the equator)22.    5  GPA imposes a considerable burden on the patient’s socio-economic status. 80% of stable GPA patients continue to experience compromised activities of daily living.23 Up to one-third eventually receive total disability benefits. Income was reduced by a median of 26% within 1 year of disease diagnosis. In addition, its impact on the healthcare system is illustrated in a cost analysis of vasculitis in the US, which estimated charges from hospitalizations of GPA patients over a 5-year period at approximately USD$148.3million.24  1.4 Survival and Mortality in Granulomatosis with Polyangiitis Without treatment, prognosis in patients with systemic GPA was poor, with mortality rates of 80% within one year with a mean survival time of 5 months.25 Glucocorticoids were the first disease-modifying drugs introduced in necrotizing vasculitis and successfully reduced mortality from 90% to 50% at 5 years.  With the addition of cyclophosphamide, there was further improvement in survival rates.26 However, the data on current survival rates for GPA are varied depending on the patient population, time of study, intervention used and method of analysis. The 1, 5 and 10-year survival rates in GPA patients were reported to range between 81-95%, 73-83% and 65-75%, respectively.27-36  Despite improving survival, patients with GPA remain at higher risk of death relative to the general population. The reported standardized mortality ratio (SMR) in GPA patients range between 1.77-4.69.28,32,36,37 A SMR is the ratio of observed deaths in the study population against the expected deaths in the age- and sex-matched   6 standard population (usually general population statistics). The elevated SMR estimates suggest that GPA patients have excess mortality that needs to be addressed.  A summary of survival and mortality data in GPA is presented in Table 1.0.   7 Table 1 : Summary of survival rates and mortality risks in granulomatosis with polyangiitis (GPA) Author/year published (ref) Country Enrolment period Study design No. patients Female, n (%) Mean Age at Study Entry, years ±SD Mortality Rate, % Survival Rate (%) Standardized Mortality Ratio, SMR (95%CI) 1 year 5 year 10 year Walton et al/195825 United Kingdom 1943-1955 Case series 10  3 (30) 50.8 80% in 1st year - - - - Littlejohn et al/198531 Australia 1973-1983 Case series 17 6 (35) 46.9 ± 4.5 41 81 - - - Anderson et al/199238 United Kingdom 1975-1985 Retrospective cohort 265  119 (45) 50.0 ± 15.8 55 -  - - - Hoffman et al/199239 USA -  Prospective cohort 158 79 (50) 41 20 - - - - Romas et al/199334 Australia 1969-1991 Case series 37  21 (57) 48 Without treatment: 83% within 2.6yrs  Cyclophosphamide: 26% 97  (with cyclophosphamide)  - 71 (with cyclophosphamide) - Krafcik et al/199640 USA 1983-1994  Retrospective cohort  Total= 67 Elderly (≥60yo): 33  Control (<60yo): 34  30 (45) Elderly: 68.4 Control: 36.9 Elderly: 54  Control: 19 (p<0.01) - - - - Matteson et al/199632 Canada, Mexico, USA 1978-1987  Prospective cohort  77 29 (38) - 36 - 75 - Overall  4.69 (3.41-5.96)  Female  6.81 (3.73-9.89) Male  4.00 (2.72-5.27)  Blockmans et al/199741 Belgium 1980-1995  Case series 50 - - 24 - - - - Reinhold-Keller et al/200042 Germany  1966-1993 Prospective cohort 155 79 (51) Median 48 14.2  - - - - Mahr et al/200143 France  1990-1995   Prospective  cohort 49 19 (39) Median 57 36.7 *6 months 77.5%  *2 years 67.5% - -   8 Author/year published (ref) Country Enrolment period Study design No. patients Female, n (%) Mean Age at Study Entry, years ±SD Mortality Rate, % Survival Rate (%) Standardized Mortality Ratio, SMR (95%CI) 1 year 5 year 10 year Knight et al/200237  Sweden 1969-1994 Population-based cohort 1065 502 (47) - 48.5 - - - Overall:  4.0 (3.6-4.3)  Cancer mortality: 2.2 (1.7-2.8) Koldingsnes et al/200230 Norway 1984-2000  Population-based  cohort 56 21 (38) 50.3 ± 18.4 23.2 93  79 75 - Booth et al /200344 UK 1995-2000  Retrospective cohort Total = 246  120 MPA  82 GPA  33 RLV  11 EGPA  106 (43) Median 66 - 84  *With ESRF: 64   76  *With ESRF: 53 (Similar to other ESRF causes) - 2.84 (2.53-3.18)  Bligny et al/200427 France 1984-1999 Retrospective cohort 93 34 (37) Median 52 26.9 - 74 - - Weidner et al/200445 Germany 1986-2001   Retrospective cohort Total = 80 32 GPA  28 MPA  20 RLV 40 (50) Median 63 26.2   86  *With ESRF: 83  81  *With ESRF: 61 - - Lane et al/200546 UK 1988-2000  Case series Total = 99  57 GPA 24 MPA 18 EGPA 38 (39) 62.6 31.3 GPA 85.5 MPA 82.7 EGPA 83.2  GPA 75.9  MPA 45.1 EGPA 68.1  - -    Rihova et al/200547 Czech Republic 1986-1997  Retrospective cohort Total = 61 33 GPA  10 MPA 14 RLV 3 EGPA 24 (39) Median 54 31.1 - 78.3 62.2 2.65 (1.46-3.84) Flores-Suarez et al/200748 Mexico 1978-2006  Case series 65 29 (45) 47.5 ± 13.8 18.5 - 91 (Data on 35 patients) 90 - Hissaria et al/200849 Australia 1985-2005  Case series 30 14 (47) 56.8 ± 11.4  26.7 - - - -   9 Author/year published (ref) Country Enrolment period Study design No. patients Female, n (%) Mean Age at Study Entry, years ±SD Mortality Rate, % Survival Rate (%) Standardized Mortality Ratio, SMR (95%CI) 1 year 5 year 10 year Hoganson et al/200850 USA 1998-2002  Case series Total = 78 59 GPA 19 MPA Elderly (>75yo): 22 Control (≤75yo): 56 Elderly: 55 Control: 36  Elderly:  78 ± 3 Control:  53 ± 15  Elderly:  40.9 Control:  10.7 p=0.0006    - - - - Eriksson et al/200951 Sweden 1978-2005   Old cohort: 1978-1996  Recent cohort: 1997-2005 Retrospective cohort Total = 95  Old cohort:  24 GPA 8 MPA    Recent cohort: 33 GPA 30 MPA   Old cohort: 15 (47)  Recent cohort: 28 (44) Old cohort: 57.7  Recent cohort:  61.4 Old cohort: 46.9  Recent cohort: 11.1 Old cohort:  91  Recent cohort:  95  Old cohort: 81  Recent cohort: 87 - Old cohort: 1 year SMR 5.2 (1.07-15.14) 5 year SMR 2.5 (0.93-5.52)  Recent cohort:  1 year SMR 2.1 (0.43-6.09) 5 year SMR 1.6 (0.6-3.2) Mohammad et al/200928 Sweden 1997-2006 Population-based cohort Total = 140 63 GPA  65 MPA  6 EGPA 6 PAN 73 (52) Median 67.6 Overall: 32.1  GPA only: 22.2 Overall: 87.8  MPA:  80  GPA:  95  Overall: 71.6  MPA:  55  GPA:  83  Overall: 55  MPA:  43  GPA:  65 Overall:  2.77 (2.02-3.71) GPA:  1.77 (0.84-2.70) MPA: 3.95 (2.51-5.38)  Males: 55-74yo: 2.43(0.92-3.93) ≥75yo: 2.59(1.28-3.89)  Females: 55-74yo: 2.32 (0.46-4.17) ≥75yo: 4.29 (2.04-6.54)    10 Author/year published (ref) Country Enrolment period Study design No. patients Female, n (%) Mean Age at Study Entry, years ±SD Mortality Rate, % Survival Rate (%) Standardized Mortality Ratio, SMR (95%CI) 1 year 5 year 10 year Chan et al/201052 Hong Kong 2001-2008  Case series Total = 31  10 GPA 13 MPA 8 EGPA 20 (65) 63.4 ± 13.8 38.7 70  51 - - Dadoniene et al/201053 Lithuania Started 1994 Prospective cohort 35  10 (29) 48.5 ± 18.9 25.7  89  67 - - Di Benedetto et al/201054 Argentina 1992-2007 Case series Total = 29 13 GPA  6 EGPA  10 MPA 21 (72) 49.8 ± 30.2 Overall: 24.1 GPA only: 31 MPA only: 10  EGPA only: 33 - - - - Takala et al/201036 Finland 1981-2000  Old cohort: 1981-1990  Recent cohort: 1991-2000 Population-based cohort 492 249 (51) Old cohort: 49.3 ± 17.2  Recent cohort:  54.5 ± 18.2 41.3 Overall: 83.3  Old cohort: 84.9  Recent cohort: 82.2  Overall: 74.2  Old cohort: 76.2  Recent cohort: 73.5  - Overall:  3.43 (2.98-3.94)  Female:  4.38 (3.59-5.61) Male: 2.80 (2.28-3.41) Flossmann et al/201155 15 European countries 1995-2002  Prospective cohort Total = 535 281 GPA 254 MPA 247 (46) Median 61 24.9 88  78 - 2.6 (2.2-3.1)   11 Author/year published (ref) Country Enrolment period Study design No. patients Female, n (%) Mean Age at Study Entry, years ±SD Mortality Rate, % Survival Rate (%) Standardized Mortality Ratio, SMR (95%CI) 1 year 5 year 10 year Holle et al/201156 Germany  1994-2002  Cohort 1: 1966-1993 Cohort 2: 1994-1998 Cohort 3: 1999-2002  Retrospective cohort Total = 445  Cohort 1 155  Cohort 2 123 Cohort 3 167  222 (49.9)  Cohort 1 79 (51) Cohort 2 61 (49.6) Cohort 3 82 (49.1)  Median Cohort 1  48  Cohort 2  52 Cohort 3  55  Overall:  9.6   Cohort 1: 14.2 Cohort 2: 10.6 Cohort 3: 4.8 - - - Overall:  1.58 (1.14-2.13)  Cohort 1: 2.1 (1.34-3.25) Cohort 2:  1.41 (0.75-2.42) Cohort 3:  1.03 (0.44-2.03)  Female: 1.23 (0.66-2.11) Male: 1.8 (1.22-2.58)  Young patients:  5.77 (2.6-10.95) Young males:  8.87 (4.05-16.8)  Cancer mortality 0.65 (0.24-1.43)  Luqmani et al/201157 UK 1989-2004  Retrospective matched cohort 255  120 (47) 58.1 Overall: 20.8  Within 1st year: GPA 11% vs controls 1.2% (p<0.001) - - - - Abbreviations: “-“ Not provided; GPA, granulomatosis with polyangiitis; MPA, microscopic polyangiitis; EGPA, eosinophilic granulomatosis with polyangiitis; PAN, polyarteritis nodosa; ESRF, end stage renal failure; RLV, renal limited vasculitis; SMR, standardized mortality ratio; SD, standard deviation 12  1.5 Causes of Death in Granulomatosis with Polyangiitis GPA patients frequently die as a consequence of uncontrolled disease and/or the immunosuppressive effects of cyclophosphamide and high dose glucocorticoids. This is especially so in the early stages of disease.58 “Active vasculitis” as a cause of early death is an umbrella term to encompass vital organ failure due to active disease, such as alveolar haemorrhage, gastrointestinal ulcer bleeding, or coronary vasculitis. Late deaths can be due to treatment-specific complications such as infections, cardiovascular disease (CVD) or cancer, or consequent to permanent disease damage such as end-stage renal failure or respiratory failure.  1.5.1 Cardiovascular Morbidity and Mortality in Granulomatosis with Polyangiitis Cardiovascular diseases have previously been well documented as a major contributor to morbidity and mortality in autoimmune diseases such as SLE59 and rheumatoid arthritis60-62. In recent years, there is increasing recognition of the elevated risk of CVD in the GPA population as well.63,64 The incidence rates of myocardial infarction (MI) and stroke in GPA patients were estimated at 11.7 and 8.9 cases per 1000 person-years, respectively and the overall risk of MI was elevated compared to the general population (HR 1.86, 95%CI 1.05-3.31).65 Within 5 years of diagnosis, the risk of a cardiovascular event in a GPA patient is 14%.64 Independent predictors of a cardiovascular event in GPA include older age (OR 1.45, 95%CI 1.11 – 1.90), diastolic hypertension (OR 1.97, 95%CI 0.98 – 3.95) and PR3-ANCA (OR 0.39, 95%CI 0.20 – 0.74).64 13   The estimated CVD-related mortality rate in an outcome study on GPA and MPA patients enrolled in EUVAS trials was 699 per 100 000 with an age-adjusted mortality ratio of 3.7 (95% CI 3.2–4.3) compared to the general population.64 There have been no population-based studies on the cardiovascular mortality risks in GPA. Given the increased incidence of CVD in GPA patients, there is an emerging need for population based data on CVD prognosis for care-givers and patients alike.   1.5.2 Infection Morbidity and Mortality in Granulomatosis with Polyangiitis GPA patients are at an increased risk of infection-related deaths due to its natural immunosuppressed disease state and also as a result of immunosuppressive therapies. The observed incidence rate for serious infections in AAV patients is 20-60%.66 Infections or sepsis ranked as either the most common or second most common cause of death in a number of studies,27,32,43 particularly in the first year after diagnosis55,57,67.  Most of the infections are localized to the respiratory tract.68 There were also reports of increased risk of opportunistic infections, with Pneumocystis jirovecii pneumonia and systemic cytomegalovirus infections most frequently reported.66 An incident cohort study reported that patients complicated by serious infections were 4.2 times more likely to die within 12 months (HR 4.20, 95%CI 2.0-8.7).68 Infection-related mortality risk in GPA patients compared to the general population is not known.   1.5.3 Cancer Morbidity and Mortality in Granulomatosis with Polyangiitis The concerns surrounding increased cancer risk in GPA stem from exposure to cyclophosphamide, particularly in those with higher cumulative doses.69,70 The 14  cumulative risk of bladder cancer is 10% even after 16 years since diagnosis of GPA. In the European Vasculitis Study (EUVAS) cohort, the age, sex and area-standardized incidence ratio (SIR) was 1.92 for all cancers in GPA patients.71 However the majority consisted of non-melanoma skin cancers.  There is insufficient data relating to cancer mortality in GPA patients. There has been only one population-based study assessing cancer incidence and mortality in GPA patients.37 In that cohort of 1065 hospitalized GPA patients, cancer-related mortality risk was estimated to be twice that of the general population (SMR 2.2, 95%CI 1.7-2.8).      1.5.4 Renal Morbidity and Mortality in Granulomatosis with Polyangiitis 70% of GPA patients have renal involvement mainly in the form of rapidly progressive glomerulonephritis.72 At 10 years, 23% would have developed end-stage renal failure (ESRF).30 Renal involvement in GPA has been consistently reported as a predictor of poor outcomes in several studies47,73 and worsening degrees of renal impairment were associated with increasing mortality.45 In a retrospective cohort, patients with ESRF have a three times greater risk of dying compared to patients with preserved renal function (RR 3.06)45 To date, there are no long term general population data on renal-related mortality in GPA patients.    1.6 Limitations of Current Mortality Data There are various methods in the reporting of mortality data. Hence, it may be difficult or inappropriate to compare mortality risks reported in studies employing variable study designs, sources of patients and statistical techniques.  15  In studies of GPA patients, there were a number of case series, and several cohort studies but only a few were based on population cohorts28,30,36,37. The crude mortality rates derived from case series reviews were highly variable and thus do not provide an accurate descriptive measure of mortality (See Table 1.0). In comparison, studies with sufficient longitudinal data are better equipped to report mortality rates in terms of person-years contributed and perform survival analysis. However, some have from small patient numbers and selection bias32,39,43,53 Furthermore, population-based 10 year survival data is scarce.28,30 Finally, survival analysis does not account for the “cohort effect” nor provide information on how these patients compare to the general population.   Observational studies may also be affected by lead time (or immortal time) bias because of their exposure definition.74 Lead time bias artificially reduces the event/outcome rate ratio when the ‘guaranteed survival’ time of the participant leading up to the given exposure at study entry is not accounted for. Any form of time dependent analysis (Cox) is necessary to diminish the effects of this bias.  There is also limited data on the mortality trend in GPA patients over time. There is an expectation for improvement in mortality risk as a result of recent therapeutic advances in GPA. For example, pulsed intravenous cyclophosphamide has been found to be as effective as daily dose cyclophosphamide, in inducing disease remission but with a lower cumulative dose exposure, which translates to a better side effect profile.75 There has also been improvement in the past decades in the management of CVD and steroid complications, i.e., more effective screening processes, newer drugs, improved disease monitoring. A recently published study based on a UK general practice 16  database reported improving mortality risk in GPA patients between the periods 1992-2002 vs 2003-2013 (HR 4.34 vs 2.41, p = 0.04).76 However, they did not report on cause-specific mortality risks and therefore the cause for improvement will need further elucidation.     1.7 Working with Administrative Data Administrative data are information routinely collected by various agencies (usually government departments) for the purposes of population census, insurance billing, resource management or registrations, usually for the delivery of a service. Administrative databases are increasingly used in healthcare research as it has the advantage of capturing data on the entire population, free of selection biases inherent in clinical databases. This is particularly relevant in rare disease epidemiology as large longitudinal cohorts are necessary to provide sufficient power to analyze infrequent outcomes.  However, administrative databases may be flawed by poorly-measured data due to errors in data entry, coding inaccuracies and lack of validation tools. In addition, they do not usually include extensive clinical or laboratory data which impacts the specificity of identified cases. Despite that, there are distinct reasons to expand the use of administrative databases, particularly in the realm of “big data” research.  Big data refers to “volumes of large, complex, linkable information”.77 They are difficult to manage with traditional software and/or hardware but as we move towards digitizing more health records coupled with technological advancements, there exists new computing infrastructure that has made possible the ability to analyze tens of thousands, even millions of data points. Big data in healthcare may include physician 17  notes and reports, medical imaging, laboratory data, prescription information and insurance or administrative record-keeping. Big data analytics on comprehensive clinical databases have led to improved patient care and costs reduction.78  One of the expounded characteristics of big data is the veracity of the data given the sheer volume of information utilized. However, real-life healthcare data are highly variable and prone to misinformation or misinterpretation. There have been recent debates on the utility and precision of using big data information in epidemiology and public health.79 However, with reliable resources and expert data management, it can provide invaluable contribution to epidemiological research. Well established examples are in the fields of genomics and cardiovascular research but there are exciting prospects for its potential in other areas including autoimmune diseases.  In British Columbia (BC), administrative data is collected on billing for healthcare utilization, known as Population Data BC.80 The 4.6 million population of BC is universally covered by a public-funded healthcare insurance scheme (Medical Services Plan, MSP) and thus, provides extensive coverage on outpatient and inpatient visits. The recorded information includes date of visits, health providers and diagnostic codes for the visit since 1990. The diagnostic codes employed are based on the International Classification of Disease, Version 9 (ICD-9, up to year 2001) and 10 (ICD-10, 2001 onwards). Each anonymous and uniquely numbered record is linked to birth and death records (through Vital Statistics Canada) as well as drug prescription information (through Pharmanet, which keeps records from 1996 onwards). Pharmanet tracks the date, type of medication dispensed (using Canadian Drug Identity Codes [CDIC]) and 18  number of days prescribed. The use of ICD codes in the study of GPA has been successfully implemented based on several published studies.9,28   1.8 Hypotheses and Aims of Studies The variability in the previously discussed mortality data can be mostly explained by the inconsistencies in case definition, patient selection bias and lack of population-based controls. There is also a scarcity of studies focusing on cause-specific mortality risks in GPA. Furthermore, how recent therapeutic changes have altered the prognosis of this disease is not clear. Clarification of this area of knowledge would be invaluable in providing a focus for future therapeutics and health economics research, as well as to serve as guidance to health policy makers. Thus, the purpose of this thesis project is to provide an estimate of the mortality risks and trends in GPA as accurately as possible by addressing some of the limitations above. The questions underlying the hypotheses behind my research project are: what are the all-cause and cause-specific mortality risks in GPA patients and have the risks improved over time, compared to the general population?  The specific aims of my study are: 1. To determine all-cause and cause-specific mortality risks in GPA patients compared to the general population 2. To estimate the difference in mortality risks in GPA patients between 1997-2004 and 2005-2012  19   Mortality in ANCA-associated Vasculitis: A Meta-analysis of Chapter 2:Observational Studies  2.1 Background Primary systemic vasculitides are a heterogeneous group of rare diseases characterized by the presence of necrotizing inflammation of the blood vessel wall. Amongst the various hypotheses on the immunologic mechanisms seeking to explain the nature of these diseases, the antineutrophil cytoplasmic antibodies (ANCA) appear to play a prominent role in the pathologic pathways of a group of predominantly small vessel vasculitis, otherwise known as ANCA-associated vasculitis (AAV).81,82 This distinctly pauci-immune form of vasculitis includes granulomatosis with polyangiitis (GPA, formerly Wegener’s granulomatosis), microscopic polyangiitis (MPA) and, eosinophilic granulomatosis with polyangiitis (EGPA, formerly Churg-Strauss syndrome).  The spectrum of AAV ranges from isolated organ involvement to life threatening fulminant disease. The prognosis in untreated systemic GPA was initially poor, with mortality rates of 80% within one year with a mean survival time of 5 months.25 With the introduction of glucocorticoids and cyclophosphamide in management of AAV in the 1960s, significant advances have been made in survival.83 The 1, 5, and 10-year survival rates in GPA patients are now reported to range between 81-95%, 73-83%, and 55-75%, respectively.27,29,31-36,84 Similar improvements were also noted in MPA and EGPA studies. With treatment, MPA survival rate at 1 year is 80%, 5 years 45-85%, 10 years ~74%.46,85-87 Recent EGPA studies have estimated 5-year survival rates at 89-97%.88,89 20   Despite improving survival, patients with AAV still remain at a higher risk of death relative to the general population.33 Standardized mortality ratio (SMR) provides an estimate of the true death risk, as it compares the number of observed patient deaths to the number of expected deaths of age- and sex-matched individuals from the general population. Several studies have reported an elevated SMR for AAV patients, ranging from 1.6 to 4.8,32,36,37,44,46,55,56 although others have found that contemporary mortality risks were not significantly different from the general population.28,51,56,89 The conflicting results from these reports may be due to biases from small sample sizes, and cohort types (e.g., community based versus clinic based).  The purpose of our study was to estimate all cause mortality risk of patients with AAV through a systematic review and meta-analysis from observational studies.  2.2 Methods 2.2.1 Search strategies A search was performed by an experienced research librarian to identify primary studies and review literature using Medline and EMbase databases on the OVID platform. Records were captured for the full date range for each database through April 2015 (Medline from 1948, EMbase from 1980) in any language. Database specific indexing was used (Medline MeSH and EMbase subject headings), along with text words in titles and abstracts. Two search concepts were combined with the Boolean operator “AND”: 1) ANCA-associated vasculitis (AAV) or vasculitis, and 2) mortality or survival. Conference abstracts were captured with this approach, as they were not 21  specifically excluded as a publication type. The exact search strategy is included as  Appendix A. Abstracts for all articles of interest were reviewed for relevance, that is those that reported mortality or survival data in AAV. Full papers of selected abstracts were retrieved and assessed for eligibility based on the inclusion criteria listed below. We also searched the reference lists of identified papers and conference abstracts for additional relevant publications.  All English-language peer-reviewed articles that met the following inclusion criteria were considered eligible: 1) clearly defined AAV identified by either the American College of Rheumatology (ACR) 1990 classification criteria 2,90 or the 2012 Chapel Hill Consensus Conference (CHCC) on disease definitions,91, 2) reported SMRs and 95% confidence intervals (95%CI), or available data to calculate SMRs. In cases of duplicate data used in more than one study, the sample with the most up to date data was selected for review. 2.2.2 Data extraction Two authors (JAT and ND) independently reviewed and assessed the selected articles for eligibility. From eligible studies, JAT and ND extracted data on year of publication, enrolment period, study design, country, population setting, definition of AAV, sample size and demographics, proportion of ANCA positivity, proportion of renal involvement at diagnosis, and survival or mortality data. Gender-specific SMR was also noted, where available. In two studies, we calculated the 95% confidence intervals (95% CI) for SMR from available information.32,44 In studies where the overall cohort was divided into time cohorts (by year of enrolment), each time cohort was computed as an 22  individual cohort during meta-analysis.51,56 One study provided 1-year and 5-year SMRs and the latter was selected for the meta-analyses,51 as the median or mean follow-up times for all studies were greater than 1 year. Any differences between the two authors (JA-T and ND) were resolved by consensus together with a third author (JAA-Z).  2.2.3 Quality scores of included studies We assessed study quality based on a 12-point scale that was adapted from previously published scales for observational studies.92,93 We used a similar scoring system in our previously published meta-analyses on the risk of mortality in rheumatoid arthritis94,95 and systemic lupus erythematosus.96 Points are allocated on an ordinal scale for each of the 6 items recorded; source of the study population (population based = 2 points, clinic/hospital based = 1 point, and undefined = 0); cohort type (inception cohort = 2, non-inception cohort = 1, undefined = 0); definition of AAV (ACR or CHCC classification criteria = 2, other validated classification criteria = 1, other pre-defined but non-validated classification criteria = 0); ascertainment of death outcome (validated criteria = 2, non-validated, but clearly defined criteria [e.g., death certificates] = 1, not mentioned = 0); AAV exposure (≥10 years = 2, ≥5years and <10years = 1, <5years = 0); and loss to follow up (≤20% = 2, >20% and ≤40% = 1, >40% or not mentioned = 0). Studies with scores ≥7points were considered higher quality and those with ≤6 points were lower quality studies. Two authors (JA-T and ND) performed quality scoring independently, with differences resolved by consensus together with a third author (JAA-Z). 23  2.2.4 Statistical analysis We calculated the meta-SMR for all-cause mortality in AAV, which is a weighted-pooled summary estimate of SMRs (weighted by the sample size of each study) using HEpiMA statistical software, version 2.1.2.0.97 A GPA meta-SMR was determined from study cohorts that included only GPA cases, excluding MPA and EGPA. Separate meta-SMRs were also calculated for males and females. Initial calculations were performed using SMRs from the individual studies on a log scale to approximate a normal sampling distribution. The resulting pooled values were then transformed back to the SMR scale. Results from the pooled statistics were based on the random-effects model. Statistical heterogeneity was assessed using the I2 statistic, which indicates the proportion of variation in effect size due to heterogeneity.98 Source of heterogeneity was determined by subgroup analysis. To do so, all included studies were stratified accordingly; population setting (population-based versus hospital/clinic-based samples), cohort type (inception versus non-inception), midpoint of enrolment periods (1980-1993, 1994-1999 and 2000-2005) and center (single center versus multi-center). Furthermore, a univariate meta-regression analysis was then used to study and interpret the difference in meta-SMRs between the subgroups.99 The time cut-offs for our enrolment period analysis were chosen as such because of the increased usage of ANCA testing in the mid-1990s and because in the early 2000s, there was a paradigm shift in treatment strategies, with an emphasis on improving the safety profile of induction therapy.100   24  We evaluated the robustness of the results using jack-knife sensitivity analysis, by repeated meta-SMR analyses with removal of a single study in succession each time.101 2.2.5 Assessment of publication bias/small-study effect We constructed a funnel plot in which a measure of the study size is plotted as a function of the measure of interest.102 We used the log of the SMRs from individual studies as well as the log of precision (1/variance). This was done to detect publication bias (i.e., bias resulting from the greater likelihood of studies with positive results to be published compared to negative results), or the small-study effect (i.e., a tendency for treatment effect estimates in small studies to differ from those in larger studies).103 In the absence of publication bias and small-study effect, the distribution of the data points will be symmetric. Furthermore, we used Egger’s regression as an objective, quantitative test statistic to test for presence of asymmetry in the data.104  2.3 Results We screened 570 abstracts published over the last 38 years (324 Medline and 238 EMbase and 8 from reference lists). A total of 58 studies were retrieved for detailed evaluation and 10 studies met the inclusion criteria (Figure 2 and Table 2). Forty-eight studies were excluded: 43 did not provide SMRs or data to calculate them, 3 were review papers, and 2 included only patients with renal vasculitis.  The ten studies included 3,338 AAV patients (2,619 with GPA, 501 with MPA, 185 with EGPA, and 33 with renal limited vasculitis) enrolled from 1966-2009 and a total of 1,091 observed deaths.28,32,36,37,44,46,51,55,56,89 Three were population-based studies 25  (n=1,691), whereas 7 were hospital/clinic-based studies (n=1,647). Four of these studies included only GPA patients (n=1,987).   There were 14 unique cohorts available for the meta-analysis. Overall, the mortality risk in AAV patients was significantly increased when compared with the general population (meta-SMR 2.71 [95% CI 2.26-3.24]). See Figure 3.  Analysis on GPA patients alone also showed a similar increase in risk of mortality (GPA meta-SMR 2.63, [95% CI 2.02-3.43]). Five studies reported sex-specific mortality estimates with no differences in mortality risks between sexes (meta-SMR 3.36 [95% CI 2.10-5.38] and 3.11 [95% CI 2.21-4.36] for females and males, respectively).  There was significant heterogeneity among the studies (I2 = 84.4%, 95% CI 72.6-96.3). Subgroup analyses showed that a number of factors might have influenced the mortality risk. Meta-SMRs were higher in population-based studies, in non-inception cohorts, in multicenter studies, and in cohorts enrolled prior to 2000 (Table 3). All subgroups showed significantly increased mortality risk compared to the general population, although we observed a decreasing mortality trend in newer cohorts. Despite the differences in mortality within subgroups, only “center” was significantly associated with the observed heterogeneity using meta–regression analysis (p=0.05).  Results of the jack-knife sensitivity analysis are shown in Table 4. The meta-SMR remained significantly increased with every sequential study exclusion, with the point estimates ranging from 2.6 to 2.9 and the corresponding 95% CI remaining >1 in all analyses. This suggested that the meta-SMR result was robust and not skewed by a single dominant study.  26  The funnel plot is shown in Figure 4. Each plot represents individual cohorts and the solid line is the log of the meta-SMR. The distribution of our data points was symmetrical; therefore, we concluded that there was no significant publication bias or small-study effect. The Egger’s test for presence of asymmetry in the data was not significant (p=0.308).  Figure 2 : Flow chart of study selection from literature search                  Potentially revelant records identified through MEDLINE and EMBase database searching (n =  873) Additional records identified through citation search (n = 8)   Records after 311 duplicates removed (n = 562)  Abstracts of records screened for relevance (n = 570) Records excluded because did not study AAV, did not report mortality or survival data, or were review articles  (n = 512 )  Full-text articles assessed for eligibility (n = 58) Full-text articles excluded because AAV not clearly defined, did not report SMR and did not provide data to calculate SMR or were review papers (n = 48) Studies included in meta-analysis (n =10) 27  Table 2 : Summary of studies included in meta-analysis Author/ Year published Country (single/ multi-center) Study design Enrolment period Mean follow-up, years No. patients Female (%) Setting Cohort type AAV classification criteria Mean age at study entry, years No. death events (%) Survival rate Standardized mortality ratio, SMR (95% CI) Quality score Matteson/ 1996 Canada, Mexico, USA (multi-center) Retrospective cohort 1978-1987 7.1 77 GPA 29 (37.7)   Tertiary hospital/clinic Inception ACR N/A 28 (36) 5-year survival 75% 4.69 (3.41-5.96)  Female  6.81 (3.73-9.89) Male  4.00 (2.72-5.27)  10   Knight/ 2002 Sweden (multi-center) Retrospective cohort 1969-1994 Up to 31, Dec 1995 1065 GPA 502 (47.1) Population based Non- inception ICD 8 and 9 N/A 516 (48.5) N/A 4.0 (3.6-4.3)  All Cancer  2.2 (1.7-2.8)  10  Booth/ 2003 UK  (multi-center) Retrospective cohort 1995-2000 3.1 (median) 246 AAV - 82 GPA - 120 MPA - 33 RLV - 11 EGPA  106 (43) Tertiary hospital/clinic Non- inception CHCC 66 (median) 59 (24) 1-year survival 84% 5-year survival 76% 2.84 (2.53-3.18) 5  Lane/2005 UK  (single center) Retrospective cohort 1988-2000 3.3 99 AAV - 57 GPA - 24 MPA - 18 EGPA 38 (38.4) Secondary district general hospital/clinic Non- inception ACR, CHCC and Lanham, plus case note reviews 62.6 31 (31.3) 1-year survival GPA 85.5% MPA 82.7% EGPA 83.2%  5-year survival  GPA 75.9% MPA 45.1% EGPA 68.1%  4.8 (2.9-6.6)  Female  3.05 (1.2-4.9) Male  5.9 (3.1-8.8)  8  Mohammad/2009 Sweden (multi-center) Retrospective cohort 1997-2006 4.9 (median) 140 AAV - 63 GPA - 65 MPA - 6 EGPA - 6 PAN (excluded from analysis) 73 (52.1) Population based Inception EMEA algorithm, plus case note reviews 67.6 (median) GPA 14 (22.2) MPA 29 (44.6) EGPA 1 (16.7) 1-year survival GPA 95% MPA 80%  5-year survival  GPA 83% MPA 55%  GPA  1.77 (0.84-2.70)* MPA  3.95 (2.51-5.38)*  Female  3.27 (1.99-5.04) Male  2.48 (1.60-3.65)  Renal SMR  3.22 (2.21-4.23)  7  Eriksson/ 2009 Sweden (single center) Retrospective cohort 1978-2005  Old cohort 1978-1996 Recent cohort 1997-2005 -  Old cohort 11.1 Recent cohort 4.4 95 AAV  Old cohort 32 AAV (24 GPA, 8 MPA) Recent cohort 63 AAV (33 43 (45.3)  Old cohort 15 (46.9) Recent cohort 28 (44.4) Tertiary hospital/clinic Inception CHCC -  Old cohort 57.7 Recent cohort 61.4 22 (23.2)  Old cohort 15 (46.9) Recent cohort 7 (11.1) -  Old cohort  1-year survival 91% 5-year survival 81%  Recent -  Old cohort 1 year SMR 5.2 (1.07-15.14) 5 year SMR 2.5 (0.93-5.52)^ Recent cohort  7 28  GPA, 30 MPA)  cohort  1-year survival 95% 5-year survival 87%  1 year SMR 2.1 (0.43-6.09) 5 year SMR 1.6(0.6-3.2)^  Takala/ 2010 Finland (multi-center) Retrospective cohort 1981-2000  Old cohort 1981-1990 Recent cohort 1991-2000   Up to 30 July 2005 492 GPA  Old cohort 126 GPA Recent cohort 366 GPA 249 (50.6)  Old cohort 67 (53.2) Recent cohort 182 (49.7) Population based Non- inception ICD 8,9 and 10 plus case note reviews with ACR criteria -  Old cohort 49.3 Recent cohort 54.5 203 (41.3)  Old cohort 67 (53.2) Recent cohort 136 (37.2)  1-year survival 83% 5-year survival 74%  Old cohort 1-year survival 4.9% 5-year survival 6.2% Recent cohort 1-year survival 82% 5-year survival 74%  3.43 (2.98-3.94)  Female  4.38 (3.59-5.61) Male  2.80 (2.28-3.41) 8  Flossmann/2011 15 European countries (multi- center) Prospective cohort 1995-2002 5.2 (median) 535 AAV - 281 GPA - 254 MPA 247 (46.2) Tertiary hospital Inception CHCC 61 (median) 133 (24.9) 1-year survival 88% 5-year survival 78% 2.6 (2.2-3.1) 8 Holle/2011 Germany (single center) Retrospective cohort 1994-2002  Cohort 1 1966-1993 Cohort 2 1994-1998 Cohort 3 1999-2002 - (median) Cohort 1 6.6 Cohort 2 7.3 Cohort 3 3.9 445 GPA  Cohort 1 155 GPA Cohort 2 123 GPA Cohort 3 167 GPA  222 (49.9)  Cohort 1 79 (51) Cohort 2 61 (49.6) Cohort 3 82 (49.1) Tertiary hospital Inception ACR - (median) Cohort 1 48 Cohort 2 52 Cohort 3 55 43 (9.6)  Cohort 1 22 (14.2) Cohort 2 13 (10.6) Cohort 3 8 (4.8) N/A 1.58 (1.14-2.13)  Cohort 1  2.1 (1.34-3.25)# Cohort 2  1.41 (0.75-2.42)# Cohort 3  1.03(0.44-2.03)#  Female 1.23 (0.66-2.11) Male 1.8 (1.22-2.58)  Young patients  5.77 (2.6-10.95) Young males  8.87 (4.05-16.8)  Cancer mortality 0.65 (0.24-1.43)  9  Moosig/ 2013 Germany (single center)  Retrospective cohort 1990-2009 5.2 150 EGPA 74 (49.3) Tertiary hospital Non- inception ACR 49.1 12/142 (8.5) 5-year survival 97% 10-year survival 89% 1.29 (0.66-2.12)  EGPA-associated heart 10  29  failure SMR 3.06 (1.10-6.00)  Abbreviations: RLV, renal limited vasculitis; PAN, polyarteritis nodosa; EMEA, European Medicines Evaluation Agency; ICD, International Classification of Diseases (8, 9 and 10 denotes 8th, 9th and 10th revision respectively) ^ 5-year SMRs computed into meta-SMR as 2 cohorts  * Computed into meta-SMR as 2 cohorts  # Computed into meta-SMR as 3 cohorts  30  Figure 3: Meta-analysis of ten studies on all-cause mortality in patients with ANCA-associated vasculitis.   Abbreviations: Meta-SMR = weighted-pooled summary estimates of standardized mortality ratios. GPA = granulomatosis with polyangiitis        31  Table 2: Overall mortality and sensitivity analyses for the 10 studies (14 unique cohorts) in patients with AAV Study subset No. cohorts No. patients No. death events Random-effects meta-SMR  (95% CI) p All studies 14 3338 1091 2.71 (2.26-3.24)  Disease definition        GPA only (homogeneous)        AAV mixed (heterogeneous)  7 6  1987 1125  804 257  2.63 (2.02-3.43) 2.59 (1.99-3.37)  NS Sex       Females         Males  5 5  611 636  147 172  3.36 (2.10-5.38) 3.11 (2.21-4.36)  NS Study population       Population-based       Hospital/clinic-based  4 10  1691 1647  763 328  3.37 (2.73-4.17) 2.39 (1.86-3.09)  NS Cohort type       Inception       Non-inception  9 5  1286 2052  270 821  2.30 (1.69-3.13) 3.22 (2.57-4.05)  NS Midpoint of enrolment period       1980-1993       1994-1999       2000-2005  5 4 5  1821 1003 514  784 236 71  3.43 (2.79-4.21) 2.82 (2.14-3.72) 1.92 (1.12-3.29)  NS Center       Multi-center       Single center  7 7  2549 789  983 108  3.27 (2.73-3.91) 1.89 (1.17-3.07)  0.05                    32  Table 3: Sensitivity analysis using the jack-knife approach Author/Year published  All-cause mortality SMR (95% CI) Study excluded, meta-SMR (95% CI) All studies 2.7 (2.3-3.2) Not applicable Matteson 1996 4.7 (3.4-6.0) 2.6 (2.1-3.1) Knight 2002 4.0 (3.6-4.3) 2.6 (2.1-3.1) Booth 2003 2.8 (2.5-3.2) 2.7 (2.2-3.3) Lane 2003 4.8 (2.9-6.6) 2.6 (2.1-3.1) Mohammad 2009 (GPA cohort) 1.8 (0.8-2.7) 2.8 (2.3-3.4) Mohammad 2009 (MPA cohort) 4.0 (2.5-5.4) 2.6 (2.2-3.2) Eriksson 2009 (Old cohort) 2.5 (0.9-5.5) 2.7 (2.3-3.3) Eriksson 2009 (New cohort) 1.6 (0.6-3.2) 2.8 (2.3-3.3) Takala 2010 3.4 (3.0-3.9) 2.6 (2.1-3.2) Flossmann 2010 2.6 (2.2-3.1) 2.7 (2.2-3.3) Holle 2011 (Cohort 1) 2.1 (1.3-3.3) 2.8 (2.3-3.3) Holle 2011 (Cohort 2) 1.4 (0.8-2.4) 2.8 (2.4-3.4) Holle 2011 (Cohort 3) 1.0 (0.4-2.0) 2.8 (2.4-3.4) Moosig 2013 1.3 (0.7-2.1) 2.9 (2.4-3.4)                    33  Figure 4: Funnel plot of 14 cohorts evaluating publication bias of mortality studies in ANCA associated vasculitis.                                   34  2.4 Discussion This is the first systematic review and meta-analysis of observational studies assessing the mortality risk in patients with AAV. We found a 2.7-fold increased risk of death in AAV patients when compared to the general population with no differences between sexes. Analysis on studies that included only GPA cases also indicated a similar mortality risk. Of interest, mortality risks were higher in earlier cohorts i.e., those with their midpoint of enrolment periods that were between 1980-1993 and 1994-1999, relative to those between 2000-2005 with a trend towards improvement over time.  Our meta-analyses did not show any significant difference in mortality between females and males. Individual studies have reported contrasting mortality risks between genders, with some favoring females46,56 and others favoring males.28,32,36 It was interesting to note that in the study by Holle et al., young AAV patients (median age 31.7 years) were almost 6-times more likely to die than the age-matched general population with the entire risk contributed by young males (SMR 8.87 [95% CI 4.05-16.8) as there were no deaths amongst the 80 females within the same cohort.56 The authors postulated that the higher mortality risk in young males were due to a higher frequency of renal involvement at diagnosis.   The secular decline in mortality risks was an interesting observation. Although the overall comparison between the cohorts was non-significant, there was a trend towards significance when we compared the earliest to the most recent cohorts (1980-1993 vs 2000-2005, p=0.06). A similar finding was reported in a recent mortality study in GPA patients.76 In that study, 465 GPA patients were followed over a 20-year period and the authors found significantly improved hazard ratios for mortality between an early cohort 35  (1992-2002) and a late cohort (2003-2013) (4.34 [95%CI 2.72-6.92] vs 2.41 [95%CI 1.74-3.34], respectively, p=0.04). We hypothesize that this observation may have resulted from therapeutic improvements, earlier diagnosis with increased availability of ANCA testing and increased physician awareness, as well as improved overall patient care in terms of CVD risk modification, drug toxicity prevention strategies and cancer surveillance. Significant changes in the past decade on the way we treat AAV patients include the use of pulsed cyclophosphamide and rituximab, as less toxic therapeutic options.75,105,106 There was insufficient data to directly assess impact of treatment strategies on mortality in this meta-analysis. Future studies will be needed to confirm the improvement in mortality.  We found a significant difference in reported mortality risks from multi-center studies compared to single center studies. In fact, single center studies had the lowest meta-SMR of 1.89 (95% CI 1.17-3.07). The observed mortality difference between single and multi-center studies were likely due to clinical differences in the respective patient populations, particularly in terms of the proportion and severity of renal involvement. Unfortunately, we were unable to test this hypothesis given that not all of the primary studies adequately described this type of data. Unexpectedly, there was a trend towards higher mortality in the non-inception cohorts when compared to inception cohorts, although this did not reach statistical significance. One might expect higher mortality to be associated with inception cohorts as they capture the entire natural history up until the end of follow up. However, inception cohorts may not follow patients for sufficiently long periods of time to capture late mortality risks, i.e., deaths due to long term disease or treatment-related 36  complications such as cancer, cardiovascular disease or chronic renal failure. Non-inception cohorts by design would include prevalent as well as incident cases and late mortality may be captured as the observation time begins at any point of the natural history. Unfortunately, we were unable to compare mean disease duration for the inception vs. non-inception cohorts given that some reported mean times (n=5), some median times (n=6) and others none provided (n=2). It was also interesting to note the trend for increased risk of death in studies that were population-based compared to those that were hospital/clinic-based. The risk estimates from population-based studies were more consistent, whereas there was wider variability in the estimates from hospital/clinic based studies. The variability in the latter subgroup was not unexpected, given the likelihood of biases inherent in selected or referral cohorts. We suggest that further research in population-based cohorts is necessary to add to the current pool of knowledge. Our study has several limitations. A common issue with meta-analyses is the comparability of the cohorts and the appropriateness of the comparison. We included cohorts that were clinically different in terms of enrolment period, AAV subgroups, classification criteria, follow up, disease severity, and study design. We adopted the random-effects model to incorporate the between-study heterogeneity into the analysis and provided an objective measure of the heterogeneity in the form of I2. Significant heterogeneity was detected, as expected in meta-analyses of observational studies.103 From the univariable meta-regression analysis, “center” and “enrolment period” were possible explanations for the heterogeneity (p=0.05 and p=0.06[cohorts 1980-1993 vs 2000-2005], respectively). Furthermore, we performed a limited multivariable meta-37  regression analysis using these two variables. However, both variables were not significant predictors in the multivariable model. For this reason, our findings suggest that study center is associated with between-study heterogeneity, but its effects may be confounded by enrolment period.   The remaining between-study heterogeneity may be partially explained by the variability of renal involvement in the study cohorts. However, the lack of uniformity in the definition of “renal involvement” in the studies did not allow for grouping into a categorical “renal characteristic”, which would be necessary for meta-regression analysis. In addition, we were also unable to include “quality score” in our meta-regression analysis as we only had one study scored as a lower quality study (≤6).  Current available data allowed us to report a meta-SMR on GPA, but not MPA or EGPA. A report on SMRs for each disease subcategory would be more clinically relevant than an overall SMR for AAV as they are clinically distinct diseases. However, the SMR for AAV may serve as a reference point for future studies seeking to compare mortality risk differences over time. In our meta-analysis, the SMR evaluated the mortality risk adjusted only for age and gender but did not account for other confounders. However, there is no method for adjusting the results of meta-analyses using SMRs. Meta-analyses on studies assessing risk factors or predictors of mortality in AAV is required to address these issues.  In summary, our meta-analysis indicated that there was a 2.7-fold increase in mortality amongst AAV patients compared to the general population. The pooled SMR for only GPA patients was elevated at 2.6 times the general population. The risk of death was elevated for both male and female AAV patients, with no significant difference 38  between the genders. Furthermore, there was a trend towards improvement in mortality risks over time, which warrants further investigation. There is a need for longitudinal studies in contemporary cohorts to evaluate mortality benefits of modern therapies. 39   All-cause and Cause-specific Mortality in Granulomatosis with Chapter 3:Polyangiitis : A Population-based study 3.1 Background Granulomatosis with polyangiitis (GPA), a rare form of ANCA-associated vasculitis (AAV), is characterized by necrotizing and granulomatous inflammation of small vessels.1 It was initially a disease of poor prognosis as 80% did not survive beyond the first year without treatment.25 Over time, the use of cyclophosphamide and glucocorticoids has dramatically improved survival. Current one and 5-year survival rates are estimated to range between 81-95% and 73-83%, respectively.27,29,31-36,84 Studies on GPA mortality compared to the general population reported variable standardized mortality ratios (SMR) ranging between 1.77 to 4.69. 28,32,36,37,51,55,56  With improved patient care, mortality in GPA patients is expected to decline, as evident from a recent report on GPA in-hospital mortality rates in the US.107 However, longitudinal data on secular trends in GPA mortality are scarce. These mostly came from studies on selected populations51,56 and although there was one study in a general population setting, it identified GPA individuals seen by primary care physicians.76  Once an immediately life-threatening disease, GPA has now evolved into a chronic disease with a considerable burden of accrued complications33,108, including cardiovascular disease (CVD), infections, cancer and renal failure. There are limited studies evaluating cause-specific mortality in patients with GPA. To the best of our knowledge, only two studies have evaluated cancer mortality with one reporting a 2-fold increased risk compared to general population37 whilst the other reported no excess 40  mortality due to cancer56. We are not aware of any study evaluating CVD, infection or renal-related mortality risks in patients with GPA at the general population level.    To address these knowledge gaps in GPA mortality, we conducted a population-based study to investigate all-cause and cause-specific mortality in patients with newly diagnosed GPA. We also evaluated if all-cause and cause-specific mortality risks differed between two calendar time periods, 1997-2004 and 2005-2012.  3.2 Methods 3.2.1 Data sources Universal healthcare coverage is available for all residents of British Columbia (BC), Canada (population ~ 4.6 million). Population Data BC captures all provincially funded health care services from 1990, including: all outpatient medical visits109, hospital admissions and discharges110, interventions109, investigations109, demographic data111, cancer registry112, and vital statistics113. Furthermore, Population Data BC encompasses the comprehensive prescription drug database PharmaNet114  which includes all dispensed medications for all BC residents since 1996 regardless of source of funding. Several other studies have been successfully conducted using Population Data BC databases65,115-117. All accessible data has been de-identified by the data providers and each subject received a unique ID number to protect privacy. 3.2.2 Study design and cohort definition Using Population Data BC, we conducted a matched cohort analysis comparing individuals with newly diagnosed GPA (GPA cohort) with age, sex, and entry time-matched individuals without GPA (non-GPA cohort).   41  We identified an incident cohort of GPA patients (>18 years of age) diagnosed between 1 January 1997 and 31 December 2012. GPA definitions for inclusion into the cohort were: a) Two ICD codes for GPA from outpatient medical visits (ICD-9 446.4) or from hospitalization (ICD-9 446.4 or ICD-10 M31.3), at least two months apart and within a two-year period; and b) At least one prescription of oral glucocorticoids, methotrexate, cyclophosphamide, leflunomide, azathioprine, cyclosporine, mycophenolate mofetil, or rituximab in the period one month before and six months after the index date. The latter of the two ICD codes was considered the index date. Individuals were excluded if there was less than 7 years run-in time between start of follow-up and the first diagnostic code for GPA in that subject (earliest health data from 1990 onwards) to ensure incident GPA individuals. The validity of ICD codes to identify GPA cases in administrative health databases was demonstrated in previous studies, with up to 89% of identified patients fulfilling the American College of Rheumatology (ACR) diagnostic criteria upon chart review.9,37  To assess time trends we divided the cohorts into an early cohort (GPA diagnosed between January 1997 and December 2004) and a late cohort (diagnosed between January 2005 and December 2012). To allow equal observation time for both cohorts, follow-up for the early cohort was right-censored on December 31, 2004. Therefore, person-time and events occurring after this time point did not contribute to early cohort-related analyses. The specific time intervals were selected as it was the midpoint of the time range for the entire cohort and also because it corresponded with a treatment shift towards reduced cumulative dose of cyclophosphamide and introduction 42  of newer immunosuppressive agents such as mycophenolate mofetil and rituximab.106,118,119 For each individual with GPA, we matched 10 individuals without GPA randomly selected from the general population based on age, sex, and calendar year of study entry (i.e. index date). 3.2.3 Assessment of outcome The outcome of this study was death (all-cause and cause-specific) during the follow up period. Causes of death were collected from death certificates using ICD-9 and ICD-10 codes. Cause-specific mortality events include CVD (ICD-9 390-459 and ICD-10 I00-I99), infections (ICD-9 001-139, 460-466, 480-488, 680-686, 449, 590, 670, 566, 567, 730, 321.8, 321.2, 320.9, 321.1, 320.7, 790.7, 999.3, 322.1-322.9, 599.0, 595.0, 639.0, 569.5, 572.0, 711.9, 711.4, 711.0, 995.91, 995.92, 996.64 and ICD-10 A00-A99, B00-B99, J00-J06, J09-J18, J20-J22, K61, K65, K67, L00-L08, M00-M01, M86, O85-O86, G01, G00, G07, K750, O080, O753, G020, G021, G028, G042, G050-G052, G060-G062, K630), cancer (ICD-9 140-209 230-234 and ICD-10 C00-C97 D37-D48) and renal-related deaths (ICD-9 584-586 and ICD-10 N17-N19).  3.2.4 Assessment of covariates We assessed covariates that were potential risk factors for mortality during the year before the index date. These included healthcare resource utilization (outpatient medical and hospital visits), medication use (hormone replacement therapy, oral contraceptives, cyclooxygenase-2 [Cox-2] inhibitors, non-steroidal anti-inflammatory drugs [NSAIDs], cardiovascular medications [anti-anginals, anti-hypertensives, cardiac glycosides, diuretics, anti-arrhythmics, and nitrates] or anti-diabetic medications [oral 43  hypoglycemics and insulin], aspirin, statins, fibrates, dipyridamole; and comorbidities (hypertension, angina, chronic obstructive pulmonary disease, obesity and alcoholic liver disease). We also assessed socioeconomic status (SES) using census-derived neighbourhood income quintiles, with quintile 1 representing lowest SES and quintile 5 the highest. We furthermore calculated the Romano modified Charlson Comorbidity Index (CCI) score from the year before the index date.120,121  The clinical parameters and ICD codes included in CCI are presented in Appendix B. 3.2.5 Statistical analysis We compared baseline characteristics between GPA and non-GPA cohorts. All subjects were followed from index date until death, moved out of province or end of follow up period (either December 31st, 2004 for the early cohort or December 31st, 2012 for the late cohort), whichever occurred first. Approximately 2% of cases and 4% of controls moved out of the province during the follow up. Follow up was computed in person-years contributed per subject. We calculated all-cause and cause-specific mortality rates (MR) per 1,000 person-years for each cohort. Survival curves were constructed using the Kaplan-Meier method. Differences in survival between GPA and non-GPA cohorts were tested using the non-parametric log-rank test.122  Cox proportional hazard regression models were used to assess the relationship between GPA and all-cause as well as cause-specific mortality.123 Effect size was reported as hazard ratio (HR), first adjusted for age, sex and entry time and then adjusted for selected covariates. Selected covariates were computed into the Cox models in a forward selection based on a purposeful selection algorithm.124 The 44  minimum accepted effect was a change in estimate of ≥5% in the HR.  An interaction term was included to determine whether the relationship between GPA and mortality changes over time (i.e., calendar time cohort*disease status). To evaluate the impact of duration of GPA (i.e., time after diagnosis), we estimated HRs in varying durations: <1, <2, <3, <4, <5, and ≥5 years. Testing of the proportional hazard assumption was done by graphically plotting log (-log(survival)) versus log (time). We used SAS V.9.4 (SAS Institute, Cary, North Carolina, USA) for all analyses. For all HRs, we calculated 95% confidence intervals (95%CIs). The level of significance was accepted at 0.05 and all p-values were two-sided. Procedures used were in compliance with BC’s Freedom of Information and Privacy Protection Act. Ethics approval was obtained from the University of British Columbia.  3.3 Results A total of 370 patients with newly diagnosed GPA and 3,700 individuals without GPA were included in this study contributing 1,624.8 and 1,8671.3 person-years of follow up, respectively. The age and sex standardized incidence rates of GPA were 2.7 per 106 for the early cohort (1997-2004) and 7.9 per 106 for the late cohort (2005-2012), consistent with previous reports of an increase in incidence rates over time.5,12 We observed 68 deaths in the GPA cohort and 310 in the non-GPA cohort, over the entire study period.  Baseline characteristics of the GPA and non-GPA cohorts are described in Table 5. As expected, GPA patients had more co-morbidities at baseline compared to their 45  matched individuals without GPA, with higher rates of hospitalizations and outpatient visits, pre-existing comorbidities, use of glucocorticoids and NSAIDs. Furthermore, the CCI score was significantly higher in the GPA cohorts compared to non-GPA cohorts, but similar between the early and late GPA cohorts.  Cumulative GPA patient survival rates at 1, 5 and 10 years were estimated at 93.1%, 83.1% and 68.5% respectively, and was significantly lower than those without GPA (p log-rank test <0.001). However, survival in the GPA patients significantly improved from the early cohort to the late cohort (p= 0.002), thus reducing the magnitude of survival difference between the GPA and non-GPA cohorts (Figure 5). Mortality risks in the GPA cohort compared to those without GPA are presented in Table 6. Overall, the age, sex and entry time-adjusted all-cause mortality risk in the GPA cohort was greater than three-fold compared to non-GPA cohort (HR 3.12, 95%CI 2.35-4.14). Even after adjusting for selected covariates including SES quintiles, outpatient visits, CCI score and presence of hypertension, the risk remained significant. There was also excess mortality from CVD in the GPA cohort, relative to the non-GPA cohort (age, sex and entry time-adjusted HR 2.41, 95% CI 1.35-4.29), which persisted in the fully adjusted model. Cancer-related mortality was not significantly increased. As a consequence of the residual disclosure policy of Population Data BC to ensure patient confidentiality, we are not allowed to report on the deaths due to infection or renal disease as there were <6 deaths for each outcome. Mortality risk in the GPA cohort improved over time (Table 6). In the age, sex and entry time-adjusted models, we observed a significant improvement in all-cause mortality between the early and late GPA cohorts (HR= 5.61 [95%CI, 3.14-10.04] vs. 46  2.33 [95%CI 1.53-3.55], respectively; p value for interaction= 0.017). This improvement remained significant after full adjustment (p=0.033). There was a trend towards improvement in CVD-related mortality, although this did not reach statistical significance (p value for interaction = 0.188 in the fully adjusted model).   Analyses stratified by year since GPA diagnosis showed persistently elevated mortality risks for each year of disease duration (Table 7). The all-cause mortality risk remained greater than 2-fold compared to the non-GPA cohort even after 5 years. Although the overall CVD mortality was significantly elevated, the stratified risks by year of disease duration was not, likely due to small numbers of CVD deaths in each year. Cancer mortality risks were not different from population risk for any year of disease duration. 47  Table 4: Baseline characteristics of GPA and non-GPA cohort in the early (1997-2004) and late (2005-2012) cohorts Variable Early cohort (1997-2004) Late cohort (2005-2012) GPA (n=91) Non-GPA (n=910) P value GPA (n=279) Non-GPA (n=2790) P value Female, n (%) 49 (53.8) 490 (53.8) NS 163 (58.4) 1630 (58.4) NS Age, mean (SD) 56.8 (16.9) 56.8 (16.9) NS 54.9 (15.6) 54.8 (15.5) NS Hospitalizations, n (%) 45 (49.5) 136 (14.9) <0.001 148 (53.0) 429 (15.4) <0.001 Outpatient visits, mean (SD) 24 (20.2) 9 (9.4) <0.001 25 (18.0) 9 (10.5) <0.001 Medications, n (%) Cardiovascular drugs 26 (28.6) 237 (26.0) NS 92 (33.0) 723 (25.9) 0.013 Anti-diabetic drugs 7 (7.7) 47 (5.2) NS 24 (8.6) 200 (7.2) NS Hormone replacement therapy 7 (7.7) 54 (5.9) NS 14 (5.0) 120 (4.3) NS Glucocorticoids 47 (51.6) 40 (4.4) <0.001 163 (58.4) 108 (3.9) <0.001 NSAIDs 26 (28.6) 134 (14.7) 0.001 78 (28.0) 392 (14.1) <0.001 Cox-2 inhibitors 10 (11.0) 32 (3.5) 0.003 12 (4.3) 72 (2.6) NS Aspirin <6* 13 (1.4) NA <6*  47 (1.7) NA Dipyridamole 0 0 NA 0 0 NA Oral contraceptives <6 41 (4.5) NA <6 91 (3.3) NA Fibrates <6 13 (1.4) NA <6 17 (0.6) NA Statins <6 99 (10.9) NA <6 392 (14.1) NA  CCI, mean (SD) 1.41 (1.99) 0.28 (0.89) <0.001 1.36 (1.6) 0.32 (0.95) <0.001  Co-morbidity, n (%) Angina 11 (12.1) 55 (6.0) 0.042 13 (4.7) 85 (3.0) NS Obesity <6 <6 NA <6 <6 NA Hypertension 10 (11.0) 192 (21.1) 0.020 79 (28.3) 655 (23.5) 0.077 Alcoholic liver disease <6 <6 NA <6 <6 NA COPD 19 (20.9) 64 (7.0) <0.001 70 (25.1) 191 (6.8) <0.001 48  Socio-economic status (SES), n (%) Quintile 1 15 (16.5) 198 (21.8) NS 41 (14.7) 548 (19.6) 0.046 Quintile 2 22 (24.2) 181 (19.9) NS 66 (23.7) 514 (18.4) 0.037 Quintile 3 14 (15.4) 166 (18.2) NS 64 (22.9) 609 (21.8) NS Quintile 4 19 (20.9) 190 (20.9) NS 52 (18.6) 541 (19.4) NS Quintile 5 21 (23.1) 175 (19.2) NS 56 (20.1) 578 (20.7) NS Abbreviations: SD, standard deviation; NSAIDs, non-steroidal anti-inflammatory drugs; Cox-2, cyclooxygenase-2; CCI, Charlson Comorbidity Index; COPD, chronic obstructive pulmonary disease; NS, non-significant; NA, not available as cell size <6 * Cell size <6 in either early or late is listed as <6 in both early and late in table, to prevent residual disclosure                49  Figure 5:  Survival from all-cause, cardiovascular disease and cancer deaths in GPA and non-GPA cohorts 5A) All-cause                  Number At Risk Early GPA  91 66 43 28 17 13 7 <6 Early non-GPA  910 769 551 412 259 198 101 47 Late GPA 279 222 176 143 116 79 42 22 Late non-GPA 2790 2284 1823 1487 1203 846 493 265 p(early	GPA	vs	non-GPA)	<0.001	p(late	GPA	vs	non-GPA)	<0.001	p(early	GPA	vs	late	GPA)	=	0.002   50  5B) Cardiovascular disease                  Number At Risk Early GPA  91 66 43 28 17 13 7 <6 Early non-GPA  910 769 551 412 259 198 101 47 Late GPA 279 222 176 143 116 79 42 22 Late non-GPA 2790 2284 1823 1487 1203 846 493 265 p	(early	GPA	vs	non-GPA)	=	0.014	p	(late	GPA	vs	non-GPA)	=	0.292	p	(early	GPA	vs	late	GPA)	=	0.235		51  5C) Cancer                  Number At Risk Early GPA  91 66 43 28 17 13 7 <6 Early non-GPA  910 769 551 412 259 198 101 47 Late GPA 279 222 176 143 116 79 42 22 Late non-GPA 2790 2284 1823 1487 1203 846 493 265 p	(early	GPA	vs	non-GPA)	=	0.631	p	(late	GPA	vs	non-GPA)	=	0.784	p	(early	GPA	vs	late	GPA)	=	0.423		52  Table 5: Summary of all-cause and cause-specific mortality risks in GPA and non-GPA cohorts (stratified by overall, early and late cohorts)  Variables Overall cohort Early Cohort (1997-2004) Late Cohort (2005-2012) GPA (n=370) Non-GPA (n=3700) GPA (n=91) Non-GPA (n=910) GPA  (n=279) Non-GPA (n=2790) Total follow up (person-years) 1624.8 18671.3 217.8 2783.0 939.0 9802.4 Mean follow-up (years) 4.39 5.05 2.39 3.06 3.37 3.51 All-cause Mortality DeathsΦ 68 310 19 45 29 130 MR (cases per 1000 person-years) 41.9 16.6 87.2 16.2 30.9 13.3 Age, sex and entry time-adjusted HR (95% CI) 3.12  (2.35-4.14) 1.00 5.61 (3.14-10.04) 1.00 2.33 (1.53-3.55) 1.00 Fully-adjusted HR* (95% CI) 2.02 (1.47-2.78) 1.00 3.58 (1.91-6.73) 1.00 1.60 (1.02-2.48) 1.00 CVD Mortality DeathsΦ 16 92 <6 <6 6 39 MR (cases per 1000 person-years) 9.8 4.9 NR NR 6.4 4.0 Age, sex and entry time-adjusted HR (95% CI) 2.41 (1.35-4.29) 1.00 4.37 (1.24-15.44) 1.00 1.65 (0.68-4.01) 1.00 53  Fully-adjusted HR# (95% CI) 1.99 (1.03-3.85) 1.00 5.50 (1.42-21.27) 1.00 1.89 (0.70-5.14) 1.00 Cancer Mortality DeathsΦ 12 112 <6 <6 6 56 MR (deaths per 1,000 person-years) 7.4 6.0 NR NR 6.4 5.7 Age, sex and entry time-adjusted HR (95% CI) 1.33 (0.72-2.46) 1.00 1.33 (0.30-5.91) 1.00 1.02 (0.43-2.43) 1.00 Fully-adjusted HR^ (95% CI) 0.96  (0.50-1.85) 1.00 1.00 (0.21-4.75) 1.00 0.73 (0.29-1.79) 1.00 Abbreviations: CVD, cardiovascular disease; MR, mortality rate; HR, hazard ratio; NR, not reported due to restrictions on death counts <6  ΦThe summation of the respective death numbers in the early and late cohorts is not equal to the overall numbers (observations were right censored in early cohort) * Adjusted for SES quintile, outpatient visits, CCI and hypertension # Adjusted for SES quintile, outpatient visits, use of CVD drugs and statin, hypertension and angina ^ Adjusted for SES quintile, CCI, and use of NSAIDs            54    Table 6: All-cause and cause-specific mortality risks in GPA from time of diagnosis Years since GPA diagnosis All-cause mortality CVD mortality Cancer mortality HR (95% CI) p HR (95% CI) p HR (95% CI) p <1 2.26 (1.31-3.89) 0.003 2.39 (0.65-8.84) NS 1.17 (0.39-3.52) NS <2 2.15 (1.36-3.41) 0.001 2.07 (0.65-6.60) NS 0.64 (0.22-1.88) NS <3 2.15 (1.42-3.25) <0.001 2.64 (1.00-6.97) 0.050 0.88 (0.36-2.14) NS <4 2.07 (1.41-3.03) <0.001 1.84 (0.78-4.39) NS 0.96 (0.42-2.18) NS <5 2.00 (1.39-2.88) <0.001 2.15 (0.96-4.78) NS 0.94 (0.45-1.99) NS ≥5 2.33 (1.20-4.51) 0.012 1.83 (0.48-6.91) NS 1.32 (0.33-5.32) NS Overall 2.02 (1.47-2.78) <0.001 1.99 (1.03-3.85) 0.042 0.96 (0.50-1.85) NS Abbreviations: CVD, cardiovascular disease; HR, hazard ratio; NS, non-significant 55  3.4 Discussion This is the first population-based study assessing all-cause as well as cause-specific mortality risks in patients with GPA. We observed a 3-fold elevated risk for all-cause mortality in GPA patients relative to the general population. However, there was significant improvement in all-cause mortality risk from the early to the late cohort (HR= 5.61 [95%CI, 3.14-10.04] vs. 2.33 [95%CI 1.53-3.55]). Overall, there was excess mortality from cardiovascular causes but not from cancer. All-cause mortality risk persisted for every year of disease duration, up to and beyond five years.  Our observation of increased all-cause mortality risk in GPA patients was consistent with the recent findings of a large general practice cohort study in UK.76 465 GPA patients were included in that study, with an overall co-morbidity and medication-adjusted mortality HR of 2.52 (95%CI 1.91-3.32). They found an improvement in mortality between two time periods (1992-2002 HR= 4.34 vs. 2003-2013 HR= 2.41, p=0.04). Our results are almost identical. The authors hypothesized a shift towards less cyclophosphamide exposure as well as improved management of long-term comorbidities such as CVD, cancer and vasculitis-related complications in GPA patients. Nevertheless, they did not have data on cause-specific deaths and thus, their reasons for the observed improvement remained speculative.  Our study suggests that the improvement in GPA mortality may have been partially driven by the diminishing CVD mortality risk between the early and late GPA cohorts.  The significantly elevated CVD mortality risk seen in the early cohort was no longer present in the recent cohort as it approached population level risk. This trend in improvement could be attributed to the increasing recognition of CVD as a major 56  contributor to late morbidity and mortality in GPA patients.36,55 As widely reported in other population studies, recent advances in medical therapies and reduction in risk factors have led to the steady decline in deaths from coronary heart disease in the general population.125,126 There were two older studies that assessed secular trends in GPA mortality.36,56 We confirmed the results of a study in Germany, which reported declining mortality risks over 3 time periods from 1966-2002.56 In fact, the risk in their GPA cohort was equivalent to general population risk in their latest time period (1999-2002). However, their cohort was a hospital-based referral cohort whereas our results are population-based, rendering our results generalizable to the population at large. The other study was a retrospective study on hospital discharge records in Finland, comparing the time periods 1981-1990 vs. 1991-2000.36 The authors reported no significant improvement in mortality. We noted that this particular study included hospitalized cases, i.e., cases that were more likely in the acute stage of disease. Therefore, this would suggest that the early mortality risk component (within 1 year of disease) has not improved within the study period.  From our overall GPA cohort, we determined an approximately 2-fold increase in CVD mortality risk (age, sex and entry time-adjusted HR 2.41, 95% CI 1.35-4.29). This was confirmed even after adjustment for selected covariates. There is a lack of reported CVD-related mortality risks in the literature for comparison. A previous estimate of CVD mortality rate ratio in GPA and microscopic polyangiitis patients enrolled in clinical trials was greater than 3-fold compared to the general population (age-standardized MRR 3.68).64 Furthermore, that study only assessed the first 5 years after GPA diagnosis.  57  We did not observe any excess mortality risk due to cancer. Our results differed from a Swedish population-based study in which an all-cancer standardized mortality ratio (SMR) of 2.2 (95% CI 1.7–2.8) was noted in GPA patients (n=1,065).37 Compared with that larger study, our cancer mortality rate was also lower at 7.4 cases per 1000 person-years, compared to 11.9 cases per 1000 person-years from their data. This may be due to population characteristics and environmental differences contributing to cancer occurrences. We were unable to analyze further on the remaining specific causes, that is infection and renal, as we were restricted by the residual disclosure clause in Population Data BC which prevents reporting of event counts <6.  All-cause mortality risk in GPA patients remained elevated for every year of disease, up to and beyond 5 years of disease. The risk was stable, maintaining at approximately 2-fold higher than the general population. Other studies that have evaluated similar risk patterns reported a markedly elevated risk during the early stage of disease (<1 year), which gradually declined with subsequent years.76,127 It would appear from our study, that disease duration has minimal or no impact on all-cause mortality in our patient population.   Reasons for the dissimilarities in our findings include possible difference in disease severity at presentation (i.e., proportion of localized to systemic disease), although our data did not allow us to analyze specific clinical features.   There are some limitations to this study. As it was an observational study utilizing administrative data, there were potential inaccuracies with GPA case coding. To improve the identification of GPA cases, we imposed a minimum of two months between two ICD codes and assigned the date of later code as the index date. This 58  would affect duration of follow up and disease exposure, but cases in both early and late cohorts would have been affected equally.  Furthermore, to improve the specificity of the case definition, we included drug prescription criterion and this approach has a reported positive predictive value of 83.9-90.8%.128 We were confident that we have identified true cases with minimal margins of error using our case definition algorithm as the incidence rates of GPA in our cohort were in keeping with data from UK129 and Sweden.9 We speculate that the rising incidence rate between the periods 1997-2004 to 2005-2012 were due to increasing availability of ANCA testing, improved classification and heightened awareness amongst medical professionals.    This study was also limited by insufficient power to report on infection or renal-related mortality risks. Moreover, the relatively few cancer and CVD deaths also limits the precision of the Kaplan-Meier survival curves. Further studies in this area with longer follow up will be necessary to address these questions.  Our study has several strengths. As it is a population-based study, the external validity of our results is increased. Furthermore, we included only incident GPA cases. We also imposed a minimum lead-in period of 7 years to minimize underestimation of mortality risks, since prevalent cases would potentially include those who had survived to enter the cohort, i.e., survival bias. Finally, due to the longitudinal nature of our cohort we were able to demonstrate the changing mortality trends of GPA. Previous studies have mainly reported SMRs, which are limited sex- and age-adjusted estimates.  In conclusion, our population-based study showed increased all-cause and CVD mortality risks in GPA patients. There was a significant improvement in all-cause mortality risks over time. However, reasons for this improvement, such as evidence-59  based medical treatments, warrant further study. There is an ongoing need to develop strategies to bridge the mortality gap between GPA patients and general population.  60   Discussion Chapter 4:4.1 Summary of Key Findings In Chapter 2 which describes our meta-analysis of all-cause mortality risk in ANCA-associated vasculitis (AAV), we found a 2.7-fold increased risk of death in AAV patients when compared to the general population (meta-SMR 2.71 [95% CI 2.26-3.24]). Analysis using studies that included only granulomatosis with polyangiitis (GPA) cases also indicated a similar mortality risk (meta-SMR 2.63, [95% CI 2.02-3.43]). Subgroup analyses showed that mortality risks were higher in older cohorts with a trend towards improvement over time (i.e., those with their midpoint of enrolment periods that were between 1980-1993 and 1994-1999, versus 2000-2005). Chapter 3 is a report on our population-based matched cohort study on all-cause and cause-specific mortality risks in patients with GPA. We observed a 3-fold increase in all-cause mortality risk in the GPA cohort compared to the non-GPA cohort. As for specific mortality risk, GPA patients were more than twice as likely as the general population to die from cardiovascular disease (CVD). There was no excess mortality risk due to cancer in GPA patients. There was significant improvement in all-cause mortality risk between the time periods of 1997-2004 (early cohort) and 2005-2012 (late cohort) (HR 5.61 vs. 2.33, respectively; p = 0.017). We attributed this observation to advances in medical treatment in GPA and improved management of comorbidities such as CVD, steroid-induced complications, cancer and renal failure. This was supported by our finding of a trend towards improvement in CVD mortality risk over time (early vs. late cohorts, HR 4.37 vs 1.65 respectively, p=0.215). 61  4.2 Implications of Key Findings There are several possible explanations for the persistent mortality gap between our GPA cohort and the general population. Firstly, the selection of cut-off for the time periods may have been too early to demonstrate any mortality benefits from changes in recommended therapies. The majority of cyclophosphamide clinical trials were clustered in the late 1990s and early 2000s. In reality, any evidence-based changes in clinical practice are likely to take place gradually over years. Furthermore, treatment recommendations were only published as early as 2009.130   Secondly, it is possible that treatment advances did not translate into sufficient mortality benefits. In other words, drug-related complications contributes to patient mortality as do active vasculitis with vital organ involvement.  Cyclophosphamide, the cornerstone of GPA treatment, is known to associate with urinary tract complications, such as acute haemorrhagic cystitis and long-term increased risk of bladder cancer.131 The other drugs used including glucocorticoids, are potent immune-suppressors and exposes the patient to severe infections and sepsis, particularly during the remission induction phase for acute vasculitis. Cumulative adverse events in the induction phase are major contributors to early mortality.132  Thirdly, there is room for improvement in terms of recognition and effective management of disease- and treatment-related comorbidities. As discussed previously, CVD is a recognized cause of late GPA mortality. In view of this, management of traditional CVD risk factors is of clinical importance.133 However, the pathophysiology of CVD in GPA has not been fully elucidated, which means the potential for effective targeted therapy is unknown. This knowledge gap needs to be addressed considering 62  the significant CVD mortality burden we have demonstrated. Some authors have hypothesized the role of impaired renal function and increased metabolic syndrome, as potential risk factors for ischemic heart disease in GPA.65 Studies in RA patients suggest that chronic inflammation and endothelial dysfunction potentiate accelerated atherosclerosis in those patients134, although this has yet to be convincingly demonstrated in ANCA-associated vasculitis.135 Further studies are needed in this area to fully understand how or why GPA patients are susceptible to CVD deaths.   Furthermore, steroids may be pro-atherogenic and prolonged glucocorticoid use also leads to well-known steroid-induced complications such as diabetes, osteoporosis, poor wound healing or cataracts. There are published guidelines on management of disease and treatment-related comorbidities130,136 but adoption into clinical practice is not known.   Finally, the lack of targeted therapy in this disease, preferably one with a better side effect profile, should be a research priority in order to improve mortality rates in AAV. Recent trials in mycophenolate mofetil and rituximab suggest they might be reasonable alternatives to cyclophosphamide.106,119,137 In 2012, Health Canada approved rituximab for use in the remission induction phase but its cost limits consideration for widespread use. Ongoing trials on new therapeutic agents include a phase 3 randomized controlled trial evaluating efficacy of avacopan, a complement 5a-receptor inhibitor in AAV (ADVOCATE trial, clinicaltrials.gov identifier NCT02994927). As the therapeutics landscape changes, continued study in this area will show any further mortality benefits from use of these drugs, or lack thereof. 63  4.3 Strengths and Limitations Study-specific strengths and limitations were discussed in detail within their respective chapters 2 and 3. The following paragraphs will discuss the strengths and limitations of this thesis project as a whole.  The limitations of this thesis project pertain mainly to the administrative data that was used for the analyses. The data was collected as part of the Medical Services Plan (MSP) funding process and thus not collected specifically for the purpose of research, which meant that it is vulnerable to insufficient clinical data collection or errors during data entry. There is also a possibility of ICD coding misclassification, which would have affected identification of true GPA cases.138 Furthermore, at the time of the study, there was no linkage with laboratory data i.e., ANCA serology results which could be used to validate the ICD codes. To address these limitations, we employed a case identification algorithm consisting of two ICD-9 or ICD-10 codes, at least two months apart and within a two-year period, plus relevant immunosuppressive medications. This was recently supported in a study that attempted to develop case-finding algorithms using ICD codes to accurately identify GPA patients in administrative databases, for the assembly of population-based cohorts.139 Of the 16 developed algorithms, they reported a positive predictive value of 84-91% and sensitivity of 58-89% using ICD-9 code 446.4 + encounter (repeat code separated by time) + medications (standard and biological DMARDs). As discussed in Chapter 3, we were confident we had identified true incident GPA cases as our incidence rates were consistent with other reported studies.9,129 As this was a retrospective, observational study, a direct causal relationship between disease state and death cannot be confirmed, and may only be inferred upon. 64  On other hand, the mortality risks analyses were performed against a matched healthy population cohort, allowing for a standardized comparison and results ultimately suggesting that GPA patients have an associated increased risk of death compared to the general population.    Although we adjusted for confounders during regression analyses, we were unable to account for other potential mortality risk factors such as smoking, or other unknown confounders using an administrative database. One method to assess the potential impact of these unmeasured confounders is to perform sensitivity analyses using simulated unmeasured confounders, assuming prevalence and odds ratios of these confounders based on previous studies and odds ratios.  Given our small mortality numbers there was also the potential for over-adjustment which would have introduced bias to the model or reduced the precision of the effect size. Finally, we were unable to report deaths due to renal or infectious causes due to insufficient numbers. We suggest that ongoing studies with longer follow up periods and increased sample sizes would be necessary to confirm the validity of our results.      This thesis project has several strengths. This was a population-based matched cohort study evaluating the all-cause and cause-specific mortality risks in GPA. By virtue of its unselected patient population and longitudinal data, the results provided an estimate of the patient's prognosis and cause-specific death risk in a real-life setting. In addition, the design of the study and inclusion criteria served to minimize the selection, immortal/lead time and misinformation biases found in previous reported studies.  65  Furthermore, we were able to adjust for known mortality confounders and confirmed an improvement in mortality risks over time. 4.4 Future Directions As we have now shown an improving but persistent mortality gap between GPA patients and the general population, we intend to continue observing this cohort to ascertain changes in the mortality trend of GPA patients. Henceforth, we hope to obtain data on usage of newer treatments such as rituximab or mycophenolate mofetil and assess their impact on mortality. With the eventual linkage of laboratory data, this will further strengthen the case identification algorithm and improve on the validity of our results. A longer follow-up period will also allow us to determine other cause-specific mortality risks due to renal failure or infections. This clinically relevant information will only serve to improve our knowledge and understanding of this complex but rare disease. It will help identify focus areas of research, such as improving therapeutics, strategies to prevent and/or manage comorbidities, assessing quality of life. A well-designed observational study can provide important information that a randomized, controlled trial is not able to, such as determining associative risk factors and disease natural history as one would see in daily clinical practice, rather than in a tightly-controlled regulated environment of clinical trials. For that reason, observational studies are an important component of clinical research and should be continued and supported. 4.5 Conclusion In summary, our meta-analysis of observational studies found that patients with GPA have a greater than 2-fold mortality risk compared to the general population. This 66  was confirmed in our population-based, matched cohort study of incident GPA cases from 1997-2012, with an age, sex and entry time-adjusted HR of 3.12 (95%CI 2.35-4.14). Furthermore, there was significant improvement in all-cause mortality risks in GPA patients between the time periods 1997-2004 and 2005-2012. There was excess mortality from CVD-related causes but not from cancer. These results highlight the persistent mortality gap between GPA patients and the general population, identifying CVD as a major cause of death. This would indicate that there is room for further improvement in our management of GPA patients. 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Rituximab versus cyclophosphamide for ANCA-associated vasculitis. N Engl J Med 2010;363:221-32. 107. Wallace ZS, Lu N, Miloslavsky E, Unizony S, Stone JH, Choi HK. Nationwide Trends in Hospitalizations and In-Hospital Mortality of Granulomatosis with Polyangiitis. Arthritis Care Res (Hoboken) 2016. 108. Robson J, Doll H, Suppiah R, et al. Damage in the anca-associated vasculitides: long-term data from the European vasculitis study group (EUVAS) therapeutic trials. Ann Rheum Dis 2015;74:177-84. 109. Medical Services Plan (MSP) Payment Information File. Population Data BC, 26 August 2017. at http://www.popdata.bc.ca/data.) 110. Discharge Abstract Database (Hospital Separations). . Population Data BC, 26 August 2017. at http://www.popdata.bc.ca/data.) 73  111. Consolidation File (MSP Registration & Premium Billing). Population Data BC, 26 August 2017. at http://www.popdata.bc.ca/data.) 112. BC Cancer Agency Registry Data. 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Mukhtyar C, Guillevin L, Cid MC, et al. EULAR recommendations for the management of primary small and medium vessel vasculitis. Ann Rheum Dis 2009;68:310-7. 131. Talar-Williams C, Hijazi YM, Walther MM, et al. Cyclophosphamide-induced cystitis and bladder cancer in patients with Wegener granulomatosis. Ann Intern Med 1996;124:477-84. 132. Little M, Nightingale P, Hauser T, et al. Early mortality in systemic vasculitis: Relative contribution of therapy-associated adverse events and active vasculitis. Apmis 2009;117:137. 133. Ntatsaki E, Carruthers D, Chakravarty K, et al. BSR and BHPR guideline for the management of adults with ANCA-associated vasculitis. Rheumatology (Oxford) 2014;53:2306-9. 134. Skeoch S, Bruce IN. Atherosclerosis in rheumatoid arthritis: is it all about inflammation? Nat Rev Rheumatol 2015;11:390-400. 135. Tervaert JW. Translational mini-review series on immunology of vascular disease: accelerated atherosclerosis in vasculitis. Clin Exp Immunol 2009;156:377-85. 136. Lapraik C, Watts R, Bacon P, et al. BSR and BHPR guidelines for the management of adults with ANCA associated vasculitis. Rheumatology (Oxford) 2007;46:1615-6. 137. Jones RB, Tervaert JWC, Hauser T, et al. Rituximab versus cyclophosphamide in ANCA-associated renal vasculitis. N Engl J Med 2010;363:211-20. 138. Boyd M, Specks U, Finkielman JD. Accuracy of the ICD-9 code for identification of patients with Wegener's granulomatosis. J Rheumatol 2010;37:474. 139. Sreih AG, Annapureddy N, Springer J, et al. Development and validation of case-finding algorithms for the identification of patients with anti-neutrophil cytoplasmic antibody-associated vasculitis in large healthcare administrative databases. Pharmacoepidemiol Drug Saf 2016;25:1368-74.  75  Appendices Appendix A   ANCA Vasculitis Search Strategy  Research Question(s) What is the risk of death of ANCA associated vasculitis when compared to the general population? (The metric for measuring mortality has to be standardized mortality ratios.)  Search Parameters Any language Adult population Update to April 14, 2015 from previous search (done October 22, 2013) Peer reviewed literature   1-A-ARCC ANCA vasculitis 2015 update medline-embase 20140414 medline-embase  2015 update – April 14, 2015.  Medline FINAL  Database: Ovid MEDLINE(R) In-Process & Other Non-Indexed Citations and Ovid MEDLINE(R) <1946 to Present> Search Strategy: -------------------------------------------------------------------------------- 1     exp Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis/ (8146) 2     ANCA associated vasculitis/ (751) 3     Churg Strauss syndrome/ (1740) 4     Wegener granulomatosis/ (5918) 5     (anti neutrophil cytoplasmic antibody associated vasculitis or ANCA-associated vasculitis or ANCA associated vasculitis or pauci-immune vasculitis or pauci immune vasculitis or pauci-immune vasculitides or ANCA-associated vasculitides or ANCA associated vasculitides or ANCA-associated vasculitide).ti,ab. (1307) 6     (Churg Strauss Syndrome or Churg-Strauss Syndrome or Churg Strauss vasculitis or Churg-Strauss Vasculitis or Allergic Granulomatous Angiitis or Allergic Granulomatous Angiitides or Allergic Angiitis or Allergic Angiitides).ti,ab. (1897) 7     Microscopic Polyangiitides.ti,ab. (1) 8     ("Wegener's granulomatosis" or Wegener granulomatosis or Klinger Wegener syndrome or morbus Wegener or necrotizing respiratory granulomatosis or necrotising respiratory gramulomatosis or "Wegener’s syndrome" or Wegener syndrome or "Wegener’s granuloma" or Wegener granuloma or "Wegener’s disease Wegener 76  disease" or Wegener Klinger Churg syndrome or Wegener Klinger granulomatosis).ti,ab. (5591) 9     or/5-8 (8017) 10     1 or 9 (10283) 11     glomerulonephritis/ (24479) 12     "Antibodies, Antineutrophil Cytoplasmic"/ (4638) 13     neutrophil cytoplasmic antibody/ (0) 14     (Antineutrophil Cytoplasmic Antibody or Anti Neutrophil Cytoplasmic Antibody or Anti-Neutrophil Cytoplasmic Antibody or Antineutrophil Cytoplasmic Antibodies or ANCA or p-ANCA or p ANCA or c-ANCA or c ANCA).ti,ab. (5909) 15     or/11-14 (30534) 16     Vasculitis/ (11259) 17     Systemic Vasculitis/ (221) 18     vasculitis.ti,ab. (24092) 19     or/16-18 (28220) 20     15 and 19 (4617) 21     10 or 20 [ANCA VASCULITIS] (12313) 22     (standardized mortality ratio* or standardised mortality ratio*).ti,ab. (3882) 23     (SMR or SMRs).ti,ab. (4308) 24     22 or 23 (5762) 25     21 and 24 [ANCA VASCULITIS + SMRs] (9) 26     mortality/ or mortality.ti,ab. or mortality.fs. (789129) 27     21 and 26 [ANCA VASCULITIS + MORTALITY (BROAD)] (568) 28     Survival Rate/ (130669) 29     Treatment Outcome/ or Treatment Failure/ (690719) 30     Incidence/ (183715) 31     (survival rate* or outcome* or incidence).ti,ab. (1529494) 32     or/28-31 (2080099) 33     27 and 32 [ANCA VASCULITIS + MORTALITY (BROAD) + SURVIVAL RATE/OUTCOMES/INCIDENCE] (330) 34     25 or 33 (332) 35     limit 34 to "all adult (19 plus years)" (233) 36     34 not 35 (99) 37     limit 34 to yr="2013 - 2015" (71) 38     remove duplicates from 37 (70) SAVED EN (42 UNIQUE IN ENDNOTE)  Medline-EMbase FINAL Database: Embase <1980 to 2015 April 13>, Ovid MEDLINE(R) In-Process & Other Non-Indexed Citations, Ovid MEDLINE(R) Daily, Ovid MEDLINE(R) and Ovid OLDMEDLINE(R) <1946 to Present> Search Strategy: -------------------------------------------------------------------------------- 1     exp Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis/ (10765) 2     ANCA associated vasculitis/ (3362) 3     Churg Strauss syndrome/ (5466) 4     Wegener granulomatosis/ (15589) 77  5     (anti neutrophil cytoplasmic antibody associated vasculitis or ANCA-associated vasculitis or ANCA associated vasculitis or pauci-immune vasculitis or pauci immune vasculitis or pauci-immune vasculitides or ANCA-associated vasculitides or ANCA associated vasculitides or ANCA-associated vasculitide).ti,ab. (3263) 6     (Churg Strauss Syndrome or Churg-Strauss Syndrome or Churg Strauss vasculitis or Churg-Strauss Vasculitis or Allergic Granulomatous Angiitis or Allergic Granulomatous Angiitides or Allergic Angiitis or Allergic Angiitides).ti,ab. (4426) 7     Microscopic Polyangiitides.ti,ab. (2) 8     ("Wegener's granulomatosis" or Wegener granulomatosis or Klinger Wegener syndrome or morbus Wegener or necrotizing respiratory granulomatosis or necrotising respiratory gramulomatosis or "Wegener’s syndrome" or Wegener syndrome or "Wegener’s granuloma" or Wegener granuloma or "Wegener’s disease Wegener disease" or Wegener Klinger Churg syndrome or Wegener Klinger granulomatosis).ti,ab. (12258) 9     or/5-8 (18050) 10     1 or 9 (21486) 11     glomerulonephritis/ (51672) 12     "Antibodies, Antineutrophil Cytoplasmic"/ (12182) 13     neutrophil cytoplasmic antibody/ (7540) 14     (Antineutrophil Cytoplasmic Antibody or Anti Neutrophil Cytoplasmic Antibody or Anti-Neutrophil Cytoplasmic Antibody or Antineutrophil Cytoplasmic Antibodies or ANCA or p-ANCA or p ANCA or c-ANCA or c ANCA).ti,ab. (14645) 15     or/11-14 (67704) 16     Vasculitis/ (38313) 17     Systemic Vasculitis/ (2865) 18     vasculitis.ti,ab. (56165) 19     or/16-18 (72593) 20     15 and 19 (12083) 21     10 or 20 [ANCA VASCULITIS] (27153) 22     (standardized mortality ratio* or standardised mortality ratio*).ti,ab. (8328) 23     (SMR or SMRs).ti,ab. (9494) 24     22 or 23 (12561) 25     21 and 24 [ANCA VASCULITIS + SMRs] (20) 26     mortality/ or mortality.ti,ab. or mortality.fs. (1653261) 27     21 and 26 [ANCA VASCULITIS + MORTALITY (BROAD)] (1392) 28     Survival Rate/ (291073) 29     Treatment Outcome/ or Treatment Failure/ (1412690) 30     Incidence/ (409174) 31     (survival rate* or outcome* or incidence).ti,ab. (3556760) 32     or/28-31 (4685833) 33     27 and 32 [ANCA VASCULITIS + MORTALITY (BROAD) + SURVIVAL RATE/OUTCOMES/INCIDENCE] (759) 34     25 or 33 (764) 35     limit 34 to "all adult (19 plus years)" [Limit not valid in Embase; records were retained] (665) 36     34 not 35 (99) 78  37     limit 34 to yr="2013 - 2015" (183) 38     remove duplicates from 37 (145) 39     from 38 keep 1-75 (75) SAVED EN (55 UNIQUE IN ENDNOTE)                                            79  Appendix B   Romano modified Charlson weighted index of comorbidity Assigned weights for diseases Diagnostic Categories ICD-CM-9 Codes 1 Myocardial infarct 410.xx, 412 Congestive heart failure 402.01, 402.11, 402.91, 425.x, 428.x, 429.3 Peripheral vascular disease 440.x, 441.x, 442.x, 443.1-443.9, 447.1, 785.4, 38.13-38.14(P), 38.16(P), 38.18(P), 38.33-38.34(P), 38.36(P), 38.38(P), 38.43-38.44(P), 38.46(P), 38.48(P), 39.22-39.26(P), 39.29(P) Cerebrovascular disease 362.34, 430-436, 437-437.1, 437.9, 438, 781.4, 784.3, 997.0, 38.12(P), 38.42(P) Dementia 290.x, 331-331.2 Chronic pulmonary disease 415.0, 416.8-416.9, 491.x-494, 496 Connective tissue disease 710.x, 714.x Ulcer disease 531.xx-534.xx Mild liver disease 571.2, 571.5-571.6, 571.8-571.9 Diabetes (mild to moderate) 250.0x-250.3x 2 Hemiplegia 342.x, 344.x Moderate or severe renal disease 585-586, V42.0, V45.1, V56.x. 39.27(P), 39.42(P), 39.93-39.95(P), 54.98(P) Diabetes with end organ damage 250.4x-250.9x Any tumor, including leukemia or lymphoma 140.x-171.x, 174.x-195.x, 200.xx-208.x, 273.0, 273.3, V10.46, 60.5(P), 62.4-62.41(P) 3 Moderate or severe liver disease 572.2-572.4, 456.0-456.2x, 39.1(P), 42.91(P) 6 Metastatic solid tumor 196.x-199.x AIDS 042.x-044.x (P) denotes codes for procedures rather than diagnoses 

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