Sasha Bernatsky1, Audrey Smargiassi2, Cheryl Barnabe3, Lawrence W Svenson4, Allan Brand2, Randall V Martin5, Marie Hudson6, Ann E Clarke7, Paul R Fortin8, Aaron van Donkelaar9, Steven Edworthy3, Patrick Bélisle10, Lawrence Joseph11. 1. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Purvis Hall 1020 Pine Avenue West, Montreal, QC, Canada H3A 1A2; Division of Clinical Epidemiology, McGill University Health Centre, Royal Victoria Hospital, V Pavilion 687, Pine Avenue West, Montreal, QC, Canada H3A 1A1. Electronic address: sasha.bernatsky@mcgill.ca. 2. Département de santé environnementale et de santé au travail, Université de Montréal, Pavillon Marguerite d'Youville, École de santé publique, C.P. 6128, Succursale Centre-Ville, Montréal, QC, Canada; Institut National de Santé Publique du Québec, 190 Crémazie Blvd. E, Montréal, QC, Canada H2P 1E2. 3. Department of Medicine, University of Calgary, 2500 University Dr NW, Calgary, AB, Canada T2N 1N4; Department of Community Health Sciences, University of Calgary, 2500 University Dr NW, Calgary, AB, Canada T2N 1N4. 4. Department of Community Health Sciences, University of Calgary, 2500 University Dr NW, Calgary, AB, Canada T2N 1N4; Surveillance and Assessment Branch, Alberta Ministry of Health, 10025 Jasper Avenue, Edmonton, Canada AB T5J 1S6; Canada School of Public Health, University of Alberta, 11405 87 Avenue NW, Edmonton, Canada AB T6G 1C9; Canada Department of Community Health Sciences, University of Calgary, 2500 Hospital Drive NW, Calgary, Canada AB T2N 1N4. 5. Department of Physics and Atmospheric Science, Dalhousie University, 6310 Coburg Rd. PO BOX 15000, Halifax, NS, Canada B3H 4R2; Harvard-Smithsonian Center for Astrophysics, 60 Garden St, Cambridge, MA 02138, USA. 6. Department of Medicine, McGill University, 1001 Decarie Boulevard, Mail Drop Number D05-2214, Suite D05-2212, Montreal, QC, Canada H4A 3J1; Division of Rheumatology, Jewish General Hospital, 3755 Côte-Ste-Catherine Road, Montreal, QC, Canada H3T 1E2. 7. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Purvis Hall 1020 Pine Avenue West, Montreal, QC, Canada H3A 1A2; Division of Allergy and Clinical Immunology, McGill University Health Centre, 1547 Avenue des Pins, Montréal, QC, Canada H3G 1B3. 8. Division of Rheumatology, Department of Medicine, Pavilion Ferdinand-Vandry 1050, ave de la Médecine, bureau 4633, Université Laval, Québec, QC, Canada G1V 0A6. 9. Department of Physics and Atmospheric Science, Dalhousie University, 6310 Coburg Rd. PO BOX 15000, Halifax, NS, Canada B3H 4R2. 10. Division of Clinical Epidemiology, McGill University Health Centre, Royal Victoria Hospital, V Pavilion 687, Pine Avenue West, Montreal, QC, Canada H3A 1A1. 11. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Purvis Hall 1020 Pine Avenue West, Montreal, QC, Canada H3A 1A2; Division of Clinical Epidemiology, McGill University Health Centre, Royal Victoria Hospital, V Pavilion 687, Pine Avenue West, Montreal, QC, Canada H3A 1A1.
Abstract
OBJECTIVE: To estimate the degree to which fine particulate (PM2.5) air pollution is associated with systemic autoimmune rheumatic diseases (SARDs). METHODS: We used population-based administrative data from Alberta (1993-2007) and Quebec (1989-2011). SARD algorithms included ≥2 physician billing codes, or ≥1 rheumatology billing code, or ≥1 hospitalization diagnostic code (for systemic lupus, Sjogren's Syndrome, scleroderma, polymyositis, dermatomyositis, or undifferentiated connective tissue disease). Bayesian hierarchical latent class regression models estimated the probability that any given resident was a SARD case, based on the algorithms. Mean 2001-2006 residential ambient PM2.5 levels were assigned using satellite-derived data for dissemination area regions in Alberta and CLSC regions in Quebec. The sum of individual level probabilities provided the estimated total cases per region in each province, according to age, sex, urban-versus-rural residence, income, and PM2.5 levels. In Alberta, we ran separate models for First-Nations (FN) and non-First Nations subgroups. Bayesian logistic regression modeling generated odds ratio (OR) estimates for being a SARD case, accounting concurrently for demographics, as well as an interaction term between age and sex. RESULTS: Our data suggested that the probability of being a SARD case was higher among females versus males and for residents aged >45 versus younger, with the highest ORs for older females. Independently, the odds of being a SARDs case increased with PM2.5 levels in both provinces. CONCLUSION: Our data suggest that PM2.5 exposure may be associated with an increased risk of SARDs.
OBJECTIVE: To estimate the degree to which fine particulate (PM2.5) air pollution is associated with systemic autoimmune rheumatic diseases (SARDs). METHODS: We used population-based administrative data from Alberta (1993-2007) and Quebec (1989-2011). SARD algorithms included ≥2 physician billing codes, or ≥1 rheumatology billing code, or ≥1 hospitalization diagnostic code (for systemic lupus, Sjogren's Syndrome, scleroderma, polymyositis, dermatomyositis, or undifferentiated connective tissue disease). Bayesian hierarchical latent class regression models estimated the probability that any given resident was a SARD case, based on the algorithms. Mean 2001-2006 residential ambient PM2.5 levels were assigned using satellite-derived data for dissemination area regions in Alberta and CLSC regions in Quebec. The sum of individual level probabilities provided the estimated total cases per region in each province, according to age, sex, urban-versus-rural residence, income, and PM2.5 levels. In Alberta, we ran separate models for First-Nations (FN) and non-First Nations subgroups. Bayesian logistic regression modeling generated odds ratio (OR) estimates for being a SARD case, accounting concurrently for demographics, as well as an interaction term between age and sex. RESULTS: Our data suggested that the probability of being a SARD case was higher among females versus males and for residents aged >45 versus younger, with the highest ORs for older females. Independently, the odds of being a SARDs case increased with PM2.5 levels in both provinces. CONCLUSION: Our data suggest that PM2.5 exposure may be associated with an increased risk of SARDs.
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