J
J Antonio Aviña-Zubieta
Researcher at University of British Columbia
Publications - 109
Citations - 4978
J Antonio Aviña-Zubieta is an academic researcher from University of British Columbia. The author has contributed to research in topics: Population & Medicine. The author has an hindex of 28, co-authored 74 publications receiving 3835 citations.
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Risk of cardiovascular mortality in patients with rheumatoid arthritis: a meta-analysis of observational studies.
J Antonio Aviña-Zubieta,Hyon K. Choi,Mohsen Sadatsafavi,Mahyar Etminan,John M. Esdaile,Diane Lacaille +5 more
TL;DR: In this article, a meta-analysis of observational studies was conducted to determine the magnitude of risk of cardiovascular mortality in patients with rheumatoid arthritis (RA) compared with the general population.
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Validity of Diagnostic Codes for Acute Stroke in Administrative Databases: A Systematic Review.
TL;DR: While most cases of prevalent cerebrovascular disease can be detected using 430-438/I60-I69 collectively, acute stroke must be defined using more specific codes, and linking vital statistics and hospitalization data may improve the ascertainment of fatal stroke.
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Overall and cause-specific mortality in patients with systemic lupus erythematosus: a meta-analysis of observational studies.
TL;DR: To determine the magnitude of risk from all‐cause and cause‐specific mortality in patients with systemic lupus erythematosus (SLE) compared to the general population through a meta‐analysis of observational studies, a large number of studies were analyzed.
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A systematic review identifies valid comorbidity indices derived from administrative health data
TL;DR: A systematic review of studies reporting on the development or validation of comorbidity indices using administrative health data and compare their ability to predict outcomes related to comor bidity (ie, construct validity) found a number of valid comorbridity indices derived from administrative data are available.
Journal ArticleDOI
Past and Future Burden of Inflammatory Bowel Diseases Based on Modeling of Population-Based Data.
Stephanie Coward,Fiona Clement,Eric I Benchimol,Charles N. Bernstein,J Antonio Aviña-Zubieta,Alain Bitton,Mathew W. Carroll,Glen Hazlewood,Kevan Jacobson,Susan Jelinski,Susan Jelinski,Rob Deardon,Jennifer Jones,M Ellen Kuenzig,Desmond Leddin,Kerry McBrien,Sanjay K. Murthy,Geoffrey C. Nguyen,Anthony R. Otley,Remo Panaccione,Ali Rezaie,Greg Rosenfeld,Juan Nicolás Peña-Sánchez,Harminder Singh,Laura E. Targownik,Gilaad G. Kaplan +25 more
TL;DR: Forecasting prevalence will allow health policy makers to develop policy that is necessary to address the challenges faced by health systems in providing high-quality and cost-effective care.