Tara Kiran1, Richard H Glazier1, Michael A Campitelli1, Andrew Calzavara1, Therese A Stukel1. 1. Department of Family and Community Medicine (Kiran, Glazier), St. Michael's Hospital, University of Toronto; Centre for Research on Inner City Health (Kiran, Glazier), Li Ka Shing Knowledge Institute of St. Michael's Hospital; Institute for Clinical Evaluative Sciences (Campitelli, Calzavara, Stukel); Dalla Lana School of Public Health, and Institute for Health Policy, Management and Evaluation (Stukel), University of Toronto, Toronto, Ont.
Abstract
BACKGROUND: Higher primary care physician supply is associated with lower mortality due to heart disease, cancer and stroke, but its relation to diabetes care and outcomes is unknown. We examined the association between primary care physician supply and evidence-based testing and hospital visits for people with diabetes in naturally occurring multispecialty physician networks in Ontario, Canada. METHODS: We conducted a cross-sectional analysis between Apr. 1, 2009, and Mar. 31, 2011, using linked administrative data. We included all Ontario residents over 40 years of age with a diagnosis of diabetes before Apr. 1, 2007, who were alive on Apr. 1, 2009 (N = 712 681). We tested the association between physician supply and outcomes at the network level using separate Poisson regression models for urban and nonurban physician networks. We accounted for clustering at the physician and network level and adjusted for patient characteristics. RESULTS: Patients in physician networks with a high supply of primary care physicians were more likely to receive the optimal number of evidence-based tests for diabetes than patients in networks with low primary care physician supply (urban relative risk [RR] 1.06, 95% confidence interval [CI] 1.04-1.07; nonurban RR 1.17, 95% CI 1.14-1.21) but were no different regarding emergency department visits (urban RR 1.05, 95% CI 0.94-1.17; nonurban RR 0.96, 95% CI 0.85-1.08) or hospital admissions for diabetes complications (urban RR 1.01, 95% CI 0.89-1.14; nonurban RR 0.91, 95% CI 0.77-1.07). INTERPRETATION: Having more primary care physicians per capita is associated with better diabetes care but not with reduced hospital visits in this setting. Further research to understand this relation and how it varies by setting is important for resource planning.
BACKGROUND: Higher primary care physician supply is associated with lower mortality due to heart disease, cancer and stroke, but its relation to diabetes care and outcomes is unknown. We examined the association between primary care physician supply and evidence-based testing and hospital visits for people with diabetes in naturally occurring multispecialty physician networks in Ontario, Canada. METHODS: We conducted a cross-sectional analysis between Apr. 1, 2009, and Mar. 31, 2011, using linked administrative data. We included all Ontario residents over 40 years of age with a diagnosis of diabetes before Apr. 1, 2007, who were alive on Apr. 1, 2009 (N = 712 681). We tested the association between physician supply and outcomes at the network level using separate Poisson regression models for urban and nonurban physician networks. We accounted for clustering at the physician and network level and adjusted for patient characteristics. RESULTS:Patients in physician networks with a high supply of primary care physicians were more likely to receive the optimal number of evidence-based tests for diabetes than patients in networks with low primary care physician supply (urban relative risk [RR] 1.06, 95% confidence interval [CI] 1.04-1.07; nonurban RR 1.17, 95% CI 1.14-1.21) but were no different regarding emergency department visits (urban RR 1.05, 95% CI 0.94-1.17; nonurban RR 0.96, 95% CI 0.85-1.08) or hospital admissions for diabetes complications (urban RR 1.01, 95% CI 0.89-1.14; nonurban RR 0.91, 95% CI 0.77-1.07). INTERPRETATION: Having more primary care physicians per capita is associated with better diabetes care but not with reduced hospital visits in this setting. Further research to understand this relation and how it varies by setting is important for resource planning.
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