Laura C Rosella1, Michael Lebenbaum2, Ye Li3, Jun Wang2, Douglas G Manuel4. 1. Public Health Ontario, Toronto, Ontario, Canada; Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada. Electronic address: laura.rosella@oahpp.ca. 2. Public Health Ontario, Toronto, Ontario, Canada. 3. Public Health Ontario, Toronto, Ontario, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada. 4. Public Health Ontario, Toronto, Ontario, Canada; Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada; Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; Department of Family Medicine and Epidemiology and Community Medicine, University of Ottawa, Canada; Statistics Canada, Ottawa, Ontario, Canada.
Abstract
OBJECTIVE: To quantify the influence of type 2 diabetes risk distribution on prevention benefit and apply a method to optimally identify population targets. METHODS: We used data from the 2011 Canadian Community Health Survey (N=45,040) and the validated Diabetes Population Risk Tool to calculate 10-year diabetes risk. We calculated the Gini coefficient as a measure of risk dispersion. Intervention benefit was estimated using absolute risk reduction (ARR), number-needed-to-treat (NNT), and number of cases prevented. RESULTS: There is a wide variation of diabetes risk in Canada (Gini=0.48) and with an inverse relation to risk (r=-0.99). Risk dispersion is lower among individuals meeting an empirically derived risk cut-off (Gini=0.18). Targeting prevention based on a risk cut-off (10-year risk ≥ 16.5%) resulted in a greater number of cases prevented (340 thousand), higher ARR (7.7%) and lower NNT (13) compared to targeting individuals based on risk factor targets. CONCLUSIONS: This study provides empirical evidence to demonstrate that risk variability is an important consideration for estimating the prevention benefit. Prioritizing target populations using an empirically derived cut-off based on a multivariate risk score will result in greater benefit and efficiency compared to risk factor targets.
OBJECTIVE: To quantify the influence of type 2 diabetes risk distribution on prevention benefit and apply a method to optimally identify population targets. METHODS: We used data from the 2011 Canadian Community Health Survey (N=45,040) and the validated Diabetes Population Risk Tool to calculate 10-year diabetes risk. We calculated the Gini coefficient as a measure of risk dispersion. Intervention benefit was estimated using absolute risk reduction (ARR), number-needed-to-treat (NNT), and number of cases prevented. RESULTS: There is a wide variation of diabetes risk in Canada (Gini=0.48) and with an inverse relation to risk (r=-0.99). Risk dispersion is lower among individuals meeting an empirically derived risk cut-off (Gini=0.18). Targeting prevention based on a risk cut-off (10-year risk ≥ 16.5%) resulted in a greater number of cases prevented (340 thousand), higher ARR (7.7%) and lower NNT (13) compared to targeting individuals based on risk factor targets. CONCLUSIONS: This study provides empirical evidence to demonstrate that risk variability is an important consideration for estimating the prevention benefit. Prioritizing target populations using an empirically derived cut-off based on a multivariate risk score will result in greater benefit and efficiency compared to risk factor targets.
Keywords:
ARR; CCHS; Canadian Community Health Survey; DPoRT; Diabetes Population Risk Tool; Diabetes mellitus, type 2; Primary prevention; Risk assessment; absolute risk reduction
Authors: Laura C Rosella; Kathy Kornas; Michael E Green; Baiju R Shah; Jennifer D Walker; Eliot Frymire; Carmen Jones Journal: CMAJ Open Date: 2020-03-16
Authors: Stacey Fisher; Carol Bennett; Deirdre Hennessy; Tony Robertson; Alastair Leyland; Monica Taljaard; Claudia Sanmartin; Prabhat Jha; John Frank; Jack V Tu; Laura C Rosella; JianLi Wang; Christopher Tait; Douglas G Manuel Journal: Health Rep Date: 2020-07-29 Impact factor: 6.094