title: Risk distribution and its influence on the population targets for diabetes prevention.
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.
authors.affiliation: 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.
authors.forename: Laura C
authors.initials: LC
authors.lastname: Rosella
authors.affiliation: Public Health Ontario, Toronto, Ontario, Canada.
authors.forename: Michael
authors.initials: M
authors.lastname: Lebenbaum
authors.affiliation: Public Health Ontario, Toronto, Ontario, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Ye
authors.initials: Y
authors.lastname: Li
authors.affiliation: Public Health Ontario, Toronto, Ontario, Canada.
authors.forename: Jun
authors.initials: J
authors.lastname: Wang
authors.affiliation: 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.
authors.forename: Douglas G
authors.initials: DG
authors.lastname: Manuel
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D002170:Canada / Q000453:epidemiology; D003153:Community Health Services; D003920:Diabetes Mellitus / Q000175:diagnosis / Q000453:epidemiology / Q000517:prevention & control*; D005260:Female; D006306:Health Surveys; D006801:Humans; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D061366:Numbers Needed To Treat; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000592:standards*; D012307:Risk Factors
keywords: ARR; CCHS; Canadian Community Health Survey; DPoRT; Diabetes Population Risk Tool; Diabetes mellitus, type 2; Primary prevention; Risk assessment; absolute risk reduction