The use of predictive algorithms is an efficient approach to identifying risk cut-offs for targeted interventions that allows for the inclusion of multiple risk factors (McLaren et al., 2010). These approaches have recently been developed and validated for use at the population level (Manuel et al., 2012 and Rosella et al., 2011). While risk algorithms are increasingly being used in clinical and recently in population settings, further research is needed on how to best interpret and apply risk-cut-offs click here to inform intervention

approaches. For example, it is not clear what magnitude of diabetes risk (e.g. 10-year risk ≥ 20%) would result in the greatest population benefit from a given diabetes prevention strategy. Most risk cut-offs identified from other algorithms appear arbitrary and are not designed to specifically maximize prevention outcomes. An important cut-off

attribute that is currently missing from prevention strategies is maximizing strategy effic\acy, meaning the risk level used to identify target populations balances the number of individuals targeted with the potential benefit. In addition, few studies have directly examined how dispersion and concentration of diabetes risk in the population can influence the impact of a given strategy. The objectives of this study are to demonstrate how the dispersion of risk in the population, measured by the Gini coefficient, is correlated with the population risk of diabetes and to generate empiric risk cut-offs based on a validated risk score in order to maximize the population benefit as measured by absolute risk reduction in the population. selleck screening library We first updated an existing validated risk prediction algorithm for incident diabetes, referred herein as DPoRT 2.0. DPoRT is a statistical model based on the Weibull survival distribution and is validated to calculate up to 10-year

diabetes risk in any population-based data that contains Montelukast Sodium self-reported risk factor information on age, height and weight, ethnicity, education, immigrant status, hypertension, self-reported heart disease, income, smoking and sex for those age 20 years and older and who are currently without diabetes. The original risk algorithm was based on a cohort of individuals 19,861 ≥ 20 years of age without diabetes followed between 1996 and 2005 and validated in two external cohorts in Ontario (N = 26,465) and Manitoba (N = 9899). Full details of development and validation can be found in a previous study (Rosella et al., 2011). DPoRT 2.0 follows the same methodology with updated coefficients based on more recent data including individuals from the original 1996 Ontario cohort and the Ontario respondents of Cycle 1.1 (2001) and 2.1 (2003) of the Canadian Community Health Survey (CCHS) linked to the Ontario Diabetes Database (ODD) with follow-up until 2011 (Hux and Ivis, 2005) resulting in a total sample size of 69,606 individuals and 667,337 person-years of follow-up. DPORT 2.