, 2009). This value is represented

as solid black line in Fig. 2. The updated algorithm (DPoRT 2.0) demonstrates excellent accuracy (H–L χ2 < 20, p < 0.01?) and similar discrimination to the original DPoRT (C-statistic = 0.77) (Fig. 1) (Appendix A). Overall, based on the 2011 population, diabetes risk is 10% (9.6%, 10.4%) translating to over 2.25 million new diabetes cases expected in Canada between 2011 and 2020. The 10-year baseline ISRIB mouse risk for diabetes in the overall population and by important subgroups is reported in Table 1. Ten-year diabetes risk varies by age, Body Mass Index (BMI), sex, ethnicity, and quartile of risk. The absolute numbers of expected new cases reflect variation in risk across the population, in addition to distribution of sub-groups within the Canadian population. Risk is variable in the Canadian population (Gini = 0.48); however, within subgroups there is a range of risk dispersions from as low as 0.11 to as high as 0.52 (Table 1). Diabetes risk is less variable within older ages, among those that are obese, and within quartiles of risk. High variability in 10-year diabetes risk is

noted within certain ethnic groups and among those under 45. The degree of variability in diabetes risk is related to the magnitude of diabetes risk such that the higher the diabetes risk score, the lower the dispersion among the population that Screening Library solubility dmso falls below that risk cut-off (r = − 0.99, Fig. 2). The empirically derived cut-off was determined to be a risk of however 16.5% (Fig. 3). Table 2 demonstrates the benefit in targeting individual or dual risk factors compared to targeting based on an empirically derived risk cut-off. Risk dispersion is lower when using the empirically derived risk

cut-off based on DPoRT compared to a single factor target, although they represent similar proportions of the population (20% vs. 17%). Furthermore, targeting the population that falls above the empirically derived cut-off would result in more diabetes cases prevented and a greater ARR assuming the same intervention effect (Table 2). Targeting based on an empirically derived risk cut-off would result in the lowest NNT of 13, which represents the number of people that would need to receive the intervention to prevent one diabetes case (Table 2). This study quantified how risk dispersion (variability in diabetes risk) is related to the magnitude of risk using a statistical measure of dispersion and a validated risk tool. Other studies have used risk algorithms to understand, compare and contrast different prevention strategies for diabetes (Chamnan et al., 2012, Harding et al., 2006 and Manuel et al., 2013a). This is the first that statistically characterizes diabetes risk dispersion using a validated population risk algorithm in order to quantify its impact on benefit and empirically derives an optimal cut-point to target populations based on maximizing differences in the absolute risk reduction between those who meet and do not meet the cut-point.