A Gini coefficient of zero expresses perfect equality where all values are the same for all individuals in a population (e.g. where everyone has exactly the same diabetes risk). A Gini coefficient

of one expresses maximal inequality among values (e.g. where only one person has all the diabetes risk). We examine the relationship between level of risk in the population and dispersion of diabetes risk by ranking percentiles of the population and then calculating the Gini coefficient of the population included within percentile groups (e.g. 0.1 represents the top 10% of the population ordered by risk of diabetes). We plotted the relationship where the x axis represents sections taken from the population ranked from the highest diabetes risk to the lowest risk. As a greater find more Venetoclax proportion of the population is included, the average risk in that section of the population decreases given that lower risk groups are included. The y-axis represents the Gini coefficient for that section of the population. We then calculated the correlation coefficient of this relationship. We examined how risk distribution measures would affect population intervention strategies by calculating the

benefits of a hypothetical targeted intervention strategy using different approaches for identifying the target group that will receive the intervention. Specifically we quantified the impact of an intervention targeting the general population and high-risk groups based on single or dual risk factors (obesity and overweight among non-white ethnicities) or based on an empirically-derived risk cut-off estimated from DPoRT 2.0. We defined population benefit as the absolute risk reduction (ARR) in 10-year diabetes risk (absolute difference in diabetes risk before and after the intervention) and the corresponding number of diabetes cases PDK4 prevented. The number of diabetes cases prevented was determined by summating

the ARR multiplied by the survey weight for all targeted individuals. The Number Needed to Treat (NNT) is equal to one over the mean value of the ARR in the population. We based the effect of the diabetes prevention strategy on a plausible range seen from meta-analyses of intervention studies involving an intensive lifestyle intervention, typically a combination of diet and physical activity, which would have a larger effect on reducing 10-year diabetes risk (Gillies et al., 2008). For the intervention strategy we used a 10-year risk reduction of 30%; although, we examined a range of effect sizes (10–60%). We derived an optimal cut-point to identify a diabetes risk score that would identify individuals or groups that would benefit from intervention.