A population-based risk algorithm for the development of diabetes: development and validation of the Diabetes Population Risk Tool (DPoRT)

BACKGROUND: National estimates of the upcoming diabetes epidemic are needed to understand the distribution of diabetes risk in the population and to inform health policy. OBJECTIVE: To create and validate a population-based risk prediction tool for incident diabetes using commonly collected national survey data. METHODS: With the use of a cohort design that links baseline risk factors to a validated population-based diabetes registry, a model (Diabetes Population Risk Tool (DPoRT)) was developed to predict 9-year risk for diabetes. The probability of developing diabetes was modelled using sex-specific Weibull survival functions for people > 20 years of age without diabetes (N=19,861). The model was validated in two external cohorts in Ontario (N=26,465) and Manitoba (N=9899). Predictive accuracy and model performance were assessed by comparing observed diabetes rates with predicted estimates. Discrimination and calibration were measured using a C statistic and Hosmer-Lemeshow χ² statistic (χ²(H-L)). RESULTS: Predictive factors included were body mass index, age, ethnicity, hypertension, immigrant status, smoking, education status and heart disease. DPoRT showed good discrimination (C=0.77-0.80) and calibration (χ²(H-L) < 20) in both external validation cohorts. CONCLUSIONS: This algorithm can be used to estimate diabetes incidence and quantify the effect of interventions using routinely collected survey data.