Résumé :
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[BDSP. Notice produite par INIST-CNRS BmR0xqrt. Diffusion soumise à autorisation]. 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=26465) 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 khi2 statistic (khi2H-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 (khi2H-L
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