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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).
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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 (DPo RT)) was developed to predict 9-year risk for diabetes.
In addition, a population prediction tool for diabetes can identify the optimal target groups for new intervention strategies, and determine how extensive a strategy must be to achieve the desired reduction in new cases.
Global estimates place the number of people with diabetes at approximately 200 million, and increasing rapidly.3 There is a growing concern that these trends may slow or even reverse life expectancy gains in the USA and other developed countries.4 Planning for healthcare and public health resources can be informed by robust prediction tools.Estimates of future diabetes incidence will alert policy makers, planners and physicians to the extent and urgency of the diabetes epidemic.DPo RT showed good discrimination (C=0.77–0.80) and calibration (χ In medicine, prediction tools are used to calculate risk, defined as the probability of developing a disease or state in a given time period.Within the clinical setting, predictive tools such as the Framingham Heart Score1 have contributed important advances in individual patient treatment and disease prevention.2 Similarly, applying predictive risk tools to populations can provide insight into the influence of risk factors on the future burden of disease in an entire region or nation and the value of interventions at the population level.
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 χ Results Predictive factors included were body mass index, age, ethnicity, hypertension, immigrant status, smoking, education status and heart disease.