Journal
JOURNAL OF EPIDEMIOLOGY AND COMMUNITY HEALTH
Volume 65, Issue 7, Pages 613-620Publisher
BMJ PUBLISHING GROUP
DOI: 10.1136/jech.2009.102244
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Funding
- Canadian Institutes of Health Research
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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 chi(2) statistic (chi(2)(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 (chi(2)(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.
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