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Chronic Disease Population Risk Tool (CDPoRT): a study protocol for a prediction model that assesses population-based chronic disease incidence

Ng R, Sutradhar R, Wodchis WP, Rosella LC. Diagn Progn Res. 2018; 2:19. Epub 2018 Oct 1.

Background — Population-based risk prediction tools exist for individual chronic diseases. From a population health perspective, studying chronic diseases together provides a comprehensive view of the burden of disease in the population. Thus, public health officials and health policymakers would benefit from a prediction tool that measures the incidence of chronic diseases compositely. This study protocol proposes the development and validation of the Chronic Disease Population Risk Tool (CDPoRT) that will predict the incidence of six chronic diseases in the population setting using multivariable modeling techniques.

Methods — CDPoRT will be built using population-based responses to the first six cycles of the Canadian Community Health Survey linked to health administrative data in Ontario and Manitoba from 2000 to 2014. Predictors including modifiable lifestyle risk factors (i.e., alcohol consumption, cigarette smoking, diet, and physical activity) will be used to predict time-to-chronic disease incidence (i.e., congestive heart failure, chronic obstructive pulmonary disease, diabetes, lung cancer, myocardial infarction, and stroke including transient ischemic heart attack). Sex-specific Royston-Parmar models will be used for model development and validation with death free of chronic disease as a competing risk. CDPoRT will be developed using an Ontario derivation cohort consisting of 47,960 females and 38,267 males with 7035 and 6220 chronic disease events, respectively. The model will be validated using split-sample validation using an Ontario validation cohort consisting of 20,325 females and 16,627 males with 2972 and 2658 chronic disease events, respectively. The model will be externally validated in the Manitoba validation cohort (i.e., geographic validation) expected to consist of 11,800 females and 9700 males with 1650 and 1550 chronic disease events, respectively. Measures of overall predictive accuracy (e.g., Nagelkerke’s R2), discrimination (e.g., Harrell’s concordance statistic), and calibration (e.g., calibration plots) will be used to assess predictive performance.

Discussion — To the extent of our knowledge, CDPoRT will be the first population-based regression prediction model that will predict the incidence of multiple chronic diseases simultaneously at the population level.

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