Ryan Ng1, Rinku Sutradhar2, Kathy Kornas1, Walter P Wodchis2,3,4, Joykrishna Sarkar5, Randall Fransoo5, Laura C Rosella1,2. 1. Dalla Lana School of Public Health, Division of Epidemiology, University of Toronto, Toronto, Ontario, Canada. 2. ICES, Toronto, Ontario, Canada. 3. Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada. 4. Trillium Health Partners' Institute for Better Health, Mississauga, Ontario, Canada. 5. Manitoba Centre for Health Policy, University of Manitoba, Winnipeg, Manitoba, Canada.
Abstract
Importance: Predicting chronic disease incidence for the population provides a comprehensive picture to health policy makers of their jurisdictions' overall future chronic disease burden. However, no population-based risk algorithm exists for estimating the risk of first major chronic disease. Objective: To develop and validate the Chronic Disease Population Risk Tool (CDPoRT), a population risk algorithm that predicts the 10-year incidence of the first major chronic disease in the adult population. Design, Setting, and Participants: In this cohort study, CDPoRT was developed and validated with 6 cycles of the Canadian Community Health Survey, linked to administrative data from January 2000 to December 2014. Development and internal validation (bootstrap and split sample) of CDPoRT occurred in Ontario, Canada, from June 2018 to April 2019 followed by external validation in Manitoba from May 2019 to July 2019. The study cohorts included 133 991 adults (≥20 years) representative of the Ontario and Manitoba populations who did not have a history of major chronic disease. Exposures: Predictors were routinely collected risk factors from the Canadian Community Health Survey, such as sociodemographic factors (eg, age), modifiable lifestyle risk factors (ie, alcohol consumption, cigarette smoking, unhealthy diet, and physical inactivity), and other health-related factors (eg, body mass index). Main Outcomes and Measures: Six major chronic diseases were considered, as follows: congestive heart failure, chronic obstructive pulmonary disease, diabetes, myocardial infarction, lung cancer, and stroke. Sex-specific CDPoRT algorithms were developed with a Weibull model. Model performance was evaluated with measures of overall predictive performance (eg, Brier score), discrimination (eg, Harrell C index), and calibration (eg, calibration curves). Results: The Ontario cohort (n = 118 747) was younger (mean [SD] age, 45.6 [16.1] vs 46.3 [16.4] years), had more immigrants (23 808 [20.0%] vs 1417 [10.7%]), and had a lower mean (SD) body mass index (26.9 [5.1] vs 27.7 [5.4]) than the Manitoba cohort (n = 13 244). During development, the full and parsimonious CDPoRT models had similar Brier scores (women, 0.087; men, 0.091), Harrell C index values (women, 0.779; men, 0.783), and calibration curves. A simple version consisting of cigarette smoking, age, and body mass index performed slightly worse than the other versions (eg, Brier score for women, 0.088; for men, 0.092). Internal validation showed consistent performance across models, and CDPoRT performed well during external validation. For example, the female parsimonious version had C index values for bootstrap, split sample, and external validation of 0.778, 0.776, and 0.752, respectively. Conclusions and Relevance: In this study, CDPoRT provided accurate, population-based risk estimates for the first major chronic disease.
Importance: Predicting chronic disease incidence for the population provides a comprehensive picture to health policy makers of their jurisdictions' overall future chronic disease burden. However, no population-based risk algorithm exists for estimating the risk of first major chronic disease. Objective: To develop and validate the Chronic Disease Population Risk Tool (CDPoRT), a population risk algorithm that predicts the 10-year incidence of the first major chronic disease in the adult population. Design, Setting, and Participants: In this cohort study, CDPoRT was developed and validated with 6 cycles of the Canadian Community Health Survey, linked to administrative data from January 2000 to December 2014. Development and internal validation (bootstrap and split sample) of CDPoRT occurred in Ontario, Canada, from June 2018 to April 2019 followed by external validation in Manitoba from May 2019 to July 2019. The study cohorts included 133 991 adults (≥20 years) representative of the Ontario and Manitoba populations who did not have a history of major chronic disease. Exposures: Predictors were routinely collected risk factors from the Canadian Community Health Survey, such as sociodemographic factors (eg, age), modifiable lifestyle risk factors (ie, alcohol consumption, cigarette smoking, unhealthy diet, and physical inactivity), and other health-related factors (eg, body mass index). Main Outcomes and Measures: Six major chronic diseases were considered, as follows: congestive heart failure, chronic obstructive pulmonary disease, diabetes, myocardial infarction, lung cancer, and stroke. Sex-specific CDPoRT algorithms were developed with a Weibull model. Model performance was evaluated with measures of overall predictive performance (eg, Brier score), discrimination (eg, Harrell C index), and calibration (eg, calibration curves). Results: The Ontario cohort (n = 118 747) was younger (mean [SD] age, 45.6 [16.1] vs 46.3 [16.4] years), had more immigrants (23 808 [20.0%] vs 1417 [10.7%]), and had a lower mean (SD) body mass index (26.9 [5.1] vs 27.7 [5.4]) than the Manitoba cohort (n = 13 244). During development, the full and parsimonious CDPoRT models had similar Brier scores (women, 0.087; men, 0.091), Harrell C index values (women, 0.779; men, 0.783), and calibration curves. A simple version consisting of cigarette smoking, age, and body mass index performed slightly worse than the other versions (eg, Brier score for women, 0.088; for men, 0.092). Internal validation showed consistent performance across models, and CDPoRT performed well during external validation. For example, the female parsimonious version had C index values for bootstrap, split sample, and external validation of 0.778, 0.776, and 0.752, respectively. Conclusions and Relevance: In this study, CDPoRT provided accurate, population-based risk estimates for the first major chronic disease.