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A study protocol for a predictive algorithm to assess population-based premature mortality risk: Premature Mortality
Laura C Rosella1,2,3, Meghan O'Neill4, Stacey Fisher4,5,6
1Dalla Lana School of Public Health, University of Toronto, 155 College St, 6th floor, Toronto, Ontario, M5T 3M7, Canada. laura.rosella@utoronto.ca.
Diagnostic and Prognostic Research
|December 9, 2020
Summary
This study introduces the Premature Mortality Risk Prediction Tool (PreMPoRT) to forecast deaths before age 75. The tool uses health survey data to inform public health strategies and promote equitable policies.
Area of Science:
- Public Health
- Epidemiology
- Health Services Research
Background:
- Premature mortality is a key indicator of health system performance and intervention needs.
- Predicting premature mortality aids in developing equitable health policies and effective public health services.
Purpose of the Study:
- To develop and validate the Premature Mortality Risk Prediction Tool (PreMPoRT).
- To predict the incidence of premature mortality using population-based health surveys and multivariable modeling.
Main Methods:
- Development and validation using Canadian Community Health Survey (CCHS) data linked to the Canadian Vital Statistics Database (2000-2017).
- Utilizing population-level risk factors (demographics, behaviors, area measures) with sex-specific Weibull accelerated failure time models.
- External validation using separate CCHS linked files to assess model robustness and predictive performance.
Main Results:
- Anticipate the development of a robust prediction model for premature mortality (death before age 75).
- The tool will provide population-based estimates of premature mortality risk.
Conclusions:
- The Premature Mortality Risk Prediction Tool (PreMPoRT) will leverage routinely collected risk factor data.
- PreMPoRT is expected to inform population-level prevention strategies and policy development.
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