Development and validation of a population-based risk algorithm for premature mortality in Canada: the Premature Mortality Population Risk Tool (PreMPoRT)

Abstract

Introduction To develop and validate the Premature Mortality Population Risk Tool (PreMPoRT), a population-based risk algorithm that predicts the 5-year incidence of premature mortality among the Canadian adult population.Methods Retrospective cohort analysis used six cycles of the Canadian Community Health Survey linked to the Canadian Vital Statistics Database (2000–2017). The cohort comprised 500 870 adults (18–74 years). Predictors included sociodemographic factors, self-perceived measures, health behaviours and chronic conditions. Three models (minimal, primary and full) were developed. PreMPoRT was internally validated using a split set approach and externally validated across three hold-out cycles. Performance was assessed based on predictive accuracy, discrimination and calibration.Results The cohort included 267 460 females and 233 410 males. Premature deaths occurred in 1.40% of females and 2.05% of males. Primary models had 12 predictors (females) and 13 predictors (males). Shared predictors included age, income quintile, education, self-perceived health, smoking, emphysema/chronic obstructive pulmonary disease, heart disease, diabetes, cancer and stroke. Male-specific predictors were marital status, Alzheimer’s disease and arthritis while female-specific predictors were body mass index and physical activity. External validation cohort differed slightly in demographics. Female model performance: split set (c-statistic: 0.852), external (c-statistic: 0.856). Male model performance: split set and external (c-statistic: 0.846). Calibration showed slight overprediction for high-risk individuals and good calibration in key subgroups.Conclusions PreMPoRT achieved the strongest discrimination and calibration among existing prediction models for premature mortality. The model produces reliable estimates of future incidence of premature mortality and may be used to identify subgroups who may benefit from public health interventions.