A study protocol for a predictive algorithm to assess population-based premature mortality risk: Premature Mortality Population Risk Tool (PreMPoRT)

Abstract

Background Premature mortality is an important population health indicator used to assess health system functioning and to identify areas in need of health system intervention. Predicting the future incidence of premature mortality in the population can facilitate initiatives that promote equitable health policies and effective delivery of public health services. This study protocol proposes the development and validation of the Premature Mortality Risk Prediction Tool (PreMPoRT) that will predict the incidence of premature mortality using large population-based community health surveys and multivariable modeling approaches. Methods PreMPoRT will be developed and validated using various training, validation, and test data sets generated from the six cycles of the Canadian Community Health Survey (CCHS) linked to the Canadian Vital Statistics Database from 2000 to 2017. Population-level risk factor information on demographic characteristics, health behaviors, area level measures, and other health-related factors will be used to develop PreMPoRT and to predict the incidence of premature mortality, defined as death prior to age 75, over a 5-year period. Sex-specific Weibull accelerated failure time models will be developed using a Canadian provincial derivation cohort consisting of approximately 500,000 individuals, with approximately equal proportion of males and females, and about 12,000 events of premature mortality. External validation will be performed using separate linked files (CCHS cycles 2007–2008, 2009–2010, and 2011–2012) from the development cohort (CCHS cycles 2000–2001, 2003–2004, and 2005–2006) to check the robustness of the prediction model. Measures of overall predictive performance (e.g., Nagelkerke’s R2), calibration (e.g., calibration plots), and discrimination (e.g., Harrell’s concordance statistic) will be assessed, including calibration within defined subgroups of importance to knowledge users and policymakers. Discussion Using routinely collected risk factor information, we anticipate that PreMPoRT will produce population-based estimates of premature mortality and will be used to inform population strategies for prevention.

A study protocol for a predictive model to assess population-based avoidable hospitalization risk: Avoidable Hospitalization Population Risk Prediction Tool (AvHPoRT)

“…Predictor variables from the CCHS capture baseline information of the study cohort. We selected variables based on their availability across provinces and cycles, reviewed existing literature on avoidable hospitalization risk factors, observational studies based on survey data linked to avoidable hospitalizations in Canada [ 16 , 27 ], recommendations from knowledge users in public health, and expertise from our team with prior experience developing and validating predictive algorithms for healthcare use [ 50 ], acute and chronic conditions [ 51 – 56 ] and mortality [ 57 , 58 ]. We limited to variables consistent across provinces and CCHS survey cycles.…”

The impact of different imputation methods on estimates and model performance: an example using a risk prediction model for premature mortality

“…The Premature Mortality Population Risk Tool (PreMPoRT) [ 10 , 11 ] was developed and validated to predict the five-year incidence of premature mortality among Canadian adults. Model predictors included sociodemographic characteristics, self-perceived measures, health behaviours, and chronic conditions from national survey data.…”

The Economic and Long-Term Health Consequences of Canadian COVID-19 Lockdowns

“…This estimated income distribution is fed into two well-validated population-level risk tools. The first estimates the change in early mortality, defined as death before age 75, over the next five years ( Rosella et al 2020 ). The second estimates the change in high health care resource utilization over the next five years, defined as an individual on whom health system spending is in the top 5% of historic Canadian data ( Rosella et al 2018 ).…”

“…The public health model uses well-calibrated estimates of excess mortality and health care utilization from reduced income to map these sector-by-sector economic projections into negative health outcomes ( Rosella et al 2018 ; Rosella, O’Neill, and Fisher 2020 ). Income loss harms mental health, cardiovascular health, food security, and continuity of medical care, which in turn result in higher rates of chronic disease, multimorbidity, and early death.…”