Measuring Burden of Unhealthy Behaviours Using a Multivariable Predictive Approach: Life Expectancy Lost in Canada Attributable to Smoking, Alcohol, Physical Inactivity, and Diet

Abstract

BackgroundBehaviours such as smoking, poor diet, physical inactivity, and unhealthy alcohol consumption are leading risk factors for death. We assessed the Canadian burden attributable to these behaviours by developing, validating, and applying a multivariable predictive model for risk of all-cause death.MethodsA predictive algorithm for 5 y risk of death—the Mortality Population Risk Tool (MPoRT)—was developed and validated using the 2001 to 2008 Canadian Community Health Surveys. There were approximately 1 million person-years of follow-up and 9,900 deaths in the development and validation datasets. After validation, MPoRT was used to predict future mortality and estimate the burden of smoking, alcohol, physical inactivity, and poor diet in the presence of sociodemographic and other risk factors using the 2010 national survey (approximately 90,000 respondents). Canadian period life tables were generated using predicted risk of death from MPoRT. The burden of behavioural risk factors attributable to life expectancy was estimated using hazard ratios from the MPoRT risk model.FindingsThe MPoRT 5 y mortality risk algorithms were discriminating (C-statistic: males 0.874 [95% CI: 0.867–0.881]; females 0.875 [0.868–0.882]) and well calibrated in all 58 predefined subgroups. Discrimination was maintained or improved in the validation cohorts. For the 2010 Canadian population, unhealthy behaviour attributable life expectancy lost was 6.0 years for both men and women (for men 95% CI: 5.8 to 6.3 for women 5.8 to 6.2). The Canadian life expectancy associated with health behaviour recommendations was 17.9 years (95% CI: 17.7 to 18.1) greater for people with the most favourable risk profile compared to those with the least favourable risk profile (88.2 years versus 70.3 years). Smoking, by itself, was associated with 32% to 39% of the difference in life expectancy across social groups (by education achieved or neighbourhood deprivation).ConclusionsMultivariable predictive algorithms such as MPoRT can be used to assess health burdens for sociodemographic groups or for small changes in population exposure to risks, thereby addressing some limitations of more commonly used measurement approaches. Unhealthy behaviours have a substantial collective burden on the life expectancy of the Canadian population.

Development and validation of a cardiovascular disease risk-prediction model using population health surveys: the Cardiovascular Disease Population Risk Tool (CVDPoRT)

“…Key considerations in our approach were full prespecification of the predictor variables, use of flexible functions for continuous predictors, and preservation of statistical properties through avoidance of data-driven variableselection procedures. Table 1 shows the 21 predictor variables that were identified, including 7 sociodemographic, 8 behavioural, 7,8,21 5 general health and chronic conditions, and 1 design variable. 9,22 The model included interactions between age and smoking, alcohol, diet, physical activity, body mass index (BMI), diabetes and hypertension.…”

“…7,9 The CVDPoRT differs from existing cardiovascular disease algorithms in its use of the competing risk approach. To facilitate algorithm comparison and to examine the role of competing risks for cardiovascular disease prediction, we conducted sensitivity analyses in which we used a standard Cox model, but otherwise maintained all predictor specifications.…”

“…For population uses, health surveys (which are available in more than 100 countries) can be used to predict the number of people who will develop cardiovascular disease. 7,9 How risk varies across populations -whether risk is diffused or concentrated -is a cornerstone of modern population planning. 34 Multivariable predictive algorithms are the most robust approach to describe population risk, and their use improves the assessment of population strategies.…”

“…10 Although the World Health Organization and others currently measure burden of disease without including an equity perspective, 35 the inclusion of sociodemographic factors in the CVDPoRT allows a straightforward assessment of cardiovascular disease risk, burden and intervention effectiveness by socioeconomic status. 7,8 For individual use, the CVDPoRT's discrimination is exceeded only by QRISK3 -an algorithm that was also developed using "big" data; however, QRISK3 used clinical data that focused on clinical measures (lipids, blood pressure and disease states). 36,37 Clinicians and patients appear to favour health behaviour interventions over medications for patients at low and medium risk, 38 but existing cardiovascular risk algorithms seldom assess the role of health behaviours beyond smoking.…”

“…Examples of population uses include estimating the future incidence of cardiovascular disease events in populations and estimating the future burden of health behaviours (e.g., smoking, physical activity, alcohol and diet). 7,8 A precision population health approach tailors preventive strategies for specific subgroups at risk. 9,10 For example, subgroups with a low socioeconomic position typically have higher cardiovascular disease risk and may benefit from a wider range and more intensive interventions for effective prevention and health promotion.…”