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Background Behaviours 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. Methods A 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. Findings The 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). Conclusions Multivariable 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., Author(s): Douglas G. Manuel 1,2,3,4,5,6,7,*, Richard Perez 1,2,5, Claudia Sanmartin 3, Monica Taljaard 1,5, Deirdre Hennessy 1,3, Kumanan Wilson 1, Peter Tanuseputro 1,2,6, Heather Manson 7, Carol Bennett 1,2, Meltem [...]

Background: Health behaviours, important factors in cardiovascular disease, are increasingly a focus of prevention. We appraised whether stroke risk can be accurately assessed using self-reported information focused on health behaviours. Methods: Behavioural, sociodemographic and other risk factors were assessed in a population-based survey of 82 259 Ontarians who were followed for a median of 8.6 years (688 000 person-years follow-up) starting in 2001. Predictive algorithms for 5-year incident stroke resulting in hospitalization were created and then validated in a similar 2007 survey of 28 605 respondents (median 4.2 years follow-up). Results: We observed 3 236 incident stroke events (1 551 resulting in hospitalization; 1 685 in the community setting without hospital admission). The final algorithms were discriminating (C-stat: 0.85, men; 0.87, women) and well-calibrated (in 65 of 67 subgroups for men; 61 of 65 for women). An index was developed to summarize cumulative relative risk of incident stroke from health behaviours and stress. For men, each point on the index corresponded to a 12% relative risk increase (180% risk difference, lowest (0) to highest (9) scores). For women, each point corresponded to a 14% relative risk increase (340% difference, lowest (0) to highest (11) scores). Algorithms for secondary stroke outcomes (stroke resulting in death; classified as ischemic; excluding transient ischemic attack; and in the community setting) had similar health behaviour risk hazards. Conclusion: Incident stroke can be accurately predicted using self-reported information focused on health behaviours. Risk assessment can be performed with population health surveys to support population health planning or outside of clinical settings to support patient-focused prevention. [ABSTRACT FROM AUTHOR]

Background: Management of chronic diseases requires patients to adhere to recommended health behavior change and complete tests for monitoring. While studies have shown an association between low income and lack of adherence, the reasons why people with low income may be less likely to adhere are unclear. We sought to determine the association between household income and receipt of health behavior change advice, adherence to advice, receipt of recommended monitoring tests, and self-reported reasons for non-adherence/non-receipt. Methods: We conducted a population-weighted survey, with 1849 respondents with cardiovascular-related chronic diseases (heart disease, hypertension, diabetes, stroke) from Western Canada (n = 1849). We used log-binomial regression to examine the association between household income and the outcome variables of interest: receipt of advice for and adherence to health behavior change (sodium reduction, dietary improvement, increased physical activity, smoking cessation, weight loss), reasons for non-adherence, receipt of recommended monitoring tests (cholesterol, blood glucose, blood pressure), and reasons for non-receipt of tests. Results: Behavior change advice was received equally by both low and high income respondents. Low income respondents were more likely than those with high income to not adhere to recommendations regarding smoking cessation (adjusted prevalence rate ratio (PRR): 1.55, 95%CI: 1.09–2.20), and more likely to not receive measurements of blood cholesterol (PRR: 1.72, 95%CI 1.24–2.40) or glucose (PRR: 1.80, 95%CI: 1.26–2.58). Those with low income were less likely to state that non-adherence/non-receipt was due to personal choice, and more likely to state that it was due to an extrinsic factor, such as cost or lack of accessibility. Conclusions: There are important income-related differences in the patterns of health behavior change and disease monitoring, as well as reasons for non-adherence or non-receipt. Among those with low income, adherence to health behavior change and monitoring may be improved by addressing modifiable barriers such as cost and access. [ABSTRACT FROM AUTHOR]