In a recent paper, Dr. Laura Rosella and Emmalin Buajitti shed light on the intricate relationship between income, health behaviours, and premature mortality due to different health behaviours. Conducted over a span of nearly a decade with data from the Canadian Community Health Surveys, the study examined the impact of smoking, alcohol use, obesity, and physical activity on premature death, and how their associated risks vary by income level. The findings revealed a stark reality: individuals in lower-income brackets faced higher risks of premature mortality associated with smoking, alcohol use, obesity, and physical inactivity. Furthermore, the study highlighted a widening gap in premature mortality between income groups over time, emphasizing the urgent need for targeted interventions to mitigate these disparities.
The study, titled Risk of premature mortality due to smoking, alcohol use, obesity and physical activity varies by income: A population-based cohort study, reaffirms the link between health behaviours and premature mortality while highlighting the crucial role of socioeconomic factors in shaping health outcomes. The findings stress the need for comprehensive strategies that address both behavioral risk factors and structural inequalities to combat premature mortality effectively. These insights emphasize the importance of ensuring that efforts to improve population health are equitable and inclusive, leaving no one behind in the pursuit of a healthier society.
This study was published in Social Sciences and Medicine-Population Health and funded by the Canadian Institutes for Health Research.
Read the study abstract here.
Background
Premature deaths are a strong population health indicator. There is a persistent and widening pattern of income inequities for premature mortality. We sought to understand the combined effect of health behaviours and income on premature mortality in a large population-based cohort.
Methods
We analyzed a cohort of 121,197 adults in the 2005–2014 Canadian Community Health Surveys, linked to vital statistics data to ascertain deaths for up to 5 years following baseline. Information on household income quintile and mortality-relevant risk factors (smoking status, alcohol use, body mass index (BMI), and physical activity) was captured from the survey. Hazard ratios (HR) for combined income-risk factor groups were estimated using Cox proportional hazards models. Stratified Cox models were used to identify quintile-specific HR for each risk factor.
Results
For each risk factor, HR of premature mortality was highest in the lowest-income, highest-risk group. Additionally, an income gradient was seen for premature mortality HR for every exposure level of each risk factor. In the stratified models, risk factor HRs did not vary meaningfully between income groups. All findings were consistent in the unadjusted and adjusted models.
Conclusion
These findings highlight the need for targeted strategies to reduce health inequities and more careful attention to how policies and interventions are distributed at the population level. This includes targeting and tailoring resources to those in lower income groups who disproportionately experience premature mortality risk to prevent further widening health inequities.