Identifying hot spots of cardiometabolic risk factors in a Swiss city: impact of individual and environmental factors

Abstract Background: Cardiovascular disease (CVD) remains a major public health concern, and developing effective interventions at the population level requires a thorough understanding of the spatial distribution and contextual determinants of cardiometabolic risk factors (CMRFs). However, knowledge on these issues at small spatial scales is still limited, especially in Europe. The aim of this study was to explore the spatial patterns of CMRFs and to identify key individual and contextual factors associated with these risk factors in the city of Lausanne, Switzerland. Methods: Using individual-level data from a population-based cohort of middle-aged and older adults (CoLaus), we examined hypertension, obesity, diabetes, and dyslipidemia as key CMRFs. Intensity functions were used to identify areas of elevated risk for each outcome. Geographically weighted regressions were then employed to examine local associations between outcomes adjusted for individual confounders, and characteristics of physical and social environments such as air pollution, noise, greenness, street connectivity, socioeconomic position, and ethnic composition. Results: We analyzed data from 3,695 participants (mean age of 64.1 years, 56.8% females). The prevalence of hypertension was 48.2%, obesity 17.7%, diabetes 10.7% and dyslipidemia 33.2%. Among the individual factors associated with CMRFs, older age, male sex, lower education level, and being foreign-born emerged as common contributors. Persistent spatial clusters for hypertension, obesity, and diabetes were identified across the city, as well as areas with concurrent increased risk for multiple CMRFs. After adjustment for individual confounders, a global association between neighborhood income and hypertension, obesity, and diabetes emerged. Obesity showed the strongest contextual influence, with significant local associations identified between obesity and higher unemployment rates, lower income, education, and greenness. Notably, hypertension showed local associations with lower neighborhood income and PM2.5 concentrations, while diabetes was associated with lower income and higher unemployment. Dyslipidemia did not show significant associations with contextual factors. Conclusions: Using individual-level data, the study combined spatial approaches to delineate high-risk areas for CMRFs within an urban area and to reveal significant associations with characteristics of physical and social environments. This methodological framework can be applied elsewhere, providing public health practitioners with essential insights to prioritize and tailor local population-level initiatives for CVD prevention.