Abstract
OBJECTIVE: Conceptual models underpinning much epidemiological research on ageing acknowledge that environmental, social and biological systems interact to influence health outcomes. Recursive partitioning is a data-driven approach that allows for concurrent exploration of distinct mixtures, or clusters, of individuals that have a particular outcome. Our aim is to use recursive partitioning to examine risk clusters for metabolic syndrome (MetS) and its components, in order to identify vulnerable populations. STUDY DESIGN: Cross-sectional analysis of baseline data from a prospective longitudinal cohort called the International Mobility in Aging Study (IMIAS). SETTING: IMIAS includes sites from three middle-income countries-Tirana (Albania), Natal (Brazil) and Manizales (Colombia)-and two from Canada-Kingston (Ontario) and Saint-Hyacinthe (Quebec). PARTICIPANTS: Community-dwelling male and female adults, aged 64-75 years (n=2002). PRIMARY AND SECONDARY OUTCOME MEASURES: We apply recursive partitioning to investigate social and behavioural risk factors for MetS and its components. Model-based recursive partitioning (MOB) was used to cluster participants into age-adjusted risk groups based on variabilities in: study site, sex, education, living arrangements, childhood adversities, adult occupation, current employment status, income, perceived income sufficiency, smoking status and weekly minutes of physical activity. RESULTS: 43% of participants had MetS. Using MOB, the primary partitioning variable was participant sex. Among women from middle-incomes sites, the predicted proportion with MetS ranged from 58% to 68%. Canadian women with limited physical activity had elevated predicted proportions of MetS (49%, 95% CI 39% to 58%). Among men, MetS ranged from 26% to 41% depending on childhood social adversity and education. Clustering for MetS components differed from the syndrome and across components. Study site was a primary partitioning variable for all components except HDL cholesterol. Sex was important for most components. CONCLUSION: MOB is a promising technique for identifying disease risk clusters (eg, vulnerable populations) in modestly sized samples.