Longitudinal clustering of health behaviours and their association with multimorbidity: Evidence from Understanding Society (UKHLS)

BackgroundSmoking, unhealthy nutrition, alcohol consumption, and physical inactivity (SNAP behaviours) are major risk factors for multimorbidity but are often studied in isolation. Using longitudinal data, Suhag et al. identified clusters of older adults (aged [≥]50) with common SNAP behaviour patterns and distinct sociodemographic profiles and multimorbidity prevalence; whether and how these patterns generalise across adulthood remains unclear. AimTo conceptually replicate Suhag et al. across a wider age range using an independent panel study. MethodsWe used data from Waves 7-13 of the UK Household Longitudinal Study, analysing adults (aged [≥]16) participating across all seven waves (n=18,008). Repeated-measures latent class analysis identified clusters of adults with common SNAP behaviours at Waves 7, 9, 11 and 13. Multinomial and binomial logistic regression examined how clusters were associated with sociodemographic characteristics and disease status (six disease groups plus multimorbidity), respectively. FindingsSeven clusters were identified: Overall Low-risk (20% of the sample), Insufficiently active (18%), Poor diet and Insufficiently active (23%), Hazardous and Harmful drinkers (11%), Hazardous drinkers, Insufficiently active and Poor diet (14%), Smokers and Drinkers (5%), and Smokers (9%). Behavioural profiles within clusters were largely stable over time. Associations between clusters and disease outcomes were counterintuitive. The cluster labelled Overall Low-risk on the basis of SNAP behaviours had the highest prevalence of multimorbidity, whereas the Hazardous drinkers, Insufficiently active and Poor diet cluster showed lower prevalence across most conditions. These clusters also differed in sociodemographic composition: the Overall Low-risk cluster comprised mainly older women with lower education and income, while the Hazardous drinkers, Insufficiently active and Poor diet cluster was more likely to comprise individuals in the highest education and income groups. ConclusionCluster-analytic techniques can be used to identify population subgroups with distinct behavioural and disease profiles, underscoring the need to consider risk behaviours in conjunction with sociodemographic context.