Abstract
Analyses of health over time must consider the potential impacts of ageing as well as any effects relating to cohort differences. The British Household Panel Survey (BHPS) and Understanding Society longitudinal studies are employed to assess trends in mental ill-health over a 26-year period. This analysis uses cross-classified multilevel models in an exploratory, non-parametric approach to evaluate age and cohort effects net of each other. Mental ill-health evidences an initial worsening trend as people age which then reverses and exhibits improvement in late-middle-age, before declining again in the latter stages of life. There were less defined cohort trends. The modelling technique also reveals the relative importance of the temporal contexts in relation to inter- and intra-individual effects on mental ill-health, demonstrating that the ageing and cohort dimensions explain little variation compared to these more dominant within and between influences. Ultimately, we suggest that researchers would benefit from wider use of this exploratory modelling strategy when evaluating underlying health trends and more research is now needed to explore potential explanations of these baseline trajectories.
Highlights
The importance of how health changes as people progress through different life stages has long been recognised, across an array of demographic, health and epidemiological fields [1–3]
We aim to evaluate trajectories in mental ill-health to provide a baseline knowledge of age and cohort trends over time, and in doing so demonstrate the utility of multilevel models as a nonparametric technique for such analyses
Mental ill-health worsens over time from young adulthood to around the age of 50, improving till around retirement age (~65) where it appears to decline through old age
Summary
The importance of how health changes as people progress through different life stages has long been recognised, across an array of demographic, health and epidemiological fields [1–3]. Examining trajectories of different health dimensions provides insight into later outcomes and, through highlighting divergent patterns, informs our understanding of health disparities between different groups. Assessment of temporal trends can serve as a baseline to the later analysis of the factors which explain patterns of health, following the lifecourse epidemiological tradition of research [1, 4]. Age. When considering health and the lifecourse isolating the change in health due to ageing processes is often the central aim.
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