This chapter considers age, period and cohort (APC) as different sources of health-related change. Age (or, life course) effects are individual, often biological, sources of change, whilst periods and cohorts can be thought of as social contexts affecting individuals that reside within them. Due to the mathematical confounding of age, period and cohort, careful consideration of each is important - otherwise what appears to be, for example, a period (year) effect could in fact be a mixture of age and cohort processes. Naive life-course approaches could thus produce misleading results when APC effects are not all considered. However, the mathematical confounding also often makes modelling all three effects together impossible, and the dangers of attempting to do so, or of ignoring one effect without critical forethought, is illustrated through the example of the obesity epidemic. This example uses Yang and Land’s Hierarchical APC model which it is claimed (incorrectly) solves the identification problem. Finally, we suggest a flexible multilevel framework that extends Yang and Land’s model, and by making relatively strong assumptions (in this case that there are no long-run period trends) can model age, period and cohort effects robustly and explicitly, so long as those assumptions are correct. This is illustrated using health data from the British Household Panel Survey. We argue that this theory driven approach is often the most appropriate for conceptualising APC effects, and producing valid empirical inference about both individual life courses and the spatial and temporal contexts in which they exist.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)