The need to use multilevel models when analysing hierarchical data—to take account of the correlation in the data when estimating regression coefficients—is widely recognised in health research.^1,2 The advantages afforded by multilevel models—including the ability to partition variation to determine the relative importance of different levels of the hierarchy, to test hypotheses about variation, and to attempt to separate the influences of context and composition—are also well reported.³

The complexity of the real world means that our data are not always drawn from strict hierarchies.⁴ In this issue of the journal Chandola et al consider a multiple membership model—a situation in which individuals may belong to more than one unit at a higher level.⁵ Their data follow up individuals over nine years during which time they may belong to several households and may move from one area to another. However, it is only data on residence that are longitudinal; data on health are restricted to the end of the study. This editorial considers how multilevel modelling can be used to analyse such data to give an insight into contextual influences at different stages of the life course.

Consider a two level model of individuals in …