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Background: In this paper we present multilevel models of individuals’ residential history at multiple time points through the life course and their application and discuss some advantages and disadvantages for their use in epidemiological studies. Methods: Literature review of research using longitudinal multilevel models in studies of neighbourhood effects, statistical multilevel models that take individuals’ residential history into account, and the application of these models in the Oslo mortality study. Results: Measures of variance have been used to investigate the contextual impact of membership to collectives, such as area of residence, at several time points. The few longitudinal multilevel models that have been used suggest that early life area of residence may have an effect on mortality independently of residence later in life although the proportion of variation attributable to area level is small compared to individual level. The following multilevel models have been developed: simple multilevel models for each year separately, a multiple membership model, a cross-classified model, and finally a correlated cross-classified model. These models have different assumptions regarding the timing of influence through the life course. Conclusions: To fully recognise the origin of adult chronic diseases, factors at all stages of the life course at both individual and area level needs to be considered in order to avoid biased estimates. Important challenges in making life course residential data available for research and assessing how changing administrative coding over time reflect contextual impact need to be overcome before these models can be implemented as normal practice in multilevel epidemiology.

Analysing the effect of area of residence over the life course in multilevel epidemiology