Background: There is growing interest in the relationship between time spent in adverse circumstances across life course and increased risk of chronic disease and early mortality. This accumulation hypothesis is usually tested by summing indicators of binary variables across the life span to form an overall score that is then used as the exposure in regression models for health outcomes. This article highlights potential issues in the interpretation of results obtained from such an approach.
Methods: We propose a model-building framework that can be used to formally compare alternative hypotheses on the effect of multiple binary exposure measurements collected across the life course. The saturated model where the order and value of the binary variable at each time point influence the outcome of interest is compared with nested alternative specifications corresponding to the critical period, cumulative risk or hypotheses about the effect of changes in environment. This framework is illustrated with data on adult body mass index and socioeconomic position measured once in childhood and twice in adulthood from the Medical Research Council National Survey of Health and Development, using a series of liner regression models.
Results: We demonstrate how analyses that only consider the association of a cumulative score with a later outcome may produce misleading results.
Conclusion: We recommend comparing a set of nested models-each corresponding to the accumulation, critical period and effect modification hypotheses-to an all-inclusive (saturated) model. This approach can provide a formal and clearer understanding of the relative merits of these alternative hypotheses.