Incorporating interactions into structured life course modelling approaches: A simulation study and applied example of the role of access to green space and socioeconomic position on cardiometabolic health

BackgroundStructured life course modelling approaches (SLCMA) have been developed to understand how exposures across the lifespan relate to later health, but have primarily been restricted to single exposures. As multiple exposures can jointly impact health, here we: i) demonstrate how to extend SLCMA to include exposure interactions; ii) conduct a simulation study investigating the performance of these methods; and iii) apply these methods to explore associations of access to green space, and its interaction with socioeconomic position, with child cardiometabolic health. MethodsWe used three methods, all based on lasso regression, to select the most plausible life course model: visual inspection, information criteria and cross-validation. The simulation study assessed the ability of these approaches to detect the correct interaction term, while varying parameters which may impact power (e.g., interaction magnitude, sample size, exposure collinearity). Methods were then applied to data from a UK birth cohort. ResultsThere were trade-offs between false negatives and false positives in detecting the true interaction term for different model selection methods. Larger sample size, lower exposure collinearity, centering exposures, continuous outcomes and a larger interaction effect all increased power. In our applied example we found little-to-no association between access to green space, or its interaction with socioeconomic position, and child cardiometabolic outcomes. ConclusionsIncorporating interactions between multiple exposures is an important extension to SLCMA. The choice of method depends on the researchers assessment of the risks of under-vs over-fitting. These results also provide guidance for improving power to detect interactions using these methods. Key messagesO_LIIn life course epidemiology, it is important to consider how multiple exposures over the lifespan may jointly influence health. C_LIO_LIWe demonstrate how to extend current structured life course modelling approaches to include interactions between multiple different exposures. C_LIO_LIA simulation study comparing different methods to detect a true interaction effect found a trade-off between false positives and false negatives, suggesting that the optimal choice of method may depend on the researchers assessment of this trade-off (e.g., exploratory studies may prefer a greater risk of false positives, while confirmatory studies may prefer to minimise the risk of false positives). C_LIO_LIWe identified key factors that improve power to detect a true interaction effect, namely larger sample sizes, centering exposures, lower exposure collinearity, continuous outcomes and larger interaction effect sizes. C_LIO_LIWe applied these methods in a UK birth cohort (ALSPAC; Avon Longitudinal Study of Parents and Children), finding little-to-no evidence of an association between access to green space and its interaction with socioeconomic position on child BMI, obesity or blood pressure. C_LI