Misspecified seasonality adjustment in temperature and preterm birth studies may introduce severe bias: a real birth cohort-based simulation.

BACKGROUND: Seasonality is typically controlled for to mitigate potential confounding by seasonally varying variables in environmental epidemiology research. We aimed to illustrate that correctly specifying the seasonality variable is crucial for effectively controlling confounding by seasonality in the context of prenatal temperature exposure and preterm birth.

METHODS: We conducted a simulation study based on an Israeli birth cohort of 131 599 births in Southern Israel, 2005-19. Weekly mean temperature exposures throughout pregnancy and last menstrual period (LMP) date were taken from the original cohort, while gestational age at birth was simulated using a Cox model under four causal scenarios: no seasonal confounding and no effect of temperature; seasonal confounding only; causal effect of temperature only; and seasonal confounding and a causal effect. Within each scenario, we compared four seasonality-adjustment approaches: no adjustment; fixed birthdate-based adjustment; fixed LMP-based adjustment; and time-varying seasonal adjustment. Finally, we compared these methods using our original cohort analysis to examine their divergence in real epidemiological study settings.

RESULTS: Birthdate-based adjustment induced strong collider bias across all scenarios and in the real study setting. In the absence of seasonal confounding, the other three adjustment methods were comparable and produced valid estimates. In the presence of seasonal confounding, however, fixed LMP-based adjustment was inferior to the use of a time-varying seasonal covariate.

CONCLUSION: Birthdate-based seasonal adjustment in studies of temperature and birth outcomes may introduce severe collider bias and should be avoided, particularly when examining outcomes strongly associated with the duration of pregnancy.