Data-Adaptive Identification of Subpopulations Vulnerable to Chemical Exposures using Stochastic Interventions

In environmental epidemiology, identifying subpopulations vulnerable to chemical exposures and those who may benefit differently from exposure-reducing policies is essential. For instance, sex-specific vulnerabilities, age, and pregnancy are critical factors for policymakers when setting regulatory guidelines. However, current semi-parametric methods for heterogeneous treatment effects are often limited to binary exposures and function as black boxes, lacking clear, interpretable rules for subpopulation-specific policy interventions. This study introduces a novel method using cross-validated targeted minimum loss-based estimation (TMLE) paired with a data-adaptive target parameter strategy to identify subpopulations with the most significant differential impact from simulated policy interventions that reduce exposure. Our approach is assumption-lean, allowing for the integration of machine learning while still yielding valid confidence intervals. We demonstrate the robustness of our methodology through simulations and application to NHANES data. Our analysis of NHANES data for persistent organic pollutants on leukocyte telomere length (LTL) identified age as the maximum effect modifier. Specifically, we found that exposure to 3,3',4,4',5-pentachlorobiphenyl (pcnb) consistently had a differential impact on LTL, with a one standard deviation reduction in exposure leading to a more pronounced increase in LTL among younger populations compared to older ones. We offer our method as an open-source software package, \texttt{EffectXshift}, enabling researchers to investigate the effect modification of continuous exposures. The \texttt{EffectXshift} package provides clear and interpretable results, informing targeted public health interventions and policy decisions.