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.

翻译：在环境流行病学中，识别对化学暴露易感的亚群以及可能从暴露减少政策中差异化获益的群体至关重要。例如，性别特异性易感性、年龄和怀孕状况是政策制定者制定监管指南时的关键考量因素。然而，当前用于异质性处理效应的半参数方法通常局限于二元暴露，且作为黑箱运行，缺乏针对特定亚群政策干预的清晰、可解释规则。本研究引入一种新方法，采用交叉验证的基于目标最小损失的估计（TMLE）结合数据自适应的目标参数策略，以识别在模拟的减少暴露政策干预下受到最显著差异化影响的亚群。我们的方法假设条件宽松，允许整合机器学习技术，同时仍能产生有效的置信区间。我们通过模拟实验及对美国国家健康与营养调查（NHANES）数据的应用，证明了该方法的稳健性。通过对NHANES数据中持久性有机污染物对白细胞端粒长度（LTL）影响的分析，我们发现年龄是最大的效应修饰因子。具体而言，3,3',4,4',5-五氯联苯（pcnb）暴露对LTL始终产生差异化影响：暴露每降低一个标准差，年轻群体比年长群体的LTL增加更为显著。我们将该方法开发为开源软件包 \texttt{EffectXshift}，使研究人员能够探究连续暴露的效应修饰作用。该软件包提供清晰可解释的结果，为精准公共卫生干预和政策制定提供科学依据。