Jones Resa M, Kulick Erin R, Snead Ryan, Wilson Robin Taylor, Hughes John, Lillys Ted
Department of Epidemiology and Biostatistics, College of Public Health, Temple University, 1301 Cecil B. Moore Ave. Ritter Annex, 9th Floor, Rm 917, Philadelphia, PA, 19122, United States.
Fox Chase Cancer Center, Temple University Health, Philadelphia, Pennsylvania, United States.
BMC Cancer. 2025 Jan 17;25(1):101. doi: 10.1186/s12885-025-13508-2.
Cancer is a complex set of diseases, and many have decades-long lag times between possible exposure and diagnosis. Environmental exposures, such as per- and poly-fluoroalkyl substances (PFAS) and area-level risk factors (e.g., socioeconomic variables), vary for people over time and space. Evidence suggests PFAS exposure is associated with several cancers; however, studies to date have various limitations. Few studies have used rigorous spatiotemporal approaches, and, to our knowledge, none have assessed cumulative exposures given residential histories or incorporated chemical mixture modeling. Thus, spatiotemporal analysis using advanced statistical approaches, accounting for spatially structured and unstructured heterogeneity in risk, can be a highly informative strategy for addressing the potential health effects of PFAS exposure.
Using population-based incident cancer cases and cancer-free controls in a 12-county area of southeastern Pennsylvania, we will apply Bayesian spatiotemporal analysis methods using historically reconstructed PFAS-contaminated water exposure given residential histories, and other potential cancer determinants over time. Bayesian group index models enable assessment of various mixtures of highly correlated PFAS chemical exposures incorporating mobility/residential history, and contextual factors to determine the association of PFAS-related exposures and cancer incidence.
The purpose of this paper is to describe the Enhanced PFAS Spatial Analysis study rationale, study design, and methods.
癌症是一组复杂的疾病，许多癌症在可能的暴露与诊断之间存在数十年的时间间隔。环境暴露，如全氟和多氟烷基物质（PFAS）以及区域层面的风险因素（如社会经济变量），会随着时间和空间的变化而在人群中有所不同。有证据表明，PFAS暴露与多种癌症有关；然而，迄今为止的研究存在各种局限性。很少有研究采用严格的时空方法，而且据我们所知，没有一项研究根据居住史评估累积暴露情况或纳入化学混合物建模。因此，使用先进统计方法进行时空分析，考虑风险中的空间结构化和非结构化异质性，对于解决PFAS暴露的潜在健康影响可能是一种极具信息量的策略。
在宾夕法尼亚州东南部的一个12县地区，我们将利用基于人群的癌症发病病例和无癌对照，运用贝叶斯时空分析方法，根据居住史以及其他潜在癌症决定因素，对历史重建的受PFAS污染的水暴露情况进行分析。贝叶斯组指数模型能够评估高度相关的PFAS化学暴露的各种混合物，纳入流动性/居住史以及背景因素，以确定与PFAS相关的暴露与癌症发病率之间的关联。
本文旨在描述强化PFAS空间分析研究的基本原理、研究设计和方法。
