Toward Advancing Precision Environmental Health: Developing a Customized Exposure Burden Score to PFAS Mixtures to Enable Equitable Comparisons Across Population Subgroups, Using Mixture Item Response Theory.

Toward Advancing Precision Environmental Health: Developing a Customized Exposure Burden Score to PFAS Mixtures to Enable Equitable Comparisons Across Population Subgroups, Using Mixture Item Response Theory.
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推进精准环境健康:利用混合项目响应理论,制定 PFAS 混合物的定制暴露负担评分,以实现不同人群亚组之间的公平比较。

DOI:
10.1021/acs.est.3c00343
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发表时间:
2023
影响因子:
11.4
通讯作者:
Buckley,JessieP
Buckley,JessieP
中科院分区:
环境科学与生态学1区
文献类型:
--
作者:
Liu,ShelleyH;Feuerstahler,Leah;Chen,Yitong;Braun,JosephM;Buckley,JessieP

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量化一个人对全氟烷基物质和多氟烷基物质 (PFAS) 混合物的累积暴露负担对于风险评估、生物监测和向参与者报告结果非常重要。然而,由于暴露来源和模式的异质性,不同的人可能会接触不同的 PFAS。对整个人群应用单一测量模型(例如,通过汇总所有 PFAS 分析物的浓度)假设每种 PFAS 分析物对所有个体的 PFAS 暴露负担具有同等的信息。如果人群中 PFAS 暴露源存在系统性差异,则该假设可能不成立。然而,系统暴露差异背后的社会人口、饮食和行为特征可能尚不清楚,或者可能是这些因素的组合造成的。因此,我们使用混合项目反应理论(一种无监督的心理测量学和数据科学方法)来开发定制的 PFAS 暴露负担评分算法。这种评分算法确保 PFAS 负担分数可以在不同人群亚组之间进行公平比较。我们将我们的方法应用于美国国家健康和营养检查调查(2013-2018)的 PFAS 生物监测数据。利用混合项目反应理论,我们发现家庭收入较高的参与者的 PFAS 负担分数较高。与非西班牙裔白人和其他种族/族裔群体相比,亚裔美国人的 PFAS 负担明显更高。然而,当使用 PFAS 浓度总和作为暴露指标时,一些差异被掩盖了。这项工作表明,我们总结的 PFAS 负担指标考虑了暴露变化的来源,可能是对 PFAS 暴露的更公平、信息更丰富的估计。
Quantifying a person’s cumulative exposure burden to per- and polyfluoroalkyl substances (PFAS) mixtures is important for risk assessment, biomonitoring, and reporting of results to participants. However, different people may be exposed to different sets of PFASs due to heterogeneity in the exposure sources and patterns. Applying a single measurement model for the entire population (e.g., by summing concentrations of all PFAS analytes) assumes that each PFAS analyte is equally informative to PFAS exposure burden for all individuals. This assumption may not hold if PFAS exposure sources systematically differ within the population. However, the sociodemographic, dietary, and behavioral characteristics that underlie systematic exposure differences may not be known, or may be due to a combination of these factors. Therefore, we used mixture item response theory, an unsupervised psychometrics and data science method, to develop a customized PFAS exposure burden scoring algorithm. This scoring algorithm ensures that PFAS burden scores can be equitably compared across population subgroups. We applied our methods to PFAS biomonitoring data from the United States National Health and Nutrition Examination Survey (2013–2018). Using mixture item response theory, we found that participants with higher household incomes had higher PFAS burden scores. Asian Americans had significantly higher PFAS burden compared with non-Hispanic Whites and other race/ethnicity groups. However, some disparities were masked when using summed PFAS concentrations as the exposure metric. This work demonstrates that our summary PFAS burden metric, accounting for sources of exposure variation, may be a more fair and informative estimate of PFAS exposure.
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