Department of Statistics, Computer Science, Applications 'G. Parenti' (DiSIA), University of Florence, 50134 Firenze, Italy.
Regional Directorate of Prevention, Food Safety, Veterinary Public Health, Regione del Veneto, 30123 Venice, Italy.
Int J Environ Res Public Health. 2021 Mar 8;18(5):2734. doi: 10.3390/ijerph18052734.
In the context of the COVID-19 pandemic, there is interest in assessing if per- and polyfluoroalkyl substances (PFAS) exposures are associated with any increased risk of COVID-19 or its severity, given the evidence of immunosuppression by some PFAS. The objective of this paper is to evaluate at the ecological level if a large area (Red Zone) of the Veneto Region, where residents were exposed for decades to drinking water contaminated by PFAS, showed higher mortality for COVID-19 than the rest of the region.
We fitted a Bayesian ecological regression model with spatially and not spatially structured random components on COVID-19 mortality at the municipality level (period between 21 February and 15 April 2020). The model included education score, background all-cause mortality (for the years 2015-2019), and an indicator for the Red Zone. The two random components are intended to adjust for potential hidden confounders.
The COVID-19 crude mortality rate ratio for the Red Zone was 1.55 (90% Confidence Interval 1.25; 1.92). From the Bayesian ecological regression model adjusted for education level and baseline all-cause mortality, the rate ratio for the Red Zone was 1.60 (90% Credibility Interval 0.94; 2.51).
In conclusion, we observed a higher mortality risk for COVID-19 in a population heavily exposed to PFAS, which was possibly explained by PFAS immunosuppression, bioaccumulation in lung tissue, or pre-existing disease being related to PFAS.
在 COVID-19 大流行背景下,鉴于一些全氟和多氟烷基物质(PFAS)具有免疫抑制作用,人们开始关注 PFAS 暴露是否会增加 COVID-19 感染或其严重程度的风险。本文旨在从生态水平评估威尼托地区(意大利北部的一个大区)一个大面积(红区)的情况,该地区的居民几十年来一直饮用被 PFAS 污染的饮用水,其 COVID-19 死亡率是否高于该地区其他地区。
我们使用贝叶斯生态回归模型,在空间和非空间结构随机分量的基础上,对市级层面(2020 年 2 月 21 日至 4 月 15 日期间)的 COVID-19 死亡率进行拟合。该模型包括教育评分、背景全因死亡率(2015-2019 年)和红区指标。这两个随机分量旨在调整潜在的隐藏混杂因素。
红区 COVID-19 粗死亡率比值为 1.55(90%置信区间 1.25;1.92)。从调整教育水平和基线全因死亡率的贝叶斯生态回归模型来看,红区的死亡率比值为 1.60(90%可信区间 0.94;2.51)。
综上所述,我们观察到一个 PFAS 暴露水平较高的人群 COVID-19 死亡率更高,这可能与 PFAS 免疫抑制、肺组织中的生物蓄积或与 PFAS 相关的预先存在的疾病有关。