Non-traditional socio-environmental and geospatial determinants of Alzheimer's disease-related dementia mortality

IF 8.2 1区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Science of the Total Environment Pub Date : 2025-05-26 DOI:10.1016/j.scitotenv.2025.179745

Skanda Moorthy , Jean-Eudes Dazard , Zhuo Chen , Ruby Charak , Shruthika Palanivel , Salil Deo , Sadeer G. Al-Kindi , Sanjay Rajagopalan

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引用次数: 0

Abstract

Importance

Recent data point to the impact of non-traditional environmental and social factors on Alzheimer's Disease-Related Dementias (ADRD) mortality. Our study aimed to determine the extent to which antecedent air pollution, social vulnerability, and geospatial features in the environment associate with ADRD mortality.

Design

This was a cross-sectional study conducted across the mainland United States. County level Social Vulnerability Index (SVI), particulate matter air pollution (PM_2.5) were linked to ADRD mortality. Patient Rule Induction Method (PRIM) was used for delineating and characterizing “bumps” or spikes in mortality. SHapley Additive exPlanations (SHAP) values were used to rank variables by predictivity and association with directional changes in ADRD mortality.

Exposures

PM_2.5 data was acquired from 1 × 1 km spatial grids using aerosol optical depth from the Atmospheric Analysis Composition Group at Washington University St. Louis. SVI was acquired from the CDC's ATSDR Data, which is a composite index scale that characterizes socio-environmental vulnerability. Google Street View imagery coupled with deep learning computational techniques was used to extract features of neighborhood level environment characteristics from across the United States.

Results

There was a significant interaction effect between PM_2.5 and SVI on ADRD mortality (β = 31.100, p < 0.001). Two clusters of elevated ADRD mortality were identified: counties with high PM_2.5 and SVI (HH) and with low PM_2.5 and SVI (LL). Analysis of LL subset revealed associations between ADRD mortality and specific SVI subdomains, as well as built environment variables. Geospatial mapping indicated a split in these clusters along northern and southern latitudes, with differences in temperature and sunlight intensity (p < 0.001) rather than urbanization driving the distribution.

Conclusions

Ambient air pollution interacts with SVI to influence ADRD mortality rates. Our findings support a role for non-traditional factors including elements of the built environment, geographical location, and natural environmental exposures contributing to ADRD mortality.

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阿尔茨海默病相关痴呆死亡率的非传统社会环境和地理空间决定因素

最近的数据指出了非传统环境和社会因素对阿尔茨海默病相关痴呆（ADRD）死亡率的影响。我们的研究旨在确定先前的空气污染、社会脆弱性和环境中的地理空间特征与ADRD死亡率的关联程度。这是一项在美国大陆进行的横断面研究。县级社会脆弱性指数（SVI）、颗粒物空气污染（PM2.5）与ADRD死亡率相关。患者规则诱导法（PRIM）用于描述和描述死亡率的“隆起”或峰值。使用SHapley加性解释（SHAP）值对ADRD死亡率的预测性和与方向性变化的相关性进行变量排序。exposurespm2.5数据来自华盛顿大学圣路易斯分校大气分析组成小组，使用气溶胶光学深度从1 × 1 km空间网格获取。SVI是从CDC的ATSDR数据中获得的，这是一个表征社会环境脆弱性的综合指数量表。谷歌街景图像与深度学习计算技术相结合，从美国各地提取邻域环境特征特征。结果PM2.5与SVI对ADRD死亡率有显著交互作用(β = 31.100, p <；0.001)。确定了两类ADRD死亡率升高的县：PM2.5和SVI高的县（HH）和PM2.5和SVI低的县（LL）。对LL子集的分析揭示了ADRD死亡率与特定SVI子域以及建筑环境变量之间的关联。地理空间制图显示，这些集群在南北纬度上存在分裂，温度和阳光强度存在差异(p <；0.001)，而不是城市化推动了分布。结论环境空气污染与SVI相互作用影响ADRD死亡率。我们的研究结果支持非传统因素的作用，包括建筑环境、地理位置和自然环境暴露因素对ADRD死亡率的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Science of the Total Environment 环境科学-环境科学

CiteScore

17.60

自引率

10.20%

发文量

8726

审稿时长

2.4 months

期刊介绍： The Science of the Total Environment is an international journal dedicated to scientific research on the environment and its interaction with humanity. It covers a wide range of disciplines and seeks to publish innovative, hypothesis-driven, and impactful research that explores the entire environment, including the atmosphere, lithosphere, hydrosphere, biosphere, and anthroposphere. The journal's updated Aims & Scope emphasizes the importance of interdisciplinary environmental research with broad impact. Priority is given to studies that advance fundamental understanding and explore the interconnectedness of multiple environmental spheres. Field studies are preferred, while laboratory experiments must demonstrate significant methodological advancements or mechanistic insights with direct relevance to the environment.