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美国老年人中的严重洪水与特定病因住院情况:一项回顾性匹配队列分析

Severe flooding and cause-specific hospitalisation among older adults in the USA: a retrospective matched cohort analysis.

作者信息

Aggarwal Sarika, Hu Jie K, Sullivan Jonathan A, Parks Robbie M, Nethery Rachel C

机构信息

Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, MA, USA.

Department of Statistics, The Ohio State University, Columbus, OH, USA.

出版信息

Lancet Planet Health. 2025 Jul;9(7):101268. doi: 10.1016/S2542-5196(25)00132-9. Epub 2025 Jul 30.

Abstract

BACKGROUND

Floods are the most common climate-related disaster; yet previous studies have investigated the impact of floods on only a few health outcomes in narrow spatiotemporal settings. We aimed to assess the association between severe flood exposure and cause-specific hospitalisation rates in adults older than 65 years in the contiguous USA.

METHODS

In this retrospective matched cohort analysis, we obtained inpatient claims data from Medicare fee-for-service beneficiaries older than 65 years living in the contiguous USA from Jan 1, 2000, to Dec 31, 2016. From each inpatient hospitalisation record, we extracted the admission date, primary International Classification of Diseases, 9th revision, clinical modification (ICD-9-CM) code (or 10th revision [ICD-10-CM] code on or after Oct 1, 2015), and self-reported residential ZIP code. Hospitalisation data were linked with satellite-based, high-resolution historical flood maps from the Global Flood Database by ZIP code. Days during and shortly after a flood exposure were matched to non-flood-affected control days by ZIP code and day-of-year. We estimated relative percentage changes in hospitalisation rates for 13 mutually exclusive, well-defined disease categories during and in the 4 weeks following flood exposure with conditional quasi-Poisson regression models.

FINDINGS

This study captured 72 major flood events and included over 4·5 million hospitalisations occurring over a 17-year period. We observed elevated rates of hospitalisation on average during and following flood exposure for skin diseases (3·1% [95% CI 1·4 to 4·9]), nervous system diseases (2·5% [1·0 to 4·1]), musculoskeletal system diseases (1·3% [0·3 to 2·3]), and injuries or poisoning (1·1% [0·2 to 2·0]). Communities with lower proportions of Black residents experienced exacerbated flood effects for nervous system diseases (7·6% [95% CI 2·8 to 12·6]), whereas skin diseases (6·1% [1·9 to 10·5]) and mental health-related impacts (3·0% [-0·3 to 6·5]) were more pronounced for areas with larger percentages of Black residents during flood exposure.

INTERPRETATION

Among adults older than 65 years, exposure to severe flood events was associated with increased hospitalisation rates for skin diseases, nervous system diseases, musculoskeletal system diseases, and injuries. Different patterns of hospital admission persisted for populations with higher versus lower proportions of Black residents. Our findings indicate a need for targeted flood-specific preparedness and adaptation strategies for socially vulnerable populations, including older individuals and racially minoritised communities.

FUNDING

National Institutes of Health, Harvard Data Science Initiative, and Alfred P Sloan Foundation.

摘要

背景

洪水是最常见的与气候相关的灾害;然而,以往的研究仅在狭窄的时空范围内调查了洪水对少数健康结果的影响。我们旨在评估美国本土65岁以上成年人中,严重洪水暴露与特定病因住院率之间的关联。

方法

在这项回顾性匹配队列分析中,我们获取了2000年1月1日至2016年12月31日期间居住在美国本土的65岁以上医疗保险按服务收费受益人的住院理赔数据。从每份住院记录中,我们提取了入院日期、主要的国际疾病分类第九版临床修订本(ICD - 9 - CM)编码(或2015年10月1日及以后的第十版[ICD - 10 - CM]编码)以及自我报告的居住邮政编码。住院数据通过邮政编码与全球洪水数据库基于卫星的高分辨率历史洪水地图相关联。洪水暴露期间及之后不久的日期,按邮政编码和一年中的日期与未受洪水影响的对照日期进行匹配。我们使用条件准泊松回归模型估计了洪水暴露期间及之后4周内13种相互排斥、明确界定的疾病类别的住院率相对百分比变化。

结果

本研究记录了72次重大洪水事件,涵盖了17年期间超过450万次住院病例。我们观察到,在洪水暴露期间及之后,皮肤病(3.1%[95%置信区间1.4至4.9])、神经系统疾病(2.5%[1.0至4.1])、肌肉骨骼系统疾病(1.3%[0.3至2.3])以及损伤或中毒(1.1%[0.2至2.0])的住院率平均有所上升。黑人居民比例较低的社区,神经系统疾病的洪水影响加剧(7.6%[95%置信区间2.8至12.6]),而在洪水暴露期间,黑人居民比例较高的地区,皮肤病(6.1%[1.9至10.5])和心理健康相关影响(3.0%[-0.3至6.5])更为明显。

解读

在65岁以上的成年人中,暴露于严重洪水事件与皮肤病、神经系统疾病、肌肉骨骼系统疾病和损伤的住院率增加有关。黑人居民比例较高和较低的人群,住院模式存在差异。我们的研究结果表明,需要针对包括老年人和少数族裔社区在内的社会弱势群体制定有针对性的洪水防范和适应策略。

资金来源

美国国立卫生研究院、哈佛数据科学计划和阿尔弗雷德·P·斯隆基金会。

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