Geospatial analysis of short sleep duration and cognitive disability in US adults: a multi-state study using machine learning techniques.

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Tue T Te, Alex A T Bui, Constance H Fung, Mary Regina Boland
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引用次数: 0

Abstract

Background: There is evidence of increased risk of cognitive disability due to short sleep duration and adverse Social Determinants of Health (SDoH). To determine whether spatial associations (correlation between spatially distributed variables within a given geographic area) exist between neighborhoods with short sleep duration and cognitive disability across the United States (US) after adjusting for other factors. We conducted a spatial analysis using a spatial lag model at the neighborhood-level with the census tract as unit-of-analysis within each state in the US. We aggregated our results nationally using a weighted analysis to adjust for the number of census tracts per state. This study used Centers for Disease Control and Prevention (CDC) data on short sleep duration, cognitive disability and other health factors. We used 2021-2022 neighborhood-level data from the CDC and US Census Bureau adjusting for social determinants of health (SDoH) and demographics, excluding Florida due to inconsistencies in data availability. Our exposure variable was self-reported short sleep defined by the CDC ("sleep less than 7 hours per 24 hour period"). Our outcome was self-reported cognitive disability defined by the CDC ("difficulty concentrating, remembering, or making decision"). We adjusted for other factors including 'health outcomes', 'preventive practices', and the CDC's Social Vulnerability Index.

Results: The spatial analysis revealed a significant association between short sleep duration and an increased risk of cognitive disability across the US (estimate range [0.29; 1.27], p < 0.005) after adjustment. Notably, six Western states (New Mexico, Alaska, Arizona, Nevada, Idaho, and Oregon) were at increased risk of cognitive disability due to short sleep duration and this pattern was significant (p = 0.007).

Conclusions: Our study highlights the importance of short sleep duration as a significant predictor of cognitive disability across the US after adjusting for other confounders. The association between short sleep and cognitive disability was especially strong in the Western region of the US providing a deeper understanding of how geographic context and local factors can shape health outcomes.

美国成年人短睡眠时间和认知障碍的地理空间分析:一项使用机器学习技术的多州研究。
背景:有证据表明,由于睡眠时间短和不利的健康社会决定因素(SDoH),认知障碍的风险增加。在调整其他因素后,确定美国各地睡眠时间短的社区与认知障碍之间是否存在空间关联(给定地理区域内空间分布变量之间的相关性)。我们以美国各州的人口普查区为分析单位,在社区层面使用空间滞后模型进行了空间分析。我们在全国范围内汇总了我们的结果,使用加权分析来调整每个州的人口普查区数量。这项研究使用了美国疾病控制与预防中心(CDC)关于睡眠时间短、认知障碍和其他健康因素的数据。我们使用了来自疾病预防控制中心和美国人口普查局的2021-2022年社区数据,对健康的社会决定因素(SDoH)和人口统计学进行了调整,由于数据可用性不一致,不包括佛罗里达州。我们的暴露变量是疾病控制与预防中心定义的自我报告的短睡眠(“每24小时睡眠少于7小时”)。我们的结果是CDC定义的自我报告的认知障碍(“难以集中注意力、记忆或做决定”)。我们调整了其他因素,包括“健康结果”、“预防措施”和疾病预防控制中心的社会脆弱性指数。结果:空间分析显示,在美国,短睡眠时间与认知障碍风险增加之间存在显著关联(估计范围[0.29;[1.27]结论:在调整了其他混杂因素后,我们的研究强调了短睡眠时间作为美国认知障碍的重要预测因素的重要性。睡眠不足和认知障碍之间的联系在美国西部地区尤为明显,这让人们对地理环境和当地因素如何影响健康结果有了更深入的了解。
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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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