居住在单户独立式住宅中的老年人跌倒较多的地区的住房特征:使用地理空间分析的队列研究

Paul Y. Takahashi MD, MPH , Euijung Ryu PhD , Katherine S. King MS , Rachel E. Dixon BA , Julie C. Porcher MS , Philip H. Wheeler , Chung Il Wi MD , Young J. Juhn MD, MPH
{"title":"居住在单户独立式住宅中的老年人跌倒较多的地区的住房特征:使用地理空间分析的队列研究","authors":"Paul Y. Takahashi MD, MPH ,&nbsp;Euijung Ryu PhD ,&nbsp;Katherine S. King MS ,&nbsp;Rachel E. Dixon BA ,&nbsp;Julie C. Porcher MS ,&nbsp;Philip H. Wheeler ,&nbsp;Chung Il Wi MD ,&nbsp;Young J. Juhn MD, MPH","doi":"10.1016/j.mcpdig.2024.04.001","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><p>To identify geographic locations with high numbers of medically attended falls (ie, hotspots) by older adults and to test the associations between fall hotspots and resident/housing characteristics.</p></div><div><h3>Patients and Methods</h3><p>In this cohort study, we retrospectively reviewed adults who were 65 years or older, lived in a single-family detached dwelling, and had a medically attended fall in Olmsted County, MN, between April 1, 2012, and December 31, 2014. We identified medically attended falls by using billing codes and confirmed by manual review of the electronic health records. We performed geospatial analysis to identify fall hotspots and evaluated the association between fall hotspots and resident or housing characteristics with logistic regression models, adjusting for age, sex, socioeconomic status, chronic health conditions, and/or a history of falls.</p></div><div><h3>Results</h3><p>Among 12,888 residents living in single-family detached dwellings in our community, 587 residents (4.6%) had documented accidental falls. Falls were more common in older residents and in women. Residents who had more chronic diseases, lower socioeconomic status, and a history of falls also had higher odds of a fall. Geospatial analysis identified 2061 (16.0%) residents who lived in a fall hotspot. Houses in hotspots were more likely to have more stories with fewer stairs (split level) (odds ratio [OR], 1.75; 95% CI, 1.57-1.94, for split level vs 1-story houses), smaller square feet (OR, 0.29; 95% CI, 0.24-0.35, for largest vs smallest houses), and in the highest quartile for age (OR, 1.46; 95% CI, 1.26-1.70, for oldest built vs newest built houses).</p></div><div><h3>Conclusion</h3><p>Falls were more common in locations in our community that had older, smaller homes and lower housing-based socioeconomic status. These findings can be used by clinicians to identify residents who are at higher risk for falls.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 259-269"},"PeriodicalIF":0.0000,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000282/pdfft?md5=677afcbfea9f9ecd229c0c3ed4369544&pid=1-s2.0-S2949761224000282-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Housing Characteristics of Areas With More Falls by Older Adults Living in Single-Family Detached Dwellings: A Cohort Study Using Geospatial Analysis\",\"authors\":\"Paul Y. Takahashi MD, MPH ,&nbsp;Euijung Ryu PhD ,&nbsp;Katherine S. King MS ,&nbsp;Rachel E. Dixon BA ,&nbsp;Julie C. Porcher MS ,&nbsp;Philip H. Wheeler ,&nbsp;Chung Il Wi MD ,&nbsp;Young J. Juhn MD, MPH\",\"doi\":\"10.1016/j.mcpdig.2024.04.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><p>To identify geographic locations with high numbers of medically attended falls (ie, hotspots) by older adults and to test the associations between fall hotspots and resident/housing characteristics.</p></div><div><h3>Patients and Methods</h3><p>In this cohort study, we retrospectively reviewed adults who were 65 years or older, lived in a single-family detached dwelling, and had a medically attended fall in Olmsted County, MN, between April 1, 2012, and December 31, 2014. We identified medically attended falls by using billing codes and confirmed by manual review of the electronic health records. We performed geospatial analysis to identify fall hotspots and evaluated the association between fall hotspots and resident or housing characteristics with logistic regression models, adjusting for age, sex, socioeconomic status, chronic health conditions, and/or a history of falls.</p></div><div><h3>Results</h3><p>Among 12,888 residents living in single-family detached dwellings in our community, 587 residents (4.6%) had documented accidental falls. Falls were more common in older residents and in women. Residents who had more chronic diseases, lower socioeconomic status, and a history of falls also had higher odds of a fall. Geospatial analysis identified 2061 (16.0%) residents who lived in a fall hotspot. Houses in hotspots were more likely to have more stories with fewer stairs (split level) (odds ratio [OR], 1.75; 95% CI, 1.57-1.94, for split level vs 1-story houses), smaller square feet (OR, 0.29; 95% CI, 0.24-0.35, for largest vs smallest houses), and in the highest quartile for age (OR, 1.46; 95% CI, 1.26-1.70, for oldest built vs newest built houses).</p></div><div><h3>Conclusion</h3><p>Falls were more common in locations in our community that had older, smaller homes and lower housing-based socioeconomic status. These findings can be used by clinicians to identify residents who are at higher risk for falls.</p></div>\",\"PeriodicalId\":74127,\"journal\":{\"name\":\"Mayo Clinic Proceedings. Digital health\",\"volume\":\"2 2\",\"pages\":\"Pages 259-269\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2949761224000282/pdfft?md5=677afcbfea9f9ecd229c0c3ed4369544&pid=1-s2.0-S2949761224000282-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mayo Clinic Proceedings. Digital health\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2949761224000282\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mayo Clinic Proceedings. Digital health","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949761224000282","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

患者和方法在这项队列研究中,我们对 2012 年 4 月 1 日至 2014 年 12 月 31 日期间明尼苏达州奥姆斯特德县 65 岁或以上、居住在单户独立式住宅中并发生过医疗护理跌倒的成年人进行了回顾性回顾。我们通过使用账单代码来识别医疗护理跌倒,并通过人工审核电子健康记录来确认。我们进行了地理空间分析以确定跌倒热点,并通过逻辑回归模型评估了跌倒热点与居民或住房特征之间的关联,同时对年龄、性别、社会经济地位、慢性健康状况和/或跌倒史进行了调整。结果在我们社区居住在单户独立住宅中的 12888 名居民中,有 587 名居民(4.6%)有意外跌倒的记录。跌倒在老年居民和女性中更为常见。患有慢性疾病、社会经济地位较低和有跌倒史的居民发生跌倒的几率也较高。通过地理空间分析发现,有 2061 名(16.0%)居民居住在跌倒热点地区。热点地区的房屋更有可能层数较多,楼梯较少(分层)(分层房屋与单层房屋的几率比 [OR], 1.75; 95% CI, 1.57-1.94),面积较小(最大房屋与最小房屋的几率比 [OR], 0.29; 95% CI, 0.24-0.结论在我们的社区中,房屋较老、较小、社会经济地位较低的地方更容易发生跌倒。临床医生可以利用这些发现来识别跌倒风险较高的居民。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Housing Characteristics of Areas With More Falls by Older Adults Living in Single-Family Detached Dwellings: A Cohort Study Using Geospatial Analysis

Objective

To identify geographic locations with high numbers of medically attended falls (ie, hotspots) by older adults and to test the associations between fall hotspots and resident/housing characteristics.

Patients and Methods

In this cohort study, we retrospectively reviewed adults who were 65 years or older, lived in a single-family detached dwelling, and had a medically attended fall in Olmsted County, MN, between April 1, 2012, and December 31, 2014. We identified medically attended falls by using billing codes and confirmed by manual review of the electronic health records. We performed geospatial analysis to identify fall hotspots and evaluated the association between fall hotspots and resident or housing characteristics with logistic regression models, adjusting for age, sex, socioeconomic status, chronic health conditions, and/or a history of falls.

Results

Among 12,888 residents living in single-family detached dwellings in our community, 587 residents (4.6%) had documented accidental falls. Falls were more common in older residents and in women. Residents who had more chronic diseases, lower socioeconomic status, and a history of falls also had higher odds of a fall. Geospatial analysis identified 2061 (16.0%) residents who lived in a fall hotspot. Houses in hotspots were more likely to have more stories with fewer stairs (split level) (odds ratio [OR], 1.75; 95% CI, 1.57-1.94, for split level vs 1-story houses), smaller square feet (OR, 0.29; 95% CI, 0.24-0.35, for largest vs smallest houses), and in the highest quartile for age (OR, 1.46; 95% CI, 1.26-1.70, for oldest built vs newest built houses).

Conclusion

Falls were more common in locations in our community that had older, smaller homes and lower housing-based socioeconomic status. These findings can be used by clinicians to identify residents who are at higher risk for falls.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Mayo Clinic Proceedings. Digital health
Mayo Clinic Proceedings. Digital health Medicine and Dentistry (General), Health Informatics, Public Health and Health Policy
自引率
0.00%
发文量
0
审稿时长
47 days
期刊最新文献
Developing a Research Center for Artificial Intelligence in Medicine Strategic Considerations for Selecting Artificial Intelligence Solutions for Institutional Integration: A Single-Center Experience Reviewers for Mayo Clinic Proceedings: Digital Health (2024) A Blueprint for Clinical-Driven Medical Device Development: The Feverkidstool Application to Identify Children With Serious Bacterial Infection Cost-Effectiveness of Artificial Intelligence-Enabled Electrocardiograms for Early Detection of Low Ejection Fraction: A Secondary Analysis of the Electrocardiogram Artificial Intelligence-Guided Screening for Low Ejection Fraction Trial
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1