Takahashi Paul Y, Ryu Euijung, King Katherine S, Dixon Rachel E, Porcher Julie C, Wheeler Philip H, Wi Chung Il, Juhn Young J
Division of Community Internal Medicine, Geriatrics, and Palliative Care, Mayo Clinic, Rochester, MN.
Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN.
Mayo Clin Proc Digit Health. 2024 Apr 5;2(2):259-269. doi: 10.1016/j.mcpdig.2024.04.001. eCollection 2024 Jun.
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.
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.
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).
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.
确定老年人就医跌倒数量较多的地理位置（即热点地区），并检验跌倒热点地区与居民/住房特征之间的关联。
在这项队列研究中，我们回顾性分析了2012年4月1日至2014年12月31日期间居住在明尼苏达州奥尔姆斯特德县、年龄在65岁及以上、居住在独栋住宅且有就医跌倒记录的成年人。我们通过使用计费代码识别就医跌倒情况，并通过人工查阅电子健康记录进行确认。我们进行了地理空间分析以确定跌倒热点地区，并使用逻辑回归模型评估跌倒热点地区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。
在我们社区居住在独栋住宅的12888名居民中，587名居民（4.6%）有记录的意外跌倒。跌倒在老年居民和女性中更为常见。患有更多慢性疾病、社会经济地位较低且有跌倒史的居民跌倒几率也更高。地理空间分析确定了2061名（16.0%）居住在跌倒热点地区的居民。热点地区的房屋更有可能楼层较多且楼梯较少（错层式）（与单层房屋相比，错层式房屋的比值比[OR]为1.75；95%置信区间[CI]为1.57 - 1.94）、平方英尺面积较小（与最大房屋相比，最小房屋的OR为0.29；95% CI为0.24 - 0.35），并且处于年龄最高四分位数（与最新建造房屋相比，最旧建造房屋的OR为1.46；95% CI为1.26 - 1.70）。
在我们社区中，房屋较旧、面积较小且基于住房的社会经济地位较低的地区跌倒更为常见。这些发现可供临床医生用于识别跌倒风险较高的居民。
