Danek Robin, Blackburn Justin, Greene Marion, Mazurenko Olena, Sanner Lindsey, Menachemi Nir
Indiana University School of Medicine, Department of Family Medicine, 980 Indiana Ave, Indianapolis, IN, 46202, USA.
Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, IN, USA.
BMC Health Serv Res. 2025 Jun 9;25(1):818. doi: 10.1186/s12913-025-12815-5.
BACKGROUND/PURPOSE: To examine how the choice of rural measurements affects estimates of hospitalization rates for depression and substance use disorders (SUD).
We conducted cross-sectional analyses using the 2018 State Inpatient Database (SID) for 5 states, including Arizona, Kentucky, Maryland, Washington, and Florida, to determine how (1) estimates of hospitalization rates for depression and SUDs; and (2) patient characteristics among those hospitalized differ. Five measurements of rurality including rural-urban commuting areas (RUCA) codes, core-based statistical areas (CBSA), urban-rural category four (URCategory4) and two definitions of rural urban continuum codes (RUCC) were used. For each measurement, we calculated frequencies and percentages for age, race, sex, and insurance type. We conducted Spearman's rank correlations to compare associations and internal agreement. We created an UpSet chart to visualize the overlap in different measurements.
There were 152,771 hospitalizations for depression and 43,760 hospitalizations for SUDs. The percentage of hospitalizations for depression or SUD differed significantly (3.2-8.1% for depression and 5.0-11.6% for SUDs ) based on rurality measure. Race and insurance characteristics of those identified as rural varied by rural measurement for depression and SUD hospitalizations. Spearman's correlations were higher for hospitalizations for SUD than for depression, ranging from r = 0.61 (RUCC and RUCA) to r = 0.99 (CBSA and URCategory4).
Different rurality measurements result in differing estimates of hospitalizations for SUD or depression. Stakeholders should be aware that the choice of rural measurements can impact policy decisions and resource allocation for programs intended to improve care in rural areas.
背景/目的：探讨农村测量方法的选择如何影响抑郁症和物质使用障碍（SUD）住院率的估计。
我们使用2018年亚利桑那州、肯塔基州、马里兰州、华盛顿州和佛罗里达州5个州的住院患者数据库（SID）进行横断面分析，以确定：（1）抑郁症和SUD住院率的估计；（2）住院患者的特征差异。使用了五种农村测量方法，包括城乡通勤区（RUCA）代码、基于核心的统计区（CBSA）、城乡类别4（URCategory4）以及城乡连续代码（RUCC）的两种定义。对于每种测量方法，我们计算了年龄、种族、性别和保险类型的频率和百分比。我们进行了斯皮尔曼等级相关性分析以比较关联和内部一致性。我们创建了一个UpSet图以直观显示不同测量方法中的重叠部分。
抑郁症住院患者有152,771例，SUD住院患者有43,760例。根据农村测量方法的不同，抑郁症或SUD的住院百分比存在显著差异（抑郁症为3.2%-8.1%，SUD为5.0%-11.6%）。在抑郁症和SUD住院患者中，被认定为农村地区的患者的种族和保险特征因农村测量方法而异。SUD住院患者的斯皮尔曼相关性高于抑郁症住院患者，范围从r = 0.61（RUCC和RUCA）到r = 0.99（CBSA和URCategory4）。
不同的农村测量方法会导致对SUD或抑郁症住院率的估计不同。利益相关者应意识到，农村测量方法的选择可能会影响旨在改善农村地区医疗服务的项目的政策决策和资源分配。
