The Association Between Patient-Reported Social Risks and the HOUSES Index: A Rural-Urban Comparison.

IF 2.5 Q1 PRIMARY HEALTH CARE
Jessica L Sosso, Karen M Fischer, Chung-Il Wi, Dominika A Jegen, Marc Matthews, Julie Maxson, Matthew E Bernard, Stephen K Stacey, Randy M Foss, Brandon Hidaka, Rachael Passmore, Gregory M Garrison, Tom D Thacher
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Abstract

Introduction/objectives: Little is known about the prevalence of patient-reported social risk factors and the use of the HOUSES Index, a simple, reliable method of assessing socioeconomic status (SES) based on publicly available housing data, in a predominantly rural, primary care population.

Methods: We conducted a cross-sectional analysis of adult patients paneled to family medicine clinicians in a US Midwest health system as of December 31, 2022. Patients' listed address determined HOUSES Index as quartile rank (Q1 lowest SES) and rural/urban status. Social risk data including housing, food, transportation, finances, and violence were collected from health record questionnaires. A mixed effect model was used to assess associations between social risk, HOUSES Index, and rurality.

Results: Of the 352 355 patients included, rural patients were more likely than urban patients to report all social risk factors and had lower SES as measured by HOUSES quartiles. In the mixed effects analysis, HOUSES quartile was independently predictive of reporting an at-risk social risk factor (Q1 vs Q4 OR = 2.27, 95% CI = 2.19-2.37), but rurality was not (OR = 1.02, 95% CI = 0.97-1.07) after adjusting for HOUSES.

Conclusions: The increased prevalence of social risk factors among rural residents is largely explained by individual SES measured by HOUSES Index.

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病人报告的社会风险与住房指数之间的关系:一个城乡比较。
前言/目标:对于患者报告的社会风险因素的普遍程度和house指数(一种基于公开住房数据评估社会经济地位(SES)的简单、可靠方法)在主要是农村初级保健人口中的使用情况知之甚少。方法:我们对截至2022年12月31日美国中西部卫生系统家庭医学临床医生的成年患者进行了横断面分析。患者所列地址决定了house指数为四分位数排名(Q1最低SES)和农村/城市状况。从健康记录问卷中收集社会风险数据,包括住房、食物、交通、财务和暴力。采用混合效应模型评估社会风险、住房指数和乡村性之间的关系。结果:在352 355例纳入的患者中,农村患者比城市患者更有可能报告所有社会风险因素,并且通过house四分位数测量的SES较低。在混合效应分析中,house四分位数能够独立预测存在风险的社会风险因素(Q1 vs Q4 OR = 2.27, 95% CI = 2.19-2.37),但在调整house后,乡村性不能(OR = 1.02, 95% CI = 0.97-1.07)。结论:农村居民社会风险因素患病率的增加,在很大程度上是由house指数测量的个体SES所解释的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.80
自引率
2.80%
发文量
183
审稿时长
15 weeks
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