Sosso Jessica L, Fischer Karen M, Wi Chung-Il, Jegen Dominika A, Matthews Marc, Maxson Julie, Bernard Matthew E, Stacey Stephen K, Foss Randy M, Hidaka Brandon, Passmore Rachael, Garrison Gregory M, Thacher Tom D
Mayo Clinic Health System, Sparta, WI, USA.
Mayo Clinic, Rochester, MN, USA.
J Prim Care Community Health. 2025 Jan-Dec;16:21501319251369673. doi: 10.1177/21501319251369673. Epub 2025 Sep 2.
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.
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.
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.
The increased prevalence of social risk factors among rural residents is largely explained by individual SES measured by HOUSES Index.
引言/目的：在以农村为主的基层医疗人群中，关于患者报告的社会风险因素的患病率以及HOUSES指数（一种基于公开可用住房数据评估社会经济地位（SES）的简单、可靠方法）的使用情况，人们了解甚少。
我们对截至2022年12月31日在美国中西部卫生系统中由家庭医学临床医生诊治的成年患者进行了横断面分析。患者列出的地址决定了HOUSES指数的四分位数排名（Q1表示最低的社会经济地位）以及农村/城市状况。从健康记录问卷中收集包括住房、食品、交通、财务和暴力在内的社会风险数据。使用混合效应模型评估社会风险、HOUSES指数和农村地区之间的关联。
在纳入的352355名患者中，农村患者比城市患者更有可能报告所有社会风险因素，并且根据HOUSES四分位数衡量，其社会经济地位较低。在混合效应分析中，HOUSES四分位数可独立预测报告有风险的社会风险因素（Q1与Q4相比，OR = 2.27，95% CI = 2.19 - 2.37），但在调整HOUSES因素后，农村地区并非如此（OR = 1.02，95% CI = 0.97 - 1.07）。
农村居民中社会风险因素患病率的增加在很大程度上可由HOUSES指数衡量的个体社会经济地位来解释。
