Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
J Urban Health. 2022 Dec;99(6):984-997. doi: 10.1007/s11524-022-00692-w. Epub 2022 Nov 11.
There is tremendous interest in understanding how neighborhoods impact health by linking extant social and environmental drivers of health (SDOH) data with electronic health record (EHR) data. Studies quantifying such associations often use static neighborhood measures. Little research examines the impact of gentrification-a measure of neighborhood change-on the health of long-term neighborhood residents using EHR data, which may have a more generalizable population than traditional approaches. We quantified associations between gentrification and health and healthcare utilization by linking longitudinal socioeconomic data from the American Community Survey with EHR data across two health systems accessed by long-term residents of Durham County, NC, from 2007 to 2017. Census block group-level neighborhoods were eligible to be gentrified if they had low socioeconomic status relative to the county average. Gentrification was defined using socioeconomic data from 2006 to 2010 and 2011-2015, with the Steinmetz-Wood definition. Multivariable logistic and Poisson regression models estimated associations between gentrification and development of health indicators (cardiovascular disease, hypertension, diabetes, obesity, asthma, depression) or healthcare encounters (emergency department [ED], inpatient, or outpatient). Sensitivity analyses examined two alternative gentrification measures. Of the 99 block groups within the city of Durham, 28 were eligible (N = 10,807; median age = 42; 83% Black; 55% female) and 5 gentrified. Individuals in gentrifying neighborhoods had lower odds of obesity (odds ratio [OR] = 0.89; 95% confidence interval [CI]: 0.81-0.99), higher odds of an ED encounter (OR = 1.10; 95% CI: 1.01-1.20), and lower risk for outpatient encounters (incidence rate ratio = 0.93; 95% CI: 0.87-1.00) compared with non-gentrifying neighborhoods. The association between gentrification and health and healthcare utilization was sensitive to gentrification definition.
人们对于理解邻里环境如何通过将现有的社会和环境健康驱动因素(SDOH)数据与电子健康记录(EHR)数据联系起来来影响健康有着浓厚的兴趣。研究量化这种关联的研究通常使用静态的邻里措施。很少有研究使用 EHR 数据来研究邻里变化的 gentrification(邻里变化的衡量标准)对长期居住在邻里的居民的健康的影响,这种方法可能比传统方法更具普遍性。我们通过将美国社区调查中的纵向社会经济数据与北卡罗来纳州达勒姆县长期居民在 2007 年至 2017 年期间使用的两个医疗系统的 EHR 数据相联系,量化了 gentrification 与健康和医疗保健利用之间的关联。如果邻里的社会经济地位相对于全县平均水平较低,则有资格成为 gentrified 的普查块组级邻里。使用 2006 年至 2010 年和 2011 年至 2015 年的社会经济数据,采用 Steinmetz-Wood 定义来定义 gentrification。多变量逻辑和泊松回归模型估计了 gentrification 与健康指标(心血管疾病、高血压、糖尿病、肥胖、哮喘、抑郁症)或医疗保健接触(急诊、住院或门诊)的发展之间的关联。敏感性分析检验了两种替代 gentrification 措施。在达勒姆市的 99 个街区中,有 28 个符合条件(N=10807;中位数年龄=42;83%黑人;55%女性),有 5 个街区 gentrified。在 gentrifying 邻里的个体中,肥胖的可能性较低(优势比[OR]=0.89;95%置信区间[CI]:0.81-0.99),急诊接触的可能性较高(OR=1.10;95% CI:1.01-1.20),门诊接触的风险较低(发病率比[IRR]=0.93;95% CI:0.87-1.00)与非 gentrifying 邻里相比。 gentrification 与健康和医疗保健利用之间的关联对 gentrification 定义很敏感。