Ettman Catherine K, Goicoechea Elena Badillo, Stuart Elizabeth A, Dean Lorraine T
Department of Health Policy & Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
Am J Epidemiol. 2025 May 7;194(5):1208-1220. doi: 10.1093/aje/kwae275.
Area-level credit scores may capture aspects of a neighborhood's resources and history that may affect population mental health beyond income and other demographic determinants. Using a sample of 511 363 adults in 1438 Pennsylvania ZIP codes who completed the COVID-19 Trends and Impact Survey, we assessed the relationship between area-level VantageScores (9 categories from <675 to ≥850) and individual mental health. We estimate odds and predicted probability of depression and anxiety symptoms, adjusting for demographics and median household income. Given a history of discrimination and unequal access to the benefits of assets, we assess whether the relationship between area credit scores and individual mental health was different for different gender, age, race and ethnicity, and education groups. Persons who lived in ZIP codes with average credit scores of 700-725 reported 1.31 (95% CI, 1.13-1.52) and 1.22 (95% CI, 1.07-1.40) times the odds of frequent feelings of depression and anxiety symptoms, respectively, relative to persons living in ZIP codes with an average area level credit score ≥850. This translated to a difference of 2.8 and 2.5 percentage points in the predicted probability of symptoms of depression and anxiety, respectively, for persons living in lower vs higher area credit score ZIP codes. Stratified models appeared to show differences in the association of depression and anxiety with credit scores across demographic groups, but tests of interaction did not show significant differences between most groups. Findings suggest that area credit scores may capture assets that shape mental health over and above income and other demographic determinants.
地区层面的信用评分可能反映了社区资源和历史的某些方面，这些方面可能会影响人口心理健康，其影响程度超出了收入和其他人口统计学决定因素。我们以宾夕法尼亚州1438个邮政编码区域内511363名完成了新冠疫情趋势与影响调查的成年人为样本，评估了地区层面的信用评分（从低于675到大于等于850共9个类别）与个人心理健康之间的关系。我们估算了抑郁和焦虑症状的比值比及预测概率，并对人口统计学特征和家庭收入中位数进行了调整。鉴于存在歧视历史以及获取资产收益的机会不平等，我们评估了地区信用评分与个人心理健康之间的关系在不同性别、年龄、种族和族裔以及教育程度群体中是否存在差异。居住在平均信用评分为700 - 725的邮政编码区域的人群，其频繁出现抑郁情绪和焦虑症状的几率分别是居住在平均地区信用评分≥850的邮政编码区域人群的1.31倍（95%置信区间，1.13 - 1.52）和1.22倍（95%置信区间，1.07 - 1.40）。这意味着，居住在地区信用评分较低的邮政编码区域的人群，其抑郁和焦虑症状的预测概率分别比居住在评分较高区域的人群高出2.8和2.5个百分点。分层模型似乎显示出不同人口统计学群体中抑郁和焦虑与信用评分之间的关联存在差异，但交互作用检验表明大多数群体之间没有显著差异。研究结果表明，地区信用评分可能反映了除收入和其他人口统计学决定因素之外，对心理健康有影响的资产情况。
