Schreibman Alana, Lactaoen Kimberly, Joo Jaehyun, Gleeson Patrick K, Weissman Gary E, Apter Andrea J, Hubbard Rebecca A, Himes Blanca E
Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Division of Pulmonary, Allergy and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
PLOS Digit Health. 2025 Jun 23;4(6):e0000677. doi: 10.1371/journal.pdig.0000677. eCollection 2025 Jun.
Electronic health records (EHRs) provide rich data for diverse populations but often lack information on social and environmental determinants of health (SEDH) that are important for the study of complex conditions such as asthma, a chronic inflammatory lung disease. We integrated EHR data with seven SEDH datasets to conduct a retrospective cohort study of 6,656 adults with asthma. Using Penn Medicine encounter data from January 1, 2017 to December 31, 2020, we identified individual-level and spatially-varying factors associated with asthma exacerbations. Black race and prescription of an inhaled corticosteroid were strong risk factors for asthma exacerbations according to a logistic regression model of individual-level risk. A spatial generalized additive model (GAM) identified a hotspot of increased exacerbation risk (mean OR = 1.41, SD 0.14, p < 0.001), and inclusion of EHR-derived variables in the model attenuated the spatial variance in exacerbation odds by 34.0%, while additionally adjusting for the SEDH variables attenuated the spatial variance in exacerbation odds by 66.9%. Additional spatial GAMs adjusted one variable at a time revealed that neighborhood deprivation (OR = 1.05, 95% CI: 1.03, 1.07), Black race (OR = 1.66, 95% CI: 1.44, 1.91), and Medicaid health insurance (OR = 1.30, 95% CI: 1.15, 1.46) contributed most to the spatial variation in exacerbation odds. In spatial GAMs stratified by race, adjusting for neighborhood deprivation and health insurance type did not change the spatial distribution of exacerbation odds. Thus, while some EHR-derived and SEDH variables explained a large proportion of the spatial variance in asthma exacerbations across Philadelphia, a more detailed understanding of SEDH variables that vary by race is necessary to address asthma disparities. More broadly, our findings demonstrate how integration of information on SEDH with EHR data can improve understanding of the combination of risk factors that contribute to complex diseases.
电子健康记录（EHRs）为不同人群提供了丰富的数据，但往往缺乏对健康的社会和环境决定因素（SEDH）的信息，而这些因素对于研究诸如哮喘（一种慢性炎症性肺病）等复杂疾病至关重要。我们将EHR数据与七个SEDH数据集相结合，对6656名成年哮喘患者进行了一项回顾性队列研究。利用宾夕法尼亚大学医学中心2017年1月1日至2020年12月31日期间的就诊数据，我们确定了与哮喘加重相关的个体层面和空间变化因素。根据个体层面风险的逻辑回归模型，黑人种族和吸入性糖皮质激素的处方是哮喘加重的强烈风险因素。空间广义相加模型（GAM）确定了一个加重风险增加的热点区域（平均比值比=1.41，标准差0.14，p<0.001），并且在模型中纳入源自EHR的变量使加重几率的空间方差降低了34.0%，而在另外调整SEDH变量后，加重几率的空间方差降低了66.9%。一次调整一个变量的其他空间GAM显示，邻里贫困（比值比=1.05，95%置信区间：1.03，1.07）、黑人种族（比值比=1.66，95%置信区间：1.44，1.91）和医疗补助健康保险（比值比=1.30，95%置信区间：1.15，1.46）对加重几率的空间变化贡献最大。在按种族分层的空间GAM中，调整邻里贫困和健康保险类型并没有改变加重几率的空间分布。因此，虽然一些源自EHR的变量和SEDH变量解释了费城哮喘加重的大部分空间方差，但有必要更详细地了解因种族而异的SEDH变量，以解决哮喘差异问题。更广泛地说，我们的研究结果表明，将SEDH信息与EHR数据相结合可以如何提高对导致复杂疾病的风险因素组合的理解。
