Population Studies with Diverse Data Types
Disparities in asthma prevalence and severity in the U.S. are well known. For example, Black/African American people are 2.8 times more likely than White people to die from asthma. Although asthma disparities arise in part from non-modifiable factors such as differences in genetic ancestry, modifiable factors such as poor health, medication non-adherence, and limited access to health care due to social, economic and environmental disadvantages also play a prominent role. We use Electronic Health Record (EHR) and epidemiologic study data to understand the factors that contribute to striking asthma disparities. Additionally, we expand the power of these data by integrating them with rich and diverse social, economic, and environmental data from publicly available secondary sources. We continue to use this approach to identify interventions that may be effective to reduce disparities and improve health of all asthma and COPD patients in our region.
An example of a U.S.-level study is one in which we used data corresponding to more than one million adults from two large epidemiologic studies (i.e., BRFSS and NHANES) to determine whether demographic factors are associated differently with asthma among men and women. In adults, asthma occurs more frequently in women than in men although the reasons for this gender difference are not completely understood. We found that obesity and smoking were much greater risk factors for women than men, as described further in this paper:
Using EHR-derived data from Penn Medicine patients, we have characterized asthma exacerbation risk factors, demonstrated that spatial analyses based on residential addresses permit the identification of regions with more exacerbations, and we showed that linking EHR data to rich and diverse sources of social, economic and environmental variables aids in the identification of factors driving health risks beyond what is possible with EHR data alone. Two representative papers on this topic are:
Factors Associated with Exacerbations among Adults with Asthma According to Electronic Health Record Data
Although approaches to enhance EHR data for the study of conditions in which social and economic variables play a prominent role via linkage of clinical data to sources of external information are convenient, whether geographic-area-level information is an adequate surrogate of individual-level information is not fully understood. We have conducted analyses to address this issue. For example, using data from two population-based surveys administered across eight Pennsylvania counties spanning the urban-rural spectrum, we found that neighborhood-level measures of socioeconomic status were poorly correlated with individual-level measures outside of urban areas. Specifically, concordance between household income and neighborhood advantage increased with county urbanicity (ρ = 0.16–0.26 vs. 0.31–0.45 vs. 0.47 in medium metro/micropolitan, suburban, and large metro counties, respectively), while confounding by individual SES on the obesity and self-rated health association decreased with urbanicity (15%–22% vs. 6%–13% vs. 3% in medium metro/micropolitan, suburban, and large metro counties, respectively). These results suggest that analyses that include only neighborhood-level measures may result in significant residual confounding by individual-level SES in non-urban and rural settings. More details are in this paper:
Health information technologies (HITs), including phone apps and sensors, are increasingly used for the management of asthma and COPD, conditions that share some similarities in that they are chronic pulmonary diseases that must be managed by patients and providers, but cannot be cured. HITs are also improving our ability to conduct research studies with rich phenotype information, specifically environmental exposure, symptoms, and medication use, which will yield advances in asthma and COPD patient care and generate hypotheses for experimental studies. Two reviews of this topic are: