Your neighborhood matters: A machine‐learning approach to the geospatial and social determinants of health in 9‐1‐1 activated chest pain

Abstract

Healthcare disparities in the initial management of patients with acute coronary syndrome (ACS) exist. Yet, the complexity of interactions between demographic, social, economic, and geospatial determinants of health hinders incorporating such predictors in existing risk stratification models. We sought to explore a machine-learning-based approach to study the complex interactions between the geospatial and social determinants of health to explain disparities in ACS likelihood in an urban community.This study identified consecutive patients transported by Pittsburgh emergency medical service for a chief complaint of chest pain or ACS-equivalent symptoms. We extracted demographics, clinical data, and location coordinates from electronic health records. Median income was based on US census data by zip code. A random forest (RF) classifier and a regularized logistic regression model were used to identify the most important predictors of ACS likelihood. Our final sample included 2400 patients (age 59 ± 17 years, 47% Females, 41% Blacks, 15.8% adjudicated ACS). In our RF model (area under the receiver operating characteristic curve of 0.71 ± 0.03) age, prior revascularization, income, distance from hospital, and residential neighborhood were the most important predictors of ACS likelihood. In regularized regression (akaike information criterion = 1843, bayesian information criterion = 1912, χ 2 = 193, df = 10, p < 0.001), residential neighborhood remained a significant and independent predictor of ACS likelihood. Findings from our study suggest that residential neighborhood constitutes an upstream factor to explain the observed healthcare disparity in ACS risk prediction, independent from known demographic, social, and economic determinants of health, which can inform future work on ACS prevention, in-hospital care, and patient discharge.

The Association of Racial Residential Segregation and Survival After Out-of-Hospital Cardiac Arrest in the United States

“…An emerging body of research has identified both broader area level SDOH domains as well as individual level health-related social needs (HRSN) as important non-clinical drivers of health outcomes. Studies of other emergent conditions, such as sepsis, 21, 22 heart failure, 23 and acute coronary syndrome, 24 have shown that SDOH and HRSN data can be important for identifying drivers of disparities in outcomes and improving prediction specific clinical endpoints for patients. Specific to OHCA, a study examining 22,816 patients contained within the Resuscitation Outcomes Consortium (ROC) database found predominantly neighborhoods with a greater proportion of Black individuals were associated with lower rates of survival to hospital discharge when compared with neighborhoods with a greater proportion of White individuals.…”

Health equity and social determinants of health

“…Our special issue, similarly, highlights the importance of considering the residential neighborhood and housing status when evaluating utilization and access to health care. Using a machine learning approach Faramand et al (2021) found that residential neighborhood and distance to hospital predicted disparities among Black acute coronary syndrome patients. Similarly, in their study of nurse and case managers, Schneiderman et al (2021) noted that healthcare utilization among previously unhoused individuals was shaped by a complex interplay of trauma related to previous homelessness, financial insecurity, and negative interactions with healthcare providers.…”