Thompson, Emily

English

ISU ReD: Research and eData

Presentation for the Symposium on BEER November 2023Modeling Maternal Outcomes By PredictingGeospatial and Social Determinants of HealthEmily Thompson11Department of Mathematics, Southern Methodist University, Dallas, TX 75205thompsone@mail.smu.eduParkland Community Health Plan (PCHP) is a Medicaid HMO that offers low-cost insurance plans topregnant women and children within seven counties in North Texas. PCHP members accrue higher thanaverage costs due to repeated emergency room visits and treatment for acute and/or chronic conditionsassociated with pregnancy complications. To identify social determinants of health (SDOH) that mightcontribute to this, we developed an algorithm that utilizes geospatial analysis and logistic regression toselect features from PCHP Medicaid claims data and Parkland Center for Clinical Innovation’s CommunityVulnerability Compass (CVC). Using the 27 selected features from the algorithm, we built a logistic regressionmodel to predict higher or lower average percentages of premature births on a block group level. Our resultsshow that the model is 65% accurate, with two CVC features, mental health and alcohol abuse, contributingthe most to the model’s performance. Using the model, we can make more informed decisions on which CVCfeatures contribute the most to predicting specific adverse maternal outcomes, which will allow us to betteraddress socioeconomical barriers that prevent PCHP members from receiving adequate healthcare.

Modeling Maternal Outcomes By Predicting Geospatial and Social Determinants of Health

https://ir.library.illinoisstate.edu/cgi/viewcontent.cgi?article=1767&context=beer

Modeling Maternal Outcomes By Predicting Geospatial and Social Determinants of Health

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ISU ReD: Research and eData