Session: Health Equity/Social Determinants of Health 1 Works in Progress
WIP 74 - Retrospective Analysis of Hospital Readmission Data to Characterize Demographics and Identify Potential “Hot Spots” through Geospatial Analysis
Pediatric Resident Dell Children's Medical Center of Central Texas Austin, Texas, United States
Background: Preventable and non-preventable readmissions are a burden to both families and healthcare systems and are used as a marker of quality in pediatric healthcare centers. It is well known that the environment a child is raised in has a significant contribution to their overall health and well-being. Childhood opportunity index (COI) is a measure of the quality of resources and conditions in neighborhoods that affect children’s health and development. COI is calculated by 29 neighborhood level indicators across 3 domains: (1) education, (2) health and environment, and (3) social and economic. Literature review has found associations of lower COI and poorer health outcomes, including increased rates of readmission, PICU utilization, and ER visits. Geospatial Analysis has been used to identify “hot spots'' for both PICU asthma and admissions for acute respiratory failure requiring mechanical ventilation. These “hot spots” had associations with lower COI. Recognizing areas more prone to readmission in our community will allow for targeted approaches to reduce readmissions. Objective: Our study aims to analyze pediatric readmission data to understand general demographic information as well as use geospatial analysis to identify “hot spot” regions for readmission. Design/Methods: Our study includes all patients ≤ age 18 readmitted within 30 days of discharge from January 2022 to January 2023, excluding readmissions to our mental health unit, special delivery unit, or planned readmissions for chemotherapy. Descriptive statistics will be performed to determine patient characteristics and outcomes. Overall remission rate as well as readmission rate per census tract is calculated with a comparative group of all admissions. Hot spots will be identified through geospatial analysis and heat maps. Test statistics will be utilized to test discrepancies of patient characteristics and outcome measures between “hot spot'' and “non-hot spot” areas. Density maps using COI data will be utilized to identify if there is overlap with readmission “hot spot'' areas. This study has been IRB approved.
