619: THE IMPACT OF NEIGHBORHOOD FACTORS ON PATIENT DISEASE SEVERITY ON PRESENTATION TO THE PICU

Abstract

Introduction: Health disparities yield worse health outcomes for critically ill patients. Neighborhoods capture social and physical attributes experienced by residents. Increasingly, neighborhood-level geographic variations in health disparities have been reported. Individuals are commonly clustered into neighborhoods based on socioeconomic status. The impact of sociodemographic factors on outcomes for PICU patients is understudied. Few studies delve into the specific neighborhood characteristics affecting disease severity on PICU admission. To address this gap, we conducted a retrospective study exploring the association between geography and disease severity on PICU presentation, with sociodemographic neighborhood data and clinical data as additional predictors. We anticipate patients with higher disease severity will be clustered in low opportunity neighborhoods. Methods: Electronic medical record patient data were collected for non-elective admissions to the PICU from Jan 2013 to Dec 2019. The peak Pediatric Logistic Organ Dysfunction-2 (PELOD-2) score for the first 72 hours of admission was calculated for each encounter. Addresses were geocoded into census tracts. The Childhood Opportunity Index (COI), Social Vulnerability Index (SVI), and Neighborhood Disorder Index (NDI) were matched to each census tract. We report descriptive analyses, Spearman correlation coefficients for continuous variables, and Kruskal-Wallis test results for categorical variables. Results: There were 8497 unique patient encounters for 5874 distinct patients. Data were skewed right with a PELOD-2 score mean of 3.2 and median of 3.0. There was no correlation between the SVI (p=0.129), COI (p=0.957) or NDI (p=0.363) and peak PELOD-2 score. There was a correlation between age (rho 0.102, p< 0.001) and number of comorbidities (rho 0.373, p< 0.001) and peak PELOD-2 scores. There was a difference between median PELOD-2 scores for varying races and insurance types (p< 0.001). Conclusions: There was no association between neighborhood resources and peak PELOD-2 scores; however, there was a difference in mean PELOD-2 scores across varying races and insurance types. This indicates a need for more analyses to evaluate the association between neighborhood indices and peak PELOD-2 scores when accounting for other sociodemographic variables.