Racialized economic segregation and potentially preventable hospitalizations among Medicaid/CHIP‐enrolled children

• Published in: Health services research 2023-06, Vol.58 (3), p.599-611
• Main Authors: Smith, Laura Barrie, O'Brien, Claire, Kenney, Genevieve M., Tabb, Loni Philip, Verdeflor, Alaisha, Wei, Keqin, Lynch, Victoria, Waidmann, Timothy
• Format: Article
• Language: English
• Subjects: Access control; Adolescent; Algorithms; Analysis; Asthma; Avoidable; Child; child and adolescent health; Child health; Child, Preschool; Children; Children & youth; Childrens health; CHIP; Demographic aspects; Economics; Extraction; Geographical variations; Government programs; Health aspects; health care disparities; Health care policy; Health disparities; health equity; High risk; Hispanic people; Hospital care; Hospitalization; Humans; Income; index of concentration at the extremes; Infant; Infant, Newborn; Managed care; Managed Care Programs; Medicaid; Medical care; Medical care (Private); Neighborhoods; Pediatrics; Postal codes; Poverty; Preventive medicine; Public health; Race; racial/ethnic differences in health and health care; Racialization; racialized economic segregation; Residential segregation; Rural areas; Segregation; Services; social determinants of health; Statistical analysis; United States
• ISSN: 0017-9124; 1475-6773
• DOI: 10.1111/1475-6773.14120

Objective To examine geographic variation in preventable hospitalizations among Medicaid/CHIP‐enrolled children and to test the association between preventable hospitalizations and a novel measure of racialized economic segregation, which captures residential segregation within ZIP codes based on race and income simultaneously. Data Sources We supplement claims and enrollment data from the Transformed Medicaid Statistical Information System (T‐MSIS) representing over 12 million Medicaid/CHIP enrollees in 24 states with data from the Public Health Disparities Geocoding Project measuring racialized economic segregation. Study Design We measure preventable hospitalizations by ZIP code among children. We use logistic regression to estimate the association between ZIP code‐level measures of racialized economic segregation and preventable hospitalizations, controlling for sex, age, rurality, eligibility group, managed care plan type, and state. Data Extraction Methods We include children ages 0–17 continuously enrolled in Medicaid/CHIP throughout 2018. We use validated algorithms to identify preventable hospitalizations, which account for characteristics of the pediatric population and exclude children with certain underlying conditions. Principal Findings Preventable hospitalizations vary substantially across ZIP codes, and a quarter of ZIP codes have rates exceeding 150 hospitalizations per 100,000 Medicaid‐enrolled children per year. Preventable hospitalization rates vary significantly by level of racialized economic segregation: children living in the ZIP codes that have the highest concentration of low‐income, non‐Hispanic Black residents have adjusted rates of 181 per 100,000 children, compared to 110 per 100,000 for children in ZIP codes that have the highest concentration of high‐income, non‐Hispanic white residents (p