Inequalities in Neighborhood Child Asthma Admission Rates and Underlying Community Characteristics in One US County

• Published in: The Journal of pediatrics 2013-08, Vol.163 (2), p.574-580.e1
• Main Authors: Beck, Andrew F., MD, MPH, Moncrief, Terri, MD, MS, Huang, Bin, PhD, Simmons, Jeffrey M., MD, MS, Sauers, Hadley, BA, Chen, Chen, PhD, Kahn, Robert S., MD, MPH
• Format: Article
• Language: English
• Subjects: Adolescent; asthma; Asthma - epidemiology; Asthma - therapy; Child; Child, Preschool; children; Cohort Studies; environmental factors; Female; geocoding; Healthcare Disparities - statistics & numerical data; Humans; Infant; linear models; Male; Ohio; Patient Admission - statistics & numerical data; Pediatrics; population density; Prospective Studies; Socioeconomic Factors
• ISSN: 0022-3476; 1097-6833
• DOI: 10.1016/j.jpeds.2013.01.064

Objectives To characterize variation and inequalities in neighborhood child asthma admission rates and to identify associated community factors within one US county. Study design This population-based prospective, observational cohort study consisted of 862 sequential child asthma admissions among 167 653 eligible children ages 1-16 years in Hamilton County, Ohio. Admissions occurred at a tertiary-care pediatric hospital and accounted for nearly 95% of in-county asthma admissions. Neighborhood admission rates were assessed by geocoding addresses to city- and county-defined neighborhoods. The 2010 US Census provided denominator data. Neighborhood admission distribution inequality was assessed by the use of Gini and Robin Hood indices. Associations between neighborhood rates and socioeconomic and environmental factors were assessed using ANOVA and linear regression. Results The county admission rate was 5.1 per 1000 children. Neighborhood rates varied significantly by quintile: 17.6, 7.7, 4.9, 2.2, and 0.2 admissions per 1000 children ( P < .0001). Fifteen neighborhoods containing 8% of the population had zero admissions. The Gini index of 0.52 and Robin Hood index of 0.38 indicated significant inequality. Neighborhood-level educational attainment, car access, and population density best explained variation in neighborhood admission rates (R2 = 0.55). Conclusion In a single year, asthma admission rates varied 88-fold across neighborhood quintiles in one county; a reduction of the county-wide admission rate to that of the bottom quintile would decrease annual admissions from 862 to 34. A rate of zero was present in 15 neighborhoods, which is evidence of what may be attainable.