Neighborhood Child Opportunity and Individual-Level Pediatric Acute Care Use and Diagnoses

Abstract

: media-1vid110.1542/5751513300001PEDS-VA_2017-2309Video Abstract OBJECTIVES: Although health care providers and systems are increasingly interested in patients' nonmedical needs as a means to improve health, little is known about neighborhood conditions that contribute to child health problems. We sought to determine if a novel, publicly available measure of neighborhood context, the Child Opportunity Index, was associated with pediatric acute care visit frequency and diagnoses.

Methods: This cross-sectional study included San Francisco residents <18 years of age with an emergency department and/or urgent care visit to any of 3 medical systems (N = 47 175) between 2007 and 2011. Hot-spot analysis was used to compare the spatial distribution of neighborhood child opportunity and income. Generalized estimating equation logistic regression models were used to examine independent associations between neighborhood child opportunity and frequent acute care use (≥4 visits per year) and diagnosis group after adjusting for neighborhood income and patient age, sex, race and/or ethnicity, payer, and health system.

Results: Neighborhood child opportunity and income had distinct spatial distributions, and we identified different clusters of high- and low-risk neighborhoods. Children living in the lowest opportunity neighborhoods had significantly greater odds of ≥4 acute care visits per year (odds ratio 1.33; 95% confidence interval 1.03-1.73) compared with those in the highest opportunity neighborhoods. Neighborhood child opportunity was negatively associated with visits for respiratory conditions, asthma, assault, and ambulatory care-sensitive conditions but positively associated with injury-related visits.

Conclusions: The Child Opportunity Index could be an effective tool for identifying neighborhood factors beyond income related to child health.

FIGURE 1

A, Spatial distribution and clustering of the Child Opportunity Index by census tract quintiles in San Francisco, California (2007–2011). B, Spatial distribution and clustering of the median household income by census tract quintiles in San Francisco, California (2007–2011). The clusters of high or low values (black lines) were calculated by using the local Getis-Ord Gi* statistic. Clusters of high values (hot spots) are tracts with high values that are surrounded by tracts with high values; clusters of low values (cold spots) are tracts with low values that are surrounded by tracts with low values. A P value < .05 indicates a probability that is less than our α level that the observed concentration of high (or low) values is the result of a random spatial distribution.