Revealing the racial and spatial disparity in pediatric asthma: A Kansas City case study.

Document Type

Article

Publication Date

1-2022

Identifier

DOI: 10.1016/j.socscimed.2021.114543

Abstract

Black and other socially disadvantaged children are disproportionately burdened by high rates of pediatric asthma. Intraurban variation in environmental risk factors and limited access to high-resolution health data make it difficult to identify vulnerable patients, communities, or the immediate exposures that may contribute to pediatric asthma exacerbation. This article presents a novel, interdisciplinary health disparities research and intervention strategy applied to the problem of pediatric asthma in Kansas City. First, address-level electronic health records from a major children's hospital in the Kansas City region are used to map the distribution of asthma encounters in 2012 at a high spatial resolution. Census tract Environmental Justice Screening Method (EJSM) indicators are then developed to scan for patterns in both the population health risks and vulnerabilities that may contribute to the burden of asthma in different communities. A Bayesian Profile Regression cluster analysis is used to systematically explore the complex relationships between census tract EJSM indicators and pediatric asthma incidence rates, helping to identify population characteristics and risk factors associated with both high and low rates of pediatric asthma throughout the region. The EJSM scanning exercise and BPR analysis demonstrate that each community faces a distinct set of risks and vulnerabilities that can contribute to the rate of acute pediatric asthma acute care encounters, providing targets for research and intervention. It is clear, however, that different forms of social disadvantage are driving high rates of pediatric asthma, which is closely tied to uneven development patterns and racial residential segregation. The results provide a starting point for designing place-based health disparities research and intervention strategies catered to the unique needs of vulnerable patients and communities; disparities-focused research and intervention strategies that leverage local knowledge and resources through community-based practices.

Journal Title

Social science & medicine (1982)

Volume

292

First Page

114543

Last Page

114543

Keywords

Asthma; Bayesian statistics; Data science; Geographic information science; Health disparities; Social determinants

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