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Using Address Information to Identify Hardships Reported by Families of Children Hospitalized With Asthma
Katherine A Auger1, Robert S Kahn2, Jeffrey M Simmons1
1Division of Hospital Medicine, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio; James M. Anderson Center for Health Systems Excellence, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
Insights
Neighborhood data can effectively identify children with asthma facing socioeconomic hardship. This approach offers a practical way to assess financial strain and healthcare access for better resource allocation.
Area of Science:
- Pediatric Asthma Research
- Health Services Research
- Socioeconomic Determinants of Health
Background:
- Socioeconomic hardship is prevalent in children hospitalized for asthma, yet difficult to measure directly.
- Geographic information (residence location) is universally available and can serve as a proxy for socioeconomic status.
- Existing methods for assessing hardship lack practical, widespread applicability.
Purpose of the Study:
- To investigate the correlation between neighborhood-level socioeconomic data and family-reported hardships in children hospitalized with asthma.
- To determine if readily available area-level data can serve as an efficient proxy for individual family socioeconomic status.
- To assess the predictive power of neighborhood characteristics for identifying families in need.
Main Methods:
- Caregivers of 774 children hospitalized for asthma completed surveys on income, financial strain, and primary care access.
- Residential addresses were geocoded and linked to US Census data at zip code, census tract, and block group levels.
- Correlations were quantified using Kendall rank correlation, and predictive abilities were assessed with receiver operating characteristic curves.
Main Results:
- Neighborhood median household income correlated moderately with reported household income.
- Neighborhood poverty levels were similarly related to reported financial strain.
- Neighborhood vehicle availability showed a weak correlation with reported primary care access; zip code data performed comparably to finer-grained neighborhood data.
Conclusions:
- Universally available neighborhood socioeconomic data can efficiently identify children and families experiencing hardship.
- Systematic screening using area-level socioeconomic measures can improve the efficiency of resource allocation for vulnerable populations.
- This approach provides a scalable method for healthcare systems to identify and support families facing socioeconomic challenges.
Objective:
Socioeconomic hardship is common among children hospitalized for asthma but often not practically measurable. Information on where a child resides is universally available. We sought to determine the correlation between neighborhood-level socioeconomic data and family-reported hardships.
Methods:
Caregivers of 774 children hospitalized with asthma answered questions regarding income, financial strain, and primary care access. Addresses were geocoded and linked to zip code-, census tract-, and block group-level (neighborhood) data from the US Census. We then compared neighborhood median household income with family-reported household income; percentage of neighborhood residents living in poverty with family-reported financial strain; and percentage of neighborhood households without an available vehicle with family-reported access to primary care. We constructed heat maps and quantified correlations using Kendall rank correlation coefficient. Receiver operator characteristic curves were used to assess predictive abilities of neighborhood measures.
Results:
The cohort was 57% African American and 73% publicly-insured; 63% reported income <$30,000, 32% endorsed ≥4 financial strain measures, and 38% reported less than adequate primary care access. Neighborhood median household income was significantly and moderately correlated with and predictive of reported household income; neighborhood poverty was similarly related to financial strain; neighborhood vehicle availability was weakly correlated with and predictive of primary care access. Correlations and predictions provided by zip code measures were similar to those of census tract and block group.
Conclusions:
Universally available neighborhood information might help efficiently identify children and families with socioeconomic hardships. Systematic screening with area-level socioeconomic measures has the potential to inform resource allocation more efficiently.
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