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The Child Opportunity Index and Pediatric Hospitalizations: Are ZIP Codes Good Enough?
Alexander H Hogan1, Natalie Grills2, Matt Hall2
1Division of Hospital Medicine, Connecticut Children's, Hartford, CT; Department of Pediatrics, University of Connecticut School of Medicine, Farmington, CT.
Insights
Using ZIP codes for Child Opportunity Index (COI) analysis is acceptable for quintiles and percentiles. However, ZIP code COI z-scores may underestimate neighborhood opportunity's impact on pediatric ambulatory care sensitive condition hospitalizations.
Area of Science:
- Public Health
- Geospatial Health
- Health Disparities
Background:
- Neighborhood socioeconomic factors significantly influence child health outcomes.
- The Child Opportunity Index (COI) is a key metric for assessing neighborhood opportunity.
- Geographic unit of analysis (e.g., ZIP code vs. census tract) can impact health disparity research.
Purpose of the Study:
- To quantify misclassification of COI quintiles when using ZIP codes versus census tracts.
- To compare the association strength between COI and pediatric ambulatory care sensitive condition (ACSC) hospitalization rates using these two geographic levels.
Main Methods:
- Retrospective analysis of pediatric ACSC hospitalizations (2013-2018) from two Midwest children's hospitals.
- Geocoded patient addresses linked to COI 3.0 at both ZIP code and census tract levels.
- Misclassification defined as ≥2 quintile difference between ZIP code and census tract COI; geospatial and regression analyses performed.
Main Results:
- 8.1% of pediatric ACSC hospitalizations showed COI misclassification between ZIP codes and census tracts.
- No significant difference in ACSC hospitalization rate associations with COI quintiles or percentiles between geographies.
- Significant underestimation of the association between neighborhood opportunity and ACSC hospitalizations when using COI z-scores from ZIP codes compared to census tracts.
Conclusions:
- ZIP code-based COI using quintiles or percentiles is generally acceptable for correlational health disparity studies.
- COI z-scores derived from ZIP codes may attenuate the observed relationship between neighborhood opportunity and pediatric health outcomes.
- Accurate geographic unit selection is crucial for robust health equity research.
Objective:
To quantify the misclassification of Child Opportunity Index (COI) quintiles when using ZIP codes instead of census tracts and to compare the strength of associations with ambulatory care sensitive condition (ACSC) hospitalization rates when using COI linked to these 2 geographies.
Study Design:
This retrospective, cross-sectional study analyzed pediatric ACSC hospitalizations from 2 Midwest children's hospitals between 2013 and 2018. Patient home addresses were geocoded and linked to the COI 3.0 at ZIP code and census tract levels. COI scores were assessed as nationally-normed quintiles, percentiles, and z-scores. Misclassification was defined as ZIP code COI differing by ≥ 2 quintile levels from census tract quintile assignment. Geospatial and regression analyses assessed the impact of misclassification on associations with ACSC hospitalization rates.
Results:
There were 26 512 ACSC hospitalizations for youth from 2 metropolitan areas comprised of 604 943 children. Misclassification occurred in 8.1% of ACSC hospitalizations. Regression analyses indicated lower hospitalization rates for areas with higher opportunity. Between the census tract and ZIP code geographies, there was no significant difference in the percent change of ACSC hospitalization rates for every COI quantile increase when using COI quintiles (-21.7% vs -22.0%; P = .51) and percentiles (-1.2% vs -1.2%; P = .6); however, there was a significant difference between the geographies when using COI z-scores (-35.1% vs -28.5%; P < .001).
Conclusions:
Using ZIP codes to assign COI scores with the most common approaches - quintiles and percentiles - is likely acceptable for correlational analyses in large datasets. However, ZIP code COI scores may underestimate true associations between neighborhood opportunity and health outcomes when using z-scores.
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