Precision risk assessment for pediatric hospitalization using address-level data in Cincinnati, Ohio

Area of Science:

• Environmental Health
• Public Health
• Health Informatics

Background:

• Persistent child health disparities necessitate research with enhanced spatial precision.
• Traditional area-level analyses may not capture localized health risks effectively.

Purpose of the Study:

• To link socio-environmental data to healthcare data for precise pediatric hospitalization risk assessment at the address level.
• To develop and validate models for identifying high-risk residential addresses for children.

Main Methods:

• Linked hospitalization data (2016-2022) with parcel-level housing, crime, census, and eviction data in Cincinnati, Ohio.
• Utilized generalized random forest models to estimate address-level and birth-adjusted hospitalization risks.
• Assessed model performance using ROC-AUC and PR-AUC, and evaluated fairness by racial demographics.

Main Results:

• Successfully matched 81.5% of hospitalizations to residential addresses.
• Models accurately characterized high-risk addresses, with housing violations, violent crime, and property value as key predictors.
• The birth-adjusted model demonstrated high performance and moderate agreement with the primary hospitalization risk model.

Conclusions:

• Address-level modeling and multiscale data integration advance precision population health beyond traditional methods.
• This scalable approach precisely identifies pediatric health risks, informing targeted clinical and policy interventions.
• Future work includes geographic expansion, stakeholder engagement, and patient-level validation.