Predicting pediatric severe asthma exacerbations: an administrative claims-based predictive model.

ABSTRACT:

Objective

Previous machine learning approaches fail to consider race and ethnicity and social determinants of health (SDOH) to predict childhood asthma exacerbations. A predictive model for asthma exacerbations in children is developed to explore the importance of race and ethnicity, rural-urban commuting area (RUCA) codes, the Child Opportunity Index (COI), and other ICD-10 SDOH in predicting asthma outcomes.

Methods

Insurance and coverage claims data from the Arkansas All-Payer Claims Database were used to capture risk factors. We identified a cohort of 22,631 children with asthma aged 5-18 years with 2 years of continuous Medicaid enrollment and at least one asthma diagnosis in 2018. The goal was to predict asthma-related hospitalizations and asthma-related emergency depa

SUBMITTER: Rezaeiahari M 

PROVIDER: S-EPMC11195303 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature