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Patterns of Children With Complex Chronic Conditions: A Latent Class Analysis.

Eyal Cohen1,2, Maria Osipovich3, Hallie Benjamin4

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Summary

Children with complex chronic conditions (CCCs) can be classified into three distinct groups based on their health needs and technology dependence. These classifications predict future healthcare utilization and costs, aiding in resource allocation for pediatric care.

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Area of Science:

  • Pediatric Health Services Research
  • Health Informatics
  • Biostatistics

Background:

  • Complex chronic conditions (CCCs) affect a significant pediatric population.
  • Understanding heterogeneity in CCCs is crucial for predicting healthcare needs.
  • Previous classifications may not fully capture the nuances of care utilization.

Purpose of the Study:

  • To identify distinct empirical classes of children with CCCs.
  • To evaluate if these identified classes predict future healthcare use and spending.
  • To inform targeted healthcare resource allocation for pediatric populations with CCCs.

Main Methods:

  • Latent class analysis of Medicaid claims data (2017-2019) for children aged 1-18 with CCCs.
  • Inclusion of demographic factors, clinical characteristics, and 2017 healthcare utilization.
  • Negative binomial and logistic regression models to assess 2018-2019 healthcare spending and use.

Main Results:

  • A 3-class solution emerged from 185,672 children with CCCs.
  • Class 1 (9.1%): High neuro-disability, high technology dependence, high multimorbidity.
  • Class 2 (14.8%): High neuro-disability, low technology dependence.
  • Class 3 (76.0%): Low neuro-disability, low technology dependence.
  • Classes 1 and 2 exhibited significantly higher healthcare spending compared to Class 3 (RR 6.9 and 2.5, respectively).
  • Inpatient and outpatient specialist services drove costs in Class 1; outpatient drugs, specialists, and mental health in Class 2.

Conclusions:

  • Children with CCCs can be meaningfully categorized into distinct classes.
  • These classes are identifiable using readily available data.
  • The identified classes demonstrate differential patterns of future healthcare use and associated costs.