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Comorbidity patterns and socioeconomic inequalities in children under 15 with medical complexity: a population-based

Neus Carrilero1,2,3, Albert Dalmau-Bueno1, Anna García-Altés4,5,6

  • 1Agència de Qualitat i Avaluació Sanitàries de Catalunya (AQuAS), Barcelona, Spain.

BMC Pediatrics
|August 1, 2020
PubMed

Insights

Children with medical complexity (CMC) often come from lower socioeconomic positions, with four distinct comorbidity patterns identified: oncology, neurodevelopment, congenital/perinatal, and respiratory. These findings highlight the need for tailored care addressing both medical and socioeconomic needs.

Area of Science:

  • Pediatrics
  • Public Health
  • Health Services Research

Background:

  • Children with medical complexity (CMC) have diverse health needs and high healthcare utilization.
  • Socioeconomic position (SEP) is a known factor affecting CMC.
  • Understanding comorbidity patterns and SEP inequalities is crucial for optimizing care.

Purpose of the Study:

  • To describe the pathologic patterns of CMC.
  • To analyze socioeconomic inequalities within these patterns.
  • To inform healthcare management and service planning for CMC.

Main Methods:

  • Cross-sectional study using administrative data from Catalonia (2016).
  • Latent Class Analysis (LCA) to identify multimorbidity classes in CMC.
  • Analysis of socioeconomic position (SEP) and Adjusted Morbidity Groups (GMA).

Main Results:

  • 71% of CMC had parents with low employment or income (<€18,000).
  • Four comorbidity classes identified: oncology (36.0%), neurodevelopment (13.7%), congenital/perinatal (19.8%), and respiratory (30.5%).
  • Significant associations found between SEP and all comorbidity classes, with higher odds for lower SEP children.

Conclusions:

  • Four distinct comorbidity patterns exist in CMC.
  • A high proportion of CMC children belong to lower socioeconomic groups across all patterns.
  • Results support tailored multidisciplinary teams and holistic care considering socioeconomic vulnerability.
Abstract

Keywords:
Administrative dataChildComorbidityHealth inequalitiesLatent class analysisMedical complexitySocioeconomic factors

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