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Identifying Unique Subgroups of High-Cost Patients With Schizophrenia: A Population-Based Study Using Latent Class

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This study identified five distinct subgroups of high-cost schizophrenia patients using administrative health data. Understanding these patient profiles can improve care and resource allocation in healthcare systems.

Keywords:
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Area of Science:

  • Health Services Research
  • Psychiatry
  • Data Science

Background:

  • Schizophrenia patient care is highly variable, necessitating tailored healthcare support.
  • Limited research exists on the heterogeneity of patient needs within schizophrenia.
  • High healthcare costs associated with schizophrenia demand efficient resource allocation.

Purpose of the Study:

  • To identify patient subgroups among high-cost individuals with schizophrenia.
  • To inform targeted interventions for improved patient outcomes.
  • To guide efficient resource allocation within healthcare systems.

Main Methods:

  • Retrospective analysis of administrative health data from Alberta, Canada (2017).
  • Inclusion of costs from inpatient, outpatient, emergency department, and drug expenditures.
  • Latent class analysis applied to 1659 high-cost adult schizophrenia patients.

Main Results:

  • Five distinct patient subgroups were identified based on clinical profiles.
  • Subgroups include: young high-needs males, actively managed middle-aged patients, elderly patients with comorbidities and polypharmacy, and unstably housed males and females with varying treatment rates and acute care utilization.
  • These classifications highlight diverse needs related to age, housing stability, treatment engagement, and healthcare utilization.

Conclusions:

  • A data-driven taxonomy of high-cost schizophrenia patients can inform policy.
  • Subgroup-specific interventions may enhance care quality and reduce healthcare spending.
  • This approach supports more efficient and effective resource management for schizophrenia care.