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Related Experiment Videos

An event count model for studying health services utilization

C Chi1

  • 1Department of Public Health, Oregon State University, Corvallis 97331-6406, USA. chunhuei.chi@orst.edu

Medical Care
|December 22, 1998
PubMed
Summary

The Generalized Event Count model offers a theory-driven approach to understanding health services utilization. This model effectively analyzes event counts, showing regular medical care and Chinese medicine use predict physician visits.

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Area of Science:

  • Health Services Research
  • Behavioral Science
  • Biostatistics

Background:

  • Health services utilization is often studied using various statistical models.
  • Existing models may not fully align with behavioral assumptions of utilization.
  • A need exists for theory-driven models that capture the event count nature of utilization.

Purpose of the Study:

  • To conceptualize health services utilization behavior as event counts.
  • To present the Generalized Event Count model as an alternative tool for studying health services utilization.
  • To demonstrate the model's consistency with behavioral theory and assumptions.

Main Methods:

  • Examined the Generalized Event Count model's assumptions for consistency with behavioral theory and data distributions.

Related Experiment Videos

  • Applied the Generalized Event Count model to a 1988 household interview dataset of Chinese-Americans in Boston.
  • Analyzed ambulatory care utilization patterns within this specific population.

    • Main Results:

    • The Generalized Event Count model identified regular source of medical care and use of Chinese medicine as significant predictors of physician visits.
    • The model confirmed that ambulatory visits within individuals are often correlated.
    • Demonstrated the model's capability in analyzing correlated event count data.

    Conclusions:

    • The Generalized Event Count model aligns better with health services utilization behavioral assumptions compared to other models.
    • The model efficiently utilizes utilization data for estimation, offering broad applicability.
    • This approach is particularly valuable for analyzing cross-sectional utilization data and understanding health-seeking behaviors.