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Bivariate Copula-Based Regression for Joint Modeling of Healthcare Visits.

Giampiero Marra1, Rosalba Radice2

  • 1Department of Statistical Science, University College London, London, UK.

Health Economics
|November 15, 2025
PubMed
Summary

This study models doctor and non-doctor healthcare visits together, revealing how factors like age and income influence seeking care from different providers. Understanding these interdependencies improves insights into healthcare access and utilization patterns.

Keywords:
additive predictorcopula regressioncount datadependencehealthcare utilization datapredictionunobserved heterogeneity

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Area of Science:

  • Health Services Research
  • Econometrics
  • Biostatistics

Background:

  • Doctor and non-doctor visit frequencies are crucial for understanding healthcare access, utilization, and patient behavior.
  • Analyzing these visit types separately can obscure important interdependencies and lead to incomplete conclusions regarding healthcare-seeking patterns.

Purpose of the Study:

  • To jointly model doctor and non-doctor visits using a flexible statistical framework.
  • To identify key demographic, socioeconomic, and health-related factors influencing both types of healthcare utilization.
  • To capture shared unobserved factors and the influence of one visit type on another.

Main Methods:

  • Employed a copula additive distributional regression framework for joint modeling.
  • Allowed distributional parameters (location, scale, dependence) to vary with covariates via additive predictors.
  • Utilized data from the 2012 Medical Expenditure Panel Survey for analysis.

Main Results:

  • Identified significant determinants of physician and non-physician visits, including age, income, and health status.
  • Demonstrated the ability to model shared unobserved heterogeneity between visit types.
  • Quantified how changes in one type of healthcare utilization impact the other.

Conclusions:

  • Joint modeling provides a deeper understanding of healthcare behavior than separate analyses.
  • The framework effectively captures complex interdependencies in healthcare seeking.
  • Findings offer valuable insights for healthcare policy and resource allocation.