Bivariate Copula‐Based Regression for Joint Modeling of Healthcare Visits

TL;DR

This paper introduces a statistical method to jointly model doctor and non-doctor healthcare visits, revealing how factors like age and income influence healthcare behavior.

Contribution

The novel use of a copula-based regression framework allows joint modeling of healthcare visits with flexible covariate effects on distributional parameters.

Findings

Key determinants of healthcare visits include age, income, and health status.

The model captures interdependencies between doctor and non-doctor visits.

Shared unobserved heterogeneity is effectively modeled, improving understanding of healthcare utilization.

Abstract

Doctor and non‐doctor visit frequencies are key indicators of healthcare access, utilization and individual health‐seeking behavior. While doctor visits reflect engagement with formal medical services, non‐doctor visits, such as to nurses, physiotherapists or alternative providers, offer insights into patient preferences and system adaptability. Modeling these outcomes separately can hide relevant interdependencies and hence lead to incomplete conclusions. To address this, we employ a copula additive distributional regression framework to jointly model doctor and non‐doctor visits as flexible functions of demographic, socioeconomic and health‐related covariates. The estimation approach allows all the distributional parameters, including location, scale and the dependence structure, to vary with covariates via additive predictors. Application of the model to data from the 2012 Medical Expenditure Panel Survey reveals key determinants of physician and non‐physician visits, such as age, income and health status. Importantly, the method allows for the modeling of shared unobserved heterogeneity and effectively captures how changes in one type of utilization influence the other, thereby yielding a deeper understanding of healthcare behavior.

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