Work

Bivariate Copula‐Based Regression for Joint Modeling of Healthcare Visits

Year: 2025

Type: article

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.

Source: Health Economics

DOI: 10.1002/hec.70059

Authors: Giampiero Marra, Rosalba Radice

Institutions: University College London, University of London, City, University of London, City St George's, University of London

Language: English

Topic: Healthcare Policy and Management

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: hybrid

Estimated APC paid: $4,050