Bivariate Copula-Based Regression for Joint Modeling of Healthcare Visits
Marra, G. & Radice, R.
ORCID: 0000-0002-6316-3961 (2025).
Bivariate Copula-Based Regression for Joint Modeling of Healthcare Visits.
Health Economics,
doi: 10.1002/hec.70059
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.
| Publication Type: | Article |
|---|---|
| Additional Information: | © 2025 The Author(s). Health Economics published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Publisher Keywords: | additive predictor, copula regression, count data, dependence, healthcare utilization, unobserved heterogeneity |
| Subjects: | H Social Sciences > HN Social history and conditions. Social problems. Social reform R Medicine > RA Public aspects of medicine R Medicine > RT Nursing |
| Departments: | Bayes Business School Bayes Business School > Faculty of Actuarial Science & Insurance |
| SWORD Depositor: |
Available under License Creative Commons Attribution.
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