City Research Online

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:	This is the peer reviewed version of the following article: Marra, G. & Radice, R. (2025). Bivariate Copula-Based Regression for Joint Modeling of Healthcare Visits. Health Economics, which is to be published in final form at https://onlinelibrary.wiley.com/journal/10991050. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
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:	Symplectic Administrator

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