Modelling Healthcare Demand Count Data with Excessive Zeros and Overdispersion

Myung Hyun Park and Joseph H. T. Kim

Global Economic Review, 2021, vol. 50, issue 4, 358-381

Abstract: In healthcare economics count datasets often exhibit excessive zeros or right-skewed tails. When covariates are available, such datasets are typically modelled using the zero-inflated (ZI) or finite mixture (FM) regression models. However, neither model performs adequately when the dataset has both excessive zeros and a long tail, which is often the case in practice. In this paper we combine these two models to create a more flexible, versatile class of ZIFM models. With this model we perform a comprehensive analysis on the number of visits to a physician’s office using the US healthcare demand dataset that has been used in numerous healthcare studies in the literature. After comparing to other existing models which have been reported to perform well on this dataset, we find that the ZIFM model substantially outperforms alternative models. In addition, the model offers a new interpretation that is in contrast to previous empirical findings regarding the factors associated with the demand for the physicians, which can shed a fresh light on the healthcare utilisation policies.

Date: 2021
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DOI: 10.1080/1226508X.2021.2004907

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