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      Generalized Zero-Adjusted Models to Predict Medical Expenditures

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      1 , 2 , 1 ,
      Computational Intelligence and Neuroscience
      Hindawi

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          Abstract

          In healthcare research, medical expenditure data for the elderly are typically semicontinuous and right-skewed, which involve a point mass at zero and may exhibit heteroscedasticity. The problem of a substantial proportion of zero values prevents traditional regression techniques based on the Gaussian, gamma, or inverse Gaussian distribution, which may lead to understanding the standard errors of the parameters and overestimating their significance. A common way to counter the problem is using zero-adjusted models. However, due to the right-skewness in the nonzeros' response, conventional zero-adjusted models such as zero-adjusted gamma, zero-adjusted Inverse Gaussian, and classic Tobit may not perform well. Here, we firstly generalize those three types of the conventional zero-adjusted model to solve the problem of right-skewness in health care. The generalized zero-adjusted models are very flexible and include the zero-adjusted Weibull, zero-adjusted gamma, zero-adjusted inverse Gaussian, and classic Tobit models as their special cases. Using the Chinese Longitudinal Healthy Longevity Survey, we find that, according to the AIC, SBC, and deviance criteria, the zero-adjusted generalized gamma model is the best one of these generalized models to predict the odds of zero cost accurately. In order to depict the predictors affecting the amount expenditure, we further discuss the situations where the mean, dispersion of a nonzero amount expenditure and model the probability of a zero amount of ZAGG in terms of predictor variables using suitable link functions, respectively. Our analysis shows that age, health, chronic diseases, household income, and residence are the main factors influencing the medical expenditure for the elderly, but the insurance is not significant. To the best of our knowledge, little study focused on these situations, and this is the first time.

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          Estimating the Dimension of a Model

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            Revisiting the Behavioral Model and Access to Medical Care: Does it Matter?

            The Behavioral Model of Health Services Use was initially developed over 25 years ago. In the interim it has been subject to considerable application, reprobation, and alteration. I review its development and assess its continued relevance.
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              Estimation of Relationships for Limited Dependent Variables

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                Author and article information

                Contributors
                Journal
                Comput Intell Neurosci
                Comput Intell Neurosci
                cin
                Computational Intelligence and Neuroscience
                Hindawi
                1687-5265
                1687-5273
                2021
                13 December 2021
                : 2021
                : 5874275
                Affiliations
                1School of Finance, Capital University of Economics and Business, Beijing 100070, China
                2School of Banking and Finance, University of International Business and Economics, Beijing 100029, China
                Author notes

                Academic Editor: Daqing Gong

                Author information
                https://orcid.org/0000-0002-0461-6166
                https://orcid.org/0000-0002-9382-7554
                https://orcid.org/0000-0002-1212-7653
                Article
                10.1155/2021/5874275
                8687810
                34938328
                e5f42522-b6d5-4ba0-9db1-5560abe1223b
                Copyright © 2021 Xin Xu et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 22 October 2021
                : 17 November 2021
                Categories
                Research Article

                Neurosciences
                Neurosciences

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