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Related Experiment Video

Updated: Oct 9, 2025

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Generalized Zero-Adjusted Models to Predict Medical Expenditures.

Xin Xu1, Tao Ye2, Dongxiao Chu1

  • 1School of Finance, Capital University of Economics and Business, Beijing 100070, China.

Computational Intelligence and Neuroscience
|December 23, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces generalized zero-adjusted models for analyzing elderly healthcare expenditure, finding the zero-adjusted generalized gamma model best predicts zero costs. Key factors influencing spending include age, health, and income, but not insurance.

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Area of Science:

  • Health Economics
  • Biostatistics
  • Econometrics

Background:

  • Elderly medical expenditure data are often semicontinuous, right-skewed, and have many zero values.
  • Traditional regression models struggle with these data characteristics, potentially leading to inaccurate standard errors and significance overestimation.
  • Existing zero-adjusted models may not adequately address the right-skewness of non-zero expenditures.

Purpose of the Study:

  • To generalize conventional zero-adjusted models to better handle right-skewed healthcare expenditure data in the elderly.
  • To identify the most effective model for accurately predicting the probability of zero medical costs.
  • To determine the key predictors influencing both the probability of zero expenditure and the amount of non-zero expenditure.

Main Methods:

  • Development of generalized zero-adjusted models, encompassing zero-adjusted Weibull, gamma, inverse Gaussian, and Tobit models.
  • Application of these models to data from the Chinese Longitudinal Healthy Longevity Survey.
  • Model selection based on Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and deviance.

Main Results:

  • The zero-adjusted generalized gamma model demonstrated superior performance in predicting zero medical costs.
  • Age, health status, chronic diseases, household income, and residence were identified as significant predictors of medical expenditure.
  • Health insurance status was found to be not significant in influencing medical expenditure.

Conclusions:

  • Generalized zero-adjusted models offer a flexible and effective approach for analyzing complex healthcare expenditure data.
  • The zero-adjusted generalized gamma model is recommended for predicting zero healthcare costs in elderly populations.
  • Understanding the determinants of healthcare spending is crucial for policy development, with specific demographic and socioeconomic factors playing key roles.