Paper Reading Journals #2
Forecasting the Health Transition and Medical Expenditure of the Future Elderly in China: A Longitudinal Study Based on Markov Chain and Two Part Model
Basic Info
| Item | Content |
|---|---|
| Title | Forecasting the Health Transition and Medical Expenditure of the Future Elderly in China: A Longitudinal Study Based on Markov Chain and Two Part Model |
| Journal | Frontiers in Public Health |
| Author | Gao Y, Li J and Yuan X |
| Year Published | 2022 |
| Link | https://doi.org/10.3389/fpubh.2021.774140 |
| Geography of Study | China |
| Area of Study | Aging, Medical Expenditure forecasting |
Description
(Including data and methods)
Data: 2011 and 2013 CHARLS (China Health and Retirement Longitudinal Study) Method: This study utilize CHARLS dataset and two main models to help analyze health status traditions and medical expenditures of older adults (age 60 and above). Two main models: (1) Health Transition Model based on logistic regression and Markov Chain to estimate health state to state transition probability. (2) Two-Part Model (Probit Model & Linear Expenditure Equation) to estimate medical expenditure of the elderly.
Main Contribution
(Innovative method or Application)
An innovative approach utilize Markov Chain to estimate how elderly move between health states. It also provide quantitative evidence to health policy and healthcare management related to healthy aging including populational health states estimation and medical expenditure estimation.
Data Source
2011 and 2013 CHARLS (China Health and Retirement Longitudinal Study) n = 17,596 for 2011 CHARLS n = 18,416 for 2013 CHARLS
Favorite Thing
Markov Chain could be adopt into our healthcare system framework simulation to estimate the transition probability between different layers / healthcare facilities.