BAYESIAN SPATIAL ANALYSIS OF CHRONIC DISEASES IN ELDERLY CHINESE PEOPLE USING A STAR MODEL

Gao Ping () and Hasegawa Hikaru ()
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Gao Ping: Graduate School of Economics and Business Hokkaido University, ; Hokkaido, ; Japan
Hasegawa Hikaru: Department of Economics, Hokkaido University, ; Hokkaido, ; Japan

Statistics in Transition New Series, 2018, vol. 19, issue 4, 645-670

Abstract: Chronic diseases have become important factors affecting the health of elderly Chinese people. Because the prevalence of chronic diseases varies among the provinces, it is necessary to understand the spatial effects on these diseases, as well as their relationships with potential risk factors. This study applies a structured additive regression model and the R2BayesX package to conduct a Bayesian analysis. The data are taken from the 2000, 2006, and 2010 Chinese Urban and Rural Elderly Population Surveys. The findings are as follows: (1) the following covariates have considerable effects on chronic diseases in general, and on specific chronic diseases (hypertension and heart disease) (in descending order): census register (rural or urban), gender, smoking, drinking, province, time, age, cultural activities, years of education, and sports activities; (2) the effect of marital status is negligible; (3) province is a critical factor, with the highest spatial effect appearing in two types of provinces: economically developed provinces, and economically backward provinces; and (4) time also has considerable effects. Based on these findings, the government should further strengthen its investment in rural areas and economically backward provinces as a cost-effective intervention, and should educate the population on the harmful effects of smoking and drinking alcohol on health.

Keywords: Bayesian analysis; Markov chain Monte Carlo (MCMC); R2BayesX; Spatial effect; Structured additive regression (STAR) models. (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:19:y:2018:i:4:p:645-670:n:9

DOI: 10.21307/stattrans-2018-034

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