Cluster analysis of differences in medical economic burden among residents of different economic levels in Guangdong Province, China

BMC Health Serv Res. 2020 Oct 28;20(1):988. doi: 10.1186/s12913-020-05817-y.

Abstract

Background: This study compares and analyzes the differences of residents' medical economic burden in different economic levels, explores the factors for improving the equity of health services in Guangdong, China.

Methods: Cluster analysis was carried out in 20 cities of Guangdong Province by taking 7 key factors on the equity of health services as indicators. Seven key factors were collected from Guangdong Statistical Yearbook 2017 and the Sixth National Population Census. R-type clustering was used to reduce the dimensionality of 7 candidate variables through similarity index. Q-type clustering was used to classify 20 cities in Guangdong Province.

Results: The cluster analysis divided Guangdong Province into three regions with different medical economic burden. The greater the proportion of the elderly over 65 years old, the greater the proportion of health care expenditure to per capita consumer expenditure of residents, and the heavier the medical economic burden. On average, 10.75% of the general budget expenditure of each city in Guangdong Province is spent on health care.

Conclusions: The lower per capita GDP, the higher proportion of the elderly over 65 years old and the lack of medical technicians are risk factors for the heavier medical burden of the residents and the fairness of health services. While increasing the health expenditure, the government needs to further complete the reform of the medical and health system, improve the efficiency of the medical system and curb the rapid rise of absolute health expenditures of individuals, which can reduce the economic burden of residents' medical care.

Keywords: Aging; Different economic levels; Equity; Medical economic burden.

MeSH terms

  • Aged
  • China / epidemiology
  • Cities
  • Cluster Analysis
  • Economics, Medical*
  • Health Expenditures*
  • Humans