BioScience Trends. 2018;12(1):87-93. (DOI: 10.5582/bst.2017.01244)

Exploring the determinants that influence end-of-life hospital costs of the elderly in Shanghai, China.

Li F, Zhu BF, He ZM, Zhang XX, Wang CY, Wang LN, Song PP, Ding LL, Jin CL


The aim of this study was to use data from the Information Center of the Shanghai Municipal Commission of Health and Family Planning (SMCHFP) to determine the factors affecting end-of-life hospital costs of patients. A total number of 43,806 decedents who died in medical facilities in 2015 were examined. These individuals, accounted for 34.85% of all deaths in 2015 in Shanghai. Descriptive analysis and multiple linear regression analysis were performed using STATA 13.0. Results indicated that 88.94% of the decedents who died in medical facilities were over age 60. Males accounted for 55.57% of decedents, and the insured were mostly covered by Urban Employee Basic Medical Insurance (UEBMI) (81.93%). Cancer and circulatory disease were the main causes of death, causing 34.53% and 26.19% of deaths. Hospital costs were higher for males (male vs. female: 9,013 USD vs. 7,844 USD), individuals insured by UEBMI (8,784 USD), and individuals with cancer (10,156USD). Twenty-ninepoint-zero-three percent of admissions occurred in the month before death and accounted for 37.82% of costs. Multiple linear regression analysis indicated that hospital costs were correlated with gender, cause of death (cancer, circulatory disease, or respiratory disease), time-to-death, insurance schemes, level of medical facilities, and length of stay (LOS) (p < 0.05 for all). After controlling for other factors, age was not a significant factor (p > 0.05). A proximity-to-death (PTD) phenomenon was evident in Shanghai. This study suggested that the PTD should be considered when predicting medical cost. Primary medical care should be enhanced and gaps in insurance coverage should be reduced to improve the efficiency and equity of medical funding. More attention should be paid to the population with a heavier disease burden.

KEYWORDS: Proximity to death, hospital costs, multiple linear regression analysis

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