Literature DB >> 29225282

The impact of population aging on medical expenses: A big data study based on the life table.

Changying Wang1, Fen Li1, Linan Wang1, Wentao Zhou2, Bifan Zhu1, Xiaoxi Zhang1, Lingling Ding3, Zhimin He3, Peipei Song4, Chunlin Jin1.   

Abstract

This study shed light on the amount and structure of utilization and medical expenses on Shanghai permanent residents based on big data, simulated lifetime medical expenses through combining of expenses data and life table model, and explored the dynamic pattern of aging on medical expenditures. 5 years were taken as the class interval, the study collected and did the descriptive analysis on the medical services utilization and medical expenses information for all ages of Shanghai permanent residents in 2015, simulated lifetime medical expenses by using current life table and cross-section expenditure data. The results showed that in 2015, outpatient and emergency visits per capita in the elderly group (aged 60 and over) was 4.1 and 4.5 times higher than the childhood group (aged 1-14), and the youth and adult group (aged 15-59); hospitalization per capita in the elderly group was 3.0 and 3.5 times higher than the childhood group, and the youth and adult group. People survived in the 60-64 years group, their expected whole medical expenses (105,447 purchasing power parity Dollar) in the rest of their lives accounted for 75.6% of their lifetime. A similar study in Michigan, US showed that the expenses of the population aged 65 and over accounted for 1/2 of lifetime medical expenses, which is much lower than Shanghai. The medical expenses of the advanced elderly group (aged 80 and over) accounted for 38.8% of their lifetime expenses, including 38.2% in outpatient and emergency, and 39.5% in hospitalization, which was slightly higher than outpatient and emergency. There is room to economize in medical expenditures of the elderly people in Shanghai, especially controlling hospitalization expenses is the key to saving medical expenses of elderly people aged over 80 and over.

Entities:  

Keywords:  Population aging; big data; life table; lifetime medical expenses

Mesh:

Year:  2017        PMID: 29225282     DOI: 10.5582/bst.2017.01243

Source DB:  PubMed          Journal:  Biosci Trends        ISSN: 1881-7815            Impact factor:   2.400


  5 in total

1.  Multi-dimensional vulnerability analysis on catastrophic health expenditure among middle-aged and older adults with chronic diseases in China.

Authors:  Wenqing Miao; Xiyu Zhang; Wanxin Tian; Yuze Li; Baoguo Shi; Bing Wu; Yongqiang Lai; Zhipeng Huang; Qi Xia; Huiqi Yang; Fan Ding; Linghan Shan; Ling Xin; Jingying Miao; Chenxi Zhang; Ye Li; Xiaodong Li; Qunhong Wu
Journal:  BMC Med Res Methodol       Date:  2022-05-25       Impact factor: 4.612

Review 2.  A Review of the Role and Challenges of Big Data in Healthcare Informatics and Analytics.

Authors:  Banan Jamil Awrahman; Chia Aziz Fatah; Mzhda Yasin Hamaamin
Journal:  Comput Intell Neurosci       Date:  2022-09-29

Review 3.  Brief introduction of medical database and data mining technology in big data era.

Authors:  Jin Yang; Yuanjie Li; Qingqing Liu; Li Li; Aozi Feng; Tianyi Wang; Shuai Zheng; Anding Xu; Jun Lyu
Journal:  J Evid Based Med       Date:  2020-02-22

4.  Development of an informational support questionnaire of transitional care for aged patients with chronic disease.

Authors:  Xiaoliu Shi; Guiling Geng; Jianing Hua; Min Cui; Yuhua Xiao; Juan Xie
Journal:  BMJ Open       Date:  2020-11-17       Impact factor: 2.692

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

Authors:  Jialong Chen; Liuna Yang; Zhenzhu Qian; Mingwei Sun; Honglin Yu; Xiaolei Ma; Chonghua Wan; Yunbin Yang
Journal:  BMC Health Serv Res       Date:  2020-10-28       Impact factor: 2.655

  5 in total

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