BioScience Trends. 2017;11(6):619-631. (DOI: 10.5582/bst.2017.01243)

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

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


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.

KEYWORDS: Population aging, life table, big data, lifetime medical expenses

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