Exploring Medical Expenditure Clustering and the Determinants of High-Cost Populations from the Family Perspective: A Population-Based Retrospective Study from Rural China

• Main Authors: Shan Lu, Yan Zhang, Yadong Niu, Liang Zhang
• Format: Article
• Language: English
• Published: MDPI AG 2018-11-01
• Series: International Journal of Environmental Research and Public Health
• Subjects: medical expenditure; high-cost family; clustering; rural China
• Online Access: https://www.mdpi.com/1660-4601/15/12/2673

The costliest 5% of the population (identified as the “high-cost„ population) accounts for 50% of healthcare spending. Understanding the high-cost population in rural China from the family perspective is essential for health insurers, governments, and families. Using the health insurance database, we tallied 202,482 families that generated medical expenditure in 2014. The Lorentz curve and the Gini coefficient were adopted to describe the medical expenditure clustering, and a logistic regression model was used to identify the determinants of high-cost families. Household medical expenditure showed an extremely uneven distribution, with a Gini coefficient of 0.76. High-cost families spent 54.0% of the total expenditure. The values for family size, average age, and distance from and arrival time to the county hospital of high-cost families were 4.05, 43.18 years, 29.67 km, and 45.09 min, respectively, which differed from the values of the remaining families (3.68, 42.46 years, 30.47 km, and 46.29 min, respectively). More high-cost families live in towns with low-capacity township hospitals and better traffic conditions than the remaining families (28.98% vs. 12.99%, and 71.19% vs. 69.6%, respectively). The logistic regression model indicated that family size, average age, children, time to county hospital, capacity of township hospital, traffic conditions, economic status, healthcare utilizations, and the utilization level were associated with high household medical expenditure. Primary care and health insurance policy should be improved to guide the behaviors of rural residents, reduce their economic burden, and minimize healthcare spending.