The Comparison of Various Types of Health Insurance in the Healthcare Utilization, Costs and Catastrophic Health Expenditures among Middle-Aged and Older Chinese Adults

Rapid aging in China is increasing the number of older people who tend to require health services for their poor perceived health. Drawing on the China Health and Retirement Longitudinal Study (CHARLS) 2018 data, we used two-part model and binary logistic regression to compare various types of health insurance in the healthcare utilization, costs and catastrophic health expenditures (CHE) among the middle-aged and older adults in China. Compared with uninsured, all types of health insurance promoted hospital utilization rate (ranged from 8.6% to 12.2%) and reduced out-of-pocket (OOP) costs (ranged from 64.9% to 123.6%), but had no significant association with total costs. In contrast, the association of health insurance and outpatient care was less significant. When Urban Employee Medical Insurance (UEMI) as reference, other types of insurance did not show a significant difference. Health insurance could not reduce the risk of CHE. The equity in healthcare utilization improved and healthcare costs had been effectively controlled among the elderly, but health insurance did not protect against CHE risks. Policy efforts should further focus on optimizing healthcare resource allocation and inclining toward the lower socio-economic and poor-health groups.

Bayesian profiling for cost with zeros to decompose total cost into probability of cost and mean nonzero cost

Cost of health care can vary substantially across hospitals, centers, or providers. Data from electronic health records provide information for studying patterns of cost variation and identifying high or low cost centers. Cost data often include zero values when patients receive no care, and joint two-part models have been developed for clustered cost data with zeros. Standard methods for center comparisons, sometimes called profiling, can use these methods to incorporate zero values into total cost. However, zero costs also provide opportunities to further examine sources of cost variation and outliers. For example, a hospital may have high (or low) cost due to frequency of nonzero cost, amount of nonzero cost, or a combination of those. We give methods for decomposing hospital differences in total cost with zeros into components for probability of use (i.e., of nonzero cost) and for cost of use (mean of nonzero cost). The components multiply to total cost and quantify components on the same easily interpreted multiplicative scales. The methods are based on Bayesian hierarchical models and counterfactual arguments, with Markov chain Monte Carlo estimation. We used simulated data to illustrate use, interpretation, and visualization of the methods in diverse situations, and applied the methods to 30,024 patients at 57 US Veterans Administration hospitals to characterize outlier hospitals in one year cost of inpatient care following a cardiac procedure. Twenty eight percent of patients had zero cost. These methods are useful in providing insight into cost variation and outliers for planning future studies or interventions.