Impact of Critical Illness Insurance on the Burden of High-Cost Rural Residents in Central China: An Interrupted Time Series Study

Abstract

Critical illness insurance (CII) in China was introduced to protect high-cost groups from health expenditure shocks for the purpose of mutual aid. This study aimed to evaluate the impact of CII on the burden of high-cost groups in central rural China. Data were extracted from the basic medical insurance (BMI) hospitalization database of Xiantao City from January 2010 to December 2016. A total of 77,757 hospitalization records were included in our analysis. The out-of-pocket (OOP) expenses and reimbursement ratio (RR) were the two main outcome variables. Interrupted time series analysis with a segmented regression approach was adopted. Level and slope changes were reported to reflect short- and long-term effects, respectively. Results indicated that the number of high-cost inpatient visits, the average monthly hospitalization expenses, and OOP expenses per high-cost inpatient visit were increased after CII introduction. By contrast, the RR from BMI and non-reimbursable expenses ratio were decreased. The OOP expenses and RR covered by CII were higher than those uncovered. We estimated a significant level decrease in OOP expenses (p < 0.01) and rise in RR (p < 0.01), whereas the slope decreases of OOP expenses (p = 0.19) and rise of RR (p = 0.11) after the CII were non-significant. We concluded that the short-term effect of the CII policy is significant and contributes to decreasing OOP expenses and raising RR for high-cost groups, whereas the long-term effect is non-significant. These findings can be explained by increasing hospitalization expenses, many non-reimbursable expenses, low coverage for high-cost groups, and the unsustainability of the financing methods.

Impact of DRGs-based inpatient service management on the performance of regional inpatient services in Shanghai, China: an interrupted time series study, 2013–2019

“…The change of level indicated the change of performance measures at the time of intervention, while the change of trend indicated the long-term effect of policy. The typical segmented regression model of ITS is as follows [22,23]:…”

“…The change of level indicated the change of performance measures at the time of intervention, while the change of trend indicated the long-term effect of policy. The typical segmented regression model of ITS is as follows [ 22 , 23 ]: Y t stands for performance indicators, such as CMI; time t is a continuous variable, indicating the time from the beginning of the observation period, taking quarter as the unit, with the value of 1, 2, 3; intervention t is a binary variable, with the code of 0 before the intervention and 1 after the intervention; time after intervention t is a continuous variable after the intervention, with the code of 0 before the intervention; the value after the intervention is the same as that of time. β 1 represents the trend before the intervention, β 2 represents the level change; β 3 represents the trend change; β 1 + β 3 represents the trend after the intervention.…”

Impact of DRGs-based inpatient service management on the performance of regional inpatient services in Shanghai, China: an interrupted time series study, 2013-2019

“…The change of level indicated the change of performance measures at the time of intervention, while the change of trend indicated the longterm effect of policy. The typical segmented regression model of ITS is as follows [22,23]: stands for performance indicators, such as CMI;…”

Impact of DRGS-based Inpatient Service Management on the Performance of Regional Inpatient Services in Shanghai, China: an Interrupted Time Series Study, 2013-2019

“…The change of level indicated the change of performance measures at the time of intervention, while the change of trend indicated the long-term effect of policy. The typical segmented regression model of ITS is as follows [22,23]: stands for performance indicators, such as CMI;…”