For urban insurance schemes, this study provides a comprehensive nationwide exploration ofequity in stroke inpatients’ healthcare utilization and expenses.Using the Theil (in)equality index to measure "horizontal equity", we found significant geographic variationin stroke inpatients’ healthcare utilization and hospital expenses. Furthermore, the ability to deliver healthcare (open beds), the enabling factors (regional reimbursement rate and regional education level) and the provincial-level economic factors (GDP per capita) were found to have a significant impact on healthcare equity.
A steady rise in the Theil index of ALOS both in UEBMI and URBMI group, indicated the growing inequity in ALOS. For all patients in China, Xie et al [32] also found that the overall geographical inequity in healthcare utilization rose from 2011 to 2015. We speculate that the increasingly geographical difference in UEBMI and URBMI ALOS might be attributable to policy factors. Since the ALOS was one of the important performance assessment indexes, public hospitals in China actively reduced patients' ALOS, by encouraging, for example, patients to be discharged early, to improve their performance[33]. Previous studies report that multimorbidity, specifically hypertension, was a strong predictor of longer ALOS for stroke patients [34]. There were considerable geographic variations in prevalence of hypertension in China, and high hypertension prevalence zones, which extended from parts of the southeast to the northern and northeast [35, 36]. These different geographical hypertension prevalence zones would impact the geographical utilization rates of UEBMI and URBMI in Table 3. We also expect that different regional stroke prevention and treatment systems were an important factor leading to the geographic variation in patients’ ALOS. A well-functioning stroke prevention and treatment system could send stroke patients into a qualified hospital for a shorter time and provide more effective treatment than regular hospitals. Delayed medical checks and treatment, and the limited treatment capacity of a hospital, could dramatically contribute to longer ALOS for stroke patients [37, 38]. Previous studies noted the large geographic variation in stroke prevention and treatment system in China [39], which may explain some of the regional variation in stroke patient healthcare utilization. We recommend that public hospitals in China establish a unified and more scientific assessment index system for stroke patients and improve their capacity for treating stroke patients. The government should further strengthen the stroke prevention and treatment system, especially in poor areas with diminished healthcare delivery.
Our empirical evidence clearly revealed that the UEBMI group had an overall smaller Theil index of hospital costs, OOP expenses and ALOS than the URBMI group from 2013 to 2016. That means stroke inpatients with URBMI experienced greater inequity in healthcare utilization and expenditure than those with UEBMI. Examining the equity of health services utilization in different regions, Zhang [40] also found patients covered by UEBMI had greater geographical variation in healthcare utilization than those covered by URBMI. As shown in Table 1, The UEBMI scheme provided more generous benefits, more comprehensive service coverage, as well as stronger financial protection[41]. Since the UEBMI scheme provided stronger financial protection than the URBMI scheme, UEBMI patients would seek a more comprehensive treatment than URBMI patients [42]. With lower levels of benefits and financial protection, URBMI patients would economize on their level of health services utilization subject to their family’s financial status [42]. This would contribute to geographical variations in healthcare utilization and hospital expenses. Importantly, patients covered by UEBMI had stable incomes due to their employment status and usually had a better financial situation than URBMI unemployed, retiree, student and childinpatients. Without worrying about the occurrence of catastrophic health expenditure, patients in different regions would receive treatment as required, but UEBMI patients could incur higher OOP expenses and longer hospital stays[43]. Furthermore, a large proportion of patients covered by the URBMI were unemployed and children, with lower education levels than UEBMI patients. Education level was considered an important factor which would affect the ALOS for stroke inpatients[38]. There were significant regional variations in education level, with people in eastern China having the highest education level, followed by the central and western regions59.These regional education level differences probably interacted with the type of insured patient to contribute to inequity in UEBMI-URBMI healthcare utilization and hospital expenses.
Figure 1 displays the inequity in healthcare utilization and health service expenses. We found that the internal differences within regions were the major factors contributed to inequity of healthcare utilization and health service expenses. Internal differences in the eastern region accounted for the largest part of the Theil index. When the gap of socio-economic development level between the richer eastern region and poorer western and northern regions was significant, it seemed counterintuitive that the richest region had the greatest inequity in healthcare utilization and health service expenses. The coastal areas and strong economic zones in eastern China had advantages of export-linked and foreign investment industry, enhanced infrastructure and benefited most from economic policy reforms that transformed China’s economy, but widened the economicgap between different cities in eastern China. Differences between cities inthe central and western provinces were relatively smaller than within the eastern region. Therefore, socio-economic factors may be an important reason for the greaterhealthcare inequity within the eastern region. Another possible reason for the inequalities within the eastern and between the eastern and other regions was healthcare resource allocation. Previous studies reported that the eastern region had been experiencing the worst equity in health resource allocation[19], which was reflected in 20,62inequities inhealthcare delivery. We recommend that the government should not only make policies to improve the medical system in central and western regions, but also take the less developed provinces and prefectures in eastern China into account.
Our results demonstrated that the ability of healthcare delivery (represented by number of actual open beds per 1000 residents) had a significant negative impact on the hospital expenses both in the UEBMI and URBMI group, where more beds improved the equity of hospital expenses. Similarly, Kim et al[44] found that better nurse staffing levels had a significantlynegative association with the ALOS and medical expenses of patients with hip or knee procedures. We believe that patients in areas with a stronger ability to deliver healthcare have easier access to high quality healthcare. To improve delivery, we suggest that an effective treatment protocol could reduce ALOS and hospital expenses[45].The enabling factors (regional reimbursement rate and regional education level) were identified as important factors influencing healthcare utilization and hospital expenses in the URBMI group, but not in the UEBMI group. First, higher reimbursement signified an insurance fund’s stronger financial protection, encouraging patients to utilize more health services and benefit more from central subsidies.This is reflected in our data that showsUEBMI patients enjoying higher reimbursement rates and usually higher total expense, but fewer OOP cost than URBMI patients[42]. Second, education level had a significant negative impact on URBMI inpatients. Sinceeducation level was considered an important factor causing prehospital delay in stroke treatment,better educated UEBMI patients sought stroke treatment earlier,with reduced ALOS, than URBMI patients[46]. This is consistent with Milagros et al[47]who reported that low levels of education hada strong relationship with longer length of hospitalstays.Our results also revealed that GDP per capita was significantly associated with hospital costs and OOP.Economic factors are universally acknowledged as a main factor affecting healthcare utilization. We speculate patients with UEBMI had high incomes, consequently, the reimbursement rate and GDP per capitawere not significant explanatory variables forhealthcare utilization and hospital expenses. URBMI patients with higher incomeswere probably more willing to follow recommendations from doctors in the utilization of health services, thus GDP per capita was associated with higher health expenditure[48].
This study has a number of limitations. Our data applies to healthcare utilization and hospital expenses of urban stroke inpatients, not the whole stroke patient population in China.Due to data limitations, we used ALOS to reflect healthcare utilization of stroke patients, but healthcare utilization based on need and demand cannot be easily divided, therefore, the results should be interpreted with care. We recommend that other potential factors influencing the equity in healthcare utilization and hospital expenses, such as regional health investment be considered in future studies.