What fragile factors hinder the pace of China’s alleviation efforts of the poverty-stricken population? A study from the perspective of impoverishment caused by medical expenses

This is a cross-sectional study on incidence of poverty and impoverishment by medical expense, households with the highest risk of impoverishment from aspect of household head, household and healthcare needs, and healthcare utilization would be focused on.

The data we used were from the Fifth National Health Service Survey (NHSS) in China, 2013. This survey is conducted every five years and has the most nationally representative sample obtained through a multi-stage, stratified, and random sampling method. Finally, 31 provinces, 156 districts (or counties), 780 townships, 1560 villages, and 93,613 households (273,688 people) were included in the survey [18], after data cleaning, 93,570 households were included in this study, the detailed sampling process was shown in Fig. 1.

Face-to-face interviews were conducted to collect the information of participants, including the households’ basic information, healthcare needs, and healthcare utilization. Data quality was strictly controlled during the whole survey and investigation process. Every investigator received rigorous training before the investigation. To ensure the effectiveness of the survey, a subset of the respondents was selected to answer the questionnaire again, reaching a consistency rate of 97.7%. Based on data from the sixth census in 2010, an analysis using the Maria index, delta dissimilarity coefficient, GINI concentration ratio, and fitting degree test showed that the size of the sample family and the proportion of rural versus urban households were not different from that of the whole country, but the proportion of the elderly population was higher than that of the general population [18].

Most householders were male (74.60%), employed (68.40%) and married (84.20%). 46.9% of households were covered by NCMS, the proportion of households with under 5 years old members and over 60 years old were 17.5 and 43.6% respectively. Households had at least 1 NCD number occupied 43.6%, the proportion of households with 1 in-patient member or 2 and above in-patient members were 17.9 and 2.3%, the detailed information is shown in Table 1.

The poverty and IME rates in China were 16.2 and 6.3% respectively, the rates of poverty and IME in 31 provinces are shown in Fig. 2 and Fig. 3. The poverty and IME rates in China’s 31 provinces differed significantly (P < 0.05). Those in the western region had much higher poverty rates than the other two regions, and the value in the eastern region was the lowest. The contribution of IME to overall poverty varied widely across the 31 provinces. In some provinces, such as Inner Mongolia, Shandong, and Heilongjiang, their rates of poverty were relatively low, but they suffered from a high burden of IME. For some provinces with higher poverty rates, such as Tibet and Xinjiang, the rate of IME was not so high. Overall, the rate of IME was higher in the western region (7.2%) than that in the central (6.5%) and eastern (5.1%) regions.

The rate of IME varied among people covered by different types of medical insurance schemes. Compared with people enrolled in other types and with no insurance schemes, those enrolled in the UE-BMI and a mixture of schemes had better capacity to deal with the burden brought by diseases. On the contrary, as one of the three basic medical insurance schemes, the incidence of IME (9.1%) was highest among those covered by the NCMS, even higher than that of people who were not covered by any medical insurance (4.0%). The IME incidence of IBMIUR was also considerably high (6.3%) (Table 2).

We summarized the top five household characteristics associated with the highest IME incidence and found that households with inpatient members, those with NCD members, and those at a low economic level suffered the highest incidence of IME. When the risk factors were combined, the incidence of IME for these households was nearly six times than the national average, especially for the poor households with more than two inpatient members, which had the highest incidence of 35.4% (Table 3).

A comparison of the IME groups revealed that the top three diseases associated with IME were malignant tumor, congenital heart disease, and mental disease. The total hospitalization expenses for the above-mentioned diseases all exceeded 7000 Yuan, and the highest payment was up to 11,000 Yuan for malignant tumors. Among the top 10 diseases in terms of total hospitalization expense incurred by IME households, five were chronic diseases (see Table 4).

Univariate analysis showed that all the factors were significantly associated with suffering impoverishment. Further logistic regression analysis showed that, many factors were related to the occurrence of impoverishment due to medical expenses. Households headed by a non-married person, an unemployed person or a student or a person with a low educational level; and households of small size, with members suffering from NCDs, with members who were inpatients were all more likely to suffer from IME. In addition, the smaller the number of people suffering from NCDs or receiving medical care in the household, the less risk of IME the household had. Households covered by the UE-BMI had the strongest ability to deal with the economic risk posed by diseases. NCMS-enrolled households had greater exposure to the risk of IME, at 1.84 times higher than that of UE-BMI-enrolled households. Poorer households were 15.05 times more likely to suffer from IME than richest households (Table 5).

Households in underdeveloped central and western regions of China are subject to greater risk of IME. It is noteworthy that while some central provinces are not seriously affected by poverty, some households in these areas have suffered severe IME after paying for medical expenses. Meanwhile, in the western provinces where the poverty rate is high, the rate of IME is not so high. From this survey, the admission rate in the central region (8.0%) is lower than that in the western region (8.6%), but the average hospitalization expenses in the central region (10,254 yuan) is higher than that in the western region (8342 yuan) [18]. The relatively lower utilization of health services in the central region does not contribute to a lighter economic burden. This reflects those regional differences exist in the design of medical insurance schemes, which lead to different patterns of economic burden.

The overall design of national poverty alleviation and medical health insurance reform is crucial. In the early stage of China’s poverty alleviation efforts, the country adopted several measures to address poverty in different regions. Since 1996, the Chinese government has identified poverty alleviation projects that provide financial assistance to 9 provinces in the eastern region and 10 provinces in the western region [22]. From 1995 to 2012, the average annual growth rate of per capita transfer payments in the western region was 20.3%, higher than that in the central region [23]. Meanwhile, according to our calculations, the reimbursement rate in the central region (55.9%) was lower than that in the western region (58.2%) (Additional file 1). Therefore, it is obvious that the economic burden of diseases depends not only on the level of economic development of the region itself, but also on the healthcare needs and service utilization of the people and the design of the medical insurance systems and other welfare systems.

We summarized the top five household characteristics associated with the high IME incidence and found that households with inpatient members, those with NCD members, and those at low economic levels suffered the highest incidence of IME. When the determinants of IME are combined together, the incidence of IME was nearly six times that of the national average, especially for poor households that have more than two inpatient members—these had the highest incidence of IME at 35.4%. A family’s economic level and health insurance utilization were found to be indicative of those most vulnerable to IME [15]. Households with a high economic level were better at dealing with the burden of diseases and enjoyed a higher capacity to pay, enabling them to avoid impoverishment [24]. A study in Vietnam also obtained a similar result [25] and remaindered those financial interventions should be targeted at poor households, especially those located in slum areas.

A possible explanation is that, for most developing countries with a large gap between the rich and the poor, although many financial protection measures have been implemented for economically disadvantaged people, ensuring that the poorest are able to benefit more from these reforms should be paid more attention.

The diseases that led to IME were mainly chronic diseases [26‐28]. Among the top 10 diseases that caused poverty, five were NCDs. Malignant tumors presented the highest risk of IME. China has become a place with a high incidence of malignant tumors, accounting for about 22% of the global incidence of tumor diseases [29]. Although China has launched major disease medical insurance schemes for patients to deal with the economic burden of such diseases since 2012, the compensation level and benefit coverage for major diseases still need to increase [30]. Some tumor diseases, such as benign brain tumor, are not even covered by the major disease medical health insurance schemes, contributing to the high economic burden of residents. Moreover, patients suffering from mental illness shared more OOP expenses due to insufficient reimbursement. Those who enrolled under MIUE for mental illness could only enjoy a 53.9% reimbursement rate compared with those who enrolled for diabetes, who enjoyed 81.5%. Meanwhile, due to the long treatment period for chronic diseases, related indirect expenses such as transportation, nursing, and preventive health care expenses, as well as time lost, were not included in the scope of the reimbursement [31, 32]. All these factors contributed to chronic diseases becoming the risk factor that pushed households into IME.

Our results showed that different medical insurance schemes led to different degrees of risk of IME. NCMS enrollment was associated with the highest IME risk, with incidence as high as 9.1%, approximately 4.79 times that of UE-BMI enrollment. The integrated medical insurance scheme was designed to alleviate inequity among different groups; however, at the initial stage, IME was still high at 6.3%. The fundamental reason for this inequity among different medical insurance schemes is the imbalanced design of the financing and reimbursement levels and the benefit package, making it impossible for enrollees to achieve equal access to health services. NCMS, with its relatively lower reimbursement level and insufficient benefit package, provided poor economic protection for residents in China [33]. China’s rural population is large, and it is a group that suffers from major and chronic diseases that lead to significant medical expenses [34]. Due to NCMS’s insufficient compensation level, rural residents have become the main high-risk group for impoverishment. Therefore, it is essential to strengthen the top-level design of the medical insurance system and eliminate the imbalance among insurance schemes to alleviate the risk of IME [35]. Compared to other under protected low- and middle-income countries, the IME level in China seems to be higher, this reflects the extensive use of copayments and insufficient insurance coverage. Therefore, overspending direct medical payment may be the main reason for IME, which is similar to Korea.

By the end of 2017, the size of China’s poor population was 30.5 million people [36]. Due to wide-ranging factors such as the aging of the population and social, economic, and geographical imbalances, poverty alleviation has proven to be challenging. To achieve comprehensive poverty alleviation, now is the time to be absolutely precise with regard to the steps taken toward poverty alleviation. How to define the precise target population and how to distribute the country’s resources to help the people at risk of IME have become top priorities.

Due to the multiple vulnerabilities of the poor population, it is no longer meaningful to divide the poor according to the national poverty line. The most vulnerable population can be screened out by accurately identifying specific family characteristics [37]. Having low income is one of the main causes of poverty, but it is not as significant as poor health. If we continue to use income as the only criterion for identifying the poor, the situation will arise wherein right after some people have been lifted out of poverty, other people fall into poverty because of medical expenses. Therefore, this study provides multidimensional criteria to determine vulnerable families, specifically, poor families with at least one inpatient, those with more than two NCD members, those with members suffered from tumor diseases, and those whose heads are unemployed, illiterate, or retired.

Establishing an eligibility screening method that uses multi-dimensional criteria could enable the precise identification of the vulnerable poor who are most at risk of IME. Then, multiple targeted interventions and poverty alleviation strategies can be carried out to deal with diseases. In addition to the three basic medical insurance systems, MFA—the catastrophic disease medical insurance—should also play a complementary role. It is important to abolish the deductible line and upper limit for the poor, increase the reimbursement ratio, and design special preferential medicine purchases for the poor. In this regard, Australia’s approach is worth learning from. Specifically, the Australian government has set a maximum limit for the annual OOP payment of medicines for patients. The upper limit for the normal population (A$ 1317.2) is higher than that for the vulnerable population (A$ 336). Once the medicine payment of a patient (who is vulnerable to poverty) exceeds the upper limit, the excess part is subsidized [38]. We should not only improve the security provided by medical insurance, but also standardize the terms of services of health service providers. In the market economy, doctors may take advantage of information asymmetry between doctors and patients, thereby pursuing personal benefits. As a result, patients would have no choice but to accept all the examinations and treatments prescribed by the hospital, thereby causing accumulated costs of a bottomless pit. Therefore, it is necessary to standardize the treatment plan by adopting diagnosis-related-group payments for inpatient care. It can not only reduce residents’ medical expenses, but also avoid wasting medical funds for poor people.

As the saying goes, “Give a man a fish and you feed him for a day. Teach him how to fish and you feed him for a lifetime.” Analogous to this saying, instead of simply providing economic, medical, and housing assistance to the poor, alleviating poverty is more important when it comes to helping poor people support themselves. Therefore, it is important to provide measures to alleviate industrial poverty and employment poverty among the poor. In this light, it is helpful to organize national leading enterprises to cooperate with poverty-stricken counties and expand the marketing channels of agricultural products, for instance, by selling grains and fruits online. Moreover, increasing the income of labor services can be achieved through job subsidies and loan support programs to encourage poor families to participate in cleaning, road protection, water management, disability assistance, old-age care, and so on [39]. In addition, poverty alleviation requires stronger government leadership to set up new systems that would effectively coordinate various departments in the whole society. Indeed, health poverty alleviation needs to be supplemented by industrial and employment poverty alleviation measures.

This study focused on factors hinder the pace of China’s alleviation efforts of poverty-stricken population from the perspective of IME. We investigated the incidence of poverty and IME and revealed the features of impoverishment from multi-dimensions using a large sample national survey. We also further explored and identified the precise groups that are vulnerable to IME with 1 or overlapped risk factors. This study also had some limitations. First, it was a cross-sectional study, and the association between impoverishment and predictors could not be obtained. Second, the datasets were obtained via a self-reported questionnaire, the recall bias may exist. Third, we only estimate the impoverishment caused by direct medical expenses and the indirect expenses (e.g., transport, food, accommodation costs, etc.) for patients were excluded, in the future studies, the direct medical expenses and indirect medical-related expenses should be taken into account. Additionally, those can’t afford health service payments were not included in the sample, which might lead to the under-estimation of CHE.