Background

Maternal health is decisive for the country, and maternal mortality is one of the most important indicators to measure the development of social and economic, and social equity [1]. Since 2000, 189 heads of state, including China, signed the Millennium Declaration and committing themselves to achieve target 5, which was to the reduce maternal mortality ratio by three-quarters between 1990 and 2015 [2]. Without exception, all had made continuous efforts to achieve the goal. In China, it had promulgated laws and policies such as the law of the People Republic of China on Maternal and Infant Health Care, the Program for the Development of Chinese Women (2001–2010), and implemented the project of reducing maternal mortality and eliminating neonatal tetanus in 378 counties of 12 provinces in western China, and so on [3, 4]. Under the efforts of the government and people, the target for Millennium Development Goal 5 achieved ahead of schedule in 2014. The past two decades, China has pushed down the maternal mortality ratio at an annualized rate of 6.5% per year, one of the fastest decreases in the world [5, 6]. In recent years, the maternal mortality rate showes a decreasing trend, but there are still gaps among regions and have reached the bottleneck stage [7,8,9]. The healthy China Initiative 2019 to 2030 states that the maternal mortality ratio should reduced to 12.0 per 100,000 by 2030 [10]. It is a great challenge to take measures to reduce the maternal mortality rate continuously.

In 1996, World Health Organization (WHO) and Swedish International Development Cooperation Agency (SIDA) proposed that equity in health services means that all members of society have the same access to health services, without differences based on social privileges [11]. Therefore, the same access to health services in the same state of health and disease is a fundamental right of members of society. The research showed that the frequency of prenatal check-ups, the proportion of systematic management of high-risk women, the rate of a new delivery, and hospital delivery were the influencing factors of maternal death [12,13,14]. Strengthening the management of maternal to improve the quality and level of maternal health care is an important measure to implement the Healthy China Strategy. At the same time, the impact of economics on maternal mortality is also crucial [15, 16]. Inadequate funding, resources, and level of health services can lead to poor access to health services, unreasonable allocation, and under-utilization of resources, which directly affect maternal health and make it unequal across regions. Therefore, it is urgent to solve the problem of whether the decrease in maternal mortality is caused by insufficient investment or inadequate utilization of resources, and whether health needs and health supply are balanced.

In 2009, promoting equalization of Basic Public Health services was one of the five major tasks of China’s health care reform, aiming to ensure that all residents in urban and rural areas could access the services and focus on equity [17, 18]. The seventh population census in 2020 showed that population growth has slowed, mainly due to the continuous decrease in fertile women that has led to a weakening of the momentum of population growth and a slight decline in the fertility level. Meanwhile, the proportion aged 65 and above reached 13.50%, which was higher than the world average of 9.3% [19]. With the aging population increasing and fertility level decreasing, it has put forward higher requirements for the health of fertile women, especially for maternal. Maternal health is the foundation of universal health and is the main object of public health services. Equity is the focus of research.

The study aimed to understand the relationship among maternal service needs, utilization, and resource allocation of 31 provinces (cities, autonomous regions) in China in 2009 and 2019, using a multi-index comprehensive evaluation method. Also, from the perspective of equity and efficiency to understand the changing trends and differences in the region, and provide guidance for policymakers regarding resource allocation and public health policy.

Methods

Data sources

The data that included maternal services needs, utilization, and resource allocation was retrieved from the China Health Statistics Yearbook 2010 and 2020 (http://www.nhc.gov.cn/mohwsbwstjxxzx/tjtjnj/new_list.shtml) [20, 21]. Twelve indicators were selected to comprehensively evaluate and analyze the equalization of maternal services across 31 provinces (cities, autonomous regions).

The three maternal services needs indicators X1 ~ X3 are maternal mortality rate (1/100,000), the proportion of obstetric hemorrhage in maternal deaths (%), and pregnancy-induced hypertension.

in maternal deaths(%). The five indicators X4 ~ X8 in the utilization of maternal services are.

registration rate (%), system management rate (%), prenatal checkup rate (%), postpartum visitrate (%), and hospital delivery rate (%). The maternal system management rate refers to the.

number of women who have received early pregnancy checkups, antenatal checkups, sterile.

delivery, and postpartum visits within 28 days after delivery to the number of live births in a given area during the year. Four resource allocation indicators X9 ~ X12 are the number of medical institutions (1/100,000), the number of practicing (assistant) physicians (1/1000), and the number of registered nurses (1/1000), the number of obstetrics and gynecology beds (1/100,000). Combined with the responsibilities of maternal health care and the desirability of indicators, the institutions includecomprehensive hospitals, primary medical institutions, maternal and child health hospitals (stations). Health technicians are involved physicians and nurses in maternal and child health hospitals (stations).

The RSR method

The fundamental theory of the RSR method is that a dimensionless statistical indicator is calculated from an n × m matrix using rank conversion. After this calculation, the distribution of RSR using parametric statistical methods. Generally, the RSR indicator ranges from zero (worst) to one (best) and follows a normal distribution. Additionally, the status (worst/best) uses the RSR order or a set of ordinal classifications to evalute [22].

In our study, the RSR method was used to rank and classify 31 provinces in China in terms of maternal service needs, utilization, and resource allocation, respectively. All indicators used the same weight, and considering that the integer rank method would lose the original data information, the non-integer rank RSR was adopted to overcome the disadvantage of losing the quantitative information of the original data.

The detailed processes are as follows:

  1. 1.

    Rank the indicators of maternal services in each province, the high-quality ranked in ascending,and the low-quality indicator ranked in descending order, such as

$${R}_{high- quality}=1+\frac{\left(n-1\right)\times \left(X-{X}_{\textrm{min}}\right)}{X_{\textrm{max}}-{X}_{\textrm{min}}}$$
$${R}_{low- quality}=1+\frac{\left(n-1\right)\times \left({X}_{\textrm{max}}-{X}_{\textrm{min}}\right)}{X_{\textrm{max}}-{X}_{\textrm{min}}}$$

Where R is the rank of each maternal services indicator of 31 provinces in China, X is the original value, n = 31, Except for maternal mortality rate, the other is high quality in the study.

  1. 2.

    Calculate the value of RSR, the equation as follows

$$RSR=\frac{1}{m\times n}\sum \limits_{j=1}^i{R}_{ij}$$

Where Rij is the rank of indicators in maternal services needs, utilization, and resource allocation, m is the number of indicator in each dimension, n is the number of provinces, i = 1,2∙∙∙n, j = 1,2∙∙∙m.

  1. 3.

    Determine the distribution of RSR. Sort the RSR from small to large; calculate the downward

cumulative frequency P (average rank/n*100%) according to the cumulative frequency and average rank; then convert it into Probit.

  1. 4.

    Calculate the regression equation. The value of RSR as the dependent variable and Probit as the

independent variable to fit the linear regression equations of maternal services needs, utilization, and resource allocation, then calculate the fitted RSR of each region.

$$\overset{\wedge }{RSR}=a+b\times probit$$
  1. 5.

    Grading and sorting. Concerning the commonly used three-grading table, the Probit critical values are substituted into the regression equation to calculate the RSR critical values for grading, and variance analysis is used to compare the differences among groups. SNK-q is used for pairwise comparison; the statistically significant level is set at P < 0.05

WHO’s comprehensive evaluation model

Based on the investigation of health services in many countries and regions, the WHO proposed to combine health service needs, utilization, and resource input, and grade the sample mean of the three categories of indicators to form eight evaluation types from A to H [23], as shown in Table 1.

Table 1 WHO’s comprehensive evaluation model of health service
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Type A and H indicate appropriate allocation of resources. The high resource utilization is types B and F, types C and E mean low and over resource utilization, respectively. Types D and G indicate the low and over the investment of resources.

In China, scholar Chen H and others established the comprehensive evaluation modelof Basic Public Health services, and combined the method of RSR, which is grouped into five categories, including relative balance area (types A,H, and F), low input area (type B), resource shortage area (type D), overutilization area (type E), and resource waste area (types C and G), which is detailed in Table 2 [24].

Table 2 The comprehensive evaluation model of basic public health services in China
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In our study, we used it for reference to carry out comprehensive evaluation of maternal services.

Results

The RSR value in needs, utilization, and resource allocation of maternal services across China in 2009 and 2019

According to the RSR value ranking evaluation of the maternal services in each region, in 2009, the top three maternal health needs were in **zang, Qinghai, and **njiang. The utilization of maternal health care was in Bei**g, Zhejiang, and Shandong, and the resource allocations were in Bei**g, Ningxia, and **njiang. While ten years later, the top three maternal health needs were in **zang, Tian**, and Qinghai; maternal health care utilization was in Zhejiang,Tian**, and Guangxi; resource allocations were in Guizhou, Ningxia, and Qinghai (Table 3).

Table 3 The RSR value in needs, utilization and resource allocation of maternal services across China in 2009 and 2019
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The distribution of RSR in needs, utilization, and resource allocation of maternal services across China in 2009 and 2019

According to the value of RSR and Probit (Table 4), six regression equations were obtained. In 2009, regression equations in needs, utilization, and resource allocation of maternal services were RSR1= 0.132Probit-0.37 (r = 0.798), RSR2 = 0.136Probit-0.029 (r = 0.693), RSR3 = 0.125Probit-0.293 (r = 0.964), respectively. In 2019, they were RSR4 = 0.126Probit-0.36 (r = 0.849), RSR5 = 0.113Probit + 0.281 (r = 0.444), RSR6 = 0.130Probit-0.289 (r = 0.921). The results of variance analysis showed that all regression equations were statistically significant (P < 0.05).

Table 4 The distribution of RSR in needs, utilization and resource allocation of maternal services across China in 2009 and 2019
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Grading and sorting maternal services needs, utilization, and resource allocation across China in 2009 and 2019

The Probit critical values of 4 and 6 were substituted into all regression equations to calculate the RSR as the basis of classification, and then calculated the fitted values of RSR in maternal service needs, utilization, and resource allocation of all provinces, and the final results were shown in Table 5 and Table 6.

Table 5 Grading and sorting maternal services needs, utilization and resource allocation across China in 2009
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Table 6 Grading and sorting maternal services needs, utilization and resource allocation across China in 2019
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The test of variance consistency showed no significant differences among groups in each dimension (P > 0.05). In 2009, an analysis of variance showed that the statistics value F of maternal service needs, utilization, and resource allocation were 36.424, 36.431, and 36.363, respectively, and 36.565, 36.306, and 36.507. In 2019, there were significant differences in the fitted values of RSR among groups of each dimension (P < 0.05). The SNK-q test showed a significant difference pairwise, and the grading was reasonable.

The comprehensive evaluation of maternal services in 2009 and 2019

According to the results of grading, all regions were filled into the model (Table 7). In 2009, the relative balance area, low input area, resource shortage area, resource waste area, and overutilization area were16, 3, 4, 3, and 5, respectively. In 2019, they were 18, 3, 6, 0, and 4, respectively.

Table 7 The comprehensive evaluation of maternal services in 2009 and 2019
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Discussion

Since China announced a health reform blueprint to achieve universal coverage by 2020, the equalization of public health services between different regional, urban and rural areas has gradually improved [25,26,27,28,29]. The research showed that 18 regions were in the relative balance area in maternal health resources allocation and health needs, and more than half had met the requirements for equitable development, which was higher than that of 16 provinces in 2009. In addition, the overutilization area decreased one province, and the resource shortage area decreased three. Compared to 2009, several regions experienced a type shift, eight changed from non-balanced to a relative balance area, and four showed an improvement in type. It indicated that under the guidance of the Public Health Service policy, including system construction, project promotion, serviceability strength, and the fund guarantee, the equalization of maternal services were gradually being realized.

In 2019, three regions were still in the low input area, and the resource allocation was not meet the needs. From the perspective of regional GDP, these were at the bottom in 2019. The economy could limit fund-raising ability, which leads to insufficient investment in infrastructures such as institutions, personnel, and beds; the RSR value of resource allocation ranked in the bottom five. At the same time, the limit on salary, training, and promotion opportunities resulted in brain drain, personnel shortage, insufficient service capacity, and other issues. According to research statistics, the full-time public health personnel in primary medical health institutions accounted for only 10% of health technicians in 2017, and 31% of physicians in urban community health service centers (stations) failed to meet the requirements of practicing assistant physicians in 2015 [30]. Therefore, it is critical to strengthen financial investment and consolidate the appropriation of special funds.

Ten years later, 10 regions still with low efficiency in maternal services, of which six were resource waste and four were over-utilized areas, resource waste coexists with overutilization. Under the guidance of macro policies, while the whole country was actively implementing the public health services tasks, the control of the efficiency of resource utilization had been neglected, especially for consideration of professional public health institutions. Therefore, it is urgent to integrate resources and optimize the utilization efficiency of health resources. We should combine the system of grading diagnosis and treatment to improve the capacity of primary-level medical institutions, promote the development of appropriate health technologies, and give full play to the role of primary-level medical institutions. Therefore, it is urgent to integrate and optimize the efficiency of resources. The hierarchical diagnosis and treatment and medical association should combine to improve the ability of maternal health, promote the development of appropriate health technology, and give full play to the role of primary medical institutions. Resource input was higher than health needs in four regions, which lead to oversupply, so the cost measurement mechanism should be improved to ensure that resources such as personnel and beds are set up according to demand.

Conclusions

Based on the WHO’s comprehensive evaluation model of Health Services, this study evaluated the needs and supply of maternal services from the perspective of equity and efficiency in China. The results showed that the equalization of maternal services was gradually being realized under the guidance of the equalization of Public Health Service policies. However, there were still some problems, such as the mismatch of resource input, health needs and waste, and the over-utilization of resources. So the non-balanced regions should formulate targeted policies in the light of their specific circumstances to promote their transformation to equalization. However, due to the availability of data, it is unable to include all technicians serving maternal, and unable to obtain dedicated public health funding. It is suggested the relevant departments should refine the statistical classification of information as much as possible and make more public resources available.