Impact of fiscal subsidies of health insurance system and public health programs on the efficiency of primary healthcare institutions in China

Abstract

Objectives

China’s fiscal subsidies structure has gradually evolved since the implementation of the New Healthcare Reform. Fiscal subsidies for the healthcare insurance system and basic public health programs have been steadily increasing. Whether such subsidies have effectively improved the service efficiency of Primary Health Care Institutions (PHCIs) requires further investigation.

Methods

This study employs data from China Health Statistical Yearbook, China Statistical Yearbook, and other relevant websites for the period 2010–2021. The Data Envelopment Analysis (DEA) was used to measure the Technical Efficiency (TE), Pure Technical Efficiency (PTE) and Scale Efficiency (SE) of PHCIs. Further, a panel Tobit model was applied to assess how fiscal subsidies in both the health insurance system and basic public health programs affect the efficiency of PHCIs.

Results

The efficiency of medical services in PHCIs is on a downward trend, while the efficiency of public health services shows an upward trend with fluctuations. Fiscal subsidies in the healthcare insurance system significantly improved the basic medical services efficiency of PHCIs but adversely affected public health services efficiency. In contrast, subsidies for basic public health programs improved public health services efficiency of PHCIs but adversely affected basic medical services efficiency. The division management mode of fiscal subsidies is not conducive to improving the comprehensive services efficiency of PHCIs.

Conclusions

Fiscal subsidies in both the healthcare insurance system and public health programs demonstrate suboptimal efficiency, characterized by poor coordination and integration, ultimately failing to significantly enhance the efficiency of PHCIs.

Introduction

PHCIs in developing countries often confront the challenge of low service efficiency. International studies indicate that PHCIs in countries such as Colombia, Kenya, Greece, and Iran experience underutilization, with significant efficiency disparities across service types and region [1–5]. Similarly, existing studies have documented low efficiency in basic medical services delivery by China’s primary healthcare institutions, marked by an overall declining trend and significant regional variations [6–8]. Current literature has primarily examined PHCIs efficiency in basic medical services delivery, with public health services efficiency remaining substantially understudied [9].

Primary Health Care Institutions (PHCIs) in China, including Community Health Centers (Stations), Township Health Centers, Village Clinics, Outpatient Departments, and Private Clinics, are tasked with delivering essential healthcare services and public health programs for local populations [10]. The service efficiency of PHCIs is influenced by multiple factors. First, China’s structural healthcare reforms have undermined the service capacity and market competitiveness of PHCIs. For instance, the rapid expansion of private primary and secondary hospitals, coupled with the ‘siphoning effect’ of large tertiary hospitals, has reduced the efficiency of PHCIs [10]. Second, improved economic conditions and rapid urbanization have driven patient preference toward higher-tier hospitals, resulting in suboptimal utilization of primary healthcare resources [11]. Third, fiscal subsidies have influenced service efficiency of PHCIs [12, 13].

Since the implementation of the ‘New Healthcare Reform’, China’s fiscal subsidies on healthcare have increased steadily. In terms of absolute scale, national general public budget expenditures on healthcare have risen progressively, with their share in total government budget expenditures increasing annually from 6.31% in 2009 to 8.35% in 2021. Fiscal subsidies of PHCIs grew substantially from 4.11 million yuan (2010) to 267.46 million yuan (2021), representing 12.94% of total government health expenditures. Meanwhile, fiscal subsidies structures have increasingly prioritized supporting PHCIs and raising per capita subsidies. Between 2009 and 2023, annual per capita fiscal subsidies for basic health insurance for rural and ono-working urban residents increased from 80 yuan to 640 yuan, and annual per capita fiscal subsidies for public health programs rose from 15 yuan to 89 yuan. In 2021, total fiscal subsidies on healthcare insurance systems and public health programs reached approximately 699.53 billion yuan, representing 33.83% of total financial healthcare expenditures. There are different opinions on whether the increasing fiscal subsidies are conducive to improving the efficiency of PHCIs. Some scholars believe that an increase in fiscal subsidies is beneficial for improving the efficiency of PHCIs [14, 15], but others argue that the division management mode of fiscal subsidies is not conducive to improving the efficiency of PHCIs. The division management mode of fiscal subsidies mainly refers to the separation of subjects and payment methods, for example, the management subject of Health Insurance Systems (HIS) is the health insurance bureau, while that of Public Health Programs (PHP) is the health administrative department. In addition, the payment standards are also different. The health insurance department is carrying out a reform of payment methods, combining various payment methods such as changing from payment based on service items to payment based on Diagnosis Related Groups (DRG) et., while fiscal subsidies of PHP mainly pay based on the number of permanent residents and service volumes. This article employs provincial panel data from China covering the period 2010–2021 to systematically evaluate the impact of fiscal subsidies of HIS and PHP on the efficiency of PHCIs.

Hypothesis

Many Scholars believe that fiscal subsidies for HIS and PHP enable the health care system to enhance efficiency. Fiscal subsidies for HIS can effectively improve the efficiency of hospitals through incentive mechanisms and competition mechanisms. Fiscal subsidies for PHP can improves the efficiency of public health institutions through demand side incentives and supply side optimization [14, 15]. Some other studies have also pointed out that due to the decentralized management of health insurance funds and public health funds in most regions of China, there are differences in the supply targets and incentive mechanisms of basic medical services and public health services, resulting in problems such as fragmented health care services, duplicated health care services and waste of health resources [16]. Most of these studies focus on qualitative analysis. We thought that for PHCIs that provide both basic medical services and public health services, a more systematic quantitative evaluation of the impact of fiscal subsidies for HIS and PHP on the service efficiency of PHCIs is necessary. On the one hand, the fiscal subsidies for HIS may enhance the efficiency of basic medical services in PHCIs through incentive and competition mechanisms. On the other hand, fiscal subsidies for PHP may improve the efficiency of public health services in PHCIs through demand side incentives and supply side optimization. However, the division management mode of fiscal subsidies may lead to segmented or repetitive services, which may not have a positive effect on improving comprehensive service efficiency of PHCIs. The research framework is shown (Fig. 1).

Fig. 1.

The analytical framework

Firstly, the coverage rate of basic health insurance in China has reached over 95% [10]. The government adopts three policy levers to improve the utilization rate of basic medical services in PHCIs: (1) higher outpatient copayment rate (≥ 85%); (2) lower hospitalization deductible (as low as 100 RMB); (3) cost-sensitive service packages. These policies have played a positive role in effectively incentivizing PHCIs to improve the efficiency of basic medical services. In addition, in recent years, the health insurance department has begun to implement payment method reform. The implementation of outpatient comprehensive prepayment and inpatient Diagnosis Related Groups (DRG)/ Diagnosis Intervention Packet (DIP) payment, will also play a positive role in cost control and efficiency improvement of medical services in PHCIs. Based on this, research hypothesis H1 is proposed: Fiscal subsidies of HIS will improve the basic medical services efficiency in PHCIs.

Secondly, the basic public health programs provide free public health services such as vaccination and health checkups for residents. The health department purchases public health services from PHCIs based on the number of permanent residents and service volume to promote the utilization rate of public health service in PHCIs. The funds for basic public health programs are allocated to PHCIs and provides a stable source of funding for PHCIs and promotes their innovative services models, such as family doctor contract services and “health banks”, improving the efficiency of public health services in PHCIs. Thus, the research hypothesis H2 is proposed: Fiscal subsidies of PHP will improve the public health services efficiency in PHCIs.

Thirdly, in most regions of China, PHCIs receive separate fiscal subsidies from HIS and PHP. The fiscal subsidies of HIS are mainly used to pay for the medical expenses of insured patients. Fiscal subsidies of PHP are mainly used to pay for disease prevention and health promotion of local residents. PHCIs bear dual functions, but the division management mode may lead to an imbalance in resource allocation, segmented and repetitive services. For example, the workload of managing and treating of chronic diseases in PHCIs is artificially divided. In addition, the limited human resources and facility equipment resources of PHCIs may lead to competition in resource allocation between basic medical services and public health services, resulting in low efficiency in PHCIs [17]. The research hypothesis H3 is proposed: The division management mode of fiscal subsidies may have negative impacts on the comprehensive service efficiency of PHCIs.

Method

Data envelopment analysis (DEA)

Data Envelopment Analysis (DEA) has been widely used to measure healthcare service efficiency [18]. DEA models evaluate entities as independent Decision-Making Units (DMUs) and construct an efficient production frontier using relevant input and output indicators. A composite technical efficiency score of 1 indicates efficiency. Among the various DEA models, the Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS) models are most commonly used. However, the variable returns to scale assumption of the Banker-Charnes-Cooper Model (BCC) can better fit the real-life situation to evaluate the efficiency of decision-making units, and is suitable for evaluating the efficiency of primary healthcare institutions [11]. This study assesses the service efficiency of PHCIs in China’s 31 provinces (municipalities and autonomous regions) covering the period 2010–2021 using an output-oriented BCC model. The calculation formula for the BCC model is as follows:

1

n indicates the number of decision units, and X and Y represent input and output variables, respectively. is the weight coefficients of input and output. This model solves the optimal weights through linear programming and is suitable for situations where the returns to scale are variable. It allows decision-making units to achieve optimal efficiency by adjusting the input-output scale. In the BCC model, TE measures the efficiency of a production unit in utilizing input factors at the current level of technology and management. Calculate using the following formula: TE = PTE × SE. When the efficiency score is less than 1, it indicates that the decision-making unit has not reached the forefront of production, that is, there is a problem of input redundancy or insufficient output. PTE reflects the input utilization efficiency of the production unit under the existing technology and management level, that is, maximizing output under the same input or minimizing input under the same output. If the PTE value is less than 1, it indicates the decision-making unit has not fully utilized resources under the existing technology and management level, and there may be management or technical defects. SE measures whether a production unit operates at its optimal scale, that is, whether it has achieved maximum economies of scale. If the SE value is less than 1, it indicates that the production scale of the decision-making unit has not reached the optimal state, which may lead to resource waste due to either excessive scale or insufficient scale.

The Malmquist index, as an extension of the basic DEA method, decomposes total factor productivity (TEP) into efficiency change and technological change. It dynamically captures changes in production efficiency and technological progress. In this study, TFP calculated using the DEA-Malmquist method reflects changes in the comprehensive service production efficiency of PHCIs. TFP greater than 1 indicates efficiency improvement from period T to T + 1, while TFP less than 1 indicates a decline.

Panel Tobit regression

The Tobit regression model, also known as a sample selection model or restricted dependent variable model, addresses the issue of limited dependent variable values. Since the technical efficiency (TE) calculated by DEA and its decomposed efficiency scores range between 0 and 1, regression analysis encounters two-sided censoring, rendering Ordinary Least Squares (OLS) estimates biased and inconsistent. Given the 2010–2021 sample period, this paper employs a panel Tobit regression model for analysis. Specific settings are as follows:

2

Among them, Y_it represents the TE and it’s decomposition efficiency of basic medical, public health and comprehensive services in PHCIs in various regions. represents the regression coefficient. X_it represents the influencing factors and represents the random error term. Since the services efficiency scores calculated by the BCC model are all greater than 0, the model is constructed as follows in the empirical analysis, using basic medical services efficiency of PHCIs as an example:

3

4

5

In the models, Cphmed_it denotes the TE estimated using the BCC model. Vrsmed_it and Scalmed_it respectively represent the PTE and SE derived from the decomposition of the TE. HIS indicates fiscal subsidies for healthcare insurance system. PHP represents fiscal subsidies for basic public health programs. Z stands for the control variables. i and t represent the provinces and years, respectively. and are the regression coefficients, and is the random error term. Model 2 examines the impact of fiscal subsidies on the TE of basic medical services in PHCIs. Models 3 and 4 estimate its effects on PTE and SE, respectively. The empirical models for public health services and comprehensive services follow a similar structure. The study uses variance inflation factors (VIF) to test for multicollinearity among variables. An average VIF is 6.28, which is below the threshold of 10, indicating that the variables passed the test.

Data sources

The data are primarily sourced from the China Health Statistical Yearbook (2011–2022) and the Statistical Yearbook of China (2011–2022), as well as relevant documents publicly available on government websites in various regions. The China Health Statistical Yearbook is an informative annual publication that reflects the development of China’s health sector and the health status of residents. Following adjustments to the statistical criteria, the China Health Statistical Yearbook has included a separate section on “Primary Medical and Health Services,” since 2011.The yearbook compiles indicators that reflect the input and output of primary healthcare in 31 provinces (municipalities, autonomous regions), including the number of visits, hospitalizations, institutions, beds, and personnel. The data meets research needs and is publicly available. For individual missing data points, interpolation is employed to fill gaps.

Input and output variable

Input index: Inputs for PHCIs mainly include capital and personnel. For capital inputs, most studies select the number of institutions and beds [11, 12, 19], while some also use indicators such as healthcare expenditure and the number of medical devices worth over 10,000. For personnel inputs, most scholars choose the number of health technicians [7, 11], while others select non-medical personnel and pharmacists. This study used the number of institutions, beds, and personnel in PHCIs as input indicators.

Output index: Yan Xiaochang (2018) classified healthcare institution outputs into three categories: outpatient care, inpatient care, and bed utilization. Most studies measure outputs using visit volumes and hospitalization rates [17]. Some studies incorporate additional metrics including bed occupancy rates, chronic disease management cases, health screenings conducted, and health education participants [11, 12]. This study used the number of outpatient services, inpatient services, and family health services of PHCIs as output indicators. Among them, the number of outpatient and inpatient services reflects the output of medical services, while the number of family health services reflects the output of public health services.

In summary, drawing on existing research and considering data availability, these indicators to some extent reflect the input and output levels of PHCIs, but there are also some shortcomings, such as the lack of quality assessment indicators in the output indicators and excessive reliance on administrative statistical data for data sources. The input-output index system of service efficiency evaluation of PHCIs are listed in Table 1.

Table 1.

Input-output index system of service efficiency evaluation of PHCIs

Input indicators	Output indicators	The type of services
Number of PHCIs (IN1) Number of beds (IN2) Number of personnel (IN3)	Number of visits to PHCIs (OM1) Number of inpatients (OM2)	Basic medical services
	Number of Family Health Services (OP3)	Public health services
	Number of visits to PHCIs (OM1) Number of inpatients (OM2) Number of Family Health Services (OP3)	Comprehensive services of “basic medical + public health”

Note: compiled by the author

Dependent variable

The service efficiency of PHCIs

Based on the input and output variables defined by the efficiency evaluation indicators system, this study employs an output-oriented Data Envelopment Analysis (DEA) method and BCC model to calculate the efficiency of PHCIs in China. It derives the TE, PTE, and SE of basic medical services, public health services, and comprehensive services across regions from 2010 to 2021.

Independent variable

Fiscal subsidies of healthcare insurance system (HIS) and fiscal subsidies for basic Public Health Programs (PHP) are the independent variables in this study. Following Zhu and Zhu (2022), fiscal subsidies of HIS is calculated by summing four parts: subsidies for the New Rural Cooperative Medical System of rural, subsidies for Residents’ Health Insurance of urban, subsidies for low-income groups to participate in health insurance and subsidies for urban and rural medical assistance. Based on Zheng and Shen (2019), fiscal subsidies of PHP are determined by multiplying the per capita public health fund compensation standard by the number of permanent residents in 31 provinces.

Control variable

Considering data availability, the study selects the following control variables (Table 2): basic health insurance coverage, the proportion of private hospitals (market competition), per capita GDP (economic development), elderly dependency ratio and provincial population (demographic structure), illiteracy rate (education level), urbanization rate, and the proportion of the number of beds/staff in PHCIs to the total number of beds/staff in healthcare institutions (resource allocation) [6, 8]. The selection of control variables fully considers various factors that may affect the efficiency of PHCI services, including population structure, economic development level, urbanization level, and health resource allocation structure, et. However, some variables are inevitably overlooked, such as policy differences across regions and the detailed classification of health resources.

Table 2.

Index system of research on the influence of fiscal subsidies on efficiency of PHCIs

Primary index	Secondary index	Three-level index
Dependent variable	Efficiency of PHCIs	TE of basic medical services, public health services and comprehensive services in PHCIs is calculated by DEA-BCC.
		PTE of basic medical services, public health services and comprehensive services in PHCIs is calculated by DEA-BCC.
		SE of basic medical services, public health services and comprehensive services in PHCIs is calculated by DEA-BCC.
Independent variable	Fiscal subsidies	Fiscal subsidies for Healthcare Insurance System (HIS): “fiscal subsidies for New Rural Cooperative Medical System of rural"+ “fiscal subsidies for Residents’ Health Insurance of urban"+ " subsidies for low-income groups to participate in health insurance “+ " subsidies for urban and rural medical assistance”
		Fiscal subsidies for basic Public Health Programs (PHP): “per capita basic public health funds subsidy standard” × “resident population in each province”
Control variables	Level of health insurance security	Coverage of basic health insurance
	Level of market competition	Number of private hospitals / number of hospitals
	Level of economic development	Per capita GDP
	population size	Population of each province
	Aging degree	Proportion of the population aged 65 and above
	Level of educational	Proportion of illiterate population in the population aged 15 and above.
	Level of urbanization	Urbanization rate
	Distribution of medical resources	Proportion of beds in PHCIs to the total number of beds in healthcare institutions
		Proportion of staffs in PHCIs to the total number of staffs in healthcare institutions

Note: compiled by the author

The definitions of the dependent variables, independent variables, and control variables are listed in Table 2, while the descriptive statistics for these three types of variables (i.e., dependent, independent, and control variables) are summarized in Table 3.

Table 3.

Descriptive statistics of variables

Variable	Full sample (N = 372)
	average value	standard deviation	minimum value	Maximum
Fiscal subsidies of healthcare insurance systems and basic public health programs
Fiscal subsidies for Health Insurance system (HIS, billion yuan)	13.851	11.521	0.462	55.291
Fiscal subsidies for basic public health programs (PHP, billion yuan)	2.108	1.715	0.075	10.654
Comprehensive technical efficiency and its decomposition of basic medical services in PHCIs
Comprehensive technical efficiency of basic medical services (Cphmed1)	0.780	0.212	0.256	1.000
Pure technical efficiency of basic medical services (Vrsmed1)	0.852	0.202	0.315	1.000
Scale efficiency of basic medical services (Scalmed1)	0.921	0.135	0.256	1.000
Comprehensive technical efficiency and its decomposition of public health service in PHCIs
Comprehensive technical efficiency of public health services (Cpublicedu1)	0.300	0.199	0.331	1.000
Pure technical efficiency of public health services (Vrspubedu1)	0.444	0.309	0.056	1.000
Scale efficiency of public health services (Scalpubedu1)	0.696	0.173	0.199	0.998
Comprehensive technical efficiency and its decomposition of comprehensive services in PHCIs
Comprehensive technical efficiency of comprehensive services (Cprimary)	0.802	0.206	0.256	1.000
Pure technical efficiency of comprehensive services (Vrspri)	0.861	0.184	0.468	1.000
Scale efficiency of comprehensive services (Scalpri)	0.934	0.109	0.475	1.000
Control variables
Basic health insurance coverage rate (Insurate)	0.636	0.318	0.129	1.211
Level of market competition (Mnumrate)	0.018	0.011	0.001	0.050
Per capita GDP (PGDP, trillion yuan)	5.224	2.778	0.208	16.493
Population of each province (population, million person)	44.354	28.220	3.002	126.840
The ratio of the elderly (ODR)	0.146	0.042	0.067	0.267
The rate of Illiteracy (illiteracy)	0.057	0.060	0.008	0.412
Urbanization rate (Town)	0.574	0.134	0.227	0.896
Proportion of beds in PHCIs to the total number of beds in healthcare institutions (Pbed)	0.260	0.107	0.039	0.579
Proportion of staffs in PHCIs to the total number of staffs in healthcare institutions (Pstaff)	0.753	0.295	0.375	2.770

Note: compiled by the author

Results

The efficiency of basic medical services in PHCIs shows a downward trend

Figure 2 illustrates that from 2010 to 2021, the average TE of basic medical services in PHCIs in China fluctuated between 0.701 and 0.813, with an overall downward trend—decreasing from 0.786 in 2010 to 0.701 in 2021. Decomposition using the DEA-BCC model showed that the average PTE ranged from 0.804 to 0.871, while the average SE ranged from 0.887 to 0.938. The respective average values for TE, PTE, and SE were 0.780, 0.852, and 0.921. These results indicate that the inefficiency of basic medical services in PHCIs is primarily attributable to low PTE, suggesting that input and output have not yet reached optimal levels.

Fig. 2.

Average efficiency of basic medical services in PHCIs from 2010 to 2021. Note: The average efficiency of basic medical services in PHCIs is calculated according to deap2.1. The blue, red, and green lines represent TE, PTE, and SE respectively

The efficiency of public health services in PHCIs is fluctuating and rising

Figure 3 shows that from 2010 to 2021, the average TE of public health services in PHCIs in China fluctuated between 0.194 and 0.418, with all efficiency scores falling below 0.5. This indicates significant inefficiency in public health services, suggesting substantial room for improvement in input and output optimization. Over this period, the average efficiency score exhibited a fluctuating upward trend, increasing from 0.194 in 2010 to 0.402 in 2021. Decomposition using the DEA-BCC model revealed that the average PTE ranged from 0.380 to 0.554, while the average SE ranged from 0.509 to 0.927. The respective average values for TE, PTE, and SE were 0.299, 0.444, and 0.705. These results suggest that the primary reason of inefficiency in public health services is low PTE. Additionally, there was a notable improvement in the TE and SE of public health services from 2016 to 2017.

Fig. 3.

Average value of public health services efficiency in PHCIs from 2010 to 2021. Note: The average efficiency of public health services in PHCIs is calculated according to deap2.1. The blue, red, and green lines represent TE, PTE, and SE respectively

The comprehensive services efficiency of PHCIs has not reached the optimal level

Figure 4 shows that from 2010 to 2021, the average TE of comprehensive services in PHCIs in China fluctuated between 0.754 and 0.825, with all scores falling below the effective value of 1. This indicates inefficiency in comprehensive services and suggests that input and output levels have not reached optimality. Over this period, the average efficiency score exhibited a downward trend, decreasing from 0.789 in 2010 to 0.754 in 2021. Decomposition using the DEA-BCC model revealed that the average PTE ranged from 0.825 to 0.880, while the average SE ranged from 0.912 to 0.944. The respective average values for TE, PTE, and SE were 0.802, 0.861, and 0.934.

Fig. 4.

Average value of comprehensive services efficiency of PHCIs from 2010 to 2021. Note: The average service efficiency of PHCIs calculated by the author is sorted according to deap2.1. The blue, red, and green lines represent TE, PTE, and SE respectively

The total factor productivity of comprehensive services in PHCIs fluctuates

Table 4 shows that the average total factor productivity of PHCIs in China was 0.986 from 2010 to 2021. TFP exhibited fluctuating trends over the period: it increased from 2010 to 2012, decreased from 2012 to 2015, rose again from 2015 to 2017, declined from 2017 to 2018, and then increased from 2018 to 2019 before falling to its lowest value of 0.866 in 2020. In 2020–2021, TFP rebounded to its highest value of 1.081. Overall, TFP showed a fluctuating upward trend from 2010 to 2021. Decomposition using the DEA-Malmquist index method revealed that the average annual growth rates of the technical efficiency change index and the technical change index were − 0.9% and − 0.6%, respectively. These results suggest that low management levels are the primary cause of the decline in TFP.

Table 4.

Average total factor productivity of comprehensive services in PHCIs in China from 2010 to 2021

Year	Technical efficiency change index	Technical change index	Pure technical efficiency change index	Scale efficiency change index	Productivity change index
2010—2011	1.003	0.978	0.967	1.038	0.981
2011—2012	1.009	1.046	1.024	0.986	1.055
2012—2013	0.985	1.017	0.970	1.016	1.002
2013—2014	1.018	0.952	1.016	1.002	0.969
2014—2015	1.002	0.966	1.002	1.000	0.968
2015—2016	1.014	0.991	1.017	0.997	1.004
2016—2017	0.996	1.012	1.002	0.994	1.008
2017—2018	0.980	0.961	0.996	0.983	0.941
2018—2019	0.958	1.030	0.966	0.991	0.987
2019—2020	0.979	0.884	0.974	1.005	0.866
2020—2021	0.965	1.120	0.980	0.985	1.081
Average	0.991	0.994	0.992	0.999	0.986

Note: The average total factor productivity of comprehensive services in PHCIs is calculated according to deap2.1

Influence of fiscal subsidies on the efficiency of basic medical services in PHCIs

Table 5 presents the regression results of the panel Tobit model. The HIS has a significantly positive effect on the TE of basic medical services in PHCIs at the 10% significance level, indicating that increased funding improves service efficiency. Conversely, PHP has a significantly negative effect at the 5% level, suggesting that higher funding for public health services reduces the efficiency of basic medical services. The influence coefficient of PHP (-0.069) is larger in magnitude than that of HIS (0.0442), indicating a stronger negative impact. Model (3) further confirms these findings with higher statistical significance.

Table 5.

Influence of fiscal subsidies on the efficiency of basic medical services in PHCIs

	Comprehensive technical efficiency of basic medical services and its decomposition
	(1) TE	(2) PTE	(3) SE
HIS	0.0442* (0.0251)	0.004 (0.0484)	0.0603*** (0.0214)
PHP	-0.069** (0.0330)	0.103* (0.0575)	-0.161*** (0.0276)
Control variable	Control	control	Control
Constant	-1.853* (1.015)	2.130** (0.992)	-2.736*** (0.600)
Sample	372	372	372
LR test of chibar2	560.77	419.79	349.12
Prob > = chibar2	0.000	0.000	0.000

Note: The brackets are the robust standard errors of clustering by province; *, * * and * * mean significant at the level of 10%, 5% and 1% respectively (the same in the following table)

Influence of fiscal subsidies on the efficiency of public health services in PHCIs

Table 6 presents the regression results of the panel Tobit model. The coefficient of HIS on the TE of public health services in PHCIs is -0.212, significantly negative at the 1% level, indicating that increased fiscal subsidies for medical services significantly reduces the efficiency of public health services. Conversely, the coefficient of PHP is 0.310, significantly positive at the 1% level, suggesting that increased fiscal subsidies for basic public health services significantly improves their efficiency. Models (2) and (3) confirm these findings, showing that higher medical service subsidies improves basic medical services efficiency but reduces public health services efficiency, while increased public health services subsidies have the opposite effect.

Table 6.

Influence of fiscal subsidies on the efficiency of public health services in PHCIs

	Comprehensive technical efficiency of public health service and its decomposition
	(1) TE	(2) PTE	(3) SE
HIS	-0.212*** (0.0432)	-0.231*** (0.0667)	-0.0969** (0.0449)
PHP	0.310*** (0.0568)	0.459*** (0.0853)	0.192*** (0.0607)
Control variable	Control	control	Control
constant term	1.757* (0.955)	8.510*** (2.109)	1.448 (1.066)
Sample size	372	372	372
LR test of chibar2	116.72	284.38	113.11
Prob > = chibar2	0.000	0.000	0.000

Influence of fiscal subsidies on comprehensive service efficiency of PHCIs

Table 7 presents the regression results of the panel Tobit model. The coefficient of HIS on the comprehensive service efficiency of PHCIs is -0.0109, indicating a negative effect, while the coefficient of PHP is 0.0315, indicating a positive but statistically insignificant effect. These results suggest that the divergent impacts of fiscal subsidies on comprehensive service efficiency highlight the inefficiency of decentralized subsidies mechanisms in improving comprehensive service efficiency in PHCIs.

Table 7.

Influence of fiscal subsidies on comprehensive service efficiency of PHCIs

	Comprehensive technical efficiency of comprehensive services and its decomposition
	(1) TE	(2) PTE	(3) SE
HIS	-0.0109 (0.0326)	-0.0111 (0.0496)	0.0294 (0.0260)
PHP	0.0315 (0.0421)	0.0927 (0.0615)	-0.0535 (0.0335)
Control variable	Control	control	Control
Constant	-0.801 (1.057)	1.990** (1.008)	-1.263** (0.590)
Sample	372	372	372
LR test of chibar2	434.47	403.30	177.96
Prob > = chibar2	0.000	0.000	0.000

Robustness test

To test the robustness of the Tobit benchmark regression results regarding the impact of fiscal subsidies on the service efficiency of PHCIs, the more robust merged minimum absolute deviation method (CLAD) is employed. CLAD requires the random disturbance term to be independent and identically distributed, and it allows for uniform estimation under conditions of non-normal distribution and heteroscedasticity of censored data. Under certain regularity conditions, the model estimator also follows an asymptotic normal distribution. The regression results are presented in Table 8. The influence coefficients are as follows: HIS has a significantly positive effect on basic medical services efficiency (0.187) and comprehensive services efficiency (0.168) at the 1% level, while it has a significantly negative effect on public health services efficiency (-0.284) at the 1% level. Conversely, PHP have a significantly negative effect on basic medical services efficiency (-0.102) and comprehensive services efficiency (-0.119) at the 1% level, but a significantly positive effect on public health services efficiency (0.332) at the 1% level. The similarity between CLAD and Tobit regression results indicates stable benchmark regression results.

Table 8.

Influence of fiscal subsidies on service efficiency of PHCIs–CLAD method

	(1) Efficiency of basic medical services		(2) Efficiency of public health services		(3) Efficiency of Comprehensive service
	Tobit method	CLAD method	Tobit method	CLAD method	Tobit method	CLAD method
HIS	0.0442* (0.0251)	0.187*** (0.0195)	-0.217*** (0.0526)	-0.284*** (0.0319)	-0.0436 (0.0388)	0.168*** (0.00226)
PHP	-0.0690** (0.0330)	-0.102*** (0.0259)	0.224*** (0.0690)	0.332*** (0.0365)	0.0536 (0.0501)	-0.119*** (0.00277)
Control variable	control	Control	Control	control	control	Control
Constant	-1.853* (1.015)	-1.003*** (0.340)	0.109 (1.085)	0.180 (0.530)	-0.828 (1.238)	-0.992*** (0.0425)
Sample	372	372	372	353	372	372

Discussion

The service efficiency of PHCIs has not reached the optimal level and is showing a downward trend in China. Although the Chinese government’s investment in PHCIs has been increasing in the new round of healthcare reform, the separation of fiscal subsidies entities and processes has posed certain obstacles for PHCIs to provide integrated and effective basic medical and public health services. In recent years, the Chinese health insurance department has begun to implement payment reform for outpatient and inpatient services, hoping to promote cost reduction and efficiency improvement of medical institutions. The health department hopes to promote the efficiency of public health institutions through government purchases of public health services. However, for PHCIs, the dual incentives of the health insurance department and the health department may lead to problems of unreasonable resource allocation and incentive misalignment, PHCIs may provide segmented or repetitive basic medical and public health services. It is difficult to effectively improve the efficiency of PHCIs through fiscal subsidies.

Based on this, the study proposes the following policy recommendations.

Encourage to explore institutional reforms to provide support for the integrated fiscal subsidies for PHCIs

China has started pilot reforms in some regions. For example, Huanghua City in Hebei Province is advancing institutional reforms in the health sector. In 2022, the health and health insurance departments were merged into a health insurance and health bureau at the county level, which unified the management of health insurance funds and public health funds. A comprehensive performance evaluation system was also designed to combine the evaluation indicators of health insurance and public health services, promoting the integration of medical and public health services in PHCIs. However, due to the short implementation period of the reform, the effectiveness of the policy requires further observation.

Based on the family doctor contract service, integrate the fiscal subsidies for HIS and PHP for PHCIs

Medical teams consisting of specialized doctors, general practitioners, and public health worker has been established in Xiamen, Fujian Province to provide medical and public health services to contracted residents. The cost of contracted services is shared among the health insurance fund, public health programs funds, and personal cash of contracted residents in a certain proportion. This integrated payment approach is recognized as an effective measure to encourage residents to choose PHCIs and improve the service efficiency of PHCIs (Liang jingang, 2023).

Adjusting the payment scope of health insurance fund to include public health services

It is an international trend to include public health services such as health checkups in the scope of health insurance payments [15]. At present, some regions in China have begun to explore the practice of adjusting the payment scope of health insurance fund [20], such as Sanmin City in Fujian Province has allocated a portion of the health insurance fund to purchase services such as disease prevention management and health education, and used the surplus of the health insurance fund to pay for family doctor contract services. Ningbo, Zhejiang Province has also included a portion of the family doctor contract service fee in the scope of health insurance payment, which has incentivized family doctors to provide family health services and improve the efficiency of both medical and public health services in PHCIs (Tian, 2020).

Limitations of the study

The limitations of this study are mainly reflected in the following aspects: firstly, due to the influence of data accessibility, the study cannot systematically conduct heterogeneity analysis between urban and rural areas. The case studies will be used to further compare the impact and mechanism differences of fiscal subsidies on service efficiency of PHCIs between urban and rural areas. Secondly, there is the issue of omitted control variables, as the service efficiency of PHCIs is influenced by other unobserved factors such as policy differences. The qualitative interviews will be conducted to further understand the impact of policy differences and other factors on the service efficiency of PHCIs. Thirdly, the data source of this study heavily relies on administrative statistical data. The next step, different data sources will be collected to further validate the research results.

Conclusion

This study reveals that the efficiency of PHCIs is influenced by multiple factors, including external environment, internal management, and fiscal subsidies mechanisms. Specifically, financial division management mode approaches negatively impact the comprehensive service efficiency of PHCIs. China should implement coordinated reforms to integrate fiscal subsidies between the health insurance system and public health programs to improve the service efficiency of PHCIs.

Abbreviations

PHCIs

Primary Health Care Institutions

DEA

Data Envelopment Analysis

BCC

Banker, Charnes and Cooper Model

TE

Technical Efficiency

PTE

Pure Technical Efficiency

SE

Scale Efficiency

TEP

Total Factor Productivity

HIS

Healthcare Insurance System

PHP

Public Health Programs

DRG

Diagnosis Related Groups

DIP

Diagnosis Intervention Packet

Author contributions

Dong WY: Writing–original draft. Shu Z & Huang ZF: Writing–review & editing.

Funding

There was no financial support or funding source for this study.

Data availability

The yearbook datasets used and/or analysed during the current study are public and available from the sources informed in the article.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.