Abstract

Background

Price levels of medical services may vary across regions with different income levels, which would raise concerns about the equal access to medical services. This study aimed to estimate the spatial price index of medical services to measure price levels across 31 provincial regions in China.

Methods

Price data were collected from medical service price schedule in each region. Two methods based on the Purchasing Power Parities were used to estimate the spatial price index and measure price differences across regions. The two-way fixed effects models were used to examine the association between medical service price levels and income levels, and further investigate the impacts of price differences on utilization of medical services and medical expenditure.

Results

The consistent estimation results were given by two methods. Medical service price level in the highest-price region was found to be 74% higher than the lowest. There was a significant negative correlation between price levels and income levels, as well as price levels and the utilization of outpatient services. Moreover, we also found a 1% increase in medical service price level was significantly associated with a 0.34% and 0.24% increase in the medical service expense per outpatient visit and per inpatient respectively.

Conclusions

Regions in China had significant gaps in medical service price levels. Policymakers should pay more attention to regional price differences and take great measures such as enhancing financial protection to ensure the equal access to medical services and better achieve the universal health coverage.

Background

The price of goods or services differs not only across countries, but also across regions within a country. It鈥檚 of great importance to take regional price differences into account especially for assessing regional inequality, comparing the real Gross Domestic Product (GDP) and the income, and ensuring the balanced development across regions [1, 2]. Several national institutes, such as the U.S. Bureau of Economic Analysis and the Office for National Statistics of the UK, have officially released the spatial price index to measure the price differences across states and metropolitan areas [3, 4]. With the development of the International Comparison Program (ICP), the methodology of spatial price index (SPI) based on the Purchasing Power Parities (PPPs) has been widely used in measuring regional price differences [2, 5]. A number of studies have measured the general price levels across regions using the spatial price index [6, 7], and found a positive correlation between price levels and income levels [8,9,10]. This correlation is also known as the Penn Effect, where high-income regions tend to have higher price levels [11].

In the healthcare sector, the price of medical services can ensure the costs of services are covered and provide incentives for providers [12]. It鈥檚 also the critical factor accounting for growth in health expenditure [13]. Measuring regional price differences of medical services may help explain regional variations in health expenditure and help policymakers take actions to improve health system performance and equity [14]. Theoretically, the price level of medical services should also have a positive association with the income level. Providers in high-income regions tend to have higher costs for delivering medical services including employee wages and office rent, so they need to charge higher prices to offset the variation of costs due to geographic location [15]. Meanwhile, patients in low-income regions could be more likely to experience financial hardship when faced with higher prices, especially for those not covered by health insurance. This would raise concerns about the access to medical services and the health equity. Some countries have taken into account the price differences of medical services across regions. For example, the Centers for Medicare & Medicaid Services in the U.S. has employed the Geographic Practice Cost Index to adjust fee-for-service payments in the Medicare Physician Fee Schedule [15], and the National Health Service in England has calculated the market forces factor presented in the payment index to vary the prices to reflect differences in unavoidable costs between providers [16]. However, studies on price comparisons across regions in the field of healthcare mainly focused on drug and consumable prices [17,18,19], whereas medical service prices were often compared in time series to reflect the price change over time [20]. Few studies have measured regional price differences of medical services and it鈥檚 still unclear the association between medical service price levels and income levels.

In China, medical services are usually provided by public medical institutions. The specification of medical service price schedule was formulated at the national level, while the administration of medical service price was decentralized to the provincial level. The provincial department of medical service price administration in each region has established its own medical service price schedule with a unique code for each service and set the price ceiling for each service in public medical institutions within the region. Some provinces have further devolved the price administration authority to the prefecture-level. This directly led to the differences in codes and prices of medical services across regions. From 2012, China has implemented the nationwide reform of public hospitals and gradually cancelled the drug markup with price adjustments for medical services, which made medical service revenue become the major source of hospital revenue [21]. With the increasing health expenditure and the budget constraints of medical insurance funds, medical service price has gradually become the focus of healthcare reforms. To further establish the dynamic price adjustment mechanism, the National Healthcare Security Administration (NHSA) of China has launched a pilot program for deepening medical service price reform in 2021 and proposed that regions with similar socioeconomic status should establish reasonable price linkage for medical services. Therefore, it鈥檚 important to measure price differences of medical services across regions in China, especially when considering the regional disparities of medical resources [22]. However, the differences of price schedules due to the decentralization of administration made it difficult to develop a basket of medical services that were comparable across regions, which also limited related research. No study has measured the price differences of medical services across 31 provincial regions in China.

With the rapid development of information technology, the NHSA of China has established the national unified healthcare security information system in 2022 and constructed a national coding system for medical services. Each provincial region has also mapped its own medical service codes with the national codes so that we could match the comparable services by the national codes, which makes price comparisons across regions possible. Take these needs into account, the present study aimed to estimate the spatial price index of medical services to measure price differences across 31 provincial regions in China. On this basis, this study examined the correlation between price levels of medical services and income levels, and further investigated the impact of price differences on utilization of medical services and medical expenditure. Findings from this study may have implications for further price adjustment in China, and may also provide lessons for other countries.

Methods

Data sources

The medical service price schedules by September 2023 were obtained from the Health Commission and the Healthcare Security Administration of each provincial region. Furthermore, price schedules were updated to December 2023 based on relevant price adjustment policies. The highest prices in price schedules of each region were selected to ensure comparability. The annual average price for 2023, estimated as the simple arithmetic average of prices at these two time points, was used as price data in the calculation of the spatial price index [23].

After matching using the national codes, 1116 medical services were identified and formed the basket for price comparison. Some provinces didn鈥檛 set the specific price for certain services in price schedules and therefore only 34,158 price data of 1116 services were obtained. The national specification divided medical services into four categories, followed as general medical services (including registration services, nursing services, etc.), medical diagnosis services (including examinations, testing services), clinical treatment services (including therapy services, surgery services), and traditional treatment services (including traditional therapy services). These 1116 services could be divided into above four categories based on the national specification. However, considering the availability of weights data, services in Category 鈥淐linical Treatment鈥� and Category 鈥淭raditional Treatment鈥� were merged into one category (Category 鈥淐linical and Traditional Treatment鈥�). Therefore, 1116 services were divided into three categories followed as general medical services, medical diagnosis services, and clinical and traditional treatment services. The three categories served as the basic heading in the calculation of the spatial price index. Table听1 showed the structure of the basket compared with the national specification. The Cramer鈥檚 V coefficient was 0.09, which indicated the similarity and the representativeness in structure.

Table 1 The components of the basket compared with the national specification

To ensure the comparability as much as possible, we used the Resistant Fences method to detect the outlier price data [24]. For the log transform price of each service in the basket, let $\:{q}_{25}=$ the first quartile, $\:{q}_{75}=$ the third quartile, and $\:H={q}_{75}-{q}_{25}$, the interquartile range. The incomparable price was defined as less than $\:{q}_{25}-k*H$ or greater than $\:{q}_{75}+k*H$, where $\:k$ is a constant and equal to 2 in this study. After that, 772 (2.26%) price data were excluded, and 33,386 price data were used finally to estimate the spatial price index.

The expenditure weight of each basic heading in each region were also needed to estimate the spatial price index, which could show the relative importance of each category. The weights were computed using the revenue data of public hospitals from the China Health Yearbook, which provided data on medical services revenue for different categories including registration, nursing, examination, therapy, and surgery services in 2021. Specifically, we directly obtained the amounts of examinations and surgery services in each region. For other categories, we used the national average proportions in total outpatient and inpatient revenue for each category, and the outpatient and inpatient revenue in each region to estimate the amounts of each category for each region. After that, we were able to calculate expenditure weights of basic headings in each region.

Methodology of the spatial price index

Due to 31 regions were involved in the price comparison, the spatial price index was constructed as a multilateral price index rather than a bilateral price index for comparison only between two regions. Compared with the bilateral price index, the multilateral price index could satisfy several fundamental properties such as transitivity and base country invariance [2, 25]. Transitivity means that the direct price index between any two regions yields the same result as an indirect comparison via any other region, and base country invariance means that the price index between any two regions should be the same regardless of the choice of base region [26]. The PPPs, developed by the ICP, has been a widely used multilateral index for comparing price levels across regions [27], which showed the ratio of prices for the same basket of goods and services in different regions. This study used PPPs to estimate the spatial price index of medical services to measure price differences across 31 provincial regions in China. The detailed steps are as follows.

The basic heading index

The first step was to estimate the basic heading index between every two regions. The Country Product Dummy (CPD) method, which was first introduced by Summers (1973) [28], was used to estimate the basic heading index. This method could deal with the fact that not all prices of medical services were available in all regions and was the standard method used in the ICP. The CPD method can be described as the following regression model:

$$\:ln\:{p}_{ij}=\sum\:_{j=1}^{M}{\gamma\:}_{i}{D}_{j}+\sum\:_{i=1}^{N}{\pi\:}_{i}{D}_{i}^{*}+{\mu\:}_{ij}$$

(1)

where $\:ln\:{p}_{ij}$ is the price of medical service $\:i$ in region $\:j$, $\:{D}_{j\:}(j=\text{1,2},\dots\:,M)$ and $\:{D}_{i}^{*}(i=\text{1,2},\dots\:,N)$ represent, respectively, the dummy variables for $\:M$ regions in the comparisons and the dummy variables for $\:N$ medical services in basic heading. The basic heading index between region $\:j$ and $\:k$ can be derived as:

$$\:{PPP}_{jk}=\frac{\text{e}\text{x}\text{p}\left({\widehat{\gamma\:}}_{j}\right)}{\text{e}\text{x}\text{p}\left({\widehat{\gamma\:}}_{k}\right)}$$

(2)

Aggregation above basic headings

The second step was to aggregate basic heading index into a bilateral price index between every two regions using the expenditure weights data in each region. The Fisher index, which was the geometric mean of Laspeyres index and Paasche index, was used and shown as following formulas:

$$PPP_{jk}^{Laspeyres}\; = \;\frac{{\sum\nolimits_i^N {pij \cdot qik} }}{{\sum\nolimits_i^N {pik \cdot qik} }}\; = \;\sum\nolimits_i^N {PPP_{jk}^i \cdot {W_{ik}}}$$

(3)

$$PPP_{jk}^{Paasche}\; = \;\frac{{\sum\nolimits_i^N {pij \cdot qij} }}{{\sum\nolimits_i^N {pik \cdot qij} }}\; = \;\frac{1}{{\sum\nolimits_i^N {\frac{1}{{PPP_{jk}^i}} \cdot {W_{ij}}} }}$$

(4)

$$PPP_{jk}^{Fisher}\; = \;{\left( {PPP_{jk}^{Laspeyres}\; \cdot \;PPP_{jk}^{Paasche}} \right)^{\frac{1}{2}}}$$

(5)

where $\:{W}_{ik}$ represents the expenditure weight of basic heading $\:i$ in region $\:k$ and $\:{W}_{ij}$ represents the expenditure weight of basic heading $\:i$ in region $\:j$.

The multilateral index

The final step was to adjust the bilateral index between every two regions to the multilateral index, which could measure price differences across regions. There have been various methods proposed to compute the multilateral index with the development of the ICP, and two methods were used in this study. The first and main method was the 骋颈苍颈-脡濒迟别迟枚-碍枚惫别蝉-厂锄耻濒肠 (GEKS) method, which originated with Gini (1930), and was independently rediscovered by 脡ltet枚 and K枚ves (1964) and Szulc (1964). It鈥檚 the standard method in ICP, which has the advantage of that each region is treated in a symmetric way and is fully consistent with the economic approach to index number theory [23]. The multilateral index between region j and k based on GEKS method can be calculated by the following formula:

$$\:{GEKS}_{jk}={\prod\:}_{l}^{M}{\left[{F}_{jl}\bullet\:{F}_{lk}\right]}^{1/M}$$

(6)

where $\:{F}_{jl}$ represents the Fisher index between region $\:j$ and $\:l$, $\:{F}_{lk}$ represents the Fisher index between region $\:l$ and $\:k$, and $\:M$ represents the number of regions for comparison.

Another one was the minimum spanning tree (MST) method, which was introduced by R. J. Hill (1999). It鈥檚 an alternative to the GEKS method in the ICP, which has the advantages that it uses a superlative index number formula for forming bilateral links and takes into account substitution effects [23]. The multilateral index based on MST method was calculated through a spanning tree from the Paasche-Laspeyres Spread, which showed the similarity between the Paasche index and the Laspeyres index [29], and was given by the following formula:

$$\:{PLS}_{jk}=\left|log\frac{{PPP}_{jk}^{\text{L}\text{a}\text{s}\text{p}\text{e}\text{y}\text{r}\text{e}\text{s}}}{{PPP}_{jk}^{\text{P}\text{a}\text{a}\text{s}\text{c}\text{h}\text{e}}}\right|$$

(7)

The intraclass correlation coefficient (ICC) was used to assess the agreement between two methods and ensure the robustness of the estimation [30].

Statistical analysis

To further estimate the long-term spatial price index for statistical analysis, this study first used the Medical Services Consumer Price Index (MSCPI) for each region to extrapolate the GEKS results of 2023 by the following formula [10]:

$$\:{GEKS}_{jk,t-1}=\frac{{GEKS}_{jk,t}\times\:{MSCPI}_{k,t}}{{MSCPI}_{j,t}}$$

(8)

where $\:{GEKS}_{jk,t}$ and $\:{GEKS}_{jk,t-1}$ represent GEKS index between region $\:j$ and $\:k$ in period $\:t$ and $\:t-1$, $\:{MSCPI}_{j,t}$ and $\:{MSCPI}_{k,t}$ are MSCPI of region $\:j$ and $\:k$, respectively. The MSCPI in some regions were unavailable and the Healthcare Price Index was used instead.

Based on the extrapolation, we constructed a panel data on medical service price levels of 31 regions from 2015 to 2023. To examine the correlation between price levels and income levels, we estimated a two-way fixed effects regression model (Model 1), which could be written as follows:

$$\:{lnPrice}_{it}={\beta\:}_{0}+{{\beta\:}_{1}lnIncome}_{it}+{\mu\:}_{i}+{\gamma\:}_{t}+{\epsilon\:}_{it}$$

(9)

where $\:{lnPrice}_{it}$ represents the log-transformed GEKS index estimated by the formula 8 of region $\:i$ in year $\:t$, $\:{lnIncome}_{it}$ is the log-transformed disposable income per capita of region $\:i$ in year $\:t$, $\:{\mu\:}_{i}$ and $\:\:{\gamma\:}_{t}$ are region fixed effect and time fixed effect respectively, $\:{\epsilon\:}_{it}$ is the error term. We also added a set of time-varying control variables to the basic model (Model 2). According to relevant policies, the capacity of medical insurance fund balance was the key factor when the departments of price administration considered price adjustments. To avoid endogeneity problems, we used the affordable months by the fund at the beginning of the year as the control variable, which was defined as the accumulated fund balance at last year-end divided by the average monthly expenditure over the last year. Besides, the population characteristics including the proportion of population aged 65 years and above, the health system characteristics including the basic medical insurance coverage, the density of physicians and hospital beds, were also included as the control variables. In addition, we selected the time interval before the COVID-19 pandemic (from 2015 to 2019) to repeat the above regression as the robustness check (Model 3).

We also investigated the impact of price differences using the two-way fixed effects regression model, which could be written as follows:

$$\:{ln\:Y}_{it}={\beta\:}_{0}+{{\beta\:}_{1}lnPrice}_{it}+{{\rm\:X}}_{it}^{{\prime\:}}\tau\:+{\mu\:}_{i}+{\gamma\:}_{t}+{\epsilon\:}_{it}$$

(10)

where $\:{ln\:Y}_{it}$ represent the log-transformed variables of interest, including the average number of annual visits per capita (Model 4), the hospitalization rate (Model 5), the average medical expense per visit (Model 6), the average medical service expense per visit (Model 7), the average medical expense per inpatient (Model 8), and the average medical service expense per inpatient (Model 9). $\:{lnPrice}_{it}$ is the log-transformed GEKS index estimated by the formula 8 of region $\:i$ in year $\:t$. $\:{{\rm\:X}}_{it}^{{\prime\:}}$ is a set of time-varying control variables including the GDP per capita, the proportion of population aged 65 years and above, the basic medical insurance coverage, the density of physicians and hospital beds, the average length of stay in hospitals, and the out-of-pocket health expenditure as a share of the total health expenditure (THE). $\:{\mu\:}_{i}$ and $\:\:{\gamma\:}_{t}$ are region fixed effect and time fixed effect respectively, $\:{\epsilon\:}_{it}$ is the error term.

The average medical service expense was estimated by deducting the average medication expense from the average medical expense. All variables above except for the proportion variable were log transformed. Two-way cluster-robust standard errors (at region and time level) were used in all models to account for the potential cross-sectionally and serially correlation [31]. All variables鈥� data were collected from the China Statistical Yearbook and the China Health Yearbook. All calculation process and statistical analysis were done using R (version 4.3.2).

Results

Regional price differences of medical services

Figure听1 shows the geographic distribution of medical service price levels across 31 provincial regions in China. Based on the GEKS method, Liaoning province had the highest price level of medical services with an SPI of 118.7, which means the price level of Liaoning was 18.7% higher than that in Shanghai, whereas in Guangxi province the price level was the lowest with an SPI of 68.22 and medical services were priced at 68.22% of that in Shanghai. The max/min ratio of the SPI was 1.74, which means the price level of medical services in Liaoning province was 74% higher than that in Guangxi province. The detailed estimates of the SPI are shown in Table听2.

Fig. 1

The price levels of medical services across 31 provincial regions in China. Notes The price level of Shanghai was 100, based on the GEKS method

Table 2 The estimated spatial price index of medical services for 31 provincial regions in China

The descriptive statistics of the spatial price index under different estimation methods are shown in Table听3. The estimation results given by the two methods were similar in terms of descriptive statistics. The ICC was close to 1, which showed the consistency of results from different methods.

Table 3 Descriptive statistics of the spatial price index for medical services

Table听4 presents the descriptive statistics of the extrapolated spatial price index. The standard deviation of the SPI was 15.05 in 2015 and 12.77 in 2023. Similarly, the max/min ratio also dropped from 1.95 in 2015 to 1.74 in 2023. The descending trend of these descriptive statistics shows that the price differences of medical services across regions has narrowed over this period.

Table 4 Descriptive statistics of the spatial price index from 2015 to 2023

Correlation between price levels and income levels

The regression estimates from Model 1, Model 2 and Model 3 are presented in Table听5. In Model 1, there was a significant negative correlation between medical service price levels and income levels. After controlling for the covariates (Model 2), the sign and significance of the correlation are consistent with Model 1, which is also the case in the robustness check (Model 3). Besides, there was a positive association between medical service price levels and the affordable months by the fund in both Model 2 and Model 3, which further indicated the capacity of medical insurance fund balance may be the critical factor considered by the departments of price administration when implementing price adjustment.

Table 5 The two-way fixed effects model results of correlation between medical service price levels and income levels

Impacts of price differences

Table听6 shows the impacts of medical services price differences on variables of interest. A significant negative association between medical service price levels and annual visits per capita was found in Model 4. By contrast, there was no significant association between medical service price levels and hospitalization rates according to Model 5. In terms of the impacts on medical expenditure, we found that a 1% increase in medical service price levels was significantly associated with a 0.34% and 0.24% increase in the medical service expense per outpatient visit and per inpatient respectively, while there was no significant association between price levels and total medical expense.

Table 6 The two-way fixed effects model results for the impact of medical service price differences

Discussion

To our best knowledge, this is the first study to estimate the spatial price index of medical services and measure price differences across 31 provincial regions in China. The sample price data involved in this study covered over 1000 medical services and had similar structure compared with the national specification, which highly demonstrated the representativeness. In order to ensure the robustness of the estimation, this study used two different methods based on PPPs to estimate the spatial price index and obtained consistent estimation results. According to the results, this study found that there were significant price differences of medical services across regions in China. The price level in the highest-price region was found to be 74% higher than the lowest. This was understandable due to the decentralization of price administration. This study also found a descending trend in the descriptive statistics for the extrapolated results of spatial price index, which shows the spatial price convergence of medical services and is consistent with general price converging in China found by previous studies [32]. This study presented the methodology for estimating the spatial price index of medical services and measured current price differences across regions, which could provide implications for future price adjustments. With the advancement of the deepening medical service price reform in China, the departments of medical service price administration should pay more attention to the price differences across regions and the spatial price index of medical services could be used as a decision tool to monitor regional price levels and provide references for price adjustments.

To examine the association between price levels and income levels, this study constructed a two-way fixed effects model based on the panel data of 31 provincial regions. A significant negative correlation between price levels and income levels was found, which is in contrast to the Penn effect shown in the general price level [10]. This is not surprising considering the health system and the medical service price administration in China. The commonweal of public medical institutions was emphasized when the departments of price administration set the price ceiling for public medical institutions to ensure the access to medical services. Such price may not fully cover the cost of delivering medical services, so China has implemented medical service price reform and established the dynamic price adjustment mechanism in recent years. However, the regional price levels were given less consideration due to the decentralization, while the capacity of medical insurance fund balance was the critical factor during the price adjustment process. This was further confirmed by our empirical analysis. A positive association between price levels and affordable months by medical insurance fund was found, which indicates the capacity of fund balance could facilitate the price adjustment and thus drive the price level of medical services higher. This may also explain to a certain extent why price levels of medical services was negatively associated with income levels. Previous studies have found that regions with higher socioeconomic levels tend to have less medical insurance fund balance [33], which may further restrain the rise in price level. This study aimed to examine the correlation between price levels of medical services and income levels, the determinants and influencing mechanisms of medical service price levels need to be further explored in future works. What鈥檚 more, further studies should focus on the reasonable price differences across regions. Considering the specific nature of medical services, more indicators should be included in addition to the cost and the income level, such as the insurance fund balance and the economic burden for patients. The pilot program for deepening medical service price reform has proposed to establish the dynamic price adjustment mechanism by medical service categories, so regional price levels could be further measured for different categories.

As highlighted before, price differences of medical services across regions may have substantial impacts on the utilization of medical services and regional medical expenditure variations, which were also indicated by the results of our empirical analysis. This study found a significant negative association between medical service price levels and average number of annual visits per capita, which presents the price elasticity of demand for medical services and is consistent with previous studies [34]. In contrast, no significant association was found between medical service price levels and hospitalization rates, which may be due to higher reimbursement rates for inpatient services than outpatients in China, making inpatient services less sensitive to price levels [35]. In terms of the impacts on medical expenditure, no significant association between medical service price levels and total medical expense was found for outpatients or inpatients. This is mainly because the total medical expense could also be determined by pharmaceutical prices and other nonprice factors. Nevertheless, we found a positive association between medical service price levels and medical service expense for both outpatients and inpatients. More specifically, a 1% increase in medical service price levels may result in a 0.34% increase in medical service expense per outpatient visit, while the elasticity for inpatients was lower at 0.24%. However, the medical service expense in this study was estimated by deducting the average medication expense from the average medical expense, and such estimations for inpatients may still contain non-service expense such as consumable expense, thereby underestimating the impact of medical service price levels for inpatients. Combined with the correlation between price levels and income levels, price differences of medical services across regions may raise concerns about the access to medical services and the equity. In future price adjustments, China should narrow and keep reasonable price differences of medical services across regions and take great measures to reduce the impacts of price differences such as increasing medical insurance coverage and providing more financial protection to patients in low-income regions.

This study also has the following limitations. Firstly, the comparability is an important principle in regional price comparisons. We matched the same services using national codes and detected the outlier price data to ensure the comparability. However, the differential characteristics of medical services may still exist due to different price schedule across regions, which may lead to misestimation of regional price levels. Furthermore, the same service may still have different quality in different regions, which could lead to the underestimation or overestimation of price levels. Future studies should measure and account for differences in the quality of medical services to estimate more accurate spatial price index. Secondly, we used the price data in the medical service price schedule of each region, which was the ceiling price for public medical institutions. However, the medical service price may vary within regions due to the decentralization, which may lead to price data used in this study deviating from actual prices. Future studies should consider prices from hospital charge data. Thirdly, the ideal weights data should be from the same year as price data, while this study used the estimated weights data for 2021 due to data availability. Similarly, we used the MSCPI to extrapolate the spatial price index due to lack of the historical price data. In future studies, the periodic price and weights data need to be gathered to facilitate the estimation of the spatial price index.

Conclusion

Measuring price differences of medical services across regions has significant importance for ensuring the equal access and future price adjustments. This study estimated the spatial price index of medical services to measure price differences and found that regions in China had significant gaps in medical service price levels. What鈥檚 more, price levels tended to have a negative association with income levels and the price differences may have further impacts on utilization of medical services and medical expenditure. This would raise concerns about the access to medical services and the health equity especially for patients in low-income region. Findings from this study may suggest that countries including China should pay more attention to regional price levels of medical services and promote the regional coordination to further optimize the price levels and keep reasonable price differences across regions. Great measures should be taken including increasing medical insurance coverage and enhancing financial protection to ensure patients have equal access to medical services regardless of the income level and better achieve the universal health coverage.