Equity of Health Resources Allocation in Rural Guangxi from 2016 to 2019: An Empirical Analysis

Background: Since 2009, the main task of the new health reform in China is to increase the equity of health resources allocation in primary health care institutions. Health policies and strategies have been established to increase the capacity of PHC services, with improved equity as the most important goal. The objective of this study is to analyze the status quo and equity of health resources distribution in rural Guangxi from 2016 to 2019. Methods: Descriptive statistics analysis was used to analyze the status quo of health resource allocation in rural health center in Guangxi from 2016 to 2019. Lorenz curve, Gini coecient and Theil index were used to evaluate the equity of health resource allocation in rural health center in Guangxi from 2016 to 2019, from three dimensions of population, geography and economy. Results: From 2016 to 2019, the total amount of health resources in rural health center in Guangxi was increased, but the professional title and education background of health workers is still low. In 2019, the Gini coecient was 0.085-0.217 geographically, 0.080-0.367 demographically and 0.135-0.340 economically. The total Theil index was 013-0.211, and the majority of the contribution rate of within regions was greater than the between regions. Conclusion: From 2016 to 2019, the distribution of health resources in rural Guangxi was uneven among regions, and with great differences within regions.


Data sources
Demographic and geographic area data was sourced from Guangxi Statistical Yearbook 2017-2020 [12][13][14][15]. Data related to Rural health center was obtained from Guangxi Health Statistics Yearbook 2017-2020. Microsoft Excel 2019 was used to calculate the Gini coe cient, Theil index and drawn Lorenz curve.

Equity assessment
Gini coe cient and Lorenz curve Gini coe cient, Lorenz curve and Theil index has been identi ed as the superior tool for assessing the equity of resources allocation [16]. The Gini coe cient is derived from the Lorenz curve, which re ecting the ratio of the area between the curve and the diagonal line, to the whole area below the 45°line [17]. The following formula was used to calculate the Gini coe cient: S B = p 1 q 1 + , i=1, 2, 3……n (1) G = 1-p 1 q 1 -, i = 1,2 3……n (2) Where S B is the area bounded by the Lorenz curve and the axes; G standards for the value of the Gini coe cient, pi is the cumulative percentage of population/GDP/geographical area in each group, and qi is the cumulative percentage of health resources in each group.
G ranges from 0 to 1, the lower the G, the higher the degree of equity. In addition, G < 0.2 indicates absolutely equity in the allocation of health resources, G between 0.2 to 0.3 indicates relatively equality, G between 0.3 to 0.4 indicates lower equity level, G between from 0.4 to 0.5 indicates highly inequality, G > 0.5 indicates extreme inequity [18].
Compared with the Gini coe cient, Theil index could analyze the contribution rate between and within each group to the total, which is complementary to the Gini coe cient [20]. Theil index ranges from 0 to 1, the smaller the value, the higher the degree of equity [21]. The following formula was used to calculate the Theil index: Where T is the Theil index, T total is the total Theil index, T within is the within group Theil index, T between is the between group Theil index; Pi is the proportion of population/economic (GDP) /geographical area of each city in the total of the region; Yi is the ratio of the number of health resources of each city in the population/economy/geographical area to the total; Pg is the proportion of the population/economy/geographical area of each city in the in the total; Yg is the proportion of the total health resources of each region in the total of the whole region; Tg is the Theil index of each region.

Indicators
Previous study has been identi ed health worker, equipment as constituting quality of primary care [22]. Two groups of indicators were selected to re ecting the general information of health resources distributions and equity of health resources allocation, respectively [23,24]. General information of health resources allocation was measured by the total number of rural health centers (institutions)/ health care beds in the rural health centers (beds)/ health workers in the rural health centers (health workers)/doctors in the rural health centers (doctors)/ nurses per 1,000 rural population, institutions/ beds/ health workers/ doctors/ nurses per square kilometer (Km 2 ), and the ratio of doctors to nurses in Guangxi. Institutions, health workers and beds were selected as objects of equity assessment [17].

Results
General information of health resource distribution in rural Guangxi

Comparative analysis of per square kilometer health resource distribution in different regions
In 2019, the average number of institutions, beds, health workers, doctors and nurses per Km 2 in Guangxi were 0.005, 0.29, 0.33, 0.08 and 0.1, respectively, which were lower than the Mid-China region. In Guangxi, the average number of rural health resources per Km 2 in Baise, Hechi, Laibin and Chongzuo were lower than the average number of Guangxi (Table 1).

Structure of health workers in rural Guangxi
In 2019, the proportion of Junior college degree was acecounted for the largest (44.72%), followed by the Technical secondary school degree (43.77%), while the Senior High school and below degree was accounted for the least (0.57%). The proportion of No titles/ unknown was accounted for the largest (40.87%), while the proportion of Senior professional title was accounted for the least (0.04%) ( Table 3).  (Table 4).
By the population size, the Lorenz curves of the health workers was closest to the absolute equity curve, while the institutions was the farthest (Figure 1-2). This nding a rmed that the equity of health workers was the best while the institutions was the worst against population dimension. By the geographic size, the Lorenz curve of the institutions was the closest to the absolute equity curve, while the beds was the farthest (Figure 3-4). This nding veri ed that the equity of institutions was the best and that of the health workers was the worst in terms of the geographical dimension. By the GDP size, the Lorenz curve of the health workers was the closest to the absolute equity curve, while the institutions was the farthest (Figure 5-6). This nding veri ed that the equity of health workers was the best and that of the institutions was the worst in terms of the GDP dimension. Furthermore, the trends of G of health workers uctuated the most, dropping from 0.276 in 2016 to 0.135 in 2019. G of institutions in geographic and population size uctuated slightly, and all of them were less than 0.2 (Figure 7-9).

Theil index and contribution rate from 2016 to 2019
By population size, T total was lower than 0.1. By geographic size, except for beds, T total showed a decreasing trend from 2016 to 2019. By GDP size, except for health worker, T total showed a trend of increasing year by year. By population and geographic size, except for institutions, the contribution rate of Thiel index of health resources in rural Guangxi was T between < T within . In GDP size, except for beds, the contribution rate of Theil index of all health resources was T within > T between . The trends Theil index from 2016 to 2019 were similar as that of the Gini index. Overall, there was no signi cant change in the inter-group and intra-group differences in the contribution rate of Theil index (Table 5 and Figure 10-12).

Discussion
From the absolute number point of view, compared to the China, Eastern China, Mid-China and Western China regions, the AAGR of beds and doctors per 1,000 rural population was the highest. Furthermore, from 2016 to 2019, the number of health resources per 1,000 rural population in Guangxi is higher than that of China, Eastern China, Mid-China and Western China regions. Illustrating that the health service capacity in rural Guangxi has been improved from 2016 to 2019. However, the ratio of doctors and nurses in rural Guangxi was decreased from 0.91 in 2016 to 0.82 in 2019. This may be partially due to the fact that China has attaches great importance to the training of doctors, while neglecting the nurses [25]. Furthermore, from 2016 to 2019, the primary health investment in Guangxi was mainly used for the infrastructure construction of primary medical institutions, while ignoring the introduction and training of health workers in rural health centers to some extent, which was consistent with the ndings of other study [26]. In terms of the structure of health workers in rural Guangxi, the proportion of Junior college degree (44.72%) was the largest, while the Postgraduate degree was the least (0.06%). Furthermore, the proportion of No titles/unknown was the largest (40.87%), while the proportion of High professional title was the least (0.04%). In rural areas in China, health workers are badly quali ed, due to the lack of the training opportunities, which was consistent with the ndings of Zhu and Xiao [27].
Studies in China also found that the quality health resources tend to be concentrated in the general hospitals [28,29]. On account of the "Siphon effect", the majority of the health resources are concentrated in Nanning, the most developed city in Guangxi, while the underdeveloped rural areas are lack of health resources, this observation was in line with other studies [8, 19,30]. Low wages and restricted career development have often been blamed for the loss of health workers in rural health centers [31]. Meanwhile, general hospitals developed far more rapidly than rural health centers [32]. It may impose a risk of further enlarging the health service capacity gap between developed areas and remote poor regions, which is against the governmental effort to strengthen primary health service capacity in the new health reform. Although the government has invested heavily in PHC, the health service capacity of rural health centers still lags behind the hospital sector [6].
From 2016 to 2019, G of population size (0.068-0.217) was lower than that of geographic size (0.080-0.367) . Moreover, the trends of Theil index and Gini coe cient were similar from 2016 to 2019. The Chinese government were based on the number of population to allocating health resource, while ignoring the different geographical factors of each region [24]. Correspondingly, the fairness of health resource allocation by population size was much better than geographic size, which was consistent with the ndings of other researches [33,34]. By GDP and geographical size, the contribution rate were: T between >T within . It suggests that the regional economic differences might be the main reason for the unfairness of health resources in rural Guangxi, which was in line with the other study [19].
Furthermore, Baise, Hechi, Chongzuo and Laibin are the cities inhabited by ethnic minorities in Guangxi, where the population of ethnic minorities accounts for more than 75% of the total [5]. In 2019, the health resources per Km 2 in the ethnic minorities region in Guangxi was higher than the average level of Guangxi. In recent years, the government has formulated documents and increased investment for PHC to promote the development of health services in ethnic minorities regions. Currently, the health workers working in rural health centers have several encouragements: short-term centralism training, more wages and welfare, and cash bonuses. Furthermore, "Internet + medical" model was implemented in remote poor ethnic regions to promote the sharing of health resources, and encouraged the hospitals in the developed cities in Guangxi such as Nanning and Liuzhou, to provide health support for poor and remote ethnic minority regions, including technological resources, health manpower and nancing.
Based on our analysis, several policy implications to improve the overall equity of health resources allocation in rural Guangxi was as follows. First of all, the government guidance should be strengthened and increase the nancial support to further reducing the gap between the urban and rural areas. Secondly, the geographic of health resource allocation in vast and sparsely populated areas need to be addressed. Moreover, the health institutions are supposed to introduce adequate health workers in remote and economically underdeveloped areas by giving extra subsidies and other preferential policies to ameliorate the inequity status. Last but not least, health workers determined the prosperity and decline of rural health centers to some extent. The salary and welfare of health workers in rural areas should be improved gradually. In order to reduce the inequity of health resources allocation in Guangxi, stakeholders, including policymakers, governments, health workers and patients, should strive to cooperate jointly in order to ameliorate the situation. In addition, investments for PHC needs to be increased signi cantly in underdeveloped regions.

Conclusion
In conclusion, this study provides suggestive evidence for the equity in the distribution of health resources in rural Guangxi by elucidating the changing trends of health resources allocation from 2016 to 2019. The continuous growth in the number of health resources in rural areas would further meet the health needs of rural residents. This study found that the equity of health resources in rural Guangxi is signi cantly higher by population than by geographic area and GDP, which substantiates the results of previous studies [8,21]. In addition, the equity status in the distribution of the institutions, beds and health workers in rural Guangxi have deteriorated since 2016. Current development of health manpower practice in multiple sites and telemedicine are very encouraging, which would greatly improve the PHC service capacity and alleviate the problem of shortage of health resources in rural areas. In addition, the assessment for fair distribution of other health resources needs to be performed by using multiple analysis methods.