Equity and trend predictions of human resources for health allocation at the Centers for Disease Control and Prevention in China, 2005-2020

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This study aimed to comprehensively analyze CDC HRH in terms of the quantity, quality and equity of allocation, and offer sound recommendations for strengthening HRH at the CDCs. Method This study provided a descriptive analysis of the quantity and quality of CDC HRH using indicators such as the total number of CDC staff, public health workforce density, age, education level and technical title. The Gini coefficient and agglomeration degree were used to measure the equity of CDC HRH allocation. The grey model first order one variable (GM (1,1)) was used to predict the number of HRH at the CDCs. Results From 2005 to 2020, the public health workforce density of CDCs was below the Chinese government's required standard of 1.75 per 10,000 residents. The CDCs have always faced the problem of understaffing and attrition. The GM (1,1) model showed that the number of CDC HRH will continue to decrease from 2021 to 2025. In addition, the quality of CDC HRH showed a gradual aging trend and a lack of high-quality talent. The Gini coefficient indicated that the overall equity of CDC HRH allocation by population was relatively better than that by geographical area. The aggregation degree showed significant differences in the equity of CDC HRH allocation in the eastern, central and western regions. Conclusions The findings indicate that it is necessary to further optimize the number and structure of CDC HRH and enhance the equity of resource allocation among different regions. However, these results were not due to a single cause. It is essential to improve existing policies and establish effective planning to strengthen the public health workforce at the CDCs and meet the needs of the public health system. Human resources for health CDC Inequality Quality GM (1 1) Agglomeration degree Figures Figure 1 Background In China, the Centers for Disease Control and Prevention (CDCs) play a critical role in China's public health system as the leading technical organizations and operating divisions of health commissions at all levels[ 1 ]. The basic functions of CDCs include developing, implementing or evaluating proven strategies, tracking diseases, responding to public health emergencies and so on[ 2 , 3 ]. In the face of the COVID-19 pandemic[ 4 ] and the growing burden of major chronic diseases[ 5 ], efforts to improve the accessibility and efficiency of high-quality human resources for health (HRH) at the CDCs have become the key issue in future-oriented public health system development. HRH are widely recognized as a decisive factor of health system performance and health outcomes[ 6 ]. Strengthening the CDC HRH has been a priority for China's public health system since the severe acute respiratory syndrome (SARS) epidemic in 2003[ 7 ], but understaffing and brain drain have been persistent problems in CDCs[ 8 ]. The number of CDC staff was 206,485 in 2005 after the SARS epidemic, but only 187,564 CDC staff responded to the COVID-19 outbreak in 2019. The shortage of CDC HRH has been further exacerbated by the COVID-19 epidemic. It is necessary to establish effective HRH planning to ensure the sustainability of CDC HRH. Regarding the lack of HRH, the World Health Organization (WHO) emphasized the need to focus on the availability, accessibility, acceptability and quality of HRH[ 9 ]. Thus, HRH planning is based on maintaining the most appropriate rationality in quantity, quality and equity. Forecasts of human resource development trends can contribute quantitatively to HRH planning and the optimal allocation of health resources[ 10 ]. There are various HRH projection approaches, and each of these approaches has advantages and limitations[ 11 ]. The grey model (GM) was proposed by Professor Deng Julong in the 1980s[ 12 ] and introduced by health management experts into health manpower forecasting. It can suit various data types. The grey model first order one variable (GM (1,1)) is the core of the grey model, which has the advantages of high accuracy, small sample data, and simple calculation principles and methods[ 13 ]. The GM (1,1) has been widely used in the prediction of HRH in China and has shown good forecasting results[ 14 ]. While in CDC HRH prediction, the GM (1,1) was less used. The equitable allocation of HRH is a prerequisite for health equity[ 15 ], and the Chinese government has always been committed to promoting equal health resources allocation to ensure health equity. The “Healthy China 2030” plan issued and implemented by the State Council in 2016 once again emphasized the need to adhere to the principle of equity [ 16 ]. However, inequality in the allocation of HRH is an ongoing issue within the CDCs[ 17 ]. The shortage of HRH complicates inequality and leads to an excessive focus on the quantity of HRH [ 18 ]. In addition, mainland China has a vast territory, with 31 provinces, autonomous regions and municipalities that can be divided into eastern, central and western regions according to economics and geographical location[ 19 ]. CDCs are divided into national, provincial, municipal and district/county levels[ 17 ], covering the eastern, central and western regions and providing public services to residents within their jurisdictions. There were 3,384 local CDCs in 2020. The Chinese government determines the allocation of CDC HRH mainly on the basis of residents[ 20 ], with insufficient consideration given to the different demographic and geographical characteristics of the regions, which further leads to inequities in CDCs. Thus, when developing HRH plans, it is necessary to measure the inequality of HRH allocation in CDCs across regions, identify priority geographical areas and consider the causes of inequalities. The main research methods used in studies on the equity of HRH allocation are the Gini coefficient/Lorenz curve and Thayer index[ 21 , 22 ]. The Gini coefficient is limited to reflecting the overall allocation and is usually used in combination with other methods[ 23 ]. The Thayer index cannot take geography into account when decomposing regional inequality[ 19 ]. The agglomeration degree proposed in this study is able to incorporate the combined effects of geographic and demographic factors[ 24 ]. When analyzing the inequality in CDC HRH across regions, the combination of the Gini coefficient and aggregation degree is more appropriate. Indeed, inadequate and unequal HRH is prevalent in many countries [ 21 , 25 ]and solving this issue has become a priority in recent years. However, the quality of human resources has been neglected, and relevant research is lacking. There has been little improvement in strengthening the structure and skills of the health workforce in the health system. The structure of human resources is a reflection of quality, and previous studies on human resources at the CDCs [ 2 ]or other health human resources[ 26 ] have rarely mentioned personnel structure in China. Therefore, with the above discussion, this study aimed to use CDC HRH data from 2005 to 2020 to describe trends in quantity and structure, analyze the equity of allocation through the Gini coefficient and aggregation degree and forecast the number of CDC HRH from 2021 to 2025 through the GM (1,1). This study can help to ensure effectiveness and comprehensiveness when developing human resource planning to improve the accessibility and efficiency of CDC HRH, and may provide broader information for research on HRH. Methods Data sources This study collected and analyzed data obtained from the China Health Statistical Yearbook (2006–2021). The China Health Statistical Yearbook, an informative annual publication reflecting the development of China's health and the health status of residents, is published annually by the National Health Commission[ 26 ]. The data used in this study were released by the Chinese government and are reliable and authentic. Indicators Human resources for health in CDCs CDC staff consist of health technicians, other technicians, management staff and logistics staff. Health technicians are a subset and core of CDC staff, and are primarily responsible for health-related tasks. The Chinese government requires health technicians to account for over 70% of the CDC staff, and the proportion of health technicians was 73%-77% from 2005 to 2020. Therefore, two categories of indicators were chosen to measure the HRH of CDCs: CDC staff and health technicians. In addition, the structure of the four types of personnel that make up the CDC HRH are also listed separately in the China Health Statistical Yearbook. This study used health technicians to represent the quality of HRH in CDCs. Health technicians: medical practitioners, assistant medical practitioners, registered nurses and other health professionals. Other technicians: non-health professionals who engag in technical work such as medical device repair, scientific research and teaching. Management staff: staff with leadership or management responsibilities Logistics staff: staff who undertake duties such as maintenance and logistical support services. Regional division The eastern region comprises Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong and Hainan; the central region includes Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei and Hunan; and the western region is composed of Inner Mongolia, Chongqing, Guangxi, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang. Data analysis Descriptive statistics Public health workforce density [ 7 ]and average annual growth rate were used to reflect the trend in human resources. The public health workforce density of CDCs = number of CDC staff/number of residents (per 10,000) Equity analysis Gini coefficient The value of the Gini coefficient ranges from 0 to 1. The closer the value is to 1, the more unequal the allocation. G below 0.2 is absolute equality, 0.2–0.3 is relative equality, 0.3–0.4 is basic equality, 0.4–0.5 is relative inequality, and above 0.5 is serious inequality [ 27 ]. The calculation formula is as follows: G = 1- \(\sum _{i=1}^{n}(\) X i – X i−1 ) (Y i +Y i−1 ) Where n is the total number of provinces (n = 31 in this study), X i is the cumulative percentage of the population and geographical area, and Y i is the cumulative percentage of HRH (i = 1, 2, … n). Agglomeration degree The health resources agglomeration degree (HRAD) is used to evaluate the equity of health resources allocation among different regions according to geographical area [ 24 ]. HRAD > 1, indicates that health resources are too concentrated based on geographical area; HRAD = 1, indicates that the allocation of health resources has absolute equity by geographical area; HRAD < 1, indicates that the equity of health resources allocation by geographical area is poor. The population agglomeration degree (PAD) is an indicator of how concentrated a region's population is relative to the region as a whole. The HRAD/PAD evaluates the equity of health resources allocation between different regions by population[ 28 ]. If HRAD/PAD = 1, health resources and population are perfectly balanced; if HRAD/PAD > 1, there is a relative surplus of health resources based on the population; and if HRAD/PAD < 1, the allocation of health resources is inequitable among different regions according to the size of the population. The calculation formula is as follows: HRAD i = (HR i /HR n )/(A i /A n ) PAD i = (P i /P n )/(A i /A n ) HR i and HR n are the total amount of health resources in region i and all regions, respectively; A i and A n are the land area of region i and all regions, respectively; P i and P n are the population in region i and all regions, respectively. Forecast analysis GM (1,1) model Using the number of CDC staff and health technicians from 2005 to 2020 as raw data, the GM (1,1) model was programmed with MATLAB (R2021a) to forecast the number of CDC staff and health technicians from 2021 to 2025. In this study, the accuracy of the model predictions was tested by means of the posterior error ratio (C) and small error probability (P). The judgment of the model accuracy level is shown in Table 1 . Table 1 Judgment standard of prediction accuracy for GM (1,1) Accuracy grade Posterior error ratio (C) Small error probability (P) Grade 1 (Excellent) C ≤ 0.35 P ≥ 0.95 Grade 2 (Qualified) 0.35 < C ≤ 0.5 0.80 ≤ P < 0.95 Grade 3 (Barely qualified) 0.5 < C ≤ 0.65 0.70 ≤ P 0.65 P < 0.7 Results Trends in the development of HRH in CDCs, 2005–2020 The number of CDC staff and health technicians showed a gradual downward trend from 2005 to 2019, decreasing by 18,921 and 18,611, respectively. In 2020, there was an increase of 6,861 and 5,390 compared to 2019. The average annual growth rates for CDC staff and health technicians were − 0.40% and − 0.58%, respectively, from 2005 to 2020. While the proportion of health technicians exceeded the standard of 70%, it fell from 76.7% in 2005 to 74.7% in 2020. The Chinese standard for the public health workforce density of CDCs is no less than 1.75 CDC staff members per 10,000 residents. The public health workforce density of CDCs reduced from 1.58 per 10,000 to 1.38 per 10,000 from 2005 to 2020, which was far below the Chinese standard. Table 2 presents the specific changes in CDC HRH from 2005 to 2020. Table 2 Numbers and densities of CDC staff and health technicians, 2005–2020 Year Population (per10,000) CDC staff Health technicians Number Density Number Number of health technicians/ number of CDC staff 2005 130756 206485 1.58 158450 76.7% 2006 131448 202377 1.54 154196 76.2% 2007 132129 197209 1.49 148512 75.3% 2008 132802 197106 1.48 148519 75.3% 2009 133474 196687 1.47 148450 75.5% 2010 134091 195467 1.46 147347 75.4% 2011 134735 194593 1.44 145198 74.6% 2012 135404 193196 1.43 141261 73.1% 2013 136072 194371 1.43 143101 73.6% 2014 136782 192397 1.41 142297 74.0% 2015 137462 190930 1.39 141698 74.2% 2016 138271 191627 1.39 142492 74.4% 2017 139008 190730 1.37 142114 74.5% 2018 139538 187826 1.35 140491 74.8% 2019 140005 187564 1.34 139839 74.6% 2020 141178 194425 1.38 145229 74.7% Average annual growth rate 0.51% -0.40% -0.91% -0.58% — Changes in the structure of HRH in CDCs, 2005–2020 The proportion of health technicians aged 55 and older was 4.9% in 2005, gradually rising to 9.7% in 2010, 11.5% in 2015 and 17.1% in 2020. The ratio of young (34 years and below), middle-aged (35–54 years old) and older (55 years and older) health technicians was 6.73:12.67:1 in 2005 and 1.30:3.55:1 in 2020. The health technicians showed an aging trend. In 2020, 46.9% of health technicians had a bachelor's degree or above, an increase of 31.5% compared to 2005. The level of education improved substantially. The proportion of health technicians with a master’s degree in 2020 was 7.1%, an increase of only 1.9% compared to 2015. The proportion of highly educated health technicians was low and increased slowly. From 2005 to 2020, most health technicians had primary and middle title, and those with a senior and associate senior title increased by only 1.9% and 5.1%, respectively. In 2020, only 11% of health technicians had senior title. The structure of the CDC HRH is illustrated in Fig. 1 . Overall inequality of CDC HRH based on the Gini coefficient, 2005–2020 Table 3 shows the results of the equity analysis of CDC HRH by the Gini coefficient. The overall inequality in the allocation of CDC staff and health technicians was similar from 2005 to 2020. The Gini coefficients of CDC staff and health technicians allocated by population were all less than 0.2, indicating absolute equity. The Gini coefficients of CDC staff and health technicians allocated by geographical area were all greater than 0.5, representing high inequity. The overall equity of CDC HRH allocation by population was relatively better than that by geographical area. Table 3 Gini coefficient of CDC HRH by population and geographical area,2005–2020 Year Allocation by population Allocation by geographical area CDC staff Health technicians CDC staff Health technicians 2005 0.1837 0.1858 0.5939 0.5903 2010 0.1615 0.1638 0.5915 0.5841 2015 0.1494 0.1491 0.5907 0.5791 2020 0.1559 0.1602 0.5874 0.5769 Regional inequality of HRH in CDCs based on the agglomeration degree, 2005–2020 From 2005 to 2020, the HRAD of CDC staff and health technicians ranged from 3.0–4.0 in the eastern region, and 1.6-2.0 in the central region. The CDC HRH allocated based on geographical area were overconcentrated in the eastern and central regions. The HRAD of CDC staff and health technicians in the western region increased each year, from 0.41 and 0.41 in 2005 to 0.48 and 0.50 in 2020, respectively. The allocation of CDC HRH remained extremely inequitable by geographic area in the western region. The order of the equity of CDC HRH allocation by geographical area was as follows: eastern region > central region > western region. The HRAD/PAD of CDC staff and health technicians in the eastern region was less than 1 and gradually decreased from 2005 to 2020, both decreasing to 0.82 in 2020. The allocation of CDC HRH in the eastern region gradually became unequal based on population. The HRAD/PAD of CDC staff and health technicians ranged from 1.11–1.32 in the western region, and 0.98–1.14 in the central region. The CDC HRH met the medical needs of the population in the central and western regions, and the residents had better access to health services. The order of the equity of CDC HRH allocation by population was as follows: western region > central region > eastern region. The results of the equity analysis of HRH in CDCs among the eastern, central and western regions by agglomeration degree are presented in Table 4 . Table 4 Agglomeration degree of CDC HRH in the eastern, central and western regions, 2005–2020 Year Region PAD CDC staff Health technicians HRAD HRAD/PAD HRAD HRAD/PAD 2005 eastern 3.46 3.19 0.92 3.16 0.91 central 1.82 2.00 1.10 2.00 1.10 western 0.36 0.41 1.14 0.41 1.14 2010 eastern 3.67 3.21 0.87 3.27 0.89 central 1.80 1.93 1.07 1.89 1.05 western 0.38 0.42 1.11 0.43 1.13 2015 eastern 3.70 3.16 0.85 3.20 0.86 central 1.79 1.85 1.03 1.74 0.97 western 0.38 0.45 1.18 0.47 1.24 2020 eastern 3.84 3.14 0.82 3.16 0.82 central 1.70 1.76 1.04 1.66 0.98 western 0.38 0.48 1.26 0.50 1.32 Equity analysis of CDC HRH in 31 provinces, municipalities and autonomous regions, 2020. As shown in the Table 5 , among 31 provinces, autonomous regions and municipalities, the HRADs in Inner Mongolia, Tibet, Gansu, Ningxia, Xinjiang, and Heilongjiang were less than 1 in 2020, of which only Heilongjiang was in the central region and the other provinces were in the western region. The allocation of CDC HRH in the eastern region of Shanghai, Beijing and Tianjin was overconcentrated by geographical area, with HRADs greater than 8. The difference between the HRADs of CDC staff and health technicians in Shanghai and those in Tibet both exceeded 22. Except for Chongqing, the HRAD/PAD values in the western region were all over 1, and among the top three with good equity were Tibet, Qinghai and Xinjiang. In contrast, the HRAD/PAD of provinces such as Hebei, Shanghai, Jiangsu, Zhejiang, Shandong, Guangdong, Anhui and Jiangxi were less than 1, and most of these provinces were located in the eastern region. Table 5 Agglomeration degree of CDC HRH in 31 provinces, municipalities and autonomous regions in 2020 Region PAD CDC staff Health technicians HRAD HRAD/PAD HRAD HRAD/PAD Eastern Region Beijing 9.11 11.13 1.22 12.51 1.37 1.37 Tianjin 7.91 8.81 1.11 8.90 1.12 1.12 Hebei 2.70 2.22 0.82 2.00 0.74 Liaoning 1.97 1.89 0.96 1.78 0.91 Shanghai 26.78 23.17 0.87 23.46 0.88 Jiangsu 5.40 4.71 0.87 4.86 0.90 Zhejiang 4.18 2.79 0.67 3.00 0.72 Fujian 2.29 2.13 0.93 2.16 0.94 Shandong 4.45 3.66 0.82 3.77 0.85 Guangdong 4.79 3.00 0.63 3.00 0.63 Hainan 1.94 2.29 1.18 2.27 1.17 Central Region Shanxi 1.52 1.53 1.00 1.43 0.94 Jilin 0.88 1.19 1.36 1.16 1.32 Heilongjiang 0.46 0.61 1.33 0.60 1.30 Anhui 2.97 1.82 0.61 1.96 0.66 Jiangxi 1.85 1.64 0.89 1.78 0.96 Henan 4.06 4.93 1.21 3.79 0.93 Hubei 2.12 2.13 1.00 2.25 1.06 Hunan 2.14 2.23 1.04 2.18 1.02 Western Region Inner Mongolia 0.14 0.24 1.75 0.26 1.84 Chongqing 2.66 1.79 0.67 1.76 0.66 Guangxi 1.44 1.66 1.15 1.75 1.21 Sichuan 1.18 1.37 1.17 1.37 1.16 Guizhou 1.49 1.53 1.02 1.64 1.10 Yunnan 0.82 1.19 1.46 1.30 1.59 Tibet 0.02 0.05 2.65 0.06 2.96 Shaanxi 1.31 1.65 1.25 1.69 1.29 Gansu 0.40 0.52 1.30 0.52 1.28 Qinghai 0.06 0.11 1.88 0.11 2.02 Ningxia 0.74 0.84 1.14 0.94 1.27 Xinjiang 0.11 0.17 1.63 0.18 1.72 Forecast analysis of HRH in CDCs from 2021 to 2025 The posterior error ratio (C) was less than 0.35 and the small error probability (P) was greater than 80% for both indicators, which means that the fit of each model was good. The results of the prediction model calculations showed that the number of CDC staff and health technicians will continue to decline from 2021 to 2025, with forecasts of 185,176 and 136,239, respectively, in 2025. Table 6 shows the detailed data. Table 6 GM (1,1) model prediction results of CDC HRH Indicator Model prediction formula C P Predicted value 2021 2022 2023 2024 2025 CDC staff \(\widehat{X}\) ( 1 ) (k)=-52443984.84 e − 0.0038( k−1) + 52650469.84 0.1651 0.875 187798 187281 186577 185875 185176 Health technicians \(\widehat{X}\) ( 1 ) (k)= -30044639.14 e − 0.0050( k−1) + 30203089.14 0.1935 0.813 138997 138302 137611 136923 136239 Discussion This study provided a comprehensive analysis of the quantity, quality and equity of HRH allocation at the CDCs and identified several key issues. First, China's public health system is facing a shortage and turnover of HRH in CDCs. From 2005 to 2020, the number of CDC staff and health technicians decreased by 12,060 and 13,211 respectively. The public health workforce density of CDCs was consistently far below the standard of 1.75 per 10,000 residents. Although the number of CDC staff rose significantly in 2020, the shortage of CDC staff was 52,637 according to current Chinese standards. The Chinese government set a development goal of 250,000 CDC staff by 2025 in the “14th Five-Year Plan for National Health” [ 29 ]. The GM (1,1) model projections for the number of CDC staff and health technicians from 2021 to 2025 declined continuously. The GM (1,1) model projected that the number of CDC staff will be 185,176 in 2025, which is 64,824 less than the target. The public health workforce is not sufficient to meet the population's demand for public health services. While the definition of the public health workforce is not entirely consistent in each country, the lack of a public health workforce is a common problem worldwide. For example, the public health workforce decreased by nearly 10% from 2012 to 2019 in the United States[ 25 ]. Low wages and a lack of long-term career opportunities are the main reasons for the decline in the global public health workforce [ 30 ], and China's public health workforce faces the same dilemma. In China, the CDCs are fully funded by the government budget, and the funding for CDCs is significantly lower than that for other health institutions [ 5 ]. The results in CDC staff being underpaid, which is not commensurate with the excessive workload. The technical title is not only a grade title to identify the business level and professional ability of HRH in CDCs but also a reflection of career advancement. The professionals with senior title increased by only 1.9% from 2005 to 2020. Furthermore, previous surveys of job willingness for Masters[ 31 ] and PhDs[ 32 ] in public health in China have also emphasized their reluctance to work in CDCs due to salary, career development, and lack of promotion opportunities. This is coupled with the low social recognition of CDC work and the lack of full awareness and respect for CDC staff[ 3 ]. These reasons combined have led to staff turnover and shortages, and directly affect the quality of HRH in CDCs. The quality of CDC HRH has shown an aging trend, with 17.1% of staff over 55 years of age in 2020, and the lack of good articulation between the older, middle-aged and younger professional. CDCs have a low percentage of health technicians with high education levels and technical titles. CDCs are knowledge-intensive units in China. The academic qualifications and technical titles are concrete expressions of the quality of HRH and are fundamental to the development of CDCs. Young people are crucial to strengthen the capacity building of CDCs[ 33 ]. The current development in the number and structure of CDC HRH may lead to inefficient delivery of public health services and affect the quality development of public health system. Attracting and retaining CDC talent is the only way to reduce staff turnover, expand staff numbers and rationalize staffing structures, which are equally critical to enhancing the public health system. Since the COVID-19 epidemic, China has gradually strengthened its public health system, establishing the National Bureau of Disease Prevention and Control in 2021 to reform China's prevention and control system[ 34 ]. The “14th Five-Year Plan for National Health” was released in 2022[ 29 ], which emphasized the need to substantially improve the capacity of public health services by 2025. Based on this, we suggest that more attention needs to be paid to the issue of public health workforce development. The Chinese government needs to focus on the current human resources situation in CDCs and retain public health workforce by enhancing the salary level of CDC HRH. The CDCs should focus on salary rationalization, improving career development opportunities and increasing the attractiveness of positions in CDCs. In addition, universities should be guided to expand enrollment and accelerate the construction of a high-level public health talent training system to provide the CDCs with sufficient high-quality talent. Finally, it is worth noting that inequalities in the allocation of HRH in CDCs are also evident, with inequality between health technicians and CDC staff being similar. The Gini coefficient indicated that the equity of CDC HRH allocation by population outperformed that by geographical area from 2005 to 2020, which is the same as the results of studies on general practitioners[ 24 ] and traditional Chinese medicine[ 28 ]. Geographical area has long been neglected in the allocation of HRH. The aggregation degree made it clear that there are significant regional differences in CDC HRH, with differences mainly in the eastern and western regions. The equity of CDC HRH allocation is best in the eastern region by geographical area, with overconcentration of HRH in some provinces and cities (e.g., Shanghai, Beijing, Tianjin, etc.), yet the equity of allocation by population is worst in the eastern region. The eastern region accounts for only 11% of the geographical area but has 43% of the population. Cities in the eastern region, such as Shanghai and Beijing, are very attractive to people due to their good economic development and geographical location, resulting in an overly dense population. CDC HRH are unable to meet the health needs of the population in the eastern region. The western region is characterized by 27% of the population and 71% of the area. The equity of CDC HRH in the sparsely populated western region is best allocated by population and worst by geographical area (mainly Inner Mongolia, Tibet and Qinghai and Xinjiang). Although the Chinese government has always favored talent policies for the western region[ 35 ], the economy of the western region is inferior to that of the central and eastern regions[ 19 ]. The western region has disadvantages in terms of resource inflow and talent introduction, and the economic and service radius are not fully taken into account when allocating HRH. Thus, the western region is still in a state of extreme inequity despite the gradual rise in geographical equity in the allocation of CDC HRH. In the central region, CDC HRH are relatively more equitable in terms of population and geographical area. However, it is noteworthy that the equity of the allocation of health technicians by population is declining in the central region. It is a distinct fact that regional differences in geographic location and economic development contribute to further inequalities in CDC HRH. To ensure equitable access to public health services for residents in all regions, the Chinese government should fully consider factors such as service population, service radius and economic disparities when setting human resources standards for each region. Moreover, certain policies and talent support should be given to provinces and cities with weak CDC construction, a lack of HRH and less economically developed areas to promote the coordinated and balanced development of CDC HRH in all regions. CDCs in different regions can strengthen cooperation and learn from each other to improve the capacity of CDC staff and maximize the use of available HRH. Our study has several limitations. The equity of allocation is limited to the number of HRH and does not reflect inequities in the quality of HRH. In addition, we chose to reflect the quality of HRH through the structure of health technicians and did not include changes in the structures of other technicians, management staff or logistics staff. Last, the GM (1,1) model used in this study is only able to predict based on the characteristics of the data itself but does not take into account social policies or emergencies. Future studies on forecasting and equity analysis of CDC HRH should be more comprehensive. Conclusions Through a comprehensive analysis of the HRH in CDCs from 2005 to 2020, we identified directions for HRH planning to ensure public health workforce that is adequate, well-trained, and capable of ensuring health equity. What clearly needs to be addressed is the lack and attrition of CDC staff, improving the aging of staff and the lack of high-level talent, and striving to narrow the equity gap in the allocation of CDC HRH in different regions. In this regard, we recommend that factors such as population, service radius and economic developments be taken into account when allocating CDC HRH. The Chinese government ensure that attract and retain more public health workforce by strengthening financial investment in CDCs and giving policy preference to areas with weak CDCs. Abbreviations CDCs Centre for Disease Control and Prevention HRH Human Resources for Health WHO World Health Organization SARS Severe Acute Respiratory Syndrome GM grey model GM (1,1) grey model first order one variable Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable. Acknowledgements Not applicable. Availability of data and material The data were collected from the China Health Statistical Yearbook (2006-2021), which were published by the National Health Commission of China (http://www.nhc.gov.cn/mohwsbwstjxxzx/tjtjnj/new_list.shtml). For access to related datasets generated by the authors, please contact the corresponding author. Competing interests we have no competing interests Funding The study was supported by the Philosophy and Social Science Fund for Colleges and Universities in Jiangsu Province (2023SJYB0292) Authors' contributions LH and CJR conceptualized this study. CJR and XSQ were responsible for data collection and analysis. XXP guided the construction of predictive models. CJR wrote the first draft of the paper. MGL and WQF read and revised the manuscript. LH critically commented the paper. All authors read and approved the final manuscript. References Meng QY. [Transformation and reform of the functions of centers for disease prevention and control in the new era]. Zhonghua Yu Fang Yi Xue Za Zhi. 2019;53(10):964–7. Li C, Sun M, Shen JJ, Cochran CR, Li X, Hao M. Evaluation on the efficiencies of county-level Centers for Disease Control and Prevention in China: results from a national survey. Trop Med Int Health. 2016;21(9):1106–14. Li J, Xu J, Zhou H, et al. Working conditions and health status of 6,317 front line public health workers across five provinces in China during the COVID-19 epidemic: a cross-sectional study. BMC Public Health. 2021;21(1):106. Cao Y, Shan J, Gong Z, Kuang J, Gao Y. Status and Challenges of Public Health Emergency Management in China Related to COVID-19. Front Public Health. 2020;8:250. Wang L, Wang Z, Ma Q, Fang G, Yang J. The development and reform of public health in China from 1949 to 2019. Global Health. 2019;15(1):45. Cheng J, Kuang X, Zeng L. The impact of human resources for health on the health outcomes of Chinese people. BMC Health Serv Res. 2022;22(1):1213. Li C, Sun M, Wang Y, et al. The Centers for Disease Control and Prevention System in China: Trends From 2002–2012. Am J Public Health. 2016;106(12):2093–102. Shan Y, Liu G, Zhou C, Li S. The Relationship Between CDC Personnel Subjective Socioeconomic Status and Turnover Intention: A Combined Model of Moderation and Mediation. Front Psychiatry. 2022;13:908844. Dubey S, Vasa J, Zadey S. Do health policies address the availability, accessibility, acceptability, and quality of human resources for health? Analysis over three decades of National Health Policy of India. Hum Resour Health. 2021;19(1):139. Van Greuningen M, Batenburg RS, Van der Velden LF. The accuracy of general practitioner workforce projections. Hum Resour Health. 2013;11:31. Pagaiya N, Phanthunane P, Bamrung A, Noree T, Kongweerakul K. Forecasting imbalances of human resources for health in the Thailand health service system: application of a health demand method. Hum Resour Health. 2019;17(1):4. Cheng T, Bai Y, Sun X, Ji Y, Zhang F, Li X. Epidemiological analysis of varicella in Dalian from 2009 to 2019 and application of three kinds of model in prediction prevalence of varicella. BMC Public Health. 2022;22(1):678. Ren W, Tarimo CS, Sun L, et al. The degree of equity and coupling coordination of staff in primary medical and health care institutions in China 2013–2019. Int J Equity Health. 2021;20(1):236. CHEN Ze-bing DZ-c, Fei SONG, et al. Analysis on equity and trend prediction of health human resources allocation in Guangdong Province Since the new health care reform[J]. Volume 36. SOFT SCIENCE OF HEALTH; 2022. pp. 61–4. 3. Li D, Zhou Z, Si Y, et al. Unequal distribution of health human resource in mainland China: what are the determinants from a comprehensive perspective? Int J Equity Health. 2018;17(1):29. Yang C, Cui D, Yin S, et al. Fiscal autonomy of subnational governments and equity in healthcare resource allocation: Evidence from China. Front Public Health. 2022;10:989625. Li YQ, Chen H, Guo HY. Examining Inequality in the Public Health Workforce Distribution in the Centers for Disease Control and Prevention (CDCs) System in China, 2008–2017. Biomed Environ Sci. 2020;33(5):374–83. Zhu B, Hsieh CW, Zhang Y. Incorporating Spatial Statistics into Examining Equity in Health Workforce Distribution: An Empirical Analysis in the Chinese Context. Int J Environ Res Public Health. 2018;15(7). Yang L, Cheng J. Analysis of inequality in the distribution of general practitioners in China: evidence from 2012 to 2018. Prim Health Care Res Dev. 2022;23:e59. National Health Commission of the People’s Republic of China. Guidelines of authorized size standard of Centers for Disease Control and Prevention. http://cyfd.cnki.com.cn/Article/N2017100162000223.htm . Accessed 24 April 2023. Mollahaliloglu S, Yardim M, Telatar TG, Uner S. Change in the geographic distribution of human resources for health in Turkey, 2002–2016. Rural Remote Health. 2021;21(2):6478. Wiseman V, Lagarde M, Batura N, Lin S, Irava W, Roberts G. Measuring inequalities in the distribution of the Fiji Health Workforce. Int J Equity Health. 2017;16(1):115. Wu X, Zhang Y, Guo X. Research on the Equity and Influencing Factors of Medical and Health Resources Allocation in the Context of COVID-19: A Case of Taiyuan, China. Healthc (Basel). 2022;10(7). Yu Q, Yin W, Huang D, et al. Trend and equity of general practitioners' allocation in China based on the data from 2012–2017. Hum Resour Health. 2021;19(1):20. Michaels I, Pirani S, Fleming M et al. Enumeration of the Public Health Workforce in New York State: Workforce Changes in the Wake of COVID-19. Int J Environ Res Public Health. 2022;19(20). Xu R, Mu T, Liu Y, Ye Y, Xu C. Trends in the disparities and equity of the distribution of traditional Chinese medicine health resources in China from 2010 to 2020. PLoS ONE. 2022;17(10):e0275712. Yu H, Yu S, He D, Lu Y. Equity analysis of Chinese physician allocation based on Gini coefficient and Theil index. BMC Health Serv Res. 2021;21(1):455. Dai G, Li R, Ma S. Research on the equity of health resource allocation in TCM hospitals in China based on the Gini coefficient and agglomeration degree: 2009–2018. Int J Equity Health. 2022;21(1):145. The State Council. The 14th Five-Year Plan for National Health. http://www.gov.cn/zhengce/content/2022-05/20/content_5691424.htm . Accessed 24 April 2023. Hilliard TM, Boulton ML. Public health workforce research in review: a 25-year retrospective. Am J Prev Med. 2012;42(5 Suppl 1):17–28. Li H, Zheng F, Zhang J, et al. Using Employment Data From a Medical University to Examine the Current Occupation Situation of Master's Graduates in Public Health and Preventive Medicine in China. Front Public Health. 2020;8:508109. Liu S, Gu Y, Yang Y, Schroeder E, Chen Y. Tackling brain drain at Chinese CDCs: understanding job preferences of public health doctoral students using a discrete choice experiment survey. Hum Resour Health. 2022;20(1):46. Wong BLH, Siepmann I, Chen TT, et al. Rebuilding to shape a better future: the role of young professionals in the public health workforce. Hum Resour Health. 2021;19(1):82. State Commission Office of Public Sectors Reform. National Bureau of Disease Control and Prevention functional configuration, internal structure and staffing requirements. http://www.gov.cn/zhengce/2022-02/16/content_5674041.htm . Accessed 24 April 2023. Wang Y, Li Y, Qin S, et al. The disequilibrium in the distribution of the primary health workforce among eight economic regions and between rural and urban areas in China. Int J Equity Health. 2020;19(1):28. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3223796","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":229786833,"identity":"812667e5-73c5-4075-9e7b-4903acc63d7f","order_by":0,"name":"Jingru Chang","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jingru","middleName":"","lastName":"Chang","suffix":""},{"id":229786834,"identity":"e61b2609-e8b1-4a41-b55f-619d12018fc6","order_by":1,"name":"Shuqian Xu","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shuqian","middleName":"","lastName":"Xu","suffix":""},{"id":229786835,"identity":"a4832b34-d7c5-4f5b-9fde-d9c95d96903f","order_by":2,"name":"Guoliang Ma","email":"","orcid":"","institution":"Nanjing Municipal Center for Disease Control and Prevention","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guoliang","middleName":"","lastName":"Ma","suffix":""},{"id":229786836,"identity":"ee0f6212-69cc-4e78-ab20-a0101aa0f54a","order_by":3,"name":"Qifeng Wu","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qifeng","middleName":"","lastName":"Wu","suffix":""},{"id":229786837,"identity":"6bda27da-a0b2-4830-b475-9ddff68dc50a","order_by":4,"name":"Xinpeng Xu","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinpeng","middleName":"","lastName":"Xu","suffix":""},{"id":229786838,"identity":"72a38d6d-6a72-4942-919e-d9c8661910a5","order_by":5,"name":"Hui Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACPgYeBoaECgZmMI+HGC1sYC1nSNbC2AblEadFIveYxMN5d9h1ZyQwPnjbxiBvTlhLXppE4rZnzGY3EpgN57YxGO5sIKglxwyo5TBIC5s0bxtDgsEBorTMAWth/02ClgaILczEaeF5Y2yRcAyo5czDZsk55yQMNxDSws+eY3jzR83hZLPjyQc/vCmzkSdoCwwkA2OnAUhLEKkeCOyIVzoKRsEoGAUjDgAAKUc4ypFK4HkAAAAASUVORK5CYII=","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2023-08-01 09:44:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3223796/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3223796/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":42511331,"identity":"4d64b743-a99a-41b8-8848-1461a1d3ea01","added_by":"auto","created_at":"2023-09-01 17:36:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":146307,"visible":true,"origin":"","legend":"\u003cp\u003eThe structure of CDC HRH in 2005-2020. (a): Age (b): Technical title (c): Education lever\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3223796/v1/9789896d943ff1b8125071fa.png"},{"id":49670219,"identity":"2e71acb1-7c45-455b-8f83-2e3bc5f871e5","added_by":"auto","created_at":"2024-01-16 08:37:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":640107,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3223796/v1/5e33757c-4958-4007-b51e-9386fe3f3e58.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Equity and trend predictions of human resources for health allocation at the Centers for Disease Control and Prevention in China, 2005-2020","fulltext":[{"header":"Background","content":"\u003cp\u003eIn China, the Centers for Disease Control and Prevention (CDCs) play a critical role in China's public health system as the leading technical organizations and operating divisions of health commissions at all levels[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The basic functions of CDCs include developing, implementing or evaluating proven strategies, tracking diseases, responding to public health emergencies and so on[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In the face of the COVID-19 pandemic[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and the growing burden of major chronic diseases[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], efforts to improve the accessibility and efficiency of high-quality human resources for health (HRH) at the CDCs have become the key issue in future-oriented public health system development.\u003c/p\u003e \u003cp\u003eHRH are widely recognized as a decisive factor of health system performance and health outcomes[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Strengthening the CDC HRH has been a priority for China's public health system since the severe acute respiratory syndrome (SARS) epidemic in 2003[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], but understaffing and brain drain have been persistent problems in CDCs[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The number of CDC staff was 206,485 in 2005 after the SARS epidemic, but only 187,564 CDC staff responded to the COVID-19 outbreak in 2019. The shortage of CDC HRH has been further exacerbated by the COVID-19 epidemic. It is necessary to establish effective HRH planning to ensure the sustainability of CDC HRH. Regarding the lack of HRH, the World Health Organization (WHO) emphasized the need to focus on the availability, accessibility, acceptability and quality of HRH[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Thus, HRH planning is based on maintaining the most appropriate rationality in quantity, quality and equity.\u003c/p\u003e \u003cp\u003eForecasts of human resource development trends can contribute quantitatively to HRH planning and the optimal allocation of health resources[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. There are various HRH projection approaches, and each of these approaches has advantages and limitations[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The grey model (GM) was proposed by Professor Deng Julong in the 1980s[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and introduced by health management experts into health manpower forecasting. It can suit various data types. The grey model first order one variable (GM (1,1)) is the core of the grey model, which has the advantages of high accuracy, small sample data, and simple calculation principles and methods[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The GM (1,1) has been widely used in the prediction of HRH in China and has shown good forecasting results[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. While in CDC HRH prediction, the GM (1,1) was less used.\u003c/p\u003e \u003cp\u003eThe equitable allocation of HRH is a prerequisite for health equity[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and the Chinese government has always been committed to promoting equal health resources allocation to ensure health equity. The \u0026ldquo;Healthy China 2030\u0026rdquo; plan issued and implemented by the State Council in 2016 once again emphasized the need to adhere to the principle of equity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, inequality in the allocation of HRH is an ongoing issue within the CDCs[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The shortage of HRH complicates inequality and leads to an excessive focus on the quantity of HRH [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In addition, mainland China has a vast territory, with 31 provinces, autonomous regions and municipalities that can be divided into eastern, central and western regions according to economics and geographical location[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. CDCs are divided into national, provincial, municipal and district/county levels[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], covering the eastern, central and western regions and providing public services to residents within their jurisdictions. There were 3,384 local CDCs in 2020. The Chinese government determines the allocation of CDC HRH mainly on the basis of residents[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], with insufficient consideration given to the different demographic and geographical characteristics of the regions, which further leads to inequities in CDCs. Thus, when developing HRH plans, it is necessary to measure the inequality of HRH allocation in CDCs across regions, identify priority geographical areas and consider the causes of inequalities.\u003c/p\u003e \u003cp\u003eThe main research methods used in studies on the equity of HRH allocation are the Gini coefficient/Lorenz curve and Thayer index[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The Gini coefficient is limited to reflecting the overall allocation and is usually used in combination with other methods[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The Thayer index cannot take geography into account when decomposing regional inequality[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The agglomeration degree proposed in this study is able to incorporate the combined effects of geographic and demographic factors[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. When analyzing the inequality in CDC HRH across regions, the combination of the Gini coefficient and aggregation degree is more appropriate.\u003c/p\u003e \u003cp\u003eIndeed, inadequate and unequal HRH is prevalent in many countries [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]and solving this issue has become a priority in recent years. However, the quality of human resources has been neglected, and relevant research is lacking. There has been little improvement in strengthening the structure and skills of the health workforce in the health system. The structure of human resources is a reflection of quality, and previous studies on human resources at the CDCs [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]or other health human resources[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] have rarely mentioned personnel structure in China. Therefore, with the above discussion, this study aimed to use CDC HRH data from 2005 to 2020 to describe trends in quantity and structure, analyze the equity of allocation through the Gini coefficient and aggregation degree and forecast the number of CDC HRH from 2021 to 2025 through the GM (1,1). This study can help to ensure effectiveness and comprehensiveness when developing human resource planning to improve the accessibility and efficiency of CDC HRH, and may provide broader information for research on HRH.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eThis study collected and analyzed data obtained from the China Health Statistical Yearbook (2006\u0026ndash;2021). The China Health Statistical Yearbook, an informative annual publication reflecting the development of China's health and the health status of residents, is published annually by the National Health Commission[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The data used in this study were released by the Chinese government and are reliable and authentic.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIndicators\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eHuman resources for health in CDCs\u003c/h2\u003e \u003cp\u003eCDC staff consist of health technicians, other technicians, management staff and logistics staff. Health technicians are a subset and core of CDC staff, and are primarily responsible for health-related tasks. The Chinese government requires health technicians to account for over 70% of the CDC staff, and the proportion of health technicians was 73%-77% from 2005 to 2020. Therefore, two categories of indicators were chosen to measure the HRH of CDCs: CDC staff and health technicians. In addition, the structure of the four types of personnel that make up the CDC HRH are also listed separately in the China Health Statistical Yearbook. This study used health technicians to represent the quality of HRH in CDCs.\u003c/p\u003e \u003cp\u003eHealth technicians: medical practitioners, assistant medical practitioners, registered nurses and other health professionals.\u003c/p\u003e \u003cp\u003eOther technicians: non-health professionals who engag in technical work such as medical device repair, scientific research and teaching.\u003c/p\u003e \u003cp\u003eManagement staff: staff with leadership or management responsibilities\u003c/p\u003e \u003cp\u003eLogistics staff: staff who undertake duties such as maintenance and logistical support services.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eRegional division\u003c/h2\u003e \u003cp\u003eThe eastern region comprises Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong and Hainan; the central region includes Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei and Hunan; and the western region is composed of Inner Mongolia, Chongqing, Guangxi, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003ePublic health workforce density [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]and average annual growth rate were used to reflect the trend in human resources. The public health workforce density of CDCs\u0026thinsp;=\u0026thinsp;number of CDC staff/number of residents (per 10,000)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eEquity analysis\u003c/h2\u003e \u003cp\u003eGini coefficient\u003c/p\u003e \u003cp\u003eThe value of the Gini coefficient ranges from 0 to 1. The closer the value is to 1, the more unequal the allocation. G below 0.2 is absolute equality, 0.2\u0026ndash;0.3 is relative equality, 0.3\u0026ndash;0.4 is basic equality, 0.4\u0026ndash;0.5 is relative inequality, and above 0.5 is serious inequality [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The calculation formula is as follows:\u003c/p\u003e \u003cp\u003e \u003cem\u003eG\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{i=1}^{n}(\\)\u003c/span\u003e\u003c/span\u003eX\u003csub\u003ei\u003c/sub\u003e \u0026ndash; X\u003csub\u003ei\u0026minus;1\u003c/sub\u003e) (Y\u003csub\u003ei\u003c/sub\u003e +Y\u003csub\u003ei\u0026minus;1\u003c/sub\u003e)\u003c/p\u003e \u003cp\u003eWhere n is the total number of provinces (n\u0026thinsp;=\u0026thinsp;31 in this study), X\u003csub\u003ei\u003c/sub\u003e is the cumulative percentage of the population and geographical area, and Y\u003csub\u003ei\u003c/sub\u003e is the cumulative percentage of HRH (i\u0026thinsp;=\u0026thinsp;1, 2, \u0026hellip; n).\u003c/p\u003e \u003cp\u003eAgglomeration degree\u003c/p\u003e \u003cp\u003eThe health resources agglomeration degree (HRAD) is used to evaluate the equity of health resources allocation among different regions according to geographical area [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. HRAD\u0026thinsp;\u0026gt;\u0026thinsp;1, indicates that health resources are too concentrated based on geographical area; HRAD\u0026thinsp;=\u0026thinsp;1, indicates that the allocation of health resources has absolute equity by geographical area; HRAD\u0026thinsp;\u0026lt;\u0026thinsp;1, indicates that the equity of health resources allocation by geographical area is poor. The population agglomeration degree (PAD) is an indicator of how concentrated a region's population is relative to the region as a whole. The HRAD/PAD evaluates the equity of health resources allocation between different regions by population[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. If HRAD/PAD\u0026thinsp;=\u0026thinsp;1, health resources and population are perfectly balanced; if HRAD/PAD\u0026thinsp;\u0026gt;\u0026thinsp;1, there is a relative surplus of health resources based on the population; and if HRAD/PAD\u0026thinsp;\u0026lt;\u0026thinsp;1, the allocation of health resources is inequitable among different regions according to the size of the population. The calculation formula is as follows:\u003c/p\u003e \u003cp\u003eHRAD\u003csub\u003ei\u003c/sub\u003e = (HR\u003csub\u003ei\u003c/sub\u003e/HR\u003csub\u003en\u003c/sub\u003e)/(A\u003csub\u003ei\u003c/sub\u003e /A\u003csub\u003en\u003c/sub\u003e) PAD\u003csub\u003ei\u003c/sub\u003e= (P\u003csub\u003ei\u003c/sub\u003e/P\u003csub\u003en\u003c/sub\u003e)/(A\u003csub\u003ei\u003c/sub\u003e/A\u003csub\u003en\u003c/sub\u003e)\u003c/p\u003e \u003cp\u003eHR\u003csub\u003ei\u003c/sub\u003e and HR\u003csub\u003en\u003c/sub\u003e are the total amount of health resources in region i and all regions, respectively; A\u003csub\u003ei\u003c/sub\u003e and A\u003csub\u003en\u003c/sub\u003e are the land area of region i and all regions, respectively; P\u003csub\u003ei\u003c/sub\u003e and P\u003csub\u003en\u003c/sub\u003e are the population in region i and all regions, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eForecast analysis\u003c/h2\u003e \u003cp\u003eGM (1,1) model\u003c/p\u003e \u003cp\u003eUsing the number of CDC staff and health technicians from 2005 to 2020 as raw data, the GM (1,1) model was programmed with MATLAB (R2021a) to forecast the number of CDC staff and health technicians from 2021 to 2025. In this study, the accuracy of the model predictions was tested by means of the posterior error ratio (C) and small error probability (P). The judgment of the model accuracy level is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eJudgment standard of prediction accuracy for GM (1,1)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccuracy grade\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosterior error ratio (C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmall error probability (P)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 1 (Excellent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u0026thinsp;\u0026le;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026ge;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 2 (Qualified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.35\u0026thinsp;\u0026lt;\u0026thinsp;C\u0026thinsp;\u0026le;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u0026thinsp;\u0026le;\u0026thinsp;P \u0026lt;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 3 (Barely qualified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u0026thinsp;\u0026lt;\u0026thinsp;C\u0026thinsp;\u0026le;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70\u0026thinsp;\u0026le;\u0026thinsp;P\u0026thinsp;\u0026lt;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 4 (Unqualified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u0026thinsp;\u0026gt;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTrends in the development of HRH in CDCs, 2005\u0026ndash;2020\u003c/h2\u003e \u003cp\u003eThe number of CDC staff and health technicians showed a gradual downward trend from 2005 to 2019, decreasing by 18,921 and 18,611, respectively. In 2020, there was an increase of 6,861 and 5,390 compared to 2019. The average annual growth rates for CDC staff and health technicians were \u0026minus;\u0026thinsp;0.40% and \u0026minus;\u0026thinsp;0.58%, respectively, from 2005 to 2020. While the proportion of health technicians exceeded the standard of 70%, it fell from 76.7% in 2005 to 74.7% in 2020. The Chinese standard for the public health workforce density of CDCs is no less than 1.75 CDC staff members per 10,000 residents. The public health workforce density of CDCs reduced from 1.58 per 10,000 to 1.38 per 10,000 from 2005 to 2020, which was far below the Chinese standard. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the specific changes in CDC HRH from 2005 to 2020.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumbers and densities of CDC staff and health technicians, 2005\u0026ndash;2020\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003cp\u003e(per10,000)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCDC staff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHealth technicians\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eNumber of health technicians/ number of CDC staff\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e158450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e76.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e154196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e76.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e148512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e75.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e148519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e75.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e148450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e75.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e147347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e75.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e194593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e145198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e74.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e141261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e73.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e194371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e143101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e73.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e74.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e141698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e74.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e74.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e74.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e187826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e74.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e187564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e139839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e74.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e194425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e145229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e74.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage annual growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eChanges in the structure of HRH in CDCs, 2005\u0026ndash;2020\u003c/h2\u003e \u003cp\u003eThe proportion of health technicians aged 55 and older was 4.9% in 2005, gradually rising to 9.7% in 2010, 11.5% in 2015 and 17.1% in 2020. The ratio of young (34 years and below), middle-aged (35\u0026ndash;54 years old) and older (55 years and older) health technicians was 6.73:12.67:1 in 2005 and 1.30:3.55:1 in 2020. The health technicians showed an aging trend. In 2020, 46.9% of health technicians had a bachelor's degree or above, an increase of 31.5% compared to 2005. The level of education improved substantially. The proportion of health technicians with a master\u0026rsquo;s degree in 2020 was 7.1%, an increase of only 1.9% compared to 2015. The proportion of highly educated health technicians was low and increased slowly. From 2005 to 2020, most health technicians had primary and middle title, and those with a senior and associate senior title increased by only 1.9% and 5.1%, respectively. In 2020, only 11% of health technicians had senior title. The structure of the CDC HRH is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOverall inequality of CDC HRH based on the Gini coefficient, 2005\u0026ndash;2020\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the results of the equity analysis of CDC HRH by the Gini coefficient. The overall inequality in the allocation of CDC staff and health technicians was similar from 2005 to 2020. The Gini coefficients of CDC staff and health technicians allocated by population were all less than 0.2, indicating absolute equity. The Gini coefficients of CDC staff and health technicians allocated by geographical area were all greater than 0.5, representing high inequity. The overall equity of CDC HRH allocation by population was relatively better than that by geographical area.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGini coefficient of CDC HRH by population and geographical area,2005\u0026ndash;2020\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAllocation by population\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eAllocation by geographical area\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDC staff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHealth technicians\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCDC staff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHealth technicians\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.1858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5903\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.1638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.1491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.1602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRegional inequality of HRH in CDCs based on the agglomeration degree, 2005\u0026ndash;2020\u003c/h2\u003e \u003cp\u003eFrom 2005 to 2020, the HRAD of CDC staff and health technicians ranged from 3.0\u0026ndash;4.0 in the eastern region, and 1.6-2.0 in the central region. The CDC HRH allocated based on geographical area were overconcentrated in the eastern and central regions. The HRAD of CDC staff and health technicians in the western region increased each year, from 0.41 and 0.41 in 2005 to 0.48 and 0.50 in 2020, respectively. The allocation of CDC HRH remained extremely inequitable by geographic area in the western region. The order of the equity of CDC HRH allocation by geographical area was as follows: eastern region\u0026thinsp;\u0026gt;\u0026thinsp;central region\u0026thinsp;\u0026gt;\u0026thinsp;western region.\u003c/p\u003e \u003cp\u003eThe HRAD/PAD of CDC staff and health technicians in the eastern region was less than 1 and gradually decreased from 2005 to 2020, both decreasing to 0.82 in 2020. The allocation of CDC HRH in the eastern region gradually became unequal based on population. The HRAD/PAD of CDC staff and health technicians ranged from 1.11\u0026ndash;1.32 in the western region, and 0.98\u0026ndash;1.14 in the central region. The CDC HRH met the medical needs of the population in the central and western regions, and the residents had better access to health services. The order of the equity of CDC HRH allocation by population was as follows: western region\u0026thinsp;\u0026gt;\u0026thinsp;central region\u0026thinsp;\u0026gt;\u0026thinsp;eastern region. The results of the equity analysis of HRH in CDCs among the eastern, central and western regions by agglomeration degree are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAgglomeration degree of CDC HRH in the eastern, central and western regions, 2005\u0026ndash;2020\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCDC staff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eHealth technicians\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHRAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHRAD/PAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHRAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHRAD/PAD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eeastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eeastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eeastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eeastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEquity analysis of CDC HRH in 31 provinces, municipalities and autonomous regions, 2020.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs shown in the Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, among 31 provinces, autonomous regions and municipalities, the HRADs in Inner Mongolia, Tibet, Gansu, Ningxia, Xinjiang, and Heilongjiang were less than 1 in 2020, of which only Heilongjiang was in the central region and the other provinces were in the western region. The allocation of CDC HRH in the eastern region of Shanghai, Beijing and Tianjin was overconcentrated by geographical area, with HRADs greater than 8. The difference between the HRADs of CDC staff and health technicians in Shanghai and those in Tibet both exceeded 22. Except for Chongqing, the HRAD/PAD values in the western region were all over 1, and among the top three with good equity were Tibet, Qinghai and Xinjiang. In contrast, the HRAD/PAD of provinces such as Hebei, Shanghai, Jiangsu, Zhejiang, Shandong, Guangdong, Anhui and Jiangxi were less than 1, and most of these provinces were located in the eastern region.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAgglomeration degree of CDC HRH in 31 provinces, municipalities and autonomous regions in 2020\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCDC staff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHealth technicians\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHRAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHRAD/PAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHRAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHRAD/PAD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEastern Region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeijing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.51 1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTianjin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.90 1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHebei\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiaoning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShanghai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJiangsu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhejiang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFujian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShandong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuangdong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHainan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCentral Region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShanxi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJilin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeilongjiang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnhui\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJiangxi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHenan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHubei\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHunan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWestern Region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInner Mongolia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChongqing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuangxi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSichuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuizhou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYunnan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTibet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShaanxi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGansu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQinghai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNingxia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXinjiang\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eForecast analysis of HRH in CDCs from 2021 to 2025\u003c/h2\u003e \u003cp\u003eThe posterior error ratio (C) was less than 0.35 and the small error probability (P) was greater than 80% for both indicators, which means that the fit of each model was good. The results of the prediction model calculations showed that the number of CDC staff and health technicians will continue to decline from 2021 to 2025, with forecasts of 185,176 and 136,239, respectively, in 2025. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the detailed data.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGM (1,1) model prediction results of CDC HRH\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eModel prediction formula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e \u003cp\u003ePredicted value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDC staff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\widehat{X}\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003e(\u003cem\u003e1\u003c/em\u003e)\u003c/sup\u003e\u0026thinsp;(k)=-52443984.84\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u0026minus;\u0026thinsp;0.0038(\u003cem\u003ek\u0026minus;1)\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;52650469.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e187798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e187281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e186577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e185875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e185176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth technicians\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\widehat{X}\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003e(\u003cem\u003e1\u003c/em\u003e)\u003c/sup\u003e\u0026thinsp;(k)=\u0026thinsp;-30044639.14\u0026nbsp;\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u0026minus;\u0026thinsp;0.0050(\u003cem\u003ek\u0026minus;1)\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;30203089.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e138302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e137611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e136923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e136239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provided a comprehensive analysis of the quantity, quality and equity of HRH allocation at the CDCs and identified several key issues. First, China's public health system is facing a shortage and turnover of HRH in CDCs. From 2005 to 2020, the number of CDC staff and health technicians decreased by 12,060 and 13,211 respectively. The public health workforce density of CDCs was consistently far below the standard of 1.75 per 10,000 residents. Although the number of CDC staff rose significantly in 2020, the shortage of CDC staff was 52,637 according to current Chinese standards. The Chinese government set a development goal of 250,000 CDC staff by 2025 in the \u0026ldquo;14th Five-Year Plan for National Health\u0026rdquo; [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The GM (1,1) model projections for the number of CDC staff and health technicians from 2021 to 2025 declined continuously. The GM (1,1) model projected that the number of CDC staff will be 185,176 in 2025, which is 64,824 less than the target. The public health workforce is not sufficient to meet the population's demand for public health services.\u003c/p\u003e \u003cp\u003eWhile the definition of the public health workforce is not entirely consistent in each country, the lack of a public health workforce is a common problem worldwide. For example, the public health workforce decreased by nearly 10% from 2012 to 2019 in the United States[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Low wages and a lack of long-term career opportunities are the main reasons for the decline in the global public health workforce [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and China's public health workforce faces the same dilemma. In China, the CDCs are fully funded by the government budget, and the funding for CDCs is significantly lower than that for other health institutions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The results in CDC staff being underpaid, which is not commensurate with the excessive workload. The technical title is not only a grade title to identify the business level and professional ability of HRH in CDCs but also a reflection of career advancement. The professionals with senior title increased by only 1.9% from 2005 to 2020. Furthermore, previous surveys of job willingness for Masters[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and PhDs[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] in public health in China have also emphasized their reluctance to work in CDCs due to salary, career development, and lack of promotion opportunities. This is coupled with the low social recognition of CDC work and the lack of full awareness and respect for CDC staff[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These reasons combined have led to staff turnover and shortages, and directly affect the quality of HRH in CDCs.\u003c/p\u003e \u003cp\u003eThe quality of CDC HRH has shown an aging trend, with 17.1% of staff over 55 years of age in 2020, and the lack of good articulation between the older, middle-aged and younger professional. CDCs have a low percentage of health technicians with high education levels and technical titles. CDCs are knowledge-intensive units in China. The academic qualifications and technical titles are concrete expressions of the quality of HRH and are fundamental to the development of CDCs. Young people are crucial to strengthen the capacity building of CDCs[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The current development in the number and structure of CDC HRH may lead to inefficient delivery of public health services and affect the quality development of public health system. Attracting and retaining CDC talent is the only way to reduce staff turnover, expand staff numbers and rationalize staffing structures, which are equally critical to enhancing the public health system.\u003c/p\u003e \u003cp\u003eSince the COVID-19 epidemic, China has gradually strengthened its public health system, establishing the National Bureau of Disease Prevention and Control in 2021 to reform China's prevention and control system[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The \u0026ldquo;14th Five-Year Plan for National Health\u0026rdquo; was released in 2022[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], which emphasized the need to substantially improve the capacity of public health services by 2025. Based on this, we suggest that more attention needs to be paid to the issue of public health workforce development. The Chinese government needs to focus on the current human resources situation in CDCs and retain public health workforce by enhancing the salary level of CDC HRH. The CDCs should focus on salary rationalization, improving career development opportunities and increasing the attractiveness of positions in CDCs. In addition, universities should be guided to expand enrollment and accelerate the construction of a high-level public health talent training system to provide the CDCs with sufficient high-quality talent.\u003c/p\u003e \u003cp\u003eFinally, it is worth noting that inequalities in the allocation of HRH in CDCs are also evident, with inequality between health technicians and CDC staff being similar. The Gini coefficient indicated that the equity of CDC HRH allocation by population outperformed that by geographical area from 2005 to 2020, which is the same as the results of studies on general practitioners[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and traditional Chinese medicine[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Geographical area has long been neglected in the allocation of HRH. The aggregation degree made it clear that there are significant regional differences in CDC HRH, with differences mainly in the eastern and western regions. The equity of CDC HRH allocation is best in the eastern region by geographical area, with overconcentration of HRH in some provinces and cities (e.g., Shanghai, Beijing, Tianjin, etc.), yet the equity of allocation by population is worst in the eastern region. The eastern region accounts for only 11% of the geographical area but has 43% of the population. Cities in the eastern region, such as Shanghai and Beijing, are very attractive to people due to their good economic development and geographical location, resulting in an overly dense population. CDC HRH are unable to meet the health needs of the population in the eastern region.\u003c/p\u003e \u003cp\u003eThe western region is characterized by 27% of the population and 71% of the area. The equity of CDC HRH in the sparsely populated western region is best allocated by population and worst by geographical area (mainly Inner Mongolia, Tibet and Qinghai and Xinjiang). Although the Chinese government has always favored talent policies for the western region[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], the economy of the western region is inferior to that of the central and eastern regions[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The western region has disadvantages in terms of resource inflow and talent introduction, and the economic and service radius are not fully taken into account when allocating HRH. Thus, the western region is still in a state of extreme inequity despite the gradual rise in geographical equity in the allocation of CDC HRH. In the central region, CDC HRH are relatively more equitable in terms of population and geographical area. However, it is noteworthy that the equity of the allocation of health technicians by population is declining in the central region. It is a distinct fact that regional differences in geographic location and economic development contribute to further inequalities in CDC HRH.\u003c/p\u003e \u003cp\u003eTo ensure equitable access to public health services for residents in all regions, the Chinese government should fully consider factors such as service population, service radius and economic disparities when setting human resources standards for each region. Moreover, certain policies and talent support should be given to provinces and cities with weak CDC construction, a lack of HRH and less economically developed areas to promote the coordinated and balanced development of CDC HRH in all regions. CDCs in different regions can strengthen cooperation and learn from each other to improve the capacity of CDC staff and maximize the use of available HRH.\u003c/p\u003e \u003cp\u003eOur study has several limitations. The equity of allocation is limited to the number of HRH and does not reflect inequities in the quality of HRH. In addition, we chose to reflect the quality of HRH through the structure of health technicians and did not include changes in the structures of other technicians, management staff or logistics staff. Last, the GM (1,1) model used in this study is only able to predict based on the characteristics of the data itself but does not take into account social policies or emergencies. Future studies on forecasting and equity analysis of CDC HRH should be more comprehensive.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThrough a comprehensive analysis of the HRH in CDCs from 2005 to 2020, we identified directions for HRH planning to ensure public health workforce that is adequate, well-trained, and capable of ensuring health equity. What clearly needs to be addressed is the lack and attrition of CDC staff, improving the aging of staff and the lack of high-level talent, and striving to narrow the equity gap in the allocation of CDC HRH in different regions. In this regard, we recommend that factors such as population, service radius and economic developments be taken into account when allocating CDC HRH. The Chinese government ensure that attract and retain more public health workforce by strengthening financial investment in CDCs and giving policy preference to areas with weak CDCs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCDCs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCentre for Disease Control and Prevention\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHRH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman Resources for Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSARS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSevere Acute Respiratory Syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egrey model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGM (1,1)\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egrey model first order one variable\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data were collected from the China Health Statistical Yearbook (2006-2021), which were published by the National Health Commission of China (http://www.nhc.gov.cn/mohwsbwstjxxzx/tjtjnj/new_list.shtml). For access to related datasets generated by the authors, please contact the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ewe have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the Philosophy and Social Science Fund for Colleges and Universities in Jiangsu Province (2023SJYB0292)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLH and CJR conceptualized this study. CJR and XSQ were responsible for data collection and analysis. XXP guided the construction of predictive models. CJR wrote the first draft of the paper. MGL and WQF read and revised the manuscript. LH critically commented the paper. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMeng QY. [Transformation and reform of the functions of centers for disease prevention and control in the new era]. Zhonghua Yu Fang Yi Xue Za Zhi. 2019;53(10):964\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi C, Sun M, Shen JJ, Cochran CR, Li X, Hao M. Evaluation on the efficiencies of county-level Centers for Disease Control and Prevention in China: results from a national survey. Trop Med Int Health. 2016;21(9):1106\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Xu J, Zhou H, et al. Working conditions and health status of 6,317 front line public health workers across five provinces in China during the COVID-19 epidemic: a cross-sectional study. 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Int J Equity Health. 2020;19(1):28.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Human resources for health, CDC, Inequality, Quality, GM (1,1), Agglomeration degree","lastPublishedDoi":"10.21203/rs.3.rs-3223796/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3223796/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eImproving the accessibility and efficiency of human resources for health (HRH) at the Centers for Disease Control and Prevention (CDCs) is an important component of China's public health system. This study aimed to comprehensively analyze CDC HRH in terms of the quantity, quality and equity of allocation, and offer sound recommendations for strengthening HRH at the CDCs.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThis study provided a descriptive analysis of the quantity and quality of CDC HRH using indicators such as the total number of CDC staff, public health workforce density, age, education level and technical title. The Gini coefficient and agglomeration degree were used to measure the equity of CDC HRH allocation. The grey model first order one variable (GM (1,1)) was used to predict the number of HRH at the CDCs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFrom 2005 to 2020, the public health workforce density of CDCs was below the Chinese government's required standard of 1.75 per 10,000 residents. The CDCs have always faced the problem of understaffing and attrition. The GM (1,1) model showed that the number of CDC HRH will continue to decrease from 2021 to 2025. In addition, the quality of CDC HRH showed a gradual aging trend and a lack of high-quality talent. The Gini coefficient indicated that the overall equity of CDC HRH allocation by population was relatively better than that by geographical area. The aggregation degree showed significant differences in the equity of CDC HRH allocation in the eastern, central and western regions.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe findings indicate that it is necessary to further optimize the number and structure of CDC HRH and enhance the equity of resource allocation among different regions. However, these results were not due to a single cause. It is essential to improve existing policies and establish effective planning to strengthen the public health workforce at the CDCs and meet the needs of the public health system.\u003c/p\u003e","manuscriptTitle":"Equity and trend predictions of human resources for health allocation at the Centers for Disease Control and Prevention in China, 2005-2020","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-01 17:36:40","doi":"10.21203/rs.3.rs-3223796/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c73eaf84-9bc4-4a8a-bbba-b6c2480cce75","owner":[],"postedDate":"September 1st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-16T08:29:12+00:00","versionOfRecord":[],"versionCreatedAt":"2023-09-01 17:36:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3223796","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3223796","identity":"rs-3223796","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}