Background
Human well-being is highly reliable on the basic health conditions. However, the improvement of health nationwide is never an easy task. Since the 21st century, the world has been facing unprecedented health challenges ranging from demographic changes, aging populations, a much broader spectrum of infectious disease outbreaks (such as SARS (Severe Acute Respiratory Syndromes), Ebola, and influenza) to rising rates of non-communicable diseases [
1]. These challenges do not only take a heavy toll on the healthcare system but also threaten the economic and social development. How we respond to these challenges profoundly influences the health of general population in one country. On the frontier against these challenges are variegated health workforce [
2]. Although the financial and equipment investment play an important role in improving population health, health workforce is the core in a country’s health system as all the successful health interventions cannot be achieved without skillful health workers [
3‐
6]. Many studies have proven the significant relationship between health workforce density (HWD, workforce-to-population ratio) and health outcomes [
6,
7]. It is also widely recognized that health human resources are central to achieving health reform goals and universal health coverage [
8,
9].
As the third-biggest country in the world, the problem of the health workforce in China was deemed as a “crisis” due to the backward health system and medical education during the past decades [
4,
10]. In order to change the situation, the government of China implemented a new round of large-scale health reforms (also referred to as New Medical Reform) in 2009, which gained enormous political and financial support [
11]. After years of efforts, the quantity of health workforce in China increased remarkably. According to the China Health and Family Planning Statistical Yearbook 2017 [
12], the health workforce stock in China has reached 11.2 million in 2016, with a growth of approximately 0.5 million per year since the New Medical Reform. However, the scope of institutional efforts to improve health service quality must go beyond the growth in the number of health workforce because the problem of health workforce misdistribution has become increasingly intricate due to urbanization. In recent decades, China’s blossoming economy has compelled millions of Chinese villagers to move into cities for better living. Accordingly, more and more health resources have been poured into densely populated cities at the expense of those who live in rural and remote areas. As inappropriate allocation of resources contributes greatly to inequities in healthcare services [
13], health workforce misdistribution has become one of the challenges faced by the Chinese health sector.
Up to now, the academic world has provided much evidence to explain the health workforce misdistribution in China. For example, Liu et al. [
14] used the number of health technicians to measure the levels of the equity in health workforce distribution in China and concluded that the overall equity of health workforce distribution improved gradually after 2009. Chen et al. [
15] analyzed the distribution of community health workforce based on the survey data from 190 health service centers in China and found the inequity in quality and geographic distribution. Zhou et al. [
16] computed the inequalities in health workforce distribution in different stages of health reforms and found that the overall inequalities in the distribution of health workers decreased to the lowest level in 2000, and then increased gently until 2011. Jin et al. [
17] used Gini coefficient to measure the equity in the distribution of medical personnel in 2013 and concluded that China’s distribution of health workforce is demographically, rather than geographically, more equal. Song et al. [
18] explored the inequity in the distribution of paediatricians and paediatric nurses with Lorenz curve, Gini coefficient and Theil index. More similar studies can be found in the provincial inequity analysis of health technicians distribution [
19] and urban-rural inequality analysis of the distribution of doctors and nurses [
3].
Even though these studies help us understand the health workforce distribution in China from a global perspective, they still bear some limitations. First, existing research methods, such as the Gini coefficient and Theil index, both face shortcomings. For example, to measure the degree of equity in health workforce distribution at the province level, the first step is often to sort provincial units on the basis of HWD. However, space position information is lost in the process of sorting. As a result, space position information has been overlook by the existing methods, resulting in difficulties in identifying the priority areas for health workforce allocation. Second, existing studies mostly investigate the distribution of a single subtype of health workforce without a comparison of different kinds of health workers. Up to now, only the distribution of several limited types of health workforce has been studied. Moreover, few compare their distribution patterns and explore the similarity and difference, which limits our understanding of the health workforce distribution.
To fill the research void and have a solid knowledge of the geography and distribution patterns of health workforce, this study aims to introduce the Local Moran’s I, which is one of the Local Indicators of Spatial Association (LISA), to describe, visualize and compare the spatial distribution of urban and rural health workforce in China. We hope that findings of this study can provide a basis for planning more effective regional-oriented distribution policies and promote a more equitable distribution of health workforce. The following parts are arranged as follows: Section 2 introduces the classification of health workforce in China. Section 3 gives a detailed introduction about the data and methods. Section 4 displays the results with tables and maps. Section 5 discusses the priority areas for health workforce allocation and relevant policy application prospect. Section 6 draws the conclusions.
Health workforce classifications in China
The Chinese health sector developed its own classification system of health workforce, which is different from the internationally commonly used ISCO-88 (the third version of the International Standard Classification of Occupations) and ISIC (International Standard Industrial Classification) [
6]. There are 5 composing sections in the health workforce in China, namely, health technicians, logistics technical workers (LTW), administrative personnel (AP), and other technical personnel (OTP), village doctors & assistants (VDA), among which health technicians can be further divided into licensed doctors (LD), registered nurses (RN), pharmacists, technologists, and other medical technical personnel (OMTP), which are direct provider of health services. The detailed definitions of these 9 categories of health workforce are listed in the following Table
1. In addition, the urban health workforce is classified as those professional who are working at the municipal districts and rural health workforce refers to the rest. It is worth noting that this study only focuses on 8 subtypes of health workforce (except VDA) which are working in both urban and rural areas.
Table 1
Health workforce classification and definition in China
Health technicians | Licensed doctors (LD) | Licensed Doctors include doctors with the certificate of medical practitioner and the certificate of assistant medical practitioner who are engaged in medical practice, exclusive of those in the managerial positions. |
Registered nurses (RN) | Registered Nurses are the nurses with a registered nurse license who are engaged in nursing practice, exclusive of the nurses in managerial positions. |
Pharmacist | Pharmacists include all levels of pharmacists from chief pharmacist to assistant pharmacist. |
Technologist | Technologists include medical laboratory technicians and diagnostic imaging technicians of different levels (including chief technologist, associate chief technologist, attending technologist etc.). |
Other medical technical personnel (OMTP) | OMTP includes various interns for medical practice (doctor interns, nurse interns, technologist interns etc.). |
Other technical personnel (OTP) | Professionals responsible for technical, research and health education & promotion support. |
Administrative personnel (AP) | Professionals responsible for the management duties and daily operation of the hospitals and clinics, including personnel engaged in health care, disease control, medical supervision, medical research and medical education duties Other personnel in party, government, human resources, finance, IT and security affairs. |
Logistics technical workers (LTW) | Professionals responsible for repairing and logistic services, including technicians and ordinary workers. |
Village doctors & assistants (VDA) | Those doctors who serve in the village clinics with the “village doctor” certification are listed as the village doctors while those serving at the same places without the certification are listed as assistants. |
Discussion
Nowadays, recent developments in geography and related software enables more and more health scholars to associate geographic location information with health-related data [
39]. By means of the LISA functions, this study explores, visualizes, and compares the spatial distribution patterns of 8 subtypes of urban and rural health workforce in China, which is beneficial for evidence-based policy-making for health workforce allocation to promote a balanced and equitable distribution of health workforce.
As reported previously, various types of spatial clustering can be observed in those univariate LISA cluster maps. The appearance of all four types of spatial clusters reveals the complex distribution characteristics of health workforce in China. Among all the four types of spatial clusters (HH, LH, HL, and LL), the areas in the low type (LL and LH) of clusters demand more attention as their appearance indicates the lack of health workforce in central and/or surrounding units. Of course, more attention should be paid on licensed doctors, registered nurses, pharmacists and technologists are much more important due to their direct, significant role in healthcare delivery. For instance, the density of urban licensed doctors in Shaanxi and Hubei showed LL cluster feature, indicating the lack of urban licensed doctors in them and their surrounding areas. While when it comes to the urban registered pharmacists, Shandong, Henan and Hubei stand out in the univariate LISA maps and displayed LL cluster feature. These provincial units should exactly be the priority areas of health workforce planning.
Unexpectedly but intriguingly, the density of urban health workforce in some geographical remote provinces (Xinjiang, Xizang etc.) in the western China displayed high-high cluster feature, indicting the abundant stock of urban health workforce in these units. While when it comes to the rural health workforce, some other western provincial units, like Yunnan and Sichuan, are always displaying low-low cluster feature for subtypes of rural health workforce. Besides, Xinjiang exhibited a high-low type of spatial clustering pertaining to the distribution of registered nurses, pharmacists and technologists, indicating the critical lack of these types of rural health workforce in its neighboring units (Xizang, Qinghai and Gansu). As quantity does not equate quality, these disadvantaged areas may have not only a lower density of rural health workforce but also less well-educated workforces [
8]. The striking differences between urban and rural health workforce densities in these western units remind us to rethink the effectiveness and rationality of previous health workforce allocation policies. All the time, these geographical remote provinces have been deemed to be suffering disadvantages of attracting and retaining healthcare workers, and policy preferences are often given to those areas to improve the health services accessibility in these units. However, as it turns out, most of the health workforce in these units is concentrated in urban areas, resulting in more huge urban-rural differences. In other words, even though much policy preferences have been given to some western units, while these policies owned limited effects in allocating health workforce to the rural areas. In addition, the vast area further reduces the accessibility to health workforce in rural west China. For example, the four largest provincial units in China account for about 50% of the total area but only 4% of the population [
40], resulting in the lower accessibility to health services under equivalent health workforce coverage level.
We can draw two take-away lessons from abovementioned spatial clusters. First, Based on the results, different types of health workforce displayed various types of spatial clustering patterns, which may be attributable to the different supply channel and training requirements of different subtypes of health workforce. This calls on subtype-specific health workforce planning and allocation policies are essential to balance the health workforce distribution. Second, China faces a tougher challenge to equally distribute rural health workforce than urban health workforce among provinces. In spite of the huge urban-rural gaps in the densities of urban and rural health workforce, more administrative units in the western China are identified as low-low cluster area in the distribution of rural health workforce. They are disadvantaged in various aspects (e.g., the education, attraction, and retention of health workforce), making them more difficult move out the low-low cluster area.
Such lessons provides a more solid and specific basis for making corresponding health workforce planning and allocation policies towards those LL and LH cluster areas. This can commonly be achieved by expanding the government and social health expenditure and strengthening the medical education system through medium-term and long-term plans [
2]. Besides, the detected LL and LH clusters are exactly the objective areas for existing area-targeted health workforce programs in China. For instance, the “Rural-oriented Medical Education Scheme (RMES),” an ongoing education program which only recruits medical students with a rural background to channel trained medical personnel into the rural and remote areas. RMES provides scholarships and tuition waiver in return for obligated medical service in the rural township hospitals of western and middle regions in China. It has been proven to be an effective approach since it’s far more difficult to urge the health workers in other provinces and urban areas to migrate into the remote provinces and the countryside [
41,
42]. The research findings will sharpen the focus of RMES by illuminating the most in need subtype personnel in the most in need provincial units. Another example is the “Medical Pairing-assistance Scheme”, a program to pair up provincial units with and without affluent medical human resources. The research findings will make such paring more accurate by paring up the geographical regions who are most in need and who are comparatively most sufficient in certain subtypes of health workforce. For the remote or border regions which cannot cultivate or attract high-quality health workers, some expediencies should be arranged with the assistance of telemedicine. Although less effective for serious diseases, telemedicine will save the time and energy for both the patients and physicians in the subsequent visits and recover period [
43]. For example, Gansu, a backward western province, built the first telemedicine consultation center in Northwest China. At present, the center covers all of the municipal hospitals and the county hospitals as well as the township hospitals in Gansu equipped with the telemedicine network, with the number of telemedicine consultations reaching 4000 cases per year [
44].
As an exploratory study attempting to investigate the issue of health workforce misdistribution in China, the present study surely has limitations that should be acknowledged. Akin to previous literature, our study on health workforce is centered on its quantity without a due consideration of its quality. As quantity does not equate quality, disadvantaged areas may have not only a lower density of health workforce but also less well-educated workforces. In addition, due to data availability, this study only targets at the health workforce distribution at the provincial level, more studies at the city or county level are encouraged in the future.
Conclusions
Evidently, China is faced with the challenge of health workforce misdistribution, which demands more attention from policy makers. Notwithstanding the heterogeneity in different kinds of health workforce, the vista that emerges is that spatial cluster is an inherent feature in the spatial distribution of health workers and it poses great challenges in the quality of health service [
45]. The cluster of each type of urban and rural health workforce, its frequency, similarities, and differences between the different types would provide much more solid evidence for area-targeted and subtype-specific health policy making in China.
To achieve the balanced distribution of health workforce in China, it is important to formulate health workforce planning and implement area-targeted health workforce programs targeting for the most prioritized areas (i.e., low-low and low-high cluster areas). More importantly, the attraction and retention of rural health workforce in remote areas should never be taken lightly, especially towards the scarce subtypes. While health workforce allocation requires not only the efforts of the health sector, but also the long-term support from other sectors (education, finance, etc.) due to its long training circle.