Spatial disparities and dynamic evolution of health human resources in primary healthcare institutions in China: a study from the perspective of aggregation degree

Abstract

Background

As a critical component of universal health coverage, the spatial distribution of human resources in primary healthcare has attracted sustained policy attention worldwide. As the world’s largest developing country, China’s experience provides an important setting for examining the spatial distribution and dynamic evolution of the primary healthcare workforce.

Objective

To examine the spatial distribution and dynamic evolution of health technical human resources in China’s primary healthcare institutions using the Health Resource Aggregation Degree (HRAD) framework.

Methods

Data on health technical personnel in China’s primary healthcare institutions from 2013 to 2022 were obtained from the China Health Statistics Yearbook and the China Statistical Yearbook. HRAD, Population Aggregation Degree (PAD), kernel density estimation, Dagum Gini coefficient decomposition, and global Moran’s I were used to assess the distribution and temporal evolution of primary healthcare human resources across 31 provinces in China from 2013 to 2022.

Results

The number of health technicians in primary healthcare institutions increased across all regions of China from 2013 to 2022, but substantial regional disparities persisted. The HRAD/PAD ratio showed different temporal patterns across regions. Overall inequality changed little, with the Gini coefficient shifting from 0.382 in 2013 to 0.378 in 2022, and the 2022 global Moran’s I was not statistically significant (I = -0.0868, p = 0.4808). The observed pattern was characterized by persistent regional disparities, geographic-population mismatch, and increasing cross-regional distributional overlap.

Conclusion

Despite sustained growth in the primary healthcare workforce, substantial spatial disparities persisted. Future policy should focus on improving targeted allocation mechanisms and strengthening the geographic accessibility of primary healthcare services, particularly in underserved areas.

Background

The 1978 Alma-Ata Declaration first established “Primary Health Care (PHC)” as a global strategic goal, emphasizing the pivotal role of grassroots health human resources in achieving health for all [1, 2]. The importance of primary health care was reaffirmed in the 2018 Astana Declaration. As a key driver of Universal Health Coverage (UHC), primary health workers serve as the fundamental engine for bridging geographical disparities in health services and promoting equitable access [3]. Rational allocation of health resources constitutes a fundamental prerequisite for achieving this strategic objective. Only through the continuous optimization of health resource distribution structures and the promotion of equitable access to essential healthcare resources can the sustainable development of the health sector be advanced [4].

From a conceptual perspective, this study distinguishes distributional equality from need-adjusted equity. In the health equity literature, horizontal equity is commonly understood as equal treatment for equal need. This perspective, associated with the work of Wagstaff and van Doorslaer, requires adjustment for differences in health need, morbidity, or expected service use when evaluating whether health care delivery is equitable [5, 6]. Because the HRAD/PAD indicator used in this study does not standardize for medical need, the present analysis is more appropriately interpreted as an assessment of spatial distributional balance in primary healthcare human resources rather than a full need-adjusted equity evaluation. This distinction is important for interpreting the observed provincial disparities and for avoiding normative claims that extend beyond the scope of the indicator framework. HRAD and PAD quantify the relationship between resource concentration, population distribution, and geographic coverage, but they do not directly adjust for differential health need, disease burden, or service utilization. Accordingly, the findings are interpreted primarily as evidence on spatial distribution and distributional balance within this metric, rather than as a complete assessment of need-adjusted equity. This positioning is consistent with health services research frameworks that differentiate equality of distribution from broader equity in access and need.

As the world’s largest developing country, China witnessed its health human resources being described as a “crisis” in past decades, largely due to the relatively lagging development in health and education sectors [7, 8]. To address this situation, the Chinese government launched a new round of large-scale healthcare reform (also known as the new healthcare reform) in 2009, which received strong political and financial support [9]. After years of sustained efforts, the total number of health human resources in China has increased significantly. According to the China Health Statistics Yearbook 2020, China’s health workforce reached 12.3 million in 2019, with an average annual increase of approximately 500,000 since the launch of the new healthcare reform. However, alongside accelerated urbanization, the issue of uneven distribution of health human resources has become increasingly complex. In recent decades, China’s rapid economic development has triggered large-scale rural-to-urban migration. Consequently, more health resources have been concentrated in densely populated urban areas, exacerbating the disadvantage faced by residents in rural and remote regions [10, 11]. Imbalanced resource allocation is a key factor leading to inequity in medical services, and the unfair distribution of health human resources has become one of the critical challenges that China’s health sector must address [12, 13].

With the deepening reform of China’s healthcare system, the rational allocation of health technical human resources in primary healthcare institutions, which serve as the “foundational network” of medical and health services, is crucial for achieving the strategic goals of the Healthy China initiative [14, 15]. Existing research indicates that, despite significant growth in the total number of China’s health human resources in recent years, persistent issues include uneven personnel quality, suboptimal structural composition, and spatial distribution imbalances. This is particularly pronounced in the allocation of health technical personnel in primary healthcare institutions, which exhibits notable regional disparities and spatial heterogeneity [16, 17]. Furthermore, current studies on the spatial distribution of health resources predominantly focus on the hospital level or macro-level aggregate analysis, with relatively little attention paid to the detailed distribution patterns, dynamic evolution, and underlying mechanisms of health technical human resources in primary healthcare institutions [18].

To address the gaps in existing research and deepen understanding of the geographic distribution patterns and dynamic changes in health human resources, this study focuses on primary healthcare technical personnel across 31 provincial-level administrative regions in China (excluding Hong Kong, Macao, and Taiwan). Using Health Resource Aggregation Degree (HRAD) and Population Aggregation Degree (PAD), the study evaluates distribution relative to population distribution and geographic coverage constraints. It then assesses matching levels through an integrated analysis of aggregation degree, kernel density estimation, Dagum Gini coefficient decomposition, and global Moran’s I. The findings aim to provide an evidence base for developing a primary healthcare workforce planning system that better aligns with economic development, population distribution, and service accessibility.

Methods

Data sources

In this study, health technical personnel in primary healthcare institutions were defined in accordance with the statistical scope of the China Health Statistics Yearbook and mainly include physicians, nurses, pharmacists, and other health technical staff working in primary healthcare institutions. Because yearbook classification practices may evolve over time, the analysis focuses on macro-level temporal and regional patterns, and possible classification-related inconsistency is acknowledged as a study limitation.

This study employs a longitudinal research design. Data were sourced from the China Health Statistics Yearbook and the China Statistical Yearbook for the period 2013–2022, as published by the National Health Commission of the People’s Republic of China and the National Bureau of Statistics of China [19–23]. These datasets encompass various indicators, including the number of health technicians, health technicians per thousand population, population size, and land area, covering 31 provincial-level administrative regions (provinces, autonomous regions, and municipalities directly under the central government, excluding Hong Kong, Macao, and Taiwan) in China for the years 2013 to 2022.

Functional regional classification

Following the standard classification of the National Bureau of Statistics of China, the 31 provinces are categorized into three regions: eastern, central, and western. The eastern region comprises 11 provinces/municipalities: Beijing, Tianjin, Liaoning, Hebei, Jiangsu, Shanghai, Zhejiang, Shandong, Guangdong, Fujian, and Hainan. The central region includes 8 provinces: Shanxi, Heilongjiang, Hunan, Jilin, Jiangxi, Anhui, Hubei, and Henan. The western region consists of 12 provinces/autonomous regions/municipalities: Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Qinghai, Gansu, Ningxia, and Xinjiang [24].

Indicators and definitions

Aggregation degree

The Health Resource Aggregation Degree (HRAD) refers to the proportion of health resources concentrated in a given region relative to 1% of the national land area [25, 26]. The Population Aggregation Degree (PAD) refers to the proportion of the population concentrated in a given region relative to 1% of the national land area. The HRAD/PAD ratio reflects population-adjusted resource concentration under geographic coverage constraints. In this study, HRAD and PAD were calculated for 31 provincial-level regions in China (excluding Hong Kong, Macao, and Taiwan) for 2013–2022 to evaluate the distribution of health technical human resources in primary healthcare institutions.

1

2

In Formula (1), represents the Health Resource Aggregation Degree of health technical human resources in primary healthcare institutions in region i. denotes the quantity of health technical human resources in primary healthcare institutions in region i, while represents the total quantity of health technical human resources in China’s primary healthcare institutions. indicates the land area of region i, and signifies the total land area of China.

In Formula (2), represents the Population Aggregation Degree of region i. denotes the population size of region i, represents the total population of China, indicates the land area of region i, and signifies the total land area of China.

When HRAD > 1, it indicates that the concentration of health technical human resources in primary healthcare institutions in that region, relative to geographic area, is above the national benchmark. Conversely, when HRAD < 1, the density of resources relative to geographic area is below the benchmark and may be less able to meet geographic service demands. When HRAD/PAD > 1, health technical human resources are more concentrated than the resident population under this metric; when HRAD/PAD = 1, the region is at parity within this metric; and when HRAD/PAD < 1, resource concentration is lower than population concentration under this metric. Accordingly, the aggregation degree framework adopted here is intended to evaluate geographic-population distributional parity over time at the provincial level, rather than to estimate horizontal inequity after standardization for differential health need.

Kernel density estimation

Kernel density estimation is a non-parametric estimation method. Through continuous density curves, it can effectively reveal trends over time in aspects such as the distribution location, shape, spread, and polarization degree of observed data [27–29]. This study employs the trends of kernel density curves to explore the distribution dynamics and evolutionary patterns of health technical human resources in China’s primary healthcare institutions. Let f(x) denote the density function of a random variable X:,where N represents the number of observations, Xi represents independently and identically distributed observed values, x is the mean value, K denotes the kernel function, and h is the bandwidth. However, the Gaussian kernel function is generally recognized for its higher computational precision [30]. Therefore, this study adopts the Gaussian kernel function to investigate the distribution and dynamic evolution of health technical human resources in China’s primary healthcare institutions. The formula is as follows:

In this study, the Gaussian kernel function was adopted because it is widely used in distributional analysis and provides a smooth representation of temporal distributional change [31]. The bandwidth parameter affects the degree of smoothing and may influence local curve shape [32]. Therefore, the KDE results in this study are interpreted primarily as descriptive evidence of distributional evolution rather than precise inferential estimates.

Compared with accessibility-oriented methods such as the two-step floating catchment area approach, the aggregation degree framework is better suited to long-period national comparison using consistent province-level data, because it jointly reflects population distribution and geographic coverage while requiring fewer facility-level travel data. The Dagum Gini decomposition was selected over conventional Gini or Theil decomposition because it can separately identify intra-regional disparity, inter-regional disparity, and cross-regional distributional overlap, thereby providing a clearer account of the sources of overall inequality.

Dagum gini coefficient decomposition

The Dagum Gini coefficient method decomposes the overall Gini coefficient into three components: intra-regional disparity, inter-regional disparity, and hypervariable density. This approach effectively addresses the issue of cross-overlap between study subjects and the inability of traditional methods to reveal the sources of overall disparity, thereby overcoming the limitations of the conventional Gini coefficient and Theil index. In this framework, hypervariable density reflects the degree of cross-overlap among the distributions of different regions. A higher contribution of hypervariable density indicates that the allocation levels of provinces in different regions are increasingly interwoven, suggesting that overall inequality cannot be explained solely by fixed average differences between regions. This study employs the Dagum Gini coefficient to analyze the disparities in regional balance and their sources in the allocation of health technical human resources in China’s primary healthcare institutions.

Global spatial autocorrelation analysis

To strengthen the spatial interpretation of provincial disparities, this study further conducted a global spatial autocorrelation test for the HRAD/PAD ratio in 2022 using Moran’s I. Moran’s I is a commonly used indicator to assess whether the spatial distribution of a variable across geographic units is clustered, dispersed, or random. In this study, the province-level HRAD/PAD ratio was used as the input variable. Spatial relationships were conceptualized using inverse distance, and Euclidean distance was adopted as the distance measure. A significantly positive Moran’s I indicates clustering of similar values, whereas a significantly negative Moran’s I suggests spatial dispersion. If Moran’s I is not statistically significant, the observed spatial pattern is considered not significantly different from a random distribution.

Data collection and analysis

Data on the number of health technicians in primary healthcare centers across different regions (eastern, central, western) from 2013 to 2022 were extracted from the China Health Statistics Yearbook. Corresponding regional population data and provincial land area data for the same period were obtained from the China Statistical Yearbook to ensure consistency and comparability across provinces. Data cleaning and tabulation were conducted in Microsoft Excel 2021. Statistical analyses were performed using Stata 17.0, MATLAB R2022b, and IBM SPSS Statistics 23.0. The main analytical procedures included calculation of the HRAD/PAD ratio, kernel density estimation, Dagum Gini coefficient decomposition, and global Moran’s I analysis. The HRAD/PAD heatmap, kernel density estimation, and Dagum Gini decomposition were conducted for the full 2013–2022 period, whereas global Moran’s I was additionally calculated for the 2022 HRAD/PAD ratio to assess whether the latest observed provincial pattern showed statistically significant global spatial autocorrelation.

Results

Overall status of health technical human resource allocation in China’s primary healthcare institutions

In terms of the total quantity of primary healthcare human resources: From 2013 to 2022, the number of health technicians in primary healthcare institutions across all regions of China increased annually. By the end of 2022, the total number had reached 11,657,878 individuals. The cumulative increase from 2013 to 2022 was 4,447,300, representing a growth rate of 61.68%. Notably, the western region exhibited the highest average annual growth rate, indicating preliminary effectiveness of national strategies aimed at promoting regional balanced development. However, the eastern region maintained a leading position in absolute numbers. Detailed results are presented in Table 1.

Table 1.

The number of health technical human resources in primary healthcare institutions across China and its regions from 2013 to 2022

Region	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	Annual growth rate (%)
China	7,210,578	7,589,790	8,007,537	8,454,403	8,988,230	9,529,179	10,154,010	10,678,019	11,244,217	11,657,878	5.47%
Eastern	3,207,385	3,348,271	3,519,912	3,711,318	3,945,466	4,201,484	4,444,050	4,649,335	4,905,260	5,089,295	5.23%
Middle	2,101,505	2,214,311	2,338,546	2,452,631	2,576,192	2,711,267	2,885,436	3,054,296	3,206,265	3,333,357	5.23%
Western	1,891,688	2,017,208	2,139,079	2,280,454	2,456,572	2,606,428	2,814,524	2,964,388	3,122,692	3,235,226	6.15%

Note: Unit: persons, except for annual growth rate (%). Data source: China Health Statistics Yearbook. The eastern, middle, and western regions were classified according to the National Bureau of Statistics of China

In terms of the density of primary health technical human resources: ① The overall level of primary health technical human resources showed steady improvement. The number of health technicians per thousand population increased from 5.27 in 2013 to 8.27 in 2022, marking an increase of 56.93%. ② The density of primary health technical human resources remained at a moderate level, with the average number of health technicians per thousand population reaching 6.716 during the 2013–2022 period. ③ The growth of health technical human resources displayed phased characteristics. Specifically, after the annual growth rate steadily increased from 2013 to a relatively high point of 6.56% in 2019, it decelerated to 3.70% by 2022, as illustrated in Fig. 1.

Fig. 1.

Overall profile of China’s primary healthcare technical human resources, 2013–2022. Note: Data source: China Health Statistics Yearbook

Results of aggregation degree matching levels for health technical human resource allocation in China’s Primary healthcare institutions

The heatmap illustrating aggregation degree matching levels reveals a general pattern of “higher in the east and lower in the west.” In this study, relative scarcity was defined as HRAD/PAD < 0.90, basic adequacy as 0.90 ≤ HRAD/PAD < 1.10, relative adequacy as 1.10 ≤ HRAD/PAD < 1.30, and surplus as HRAD/PAD ≥ 1.30. Notably, an intuitive observation indicates that resource allocation in six provinces—Shanghai, Shandong, Fujian, Xinjiang, Shanxi, and Gansu—has gradually transitioned from a state of relative scarcity to a more adequate level. In contrast, Yunnan and Anhui exhibit the opposite trend. Furthermore, five provinces, including Beijing, Chongqing, and Guangxi, have consistently remained in a state of relatively scarce allocation of primary healthcare technical personnel. Details are presented in Fig. 2. However, the visual pattern shown in the heatmap should be interpreted with caution, because the global Moran’s I test for the HRAD/PAD ratio in 2022 did not indicate statistically significant global spatial autocorrelation.

Fig. 2.

Heat map of aggregation degree matching levels for grassroots health technical human resources across provinces. Note: HRAD = Health Resource Aggregation Degree; PAD = Population Aggregation Degree. Relative scarcity was defined as HRAD/PAD < 0.90, basic adequacy as 0.90 ≤ HRAD/PAD < 1.10, relative adequacy as 1.10 ≤ HRAD/PAD < 1.30, and surplus as HRAD/PAD ≥ 1.30. Data source: China Health Statistics Yearbook and China Statistical Yearbook

Global spatial autocorrelation of the HRAD/PAD Ratio in 2022

To further examine whether the observed provincial variation in the HRAD/PAD ratio represented statistically meaningful spatial dependence, we calculated the global Moran’s I for 2022. The result showed that Moran’s I was − 0.086833, with a z-score of -0.704996 and a p-value of 0.480813, as illustrated in Table 2. This indicates that the spatial distribution of the HRAD/PAD ratio in 2022 did not exhibit statistically significant global spatial autocorrelation at the provincial level. In other words, although interprovincial differences were visually observable, these differences could not be interpreted as statistically significant spatial clustering or dispersion from a statistical perspective.

Table 2.

Global Moran’s I test for the provincial distribution of HRAD/PAD ratio in 2022

Year	Indicator	Moran’s I	Expected I	z-score	p-value	Interpretation
2022	HRAD/PAD ratio	-0.0868	-0.0333	-0.705	0.4808	No significant global spatial autocorrelation

Note: HRAD = Health Resource Aggregation Degree; PAD = Population Aggregation Degree. A p-value > 0.05 indicates no statistically significant global spatial autocorrelation

Dynamic evolution of health technical human resource allocation in China’s primary healthcare institutions

During the period 2013–2022, the kernel density curves for health technical human resources in primary healthcare institutions across China’s 31 provinces exhibited a consistent rightward shift overall, indicating an overall improvement in matching levels. Concurrently, the peak height of the curves showed a gradual upward trend, while their width slightly increased. In general, a higher peak indicates that provincial values became more concentrated, whereas a broader curve suggests greater dispersion. If multiple peaks emerge, this may indicate polarization. A regional breakdown reveals distinct patterns: in the eastern region, the peak demonstrated a decreasing trend accompanied by a continuous broadening of the curve width, suggesting increasing internal dispersion. Conversely, the central region experienced a continuous increase in peak height alongside a reduction in curve width, indicating relative convergence. For the western region, the peak continuously rose. The curve width was relatively broad during 2018–2019, indicating significant intra-regional disparity; however, from 2020 to 2022, the width decreased, suggesting a convergence in the distribution and a narrowing of regional disparities, with the number of technical personnel becoming more concentrated. See Fig. 3. It should be noted that the KDE curves are interpreted primarily as descriptive evidence of temporal distributional change, because minor local shape differences may be influenced by the smoothing parameter.

Fig. 3.

Kernel density estimation map of the matching level of the allocation of grassroots health technical human resources in China and its regions. Note: The horizontal axis represents the HRAD/PAD ratio and the vertical axis represents kernel density. Higher peaks indicate greater concentration, broader curves indicate greater dispersion, and multiple peaks may suggest polarization. Data source: authors’ calculations based on China Health Statistics Yearbook and China Statistical Yearbook

Dagum gini coefficient and its decomposition results

The overall Gini coefficient for the allocation of primary healthcare technical human resources in China was 0.382 in 2013. It declined to 0.363 in 2016 before fluctuating and increasing to 0.378 in 2022. Decomposition results for the intra-regional Gini coefficient show that from 2013 to 2022, the coefficient for the eastern region fluctuated and decreased from 0.386 to 0.367; for the central region, it slowly increased from 0.229 to 0.263; and for the western region, it slowly fluctuated and increased from 0.405 to 0.410.

Decomposition results for the inter-regional Gini coefficient indicate that the disparity in the balance of personnel allocation between the eastern and central regions followed a trajectory of initial decline followed by a rebound, with the overall gap gradually narrowing. The trajectories of the allocation balance disparities between the eastern and western regions, and between the central and western regions, both showed a trend of fluctuating decline.

Regarding contribution rates, the intra-regional contribution rate slowly increased from 30.82% to 31.29% in 2022. The inter-regional contribution rate exhibited a fluctuating downward trend, decreasing from 38.73% in 2013 to 31.54% in 2022, a drop of 7.19% points. The contribution rate of hypervariable density generally showed an opposite trend to that of the inter-regional disparity, increasing from 30.46% in 2013 to 37.17% in 2022. In this framework, hypervariable density reflects the degree of cross-overlap among the distributions of different regions. A higher contribution of hypervariable density indicates that the allocation levels of provinces in different regions are increasingly interwoven, suggesting that overall inequality cannot be explained solely by fixed average differences between regions. See Table 3; Fig. 4.

Table 3.

Gini coefficient and its decomposition for inequality in the distribution of health technical human resources in China’s primary healthcare from 2013 to 2022

Year	Gini coefficient	Gini coefficient within the group			Inter-group Gini coefficient
	Overall	Eastern	Middle	Western	Eastern& Middle	Eastern& Western	Middle& Eastern	Middle& Western	Western& Eastern	Western& Middle
2013	0.382	0.386	0.229	0.405	0.349	0.475	0.349	0.355	0.475	0.355
2014	0.365	0.369	0.233	0.402	0.327	0.442	0.327	0.353	0.442	0.353
2015	0.363	0.366	0.235	0.396	0.329	0.438	0.329	0.348	0.438	0.348
2016	0.363	0.364	0.238	0.397	0.331	0.437	0.331	0.348	0.437	0.348
2017	0.364	0.364	0.236	0.398	0.335	0.439	0.335	0.345	0.439	0.345
2018	0.369	0.368	0.234	0.397	0.345	0.446	0.345	0.342	0.446	0.342
2019	0.371	0.37	0.244	0.398	0.348	0.444	0.348	0.347	0.444	0.347
2020	0.37	0.367	0.243	0.402	0.343	0.442	0.343	0.350	0.442	0.35
2021	0.371	0.362	0.25	0.406	0.342	0.442	0.342	0.357	0.442	0.357
2022	0.378	0.367	0.263	0.410	0.349	0.448	0.349	0.366	0.448	0.366

Fig. 4.

Contribution rates to the inequality in the distribution of health technical human resources in China’s primary healthcare from 2013 to 2022

Discussion

Sustained growth in the total quantity of health technical human resources in China’s primary healthcare institutions.

The findings of this study indicate a consistent and stable increase in the total quantity of health technical human resources within China’s primary healthcare institutions from 2013 to 2022. This trend aligns with the observation from China’s kernel density estimation plot, where the curve exhibits a continuous rightward shift overall. This consistency underscores the substantial policy support provided by the Chinese government for strengthening the primary healthcare workforce. The “14th Five-Year” Plan for Health Personnel Development issued by the National Health Commission of China in 2022 [33] explicitly sets a target of reaching 16 million health personnel by 2025. The plan also notes that current challenges persist, including a relative shortage and uneven distribution of primary health technical personnel. It emphasizes the urgent need to reinforce the development of the primary health workforce and propel the advancement of primary health services by enhancing training efforts and effectively improving service capacity at the grassroots level. Government agencies across China have actively responded to national directives by implementing supportive policies aimed at optimizing the allocation of primary health technical personnel and elevating the overall caliber of the primary healthcare workforce. Consequently, within the overarching goal of expanding the total health human resources, national-level policies should play a pivotal guiding role in the continuous expansion of primary health technical human resources. In contrast, policy implementation at the local level should focus on refining the environment for talent development. This can be achieved through measures such as broadening career advancement pathways, alleviating workload burdens, and improving incentive mechanisms to attract and retain health technical professionals.

Significant spatial disparities in the distribution of health technical human resources in China’s primary healthcare institutions

This study showed persistent regional disparities in the allocation of primary healthcare human resources. Provinces such as Beijing, Tianjin, Jiangsu, Zhejiang, and Shanghai had relatively high HRAD values, whereas several provinces in the central and western regions remained below the benchmark level, indicating continued shortages in geographically and demographically less advantaged areas. This interpretation is also broadly consistent with the heatmap results, which suggested a general pattern of higher levels in the east and lower levels in the west.

Although the overall Gini coefficient declined slightly from 0.382 in 2013 to 0.378 in 2022, the magnitude of change was small, suggesting only limited improvement in distributional balance. At the same time, hypervariable density gradually became the largest contributor to overall inequality. In this framework, hypervariable density reflects cross-regional distributional overlap. In other words, provinces from different regions increasingly showed overlapping allocation levels. This suggests that overall inequality was driven not only by average gaps between broad regions, but also by the increasing overlap of provincial distributions across regions.

A cautious interpretation is that this pattern may be associated with urbanization, internal migration, and the lagged adjustment of workforce allocation mechanisms to population change. However, this study does not estimate causal effects. These factors should therefore be regarded as plausible hypotheses rather than confirmed explanations. More broadly, the observed spatial disparities may also reflect institutional frictions between need-oriented allocation principles and the actual operation of workforce financing, recruitment, retention, and regional planning mechanisms.

These findings should be interpreted cautiously. The present study is descriptive and does not identify causal effects. The observed pattern may be related to both supply-side factors, such as fiscal support, workforce training, and institutional capacity, and demand-side factors, such as population density, migration, and service need [34, 35]. In addition, the global Moran’s I result for 2022 was not statistically significant. Therefore, the mapped provincial pattern is more appropriately interpreted as spatial heterogeneity rather than statistically confirmed global spatial clustering.

From a policy perspective, improving distributional balance requires not only continued workforce expansion, but also more targeted compensation and staffing mechanisms for central, western, border, ethnic, and other underserved areas. For provinces that remained below the adequacy threshold, policy priorities should include staffing support, retention incentives, and service-radius-sensitive allocation standards. For large metropolitan areas, priorities may include retention management and more efficient deployment within densely populated service networks. Allocation standards should therefore consider not only population size, but also service radius, geographic accessibility, and local service burden [36, 37].

Insufficient stability in the allocation of health technical resources in China’s primary healthcare institutions

From 2018 to 2022, a notable decline was observed in the Health Resource Aggregation Degree (HRAD) for Beijing, Shanghai, and Zhejiang, although their values remained above 1. This pattern may reflect changing workforce distribution dynamics in highly developed regions. Existing studies suggest that employment location choices among primary healthcare workers may be associated with family responsibilities, career development opportunities, compensation, and institutional working conditions [38, 39]. These factors may help explain why high-resource regions can still experience pressure in maintaining a stable primary healthcare workforce.

These findings suggest that improving workforce stability will require continued refinement of personnel management systems in primary healthcare, together with better support for career development, compensation, and institutional capacity in underserved areas. The ongoing development of regional medical centers may also strengthen service capacity in recipient areas and, over time, improve the distribution of health professionals.

This study has several limitations. First, although a global Moran’s I test was added for the HRAD/PAD ratio in 2022, the result was not statistically significant; therefore, the observed provincial pattern should be interpreted more cautiously as spatial heterogeneity rather than statistically confirmed global spatial clustering. In addition, because the analysis is based on province-level aggregated data, it primarily captures interprovincial disparities and may obscure substantial within-province heterogeneity, especially urban-rural and county-level differences in geographically diverse western provinces. Second, although the China Health Statistics Yearbook provides authoritative national data, possible changes in statistical classification across years may affect strict measurement comparability for some personnel categories. Third, the study period covers 2013 to 2022 and does not reflect post-2022 policy or contextual changes, and the aggregation degree-based measure cannot fully capture the quality dimension of health human resources, local workload, or all aspects of service need. Fourth, this study is descriptive in nature and does not include a multivariable explanatory model or adjustment for potential provincial confounders. Accordingly, interpretations concerning population mobility, policy effects, and workforce preference changes should be regarded as cautious, non-causal explanations rather than established causal mechanisms. Future research could incorporate finer-scale data, richer provincial covariates, and multilevel or multiscale spatial approaches to provide more precise and policy-relevant evidence.

Conclusions

From 2013 to 2022, the number of health technicians in China’s primary healthcare institutions increased steadily, but substantial spatial disparities persisted. Overall inequality changed only modestly, with the Gini coefficient moving from 0.382 in 2013 to 0.378 in 2022. Notably, the contribution of hypervariable density increased from 30.46% to 37.17%, becoming the largest source of overall inequality. This indicates that the observed imbalance increasingly stemmed from cross-regional distributional overlap rather than from fixed average differences between regions. Accordingly, future policy should move beyond aggregate workforce expansion and place greater emphasis on targeted allocation mechanisms and the geographic accessibility of primary healthcare services in underserved areas.

Author contributions

JY and BM conceived and designed the study and share first authorship. BM collected and analyzed the data. BM, TW, and YN contributed to the literature review and interpretation. YN and TW contributed to manuscript drafting and revision. All authors read and approved the final manuscript.

Funding

This study was supported by the National Social Science Foundation of China (Grant No. 23XMZ038) and the Natural Science Foundation of Ningxia (Grant No. 2023AAC03234). The funders had no role in the study design, data collection, analysis, interpretation, or manuscript writing.

Data availability

This study is based on publicly available datasets. The data can be accessed from the official websites of the National Health Commission of the People’s Republic of China (http://www.nhc.gov.cn/wjw/index.shtml) and the National Bureau of Statistics of China (https://www.stats.gov.cn/).

Code availability

No custom code package was developed in this study. The analytical workflow included: calculation of HRAD and PAD in Microsoft Excel 2021; kernel density estimation in Stata 17.0 and MATLAB R2022b; Dagum Gini coefficient decomposition in IBM SPSS Statistics 23.0; and global Moran’s I analysis for the 2022 HRAD/PAD ratio using an inverse-distance spatial relationship specification. Additional workflow details are available from the corresponding author upon reasonable request.

Declarations

Ethics approval

was not required for this study because it was based exclusively on publicly available aggregated datasets and did not involve human participants, identifiable personal data, or human tissue. The study used only de-identified public statistical data and did not involve restricted-access information.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.