China’s High-Quality Hospital Resources Are Narrowing the East-West Divide, Decade-Long Analysis Finds

High-quality healthcare remains one of the most stubbornly uneven commodities in modern China, but a new decade-long study of all 31 mainland provincial-level regions shows the gap is slowly closing—while also revealing, for the first time in such detail, exactly which ingredients a province must possess before any policy recipe for world-class hospital care can succeed.

The research, published in the International Journal for Equity in Health by a team from Guangxi Medical University led by Zijian Tang and Pinghua Zhu, tracked high-quality healthcare resource allocation, or HQHRA, from 2014 to 2023. Using a balanced panel of 310 province-year observations, the investigators built a composite index from four indicators: the stock of high-level hospital resources, hospital hierarchy, the structure of tertiary-hospital accreditation, and the scale structure of hospitals. Each indicator was combined with equal weights using the TOPSIS multi-criteria method, producing a single comparable score for every province in every year of the study period.

The headline finding is a story of gradual geographic rebalancing. The eastern region’s share of China’s high-quality hospital resources declined from 43 percent to 40 percent over the decade, while the western share climbed from 29 percent to 33 percent. The eastern–western gap, once a yawning 14 percentage points, narrowed to 7. Kernel density estimation confirmed that the overall national distribution shifted rightward, meaning more provinces moved into higher quality categories as the years passed. Yet the distributions also remained dispersed, signalling that broad improvement did not translate into uniform convergence across provinces. Some regions raced ahead while others advanced far more slowly, leaving substantial interprovincial differentiation intact.

Spatial statistics added a crucial layer to the picture. Global Moran’s I values stayed positive throughout the decade, ranging from 0.100 to 0.220, indicating that provinces with high HQHRA scores tended to cluster near other high-scoring neighbours, and low-scoring provinces clustered together too. The researchers then applied traditional and spatial Markov chain analysis to examine how provinces moved between quality states over time. Transitions occurred predominantly within the same state or between adjacent states, and the probabilities of moving up or down depended significantly on the quality of a province’s geographic neighbourhood. In other words, a province embedded among high-performing neighbours faced different transition odds than one surrounded by weak ones. The pattern revealed gradual evolution, strong path dependence, and spatially conditioned persistence—history and geography jointly shaped each province’s trajectory.

To uncover why some provinces achieved high-quality allocation while others did not, the team turned to the Technology–Organization–Environment (TOE) framework, organising eight conditions across technological, organisational, and environmental dimensions. They then deployed two complementary set-theoretic and constraint-based methods. Necessary condition analysis (NCA) tested whether any single factor acted as a hard bottleneck, while panel qualitative comparative analysis (panel QCA) searched for combinations of conditions that were jointly sufficient to produce high HQHRA.

The NCA results were striking. Three factors emerged as necessary but insufficient constraints: technological innovation capacity, clinical workforce capacity, and regional economic development. At the demanding 90 percent HQHRA target level, a province would need technological innovation capacity at a minimum of 59.9 percent of its observed range, clinical workforce capacity at 39.3 percent, and regional economic development at 53.9 percent of its observed range. Falling below these floors, the analysis suggests, makes high-level allocation effectively unreachable regardless of other efforts. Yet reaching these thresholds guarantees nothing on its own—each condition is a prerequisite, not a cause.

Panel QCA found no single set-theoretically necessary condition, but it identified four distinct sufficient configurations for achieving high HQHRA. The first is an innovation-led pattern, in which strong technological innovation capacity anchors the outcome. The second and third are variants of an innovation–demand pattern, one reinforced by economic strength and the other shaped by an ageing population context that raises demand for complex care. The fourth is a multi-capacity–demand synergy pattern, combining organisational capability with demand-side pressures. The overall solution achieved a consistency of 0.864 and coverage of 0.686, indicating the configurations reliably account for a substantial share of high-HQHRA cases. The researchers describe this structure as constrained equifinality: multiple different routes lead to the same destination, but only for provinces that have cleared the minimum floors on innovation, workforce, and economic capacity.

The policy implications are pointed. The authors argue that blanket institutional expansion—simply building more hospitals everywhere—is not enough. Instead, they call for prioritising low-HQHRA provinces that are embedded in disadvantaged neighbourhood contexts, where spatial spillovers work against improvement. Regional medical-centre and counterpart-assistance programmes, they suggest, should couple infrastructure investment with technology transfer, specialist rotations, workforce development and retention, referral coordination, and local capacity building. Rather than uniform prescriptions, provincial interventions should be tailored to local bottlenecks: innovation absorption in some regions, demand-responsive specialty services in others, ageing-oriented integrated care where demographic pressure dominates, or regional capability diffusion where neighbourhood effects hold sway.

Notably, the study also proposes new yardsticks for measuring success. Rather than counting beds or buildings, policy performance should be assessed through reductions in avoidable patient outflow, increases in locally managed complex cases, workforce retention, and the adoption of transferred technologies—metrics that capture whether capacity is genuinely taking root rather than merely being distributed on paper.

For a nation of 1.4 billion people, the stakes of these findings extend well beyond academic debate. China’s hospital hierarchy system concentrates tertiary accreditation and specialist expertise in a relatively small number of elite institutions, and patients routinely travel across provincial borders for complex care, a phenomenon known as patient outflow. If high-quality resources remain locked into advantaged regions and advantaged neighbourhoods, health equity suffers in ways that ordinary expansion cannot fix. This study’s central contribution is to show that the routes out of that trap are real but conditional: a province must first build its innovation, workforce, and economic foundations, and then choose the configuration that matches its own technological, organisational, and environmental circumstances. The decade from 2014 to 2023 demonstrated that convergence is possible—the eastern–western gap nearly halved—but the persistence of spatially conditioned path dependence means the next decade’s progress will depend less on where resources are poured and more on whether the underlying capacities exist to absorb them.

Subject of Research: Spatiotemporal distribution, dynamic evolution, and configurational mechanisms of high-quality healthcare resource allocation across 31 mainland Chinese provincial regions from 2014 to 2023, analysed with panel QCA and necessary condition analysis within the TOE framework

Article Title: Spatiotemporal distribution, dynamic evolution, and configurational mechanisms of high-quality healthcare resource allocation in China—evidence from panel QCA and NCA within the TOE framework

Article References: Tang, Z., Zeng, P., Chen, H., Chen, Y., Yang, S., Zhang, W., Wang, Q., & Zhu, P. (2026). Spatiotemporal distribution, dynamic evolution, and configurational mechanisms of high-quality healthcare resource allocation in China—evidence from panel QCA and NCA within the TOE framework. International Journal for Equity in Health. https://doi.org/10.1186/s12939-026-03041-4

Image Credits: AI Generated

DOI: 10.1186/s12939-026-03041-4

Keywords: healthcare resource allocation, China, health equity, panel QCA, necessary condition analysis, TOE framework, hospital hierarchy, spatial Markov chains, regional inequality, health policy, constrained equifinality, TOPSIS