Abstract
Objects
The assessment of hospital construction projects has evolved from limited financial and operational appraisals to comprehensive socio-economic evaluations. However, the application of socio-economic evaluation remains constrained by methodological and data limitations, particularly in remote and developing regions. A pressing challenge is to develop an accessible framework capable of overcoming these barriers, thereby facilitating rapid socio-economic evaluations that provide actionable insights for hospital administrators, investors and policymakers.
Method
Hospital S was analyzed as a regional case study. Socio-economic and healthcare indicators (population, GDP per capita, health expenditure, beds, physicians, nurses) were obtained from local and supplemented national or international sources. A counterfactual framework was applied. Under the counterfactual scenario, no hospital construction was assumed and Disability-Adjusted Life Years (DALYs) and covariates were projected using the grey model. Under the factual scenario, hospital effects were incorporated through a pathway linking hospital construction to changes in indicators and subsequently to DALYs. DALYs were modelled using a log-linear multiple regression. Estimated coefficients were applied to translate changes in indicators into DALY differences between scenarios. The difference in DALYs was interpreted as disease burden averted and monetized using standard health economic thresholds to estimate socio-economic benefits.
Results
By comparing outcomes under simulated counterfactual and factual scenarios, the new hospital would be projected to avert an average of 2216 DALYs annually, with a total of 24,374 DALYs averted across the entire project lifecycle. When varying the threshold settings, our analysis indicated that the new hospital would generate socio-economic benefits ranging from USD 29.5 million to USD 2.1 billion over its lifecycle.
Conclusion
This study proposes a practical and adaptable framework based on counterfactual theory for the rapid socio-economic evaluation of hospital construction projects from a regional perspective. The framework enables estimation of DALYs differences between factual and counterfactual scenarios and the corresponding socio-economic benefits under data-limited conditions. It is intended as a flexible analytical approach that can be adjusted according to context, data availability, and policy objectives, and may support evidence-based regional health planning and resource allocation.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s13561-026-00834-9.
Keywords: Counterfactual Theory, Hospital Construction Project, Disability-Adjusted Life Years, Socio-economic Benefits
Background
Modern hospitals are the core component of healthcare systems and tasked with acute and critical care services, preventive health programs, medical education, research innovation, and public health emergency response, ensuring population health and social stability [1, 2]. Hospital construction projects hold essential significance in advancing health equity and optimizing healthcare resource allocation. Well-designed hospital infrastructure can effectively address urban-rural or interregional imbalance of health resource [3, 4]. The evaluation of hospital construction projects can provide critical evidence for evidence-based decision-making [5]. Nevertheless, current evaluations of hospital construction projects predominantly focus on hospitals as healthcare service providers, emphasizing their fulfillment of core functions and delivery of medical services. Existing evaluation frameworks primarily prioritize metrics related to clinical quality, technical capacity, and operational efficiency, with limited attention to dedicated studies on socio-economic impact assessment [6–9]. In 1984, the World Bank introduced “Social Assessment”, and also emphasized that investments in developing countries should undergo social evaluation to analyze projects’ impacts on local socio-economic structures [10]. The project evaluation has evolved from narrow financial and operational appraisals to comprehensive assessments spanning social, economic, and environmental dimensions [11, 12].
Existing literature on the socio-economic evaluation of hospital construction projects has gradually shifted from traditional financial appraisal toward broader assessments incorporating health, social, and economic outcomes. Early studies primarily relied on cost-benefit analysis, input-output analysis, and descriptive case studies, focusing on direct outputs such as healthcare utilization and short-term economic impacts [13]. More recently, quantitative approaches, including the analytic hierarchy process and fuzzy comprehensive evaluation, have been introduced to assess social benefits [14, 15]. However, these methods remain at an exploratory stage and often face challenges related to data requirements, model complexity, and practical implementation.
Previous studies have employed diverse indicators to evaluate healthcare performance and efficiency. Hospital performance assessments commonly use measures of healthcare resources and service delivery, such as hospital beds, physicians, nurses, and service utilization [16, 17]. Health system efficiency studies typically examine healthcare inputs and population health outcomes, incorporating indicators such as health expenditure, healthcare workforce, life expectancy, mortality rates, and GDP per capita [18]. However, indicator selection varies considerably across studies and is often influenced by data availability and practical considerations, reflecting the absence of a universally accepted evaluation framework [19, 20].
The literature also identifies several persistent challenges, including limited regional-level data, difficulties in forecasting long-term impacts, and uncertainty in quantifying social benefits [21, 22]. These issues are particularly pronounced in remote and developing regions, where data scarcity constrains the application of complex evaluation methods. Therefore, there remains a need for a practical and regionally oriented framework that can support rapid socio-economic evaluation under limited-data conditions. Building on these findings, this study develops a counterfactual-based approach to assess the health and socio-economic impacts of hospital construction projects from a regional planning perspective.
The study focuses on “Hospital S”, a tertiary public hospital under the jurisdiction of “City N”, a prefecture-level city in eastern China. As one of the three tertiary hospitals in the region, Hospital S is the most representative facility and accounts for approximately 50% of key service indicators among tertiary hospitals, including physicians, nurses, outpatient and emergency visits, and inpatient discharges. However, due to its geographically peripheral location, patients often face long travel distances to access services, leading to substantial out-of-region patient outflows and constraints on local service utilization. To address these challenges, Hospital S planned a new downtown campus (the “new hospital”) to improve accessibility, reduce patient outflow, and better meet regional healthcare demand, particularly for cardiovascular and cancer care. The project is scheduled from 2025 to 2035 and is designed as a regional pilot case. Based on counterfactual theory and available data, this study applies a regional socio-economic evaluation framework for the rapid assessment of similar hospital construction projects.
Method
The counterfactual theory
Counterfactual theory provides the foundational framework for defining causal effects in public policy analysis, which is also applied in the evaluation of public health project [23]. Its central premise is that causal effects can be identified and quantified by comparing the outcome that actually occurred with the outcome that would have occurred under a hypothetical alternative condition that did not materialize in reality [24]. According to this theory, causation is grounded in counterfactual dependence: if a cause had not occurred, the effect would not have occurred, or would have occurred differently. The counterfactual theory defines a causal effect as the difference between outcomes observed when an intervention, policy, exposure, or event occurs and the outcomes that would have been observed had it not occurred.
Within the counterfactual framework for causal inference, valid causal conclusions require that the observed outcome distribution in the control group (those not receiving the intervention) serves as a statistical proxy for the unobservable counterfactual—the outcomes that would have occurred had the treated group not received the intervention. This approach addresses the fundamental challenge of causality, due to no individual can simultaneously exist in both treated and untreated states, necessitating comparisons between actual outcomes and hypothetical counterfactual states. During project implementation, the target population is divided into two groups. Treated group represents that subject exposed to the policy intervention. Control group represents that subject not exposed to the intervention. To assess the causal impact of a policy, the theory compares: (a) The observed outcomes of the treated group against their hypothetical outcomes if the intervention had not occurred. (b) The hypothetical outcomes of the control group if they had received the intervention against their actual outcomes. This dual comparison isolates and quantifies the policy effect, the causal difference directly attributable to the intervention itself, by controlling for confounding external factors [25]. This framework strives to approximate the idealized counterfactual contrast between intervention-driven outcomes and empirically observed results.
Study design
This study aimed to evaluate the contribution of a hospital construction project to disease burden reduction and to quantify its socioeconomic benefits from a regional perspective using health economic valuation thresholds. The selection of indicators and data sources was informed by a literature review, the objectives of the socio-economic evaluation and the contextual characteristics of the study setting. While primary data were obtained from locally available sources, data gaps were supplemented using publicly accessible provincial, national, and international datasets to address data availability constraints.
Based on counterfactual theory, factual and counterfactual scenarios were constructed using the Gray Model (GM) and Ordinary Least Squares (OLS) multiple linear regression model [26]. Under the counterfactual scenario, Disability-Adjusted Life Years (DALYs) and related indicators were projected according to historical trends in the absence of the hospital project. Under the factual scenario, the impacts of the hospital project on DALYs were simulated through changes in relevant socio-economic and healthcare indicators. By comparing DALYs between the factual and counterfactual scenarios, the disease burden reduced (or averted) by the project was estimated and subsequently converted into socioeconomic benefits using health economic valuation thresholds. More details are shown in Fig. 1.
Fig. 1.
A conceptual framework for rapid socio-economic evaluation of hospital construction projects based on counterfactual theory
It should be noted that this study provides a practical research approach and reference framework for assessing the socio-economic benefits of hospital construction projects. As indicator selection may vary across regional contexts and data availability, the proposed framework can be adapted to local conditions and research objectives.
Indicators selection and data collection
The selection of indicators was guided by a combination of theoretical relevance, evidence from previous studies, and data availability. The final set of indicators was determined through a balance between conceptual relevance and practical data constraints, ensuring consistency with the regional evaluation framework while maintaining feasibility under real-world data limitations. Therefore, we applied DALYs to reflect the reduce the burden of disease. Used relevant regional socio-economic development, medical services and hospital operation service indicators including population, GDP per capita, government health expenditure (GHE), health expenditure (HE) per capita, number of hospitals, beds, certified (assistant) doctors, nurses, outpatients and emergency visits (OEVs) and inpatients to simulate changes of DALYs in different scenarios [13, 27, 28].
DALYs is a composite metric used to measure the burden of disease, proposed by the WHO and Harvard University in the 1990s to representing the total years of healthy life lost due to illness, disability, or premature death [29]. DALYs consist of two components: Years of Life Lost (YLLs) due to premature mortality and Years Lived with Disability (YLDs). The formula for DALYs is: DALYs = YLLs + YLDs. The calculation of DALYs reveals social value preferences, offering a scientifically rigorous and comprehensive assessment of the health impacts of disability and mortality from both medical and sociological perspectives. DALYs enable cross-disease, cross-regional, and cross-temporal comparisons, providing a unified metric to inform resource prioritization decisions in public health. Their direct integration with cost-effectiveness analyses allows policymakers to quantitatively assess the value of health interventions, such as calculating the cost per DALY averted to guide funding allocations. Furthermore, as the standardized methodology employed consistently by the GBD initiative, DALYs ensure comparability and continuity of results across decades and regions [30, 31]. This methodological consistency supports longitudinal tracking of disease burden trends and facilitates evidence-based policy adjustments in response to evolving health challenges.
To support further analysis, a comprehensive historical dataset was constructed, integrating N_DALYs and related socio-economic and healthcare indicators. The time series of data were retrieved from 2009 to 2019. Regional socio-economic development and medical services indicators could be collected through coordination with the local Health Commission and by referencing the “China Statistical Yearbook”. the N_DALYs was DALYs lost due to cardiovascular diseases and cancers in City N, which data access was challenging. We considered that proportionally scaled down national level data to the city level based on population ratios, in Eq. (1), to estimate N_DALYs. The DALYs data for cardiovascular diseases and cancers in China was collected from the Global Burden of Disease (GBD) public database maintained by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington.
 |
1 |
Projection of DALYs under the counterfactual scenario
Under the counterfactual scenario, the hospital construction project was assumed not to exist, meaning that neither DALYs nor the related socio-economic and healthcare indicators were affected by the intervention. Instead, all changes were assumed to be driven solely by historical development patterns and secular trends. Based on historical data, the GM(1,1) grey prediction model was employed to forecast future trajectories of N_DALYs and related indicators over the entire project lifecycle (2025–2035), thereby constructing the counterfactual dataset. To ensure consistency with econometric modeling conventions and to account for substantial differences in the magnitude of variables, all variables were transformed into logarithmic (Ln) form. This transformation was informed by the log-linear specification derived from the Cobb–Douglas production function, which provides a theoretical framework for characterizing the relationship between multiple input factors and an outcome variable while mitigating potential scale-related issues in subsequent analyses [32].
Simulation of DALYs under the factual scenario
Under the factual scenario, the hospital construction project was assumed to be implemented and fully operational. The impact on disease burden was modelled through a structured pathway of “new hospital construction → changes in relevant indicators → changes in DALYs”. To operationalize this mechanism, an OLS multiple linear regression model was constructed with N_DALYs as the dependent variable and relevant socio-economic and healthcare indicators as independent variables. The estimated coefficients were used to quantify the associations between changes in these indicators and DALYs. Based on projected indicator values under the factual scenario, DALYs were subsequently estimated using the fitted regression equation, thereby generating DALY estimates under the presence of the hospital project.
 |
2 |
Where, 
represents the coefficient of the i-th indicator, 
represents the specific value of the i-th indicator and E represents the constant term.
To assess the robustness and consistency of the results, DALY estimates derived from the regression-based approach were compared with those generated directly by the GM(1,1) model under the counterfactual scenario. The agreement between the two sets of projections was evaluated using the Mean Absolute Percentage Error (MAPE), which served as a measure of consistency between the alternative estimation approaches. After confirming acceptable consistency, both factual and counterfactual scenarios were constructed. The difference in DALYs between the two scenarios was then interpreted as the disease burden reduced or averted attributable to the hospital construction project.
Socio-economic benefit analysis
The socio-economic benefits of the hospital project were estimated by comparing the factual and counterfactual scenarios, with the difference in outcomes interpreted as the intervention-attributable effect after controlling for secular trends. The following Eqs. (3) and (4) were applied to estimate:
 |
3 |
 |
4 |
DALYs averted represented the projected reduction in DALYs attributable to the new hospital. Healthcare cost savings were estimated by monetizing the avoided DALYs and used as a measure of socio-economic benefits. All future benefits were discounted to 2022 values using a 3% annual discount rate.
As the estimated socio-economic benefits depend on the selected cost-effectiveness threshold, two benchmark ranges were adopted: (a) the World Health Organization (WHO) recommendation of 1–3 times GDP per capita and (b) a China-specific threshold of 47–88% of GDP per capita based on domestic studies [33]. Applying multiple thresholds provided a range of reference estimates under different valuation assumptions.
Simulation setting
Using the established multiple linear regression model and the derived coefficients for each indicator, we simulated DALYs under the factual scenario. To clarify the analytical framework, it is important to note that this study does not directly estimate the causal effect of the new hospital on DALYs within the regression model. Instead, the regression model is used to quantify the relationships between DALYs and a set of socio-economic and healthcare indicators based on historical observations. The impact of the new hospital is therefore not entered directly into the regression equation, but is transmitted through its influence on relevant indicators.
We defined three variables that have a direct connection with the new hospital: beds, doctors, and OEVs. The projected changes in beds, doctors, and OEVs were integrated into the counterfactual dataset to represent the operational impact of the new hospital. These updated indicator values were then mapped into DALY changes through the regression model. Accordingly, this study follows a mediated analytical pathway, “new hospital → changes in related indicators → changes in DALYs”, to simulate the impact of the hospital project on disease burden. This simulation framework allows for a systematic evaluation of the socio-economic benefits (expressed in monetary terms) generated by the new hospital, including improved healthcare accessibility, reduced patient outflow, and alleviation of disease-related socio-economic burdens. The simulation settings are detailed as follows:
Based on the new hospital construction plan, we simulated the completion of 1,000 beds by 2025. Starting from 2025 onward, an annual increase of 5% in additional beds was simulated, following historical simulation data from the old hospital.
Building on historical simulation data from the old hospital, we analyzed the relationships between beds, doctors, nurses, and OEVs. The results revealed strong correlations between numbers of nurses/doctors and OEVs volumes. Based on these findings, we estimated the projected OEVs for the new hospital using its planned recruitment of 300 physicians and 360 nurses by 2025 (with a doctor-to-nurse ratio of 1:1.2). The total numbers of physicians and nurses are assumed to grow annually at historical rates derived from simulation data of the old hospital.
For OEVs, the analysis also emphasized impact of the new hospital on reducing out-of-city medical visits, specifically, by estimating the increase in medical services due to improved geographical accessibility. Relevant study reported that patients are willing to pay an additional 87 km for healthcare [34]. By calculating the average distance between each district and the new or old hospitals, we determined that medical services of the new hospital would increase by one-third compared to the old hospital. Consequently, we assumed a one-third increase in OEVs attributable to the reduced travelling cost.
Analysis methods
Grey model
Gray System Theory (GST) focuses on studying uncertainty systems characterized by partially known and partially unknown information. By generating and refining partial known information, GST extracts actionable insights to accurately describe system behaviors, predict evolutionary patterns, and enable effective monitoring [35]. Notable for its minimal data requirements, computational simplicity, and high prediction accuracy, GST is widely applied to forecast and evaluate challenges in production, public health, scientific research, and other domains where incomplete data prevails.
Gray Prediction Model, the GM(1,1), is the foundational model within GST. Its core principle involves the following steps. Transform raw data into a sequence with exponential regularity through cumulative summation. Construct a first-order linear differential equation (the “whitenization equation”) to describe the system’s dynamics. Solve the equation and apply the inverse accumulative generation operation to derive predicted values [36]. The GM(1,1) model represents a first-order differential function of a single variable over time, formalized as:
 |
X
(1) represents the first-order accumulated data sequence. The t is time variable. a and u are undetermined coefficients, where a is termed the development grayness (reflecting system growth trends) and u is the endogenous control grayness (representing internal regulatory effects). Before developing the model, calculate the consecutive term ratios for the time series. Validate that all ratios fall within the admissible coverage interval(
, 
). Only validated series can proceed to GM(1,1) modeling. The model’s predictive precision is assessed using the posterior variance ratio (Value C) which is the ratio of residual variance to original data variance and the small error probability (Value P) which is the Likelihood of residuals falling within a defined tolerance range. These metrics are benchmarked against standard thresholds to determine model fitness [37, 38]. More details were shown in Table 1.
Table 1.
Criteria for model fit tests in the GM(1,1)
| Model fit class | Value C | Value P |
|---|---|---|
| Class 1 (outstanding) | C < 0.35 | P ≥ 0.95 |
| Class 2 (qualified) | 0. 35 ≤ C < 0.50 | 0. 80 ≤ P < 0.95 |
| Class 3 (reluctant) | 0. 50 ≤ C < 0.65 | 0. 70 ≤ P < 0.80 |
| Class 4 (substandard) | C ≥ 0.65 | P < 0.70 |
The multiple linear regression model
This study employs a multiple linear regression analysis based on the Ordinary Least Squares (OLS) method, with N_DALYs as the dependent variable and healthcare and socio-economic indicators as independent variables. This approach serves two purposes. One of them is identifying key indicators which significantly influence DALYs variations. The other one is establishing a regression equation to calculate DALYs using coefficients and specified values of the indicators. Additionally, a multivariate linear regression analysis was conducted with OEVs as the dependent variable and numbers of doctors, nurses, and beds as independent variables. This enables the prediction of potential OEVs after the new hospital’s completion using the derived regression equation. Furthermore, we verified linear relationships between variables using the Pearson Correlation Coefficient. Eliminated confounding variables through collinearity analysis (Variance Inflation Factor, VIF < 5). All analyses adopted a significance level of α = 0.05.
Results
GM(1,1) modeling results
GM(1,1) models were constructed for N_DALYs, healthcare and socio-economic indicators of City N, with detailed model parameters provided in Table 2. To ensure model validity, doctors count data from the earliest two years (2009 and 2010) were excluded. Only inpatients counts were omitted due to the inability to establish a meaningful GM(1,1) model. All other indicator models demonstrated excellent fit, with Value C < 0.35 and Value P > 0.95, meeting rigorous goodness-of-fit criteria. Subsequently, GM(1,1) models were used to forecast data trends for all indicators from 2020 to 2035, creating a predictive dataset for downstream analyses. These projections served as counterfactual scenario outcomes, enabling comparisons with the hospital construction project’s anticipated impacts.
Table 2.
Parameter test results of GM(1,1) models
| Indicators | a | u | Value C | Value P |
|---|---|---|---|---|
| N_DALYs | -0.010 | 269120.078 | 0.116 | 1.000 |
| Population | -0.002 | 262.964 | 0.023 | 1.000 |
| GDP Per capita | -0.100 | 26792.704 | 0.006 | 1.000 |
| GHE | -0.125 | 102846.024 | 0.079 | 1.000 |
| HE Per capita | -0.148 | 296.153 | 0.045 | 1.000 |
| Beds | -0.047 | 10927.953 | 0.200 | 1.000 |
| Doctors | 0.024 | 4777.159 | 0.128 | 1.000 |
| Nurses | -0.055 | 4667.650 | 0.086 | 1.000 |
| OEVs | -0.026 | 12749148.842 | 0.090 | 1.000 |
| Inpatients | 0.009 | 495400.710 | 0.295 | 0.778 |
Multiple linear regression results
Both multiple linear regression models demonstrated statistical significance (P < 0.001). In the first model (Ln_N_DALYs as the dependent variable), nurses and GHE were omitted due to collinearity issues and the other indicators performed statistical significance. The Ln_beds, Ln_HE Per capita and Ln_OEVs performed the negative connection with Ln_N_DALYs, and the Ln_beds (B = -0.10) was the most important indicator for reducing DALYs. The Ln_GDP Per capita, Ln_Population and Ln_Doctors performed the positive connection with Ln_N_DALYs, and the Ln_Population (B = 0.94) was the most important indicator for increasing DALYs. In the second model (OEVs as the dependent variable), beds exhibited significant collinearity with the number of doctors and nurses. The OEVs were largely dependent on the number of doctors and nurses. More details were shown in the Table 3.
Table 3.
Indicator coefficient results of multiple linear regression model
| Model | B | SE | t | P | ||
|---|---|---|---|---|---|---|
| Dependent Variable | Explanatory Variable | |||||
| Ln_N_DALYs | Ln_GDP Per capita | 0.15 | 0.01 | 15.59 | <0.001 | |
| Ln_Population | 0.94 | 0.05 | 23.38 | <0.001 | ||
| Ln_Beds | -0.10 | 0.01 | -17.02 | <0.001 | ||
| Ln_Doctors | 0.11 | 0.02 | 8.75 | <0.001 | ||
| Ln_HE Per capita | -0.02 | 0.01 | -3.97 | <0.001 | ||
| Ln_OEVs | -0.05 | 0.02 | -3.75 | 0.001 | ||
| OEVs | Docs | -3935.99 | 644.68 | -6.11 | <0.001 | |
| Nurs | 3232.11 | 240.10 | 13.46 | <0.001 | ||
Socio-economic benefits
By comparing outcomes under simulated counterfactual and factual scenarios, the analysis revealed that the new hospital would be projected to avert an average of 2216 DALYs annually, totaling 24,374 DALYs avoided over the entire project lifecycle (2025–2035). When threshold was the 47% of GDP per capita, the lowest total benefits would be approximately USD 324.2 million and the average benefit annually would be about USD 29.5 million. When threshold was the 3 times GDP per capita, the highest total benefits would be approximately USD 2.1 billion and average benefit annually would be about USD 188.1 million. More details were shown in the Table 4.
Table 4.
Results of socio-economic benefit analysis
| Year | DALYs Averted |
Threshold(N times GDP per capita) | |||
|---|---|---|---|---|---|
| 47% GDP per capita |
88% GDP per capita |
1 time GDP per capita |
3 times GDP per capita |
||
| 2025 | 2062 | $18,634,408 | $34,889,956 | $39,647,677 | $118,943,031 |
| 2026 | 2091 | $20,276,998 | $37,965,444 | $43,142,550 | $129,427,649 |
| 2027 | 2121 | $22,065,294 | $41,313,742 | $46,947,434 | $140,842,302 |
| 2028 | 2151 | $24,012,209 | $44,959,031 | $51,089,807 | $153,269,422 |
| 2029 | 2182 | $26,131,799 | $48,927,624 | $55,599,573 | $166,798,720 |
| 2030 | 2213 | $28,439,358 | $53,248,160 | $60,509,273 | $181,527,818 |
| 2031 | 2245 | $30,951,529 | $57,951,798 | $65,854,316 | $197,562,949 |
| 2032 | 2277 | $33,686,421 | $63,072,449 | $71,673,237 | $215,019,712 |
| 2033 | 2310 | $36,663,742 | $68,647,007 | $78,007,962 | $234,023,887 |
| 2034 | 2343 | $39,904,933 | $74,715,619 | $84,904,112 | $254,712,336 |
| 2035 | 2377 | $43,433,322 | $81,321,964 | $92,411,323 | $277,233,968 |
| Avg | 2216 | $29,472,729 | $55,182,981 | $62,707,933 | $188,123,800 |
| Sum | 24,374 | $324,200,014 | $607,012,793 | $689,787,265 | $2,069,361,795 |
The discounted at 3% interest rate
Discussions
Hospital construction projects exhibit strong public service and social welfare attributes, generating multidimensional societal benefits. However, the evaluation of these social benefits remains underdeveloped, with a lack of standardized indicator systems and limited methodological rigor, particularly in the quantification of health and socio-economic outcomes due to data constraints and methodological challenges. From a regional planning perspective, this study proposes a rapid socio-economic benefit assessment framework by simulating factual and counterfactual scenarios. The framework integrates disease burden indicators (DALYs) with health economic valuation thresholds to enable a structured quantification of the socio-economic benefits of hospital construction projects. This approach partially addresses data scarcity limitations and provides a feasible reference framework for evaluating infrastructure-related health interventions.
Importantly, beyond its methodological contribution, this framework may offer relevant insights for regional health policy-making and planning. It may support policymakers in prioritizing hospital infrastructure investments, improving equity in access to healthcare services across regions, enhancing the efficiency of health resource allocation, and providing an evidence base for public investment decisions based on expected health and socio-economic returns. Notably, its applicability during the pre-construction decision-making phase makes it particularly relevant for evidence-informed planning and strategic health system development.
Applicability of DALY-based socio-economic benefit analysis for hospital construction project
We define that the socio-economic benefit of the hospital construction project is measured by the reduction in the burden of diseases. It is considered that includes the direct expenses of diseases patients hospitalized (patients also have a significant amount of indirect medical expenses, such as transportation, nutrition, and lost wages), but these are difficult to obtain without surveys. Therefore, we suggested that measures the socio-economic benefit of the hospital construction project by using the DALYs (reflecting the disease burden) and a threshold from regional socio-economic indicator.
In our study, the averted DALYs was utilized to reveal the reduction of disease burden owing to the hospital construction project. The GDP, the threshold of regional socio-economic indicator used in our study, can help know the economic value that people can create without disease. Then, we can calculate the socio-economic benefit from the averted DALYs due to the hospital construction and the socio-economic benefit that can be gained per DALYs averted. In this way, the methodology demonstrates robust alignment with outcomes and performs strong adaptability, which is satisfactory for the demands of socio-economic benefit evaluation.
However, the relevant study indicated problems about DALYs for inadequately addressing health inequities or social context, and advocating for complementary equity-focused analyses [39]. For instance, DALYs do not account for the indirect effects of social determinants, such as poverty, educational attainment, or systemic discrimination, on disease burden. Additionally, DALYs do not take into account future changes in health patterns, which may overlook potential shifts driven by health technology development or policy reforms (such as universal health coverage) [40]. This issue may lead to systematic underestimation or overestimation of burden, particularly in rapidly evolving health landscapes like emerging epidemics or aging societies.
Rationalization of thresholds in the socio-economic benefits analysis
A literature review indicates that commonly used cost-effectiveness thresholds include 1–3 times GDP per capita, health opportunity cost approaches, willingness-to-pay methods, and country-specific thresholds. Among these, the WHO-recommended threshold of 1–3 times GDP per capita remains the most widely applied internationally. However, growing evidence suggests that this threshold may overestimate the value of health gains in resource-constrained settings and may not fully reflect country-specific economic conditions [41, 42].
To improve contextual relevance, this study also considered thresholds derived from Chinese cost-effectiveness research, which estimated the value of one DALY at 47%–88% of GDP per capita [33]. Additionally, Catastrophic Health Expenditure (CHE) serves as an important reference for assessing disease-related economic burdens and financial risk protection [43, 44]. Previous studies have suggested that CHE thresholds in China range from approximately 51.58% to 71.42%, depending on population characteristics and socioeconomic conditions [45]. Notably, these values broadly overlap with the estimated DALY valuation threshold of 47%–88% of GDP per capita, supporting its relevance to China’s economic and healthcare context. Therefore, this threshold may provide a more contextually appropriate basis for policy evaluation and resource allocation [46].
Importantly, the estimated socio-economic benefits were sensitive to the choice of valuation threshold, with higher thresholds generating larger monetary estimates. To account for this uncertainty, socio-economic benefits were calculated using four thresholds (47%, 88%, 1 time GDP per capita, and 3 times GDP per capita), providing a range of plausible estimates rather than a single definitive value. Although the magnitude of economic benefits varied across thresholds, the overall direction of the findings remained consistent, indicating positive health and socio-economic gains associated with the hospital construction project. Given the regional context of this study, the 47%–88% GDP per capita threshold may provide a more policy-relevant reference for decision-making than the conventional WHO threshold.
Limitation
This study has several limitations. First, the estimated socio-economic benefits were derived from simulated factual and counterfactual scenarios and therefore depend on the assumptions used in scenario construction. The results should be interpreted as scenario-based estimates rather than precise predictions. Second, data availability was the primary constraint. Several indicators were unavailable at the regional level and were supplemented using provincial, national, or international sources, which may introduce uncertainty. Finally, the proposed framework is intended as a flexible analytical approach rather than a fixed modelling specification, and alternative indicators or analytical methods may be adopted as data availability and research contexts evolve.
Conclusion
From a regional perspective, this study proposes a practical and adaptable framework based on counterfactual theory to rapidly evaluate the socio-economic benefits of hospital construction projects. By constructing factual and counterfactual scenarios, the framework enables estimation of DALYs differences attributable to the intervention, providing a structured basis for assessing disease burden changes associated with new hospital development. DALYs differences between the two scenarios are further monetized using cost-effectiveness thresholds to quantify the corresponding regional health and socio-economic benefits. The proposed framework is designed for settings with limited regional data and is intended as a flexible analytical approach rather than a fixed modelling specification. It can be adapted according to contextual conditions, data availability, and policy objectives. Overall, this study provides a feasible framework for the rapid assessment of socio-economic benefits of hospital construction projects based on counterfactual theory, supporting evidence-informed regional health planning and resource allocation.
Supplementary Information
Authors’ contributions
TC contributed to the Conceptualization, Data curation, Formal analysis, Methodology, Validation, Writing – original draft and Writing – review & editing. QD and JQ contributed to the Data curation, Investigation, Resources and Writing – review & editing. YC contributed to the Conceptualization, Funding acquisition, Project administration, Supervision and Writing – review & editing. All authors were responsible for critical revisions to and approval of the final manuscript.
Funding
This work did not receive funding.
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
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References
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
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Data Availability Statement
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.