Evidence map›Paper›PMID 41849026›Full record

ArticleHealth economics review2026

Beyond average costs: large-scale evidence on DRG payment reform and the distribution of standardized inpatient spending in China.

Zhuang Cao, Yifan Yao, Bin Cui, Jian Wang, Changli Jia

Abstract read
In one paragraph

Article in Health economics review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zhuang CaoDong Fureng Institute of Economic and Social Development, Wuhan University, Wuhan, China.
Yifan YaoDepartment of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Bin CuiSchool of Public Health, Peking University, Beijing, China.
Jian WangDong Fureng Institute of Economic and Social Development, Wuhan University, Wuhan, China.
Changli JiaDepartment of Health Management, School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. jiachangli1014@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUnder pressures of health insurance cost containment and high-quality development, diagnosis-related groups (DRGs) and other case-based payment schemes have become a major direction of China’s inpatient payment reform, yet their impacts may extend beyond changes in mean costs. Using large-scale inpatient settlement data, we evaluate the cost-containment effects of DRG payment reform from a distributional perspective, testing whether the reform is reflected not only in a decline in mean standardized inpatient spending but also in reshaping spending variability and heavy tails (tail risk).

methodsThe study covers six cities in China’s Province S from January 2019 to December 2021; three cities launched DRG reform in November 2020, January 2021, and June 2021, respectively. The individual-level sample includes 6,882,875 admissions from 172 healthcare institutions and is aggregated into a hospital–month panel; we construct individual standardized inpatient spending as “spending per admission divided by DRG weight,” and then compute the hospital–month mean, standard deviation, skewness, and kurtosis of standardized spending. Our identification strategy applies a two-stage difference-in-differences (DID) estimator suited to staggered adoption to estimate the average treatment effect, and uses the Sun & Abraham event-study approach to test parallel trends and trace dynamic effects; we control for patient composition and city-level covariates and include institution and month fixed effects; we further assess heterogeneity by hospital level and DRG design (point-based vs. rate-based).

resultsThe two-stage DID results show that DRG implementation is associated with a significant reduction in the hospital–month mean of standardized spending (β = −705.7, p < 0.001) and a significant reduction in the standard deviation (β = −1,098.0, p < 0.001); meanwhile, distributional shape metrics increase, with higher skewness (β = 0.380, p < 0.05) and higher excess kurtosis (β = 18.3, p < 0.01). In the event-study analysis, most pre-reform lead coefficients fluctuate around zero and their confidence intervals largely cover zero, providing overall support for the parallel-trends assumption. Regarding heterogeneity, relative to secondary hospitals, the interaction term for tertiary hospitals is not statistically significant across all four outcomes. Relative to the rate-based design, the point-based design shows a significant mitigating effect on distributional shape metrics, reducing skewness (β = −0.941, p < 0.001) and excess kurtosis (β = −38.4, p < 0.001), while differences in the mean and standard deviation are not statistically significant.

conclusionsWhile DRG payment reform reduces the mean level of standardized inpatient spending and within-hospital spending variability, it may be accompanied by a strengthened right tail and heavier tails, indicating that “mean-based cost containment” is insufficient to characterize the reform’s structural consequences; distributional metrics and tail risk should be incorporated into routine performance assessment and regulatory frameworks. Compared with the rate-based approach, the point-based approach appears more advantageous in curbing heavy-tail indicators, underscoring the importance of settlement mechanisms and payment-parameter design for risk discipline.

Indexed as

DRG payment reformEvent studySpending distributionStandardized inpatient spendingTwo-stage DID

Identifiers

PMID41849026
PMCPMC13112759

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.