Did the universal zero-markup drug policy lower healthcare expenditures? Evidence from Changde, China
Bibliographic record
Abstract
BACKGROUND: The zero-markup drug policy (also known as the universal zero-markup drug policy (UZMDP)) was implemented in stages beginning with primary healthcare facilities in 2009 and eventually encompassing city public hospitals in 2016. This policy has been a central pillar of Chinese health reforms. While the literature has examined the impacts of this policy on healthcare utilization and expenditures, a more comprehensive and detailed assessment is warranted. The purpose of this paper is to explore the impacts of the UZMDP on inpatient and outpatient visits as well as on both aggregate healthcare expenditures and its various components (including drug, diagnosis, laboratory, and medical consumables expenditures). METHODS: A pre-post design was applied to a dataset extracted from the Changde Municipal Human Resource and Social Security Bureau comprising discharge data on 27,246 inpatients and encounter data on 48,282 outpatients in Changde city, Hunan province, China. The pre-UZMDP period for the city public hospitals was defined as the period from October 2015 to September 2016, while the post-UZMDP period was defined as the period from October 2016 to September 2017. Difference-in-Difference negative binomial and Tobit regression models were employed to evaluate the impacts of the UZMDP on healthcare utilization and expenditures, respectively. RESULTS: Four key findings flow from our assessment of the impacts of the UZMDP: first, outpatient and inpatient visits increased by 8.89 % and 9.39 %, respectively; second, average annual inpatient and outpatient drug expenditures fell by 4,349.00 CNY and 1,262.00 CNY, respectively; third, average annual expenditures on other categories of healthcare expenditures increased by 2,500.83 CNY, 417.10 CNY, 122.98 CNY, and 143.50 CNY for aggregate inpatient, inpatient diagnosis, inpatient laboratory, and outpatient medical consumables expenditures, respectively; and fourth, men and older individuals tended to have more inpatient and outpatient visits than their counterparts. CONCLUSIONS: Although the UZMDP was effective in reducing both inpatient and outpatient drug expenditures, it led to a sharp rise in other expenditure categories. Policy decision makers are advised to undertake efforts to contain the growth in total healthcare expenditures, in general, as well as to evaluate the offsetting effects of the policy on non-drug components of care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".