Evidence map›Paper›PMID 34238290›Full record

ArticleBMC health services research2021

The impacts of Chinese drug volume-based procurement policy on the use of policy-related antibiotic drugs in Shenzhen, 2018-2019: an interrupted time-series analysis.

Ying Yang, Lei Chen, Xinfeng Ke, Zongfu Mao, Bo Zheng

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Article in BMC health services research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

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36citing papers in PubMed
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3 · Its place in the literature

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36 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Ying YangSchool of Health Sciences, Wuhan University, 115# Donghu Road, 430071, Wuhan, China.
Lei ChenSchool of Health Sciences, Wuhan University, 115# Donghu Road, 430071, Wuhan, China.
Xinfeng KeSchool of Health Sciences, Wuhan University, 115# Donghu Road, 430071, Wuhan, China.
Zongfu MaoSchool of Health Sciences, Wuhan University, 115# Donghu Road, 430071, Wuhan, China. zfmao@whu.edu.cn.
Bo ZhengInstitute of Clinical Pharmacology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, 100034, Beijing, China. doctorzhengbo@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn 2019, Chinese government implemented volume-based procurement of 25 drugs in 4 municipalities and 7 sub-provincial cities, i.e. "4 + 7" policy. Competitive bidding was conducted by the government based on the annual agreed procurement volume submitted by each public medical institution in pilot cities. Pilot cities were required to implement bid winning results in March 2019 and the use volume of bid winning products was examined to ensure the completion of agreed procurement volume. In the policy, an oral antibiotic (cefuroxime) was included. Given the current condition of the irrational use of antibiotics in China, this study aims to evaluate the impact of "4 + 7" policy on the use of policy-related antibiotics.

methodsThis study used drug purchase data from the Centralized Drug Procurement Survey in Shenzhen 2019, covering 24 months from January 2018 to December 2019. Oral antibiotic drugs related to "4 + 7" policy were selected as study samples, including cefuroxime and 12 antibiotic drugs that have an alternative relationship with cefuroxime in clinical use. Purchase volume and expenditures were selected as outcome variables, and were measured using Defined Daily Doses (DDDs) and Chinese yuan, respectively. Segmented linear regression analysis with interrupted time series was adopted to examine the effect of "4 + 7" policy.

resultsAfter the implementation of "4 + 7" policy, the overall volume of cefuroxime and its alternative drugs increased from 9.47 million DDDs to 13.42 million DDDs, with an increase of 41.8 %. The results of segmented linear regression showed that the volume of cefuroxime significantly increased 161.16 thousand DDDs after "4 + 7" policy (95 % CI: 59.43 to 262.90, p-value = 0.004). The volume of alternative drugs significantly increased 273.65 thousand DDDs (95 % CI: 90.17 to 457.12, p-value = 0.006). The overall "4 + 7" policy-related antibiotics significantly increased 436.31 thousand DDDs (95 % CI: 190.81 to 681.81, p-value = 0.001) after "4 + 7" policy.

conclusionsThis study provides evidence that the implementation of "4 + 7" volume-based procurement policy was associated with significant increases in the volume of policy-related antibiotic drugs. The increase in antibiotic use after the policy needs special attention and vigilance.

Indexed as

Anti-Bacterial AgentsPharmaceutical PreparationsChinaHealth ExpendituresHumansInterrupted Time Series AnalysisAnti-Bacterial AgentsPharmaceutical Preparations"4 + 7"antibiotic useChinaNational Centralized Drug Procurement (NCDP) policyvolume-based procurement

Identifiers

PMID34238290
PMCPMC8265121

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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.