| Literature DB >> 34238290 |
Ying Yang1,2, Lei Chen1, Xinfeng Ke1, Zongfu Mao3,4, Bo Zheng5,6.
Abstract
BACKGROUND: In 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.Entities:
Keywords: "4 + 7"; China; National Centralized Drug Procurement (NCDP) policy; antibiotic use; volume-based procurement
Mesh:
Substances:
Year: 2021 PMID: 34238290 PMCID: PMC8265121 DOI: 10.1186/s12913-021-06698-5
Source DB: PubMed Journal: BMC Health Serv Res ISSN: 1472-6963 Impact factor: 2.655
Fig. 1Flow chart of samples screening. Note: CDPS-SZ 2019, Centralized Drug Procurement Survey in Shenzhen 2019
Purchase volume and expenditures of included antibacterial agents in April to December 2018 and April to December 2019
| Categories | Volume (million DDDs) | Expenditures (million RMB) | ||||
|---|---|---|---|---|---|---|
| Apr. to Dec. | Apr. to Dec. | Relative | Apr. to Dec. | Apr. to Dec. | Relative | |
| Cefuroxime | 1.75 | 3.38 | 92.9 | 5.19 | 4.21 | -18.9 |
| Winning products | 1.13 | 3.20 | 182.9 | 1.62 | 3.29 | 102.4 |
| Non-winning products | 0.62 | 0.18 | -70.3 | 3.56 | 0.92 | -74.1 |
| Alternatives | 7.71 | 10.04 | 30.2 | 87.49 | 105.89 | 21.0 |
| Total | 9.47 | 13.42 | 41.8 | 92.67 | 110.09 | 18.8 |
Fig. 2Trends of monthly drug purchase volume and expenditures for winning and non-winning products. (a) Volume (thousand DDDs); (b) Expenditures (thousand RMB)
Results of the segmented linear regression models for the volume of winning and non-winning products
| Coefficient | Standard Error | 95 % | ||||
|---|---|---|---|---|---|---|
| Lower | Upper | |||||
| Secular trend, | 3.94 | 3.28 | 1.20 | 0.245 | -2.92 | 10.80 |
| Change in level, | 185.07 | 50.14 | 3.69 | 0.002 | 80.13 | 290.02 |
| Change in trend, | -0.82 | 8.01 | -0.10 | 0.920 | -17.58 | 15.95 |
| Cold, | 73.25 | 31.36 | 2.34 | 0.031 | 7.61 | 138.89 |
| Constant, | 87.74 | 30.10 | 2.92 | 0.009 | 24.75 | 150.73 |
| Secular trend, | 0.33 | 0.98 | 0.33 | 0.744 | -1.73 | 2.38 |
| Change in level, | -26.35 | 15.03 | -1.75 | 0.096 | -57.81 | 5.11 |
| Change in trend, | -4.27 | 2.40 | -1.78 | 0.091 | -9.30 | 0.75 |
| Cold, | 23.97 | 9.41 | 2.55 | 0.020 | 4.27 | 43.66 |
| Constant, | 59.08 | 9.02 | 6.55 | 0.000 | 40.21 | 77.95 |
Model 1, F = 25.18, p-value < 0.001, R = 0.841, Adjusted R = 0.808; Model 2, F = 13.02, p-value < 0.001, R = 0.733, Adjusted R = 0.676
Fig. 3Trends of monthly drug purchase volume and expenditures for Cefuroxime Axetil and its Alternatives. (a) Volume (thousand DDDs); (b) Expenditures (thousand RMB)
Results of the segmented linear regression models for the volume of Cefuroxime and its alternative drugs
| Coefficient | Standard Error | 95 % | ||||
|---|---|---|---|---|---|---|
| Lower | Upper | |||||
| Secular trend, | 4.12 | 3.09 | 1.33 | 0.199 | -2.35 | 10.59 |
| Change in level, | 161.16 | 48.61 | 3.32 | 0.004 | 59.43 | 262.90 |
| Change in trend, | -5.41 | 7.52 | -0.72 | 0.480 | -21.16 | 10.33 |
| Cold, | 89.67 | 32.04 | 2.80 | 0.011 | 22.62 | 156.72 |
| Constant, | 149.22 | 28.04 | 5.32 | 0.000 | 90.54 | 207.89 |
| Secular trend, | 20.52 | 5.42 | 3.79 | 0.001 | 9.18 | 31.86 |
| Change in level, | 273.65 | 87.66 | 3.12 | 0.006 | 90.17 | 457.12 |
| Change in trend, | -47.57 | 12.91 | -3.69 | 0.002 | -74.59 | -20.55 |
| Cold, | 313.94 | 65.63 | 4.78 | 0.000 | 176.56 | 451.31 |
| Constant, | 634.46 | 47.66 | 13.31 | 0.000 | 534.70 | 734.22 |
| Secular trend, | 24.70 | 7.25 | 3.41 | 0.003 | 9.52 | 39.88 |
| Change in level, | 436.31 | 117.29 | 3.72 | 0.001 | 190.81 | 681.81 |
| Change in trend, | -54.09 | 17.30 | -3.13 | 0.006 | -90.31 | -17.88 |
| Cold, | 385.09 | 87.21 | 4.42 | 0.000 | 202.55 | 567.63 |
| Constant, | 786.20 | 63.89 | 12.30 | 0.000 | 652.47 | 919.94 |
Model 1, F = 19.63, p-value < 0.001, R = 0.805, Adjusted R = 0.764; Model 2, F = 32.63, p-value < 0.001, R = 0.873, Adjusted R = 0.846; Model 3, F = 36.12, p-value < 0.001, R = 0.884, Adjusted R = 0.859