2.1 Data sources
Data used in this study was obtained from the China Drug Supply Information Platform (CDSIP) database. The CDSIP is a national drug database constructed and operated by the Statistical Information Center of the National Health Commission of the PRC, and was officially launched on October 22, 2015. Since then, health facilities upload information daily on ordering, storing and delivering, and medicines settlement to the CDSIP; the government can monitor prices, quantities, distribution and warehousing, and can organize medications purchased by medical institutions and strengthen management based on relevant information . Thus, the CDSIP database covered drug procurement order data of all provincial drug centralized procurement platforms from 31 provinces (autonomous regions and municipalities) in mainland China. Under the zero-markup drug policy in China [22], the drug purchase prices in public medical institutions are the same as the prices used by patients. Since 2015, it was required that all drugs used by public medical institutions should be purchased through the provincial-level drug centralized procurement platform [23]. Therefore, in mainland China, the drug purchase data of public medical institutions in the CDSIP database is generally consistent with the drug use data.
In the CDSIP database, each drug purchase order record included the name of the medical institution, purchase date, drug YPID (Yao Pin Identifier) code, drug generic name, dosage form, specification, conversion factor, pharmaceutical manufacturer, price per unit, purchasing unit (by box, bottle, or branch), purchase volume, purchase expenditures, etc. Details of the CDSIP database are available elsewhere [18,24]. A total of 7,647 drugs (by generic name) and 141,624 products (by YPID code) were contained in this national database.
2.2 Sample selection
The inclusion criteria of study samples was as follows: (a) The drug scope was “4+7” policy-related drugs, including 25 drugs (by generic name) in the “4+7” List and the alternative drugs (supplementary table 1). The alternative drugs referred to drugs that had an alternative relationship with “4+7” List drugs in clinical use, and was determined following the Monitoring Plan for the Pilot Work of National Centralized Drug Procurement and Use issued by the NHSA of the PRC [25]. The “4+7” List drugs were then divided into bid-winning products and non-winning products according to the "4+7" city centralized drug procurement bid-winning results announced by the Joint Procurement Office [10]. (b) The time period was 23 months from January 2018 to November 2019. (c) The scope of regions was pilot cities (i.e. pilot group) and non-pilot provinces (i.e. control group). The pilot group involved nine “4+7” pilot cities, including Beijing, Shanghai, Chongqing, Tianjin, Chengdu, Xi'an, Shenyang, Dalian, and Xiamen. For two (Guangzhou and Shenzhen) of the eleven “4+7” pilot cities that were not included in this study, their purchase order data in the CDSIP database was incomplete. The control group involved 12 provinces without the implementation of “4+7” pilot policy, including Hubei, Hunan, Guizhou, Inner Mongolia, Jilin, Heilongjiang, Anhui, Hainan, Gansu, Qinghai, Ningxia, and Xinjiang. (d) The scope of health facilities were all the public medical institutions in the included 9 pilot cities and 12 non-pilot provinces, and were divided into tertiary hospitals, secondary hospitals, and primary healthcare centers (PHCs). Purchase order records with incomplete information were excluded.
Aggregated monthly drug procurement records of 4663 public medical institutions from 9 pilot cities and 13,973 public medical institutions from 12 non-pilot provinces were retrospectively analysed (Table 1). A total of 108 policy-related drugs (by generic name) were included in this study, including 25 “4+7” List drugs and 83 alternative drugs. The flow chart of the sample selection process is shown in Figure 1.
Table 1. Distribution of sample medical institutions.
|
Pilot group
|
Control group
|
Overall
|
Tertiary
|
356
|
613
|
969
|
Secondary
|
488
|
1749
|
2237
|
Primary
|
3819
|
11611
|
15430
|
Total
|
4663
|
13973
|
18636
|
2.3 Outcome variables
This study employed the Drug Structure Index (DSI) as the main outcome variable. In 2002, Antonio Addis and Nicola Magrini of the Italian Centre for Effectiveness Evaluation of Health Services constructed a method for analyzing the drug cost variance factors, known as A.M. index method [26]. This method divided drug cost changes into three parts: quantity factor, price factor, and structural factor, in which the structural factor represents the concept of “price structure” and refers to the combined structure of drugs at different prices. Based on the classical A.M. index system method, previous scholars constructed a drug structure index system, and has been applied in relevant studies [27,28]. The DSI represents the changes in the combination of drugs with different Defined Daily Drug cost (DDDc) in the same drug group, showing the transfer of use from drugs with lower (higher) prices to drugs with higher (lower) prices [29]. DSI is calculated as follows:
![](data:image/png;base64,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)
In formula (1), Q represents the used quantity of drugs. The used quantity of each drug was measured based on its Defined Daily Dose (DDD), which is developed by WHO to compare drug consumptions and refers to the average maintenance dose per day for a drug used for its main indication in adults [30]. In this study, the DDD of the drugs which could not be coded in WHO’s ATC/DDD Index 2021 system was determined based on the dosage regimen recommended in the manufacturers’ instructions, as approved by China Food and Drug Administration. P represents the prices of drugs, which is expressed as DDDc. DDDc was calculated as drugs costs per unit DDD (DDDc=expenditures/DDDs).
The numbers of subscripts represent periods, with 0 representing the baseline period and 1 representing the reporting period. The higher the DSI, the higher the use proportion of drugs with higher DDDc in a “basket” of drugs. DSI>1 means that the use proportion of higher DDDc drugs increased in the reporting period compared with the baseline period; DSI<1 means that the use proportion of higher DDDc drugs decreased in the reporting period compared with the baseline period; DSI=1 means that the use proportion of higher DDDc drugs remains stable between reporting-period and baseline period.
This study observed the change of DSI in the pilot group and control group between the pre- and post-“4+7” policy periods. In this study, January to March 2018 was assigned as the baseline period, and April 2018 to November 2019 was assigned as reporting periods, involving a total of 20 reporting periods. Finally, the time series of DSI for policy-related drugs were constructed containing 20 monthly time points.
2.4 Statistical analysis
Descriptive analysis was used to describe the distribution of DSI in the pilot group and control group, pre- and post-“4+7” policy periods, different drug categories, and different levels of medical institutions. The line chart of DSI was drawn to describe the overall trend of drug structure changes from July 2018 to November 2019.
Difference-in-difference (DID) is a method commonly used for the quantitative effect evaluation of public policies or projects. By effectively combining “the difference before and after intervention” with “the difference with or without intervention”, this method to a certain extent can control the influence of some factors other than intervention, so as to estimate the net impacts of the intervention on the outcome variable [31-33]. In order to eliminate the net effect of “4+7” policy on the DSI change of policy-related drugs, we conducted DID models by using the DSI time series data in the pilot group and control group constructed above. The DID model is expressed as follows:
![](data:image/png;base64,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)
In formula (2), Y is the outcome variable, i.e. DSI in this study. Tt refers to “4+7” policy intervention with the value of 0 and 1, and 0 represents the pre-“4+7” policy period (from April 2018 to February 2019) and 1 represents the post-“4+7” policy period (from March 2019 to November 2019). Gt represents groups with the value of 0 and 1, and 0 represents the control group and 1 represents the pilot group. εt is the error term, representing random errors that cannot be explained by variables in the model. β0 represents the constant term. β1 estimates the change of DSI in the post-“4+7” policy period compared with the pre-“4+7” policy period. β2 estimates the change of DSI in the pilot group compared with the control group. β3 represents DID value between the Pilot Group and the Control Group, is the DID value, which represents the interaction item between intervention measures and groups, that is, the net impacts of NCDP policy on DSI. The relative change of DSI after “4+7” policy was expressed as β3/β0 [34]. In this study, we observed the monthly trends of outcome variable between pilot group and control group before the policy intervention, so as to verify if the DID model met the parallel trend conditions (supplementary figure 1) [35].
To further explore the change of DSI in different periods after the implementation of “4+7” policy, this study divided the post-“4+7” policy period into three stages: Period 1 (March to May 2019), Period 2 (June to August 2019), and Period 3 (September to November 2019). The net impact of “4+7” policy on the change of DSI was estimated in each of the three post-intervention periods compared with the pre-“4+7” policy period. Stata version 16.0 was used to perform the analyses above. A p-value <0.05 was considered statistically significant.