Study setting and design
This study was conducted in Shandong province in eastern China, which had a total population of 101, 699, 900 at the end of 2021 [20]. The purpose sampling was adopted in the study, and the counties S and Y under city L, in the western area of Shandong Province, were selected as the target locations. The two counties have similar mid-level indexes such as GDP, year-end population, number of health technicians, and number of township hospitals.
Data were collected from all township hospitals (38 in total) in counties S and Y under City L in Shandong Province. PHSs in Shandong Province are only allowed to purchase, store and distribute drugs listed in the Regulations on Drug Administration by following the pricing rule of zero makeup. At the same time, doctors in PHSs are only permitted to prescribe antibiotics listed in the List of Essential Drugs [21].
We conducted a three-year natural, before-after, quasi-experimental study in the real world. A controlled interruption time series design was used to collect data at several time points before and after the COVID-19 pandemic outbreak. The superiority of the design was that it could identify underlying long-term trends that may occur after an intervention. We explored the immediate and long-term impacts of the COVID-19 pandemic on the trend of antibiotic consumption by using an interruption time series design. The effectiveness was estimated by controlling the baseline level and the trend [22], and the longitudinal characteristics of the data ensure the robustness of the findings [23].
Data Collection And Management
Monthly data of antibiotic consumption was collected from 38 township hospitals in counties S and Y under City L. The data collection was carried out with the following method: two researchers were responsible for preparing Excel forms with headers and sending the forms to the persons in charge of township hospitals. They would provide feedback quarterly via email to the person in charge of data collection of the research group. The 36-month data of antibiotic consumption was collected from January 1, 2019 to December 31, 2021. The COVID-19 pandemic was first outbroken and spread in Wuhan, China, at the end of December 2019, and then spread to Shandong Province due to population migration during the holiday of the Chinese New Year [24]. The antibiotic consumption data in 12 months before and 24 months after the outbreak of the COVID-19 pandemic was included. The antibiotic consumption data mainly covered the name of the township hospitals, drug generic name, dosage form, specification, manufacturer, price per unit, unit (by box, bottle, or ampoule), inventory at the beginning of the month, monthly purchase quantity, monthly ex-warehouse quantity, monthly inventory at the end of the month, administration route, etc. These antibiotics were classified into 6 ATC-3 groups according to the Anatomical Therapeutic and Chemical (ATC) classification J01 (i.e., antibacterial for systemic use) system [25]. Furthermore, the WHO AWaRe category of antibiotics in 2021 was used to classify antibiotics, aiming to analyze the use patterns of antibiotics [26]. Moreover, we strictly controlled the quality of the data, carefully sorted and summarized the data after receiving the emails from the persons in charge of the township hospitals each time. After that, the data were cleaned by two doctoral or master students engaged in antibiotic resistance research and they mainly made logic proofreading and supplemented outliers or missing values. During the process of data cleaning, the data with problems were separately listed and sent back to the persons in charge of the corresponding township hospitals via email for supplementing or correction (e.g., the monthly usage, specifications, and dosage forms of drugs shall be supplemented, and whether the end-of-month inventory of the drugs is consistent with the beginning-of-month inventory of the next month shall be checked.)
Outcome Measures
In this study, the outcome indicator of antibiotic consumption was used to assess the impact of the outbreak of the COVID-19 pandemic on antibiotics use. Antibiotic consumption was measured by a defined daily dose (DDD), a maintenance dose in adults with indications [25], and it is a statistical measure of drug consumption to compare drug usage [27]. The DDD equivalence per package (DPP) of drugs was calculated in DDD unit [DPP= (unit strength * package size/DDD)]. The total consumption of each group of procured drugs (DDDs) was estimated as the summed DPPs of all-inclusive products [27].
$${\text{DDD}}_{\text{s}}\text{=}\sum _{\text{i=1}}^{\text{n}}\left({\text{DPP}}_{\text{i}}\text{×}{\text{N}}_{\text{i}}\right)$$
Where, Ni represents the number of packages of a certain antibiotic product(i)
used in the township hospitals.
Data analysis
This study conducted the descriptive statistical method to quantify the patterns and trends of antibiotic consumption. The average total antibiotic consumption among all PHSs before the COVID-19 pandemic outbreak (2019) and after its outbreak (2020/2021) was described respectively and compared horizontally. In addition, we created a monthly antibiotic consumption trend chart based on ATC classification and WHO AWaRe category to observe and describe changes in antibiotic consumption from January 2019 to December 2021, respectively.
The interrupted time series analysis was used to assess the immediate and long-term impacts of the COVID-19 pandemic on the trends of antibiotic consumption in PHSs [23, 28]. Since the data in this study were collected monthly at even intervals, one month was taken as the time unit. Considering the lag of intervention, January 2020 was set as the intervention time point, so the data of 13 months before the intervention and 23 months after the intervention was finally included in the research. A segmented regression model was established as follows:
$${\text{Y}}_{\text{t}}\text{=}{\text{β}}_{\text{0}}\text{+}{\text{β}}_{\text{1}}\text{×}{\text{Time}}_{\text{t}}\text{+}{\text{β}}_{\text{2}}\text{×}{\text{Covid-19}}_{\text{t}}\text{+}{\text{β}}_{\text{3}}\text{×}{\text{Long-term}}_{\text{t}}\text{+}{\text{β}}_{\text{4}}\text{×}\text{Cold}\text{+}{\text{ε}}_{\text{t}}$$
Where \({\text{Y}}_{\text{t}}\) is the independent outcome variable of month t (antibiotic consumption DDDs); where \({\text{Time}}_{\text{t}}\) is a continuous time series variable (1,2,3... 36); \({\text{Covid-19}}_{\text{t}}\) represents the intervention time indicator before and after the outbreak of the COVID-19 pandemic. The intervention time node lies in the 13th month before the outbreak of COVID-19 pandemic (t = 0) and after the outbreak of COVID-19 pandemic (t = 1); where \({\text{Long-term}}_{\text{t}}\) represents the time since the outbreak of the COVID-19 pandemic; its value before the outbreak of COVID-19 pandemic is 0 while its value after the outbreak of COVID-19 pandemic is 1, 2, 3, …24, corresponding to January 2020 to December 2021 (long-term effect). In addition, a dummy variable \(\text{Cold}\) was set to control the extreme value of antibiotic consumption during the coldest period of Chinese Spring Festival, which was the wild data point of this study [28–31]. By referring to previous studies, we assigned 1 to the variable \(\text{Cold}\) in December and January every year, and 0 for the rest of the year [32].
In this model, \({\text{β}}_{\text{0}}\) is used to estimate the level of antibiotic consumption at the beginning of the time series. \({\text{β}}_{\text{1}}\) is used to estimate the change trends of antibiotic consumption prior to the outbreak of the COVID-19 pandemic. \({\text{β}}_{\text{2}}\) is used to assess the changes immediately after the outbreak of the COVID-19 pandemic. \({\text{β}}_{\text{3}}\) reflects the monthly changes in antibiotic consumption after the outbreak of the COVID-19 pandemic. If it is different from the trend before the outbreak of the COVID-19 pandemic, it suggests that the outbreak of the COVID-19 pandemic has a long-lasting impact on the antibiotic consumption. \({\text{β}}_{\text{4}}\) is used to estimate the weather effect of the coldest period. \({\text{ε}}_{\text{t}}\) is an estimate of the random error at time t. In addition, Durbin-Watson test was performed to verify the existence of the first-order autocorrelation (if the value is about 2, it indicates that there is no autocorrelation) [33]. In case of an autocorrelation, the regression will be estimated using the Prais-Winsten method [32].
All statistical analyses were performed in STATA version 15.0 (STATA Crop LP, College Station, TX, USA), and P < 0.05 was considered statistically significant.