Next Article in Journal
Isolation and Characterization of a Novel Actinomycete Isolated from Marine Sediments and Its Antibacterial Activity against Fish Pathogens
Next Article in Special Issue
Impact of COVID-19 Pandemic in Antibiotic Consumption in Navarre (Spain): An Interrupted Time Series Analysis
Previous Article in Journal
Meta-Analysis of Clinical Trials Comparing Cefazolin to Cefuroxime, Ceftriaxone, and Cefamandole for Surgical Site Infection Prevention
Previous Article in Special Issue
Impact of the COVID-19 Pandemic on Antibiotic Prescribing by Dentists in Galicia, Spain: A Quasi-Experimental Approach
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Antibiotics
Volume 11
Issue 11
10.3390/antibiotics11111544
antibiotics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Impact of the COVID-19 Outbreak on the Antibiotic Use Patterns among a Rural Community Population in Eastern China

by
Bingbing Zhu
1,†,
Xinping Zhao
1,†,
Yurong Li
1,
Na Wang
1,
Helen Lambert
2,
Fei Yan
1,
Qingwu Jiang
1,* and
Chaowei Fu
1,*
1
Key Laboratory of Public Health Safety, NHC Key Laboratory of Health Technology Assessment, School of Public Health, Fudan University, Shanghai 200032, China
2
Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Antibiotics 2022, 11(11), 1544; https://doi.org/10.3390/antibiotics11111544
Submission received: 25 September 2022 / Revised: 26 October 2022 / Accepted: 1 November 2022 / Published: 3 November 2022
(This article belongs to the Special Issue Antimicrobial Use and Stewardship during the COVID-19 Era)
Browse Figures
Review Reports Versions Notes

Abstract

:
There are growing concerns that the coronavirus disease of 2019 (COVID-19) pandemic may change antibiotic use patterns and accelerate antibiotic resistance, but evidence from the community level is lacking. This study aims to estimate the impact of the COVID-19 outbreak on the antibiotic use patterns among a community population in Eastern China. A self-administered medicine diary was used to collect information on antibiotic use from July 2019 to June 2021 among a rural community in Eastern China. We analyzed the changes in antibiotic use patterns over five months from August to December 2019 and the corresponding months in 2020. The risk of antibiotic use and its changes were measured with the incidence rate (IR) and relative risk (RR). In total, 1111 participants were eligible for the final analysis (440 in 2019 and 671 in 2020). After the COVID-19 outbreak, antibiotic use increased by 137% (5.43 per 100 person months in the 2019 vs. 12.89 per 100 person months in the 2020), and after the adjustment of covariates, the adjusted RR was 1.72 (95% CI: 1.10~2.34). It was higher among those who were women (RR = 2.62), aged 35–59 years old (RR = 2.72), non-farmers (RR = 2.75), had less than six years of education (RR = 2.61), had an annual household income over CNY 100,000 (USD 14,940) (RR = 2.60), and had no history of chronic diseases (RR = 2.61) (all p < 0.05). The proportion of cephalosporins consumed increased from 54.29% in 2019 to 64.92% in 2020 (p = 0.011). Among those aged 35 years and older, the proportion of antibiotics obtained from medical facilities increased, while the proportion obtained from retail pharmacies, homes, and other sources decreased (all p < 0.05). The COVID-19 outbreak changed antibiotic use patterns in this study population (Eastern China) significantly. More efforts to monitor and enhance antibiotic stewardship activities at the community level are needed in future.

1. Introduction

Both antimicrobial resistance (AMR) and COVID-19 are currently challenging global public health [1,2,3,4]. It was estimated that AMR would cause 10 million deaths per year by 2050 if no action were taken to stop its spread [5]. The current pandemic caused by COVID-19 poses an immense threat to global public health. Until 2nd August 2022, COVID-19 has infected almost 601 million people, resulting in over 6.5 million deaths globally [6]. Although it is a viral illness, patients with COVID-19 may develop severe complications associated with bacterial pathogens; hence, different antimicrobials, especially antibiotics, were used as empirical therapy [7,8,9,10,11]. Furthermore, during the early stages of the pandemic, there were insufficient effective antivirals with proven efficacy, which, combined with the anxiety and uncertainty of available treatments, led to the empirical but rather widespread and excessive use of antibiotics [11]. However, not all antimicrobial use in COVID-19 cases is warranted [12,13], and the increased use of antimicrobials is somewhat counterproductive in the global effort to reduce AMR [14]. A study in Italy showed that, during the lockdowns for COVID-19, there was a reduction in antibiotic consumption among children due to closed daycare centers and schools, while a relative increase in azithromycin use in adults was apparent [15]. However, there was no strong evidence for the routine use of azithromycin for reducing the time to recovery or risk of hospitalization from suspected COVID-19 in the community [16]. There is growing concern that due to the COVID-19 pandemic, the disruption of antimicrobial stewardship and infection prevention programs, inappropriate antibiotic treatment in patients with COVID-19, as well as the high number of patients and limitations of personal protective equipment, may increase the risk of AMR, although other actions, such as strict lockdowns, social distancing, and the extensive implementation of hand hygiene and face masks may decrease it [17,18,19,20,21,22].
Antibiotic use is one of the key drivers in the development of antibiotic resistance, with community and hospital settings being equal ecological niches of its emergence in human health [23,24]. At the user level, the “unjustified” or “irrational” behaviors of antibiotic use are thought to include failing to start a course of therapy, an omission of doses, the early cessation or unnecessary prolongation of therapy, errors in dose quantity, “inappropriate” intervals between doses, modifications to recommended regimes, and the use of shared prescriptions and medicines [25,26,27,28]. Self-medicating with antibiotics is framed as another “problematic” behavior in the development of antibiotic resistance [29,30,31]. Compared with the convenient location and timely access of pharmacies, the limited availability of physicians coupled with long waiting times in hospitals drive the self-purchasing of antibiotics, particularly in rural areas [32,33]. In this context, there has been easy access to antibiotics without a prescription, especially in rural China. The community’s perceptions of antibiotic use and AMR are very important for their control [34].
Moreover, emerging studies have highlighted the contribution of the COVID-19 pandemic to the escalation of AMR from multiple perspectives, such as extensive hospital antibiotic overuse and misuse [35,36,37,38] and the changes in total antibiotic consumption [39,40]. Controversially, some studies suggested that the use of several antibiotics increased in 2020 compared to 2019 [41,42,43,44,45], despite the fact that many studies reported that the total antibiotic consumption decreased during or after COVID-19 in Europe [40], the United States [46,47], Singapore [48], and China [39]. So far, few studies have focused on the impact of COVID-19 on antibiotic use among the community population [49,50]. Hogberg et al. reported that community antibiotic consumption decreased in nearly all EU/EEA countries during COVID-19 [49]. Furthemore, Kalam conducted a qualitative study to understand users’ perspectives on antibiotic seeking and consumption during COVID-19 [50]. Nevertheless, the changes in antibiotic use patterns were not clear, which may prompt evidence of how COVID-19 affects antibiotic resistance. Therefore, this study aimed to estimate the impact of the COVID-19 outbreak on the overall antibiotic use patterns among the rural community population in Eastern China as reported in self-administered medication diaries.

2. Results

2.1. Characteristics of Participants

A total of 1111 eligible participants (440 in 2019 and 671 in 2020) had similar general characteristics between 2019 and 2020. More than half were female, with an average age of 59.17 ± 14.07 years. Most of them were non-farmers, had less than six years of education, and reported no history of chronic diseases. Moreover, more than half had an annual household income over CNY 100,000 (USD 14,940). Compared to 2019, the participants in 2020 had a significantly higher proportion of participants that were 60 years and older (55% vs. 48%), but a lower proportion of farmers (35% vs. 41%) and an annual household income over CNY 100,000 (43% vs. 63%) (Table 1).

2.2. Changes in the Risk of Antibiotic Use between 2019 and 2020

Overall, the incidence rate of antibiotic use was 5.43 per 100 PMs in 2019, prior to the COVID-19 outbreak in China, and 12.89 per 100 PMs in 2020 in the post-period, respectively. The risk of antibiotic use was 2.37 times greater in 2020 than in 2019 (p < 0.001). After the adjustment of covariates, including gender, age groups, education levels, farmer, annual household income, and chronic disease history, the adjusted RR was 1.72 (95% CI: 1.10~2.34) in the Cox regression model. There was a greater increase among those who were women, aged 35–59, farmers, had less than 6 years of education, had an annual household income over CNY 100,000 (USD 14,940), and had no history of chronic diseases. However, this increase was not found among the participants aged 15–34 years old (Table 2).

2.3. Changes in Antibiotic Categories and Access

Cephalosporins were always the most used antibiotics, and the proportion increased significantly from 54.29% in 2019 to 64.92% in 2020 (p = 0.011), but quinolones also increased, and macrolides and lincosamides decreased without significance (Figure 1). In 2019, the proportions of antibiotics obtained from medical facilities, retail pharmacies, homes, and other sources were 69.29%, 2.14%, 2.14%, and 26.43%, respectively, and 82.35%, 0.21%, 1.05%, and 16.39% in 2020, with medical facilities being the principal source of antibiotics. After the COVID-19 outbreak, the proportion of antibiotics obtained from medical facilities increased, while the proportion obtained from retail pharmacies, homes, and other sources decreased among those aged 35–59 and 60 years and older (all p < 0.05). Furthermore, the subgroup analysis revealed that among females aged 60 years and over, the proportion of antibiotics obtained from medical facilities increased (p = 0.013), while the proportion obtained from homes (p = 0.035) and other sources (p = 0.031) decreased, whereas males showed no significant changes in any age group (Figure 2). In addition, the reasons for antibiotic use reported by the subjects changed significantly after the COVID-19 outbreak (p = 0.005). Lower proportions were reported for treating flu-like symptoms (65.10% in 2019 vs. 60.34% in 2020) and inflammation (17.92% in 2019 vs. 8.96% in 2020), while higher proportions were reported for treating diarrhea (1.89% in 2019 vs. 2.13% in 2020) and other symptoms (15.09% in 2019 vs. 28.57% in 2020) after the COVID-19 outbreak. However, the top two reasons were treating flu-like symptoms and inflammation (Figure 3).

3. Discussion

3.1. Summary

The COVID-19 pandemic has severely impacted healthcare delivery, and there are growing concerns that the pandemic may change antibiotic use patterns and accelerate antibiotic resistance [23,24]. Thus, determining the impact of COVID-19 on antibiotic use patterns would help us to understand the impact of antibiotic resistance among community populations. In this community-level study among a rural population in Eastern China, the COVID-19 outbreak changed antibiotic use patterns significantly, including the risk for antibiotic use and the consumption proportion of cephalosporins. Additionally, the proportion of antibiotics obtained from medical facilities increased among certain groups of subjects, and the reasons for antibiotic use slightly changed.

3.2. The Potential Reasons Why the Risk of Antibiotics Increased after the COVID-19 Outbreak

Although the impact of the COVID-19 outbreak on antibiotic consumption has been documented in some epidemiological studies, few of them were conducted among community populations because the reliable data on antibiotic use are largely limited to hospitalized patients [39,40,49,50]. Our study estimated that the risk of antibiotic use increased by 137% after the COVID-19 outbreak in China, while it was reduced to 72% after the control of the covariates. This is particularly significant given that no confirmed COVID-19 cases were reported in the region during the study period. A study in Bangladesh showed an increased antibiotic use rate for illnesses reported in the preceding four weeks at the community level during the COVID-19 pandemic [51]. Another study in Italy showed that, during the lockdowns for COVID-19, there was a relevant reduction in antibiotic consumption among children due to closed daycare centers and schools, except for a relative increase in azithromycin use in adults [15]. This finding from our study can be explained. During the COVID-19 pandemic, the general population was likely sensitized regarding the use of antibiotics for treating COVID-19. According to the web-based surveillance of antibiotics used in Malaysia, 37% of the participants were aware that using antibiotics could not speed up recovery from all infections [52]. Still, 49% of the respondents reported that antibiotics were only effective against bacterial infections [52]. August to December 2020 (post-COVID-19 outbreak) was a relatively “peaceful” period, during which the government’s priority was vaccination for COVID-19. However, the perceived need for antibiotics may increase during the pandemic due to health anxiety. In such circumstances, doctors might agree to prescribe antibiotics, allowing people to obtain antibiotics easily. Another study elsewhere in Asia indicated that antibiotic-seeking behavior was related to a perceived vulnerability, management of an “unknown” illness, and anxiety [50], while an earlier Dutch study showed that if patients expected an antibiotic prescription, they were more likely to receive one [53]. Correspondingly, our study also showed that the proportion of medical facility-sourced antibiotics increased after the outbreak, in part, perhaps, because the availability of antibiotics purchased over the counter was increasingly restricted after the start of the pandemic. Additionally, there has been a surge in self-medication activities since the COVID-19 pandemic because of the societal perception of risk and the urge to accomplish something for preventative and curative reasons [54,55,56]. A recent systematic review revealed that the prevalence of COVID-19-related self-medication was 44.9%, and 79% of them were antibiotics [57]. This finding implies that the COVID-19 pandemic may increase the risk of antibiotic resistance.

3.3. A More Pronounced Impact Occurred in Some Groups

Moreover, it was observed that there was a greater increase in the risk of antibiotic use among those who were women, aged 35–59 years old, non-farmers, had less than 6 years of education, had an annual household income over CNY 100,000, and had no history of chronic diseases. The theory of planned behavior (TpB) suggests that a person’s actual use of antibiotics is best predicted by their intentions, which are influenced by attitudes, subjective norms, and perceived behavioral control [58]. Given that women are more likely to acquire gynecological infections, they require more antibiotics. A European study reported that women and those with lower levels of education were more likely to be prescribed antibiotics [59]. A previous study in rural China suggested that middle age was a potential factor related to higher levels of antibiotic utilization while younger people used antibiotics more strictly and appropriately, which concurs with our study findings [60]. In addition, those who were non-farmers or had a higher household income had a higher risk of using antibiotics, partly because their interactions between relatives and friends may foster a culture of antibiotic use and provide more chances of antibiotic exposure within this population. Subjects with chronic diseases may have good knowledge about antibiotics and use them cautiously [61,62]. These findings call for specific community-level interventions on antibiotic use for certain socio-demographic groups in rural China.

3.4. Categories and Access of Antibiotic Use Changed Moderately

It was found that the proportion of cephalosporins increased significantly in this study population in 2020 compared with 2019, which was supported by previous studies [44,50]. A recent study reported that the use of third-generation cephalosporins increased by 19% in 2020 compared with 2019 [38]. Another study found an increase in hospital consumption, but only the consumption of ceftaroline increased significantly [44]. The dispensing of antibiotics increased profoundly in Egypt during the early period of the COVID-19 pandemic without proper clinical evaluation, and azithromycin, ceftriaxone, and linezolid were the major antibiotics used [63].
Medical facilities were the main source of antibiotics in both study periods. Compared to 2019, the proportion of antibiotics accessed from medical facilities increased in 2020, while the proportion obtained from retail pharmacies, homes, and other sources decreased among those aged 35–59 and 60 years and over in this study. A previous study revealed that 57% of retail pharmacies sold antibiotics without a prescription in the Zhejiang province in 2017 [64]. However, in order to monitor the possible COVID-19 cases, retail pharmacies were not allowed to sell antibiotics in Deqing County [65], which would greatly limit the purchase of antibiotics from retail pharmacies, and in turn, this is likely to have promoted more attendance at medical facilities and the consequent purchases of prescribed antibiotics from their pharmacies. This suggests that a stricter enforcement of policies would have great significance for rationalizing antibiotic use, and it seems to be a great opportunity for improving antibiotic management after the outbreak of COVID-19.

3.5. Strengths and Limitations

To our limited knowledge, this was the first study to describe the impact of the COVID-19 outbreak on the antibiotic use patterns among the community population by analyzing the findings from self-administered medicine diaries. This study has several strengths. First, it was based on a community population and covered prior- and post-COVID-19 outbreak periods in China. Secondly, a self-administered medicine diary may more comprehensively reflect the individual and household antibiotic use in the context of sharing antibiotics with families or friends and more accurately document antibiotic access from non-medical facilities in rural China [60]. However, this study has several notable limitations. Firstly, there may be a reporting bias in the antibiotic records, especially in those unable to write, although health professionals from the village clinic helped participants document all their medication use weekly. Secondly, the participants were also asked to retain all medicine packets, including any antibiotics, and these were checked by health professionals on a monthly basis. However, the use of local health professionals to monitor the study may have urged some participants to be reluctant to disclose instances of self-medication with antibiotics that involved a procurement from sources other than the village clinic. Thirdly, due to the restriction of funds, the authorities, and the pandemic, the purpose of antibiotic use was not included, which may be important to interpret the impacts of the COVID-19 outbreak. A further limitation is that this study was carried out in Deqing County, one of the most developed areas in China, so the findings should be extended with caution to other populations in China, especially for the developing areas. Additionally, this study only covered five months of one year, so variations over the whole year were not captured.

4. Materials and Methods

4.1. Study Design and Population

(1)
Study design: This study was from research carried out in a rural community in Eastern China in June 2019, as presented in our previous paper [66]. Data collected from August to December 2019 was the “prior” period of the COVID-19 outbreak in China, and those during the same five months in 2020 were the “post” period, during which no local cases of COVID-19 infection were reported in Deqing County.
(2)
Sampling method: Based on the population cohort previously established in Deqing County of the Zhejiang province in China, one village was selected as the study site using the rapid cluster sample survey methodology [67], and a total of 1112 residents were recruited into this study (441 in 2019 and 671 in 2020). One individual had missing data for most variables in 2019 and was therefore excluded. Finally, 1111 participants were eligible for the final analysis (440 in 2019 and 671 in 2020).
(3)
Sample size estimation: According to a study in the United States, the antimicrobial use prevalence was 8.2% [68]; we conservatively expected that the prevalence was 9.0% among this study population. A two-sided test was used with the following parameters: set α = 0.05, δ = 2.0%. Then the values were inputted into the following formula:
  n = Z α / 2 × P × ( 1 − P ) / δ 2 = 400
Considering the non-response variable and increasing the sample size by 10%, the minimum sample size was estimated to be 440.
(4)
The inclusion criteria were as follows: (1) aged 15 years old and over, (2) lived in the study sites for no less than six months, and (3) consented to inclusion in the study.
(5)
Ethical statement: All participants read a consent form (including the study objectives, risks, and benefits for the participants, the confidentiality, and that participation was voluntary) and received clarification if requested before giving their oral consent to participate in the study. This study was reviewed and approved by the Institutional Review Board of the School of Public Health, Fudan University, China (number IRB#2019-03-0733).

4.2. Collections for General Information and Antibiotic Use

Two surveys in June 2019 and July 2020 using the same questionnaire through a face-to-face interview were carried out to collect information on the demographic variables (sex, age, ethnicity, education level, marriage status, occupation, and annual household income) and the history of chronic diseases in the same site. The details of the questionnaire were described in our study protocol paper [66]. The education level was the highest grade completed by the participant and was described as ≤6 years (elementary school and below) and >6 years (junior school and above). The chronic disease history covered seven common chronic diseases (including diabetes mellitus, hypertension, coronary heart disease, stroke, chronic kidney disease, chronic obstructive pulmonary disease, and cancers).
To describe the patterns of antibiotic use in the population, a structured self-administered diary was used to collect the medicine use (antibiotics and chronic disease medicine) and individual health status in each participating household over a 12-month period, as described in our study protocol paper [66]. The data on the medicines used for each household member were recorded weekly by participants using the structured form to capture the types, reasons, sources, frequencies, doses, side effects, and other details of both antibiotic use and the use of other medicines for long-term conditions. The options for the antibiotic sources included: inpatient/outpatient services in medical facilities (including village clinics, township healthcare centers, and hospitals) with prescriptions; medical stores or pharmacies; homes (including family members or saved from a previous time); and others (including friends, online, stalls, or hawkers, and somewhere or someone else). The purposes of antibiotic use included: flu-like symptoms (including cold, fever, and cough), inflammation (including urinary, digestive, respiratory, and motor system inflammation), diarrhoea, and other reasons (including pain, sore throat, vomiting, dizziness, etcetera).
Antibiotics were subsequently categorized using the anatomical therapeutic chemical (ATC) classification codes [69]. The study drew on the medicine diary for the antibiotic use data and from the surveys for the socio-demographic data on the participants.

4.3. Statistical Analysis

For the descriptive statistics, the variables were described as percentages for the categorical variables and means ± SD for the continuous variables. The categorical variables were compared over the groups using the Chi-square test, and the continuous variables were analysed using the Student’s t-test.
Given that the risk of antibiotic use varies with the length of time for observation, we applied the incidence rate (IR) to estimate it in this study, and the difference between the subgroups was estimated with the relative risk (RR). The IR and RR of the antibiotic use were defined as the following:
I n c i d e n c e   R a t e = N u m b e r   o f   s u b j e c t s   w h o   u s e d   a n t i b i o t i c ∑ T i m e   s p e n t   i n   t h i s   p o p u l a t i o n
R e l a t i v e   R i s k = I n c i d e n c e   R a t e 2020 I n c i d e n c e   R a t e 2019
in which the person-months (PMs) of the follow-ups were calculated from the date initially recorded to the date of the follow-up or end of the follow-up, whichever came first. The Z test was occupied for the incidence rate difference [70]. After controlling for gender, age, farmer, annual household income, and chronic disease history, the adjusted RRs were estimated in the Cox regression model. All the statistical tests were two-sided, and a p < 0.05 was considered statistically significant. All analyses were performed in R software (Version 4.1.0; R Foundation for Statistical Computing, Vienna, Austria).

5. Conclusions

In conclusion, this study identified an increase in the risk of antibiotic use, although the proportion of antibiotics obtained from medical facilities (and, by implication, via a prescription) increased among this rural community after the COVID-19 outbreak. It suggests that there was a window for improving antibiotic management during the pandemic. To ensure the appropriate use of antibiotics and to control AMR, more efforts need to be forged to monitor and improve antibiotic stewardship activities at the community level. More education and awareness campaigns and activities may be worthwhile, especially for older and less well-educated rural Chinese women who are otherwise healthy, since this group were found to be more likely to use antibiotics.

Author Contributions

Conceptualization, B.Z., C.F. and Q.J.; methodology, B.Z., X.Z. and Y.L.; validation, N.W. and F.Y.; formal analysis, B.Z.; investigation, N.W., X.Z. and. Y.L.; data curation, X.Z. and Y.L.; writing—original draft preparation, B.Z.; writing—review and editing, H.L., C.F. and Q.J.; supervision, Q.J.; project administration, C.F.; funding acquisition, C.F. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Nature Science Foundation of China (81861138050) and the Medical Research Council and Newton Fund through a UK-China AMR Partnership Hubaward (MR/S013717/1). The funders had no role in the design, collection, analysis, or interpretation of the data or in the decision to submit the manuscript for publication.

Institutional Review Board Statement

This study was reviewed and approved by the Institutional Review Board of the Fudan University School of Public Health (number: 2019-03-0733, date: 18 March 2019).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study can be made available upon request from the corresponding author. The dataset is not publicly available as it forms part of ongoing research and analysis. It will be made publicly available once the major study publications are completed.

Acknowledgments

We would like to thank all participants enrolled in this study and all the health professionals of the Deqing County Center for Disease Control and Prevention for contributing to the study.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

  1. Seethalakshmi, P.S.; Charity, O.J.; Giakoumis, T.; Kiran, G.S.; Sriskandan, S.; Voulvoulis, N.; Selvin, J. Delineating the impact of COVID-19 on antimicrobial resistance: An Indian perspective. Sci. Total Environ. 2022, 818, 151702. [Google Scholar] [CrossRef] [PubMed]
  2. Cars, H.; Hakansson, A. To prescribe—Or not to prescribe—Antibiotics. District physicians’ habits vary greatly, and are difficult to change. Scand. J. Prim. Health Care 1995, 13, 3–7. [Google Scholar] [CrossRef] [PubMed]
  3. Stocker, M.; Ferrao, E.; Banya, W.; Cheong, J.; Macrae, D.; Furck, A. Antibiotic surveillance on a paediatric intensive care unit: Easy attainable strategy at low costs and resources. BMC Pediatr. 2012, 12, 1–6. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. U.S. Department of Health and Human Services. Antibiotic Resistance Threats in the United States 2013; Centers for Disease Control and Prevention: Atlanta, GA, USA, 2013; pp. 1–113.
  5. O’Neill, J. Tackling Drug-Resistant Infections Globally: Final Report and Recommendations; Government of the United Kingdom: London, UK, 2016. [Google Scholar]
  6. World Health Organization. WHO Coronavirus (COVID-19) Ashboard. Available online: https://covid19.who.int/ (accessed on 23 June 2022).
  7. Beovic, B.; Dousak, M.; Ferreira-Coimbra, J.; Nadrah, K.; Rubulotta, F.; Belliato, M.; Berger-Estilita, J.; Ayoade, F.; Rello, J.; Erdem, H. Antibiotic use in patients with COVID-19: A ‘snapshot’ Infectious Diseases International Research Initiative (ID-IRI) survey. J. Antimicrob. Chemother. 2020, 75, 3386–3390. [Google Scholar] [CrossRef]
  8. Verroken, A.; Scohy, A.; Gérard, L.; Wittebole, X.; Collienne, C.; Laterre, P.-F. Co-infections in COVID-19 critically ill and antibiotic management: A prospective cohort analysis. Crit. Care 2020, 24, 1–3. [Google Scholar] [CrossRef]
  9. Nestler, M.J.; Godbout, E.; Lee, K.; Kim, J.; Noda, A.J.; Taylor, P.; Pryor, R.; Markley, J.D.; Doll, M.; Bearman, G.; et al. Impact of COVID-19 on pneumonia-focused antibiotic use at an academic medical center. Infect. Control. Hosp. Epidemiol. 2021, 42, 915–916. [Google Scholar] [CrossRef]
  10. Clancy, C.J.; Nguyen, M.H. Coronavirus disease 2019, superinfections, and antimicrobial development: What can we expect? Clin. Infect. Dis. 2020, 71, 2736–2743. [Google Scholar] [CrossRef]
  11. Huttner, B.; Catho, G.; Pano-Pardo, J.; Pulcini, C.; Schouten, J. COVID-19: Don’t neglect antimicrobial stewardship principles! Clin. Microbiol. Infect. 2020, 26, 808–810. [Google Scholar] [CrossRef]
  12. Cong, W.; Poudel, A.N.; Alhusein, N.; Wang, H.; Yao, G.; Lambert, H. Antimicrobial Use in COVID-19 Patients in the First Phase of the SARS-CoV-2 Pandemic: A Scoping Review. Antibiotics 2021, 10, 745. [Google Scholar] [CrossRef]
  13. Cong, W.; Stuart, B.; AIhusein, N.; Liu, B.; Tang, Y.; Wang, H.; Wang, Y.; Manchundiya, A.; Lambert, H. Antibiotic Use and Bacterial Infection in COVID-19 Patients in the Second Phase of the SARS-CoV-2 Pandemic: A Scoping Review. Antibiotics 2022, 11, 991. [Google Scholar] [CrossRef]
  14. Nature Microbiology. Antimicrobial resistance in the age of COVID-19. Nat. Microbiol. 2020, 5, 779. [Google Scholar] [CrossRef] [PubMed]
  15. Gagliotti, C.; Buttazzi, R.; Ricchizzi, E.; Di Mario, S.; Tedeschi, S.; Moro, M.L. Community use of antibiotics during the COVID-19 lockdown. Infect. Dis. 2020, 53, 142–144. [Google Scholar] [CrossRef] [PubMed]
  16. Butler, C.C.; Dorward, J.; Yu, L.-M.; Gbinigie, O.; Hayward, G.; Saville, B.R.; Van Hecke, O.; Berry, N.; Detry, M.; Saunders, C.; et al. Azithromycin for community treatment of suspected COVID-19 in people at increased risk of an adverse clinical course in the UK (PRINCIPLE): A randomised, controlled, open-label, adaptive platform trial. Lancet 2021, 397, 1063–1074. [Google Scholar] [CrossRef]
  17. Condes, E.; Arribas, J.R.; COVID-19 Madrid-S.P.P.M. Group. Impact of COVID-19 on Madrid hospital system. Enferm. Infecc. Microbiol. Clin. 2021, 39, 256–257. [Google Scholar] [CrossRef] [PubMed]
  18. Subramanya, S.H.; Czyż, D.M.; Acharya, K.P.; Humphreys, H. The potential impact of the COVID-19 pandemic on antimicrobial resistance and antibiotic stewardship. Virusdisease 2021, 32, 330–337. [Google Scholar] [CrossRef]
  19. Tartari, E.; Hopman, J.; Allegranzi, B.; Gao, B.; Widmer, A.; Cheng, V.C.-C.; Wong, S.C.; Marimuthu, K.; Ogunsola, F.; Voss, A. Perceived challenges of COVID-19 infection prevention and control preparedness: A multinational survey. J. Glob. Antimicrob. Resist. 2020, 22, 779–781. [Google Scholar] [CrossRef] [PubMed]
  20. Zhu, N.; Aylin, P.; Rawson, T.; Gilchrist, M.; Majeed, A.; Holmes, A. Investigating the impact of COVID-19 on primary care antibiotic prescribing in North West London across two epidemic waves. Clin. Microbiol. Infect. 2021, 27, 762–768. [Google Scholar] [CrossRef]
  21. Monnet, D.L.; Harbarth, S. Will coronavirus disease (COVID-19) have an impact on antimicrobial resistance? Eurosurveillance 2020, 25, 2001886. [Google Scholar] [CrossRef]
  22. Knight, G.M.; E Glover, R.; McQuaid, C.F.; Olaru, I.D.; Gallandat, K.; Leclerc, Q.J.; Fuller, N.M.; Willcocks, S.J.; Hasan, R.; van Kleef, E.; et al. Antimicrobial resistance and COVID-19: Intersections and implications. Elife 2021, 10, e64139. [Google Scholar] [CrossRef]
  23. O’Neill, J. Review on Antimicrobial Resistance: Tackling a Global Health Crisis; AMR Review: London, UK, 2015. [Google Scholar]
  24. Founou, L.L.; Founou, R.C.; Essack, S.Y. Antibiotic Resistance in the Food Chain: A Developing Country-Perspective. Front. Microbiol. 2016, 7, 1881. [Google Scholar] [CrossRef]
  25. Davey, P.; Pagliari, C.; Hayes, A. The patient’s role in the spread and control of bacterial resistance to antibiotics. Clin. Microbiol. Infect. 2002, 8, 43–68. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Hawkings, N.J.; Butler, C.C.; Wood, F. Antibiotics in the community: A typology of user behaviours. Patient Educ. Couns. 2008, 73, 146–152. [Google Scholar] [CrossRef] [PubMed]
  27. Beyene, K.A.; Sheridan, J.; Aspden, T. Prescription Medication Sharing: A Systematic Review of the Literature. Am. J. Public Health 2014, 104, e15–e26. [Google Scholar] [CrossRef] [PubMed]
  28. Okeke, I.N.; Lamikanra, A.; Edelman, R. Socioeconomic and behavioral factors leading to acquired bacterial resistance to antibiotics in developing countries. Emerg. Infect. Dis. 1999, 5, 18–27. [Google Scholar] [CrossRef] [PubMed]
  29. Ahmed, I.; Rabbi, M.B.; Sultana, S. Antibiotic resistance in Bangladesh: A systematic review. Int. J. Infect. Dis. 2019, 80, 54–61. [Google Scholar] [CrossRef] [Green Version]
  30. Hoque, R.; Ahmed, S.M.; Naher, N.; Islam, M.A.; Rousham, E.K.; Islam, B.Z.; Hassan, S. Tackling antimicrobial resistance in Bangladesh: A scoping review of policy and practice in human, animal and environment sectors. PLoS ONE 2020, 15, e0227947. [Google Scholar] [CrossRef] [Green Version]
  31. Aslam, A.; Gajdács, M.; Zin, C.S.; Ab Rahman, N.S.; Ahmed, S.I.; Zafar, M.Z.; Jamshed, S. Evidence of the Practice of Self-Medication with Antibiotics among the Lay Public in Low- and Middle-Income Countries: A Scoping Review. Antibiotics 2020, 9, 597. [Google Scholar] [CrossRef]
  32. Do, N.T.T.; Vu, H.T.L.; Nguyen, C.T.K.; Punpuing, S.; Khan, W.A.; Gyapong, M.; Asante, K.P.; Munguambe, K.; Gómez-Olivé, F.X.; John-Langba, J.; et al. Community-based antibiotic access and use in six low-income and middle-income countries: A mixed-method approach. Lancet Glob. Health 2021, 9, e610–e619. [Google Scholar] [CrossRef]
  33. Chowdhury, M.; Williams, J.S.; Wertheim, H.; Khan, W.A.; Matin, A.; Kinsman, J. Rural community perceptions of antibiotic access and understanding of antimicrobial resistance: Qualitative evidence from the Health and Demographic Surveillance System site in Matlab, Bangladesh. Glob. Health Action 2019, 12, 1824383. [Google Scholar] [CrossRef]
  34. World Health Organization. Antibiotic Resistance: Multi-Country Public Awareness Survey. 2015. Available online: http://apps.who.int/iris/bitstream/10665/194460/1/9789241509817_eng.pdf (accessed on 13 October 2022).
  35. Founou, R.C.; Blocker, A.J.; Noubom, M.; Tsayem, C.; Choukem, S.P.; Van Dongen, M.; Founou, L.L. The COVID-19 pandemic: A threat to antimicrobial resistance containment. Future Sci. OA 2021, 7, FSO736. [Google Scholar] [CrossRef]
  36. Vestesson, E.; Booth, J.; Hatcher, J.; McGarrity, O.; Sebire, N.J.; Steventon, A.; Alonso, C.S.; Tomlin, S.; Standing, J.F. The impact of the COVID-19 pandemic on antimicrobial prescribing at a specialist paediatric hospital: An observational study. J. Antimicrob. Chemother. 2022, 77, 1185–1188. [Google Scholar] [CrossRef] [PubMed]
  37. Nieuwlaat, R.; Mbuagbaw, L.; Mertz, D.; Burrows, L.L.; E Bowdish, D.M.; Moja, L.; Wright, G.D.; Schünemann, H.J. Coronavirus Disease 2019 and Antimicrobial Resistance: Parallel and Interacting Health Emergencies. Clin. Infect. Dis. 2021, 72, 1657–1659. [Google Scholar] [CrossRef] [PubMed]
  38. Hughes, S.; Troise, O.; Donaldson, H.; Mughal, N.; Moore, L.S.P. Bacterial and fungal coinfection among hospitalized patients with COVID-19: A retrospective cohort study in a UK secondary-care setting. Clin. Microbiol. Infect. 2020, 26, 1395–1399. [Google Scholar] [CrossRef] [PubMed]
  39. Yang, Y.; Geng, X.; Liu, X.; Wen, X.; Wu, R.; Cui, D.; Mao, Z. Antibiotic Use in China’s Public Healthcare Institutions during the COVID-19 Pandemic: An Analysis of Nationwide Procurement Data, 2018–2020. Front. Pharmacol. 2022, 13, 813213. [Google Scholar] [CrossRef]
  40. European Centre for Disease Prevention and Control. Antimicrobial Consumption in the EU/EEA (ESAC-Net)—Annual Epidemiological Report 2020; ECDC: Stockholm, Sweden, 2021. [Google Scholar]
  41. Al-Azzam, S.; Mhaidat, N.; Banat, H.; Alfaour, M.; Ahmad, D.; Muller, A.; Al-Nuseirat, A.; Lattyak, E.; Conway, B.; Aldeyab, M. An Assessment of the Impact of Coronavirus Disease (COVID-19) Pandemic on National Antimicrobial Consumption in Jordan. Antibiotics 2021, 10, 690. [Google Scholar] [CrossRef]
  42. Mustafa, Z.U.; Salman, M.; Aldeyab, M.; Kow, C.S.; Hasan, S.S. Antimicrobial consumption among hospitalized patients with COVID-19 in Pakistan. SN Compr. Clin. Med. 2021, 3, 1691–1695. [Google Scholar] [CrossRef]
  43. Hamidi, A.A.; Yilmaz, Ş. Antibiotic consumption in the hospital during COVID-19 pandemic, distribution of bacterial agents and antimicrobial resistance: A single-center study. J. Surg. Med. 2021, 5, 124–127. [Google Scholar] [CrossRef]
  44. Grau, S.; Echeverria-Esnal, D.; Gómez-Zorrilla, S.; Navarrete-Rouco, M.E.; Masclans, J.R.; Espona, M.; Gracia-Arnillas, M.P.; Duran, X.; Comas, M.; Horcajada, J.P.; et al. Evolution of Antimicrobial Consumption during the First Wave of COVID-19 Pandemic. Antibiotics 2021, 10, 132. [Google Scholar] [CrossRef]
  45. Gonzalez-Zorn, B. Antibiotic use in the COVID-19 crisis in Spain. Clin. Microbiol. Infect. 2021, 27, 646–647. [Google Scholar] [CrossRef]
  46. Buehrle, D.J.; Wagener, M.M.; Nguyen, M.H.; Clancy, C.J. Trends in Outpatient Antibiotic Prescriptions in the United States during the COVID-19 Pandemic in 2020. JAMA Netw. Open 2021, 4, e2126114. [Google Scholar] [CrossRef]
  47. King, L.M.; Lovegrove, M.C.; Shehab, N.; Tsay, S.; Budnitz, D.S.; I Geller, A.; Lind, J.N.; Roberts, R.M.; A Hicks, L.; Kabbani, S. Trends in US Outpatient Antibiotic Prescriptions during the Coronavirus Disease 2019 Pandemic. Clin. Infect. Dis. 2021, 73, e652–e660. [Google Scholar] [CrossRef] [PubMed]
  48. Ng, T.M.; Tan, S.H.; Heng, S.T.; Tay, H.L.; Yap, M.Y.; Chua, B.H.; Teng, C.B.; Lye, D.C.; Lee, T.H. Effects of coronavirus disease 2019 (COVID-19) pandemic on antimicrobial prevalence and prescribing in a tertiary hospital in Singapore. Antimicrob. Resist. Infect. Control. 2021, 10, 1–5. [Google Scholar] [CrossRef] [PubMed]
  49. Högberg, L.D.; Vlahović-Palčevski, V.; Pereira, C.; Weist, K.; Monnet, D.L. ESAC-Net study group Decrease in community antibiotic consumption during the COVID-19 pandemic, EU/EEA 2020. EuroSurveillance 2021, 26, 2101020. [Google Scholar] [CrossRef]
  50. Kalam, M.A.; Shano, S.; Afrose, S.; Uddin, M.N.; Rahman, N.; Jalal, F.A.; Akter, S.; Islam, A.; Anam, M.M.; Hassan, M.M. Antibiotics in the Community during the COVID-19 Pandemic: A Qualitative Study to Understand Users’ Perspectives of Antibiotic Seeking and Consumption Behaviors in Bangladesh. Patient Prefer. Adherence 2022, 16, 217–233. [Google Scholar] [CrossRef]
  51. Akhtar, Z.; Mah, E.M.S.; Rashid, M.M.; Ahmed, M.S.; Islam, M.A.; Chowdhury, S.; Khan, Z.; Hassan, M.Z.; Islam, K.; Parveen, S.; et al. Antibiotics Use and Its Knowledge in the Community: A Mobile Phone Survey during the COVID-19 Pandemic in Bangladesh. Antibiotics 2021, 10, 1052. [Google Scholar] [CrossRef] [PubMed]
  52. Chang, C.T.; Lee, V.; Lee, J.C.Y.; Lee, N.C.T.; Ng, T.Y.; Shafie, A.A.; Thong, K.S. Public KAP towards COVID-19 and Antibiotics Resistance: A Malaysian Survey of Knowledge and Awareness. Int. J. Environ. Res. Public Health 2021, 18, 3964. [Google Scholar] [CrossRef] [PubMed]
  53. Welschen, I.; Kuyvenhoven, M.; Hoes, A.; Verheij, T. Antibiotics for acute respiratory tract symptoms: Patients’ expectations, GPs’ management and patient satisfaction. Fam. Pract. 2004, 21, 234–237. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Quispe-Cañari, J.F.; Fidel-Rosales, E.; Manrique, D.; Mascaró-Zan, J.; Huamán-Castillón, K.M.; Chamorro–Espinoza, S.E.; Garayar–Peceros, H.; Ponce–López, V.L.; Sifuentes-Rosales, J.; Alvarez-Risco, A.; et al. Self-medication practices during the COVID-19 pandemic among the adult population in Peru: A cross-sectional survey. Saudi Pharm. J. 2021, 29, 1–11. [Google Scholar] [CrossRef]
  55. Shubham Rajkoshor, P.; Hareshwar, P.D.; Swati, S.R. Trends in the use of home-remedies and over-the-counter drugs during COVID-19 Pandemic: A Cross-Sectional Study. Indian J. Prev. Soc. Med. 2021, 52, 169–176. [Google Scholar]
  56. Azhar, H.; Tauseef, A.; Usman, T.; Azhar, Y.; Ahmed, M.; Umer, K.; Shoaib, M. Prevalence, Attitude and Knowledge of Self Medication during COVID-19 Disease Pandemic. Pak. J. Med. Health Sci. 2021, 15, 902–905. [Google Scholar] [CrossRef]
  57. Ayosanmi, O.S.; Alli, B.Y.; Akingbule, O.A.; Alaga, A.H.; Perepelkin, J.; Marjorie, D.; Sansgiry, S.S.; Taylor, J. Prevalence and Correlates of Self-Medication Practices for Prevention and Treatment of COVID-19: A Systematic Review. Antibiotics 2022, 11, 808. [Google Scholar] [CrossRef] [PubMed]
  58. Ajzen, I. Perceived Behavioral Control, Self-Efficacy, Locus of Control, and the Theory of Planned Behavior. J. Appl. Soc. Psychol. 2002, 32, 665–683. [Google Scholar] [CrossRef]
  59. McNulty, C.A.M.; Boyle, P.; Nichols, T.; Clappison, P.; Davey, P. The public’s attitudes to and compliance with antibiotics. J. Antimicrob. Chemother. 2007, 60 (Suppl. 1), i63–i68. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  60. Wang, Y.; Zhao, X.; Li, Y.; Wang, N.; Jiang, F.; Lambert, H.; Yan, F.; Fu, C.; Jiang, Q. Patterns and Determinants of Antibiotic Use Behaviors among Rural Community Residents in Eastern China. Antibiotics 2022, 11, 823. [Google Scholar] [CrossRef] [PubMed]
  61. Waaseth, M.; Adan, A.; Røen, I.L.; Eriksen, K.; Stanojevic, T.; Halvorsen, K.H.; Garcia, B.H.; Holst, L.; Ulshagen, K.M.; Blix, H.S.; et al. Knowledge of antibiotics and antibiotic resistance among Norwegian pharmacy customers—A cross-sectional study. BMC Public Health 2019, 19, 66. [Google Scholar] [CrossRef] [PubMed]
  62. Zucco, R.; Lavano, F.; Anfosso, R.; Bianco, A.; Pileggi, C.; Pavia, M. Internet and social media use for antibiotic-related information seeking: Findings from a survey among adult population in Italy. Int. J. Med. Inform. 2018, 111, 131–139. [Google Scholar] [CrossRef]
  63. Elsayed, A.A.; Darwish, S.F.; Zewail, M.B.; Mohammed, M.; Saeed, H.; Rabea, H. Antibiotic misuse and compliance with infection control measures during COVID-19 pandemic in community pharmacies in Egypt. Int. J. Clin. Pract. 2021, 75, e14081. [Google Scholar] [CrossRef]
  64. Chen, J.; Wang, Y.; Chen, X.; Hesketh, T. Widespread illegal sales of antibiotics in Chinese pharmacies—A nationwide cross-sectional study. Antimicrob. Resist. Infect. Control. 2020, 9, 1–8. [Google Scholar] [CrossRef]
  65. Deqing County Market Supervision Administration. Notice on Uspension of Sales of “Antipyretic, Antitussive, Antiviral, Antimicrobial”. Available online: http://www.deqing.gov.cn/hzgov/front/s170/zfxxgk/yqfk/tzgg/20220624/i3218597.html (accessed on 24 April 2022).
  66. Cai, S.; Wang, N.; Xu, L.; Yan, F.; Jiang, Q.; Zhao, X.; Wang, W.; Wang, H.; Jiang, L.; Cong, W.; et al. Impacts of Antibiotic Residues in the Environment on Bacterial Resistance and Human Health in Eastern China: An Interdisciplinary Mixed-Methods Study Protocol. Int. J. Environ. Res. Public Health 2022, 19, 8145. [Google Scholar] [CrossRef]
  67. World Health Organization. Manual for the Household Survey to Measure Access and Use of Medicines. 2015. Available online: https://www.scirp.org/reference/ReferencesPapers.aspx?ReferenceID=1608696 (accessed on 26 June 2022).
  68. Thompson, N.D.; Stone, N.D.; Brown, C.J.; Penna, A.R.; Eure, T.R.; Bamberg, W.M.; Barney, G.R.; Barter, D.; Clogher, P.; DeSilva, M.B. Antimicrobial use in a cohort of US nursing homes, 2017. JAMA 2021, 325, 1286–1295. [Google Scholar]
  69. World Health Organization. Guidelines for ATC Classification and DDD Assignment. 2022. Available online: https://www.whocc.no/atc_ddd_index_and_guidelines/guidelines/ (accessed on 20 April 2022).
  70. Nai-Qing, Z. Statistical Methods for incidence rate. Chin. J. Health Stat. 2006, 23, 534–535. [Google Scholar]
Antibiotics 11 01544 g001 550
Figure 1. The proportion of different categories of antibiotics in medicine diaries among the study population in Eastern China, 2019–2020. (Data shown are unstandardized percentages. The proportion of cephalosporins are different between 2019 and 2020 (p = 0.011).).
Figure 1. The proportion of different categories of antibiotics in medicine diaries among the study population in Eastern China, 2019–2020. (Data shown are unstandardized percentages. The proportion of cephalosporins are different between 2019 and 2020 (p = 0.011).).
Antibiotics 11 01544 g001
Antibiotics 11 01544 g002 550
Figure 2. Sources of antibiotics in medicine diaries among the study population in Eastern China, 2019–2020. (Data shown are unstandardized percentages. In total, the sources of antibiotics changed significantly after COVID-19 (p = 0.002)).
Figure 2. Sources of antibiotics in medicine diaries among the study population in Eastern China, 2019–2020. (Data shown are unstandardized percentages. In total, the sources of antibiotics changed significantly after COVID-19 (p = 0.002)).
Antibiotics 11 01544 g002
Antibiotics 11 01544 g003 550
Figure 3. Reasons for antibiotic use in medicine diaries among the study population in Eastern China, 2019–2020. (Data shown are unstandardized percentages. In total, the sources of antibiotics changed significantly after COVID-19 (p = 0.005)).
Figure 3. Reasons for antibiotic use in medicine diaries among the study population in Eastern China, 2019–2020. (Data shown are unstandardized percentages. In total, the sources of antibiotics changed significantly after COVID-19 (p = 0.005)).
Antibiotics 11 01544 g003
Table
Table 1. General characteristics of the participants among the study population in Eastern China by years.
Table 1. General characteristics of the participants among the study population in Eastern China by years.
CharacteristicsTotal20192020χ2 or tp Value
n = 1111n = 440n = 671
Gender
Male 533 (47.97)217 (49.32)316 (47.09)0.4410.506
Female 578 (52.03)223 (50.68)355 (52.91)
Age (years; mean ± SD)59.17 ± 14.0757.63 ± 14.7060.14 ± 13.5735.30.004
15–3468 (6.12)34 (7.73) 34 (5.07)6.630<0.036
35–59464 (41.76)195 (44.32) 269 (40.09)
≥60579 (52.12)211 (47.95) 368 (54.84)
Education (years) *
≤6 768 (69.13)297 (67.50) 471 (70.19)0.7820.377
>6 343 (30.87)143 (32.50) 200 (29.81)
Farmer *
No 687 (62.74)249 (58.73) 438 (65.28)4.4910.034
Yes408 (37.26)175 (41.27) 233 (34.72)
Annual household income (CNY) *
≤100,000491 (49.25)144 (36.64) 347 (57.45)40.419<0.001
>100,000506 (50.75)249 (63.36) 257 (42.55)
History of chronic diseases
No615 (57.32)243 (60.45) 372 (55.44)2.377<0.123
Yes 458 (42.68)159 (39.55) 299 (44.56)
Note: * Missed data; Abbreviations: CNY, Chinese Yuan.
Table
Table 2. Changes in the risk of antibiotic use between 2019 and 2020 among the study population in Eastern China.
Table 2. Changes in the risk of antibiotic use between 2019 and 2020 among the study population in Eastern China.
Characteristics20192020RRZ Valuep Value
Person MonthsCasesIR
(100 PMs)		Person MonthsCasesIR
(100 PMs)
Overall21161155.43327342212.892.3777.981<0.001
Gender
Male1020595.86152018712.302.1033.276<0.001
Female1095565.11175323513.412.6243.558<0.001
Age (years)
15–3414742.7215274.611.531.0820.280
35–59914414.49120514712.202.7232.079<0.001
≥60968666.82175425214.372.1139.053<0.001
Education (years) *
≤6 1384715.13220329513.392.6154.904<0.001
>6645406.2090811112.221.9719.937<0.001
Farmer *
No1130534.69199325712.902.7544.457<0.001
Yes899586.45111714913.342.0730.338<0.001
Annual household income (CNY) *
≤100,000624426.73161823014.222.1126.518<0.001
>100,0001185534.47119513911.632.6036.663<0.001
History of chronic diseases
No1148574.97169522012.982.6143.43<0.001
Yes724395.39141618613.142.4428.76<0.001
Note: * Missed data; Abbreviations: CNY, Chinese Yuan.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Zhu, B.; Zhao, X.; Li, Y.; Wang, N.; Lambert, H.; Yan, F.; Jiang, Q.; Fu, C. Impact of the COVID-19 Outbreak on the Antibiotic Use Patterns among a Rural Community Population in Eastern China. Antibiotics 2022, 11, 1544. https://doi.org/10.3390/antibiotics11111544

AMA Style

Zhu B, Zhao X, Li Y, Wang N, Lambert H, Yan F, Jiang Q, Fu C. Impact of the COVID-19 Outbreak on the Antibiotic Use Patterns among a Rural Community Population in Eastern China. Antibiotics. 2022; 11(11):1544. https://doi.org/10.3390/antibiotics11111544

Chicago/Turabian Style

Zhu, Bingbing, Xinping Zhao, Yurong Li, Na Wang, Helen Lambert, Fei Yan, Qingwu Jiang, and Chaowei Fu. 2022. "Impact of the COVID-19 Outbreak on the Antibiotic Use Patterns among a Rural Community Population in Eastern China" Antibiotics 11, no. 11: 1544. https://doi.org/10.3390/antibiotics11111544

APA Style

Zhu, B., Zhao, X., Li, Y., Wang, N., Lambert, H., Yan, F., Jiang, Q., & Fu, C. (2022). Impact of the COVID-19 Outbreak on the Antibiotic Use Patterns among a Rural Community Population in Eastern China. Antibiotics, 11(11), 1544. https://doi.org/10.3390/antibiotics11111544

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop