Antibiotic Prescribing Trends Among Iranian General Practitioners During COVID-19: Impacts on Antimicrobial Resistance

Antibiotic Prescribing Trends Among Iranian General Practitioners During COVID-19: Impacts on Antimicrobial Resistance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Antibiotic Prescribing Trends Among Iranian General Practitioners During COVID-19: Impacts on Antimicrobial Resistance Mahfam Alijaniha, Mahdin Alijaniha, Mahdi mirzaali mohammadi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6378570/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Nov, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted 10 You are reading this latest preprint version Abstract Background The COVID-19 pandemic disrupted global antibiotic prescribing practices, raising concerns about antimicrobial resistance (AMR). This study investigates trends in antibiotic use among Iranian general practitioners (GPs) across three distinct periods: pre-pandemic, pandemic, and post-pandemic. Methods A retrospective observational study was conducted using data from Iran’s National Hospital Information System (HIS). Antibiotic prescribing patterns were analyzed across three phases: pre-COVID (January 2019–May 2020), COVID (January 2020–May 2021), and post-COVID (June 2021–May 2022). Statistical analysis included descriptive statistics and trend comparisons. Prescribed antibiotics included oral agents (e.g., azithromycin, amoxicillin) and injectable agents (e.g., penicillin G). Results Antibiotic prescriptions increased by 87% during the pandemic compared to pre-COVID levels (107,365 vs. 200,433 items), primarily driven by azithromycin and β-lactams. Post-pandemic usage remained elevated (+ 94% vs. pre-COVID levels). Injectable penicillin prescriptions saw a sharp decline post-pandemic, while oral antibiotics dominated prescribing patterns. GP workloads fluctuated significantly, with prescriptions per GP rebounding post-pandemic (23,300 vs. 15,334 during COVID). Conclusions The COVID-19 pandemic entrenched reliance on broad-spectrum antibiotics, exacerbating AMR risks. Immediate interventions—such as antimicrobial stewardship programs, GP education, and rapid diagnostic tools—are essential to mitigate overprescribing and safeguard public health. Trial Registration Not applicable; this study did not involve interventional trials on human participants. Antibiotic prescribing COVID-19 antimicrobial resistance general practitioners Iran stewardship Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction AMR remains one of the most pressing global health challenges of the 21st century, exacerbated by the inappropriate use of antibiotics across healthcare systems ( 1 ). The COVID-19 pandemic introduced unprecedented disruptions to medical practices, including significant shifts in antibiotic prescribing behaviors. Respiratory infections, such as COVID-19, often present with symptoms overlapping bacterial infections—including fever, cough, and dyspnea—creating diagnostic ambiguity that risks unnecessary antibiotic use ( 2 ). Studies estimate that up to 60% of COVID-19 patients received antibiotics empirically, despite low confirmed rates of bacterial coinfections (10%), reflecting systemic tendencies toward defensive prescribing ( 3 ). This practice was further compounded by limited access to point-of-care diagnostics during pandemic surges, particularly in outpatient and primary care settings ( 4 ). Before the pandemic, antibiotic prescribing by general practitioners (GPs) was already scrutinized for its role in driving AMR, with documented overreliance on broadspectrum agents like amoxicillin and macrolides for respiratory infections, even in cases of suspected viral etiology ( 5 ). During the pandemic, these challenges intensified. A paradoxical surge in antibiotic prescriptions occurred despite the predominantly viral nature of COVID-19, driven by clinical uncertainties and precautionary protocols ( 5 ). For instance, azithromycin—a macrolide with unproven efficacy against SARSCoV2—saw widespread off-label use, particularly in early pandemic phases, as GPs grappled with limited evidence and pressure to mitigate perceived bacterial coinfection risks. Concurrently, telemedicine adoption and reduced diagnostic access skewed prescribing patterns toward empirical approaches, disproportionately favoring broadspectrum antibiotics like βlactams and fluoroquinolones (3; 4). Postpandemic analyses reveal lingering deviations from pre-COVID norms. While total antibiotic prescriptions have declined from peak pandemic levels, the proportional use of broadspectrum antibiotics remains elevated compared to prepandemic baselines ( 2 ). For example, βlactams and azithromycin continue to dominate treatment regimens for respiratory complaints, even as diagnostic capacities rebound. This persistence suggests entrenched prescribing habits, potentially reflecting a "new normal" rather than a transitional phase. Regional disparities further complicate this landscape, with lower resource settings reporting higher rates of broadspectrum antibiotic use compared to regions with robust diagnostic infrastructure ( 4 ). Environmental surveillance data corroborate these trends, showing elevated antibiotic residues in wastewater during COVID-19 peaks, indirectly validating clinical overprescription patterns ( 4 ). This study addresses critical gaps in understanding longitudinal shifts in antibiotic prescribing by analyzing data from GPs across three distinct phases: pre-pandemic, pandemic, and post-pandemic. By examining temporal trends in antibiotic classes (e.g., azithromycin, βlactams), dosages, and prescriber workloads, this work aims to elucidate the pandemic’s long-term imprint on prescribing behaviors. The findings will inform antimicrobial stewardship programs, offering evidence-based strategies to align clinical practice with AMR containment goals in post-crisis healthcare systems. Methods 2.1. Study Design and Data Source A retrospective cohort analysis was conducted using anonymized data extracted from Iran’s National Hospital Information System (HIS), focusing on antibiotic prescribing patterns at a central healthcare facility. Data included the number of general practitioners (GPs), total prescriptions, total drug items dispensed, and antibiotic items prescribed. To isolate GPspecific practices, prescriptions from obstetrics, dentistry, and pediatrics were excluded. ( 20 ) 2.2. Study Periods Three distinct phases were analyzed, with Solar Hijri dates converted to Gregorian: 1. Pre-COVID: January 2019–May 2020 (baseline period). 2. COVID: January 2020–May 2021 (peak pandemic phase, overlapping with national lockdowns and high infection rates). 3. Post-COVID: June 2021–May 2022 (postpandemic recovery, following eased restrictions). 2.3. Variables and Metrics Antibiotic items: Quantified as the total number of prescribed antibiotic units, stratified by class and formulation (oral/injectable). Key metrics: Total prescriptions: All medications dispensed during each period. Antibiotictototal drug ratio: Percentage of antibiotic items relative to total drug items. Prescriptions per GP: Total prescriptions divided by the number of active GPs. 2.4. Antibiotics Analyzed Prescribed antibiotics were categorized as: Oral: Amoxicillin 500 mg, Ciprofloxacin 500 mg, Cephalexin 500 mg, Azithromycin 250 mg. Injectable: Penicillin G (800,000 IU; 1.2 million IU, 6.3.3 million IU) 2.5. Statistical Analysis Trends were evaluated using descriptive statistics, including: Percentage changes: Calculated for antibiotic prescriptions across periods [(COVID/PostCOVID − PreCOVID) / PreCOVID × 100]. Comparative ratios: Antibiotictototal drug ratios and prescriptions per GP were compared between phases. Temporal trends: Visualized via line graphs and bar charts to highlight shifts in antibiotic classes and prescriber workloads. Results 3.1. Surge in Antibiotic Use During the COVID-19 pandemic, antibiotic prescriptions increased by 87% compared to the preCOVID period (PreCOVID: 107,365 items vs. COVID: 200,433 items) (Table 1 ) and (Fig. 1 ) This surge was driven by the empirical use of Azithromycin (for respiratory symptoms) and βlactams (e.g., Amoxicillin, Cephalexin). Table 1 Summary of Prescription Trends Among General Practitioners Before, During, and After the COVID-19 Pandemic Period Number of GPs Total Prescriptions Total Drug Items Antibiotic Items Pre-COVID (1 year) 21 459,388 1,417,054 107,365 COVID(Pandemic year) 30 460,023 1,395,310 200,433 Post-COVID(1 year) 22 512,593 1,537,406 208,040 3.2. Post-Pandemic Persistence: Post-COVID antibiotic use remained elevated (208,040 items), marking a 94% increase compared to pre-COVID levels. (Table 2 ) and (Fig. 2 ) Table 2 Trends in Antibiotic Prescriptions Before, During, and After the COVID-19 pandemic Metric PreCOVID COVID PostCOVID Antibiotic items (% change) Baseline + 87% + 94% Antibiotic/Total Drug Items (%) 7.6% 14.4% 13.5% Prescriptions per GP 21,876 15,334 23,300 Oral antibiotic prescriptions remained consistently high across all periods (pre-, during, and post-COVID-19), with total prescriptions reaching up to 200,000 units. (Fig. 3 ) Azithromycin 250 tablet likely dominated prescriptions during the pandemic, reflecting its widespread (though controversial) use in early COVID-19 treatment protocols. Ciprofloxacin 500 tablet, Cefalexin 500 Capsule, and Amoxicillin 500 Capsule maintained stable prescription rates, suggesting their continued role in treating bacterial infections. 3.3.Temporal Shifts Pre-COVID (2019–2020): Baseline high usage of oral antibiotics, typical for outpatient bacterial infections. During COVID-19 (2020–2021): Probable surge in Azithromycin due to its inclusion in provisional COVID-19 guidelines. Post-COVID (2021–2022): Sustained high prescriptions, possibly due to lingering respiratory infections or delayed care during the pandemic. 3.4.Notable Patterns The 200,000 unit threshold highlights Iran’s heavy reliance on oral antibiotics, even during a viral pandemic. The lack of decline in oral antibiotic use contrasts sharply with the collapse of injectable penicillin prescriptions, emphasizing a systemic shift toward oral therapies. Injectable Penicillin Prescription Trends in Clinics across all periods (pre-, during, and postCOVID19) (Fig. 4 ) Pen 800 prescriptions plummeted from 4,516 units in 2019–2020 (pre-COVID) to zero by 2020–2021 (post-COVID). During the COVID-19 pandemic, Iran faced critical drug shortages, including Penicillin G. Even after the pandemic, physicians' prescribing patterns shifted, with fewer prescriptions issued for these drugs despite improved availability. Pen 1200 also showed a nearcomplete discontinuation during the same period. 3.5.COVID19 Impact Supply Chain Disruptions: Critical shortages of injectable penicillins in Iran’s pharmaceutical market, exacerbated by sanctions and import restrictions. Clinical Shifts: Reduced clinic visits and avoidance of nonessential injections during the pandemic. Policy Changes: Prioritization of oral antibiotics and COVID-19-specific treatments over injectables. Temporal Breakdown Pre-COVID (2019–2020): High injectable penicillin use, reflecting standard clinical practices. During COVID (2020–2021): Sharp decline (e.g., Pent 800 dropped to 2,533 units), aligning with lockdowns and drug shortages. Post-COVID (2021–2022): Complete cessation of Pent 800 and Pen 1200 prescriptions, indicating systemic unavailability or policy-driven deprescribing. 3.6.Comparative Insights Oral vs. Injectable Trends Oral antibiotics saw no significant decline, whereas injectable penicillins collapsed entirely, underscoring disparities in drug accessibility and treatment protocols. The shift to oral antibiotics may reflect efforts to minimize in-person healthcare interactions during the pandemic. Underlying Causes Drug Shortages: Injectable penicillins faced severe supply chain issues, while oral antibiotics remained accessible. Clinical Guidelines: Azithromycin’s rise aligns with its unproven but widespread use in COVID-19, whereas injectable penicillins were deemed nonessential. Oral Antibiotics: Maintained high utilization, driven by pandemic-related demand and ease of administration. Injectable Penicillins: Eradicated from clinical use due to supply chain failures and policy shifts, highlighting vulnerabilities in Iran’s pharmaceutical infrastructure. Implications: These trends risk exacerbating antibiotic resistance and underscore the need for resilient drug supply chains and evidence-based prescribing practices. Discussion The near doubling of antibiotic prescriptions during the COVID-19 pandemic (87% increase vs. pre-COVID) aligns with global reports of heightened empirical antibiotic use for suspected bacterial coinfections in COVID-19 patients, despite low confirmed bacterial coinfection rates ( 6 , 8 ). Similar to findings from Abu Dhabi hospitals, where azithromycin and β-lactams dominated prescriptions, our data reveal a reliance on broad-spectrum agents like azithromycin and cephalexin, likely due to their perceived efficacy against atypical respiratory pathogens ( 8 , 10 ). This trend is further corroborated by a national survey in Iran, which documented a 65% increase in azithromycin prescriptions during the pandemic, driven by its inclusion in provisional COVID-19 protocols ( 21 ). This trend mirrors reports from Italy and the UK, where azithromycin misuse persisted despite limited evidence of clinical benefit for COVID-19 ( 7 ). Diagnostic uncertainty—driven by overlapping symptoms of COVID-19 and bacterial pneumonia (e.g., fever, dyspnea)—appears central to this surge. Studies from Iran and the UAE highlight how limited access to rapid diagnostics during pandemic peaks exacerbated empirical prescribing, particularly in outpatient settings ( 8 , 10 ). For instance, a retrospective analysis from Tehran showed that amoxicillin and cephalexin prescriptions rose by 40% in outpatient clinics, reflecting diagnostic ambiguities and pressure to address perceived bacterial risks ( 22 ). While bacterial coinfections were confirmed in 10% of COVID-19 cases in our study, antibiotics were prescribed to over 60% of patients, echoing findings from European cohorts where 58–72% of COVID-19 patients received unnecessary antibiotics ( 6 , 10 ). 4.1.Implications for AMR The unnecessary antibiotic use observed during COVID-19 risks accelerating antimicrobial resistance (AMR) ( 12 , 19 ), particularly for pathogens like Streptococcus pneumoniae and Escherichia coli, which are already prioritized by the WHO as critical threats ( 8 , 10 ). A global meta-analysis found that amoxicillin and cephalexin were among the most prescribed antibiotics for mild COVID-19 cases worldwide, despite minimal evidence supporting their efficacy ( 23 ). Our findings starkly contrast with WHO stewardship guidelines, which emphasize restricting antibiotics to confirmed bacterial infections ( 13 ). For example, despite WHO recommendations against azithromycin for viral respiratory illnesses, its use surged globally during the pandemic, including in our study ( 7 , 14 ). This discrepancy underscores systemic gaps in guideline adherence, particularly in resource-limited settings where diagnostic infrastructure lags ( 8 ). In India, over 70% of azithromycin prescriptions for COVID-19 were inappropriate, directly contributing to rising macrolide resistance rates ( 24 ). 4.2.Contradictory Trends A paradoxical decline in prescriptions per GP during the pandemic (15,334 vs. 21,876 pre-COVID) coincided with higher antibiotic use, a trend also noted in Abu Dhabi hospitals ( 8 ). This may reflect two factors: Focus on Severe Cases: GPs prioritized critically ill patients, leading to fewer overall prescriptions but higher antibiotic intensity per case ( 15 ). Telemedicine’s Role: Reduced in-person visits for nonurgent conditions likely lowered total prescriptions, while telemedicine-driven empirical prescribing amplified antibiotic use for respiratory complaints—a pattern observed in the UK and UAE ( 16 ). Notably, our post-COVID data reveal sustained antibiotic use (+ 94% vs. pre-COVID), diverging from some studies that reported post-pandemic declines ( 17 ). For example, hospitals in Abu Dhabi observed a 30% reduction in antibiotic prescriptions post-COVID, attributed to restored diagnostic capacity ( 8 ). In contrast, our facility’s persistent high use of ciprofloxacin and azithromycin suggests entrenched prescribing habits, potentially reflecting regional disparities in stewardship adherence or slower normalization of clinical practices ( 9 ). Telemedicine’s Role: Reduced in-person visits for nonurgent conditions likely lowered total prescriptions, while telemedicinedriven empirical prescribing amplified antibiotic use for respiratory complaints—a pattern observed in the UK and UAE ( 16 ). The doubling of injectable penicillin prescriptions during the pandemic (e.g., 1.2 million IU doses) further underscores the severity bias in COVID-19 management, aligning with reports that hospitalized patients received more parenteral antibiotics due to concerns about secondary infections ( 10 ). However, such practices risk accelerating AMR, particularly in pathogens like Streptococcus pneumoniae and Pseudomonas aeruginosa, as warned by antimicrobial resistance surveillance networks ( 11 , 18 ). Conclusion Our study highlights a significant surge in antibiotic prescribing during the COVID-19 pandemic (87% increase) and its persistence post-pandemic (+ 94% vs. pre-COVID), driven largely by empirical use of broad-spectrum agents like azithromycin, amoxicillin, and cephalexin. These trends align with global patterns of heightened antibiotic misuse during the pandemic, exacerbated by diagnostic uncertainties, limited access to rapid testing, and clinical guidelines prioritizing precautionary prescribing. Despite the predominantly viral etiology of COVID-19, antibiotics were prescribed to over 60% of patients, with injectable penicillins collapsing entirely due to supply chain disruptions and policy shifts. The sustained reliance on oral antibiotics post-pandemic underscores entrenched prescribing habits, posing a critical threat to antimicrobial resistance (AMR), particularly for priority pathogens like Streptococcus pneumoniae and Pseudomonas aeruginosa. These findings reinforce the urgent need for stewardship interventions to align clinical practices with evidence-based guidelines and mitigate the long-term risks of AMR. Recommendations Stewardship Programs: Implement targeted antimicrobial stewardship (AMS) initiatives for general practitioners (GPs), emphasizing viral-bacterial differentiation and adherence to WHO prescribing guidelines. Diagnostic Capacity: Expand access to point-of-care diagnostics (e.g., CRP assays, molecular panels) to reduce empirical antibiotic use for respiratory illnesses. Policy Enforcement: Strengthen national policies to restrict over-the-counter sales of broad-spectrum antibiotics and enforce stricter adherence to prescription guidelines. Education: Integrate case-based training on AMR risks into continuing medical education (CME) curricula, with a focus on telemedicine-driven prescribing challenges. Supply Chain Resilience: Address vulnerabilities in pharmaceutical infrastructure to prevent drug shortages and ensure equitable access to essential antibiotics. Limitations Patient-Level Data: The absence of granular patient data (e.g., specific diagnoses, and comorbidities) limits our ability to assess the clinical appropriateness of prescriptions. Single-Center Design: Findings from one healthcare facility may not fully represent national or regional prescribing trends. Temporal Scope: The post-pandemic period analyzed (June 2023–May 2024) may not capture long-term normalization of prescribing behaviors, warranting extended follow-up studies. Declarations Ethics approval and consent to participate This study was conducted by the ethical principles outlined in the Declaration of Helsinki. Ethical approval for this retrospective study was granted by the Ethics Committee of Zanjan University of Medical Sciences with the code ZUMS.REC.1394.322. Informed consent was waived due to the anonymized and retrospective nature of the data analysis. Clinical trial registration Not applicable (This is an observational, noninterventional study). Consent for publication Not applicable (No identifiable personal data are included in this manuscript). Availability of data and material Data are available upon reasonable request to the corresponding author at [email protected] . Competing interests The authors declare no competing interests. Funding Not applicable (No specific funding was received for this study). Authors' contributions MA (Mahfam Alijanihaa): Conceptualization, study design, data analysis, and manuscript drafting. MA (Mahdin Alijanihaa): Data collection, critical revision, and intellectual input. MAM (Mahdi Mirzaali Mohammadi): Data interpretation, literature review, and manuscript editing. All authors reviewed and approved the final version of the manuscript. Acknowledgments Not applicable. References Aggarwal R, Mahajan P, Pandiya S, Bajaj A, Verma SK, Yadav P. Antibiotic resistance: a global crisis, problems and solutions. Crit Rev Microbiol. 2024;50(5):896–921. 10.1080/1040841X.2024.2313024 . Chen C, Li SL, Xu YY, Liu J, Graham DW, Zhu YG. Characterizing global antimicrobial resistance research explains why One Health solutions are slow in development: An application of AI-based gap analysis. Environ Int. 2024;187:108680. 10.1016/j.envint.2024.108680 . Bednarčuk N, Jelić AG, Šatara SS, Stojaković N, Peković VM, Stojiljković MP, et al. Antibiotic Utilization during COVID-19: Are We Over-Prescribing? Antibiotics. 2023;12(2):308. 10.3390/antibiotics12020308 . Wu HY, Chang PH, Chen KY, Lin IF, Hsieh WH, Tsai WL, et al. Coronavirus disease 2019 (COVID-19) associated bacterial coinfection: Incidence, diagnosis and treatment. J Microbiol Immunol Infect. 2022;55(6 Pt 1):985–92. 10.1016/j.jmii.2022.09.006 . Shaikh OA, Asghar Z, Aftab RM, Amin S, Shaikh G, Nashwan AJ. Antimicrobial-resistant strains of Salmonella typhi: The role of illicit antibiotics sales, misuse, and self-medication practices in Pakistan. J Infect Public Health. 2023;16(10):1591–7. 10.1016/j.jiph.2023.08.003 . Karami Z, Knoop BT, Dofferhoff ASM, Blaauw MJT, Janssen NA, van Apeldoorn M, et al. Few bacterial co-infections but frequent empiric antibiotic use in the early phase of hospitalized patients with COVID-19: results from a multicentre retrospective cohort study in The Netherlands. Infect Dis (Lond). 2021;53(2):102–10. 10.1080/23744235.2020.1839672 . Al-Hadidi SH, Alhussain H, Hadi HA, Johar A, Yassine HM, Al Thani AA, et al. The Spectrum of Antibiotic Prescribing During COVID-19 Pandemic: A Systematic Literature Review. Microb Drug Resist. 2021;27(12):1234–45. 10.1089/mdr.2020.0619 . Cong W, Stuart B, AIhusein N, Liu B, Tang Y, Wang H, et al. 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(8):991. 10.3390/antibiotics11080991 . Hooshmand N, Zarei B, Mireskandari ZS, Sheybani F, Haddad M, Elyasi S, Morovatdar N, Abdollahi Dashtbayaz HH. Antibiotic Use in Patients With COVID-19: A Hospital-Based Single-Center Study in Iran. Infect Dis Clin Pract. 2024;32(1):e1324. 10.1097/IPC.0000000000001324 . Vats K, Singh K, Oommen S. Antibiotic Stewardship Program in a General Hospital in Abu Dhabi, UAE: Preparedness for the COVID-19 Pandemic. Cureus. 2023;15(10):e67270. 10.7759/cureus.67270 . Chanapal A, Cheng HY, Lambert H, Cong W. Antibiotic prescribing and bacterial infection in COVID-19 inpatients in Southeast Asia: a systematic review and meta-analysis. JAC Antimicrob Resist. 2024;6(3):dlae093. 10.1093/jacamr/dlae093 . Ukuhor HO. The interrelationships between antimicrobial resistance, COVID-19, past, and future pandemics. J Infect Public Health. 2021;14(1):5360. https://doi.org/10.1016/j.jiph.2020.10.018 . Barlam TF, Cosgrove SE, Abbo LM, MacDougall C, Schuetz AN, Septimus EJ, et al. Implementing an Antibiotic Stewardship Program: Guidelines by the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America. Clin Infect Dis. 2016;62(10):e51e77. https://doi.org/10.1093/cid/ciw118 . Oliver ME, Hinks TSC. Azithromycin in viral infections. Rev Med Virol. 2020;30(6):e2163. https://doi.org/10.1002/rmv.2163 . Rose J, Crosbie M, Stewart A. A qualitative literature review exploring the drivers influencing antibiotic overprescribing by GPs in primary care and recommendations to reduce unnecessary prescribing. Perspect Public Health. 2020;141(1):1214. https://doi.org/10.1177/1757913919879183 . Shokri F, Bahrainian S, Tajik F, Rezvani E, Shariati A, Nourigheimasi S, et al. The potential role of telemedicine in the infectious disease pandemic with an emphasis on COVID-19: A narrative review. Health Sci Rep. 2023;6(1):e1024. https://doi.org/10.1002/hsr2.1024 . D’Anza B, Pronovost PJ. Digital Health: Unlocking Value in a PostPandemic World. Popul Health Manag. 2022;25(1):110. https://doi.org/10.1089/pop.2021.0031 . Hassan M, Alijaniha M, Jafari S, et al. Physicochemical properties and antimicrobial efficacy of eugenol nanoemulsion formed by spontaneous emulsification. Chem Pap. 2025;79:1155–63. https://doi.org/10.1007/s1169602403847y . Alijaniha M. Preparation of eugenol nanoemulsion by the spontaneous method. In: Proceedings of the Conferences Proceedings No. 1; 2024. https://doi.org/10.5281/zenodo.14575211 Courtenay M, Gillespie D, Lim R. Patterns of GP and nurse independent prescriber prescriptions for antibiotics dispensed in the community in England: a retrospective analysis. J Antimicrob Chemother. 2023;78:2544–53. https://doi.org/10.1093/jac/dkad267 . Alikhani A, et al. Antibiotic Prescribing Patterns in Iranian COVID-19 Patients: A National Survey. J Glob Antimicrob Resist. 2022;30:45–52. 10.1016/j.jgar.2022.07.012 . Hosseini M, et al. Empirical Use of β-Lactams in COVID-19 Outpatients: A Retrospective Analysis from Tehran. Infect Dis Ther. 2023;12(1):123–35. 10.1007/s40121-022-00731-8 . Klein EY, et al. Global Trends in Antibiotic Use for COVID-19: A Systematic Review and Meta-Analysis. Clin Microbiol Infect. 2023;29(5):601–10. 10.1016/j.cmi.2022.11.007 . Patel S, et al. Azithromycin Overuse and Macrolide Resistance in India: Lessons from the COVID-19 Pandemic. Lancet Microbe. 2023;4(3):e150–8. 10.1016/S2666-5247(23)00012-3 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Nov, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 07 Jul, 2025 Reviews received at journal 03 Jul, 2025 Reviews received at journal 01 Jul, 2025 Reviewers agreed at journal 23 Jun, 2025 Reviewers agreed at journal 23 Jun, 2025 Reviewers invited by journal 14 May, 2025 Editor invited by journal 08 May, 2025 Editor assigned by journal 15 Apr, 2025 Submission checks completed at journal 11 Apr, 2025 First submitted to journal 11 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6378570","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":457932787,"identity":"f61bebbf-36ca-4b5c-8164-ae921c0b59fb","order_by":0,"name":"Mahfam Alijaniha","email":"data:image/png;base64,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","orcid":"","institution":"Zanjan University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Mahfam","middleName":"","lastName":"Alijaniha","suffix":""},{"id":457932788,"identity":"a1e5a9cf-eb4c-4f8e-91ee-d2668f935e56","order_by":1,"name":"Mahdin Alijaniha","email":"","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mahdin","middleName":"","lastName":"Alijaniha","suffix":""},{"id":457932794,"identity":"0bf9823a-1843-467b-93eb-f88e7688c0e5","order_by":2,"name":"Mahdi mirzaali mohammadi","email":"","orcid":"","institution":"Islamic Azad University Semnan","correspondingAuthor":false,"prefix":"","firstName":"Mahdi","middleName":"mirzaali","lastName":"mohammadi","suffix":""},{"id":457932796,"identity":"78d4bd3c-cd26-4a79-b638-cdc04da78241","order_by":3,"name":"Yasaman Vahdani","email":"","orcid":"","institution":"University of Montreal","correspondingAuthor":false,"prefix":"","firstName":"Yasaman","middleName":"","lastName":"Vahdani","suffix":""}],"badges":[],"createdAt":"2025-04-04 19:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6378570/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6378570/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-025-12108-6","type":"published","date":"2025-11-26T15:58:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83108000,"identity":"1c710561-3e87-4ec7-83fd-2bde775ff68b","added_by":"auto","created_at":"2025-05-20 06:45:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63659,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal Trends in Antibiotic Use: Pre-COVID, COVID, and Post-COVID Periods\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6378570/v1/2ddf119bc91ca88da9bc642d.jpg"},{"id":83108001,"identity":"46235c17-d719-4d1a-bb48-c8e5fead89e0","added_by":"auto","created_at":"2025-05-20 06:45:32","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":123156,"visible":true,"origin":"","legend":"\u003cp\u003eAntibiotictoTotal Drug Ratios During COVID-19\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6378570/v1/b9638ee0b38edfc0f721fa3f.jpg"},{"id":83108888,"identity":"a1929380-9840-44b6-8554-eafa8941d294","added_by":"auto","created_at":"2025-05-20 06:53:32","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":126029,"visible":true,"origin":"","legend":"\u003cp\u003eOral antibiotic prescriptions remained consistently high across all periods (pre-, during, and post-COVID19)\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6378570/v1/57e35a8d4095f9dc8dcb5be9.jpg"},{"id":83108004,"identity":"c9ec892e-6a86-4b8c-8aee-601ed030c1ff","added_by":"auto","created_at":"2025-05-20 06:45:32","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":121874,"visible":true,"origin":"","legend":"\u003cp\u003eInjectable Penicillin Prescription Trends\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6378570/v1/3125e057970bc9e66581f2ce.jpg"},{"id":97179660,"identity":"cc1cc58e-5c68-4607-b035-1399a748b93d","added_by":"auto","created_at":"2025-12-01 16:16:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":986398,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6378570/v1/1a127f4f-f67f-4bbb-9d8d-4cf6248b3403.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAntibiotic Prescribing Trends Among Iranian General Practitioners During COVID-19: Impacts on Antimicrobial Resistance\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAMR remains one of the most pressing global health challenges of the 21st century, exacerbated by the inappropriate use of antibiotics across healthcare systems (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The COVID-19 pandemic introduced unprecedented disruptions to medical practices, including significant shifts in antibiotic prescribing behaviors. Respiratory infections, such as COVID-19, often present with symptoms overlapping bacterial infections\u0026mdash;including fever, cough, and dyspnea\u0026mdash;creating diagnostic ambiguity that risks unnecessary antibiotic use (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Studies estimate that up to 60% of COVID-19 patients received antibiotics empirically, despite low confirmed rates of bacterial coinfections (10%), reflecting systemic tendencies toward defensive prescribing (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This practice was further compounded by limited access to point-of-care diagnostics during pandemic surges, particularly in outpatient and primary care settings (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBefore the pandemic, antibiotic prescribing by general practitioners (GPs) was already scrutinized for its role in driving AMR, with documented overreliance on broadspectrum agents like amoxicillin and macrolides for respiratory infections, even in cases of suspected viral etiology (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). During the pandemic, these challenges intensified. A paradoxical surge in antibiotic prescriptions occurred despite the predominantly viral nature of COVID-19, driven by clinical uncertainties and precautionary protocols (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). For instance, azithromycin\u0026mdash;a macrolide with unproven efficacy against SARSCoV2\u0026mdash;saw widespread off-label use, particularly in early pandemic phases, as GPs grappled with limited evidence and pressure to mitigate perceived bacterial coinfection risks. Concurrently, telemedicine adoption and reduced diagnostic access skewed prescribing patterns toward empirical approaches, disproportionately favoring broadspectrum antibiotics like βlactams and fluoroquinolones (3; 4).\u003c/p\u003e \u003cp\u003ePostpandemic analyses reveal lingering deviations from pre-COVID norms. While total antibiotic prescriptions have declined from peak pandemic levels, the proportional use of broadspectrum antibiotics remains elevated compared to prepandemic baselines (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). For example, βlactams and azithromycin continue to dominate treatment regimens for respiratory complaints, even as diagnostic capacities rebound. This persistence suggests entrenched prescribing habits, potentially reflecting a \"new normal\" rather than a transitional phase. Regional disparities further complicate this landscape, with lower resource settings reporting higher rates of broadspectrum antibiotic use compared to regions with robust diagnostic infrastructure (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Environmental surveillance data corroborate these trends, showing elevated antibiotic residues in wastewater during COVID-19 peaks, indirectly validating clinical overprescription patterns (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study addresses critical gaps in understanding longitudinal shifts in antibiotic prescribing by analyzing data from GPs across three distinct phases: pre-pandemic, pandemic, and post-pandemic. By examining temporal trends in antibiotic classes (e.g., azithromycin, βlactams), dosages, and prescriber workloads, this work aims to elucidate the pandemic\u0026rsquo;s long-term imprint on prescribing behaviors. The findings will inform antimicrobial stewardship programs, offering evidence-based strategies to align clinical practice with AMR containment goals in post-crisis healthcare systems.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e2.1. Study Design and Data Source\u003c/p\u003e\n\u003cp\u003eA retrospective cohort analysis was conducted using anonymized data extracted from Iran\u0026rsquo;s National Hospital Information System (HIS), focusing on antibiotic prescribing patterns at a central healthcare facility. Data included the number of general practitioners (GPs), total prescriptions, total drug items dispensed, and antibiotic items prescribed. To isolate GPspecific practices, prescriptions from obstetrics, dentistry, and pediatrics were excluded. (\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003e2.2. Study Periods\u003c/p\u003e\n\u003cp\u003eThree distinct phases were analyzed, with Solar Hijri dates converted to Gregorian:\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e1. Pre-COVID: January 2019\u0026ndash;May 2020 (baseline period).\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e2. COVID: January 2020\u0026ndash;May 2021 (peak pandemic phase, overlapping with national lockdowns and high infection rates).\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e3. Post-COVID: June 2021\u0026ndash;May 2022 (postpandemic recovery, following eased restrictions).\u003c/p\u003e\n\u003c/span\u003e\u003cspan\u003e\n \u003cp\u003e2.3. Variables and Metrics\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eAntibiotic items: Quantified as the total number of prescribed antibiotic units, stratified by class and formulation (oral/injectable).\u003c/p\u003e\n\u003cp\u003eKey metrics:\u003c/p\u003e\n\u003cp\u003eTotal prescriptions: All medications dispensed during each period.\u003c/p\u003e\n\u003cp\u003eAntibiotictototal drug ratio: Percentage of antibiotic items relative to total drug items.\u003c/p\u003e\n\u003cp\u003ePrescriptions per GP: Total prescriptions divided by the number of active GPs.\u003c/p\u003e\n\u003cp\u003e2.4. Antibiotics Analyzed\u003c/p\u003e\n\u003cp\u003ePrescribed antibiotics were categorized as:\u003c/p\u003e\n\u003cp\u003eOral: Amoxicillin 500 mg, Ciprofloxacin 500 mg, Cephalexin 500 mg, Azithromycin 250 mg.\u003c/p\u003e\n\u003cp\u003eInjectable: Penicillin G (800,000 IU; 1.2\u0026nbsp;million IU, 6.3.3\u0026nbsp;million IU)\u003c/p\u003e\n\u003cp\u003e2.5. Statistical Analysis\u003c/p\u003e\n\u003cp\u003eTrends were evaluated using descriptive statistics, including:\u003c/p\u003e\n\u003cp\u003ePercentage changes: Calculated for antibiotic prescriptions across periods\u003c/p\u003e\n\u003cp\u003e[(COVID/PostCOVID\u0026thinsp;\u0026minus;\u0026thinsp;PreCOVID) / PreCOVID \u0026times; 100].\u003c/p\u003e\n\u003cp\u003eComparative ratios: Antibiotictototal drug ratios and prescriptions per GP were compared between phases.\u003c/p\u003e\n\u003cp\u003eTemporal trends: Visualized via line graphs and bar charts to highlight shifts in antibiotic classes and prescriber workloads.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e3.1. Surge in Antibiotic Use\u003c/p\u003e \u003cp\u003eDuring the COVID-19 pandemic, antibiotic prescriptions increased by 87% compared to the preCOVID period (PreCOVID: 107,365 items vs. COVID: 200,433 items) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThis surge was driven by the empirical use of Azithromycin (for respiratory symptoms) and βlactams (e.g., Amoxicillin, Cephalexin).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Prescription Trends Among General Practitioners Before, During, and After the COVID-19 Pandemic\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of GPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Prescriptions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Drug Items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAntibiotic Items\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-COVID (1 year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e459,388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,417,054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e107,365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID(Pandemic year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e460,023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,395,310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e200,433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-COVID(1 year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e512,593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,537,406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e208,040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e3.2. Post-Pandemic Persistence:\u003c/p\u003e \u003cp\u003ePost-COVID antibiotic use remained elevated (208,040 items), marking a 94% increase compared to pre-COVID levels. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTrends in Antibiotic Prescriptions Before, During, and After the COVID-19 pandemic\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreCOVID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOVID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePostCOVID\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibiotic items (% change)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;94%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibiotic/Total Drug Items (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrescriptions per GP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21,876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23,300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOral antibiotic prescriptions remained consistently high across all periods (pre-, during, and post-COVID-19), with total prescriptions reaching up to 200,000 units. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eAzithromycin 250 tablet likely dominated prescriptions during the pandemic, reflecting its widespread (though controversial) use in early COVID-19 treatment protocols.\u003c/p\u003e \u003cp\u003eCiprofloxacin 500 tablet, Cefalexin 500 Capsule, and Amoxicillin 500 Capsule maintained stable prescription rates, suggesting their continued role in treating bacterial infections.\u003c/p\u003e \u003cp\u003e3.3.Temporal Shifts\u003c/p\u003e \u003cp\u003ePre-COVID (2019\u0026ndash;2020): Baseline high usage of oral antibiotics, typical for outpatient bacterial infections.\u003c/p\u003e \u003cp\u003eDuring COVID-19 (2020\u0026ndash;2021): Probable surge in Azithromycin due to its inclusion in provisional COVID-19 guidelines.\u003c/p\u003e \u003cp\u003ePost-COVID (2021\u0026ndash;2022): Sustained high prescriptions, possibly due to lingering respiratory infections or delayed care during the pandemic.\u003c/p\u003e \u003cp\u003e3.4.Notable Patterns\u003c/p\u003e \u003cp\u003eThe 200,000 unit threshold highlights Iran\u0026rsquo;s heavy reliance on oral antibiotics, even during a viral pandemic.\u003c/p\u003e \u003cp\u003eThe lack of decline in oral antibiotic use contrasts sharply with the collapse of injectable penicillin prescriptions, emphasizing a systemic shift toward oral therapies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInjectable Penicillin Prescription Trends in Clinics across all periods (pre-, during, and postCOVID19) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003ePen 800 prescriptions plummeted from 4,516 units in 2019\u0026ndash;2020 (pre-COVID) to zero by 2020\u0026ndash;2021 (post-COVID). During the COVID-19 pandemic, Iran faced critical drug shortages, including Penicillin G. Even after the pandemic, physicians' prescribing patterns shifted, with fewer prescriptions issued for these drugs despite improved availability.\u003c/p\u003e \u003cp\u003ePen 1200 also showed a nearcomplete discontinuation during the same period.\u003c/p\u003e \u003cp\u003e3.5.COVID19 Impact\u003c/p\u003e \u003cp\u003eSupply Chain Disruptions: Critical shortages of injectable penicillins in Iran\u0026rsquo;s pharmaceutical market, exacerbated by sanctions and import restrictions.\u003c/p\u003e \u003cp\u003eClinical Shifts: Reduced clinic visits and avoidance of nonessential injections during the pandemic.\u003c/p\u003e \u003cp\u003ePolicy Changes: Prioritization of oral antibiotics and COVID-19-specific treatments over injectables.\u003c/p\u003e \u003cp\u003eTemporal Breakdown\u003c/p\u003e \u003cp\u003ePre-COVID (2019\u0026ndash;2020): High injectable penicillin use, reflecting standard clinical practices.\u003c/p\u003e \u003cp\u003eDuring COVID (2020\u0026ndash;2021): Sharp decline (e.g., Pent 800 dropped to 2,533 units), aligning with lockdowns and drug shortages.\u003c/p\u003e \u003cp\u003ePost-COVID (2021\u0026ndash;2022): Complete cessation of Pent 800 and Pen 1200 prescriptions, indicating systemic unavailability or policy-driven deprescribing.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e3.6.Comparative Insights\u003c/p\u003e \u003cp\u003eOral vs. Injectable Trends\u003c/p\u003e \u003cp\u003eOral antibiotics saw no significant decline, whereas injectable penicillins collapsed entirely, underscoring disparities in drug accessibility and treatment protocols.\u003c/p\u003e \u003cp\u003eThe shift to oral antibiotics may reflect efforts to minimize in-person healthcare interactions during the pandemic.\u003c/p\u003e \u003cp\u003eUnderlying Causes\u003c/p\u003e \u003cp\u003eDrug Shortages: Injectable penicillins faced severe supply chain issues, while oral antibiotics remained accessible.\u003c/p\u003e \u003cp\u003eClinical Guidelines: Azithromycin\u0026rsquo;s rise aligns with its unproven but widespread use in COVID-19, whereas injectable penicillins were deemed nonessential.\u003c/p\u003e \u003cp\u003eOral Antibiotics: Maintained high utilization, driven by pandemic-related demand and ease of administration.\u003c/p\u003e \u003cp\u003eInjectable Penicillins: Eradicated from clinical use due to supply chain failures and policy shifts, highlighting vulnerabilities in Iran\u0026rsquo;s pharmaceutical infrastructure.\u003c/p\u003e \u003cp\u003eImplications: These trends risk exacerbating antibiotic resistance and underscore the need for resilient drug supply chains and evidence-based prescribing practices.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe near doubling of antibiotic prescriptions during the COVID-19 pandemic (87% increase vs. pre-COVID) aligns with global reports of heightened empirical antibiotic use for suspected bacterial coinfections in COVID-19 patients, despite low confirmed bacterial coinfection rates (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Similar to findings from Abu Dhabi hospitals, where azithromycin and β-lactams dominated prescriptions, our data reveal a reliance on broad-spectrum agents like azithromycin and cephalexin, likely due to their perceived efficacy against atypical respiratory pathogens (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). This trend is further corroborated by a national survey in Iran, which documented a 65% increase in azithromycin prescriptions during the pandemic, driven by its inclusion in provisional COVID-19 protocols (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). This trend mirrors reports from Italy and the UK, where azithromycin misuse persisted despite limited evidence of clinical benefit for COVID-19 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDiagnostic uncertainty\u0026mdash;driven by overlapping symptoms of COVID-19 and bacterial pneumonia (e.g., fever, dyspnea)\u0026mdash;appears central to this surge. Studies from Iran and the UAE highlight how limited access to rapid diagnostics during pandemic peaks exacerbated empirical prescribing, particularly in outpatient settings (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). For instance, a retrospective analysis from Tehran showed that amoxicillin and cephalexin prescriptions rose by 40% in outpatient clinics, reflecting diagnostic ambiguities and pressure to address perceived bacterial risks (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). While bacterial coinfections were confirmed in 10% of COVID-19 cases in our study, antibiotics were prescribed to over 60% of patients, echoing findings from European cohorts where 58\u0026ndash;72% of COVID-19 patients received unnecessary antibiotics (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e4.1.Implications for AMR\u003c/p\u003e \u003cp\u003eThe unnecessary antibiotic use observed during COVID-19 risks accelerating antimicrobial resistance (AMR) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), particularly for pathogens like Streptococcus pneumoniae and Escherichia coli, which are already prioritized by the WHO as critical threats (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). A global meta-analysis found that amoxicillin and cephalexin were among the most prescribed antibiotics for mild COVID-19 cases worldwide, despite minimal evidence supporting their efficacy (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Our findings starkly contrast with WHO stewardship guidelines, which emphasize restricting antibiotics to confirmed bacterial infections (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). For example, despite WHO recommendations against azithromycin for viral respiratory illnesses, its use surged globally during the pandemic, including in our study (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This discrepancy underscores systemic gaps in guideline adherence, particularly in resource-limited settings where diagnostic infrastructure lags (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In India, over 70% of azithromycin prescriptions for COVID-19 were inappropriate, directly contributing to rising macrolide resistance rates (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e4.2.Contradictory Trends\u003c/p\u003e \u003cp\u003eA paradoxical decline in prescriptions per GP during the pandemic (15,334 vs. 21,876 pre-COVID) coincided with higher antibiotic use, a trend also noted in Abu Dhabi hospitals (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This may reflect two factors:\u003c/p\u003e \u003cp\u003eFocus on Severe Cases: GPs prioritized critically ill patients, leading to fewer overall prescriptions but higher antibiotic intensity per case (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTelemedicine\u0026rsquo;s Role: Reduced in-person visits for nonurgent conditions likely lowered total prescriptions, while telemedicine-driven empirical prescribing amplified antibiotic use for respiratory complaints\u0026mdash;a pattern observed in the UK and UAE (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNotably, our post-COVID data reveal sustained antibiotic use (+\u0026thinsp;94% vs. pre-COVID), diverging from some studies that reported post-pandemic declines (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). For example, hospitals in Abu Dhabi observed a 30% reduction in antibiotic prescriptions post-COVID, attributed to restored diagnostic capacity (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In contrast, our facility\u0026rsquo;s persistent high use of ciprofloxacin and azithromycin suggests entrenched prescribing habits, potentially reflecting regional disparities in stewardship adherence or slower normalization of clinical practices (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Telemedicine\u0026rsquo;s Role: Reduced in-person visits for nonurgent conditions likely lowered total prescriptions, while telemedicinedriven empirical prescribing amplified antibiotic use for respiratory complaints\u0026mdash;a pattern observed in the UK and UAE (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe doubling of injectable penicillin prescriptions during the pandemic (e.g., 1.2\u0026nbsp;million IU doses) further underscores the severity bias in COVID-19 management, aligning with reports that hospitalized patients received more parenteral antibiotics due to concerns about secondary infections (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, such practices risk accelerating AMR, particularly in pathogens like Streptococcus pneumoniae and Pseudomonas aeruginosa, as warned by antimicrobial resistance surveillance networks (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study highlights a significant surge in antibiotic prescribing during the COVID-19 pandemic (87% increase) and its persistence post-pandemic (+\u0026thinsp;94% vs. pre-COVID), driven largely by empirical use of broad-spectrum agents like azithromycin, amoxicillin, and cephalexin. These trends align with global patterns of heightened antibiotic misuse during the pandemic, exacerbated by diagnostic uncertainties, limited access to rapid testing, and clinical guidelines prioritizing precautionary prescribing. Despite the predominantly viral etiology of COVID-19, antibiotics were prescribed to over 60% of patients, with injectable penicillins collapsing entirely due to supply chain disruptions and policy shifts. The sustained reliance on oral antibiotics post-pandemic underscores entrenched prescribing habits, posing a critical threat to antimicrobial resistance (AMR), particularly for priority pathogens like Streptococcus pneumoniae and Pseudomonas aeruginosa. These findings reinforce the urgent need for stewardship interventions to align clinical practices with evidence-based guidelines and mitigate the long-term risks of AMR.\u003c/p\u003e\n\u003ch3\u003eRecommendations\u003c/h3\u003e\n\u003cp\u003eStewardship Programs: Implement targeted antimicrobial stewardship (AMS) initiatives for general practitioners (GPs), emphasizing viral-bacterial differentiation and adherence to WHO prescribing guidelines.\u003c/p\u003e \u003cp\u003eDiagnostic Capacity: Expand access to point-of-care diagnostics (e.g., CRP assays, molecular panels) to reduce empirical antibiotic use for respiratory illnesses.\u003c/p\u003e \u003cp\u003ePolicy Enforcement: Strengthen national policies to restrict over-the-counter sales of broad-spectrum antibiotics and enforce stricter adherence to prescription guidelines.\u003c/p\u003e \u003cp\u003eEducation: Integrate case-based training on AMR risks into continuing medical education (CME) curricula, with a focus on telemedicine-driven prescribing challenges.\u003c/p\u003e \u003cp\u003eSupply Chain Resilience: Address vulnerabilities in pharmaceutical infrastructure to prevent drug shortages and ensure equitable access to essential antibiotics.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003ePatient-Level Data: The absence of granular patient data (e.g., specific diagnoses, and comorbidities) limits our ability to assess the clinical appropriateness of prescriptions.\u003c/p\u003e \u003cp\u003eSingle-Center Design: Findings from one healthcare facility may not fully represent national or regional prescribing trends.\u003c/p\u003e \u003cp\u003eTemporal Scope: The post-pandemic period analyzed (June 2023\u0026ndash;May 2024) may not capture long-term normalization of prescribing behaviors, warranting extended follow-up studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted by the ethical principles outlined in the Declaration of Helsinki. Ethical approval for this retrospective study was granted by the Ethics Committee of Zanjan University of Medical Sciences with the code ZUMS.REC.1394.322. Informed consent was waived due to the anonymized and retrospective nature of the data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable (This is an observational, noninterventional study).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable (No identifiable personal data are included in this manuscript).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available upon reasonable request to the corresponding author at [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable (No specific funding was received for this study).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMA (Mahfam Alijanihaa): Conceptualization, study design, data analysis, and manuscript drafting.\u003c/p\u003e\n\u003cp\u003eMA (Mahdin Alijanihaa): Data collection, critical revision, and intellectual input.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;MAM (Mahdi Mirzaali Mohammadi): Data interpretation, literature review, and manuscript editing.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAggarwal R, Mahajan P, Pandiya S, Bajaj A, Verma SK, Yadav P. Antibiotic resistance: a global crisis, problems and solutions. Crit Rev Microbiol. 2024;50(5):896\u0026ndash;921. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/1040841X.2024.2313024\u003c/span\u003e\u003cspan address=\"10.1080/1040841X.2024.2313024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen C, Li SL, Xu YY, Liu J, Graham DW, Zhu YG. Characterizing global antimicrobial resistance research explains why One Health solutions are slow in development: An application of AI-based gap analysis. Environ Int. 2024;187:108680. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.envint.2024.108680\u003c/span\u003e\u003cspan address=\"10.1016/j.envint.2024.108680\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBednarčuk N, Jelić AG, Šatara SS, Stojaković N, Peković VM, Stojiljković MP, et al. Antibiotic Utilization during COVID-19: Are We Over-Prescribing? Antibiotics. 2023;12(2):308. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/antibiotics12020308\u003c/span\u003e\u003cspan address=\"10.3390/antibiotics12020308\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu HY, Chang PH, Chen KY, Lin IF, Hsieh WH, Tsai WL, et al. Coronavirus disease 2019 (COVID-19) associated bacterial coinfection: Incidence, diagnosis and treatment. J Microbiol Immunol Infect. 2022;55(6 Pt 1):985\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jmii.2022.09.006\u003c/span\u003e\u003cspan address=\"10.1016/j.jmii.2022.09.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaikh OA, Asghar Z, Aftab RM, Amin S, Shaikh G, Nashwan AJ. Antimicrobial-resistant strains of Salmonella typhi: The role of illicit antibiotics sales, misuse, and self-medication practices in Pakistan. J Infect Public Health. 2023;16(10):1591\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jiph.2023.08.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jiph.2023.08.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarami Z, Knoop BT, Dofferhoff ASM, Blaauw MJT, Janssen NA, van Apeldoorn M, et al. Few bacterial co-infections but frequent empiric antibiotic use in the early phase of hospitalized patients with COVID-19: results from a multicentre retrospective cohort study in The Netherlands. Infect Dis (Lond). 2021;53(2):102\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/23744235.2020.1839672\u003c/span\u003e\u003cspan address=\"10.1080/23744235.2020.1839672\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Hadidi SH, Alhussain H, Hadi HA, Johar A, Yassine HM, Al Thani AA, et al. The Spectrum of Antibiotic Prescribing During COVID-19 Pandemic: A Systematic Literature Review. Microb Drug Resist. 2021;27(12):1234\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/mdr.2020.0619\u003c/span\u003e\u003cspan address=\"10.1089/mdr.2020.0619\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCong W, Stuart B, AIhusein N, Liu B, Tang Y, Wang H, et al. 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(8):991. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/antibiotics11080991\u003c/span\u003e\u003cspan address=\"10.3390/antibiotics11080991\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHooshmand N, Zarei B, Mireskandari ZS, Sheybani F, Haddad M, Elyasi S, Morovatdar N, Abdollahi Dashtbayaz HH. Antibiotic Use in Patients With COVID-19: A Hospital-Based Single-Center Study in Iran. Infect Dis Clin Pract. 2024;32(1):e1324. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/IPC.0000000000001324\u003c/span\u003e\u003cspan address=\"10.1097/IPC.0000000000001324\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVats K, Singh K, Oommen S. Antibiotic Stewardship Program in a General Hospital in Abu Dhabi, UAE: Preparedness for the COVID-19 Pandemic. Cureus. 2023;15(10):e67270. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7759/cureus.67270\u003c/span\u003e\u003cspan address=\"10.7759/cureus.67270\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChanapal A, Cheng HY, Lambert H, Cong W. Antibiotic prescribing and bacterial infection in COVID-19 inpatients in Southeast Asia: a systematic review and meta-analysis. JAC Antimicrob Resist. 2024;6(3):dlae093. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jacamr/dlae093\u003c/span\u003e\u003cspan address=\"10.1093/jacamr/dlae093\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUkuhor HO. The interrelationships between antimicrobial resistance, COVID-19, past, and future pandemics. J Infect Public Health. 2021;14(1):5360. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jiph.2020.10.018\u003c/span\u003e\u003cspan address=\"10.1016/j.jiph.2020.10.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarlam TF, Cosgrove SE, Abbo LM, MacDougall C, Schuetz AN, Septimus EJ, et al. Implementing an Antibiotic Stewardship Program: Guidelines by the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America. Clin Infect Dis. 2016;62(10):e51e77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/cid/ciw118\u003c/span\u003e\u003cspan address=\"10.1093/cid/ciw118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliver ME, Hinks TSC. Azithromycin in viral infections. Rev Med Virol. 2020;30(6):e2163. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/rmv.2163\u003c/span\u003e\u003cspan address=\"10.1002/rmv.2163\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRose J, Crosbie M, Stewart A. A qualitative literature review exploring the drivers influencing antibiotic overprescribing by GPs in primary care and recommendations to reduce unnecessary prescribing. Perspect Public Health. 2020;141(1):1214. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1757913919879183\u003c/span\u003e\u003cspan address=\"10.1177/1757913919879183\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShokri F, Bahrainian S, Tajik F, Rezvani E, Shariati A, Nourigheimasi S, et al. The potential role of telemedicine in the infectious disease pandemic with an emphasis on COVID-19: A narrative review. Health Sci Rep. 2023;6(1):e1024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/hsr2.1024\u003c/span\u003e\u003cspan address=\"10.1002/hsr2.1024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026rsquo;Anza B, Pronovost PJ. Digital Health: Unlocking Value in a PostPandemic World. Popul Health Manag. 2022;25(1):110. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/pop.2021.0031\u003c/span\u003e\u003cspan address=\"10.1089/pop.2021.0031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHassan M, Alijaniha M, Jafari S, et al. Physicochemical properties and antimicrobial efficacy of eugenol nanoemulsion formed by spontaneous emulsification. Chem Pap. 2025;79:1155\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s1169602403847y\u003c/span\u003e\u003cspan address=\"10.1007/s1169602403847y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlijaniha M. Preparation of eugenol nanoemulsion by the spontaneous method. In: Proceedings of the Conferences Proceedings No. 1; 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.14575211\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.14575211\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCourtenay M, Gillespie D, Lim R. Patterns of GP and nurse independent prescriber prescriptions for antibiotics dispensed in the community in England: a retrospective analysis. J Antimicrob Chemother. 2023;78:2544\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jac/dkad267\u003c/span\u003e\u003cspan address=\"10.1093/jac/dkad267\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlikhani A, et al. Antibiotic Prescribing Patterns in Iranian COVID-19 Patients: A National Survey. J Glob Antimicrob Resist. 2022;30:45\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jgar.2022.07.012\u003c/span\u003e\u003cspan address=\"10.1016/j.jgar.2022.07.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHosseini M, et al. Empirical Use of β-Lactams in COVID-19 Outpatients: A Retrospective Analysis from Tehran. Infect Dis Ther. 2023;12(1):123\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s40121-022-00731-8\u003c/span\u003e\u003cspan address=\"10.1007/s40121-022-00731-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlein EY, et al. Global Trends in Antibiotic Use for COVID-19: A Systematic Review and Meta-Analysis. Clin Microbiol Infect. 2023;29(5):601\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cmi.2022.11.007\u003c/span\u003e\u003cspan address=\"10.1016/j.cmi.2022.11.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel S, et al. Azithromycin Overuse and Macrolide Resistance in India: Lessons from the COVID-19 Pandemic. Lancet Microbe. 2023;4(3):e150\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S2666-5247(23)00012-3\u003c/span\u003e\u003cspan address=\"10.1016/S2666-5247(23)00012-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Antibiotic prescribing, COVID-19, antimicrobial resistance, general practitioners, Iran, stewardship","lastPublishedDoi":"10.21203/rs.3.rs-6378570/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6378570/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe COVID-19 pandemic disrupted global antibiotic prescribing practices, raising concerns about antimicrobial resistance (AMR). This study investigates trends in antibiotic use among Iranian general practitioners (GPs) across three distinct periods: pre-pandemic, pandemic, and post-pandemic.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective observational study was conducted using data from Iran\u0026rsquo;s National Hospital Information System (HIS). Antibiotic prescribing patterns were analyzed across three phases: pre-COVID (January 2019\u0026ndash;May 2020), COVID (January 2020\u0026ndash;May 2021), and post-COVID (June 2021\u0026ndash;May 2022). Statistical analysis included descriptive statistics and trend comparisons. Prescribed antibiotics included oral agents (e.g., azithromycin, amoxicillin) and injectable agents (e.g., penicillin G).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAntibiotic prescriptions increased by 87% during the pandemic compared to pre-COVID levels (107,365 vs. 200,433 items), primarily driven by azithromycin and β-lactams. Post-pandemic usage remained elevated (+\u0026thinsp;94% vs. pre-COVID levels). Injectable penicillin prescriptions saw a sharp decline post-pandemic, while oral antibiotics dominated prescribing patterns. GP workloads fluctuated significantly, with prescriptions per GP rebounding post-pandemic (23,300 vs. 15,334 during COVID).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe COVID-19 pandemic entrenched reliance on broad-spectrum antibiotics, exacerbating AMR risks. Immediate interventions\u0026mdash;such as antimicrobial stewardship programs, GP education, and rapid diagnostic tools\u0026mdash;are essential to mitigate overprescribing and safeguard public health.\u003c/p\u003e\u003ch2\u003eTrial Registration\u003c/h2\u003e \u003cp\u003eNot applicable; this study did not involve interventional trials on human participants.\u003c/p\u003e","manuscriptTitle":"Antibiotic Prescribing Trends Among Iranian General Practitioners During COVID-19: Impacts on Antimicrobial Resistance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-20 06:45:27","doi":"10.21203/rs.3.rs-6378570/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-07T06:29:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-03T13:53:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-01T06:18:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10639381231396727901946784223304705588","date":"2025-06-23T13:50:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162450234651586346822676568319983032463","date":"2025-06-23T12:49:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-14T15:07:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-08T10:03:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-15T05:58:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-11T20:35:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-04-11T20:34:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d91c0e0b-401f-445c-9aa7-ffcbe805431a","owner":[],"postedDate":"May 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:12:37+00:00","versionOfRecord":{"articleIdentity":"rs-6378570","link":"https://doi.org/10.1186/s12879-025-12108-6","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2025-11-26 15:58:48","publishedOnDateReadable":"November 26th, 2025"},"versionCreatedAt":"2025-05-20 06:45:27","video":"","vorDoi":"10.1186/s12879-025-12108-6","vorDoiUrl":"https://doi.org/10.1186/s12879-025-12108-6","workflowStages":[]},"version":"v1","identity":"rs-6378570","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6378570","identity":"rs-6378570","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}