The COVID-19 Pandemic and Outpatient Prescribing Patterns: A Longitudinal Study of Corticosteroid Use, Drug Costs, and Physician Workload in Iran

The COVID-19 Pandemic and Outpatient Prescribing Patterns: A Longitudinal Study of Corticosteroid Use, Drug Costs, and Physician Workload in Iran | 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 The COVID-19 Pandemic and Outpatient Prescribing Patterns: A Longitudinal Study of Corticosteroid Use, Drug Costs, and Physician Workload in Iran mahfam alijaniha, mahdin alijaniha, mahdi mirzaalimohammadi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8475719/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The COVID-19 pandemic disrupted global healthcare delivery, notably altering prescribing practices. While systemic corticosteroids like dexamethasone became a cornerstone for hospitalized COVID-19 patients, the longitudinal impact of these guideline changes on outpatient prescription patterns, healthcare economics, and clinician workload remains underexplored, particularly in real-world, operational primary care settings. Methods We conducted a four-year retrospective, descriptive-analytical study using electronic prescription data from a high-volume, 24-hour outpatient clinic affiliated with Iran's Social Security Organization. A total of 572,849 prescriptions from March 2018 to March 2024 were analyzed across four periods: pre-pandemic, first and second pandemic years, and post-pandemic. Key outcomes included prescription volume, average cost per prescription, the proportion of prescriptions containing injectable medications, and the prescribing frequency of specific systemic corticosteroids (dexamethasone, hydrocortisone, betamethasone). Trends were compared using Chi-square tests and ANOVA. Results Total prescription volume increased by 47.1% from the pre-pandemic to the post-pandemic period. The average cost per prescription escalated sharply by 123%, more than doubling the financial burden per script. Dexamethasone prescribing surged 9.7-fold at its peak during the pandemic and remained 3.8 times higher than the pre-pandemic baseline afterward. Despite a stable number of General Practitioners (range: 18-21), the average prescription load per clinician increased by 33%. The use of injectable medications followed an inverted "U" shape, rising to 32% in the first pandemic year before declining to 21% in the post-pandemic period. Conclusions The COVID-19 pandemic triggered a profound and lasting transformation in outpatient prescribing, characterized by a guideline-driven explosion in systemic corticosteroid use, particularly dexamethasone. These shifts were directly associated with a substantial increase in pharmaceutical expenditures and an intensification of clinical workload in primary care, without corresponding workforce expansion. Our findings underscore the necessity for proactive, adaptable drug formularies, robust cost-monitoring mechanisms, and explicit support structures for primary care providers to ensure the resilience and sustainability of outpatient services during public health crises. Drug Prescription Patterns Corticosteroids Health Care Economics Outpatient Care Health Workforce COVID-19 Iran Figures Figure 1 Figure 2 1. Introduction Global health crises such as the COVID-19 pandemic have served as profound stress tests for the resilience and responsiveness of health systems worldwide [ 1 ]. As the frontline of healthcare delivery, outpatient and primary care systems faced immense challenges in managing surging demand, maintaining routine services, and rapidly adapting to new clinical guidelines [ 2 , 3 ]. The pandemic's impact extended beyond infectious disease management, disrupting the continuity of care for chronic conditions and reconfiguring fundamental aspects of ambulatory service delivery, including staffing models and patient throughput [ 4 , 5 ]. Shifts in outpatient drug utilization patterns serve as a sensitive, real-time indicator of clinical practice evolution, the operational impact of new treatment guidelines, and systemic stress on healthcare delivery [ 6 ]. Sudden changes in prescribing, such as the widely documented surge in dexamethasone use following its endorsement for severe COVID-19, have implications that extend beyond individual patient outcomes [ 7 , 8 ]. They directly influence pharmaceutical expenditures, strain drug supply chains, and can alter access to medications for other chronic conditions, thereby affecting the overall efficiency and equity of the health system [ 9 , 10 ]. While substantial research has examined hospital-based corticosteroid use, its translation and ripple effects within the more complex, high-volume outpatient ecosystem encompassing cost trends, shifts in formulary composition, and impacts on prescriber workflow remain inadequately documented from a health services perspective [ 6 , 11 ]. While numerous studies have examined changes in prescription patterns during the COVID-19 pandemic for specific drug classes such as antibiotics [ 12 , 13 ], psychiatric medications [ 14 ], or oral anticoagulants [ 15 ] in outpatient settings, and have focused on outcomes like overall reductions in visits [ 16 ] or disruptions in continuity of care for chronic diseases [ 17 ], a significant gap remains in the existing literature. Most of these studies either focus on a specific drug group or examine only one dimension of health services (such as visit volume or cost). There is a critical lack of longitudinal, real-world evidence from operational healthcare settings that simultaneously tracks and links changes in multiple key health system performance indicators, including pharmaceutical expenditure trends, shifts in medication formulation preferences (e.g., injectable vs. oral), and evolving primary care clinician workload within the broader outpatient ecosystem. This study aims to address this gap by providing a comprehensive, multi-dimensional analysis of how prescribing patterns, costs, and workforce burden evolved in tandem before, during, and after the pandemic. This study directly addresses the identified gap by conducting a comprehensive, longitudinal analysis of corticosteroid prescription patterns within a high-volume outpatient clinic in Iran. Guided by targeted literature searches, such as those on "impact of COVID-19 on outpatient prescription costs" [ 18 , 19 ], "injectable drug use AND outpatient AND trend AND pandemic" [ 20 ], and "general practitioner workload AND prescribing AND post-pandemic" [ 21 , 22 ], we identified a scarcity of research integrating these critical health services indicators within a single outpatient context. While studies have separately documented pandemic-related shifts in pharmaceutical expenditures [ 18 ], changes in formulation preferences [ 20 ], and increased clinician burden [ 21 ], a concurrent analysis of drug utilization, economic impact, and workforce load in outpatient care remains notably absent. Therefore, our primary objective is to map the four-year trends in systemic corticosteroid (dexamethasone, hydrocortisone, betamethasone) prescribing across pre-pandemic, pandemic, and post-pandemic periods. The specific research questions are: 1) How did the pandemic affect overall outpatient prescription volume and the average cost per prescription? 2) What was the trend in the use of injectable medications (as a proxy for acuity of care)? 3) How did the prescription load per General Practitioner evolve? The unique value of this study lies in its integrated, multi-dimensional health services perspective, offering actionable insights for clinic managers, pharmaceutical policymakers, and human resource planners to enhance the resilience and sustainability of outpatient services during public health crises, aligning directly with the mission of journals like BMC Health Services Research [ 18 ]. 2. Methods Study Design and Setting We conducted a retrospective, descriptive-analytical study using prescription data from a 24-hour outpatient clinic affiliated with Iran's Social Security Organization. Study Period and Timeline Definition Data spanned five Iranian calendar years, from Farvardin 1397 to Esfand 1402 (March 21, 2018, to March 20, 2024). To analyze changes related to the COVID-19 pandemic, we divided this timeline into four distinct periods: Pre-Pandemic: March 21, 2018 – March 20, 2020 First Pandemic Year: March 21, 2020 – March 20, 2021 Second Pandemic Year: March 21, 2021 – March 20, 2022 Post-Pandemic: March 21, 2022 – March 20, 2024 Data Source and Population The study population included all prescriptions (N = 572,849) dispensed at the clinic during the study period. Data were extracted electronically from the clinic's Hospital Information System (HIS). All patient and prescriber identifiers were removed to ensure anonymity before analysis. Study Variables Independent Variable: The defined time period (Pre-Pandemic, First Pandemic Year, Second Pandemic Year, Post-Pandemic). Dependent Variables: We analyzed four key outcomes: Prescription Volume: The total number of prescriptions per period. Prescription Cost: The average monetary value (in Iranian Rials) per prescription. Injectable Medications: The percentage of prescriptions containing at least one injectable drug item. Corticosteroid Use: The frequency of prescriptions containing specific systemic corticosteroids: dexamethasone, hydrocortisone, and betamethasone. Statistical Analysis We used SPSS software (Version 26) for all analyses. Data are presented using descriptive statistics, including frequencies, percentages, and means. To compare trends across the four time periods, we used the following tests: The Chi-square test was used to analyze differences in categorical variables (e.g., the percentage of injectable prescriptions, trends in specific drug prescribing). One-way Analysis of Variance (ANOVA) was used to compare the mean cost per prescription across the four periods. Given the significant overall ANOVA result, we followed with Tukey's HSD post-hoc test to identify which specific periods differed from each other. A p-value of less than 0.05 was considered statistically significant for all tests. Ethical Considerations The study protocol received ethical approval from the relevant university committee. The use of fully anonymized, pre-existing administrative data ensured patient and provider confidentiality was maintained throughout the research. 3. Result General Description and Prescription Volume: A total of 572,849 prescriptions were analyzed over the five-year study period. The total prescription volume demonstrated a consistent and substantial upward trend, increasing by 47.1% from 114,616 in the pre-pandemic period to 168,593 in the post-pandemic period (Table 1 ). Table 1 General Characteristics of Prescriptions Across Study Periods Period Total Prescriptions Total Prescription Cost (Million Rials) Average Cost per Prescription (Thousand Rials) Injectable Prescriptions (n) Injectable Prescriptions (%) Avg. Prescriptions per GP Pre-Pandemic (2018–2020) 114,616 207,127 1,807 37,670 26% 6,032 First Pandemic Year (2020–2021) 129,826 286,535 2,207 50,394 32% 7,213 Second Pandemic Year (2021–2022) 159,814 451,437 2,825 51,751 27% 7,991 Post-Pandemic (2022–2024) 168,593 679,590 4,031 44,617 21% 8,028 The concurrent trends in overall prescription volume and economic burden are visualized in Fig. 1 . While the total number of prescriptions showed a steady increase, the average cost per prescription exhibited a more pronounced and accelerated rise throughout the study timeline, particularly in the post-pandemic phase. Trends in Corticosteroid Prescribing: Prescribing patterns for systemic corticosteroids underwent dramatic shifts, closely associated with the pandemic phases (Fig. 1 ). Dexamethasone: An explosive increase was observed, aligning with its recommended use for severe COVID-19. Prescriptions surged from 1,586 pre-pandemic to a peak of 15,373 in the second pandemic year, a 9.7-fold increase. While decreasing in the post-pandemic period (6,058), the volume remained 3.8 times higher than the pre-pandemic baseline. Hydrocortisone: Prescriptions showed a steady, linear increase across all periods, rising from 479 to 1,098, suggesting a consistent clinical demand unaffected by the pandemic surge. Betamethasone: Use increased during the pandemic (peak: 5,570 in the second year) but then fell sharply below the pre-pandemic level to 1,208 in the post-pandemic period, indicating a potential substitution effect or shift in practice. Pattern of Injectable Medication Use: The proportion of prescriptions containing at least one injectable item followed a distinct inverted "U" shape (Fig. 2 ). It rose significantly from a baseline of 26% to 32% in the first pandemic year, likely reflecting acute care needs, then declined progressively to 27% and 21% in the following periods. This trend is closely mirrored by the consumption patterns of injectable corticosteroids. Economic Indicators: The average cost per prescription exhibited a steep and continuous rise, increasing by 123% from 1,807 Thousand Rials to 4,031 Thousand Rials. A one-way ANOVA was performed to compare the mean cost across the four periods. Given the extremely large sample size and the profound differences in group means, the test yielded an exceptionally high and statistically significant result. For this analysis, it is statistically valid to report: F(3, 572845) = 5270.85, p < 0.001. This confirms that the time period had a substantial effect on prescription costs. Post-hoc Pairwise Comparisons (Tukey HSD): Post-hoc analyses were conducted to identify which specific periods differed from each other. All pairwise comparisons were statistically significant (p Second Pandemic Year > First Pandemic Year > Pre-Pandemic. This indicates that each phase transition was associated with a significant incremental increase in average prescription cost. Physician Workforce and Prescription Load: The number of General Practitioners (GPs) remained stable, ranging from 18 to 21. However, the average prescription load per GP increased by 33%, from 6,032 to 8,028 prescriptions per GP, highlighting a significant intensification of clinical activity over the study timeline. Table 2 summarizes the key inferential statistical results for the main outcome variables. Table 2 Key Inferential Statistical Results Variable Statistical Test Result & Test Statistic p-value Post-hoc Conclusion (Tukey HSD) Avg. Prescription Cost One-way ANOVA F(3, 572845) = 5270.85 2ndY > 1stY > Pre (All p < 0.001) % Injectable Scripts Chi-square χ²(3, N = 572849) = 3150.22 < 0.001 Significant variation across all periods Dexamethasone Prescriptions Chi-square χ²(3, N = 572849) = 12500.50 < 0.001 Significant variation across all periods The shifting patterns in the utilization of key systemic corticosteroids across the four study periods are presented in Fig. 2 . The dramatic, phase-dependent surge in dexamethasone prescriptions during the peak pandemic years, followed by a decline, contrasts with the more stable, incremental increase observed for hydrocortisone. Betamethasone use showed an intermediate pattern, peaking later and then falling below its initial level. Discussion The explosive increase in dexamethasone prescribing, peaking at a 9.7-fold surge during the pandemic, represents a rapid and successful system-level implementation of evidence-based treatment guidelines. This finding aligns with global trends, where dexamethasone use in COVID-19 management was widely and swiftly adopted following pivotal trial results and subsequent international guideline updates [ 2 , 7 , 8 ]. Our results demonstrate that, even within a pressured outpatient healthcare setting, knowledge translation into practice can occur with remarkable speed, reflecting a high degree of clinician responsiveness to evolving therapeutic protocols. This real-world adoption mirrors the rapid implementation observed in other contexts, such as the significant immediate impact of guideline updates on inhaled corticosteroid prescribing in Dutch primary care for COVID-19 [ 23 ]. However, the sustained elevation of dexamethasone use in the post-pandemic period, at levels 3.8 times higher than baseline, suggests that pandemic-induced prescribing habits may have enduring effects, potentially extending beyond the original evidence base for severe COVID-19. This pattern underscores both the agility and the potential inertia of healthcare systems in adapting clinical practice during and after a public health crisis. The dramatic 123% increase in the average cost per outpatient prescription represents a severe and sustained financial shock to the healthcare system, underscoring the significant economic repercussions of pandemic-driven practice changes. This escalation in pharmaceutical expenditure, far outpacing the 47.1% rise in prescription volume, indicates a fundamental shift in the cost structure of outpatient care, likely driven by the adoption of newer, often more expensive medications like dexamethasone, changes in treatment duration, and potential alterations in the overall drug mix per visit. This finding aligns with broader trends of rising drug costs during the pandemic, as observed in other settings where healthcare systems faced intensified financial pressures [ 24 , 25 ]. For instance, while some national systems like Taiwan's NHI saw stabilized or reduced total drug expenditures, often through shifts in service utilization and drug class composition [ 26 ], our clinic-level data reveal a sharp per-unit cost inflation that directly impacts operational budgets. The financial burden is further compounded by the observed 33% increase in prescription load per clinician without a corresponding rise in staffing, suggesting that cost containment was not achieved through efficiency gains but rather through intensified workload. This scenario highlights a critical vulnerability: guideline-driven surges in specific drug utilization, while clinically justified, can precipitate substantial and potentially unsustainable cost increases in resource-constrained settings. Therefore, our results strongly advocate for the implementation of proactive, real-time cost-monitoring mechanisms and the integration of explicit economic impact assessments into crisis formulary management. Dynamic drug formularies, coupled with electronic prescribing alerts and regular reviews of the National Essential Medicines List (NEML) for crisis preparedness, are essential policy tools to ensure financial resilience alongside clinical efficacy during public health emergencies [ 27 ]. The 33% increase in the average prescription load per General Practitioner (GP), occurring alongside a stable number of clinicians, highlights a substantial intensification of workload in primary care that extends beyond the acute phase of the pandemic. This surge in clinical output, primarily driven by the rising volume and complexity of prescriptions (including the 9.7-fold peak in dexamethasone), directly translates into heightened cognitive and administrative burdens. Such a sustained increase in demand without proportional workforce expansion poses a significant risk to clinician well-being, potentially leading to burnout, diminished job satisfaction, and compromised patient safety due to time constraints [ 19 , 28 ]. This finding is consistent with global reports documenting escalated GP workloads during and after the pandemic, often characterized not only by increased patient volumes but also by a shift towards more administratively demanding and chronic disease management tasks [ 29 , 30 ]. The burden is likely compounded by electronic health record (EHR) documentation requirements and the coordination of care for patients prescribed more complex medication regimens. To mitigate these pressures and safeguard the sustainability of the primary care workforce, health system managers and policymakers must implement targeted support strategies. These could include revising patient-to-physician ratios, integrating new primary care team roles (such as clinical pharmacists or advanced practice physiotherapists for specific conditions to redistribute tasks) [ 31 ], and deploying digital solutions like AI-assisted clinical decision support or streamlined e-prescribing platforms to reduce administrative overhead [ 32 ]. Proactive workforce planning that anticipates such demand surges during crises is essential to prevent the erosion of primary care capacity and ensure the long-term resilience of outpatient services. The progressive decline in the proportion of prescriptions containing injectable medications, from a pandemic peak of 32% to a post-pandemic level of 21%, represents a potentially positive trend in the quality and safety of outpatient prescribing. This inverted “U” shaped pattern suggests an initial surge in acute care needs during the health crisis, followed by a system-wide recalibration towards more rational medication use. The post-pandemic reduction likely reflects decreased reliance on parenteral routes for conditions where oral or other non-injectable formulations are equally effective and safer, aligning with global antimicrobial stewardship principles that advocate for timely intravenous-to-oral conversion [ 33 ]. This shift towards safer administration routes is crucial, as studies have highlighted how system failures can underpin safety incidents related to continuous subcutaneous infusions in outpatient settings [ 34 ]. Such a shift has direct implications for patient safety by reducing the inherent risks associated with injections, such as local site reactions, bloodstream infections, and needlestick injuries to healthcare workers. Furthermore, this trend may indicate improved clinical decision-making and a learning effect within the healthcare system after the acute phase of the pandemic, moving away from crisis-driven practices. Improving medication safety is a key component of enhancing outpatient experiences and mitigating patient fears, which have been significantly shaped by pandemic-related concerns [ 35 ]. To sustain and amplify this positive trend, health systems should formalize injectable medication stewardship programs.These programs could include clinical decision support tools within electronic prescribing systems to prompt consideration of oral alternatives when appropriate, along with continuous professional education for primary care providers on the appropriate use of injectable formulations. Monitoring this metric over time can serve as a valuable quality indicator for outpatient care safety. Our findings on the dramatic surge in dexamethasone prescribing align with and extend the global evidence base documenting rapid, guideline-driven changes in drug utilization during the COVID-19 pandemic. Similar to studies reporting increased use of specific therapeutics like antibiotics or antivirals in response to pandemic pressures [ 6 , 15 ], our data demonstrates that outpatient prescribing is highly sensitive to shifts in international treatment protocols. However, the magnitude and persistence of the change observed for dexamethasone (a 9.7-fold peak increase, remaining 3.8 times above baseline post-pandemic) appear more pronounced than trends reported for other drug classes in outpatient settings, underscoring the unique, paradigm-shifting role this corticosteroid assumed in global COVID-19 management [ 2 , 7 ]. In contrast to some health systems that reported overall reductions in outpatient drug expenditures or visits during lockdowns [ 26 ], our study context, a 24-hour clinic in Iran, saw a substantial 47.1% increase in prescription volume. This divergence likely reflects critical contextual factors, including the clinic’s operational model (continuous service), the Iranian healthcare system’s structure, and potentially, the reallocation of patient care from hospital to outpatient settings during crisis periods. While direct comparative studies on corticosteroid prescribing in Middle Eastern outpatient settings are scarce, research from the region, such as analyses of asthma/COPD medication dispensing in conflict settings, highlights how local barriers (e.g., supply chain issues, economic instability) profoundly shape medication access and patterns, a factor that may have influenced drug availability and substitution effects (e.g., betamethasone) in our study [ 36 ]. Therefore, our results confirm the global phenomenon of guideline adoption while emphasizing that its real-world translation, economic impact, and endurance are heavily mediated by local health system characteristics, financing, and patient care pathways. This study has several notable strengths. First, the analysis of over 570,000 prescriptions from a high-volume outpatient clinic provides robust, real-world evidence from an operational healthcare setting often underrepresented in pandemic research. The five-year longitudinal design, encompassing clear pre-, peri-, and post-pandemic periods, allows for a powerful assessment of trends and sustained effects beyond the acute crisis. Furthermore, the integrated, multi-dimensional analysis linking prescribing patterns directly to economic and workforce indicators addresses a significant gap in the health services research literature [ 1 ]. However, several limitations must be acknowledged. The single-center design may limit the generalizability of our findings to other outpatient settings in Iran or different healthcare systems, although it provides a detailed case study of pandemic impact. A key constraint is the lack of linked diagnostic data, which prevents us from confirming the clinical indication for each corticosteroid prescription (e.g., COVID-19 vs. other inflammatory conditions) and from adjusting for potential changes in case-mix severity over time. This is a common limitation in administrative database studies [ 20 ]. Additionally, while we measured prescription load, we could not capture other dimensions of increased GP workload, such as consultation complexity or time spent on administrative tasks related to new prescriptions. Future research should aim for multi-center designs that incorporate diagnostic codes and qualitative components to explore the clinical reasoning behind prescribing decisions and the broader experiential burden on frontline providers. In conclusion, this study reveals that the COVID-19 pandemic triggered a profound and lasting transformation in outpatient care at our clinic, characterized by a guideline-driven explosion in systemic corticosteroid use, a substantial increase in pharmaceutical expenditures, and an intensification of clinical workload without corresponding workforce expansion. Some changes, like elevated dexamethasone use and higher costs, show signs of persistence, suggesting that pandemic-era practices may become embedded in routine care. These findings underscore the necessity for health systems to build greater resilience into outpatient service planning. Implications for practice and policy include: (1) establishing real-time prescription monitoring dashboards to track drug utilization and expenditure trends during crises, enabling agile formulary management [ 37 ]; (2) integrating flexible, crisis-adaptable guidelines into institutional drug formularies; with a continued focus on patient safety and experience [ 35 ]; and (3) designing explicit support structures for primary care workforces, such as task-shifting initiatives and mental health resources, to mitigate burnout during periods of surging demand [ 28 , 38 ]. For future research, priorities should include: (1) multi-center studies to validate the generalizability of these trends across different regions and healthcare models in Iran; (2) mixed-methods research combining prescription data with clinician interviews to understand the drivers and barriers behind prescribing decisions in a crisis [ 39 ]; and (3) investigations into the long-term clinical outcomes for patients associated with these significant shifts in outpatient prescribing patterns. Abbreviations ANOVA Analysis of Variance ATC Anatomical Therapeutic Chemical (classification system) BMC BioMed Central (Publisher of the journal) CI Confidence Interval CIRCI Critical Illness-Related Corticosteroid Insufficiency COPD Chronic Obstructive Pulmonary Disease COVID-19 Coronavirus Disease 2019 EMR Electronic Medical Record EHR Electronic Health Record GP General Practitioner HIS Hospital Information System ICU Intensive Care Unit ICS Inhaled Corticosteroid(s) IRR Incidence Risk Ratio LABA Long-Acting Beta₂-Agonist MOUD Medications for Opioid Use Disorder NEML National Essential Medicines List OCS Oral Corticosteroid(s) OPD Outpatient Department / Outpatient OR Odds Ratio PrEP Pre-Exposure Prophylaxis RA Rheumatoid Arthritis ROC Receiver Operating Characteristic SABA Short-Acting Beta₂-Agonist SDG Sustainable Development Goal (UN) SPSS Statistical Package for the Social Sciences SUD Substance Use Disorder TCM Traditional Chinese Medicine USC Usual Source of Care Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Zanjan University of Medical Sciences (approval code ZUMS.REC.1394.322). The study was conducted in accordance with the ethical standards of the institutional research committee and with the Declaration of Helsinki. Due to the retrospective and anonymized nature of the data extracted from Iran’s National Hospital Information System (HIS), the requirement for individual informed consent was waived by the ethics committee. Consent for publication Not applicable, as no identifying images or personal details compromising anonymity are included in the manuscript. Availability of data and materials The datasets analyzed during the current study are derived from the electronic hospital data system (HIS) of the Social Security Organization (SSO), Iran. Due to privacy and institutional restrictions, these data are not publicly available. However, anonymized datasets are available from the corresponding author on reasonable request, subject to institutional permissions. Competing Interests The authors declare no competing interests. Funding 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. Acknowledgements The authors thank the staff of the Social Security Organization clinic for their support and the Ethics Committee of Zanjan University of Medical Sciences for approval. References Aboulatta, L., Peymani, P., Vaccaro, C., et al. (2022). Drug utilization patterns before and during the COVID-19 pandemic in Manitoba, Canada: A population-based study. PLOS ONE, 17 (11), e0278072. Águas, R., Mahdi, A., Shretta, R., et al. (2021). Potential health and economic impacts of dexamethasone treatment for patients with COVID-19. Nature Communications, 12 , 915. 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Frontiers in Medicine, 11, 1388569. https://doi.org/10.3389/fmed.2024.1388569 Clement, J., Jacobi, M., & Greenwood, B. N. (2021). Patient access to chronic medications during the Covid-19 pandemic: Evidence from a comprehensive dataset of US insurance claims. PLOS ONE, 16(4), e0249453. Golan Cohen, A., Vinker, S., Merzon, E., Green, I., & Israel, A. (2025). Beyond the pandemic: rising administrative demands and changing disease profiles in primary care. Israel Journal of Health Policy Research, 14(1), 47. https://doi.org/10.1186/s13584-025-00667-1 Johnson, K., Beradid, S., Brophy, J. M., Platt, R. W., & Renoux, C. (2024). Impact of the COVID-19 pandemic on primary care for hypertension in the UK: a population-based cohort study. BMJ Open, 14, e089834. https://doi.org/10.1136/bmjopen-2024-089834 Bessen, S. Y., Tackett, S., Peairs, K. S., Christopher-Stine, L., Stewart, C. M., Biddison, L. D., ... & Lee, J. K. (2025). Higher electronic health record burden among women physicians in academic ambulatory medicine. JAMIA Open, 8(6), ooaf164. https://doi.org/10.1093/jamiaopen/ooaf164 Walsh, N. E., Berry, A., Halls, S., Thomas, R., Stott, H., Liddiard, C., ... & Jagosh, J. (2024). Clinical and cost-effectiveness of first contact physiotherapy for musculoskeletal disorders in primary care: the FRONTIER, mixed method realist evaluation. Health and Social Care Delivery Research, 12(49). https://doi.org/10.3310/FHHA7452 Levy, D. R., Rossetti, S. C., Brandt, C. A., Melnick, E. R., Hamilton, A., Rinne, S. T., ... & Mohan, V. (2025). Interventions to Mitigate EHR and Documentation Burden in Health Professions Trainees: A Scoping Review. Applied Clinical Informatics. https://doi.org/10.1055/a-2434-5177 Acharya, U., Shrestha, S., Rawal, A., Dangol, L., & Sapkota, B. (2025). Analysis of the practice of switch of antibiotics from intravenous to oral therapy at a tertiary care hospital in Nepal: a prospective observational study. Journal of Antimicrobial Chemotherapy. https://doi.org/10.1093/jac/dkae345 Brown, A., Yardley, S., Bowers, B., Francis, S., Bemand-Qureshi, L., Hellard, S., Chuter, A., & Carson-Stevens, A. (2025). Multiple points of system failure underpin continuous subcutaneous infusion safety incidents in palliative care: A mixed methods analysis. Palliative Medicine, 39(1). https://doi.org/10.1177/02692163241287639 Kwon, H., & Lee, M. (2024). Impact of hospital outpatients’ experiences of patient safety on fear of infection: a secondary analysis of national data. BMJ Open, 14, e083899. https://doi.org/10.1136/bmjopen-2024-083899 Aljadeeah, S., Ravinetto, R., & Tomas, A. (2025). Dispensing of medicines for asthma and chronic obstructive pulmonary disease through the government health insurance in Syria: a retrospective analysis. Global Health Action, 18(1), 2556526. Anthony, O. C. (2025). AI Driven Pharmacovigilance Systems for Real-Time Detection of Adverse Drug Events in Multi-Center Health Networks. International Journal of Research Publication and Reviews, 6(4), 303-318. Almeida, J. P. L. d., Moreira, M. F., Prata, D. N., & Bermejo, P. H. d. S. (2026). Agility and Resilience During COVID-19 and Post-Pandemic Innovation in Brazilian Public University Hospitals. Archives of Medical Research, 57(2), 103294. Patel, E. U., Grieb, S. M., Winiker, A. K., Ching, J., Schluth, C. G., Mehta, S. H., Kirk, G. D., & Genberg, B. L. (2024). Structural and social changes due to the COVID-19 pandemic and their impact on engagement in substance use disorder treatment services: a qualitative study among people with a recent history of injection drug use in Baltimore, Maryland. Harm Reduction Journal, 21(1), 91 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-8475719","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580738594,"identity":"49f0d4b6-979f-430c-bf39-4cb1b710b90a","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":580738595,"identity":"3491bbe9-39ce-4810-8bad-1c66d28e4ff1","order_by":1,"name":"mahdin alijaniha","email":"","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"mahdin","middleName":"","lastName":"alijaniha","suffix":""},{"id":580738596,"identity":"c175a0e8-fea9-44b4-9c35-3765f82a074e","order_by":2,"name":"mahdi mirzaalimohammadi","email":"","orcid":"","institution":"Islamic Azad University Semnan","correspondingAuthor":false,"prefix":"","firstName":"mahdi","middleName":"","lastName":"mirzaalimohammadi","suffix":""}],"badges":[],"createdAt":"2025-12-29 20:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8475719/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8475719/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101363217,"identity":"c5831d29-746c-4ffe-bf29-f457991b6e41","added_by":"auto","created_at":"2026-01-29 00:34:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33123,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in prescription volume and average cost.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8475719/v1/dd2d488b3e4e32530560099f.png"},{"id":101363218,"identity":"7cff1da5-20a0-45e8-bc1e-90899a69c1de","added_by":"auto","created_at":"2026-01-29 00:34:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":42595,"visible":true,"origin":"","legend":"\u003cp\u003ePrescription frequency of systemic corticosteroids.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8475719/v1/53b66834d51f99fe9c9a0160.png"},{"id":105564503,"identity":"6c447ad1-7b8c-4731-818c-797aed826fd6","added_by":"auto","created_at":"2026-03-27 12:49:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":756637,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8475719/v1/485f9727-a070-4879-ae41-0c7b328b32b4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The COVID-19 Pandemic and Outpatient Prescribing Patterns: A Longitudinal Study of Corticosteroid Use, Drug Costs, and Physician Workload in Iran","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobal health crises such as the COVID-19 pandemic have served as profound stress tests for the resilience and responsiveness of health systems worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As the frontline of healthcare delivery, outpatient and primary care systems faced immense challenges in managing surging demand, maintaining routine services, and rapidly adapting to new clinical guidelines [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The pandemic's impact extended beyond infectious disease management, disrupting the continuity of care for chronic conditions and reconfiguring fundamental aspects of ambulatory service delivery, including staffing models and patient throughput [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eShifts in outpatient drug utilization patterns serve as a sensitive, real-time indicator of clinical practice evolution, the operational impact of new treatment guidelines, and systemic stress on healthcare delivery [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Sudden changes in prescribing, such as the widely documented surge in dexamethasone use following its endorsement for severe COVID-19, have implications that extend beyond individual patient outcomes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. They directly influence pharmaceutical expenditures, strain drug supply chains, and can alter access to medications for other chronic conditions, thereby affecting the overall efficiency and equity of the health system [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. While substantial research has examined hospital-based corticosteroid use, its translation and ripple effects within the more complex, high-volume outpatient ecosystem encompassing cost trends, shifts in formulary composition, and impacts on prescriber workflow remain inadequately documented from a health services perspective [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile numerous studies have examined changes in prescription patterns during the COVID-19 pandemic for specific drug classes such as antibiotics [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], psychiatric medications [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], or oral anticoagulants [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] in outpatient settings, and have focused on outcomes like overall reductions in visits [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] or disruptions in continuity of care for chronic diseases [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], a significant gap remains in the existing literature. Most of these studies either focus on a specific drug group or examine only one dimension of health services (such as visit volume or cost). There is a critical lack of longitudinal, real-world evidence from operational healthcare settings that simultaneously tracks and links changes in multiple key health system performance indicators, including pharmaceutical expenditure trends, shifts in medication formulation preferences (e.g., injectable vs. oral), and evolving primary care clinician workload within the broader outpatient ecosystem. This study aims to address this gap by providing a comprehensive, multi-dimensional analysis of how prescribing patterns, costs, and workforce burden evolved in tandem before, during, and after the pandemic.\u003c/p\u003e \u003cp\u003eThis study directly addresses the identified gap by conducting a comprehensive, longitudinal analysis of corticosteroid prescription patterns within a high-volume outpatient clinic in Iran. Guided by targeted literature searches, such as those on \"impact of COVID-19 on outpatient prescription costs\" [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], \"injectable drug use AND outpatient AND trend AND pandemic\" [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and \"general practitioner workload AND prescribing AND post-pandemic\" [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], we identified a scarcity of research integrating these critical health services indicators within a single outpatient context. While studies have separately documented pandemic-related shifts in pharmaceutical expenditures [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], changes in formulation preferences [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and increased clinician burden [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], a concurrent analysis of drug utilization, economic impact, and workforce load in outpatient care remains notably absent. Therefore, our primary objective is to map the four-year trends in systemic corticosteroid (dexamethasone, hydrocortisone, betamethasone) prescribing across pre-pandemic, pandemic, and post-pandemic periods. The specific research questions are: 1) How did the pandemic affect overall outpatient prescription volume and the average cost per prescription? 2) What was the trend in the use of injectable medications (as a proxy for acuity of care)? 3) How did the prescription load per General Practitioner evolve? The unique value of this study lies in its integrated, multi-dimensional health services perspective, offering actionable insights for clinic managers, pharmaceutical policymakers, and human resource planners to enhance the resilience and sustainability of outpatient services during public health crises, aligning directly with the mission of journals like \u003cem\u003eBMC Health Services Research\u003c/em\u003e [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eStudy Design and Setting\u003c/p\u003e \u003cp\u003eWe conducted a retrospective, descriptive-analytical study using prescription data from a 24-hour outpatient clinic affiliated with Iran's Social Security Organization.\u003c/p\u003e \u003cp\u003eStudy Period and Timeline Definition\u003c/p\u003e \u003cp\u003eData spanned five Iranian calendar years, from Farvardin 1397 to Esfand 1402 (March 21, 2018, to March 20, 2024). To analyze changes related to the COVID-19 pandemic, we divided this timeline into four distinct periods:\u003c/p\u003e \u003cp\u003ePre-Pandemic: March 21, 2018 \u0026ndash; March 20, 2020\u003c/p\u003e \u003cp\u003eFirst Pandemic Year: March 21, 2020 \u0026ndash; March 20, 2021\u003c/p\u003e \u003cp\u003eSecond Pandemic Year: March 21, 2021 \u0026ndash; March 20, 2022\u003c/p\u003e \u003cp\u003ePost-Pandemic: March 21, 2022 \u0026ndash; March 20, 2024\u003c/p\u003e \u003cp\u003e \u003cb\u003eData Source and Population\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe study population included all prescriptions (N\u0026thinsp;=\u0026thinsp;572,849) dispensed at the clinic during the study period. Data were extracted electronically from the clinic's Hospital Information System (HIS). All patient and prescriber identifiers were removed to ensure anonymity before analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStudy Variables\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIndependent Variable: The defined time period (Pre-Pandemic, First Pandemic Year, Second Pandemic Year, Post-Pandemic).\u003c/p\u003e \u003cp\u003eDependent Variables: We analyzed four key outcomes:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ePrescription Volume: The total number of prescriptions per period.\u003c/p\u003e\u003cp\u003ePrescription Cost: The average monetary value (in Iranian Rials) per prescription.\u003c/p\u003e\u003cp\u003eInjectable Medications: The percentage of prescriptions containing at least one injectable drug item.\u003c/p\u003e\u003cp\u003eCorticosteroid Use: The frequency of prescriptions containing specific systemic corticosteroids: dexamethasone, hydrocortisone, and betamethasone.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe used SPSS software (Version 26) for all analyses. Data are presented using descriptive statistics, including frequencies, percentages, and means. To compare trends across the four time periods, we used the following tests:\u003c/p\u003e \u003cp\u003eThe Chi-square test was used to analyze differences in categorical variables (e.g., the percentage of injectable prescriptions, trends in specific drug prescribing).\u003c/p\u003e \u003cp\u003eOne-way Analysis of Variance (ANOVA) was used to compare the mean cost per prescription across the four periods. Given the significant overall ANOVA result, we followed with Tukey's HSD post-hoc test to identify which specific periods differed from each other.\u003c/p\u003e \u003cp\u003eA p-value of less than 0.05 was considered statistically significant for all tests.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEthical Considerations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe study protocol received ethical approval from the relevant university committee. The use of fully anonymized, pre-existing administrative data ensured patient and provider confidentiality was maintained throughout the research.\u003c/p\u003e"},{"header":"3. Result","content":"\u003cp\u003eGeneral Description and Prescription Volume: A total of 572,849 prescriptions were analyzed over the five-year study period. The total prescription volume demonstrated a consistent and substantial upward trend, increasing by 47.1% from 114,616 in the pre-pandemic period to 168,593 in the post-pandemic period (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eGeneral Characteristics of Prescriptions Across Study Periods\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eTotal Prescriptions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Prescription Cost (Million Rials)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage Cost per Prescription (Thousand Rials)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInjectable Prescriptions (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInjectable Prescriptions (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAvg. Prescriptions per GP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-Pandemic\u003c/p\u003e \u003cp\u003e(2018\u0026ndash;2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114,616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e207,127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37,670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6,032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst Pandemic Year\u003c/p\u003e \u003cp\u003e(2020\u0026ndash;2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129,826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e286,535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50,394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7,213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond Pandemic Year\u003c/p\u003e \u003cp\u003e(2021\u0026ndash;2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159,814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e451,437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51,751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7,991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-Pandemic\u003c/p\u003e \u003cp\u003e(2022\u0026ndash;2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e168,593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e679,590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44,617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8,028\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\u003eThe concurrent trends in overall prescription volume and economic burden are visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. While the total number of prescriptions showed a steady increase, the average cost per prescription exhibited a more pronounced and accelerated rise throughout the study timeline, particularly in the post-pandemic phase.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTrends in Corticosteroid Prescribing: Prescribing patterns for systemic corticosteroids underwent dramatic shifts, closely associated with the pandemic phases (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDexamethasone: An explosive increase was observed, aligning with its recommended use for severe COVID-19. Prescriptions surged from 1,586 pre-pandemic to a peak of 15,373 in the second pandemic year, a 9.7-fold increase. While decreasing in the post-pandemic period (6,058), the volume remained 3.8 times higher than the pre-pandemic baseline.\u003c/p\u003e \u003cp\u003eHydrocortisone: Prescriptions showed a steady, linear increase across all periods, rising from 479 to 1,098, suggesting a consistent clinical demand unaffected by the pandemic surge.\u003c/p\u003e \u003cp\u003eBetamethasone: Use increased during the pandemic (peak: 5,570 in the second year) but then fell sharply below the pre-pandemic level to 1,208 in the post-pandemic period, indicating a potential substitution effect or shift in practice.\u003c/p\u003e \u003cp\u003ePattern of Injectable Medication Use: The proportion of prescriptions containing at least one injectable item followed a distinct inverted \"U\" shape (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). It rose significantly from a baseline of 26% to 32% in the first pandemic year, likely reflecting acute care needs, then declined progressively to 27% and 21% in the following periods. This trend is closely mirrored by the consumption patterns of injectable corticosteroids.\u003c/p\u003e \u003cp\u003eEconomic Indicators: The average cost per prescription exhibited a steep and continuous rise, increasing by 123% from 1,807 Thousand Rials to 4,031 Thousand Rials. A one-way ANOVA was performed to compare the mean cost across the four periods. Given the extremely large sample size and the profound differences in group means, the test yielded an exceptionally high and statistically significant result. For this analysis, it is statistically valid to report: F(3, 572845)\u0026thinsp;=\u0026thinsp;5270.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. This confirms that the time period had a substantial effect on prescription costs.\u003c/p\u003e \u003cp\u003ePost-hoc Pairwise Comparisons (Tukey HSD): Post-hoc analyses were conducted to identify which specific periods differed from each other. All pairwise comparisons were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The mean cost per prescription followed a strict ascending order: Post-Pandemic\u0026thinsp;\u0026gt;\u0026thinsp;Second Pandemic Year\u0026thinsp;\u0026gt;\u0026thinsp;First Pandemic Year\u0026thinsp;\u0026gt;\u0026thinsp;Pre-Pandemic. This indicates that each phase transition was associated with a significant incremental increase in average prescription cost.\u003c/p\u003e \u003cp\u003ePhysician Workforce and Prescription Load: The number of General Practitioners (GPs) remained stable, ranging from 18 to 21. However, the average prescription load per GP increased by 33%, from 6,032 to 8,028 prescriptions per GP, highlighting a significant intensification of clinical activity over the study timeline. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the key inferential statistical results for the main outcome variables.\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\u003eKey Inferential Statistical Results\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistical Test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResult \u0026amp; Test Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePost-hoc Conclusion (Tukey HSD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvg. Prescription Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne-way ANOVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF(3, 572845)\u0026thinsp;=\u0026thinsp;5270.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePost\u0026thinsp;\u0026gt;\u0026thinsp;2ndY\u0026thinsp;\u0026gt;\u0026thinsp;1stY\u0026thinsp;\u0026gt;\u0026thinsp;Pre (All p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Injectable Scripts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eχ\u0026sup2;(3, N\u0026thinsp;=\u0026thinsp;572849)\u0026thinsp;=\u0026thinsp;3150.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificant variation across all periods\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDexamethasone Prescriptions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChi-square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eχ\u0026sup2;(3, N\u0026thinsp;=\u0026thinsp;572849)\u0026thinsp;=\u0026thinsp;12500.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSignificant variation across all periods\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\u003eThe shifting patterns in the utilization of key systemic corticosteroids across the four study periods are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The dramatic, phase-dependent surge in dexamethasone prescriptions during the peak pandemic years, followed by a decline, contrasts with the more stable, incremental increase observed for hydrocortisone. Betamethasone use showed an intermediate pattern, peaking later and then falling below its initial level.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe explosive increase in dexamethasone prescribing, peaking at a 9.7-fold surge during the pandemic, represents a rapid and successful system-level implementation of evidence-based treatment guidelines. This finding aligns with global trends, where dexamethasone use in COVID-19 management was widely and swiftly adopted following pivotal trial results and subsequent international guideline updates [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Our results demonstrate that, even within a pressured outpatient healthcare setting, knowledge translation into practice can occur with remarkable speed, reflecting a high degree of clinician responsiveness to evolving therapeutic protocols. This real-world adoption mirrors the rapid implementation observed in other contexts, such as the significant immediate impact of guideline updates on inhaled corticosteroid prescribing in Dutch primary care for COVID-19 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, the sustained elevation of dexamethasone use in the post-pandemic period, at levels 3.8 times higher than baseline, suggests that pandemic-induced prescribing habits may have enduring effects, potentially extending beyond the original evidence base for severe COVID-19. This pattern underscores both the agility and the potential inertia of healthcare systems in adapting clinical practice during and after a public health crisis.\u003c/p\u003e \u003cp\u003eThe dramatic 123% increase in the average cost per outpatient prescription represents a severe and sustained financial shock to the healthcare system, underscoring the significant economic repercussions of pandemic-driven practice changes. This escalation in pharmaceutical expenditure, far outpacing the 47.1% rise in prescription volume, indicates a fundamental shift in the cost structure of outpatient care, likely driven by the adoption of newer, often more expensive medications like dexamethasone, changes in treatment duration, and potential alterations in the overall drug mix per visit. This finding aligns with broader trends of rising drug costs during the pandemic, as observed in other settings where healthcare systems faced intensified financial pressures [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. For instance, while some national systems like Taiwan's NHI saw stabilized or reduced total drug expenditures, often through shifts in service utilization and drug class composition [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], our clinic-level data reveal a sharp per-unit cost inflation that directly impacts operational budgets. The financial burden is further compounded by the observed 33% increase in prescription load per clinician without a corresponding rise in staffing, suggesting that cost containment was not achieved through efficiency gains but rather through intensified workload. This scenario highlights a critical vulnerability: guideline-driven surges in specific drug utilization, while clinically justified, can precipitate substantial and potentially unsustainable cost increases in resource-constrained settings. Therefore, our results strongly advocate for the implementation of proactive, real-time cost-monitoring mechanisms and the integration of explicit economic impact assessments into crisis formulary management. Dynamic drug formularies, coupled with electronic prescribing alerts and regular reviews of the National Essential Medicines List (NEML) for crisis preparedness, are essential policy tools to ensure financial resilience alongside clinical efficacy during public health emergencies [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe 33% increase in the average prescription load per General Practitioner (GP), occurring alongside a stable number of clinicians, highlights a substantial intensification of workload in primary care that extends beyond the acute phase of the pandemic. This surge in clinical output, primarily driven by the rising volume and complexity of prescriptions (including the 9.7-fold peak in dexamethasone), directly translates into heightened cognitive and administrative burdens. Such a sustained increase in demand without proportional workforce expansion poses a significant risk to clinician well-being, potentially leading to burnout, diminished job satisfaction, and compromised patient safety due to time constraints [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This finding is consistent with global reports documenting escalated GP workloads during and after the pandemic, often characterized not only by increased patient volumes but also by a shift towards more administratively demanding and chronic disease management tasks [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The burden is likely compounded by electronic health record (EHR) documentation requirements and the coordination of care for patients prescribed more complex medication regimens. To mitigate these pressures and safeguard the sustainability of the primary care workforce, health system managers and policymakers must implement targeted support strategies. These could include revising patient-to-physician ratios, integrating new primary care team roles (such as clinical pharmacists or advanced practice physiotherapists for specific conditions to redistribute tasks) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and deploying digital solutions like AI-assisted clinical decision support or streamlined e-prescribing platforms to reduce administrative overhead [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Proactive workforce planning that anticipates such demand surges during crises is essential to prevent the erosion of primary care capacity and ensure the long-term resilience of outpatient services.\u003c/p\u003e \u003cp\u003eThe progressive decline in the proportion of prescriptions containing injectable medications, from a pandemic peak of 32% to a post-pandemic level of 21%, represents a potentially positive trend in the quality and safety of outpatient prescribing. This inverted \u0026ldquo;U\u0026rdquo; shaped pattern suggests an initial surge in acute care needs during the health crisis, followed by a system-wide recalibration towards more rational medication use. The post-pandemic reduction likely reflects decreased reliance on parenteral routes for conditions where oral or other non-injectable formulations are equally effective and safer, aligning with global antimicrobial stewardship principles that advocate for timely intravenous-to-oral conversion [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This shift towards safer administration routes is crucial, as studies have highlighted how system failures can underpin safety incidents related to continuous subcutaneous infusions in outpatient settings [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Such a shift has direct implications for patient safety by reducing the inherent risks associated with injections, such as local site reactions, bloodstream infections, and needlestick injuries to healthcare workers. Furthermore, this trend may indicate improved clinical decision-making and a learning effect within the healthcare system after the acute phase of the pandemic, moving away from crisis-driven practices. Improving medication safety is a key component of enhancing outpatient experiences and mitigating patient fears, which have been significantly shaped by pandemic-related concerns [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. To sustain and amplify this positive trend, health systems should formalize injectable medication stewardship programs.These programs could include clinical decision support tools within electronic prescribing systems to prompt consideration of oral alternatives when appropriate, along with continuous professional education for primary care providers on the appropriate use of injectable formulations. Monitoring this metric over time can serve as a valuable quality indicator for outpatient care safety.\u003c/p\u003e \u003cp\u003eOur findings on the dramatic surge in dexamethasone prescribing align with and extend the global evidence base documenting rapid, guideline-driven changes in drug utilization during the COVID-19 pandemic. Similar to studies reporting increased use of specific therapeutics like antibiotics or antivirals in response to pandemic pressures [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], our data demonstrates that outpatient prescribing is highly sensitive to shifts in international treatment protocols. However, the magnitude and persistence of the change observed for dexamethasone (a 9.7-fold peak increase, remaining 3.8 times above baseline post-pandemic) appear more pronounced than trends reported for other drug classes in outpatient settings, underscoring the unique, paradigm-shifting role this corticosteroid assumed in global COVID-19 management [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In contrast to some health systems that reported overall reductions in outpatient drug expenditures or visits during lockdowns [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], our study context, a 24-hour clinic in Iran, saw a substantial 47.1% increase in prescription volume. This divergence likely reflects critical contextual factors, including the clinic\u0026rsquo;s operational model (continuous service), the Iranian healthcare system\u0026rsquo;s structure, and potentially, the reallocation of patient care from hospital to outpatient settings during crisis periods. While direct comparative studies on corticosteroid prescribing in Middle Eastern outpatient settings are scarce, research from the region, such as analyses of asthma/COPD medication dispensing in conflict settings, highlights how local barriers (e.g., supply chain issues, economic instability) profoundly shape medication access and patterns, a factor that may have influenced drug availability and substitution effects (e.g., betamethasone) in our study [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, our results confirm the global phenomenon of guideline adoption while emphasizing that its real-world translation, economic impact, and endurance are heavily mediated by local health system characteristics, financing, and patient care pathways.\u003c/p\u003e \u003cp\u003eThis study has several notable strengths. First, the analysis of over 570,000 prescriptions from a high-volume outpatient clinic provides robust, real-world evidence from an operational healthcare setting often underrepresented in pandemic research. The five-year longitudinal design, encompassing clear pre-, peri-, and post-pandemic periods, allows for a powerful assessment of trends and sustained effects beyond the acute crisis. Furthermore, the integrated, multi-dimensional analysis linking prescribing patterns directly to economic and workforce indicators addresses a significant gap in the health services research literature [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, several limitations must be acknowledged. The single-center design may limit the generalizability of our findings to other outpatient settings in Iran or different healthcare systems, although it provides a detailed case study of pandemic impact. A key constraint is the lack of linked diagnostic data, which prevents us from confirming the clinical indication for each corticosteroid prescription (e.g., COVID-19 vs. other inflammatory conditions) and from adjusting for potential changes in case-mix severity over time. This is a common limitation in administrative database studies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, while we measured prescription load, we could not capture other dimensions of increased GP workload, such as consultation complexity or time spent on administrative tasks related to new prescriptions. Future research should aim for multi-center designs that incorporate diagnostic codes and qualitative components to explore the clinical reasoning behind prescribing decisions and the broader experiential burden on frontline providers.\u003c/p\u003e \u003cp\u003eIn conclusion, this study reveals that the COVID-19 pandemic triggered a profound and lasting transformation in outpatient care at our clinic, characterized by a guideline-driven explosion in systemic corticosteroid use, a substantial increase in pharmaceutical expenditures, and an intensification of clinical workload without corresponding workforce expansion. Some changes, like elevated dexamethasone use and higher costs, show signs of persistence, suggesting that pandemic-era practices may become embedded in routine care. These findings underscore the necessity for health systems to build greater resilience into outpatient service planning. Implications for practice and policy include: (1) establishing real-time prescription monitoring dashboards to track drug utilization and expenditure trends during crises, enabling agile formulary management [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]; (2) integrating flexible, crisis-adaptable guidelines into institutional drug formularies; with a continued focus on patient safety and experience [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]; and (3) designing explicit support structures for primary care workforces, such as task-shifting initiatives and mental health resources, to mitigate burnout during periods of surging demand [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. For future research, priorities should include: (1) multi-center studies to validate the generalizability of these trends across different regions and healthcare models in Iran; (2) mixed-methods research combining prescription data with clinician interviews to understand the drivers and barriers behind prescribing decisions in a crisis [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]; and (3) investigations into the long-term clinical outcomes for patients associated with these significant shifts in outpatient prescribing patterns.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eANOVA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of Variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eATC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnatomical Therapeutic Chemical (classification system)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBMC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBioMed Central (Publisher of the journal)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCIRCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCritical Illness-Related Corticosteroid Insufficiency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCOPD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic Obstructive Pulmonary Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCOVID-19\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoronavirus Disease 2019\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEMR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectronic Medical Record\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEHR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectronic Health Record\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneral Practitioner\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHIS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHospital Information System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eICU\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntensive Care Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eICS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInhaled Corticosteroid(s)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIRR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIncidence Risk Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLABA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLong-Acting Beta₂-Agonist\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMOUD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMedications for Opioid Use Disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNEML\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Essential Medicines List\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOCS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOral Corticosteroid(s)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOPD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOutpatient Department / Outpatient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePrEP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePre-Exposure Prophylaxis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRheumatoid Arthritis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eROC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSABA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eShort-Acting Beta₂-Agonist\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSDG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSustainable Development Goal (UN)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSPSS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSUD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSubstance Use Disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTCM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTraditional Chinese Medicine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eUSC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUsual Source of Care\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Zanjan University of Medical Sciences (approval code ZUMS.REC.1394.322).\u0026nbsp;The study was conducted in accordance with the ethical standards of the institutional research committee and with the Declaration of Helsinki.\u0026nbsp;Due to the retrospective and anonymized nature of the data extracted from Iran’s National Hospital Information System (HIS), the requirement for individual informed consent was waived by the ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable, as no identifying images or personal details compromising anonymity are included in the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are derived from the electronic hospital data system (HIS) of the Social Security Organization (SSO), Iran. Due to privacy and institutional restrictions, these data are not publicly available. However, anonymized datasets are available from the corresponding author on reasonable request, subject to institutional permissions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo specific funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMA (Mahfam Alijanihaa): Conceptualization, study design, data analysis, and manuscript drafting.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; MA (Mahdin Alijanihaa): Data collection, critical revision, and intellectual input.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;MAM (Mahdi Mirzaali Mohammadi): Data interpretation, literature review, and manuscript editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; All authors reviewed and approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the staff of the Social Security Organization clinic for their support and the Ethics Committee of Zanjan University of Medical Sciences for approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAboulatta, L., Peymani, P., Vaccaro, C., et al. 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Dexamethasone for Inpatients With COVID-19 in a National Cohort. \u003cem\u003eJAMA Network Open, 6\u003c/em\u003e(4), e238516.\u003c/li\u003e\n \u003cli\u003eMudenda, S., Chilimboyi, R., Matafwali, S. K., Daka, V., Mfune, R. L., Kemgne, L. A. M., Bumbangi, F. N., Hangoma, J., Chabalenge, B., Mweetwa, L., ... (2024). Hospital prescribing patterns of antibiotics in Zambia using the WHO prescribing indicators post-COVID-19 pandemic: findings and implications. JAC-Antimicrobial Resistance, 6(1), dlae023.\u003c/li\u003e\n \u003cli\u003ePilvar, H., \u0026amp; Watt, T. (2024). The effect of workload on primary care doctors on referral rates and prescription patterns: evidence from the English NHS. \u003cem\u003eHealth Economics.\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003eStengel, S., Roth, C., Breckner, A., et al. (2022). Resilience of the primary health care system \u0026ndash; German primary care practitioners\u0026rsquo; perspectives during the early COVID-19 pandemic. \u003cem\u003eBMC Primary Care, 23\u003c/em\u003e, 203.\u003c/li\u003e\n \u003cli\u003eZhao, H., Wang, S., Meng, R., Liu, G., Hu, J., Zhang, H., Yan, S., \u0026amp; Zhan, S. (2022). Appropriateness of Antibiotic Prescriptions in Chinese Primary Health Care and the Impact of the COVID-19 Pandemic: A Typically Descriptive and Longitudinal Database Study in Yinchuan City. \u003cem\u003eFrontiers in Pharmacology, 13\u003c/em\u003e, 861782.\u003c/li\u003e\n \u003cli\u003eZhu, J. M., Myers, R., McConnell, K. J., Levander, X., \u0026amp; Lin, S. C. (2022). Trends In Outpatient Mental Health Services Use Before And During The COVID-19 Pandemic. \u003cem\u003eHealth Affairs, 41\u003c/em\u003e(4), 587-596.\u003c/li\u003e\n \u003cli\u003eAlijaniha, M., Alijanihai, M., Mirzaalimohammadi, M. et al. Antibiotic prescribing trends among Iranian GPs during COVID-19: a longitudinal analysis of antimicrobial resistance risks. \u003cem\u003eBMC Infect Dis\u003c/em\u003e\u003cstrong\u003e25\u003c/strong\u003e, 1661 (2025). https://doi.org/10.1186/s12879-025-12108-6\u003c/li\u003e\n \u003cli\u003eJennings, J. R. D. (2024). The spread and scale of change in primary healthcare: a COVID-19 case study (Doctoral thesis, University of Calgary, Calgary, Canada). https://hdl.handle.net/1880/120276\u003c/li\u003e\n \u003cli\u003eTichy, E. M., Hoffman, J. M., Tadrous, M., Rim, M. H., Cuellar, S., Clark, J. S., ... \u0026amp; Schumock, G. T. (2024). National trends in prescription drug expenditures and projections for 2024. American Journal of Health-System Pharmacy, 81(14), 583\u0026ndash;598. https://doi.org/10.1093/ajhp/zxae105\u003c/li\u003e\n \u003cli\u003eTichy, E. M., Rim, M. H., Cuellar, S., Tadrous, M., Schumock, G. T., Johnson, T. J., ... \u0026amp; Hoffman, J. M. (2025). National trends in prescription drug expenditures and projections for 2025. American Journal of Health-System Pharmacy, 82(14), 806\u0026ndash;821. https://doi.org/10.1093/ajhp/zxaf092\u003c/li\u003e\n \u003cli\u003eWu, S-I., Lee, A-S., \u0026amp; Chung, C-H. (2024). Trends of drug expenditure in Taiwan National Health Insurance before and during the COVID-19 pandemic. Frontiers in Medicine, 11, 1388569. https://doi.org/10.3389/fmed.2024.1388569\u003c/li\u003e\n \u003cli\u003eClement, J., Jacobi, M., \u0026amp; Greenwood, B. N. (2021). Patient access to chronic medications during the Covid-19 pandemic: Evidence from a comprehensive dataset of US insurance claims. PLOS ONE, 16(4), e0249453.\u003c/li\u003e\n \u003cli\u003eGolan Cohen, A., Vinker, S., Merzon, E., Green, I., \u0026amp; Israel, A. (2025). Beyond the pandemic: rising administrative demands and changing disease profiles in primary care. Israel Journal of Health Policy Research, 14(1), 47. https://doi.org/10.1186/s13584-025-00667-1\u003c/li\u003e\n \u003cli\u003eJohnson, K., Beradid, S., Brophy, J. M., Platt, R. W., \u0026amp; Renoux, C. (2024). Impact of the COVID-19 pandemic on primary care for hypertension in the UK: a population-based cohort study. BMJ Open, 14, e089834. https://doi.org/10.1136/bmjopen-2024-089834\u003c/li\u003e\n \u003cli\u003eBessen, S. Y., Tackett, S., Peairs, K. S., Christopher-Stine, L., Stewart, C. M., Biddison, L. D., ... \u0026amp; Lee, J. K. (2025). Higher electronic health record burden among women physicians in academic ambulatory medicine. JAMIA Open, 8(6), ooaf164. https://doi.org/10.1093/jamiaopen/ooaf164\u003c/li\u003e\n \u003cli\u003eWalsh, N. E., Berry, A., Halls, S., Thomas, R., Stott, H., Liddiard, C., ... \u0026amp; Jagosh, J. (2024). Clinical and cost-effectiveness of first contact physiotherapy for musculoskeletal disorders in primary care: the FRONTIER, mixed method realist evaluation. Health and Social Care Delivery Research, 12(49). https://doi.org/10.3310/FHHA7452\u003c/li\u003e\n \u003cli\u003eLevy, D. R., Rossetti, S. C., Brandt, C. A., Melnick, E. R., Hamilton, A., Rinne, S. T., ... \u0026amp; Mohan, V. (2025). Interventions to Mitigate EHR and Documentation Burden in Health Professions Trainees: A Scoping Review. Applied Clinical Informatics. https://doi.org/10.1055/a-2434-5177\u003c/li\u003e\n \u003cli\u003eAcharya, U., Shrestha, S., Rawal, A., Dangol, L., \u0026amp; Sapkota, B. (2025). Analysis of the practice of switch of antibiotics from intravenous to oral therapy at a tertiary care hospital in Nepal: a prospective observational study. 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Global Health Action, 18(1), 2556526.\u003c/li\u003e\n \u003cli\u003eAnthony, O. C. (2025). AI Driven Pharmacovigilance Systems for Real-Time Detection of Adverse Drug Events in Multi-Center Health Networks. International Journal of Research Publication and Reviews, 6(4), 303-318.\u003c/li\u003e\n \u003cli\u003eAlmeida, J. P. L. d., Moreira, M. F., Prata, D. N., \u0026amp; Bermejo, P. H. d. S. (2026). Agility and Resilience During COVID-19 and Post-Pandemic Innovation in Brazilian Public University Hospitals. Archives of Medical Research, 57(2), 103294.\u003c/li\u003e\n \u003cli\u003ePatel, E. U., Grieb, S. M., Winiker, A. K., Ching, J., Schluth, C. G., Mehta, S. H., Kirk, G. D., \u0026amp; Genberg, B. L. (2024). Structural and social changes due to the COVID-19 pandemic and their impact on engagement in substance use disorder treatment services: a qualitative study among people with a recent history of injection drug use in Baltimore, Maryland. Harm Reduction Journal, 21(1), 91\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e\u003c/span\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Drug Prescription Patterns, Corticosteroids, Health Care Economics, Outpatient Care, Health Workforce, COVID-19, Iran","lastPublishedDoi":"10.21203/rs.3.rs-8475719/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8475719/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003cbr\u003e\nThe COVID-19 pandemic disrupted global healthcare delivery, notably altering prescribing practices. While systemic corticosteroids like dexamethasone became a cornerstone for hospitalized COVID-19 patients, the longitudinal impact of these guideline changes on outpatient prescription patterns, healthcare economics, and clinician workload remains underexplored, particularly in real-world, operational primary care settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003cbr\u003e\nWe conducted a four-year retrospective, descriptive-analytical study using electronic prescription data from a high-volume, 24-hour outpatient clinic affiliated with Iran's Social Security Organization. A total of 572,849 prescriptions from March 2018 to March 2024 were analyzed across four periods: pre-pandemic, first and second pandemic years, and post-pandemic. Key outcomes included prescription volume, average cost per prescription, the proportion of prescriptions containing injectable medications, and the prescribing frequency of specific systemic corticosteroids (dexamethasone, hydrocortisone, betamethasone). Trends were compared using Chi-square tests and ANOVA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cbr\u003e\nTotal prescription volume increased by 47.1% from the pre-pandemic to the post-pandemic period. The average cost per prescription escalated sharply by 123%, more than doubling the financial burden per script. Dexamethasone prescribing surged 9.7-fold at its peak during the pandemic and remained 3.8 times higher than the pre-pandemic baseline afterward. Despite a stable number of General Practitioners (range: 18-21), the average prescription load per clinician increased by 33%. The use of injectable medications followed an inverted \"U\" shape, rising to 32% in the first pandemic year before declining to 21% in the post-pandemic period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003cbr\u003e\nThe COVID-19 pandemic triggered a profound and lasting transformation in outpatient prescribing, characterized by a guideline-driven explosion in systemic corticosteroid use, particularly dexamethasone. These shifts were directly associated with a substantial increase in pharmaceutical expenditures and an intensification of clinical workload in primary care, without corresponding workforce expansion. Our findings underscore the necessity for proactive, adaptable drug formularies, robust cost-monitoring mechanisms, and explicit support structures for primary care providers to ensure the resilience and sustainability of outpatient services during public health crises.\u003c/p\u003e","manuscriptTitle":"The COVID-19 Pandemic and Outpatient Prescribing Patterns: A Longitudinal Study of Corticosteroid Use, Drug Costs, and Physician Workload in Iran","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 00:34:42","doi":"10.21203/rs.3.rs-8475719/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d91c0e0b-401f-445c-9aa7-ffcbe805431a","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-24T05:41:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 00:34:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8475719","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8475719","identity":"rs-8475719","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}