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. 2025 Jan 3;39(1):498–508. doi: 10.21873/invivo.13854

A Time-varying Analysis of General Practice Prescribing in the COVID-19 Era: Lessons from Prescription Dynamics in a Pandemic

KINAN MOKBEL 1, KATE EMBLIN 1,2, ROB DANIELS 1, FAHAD ALGHAMDI 1,3, LEIGH JACKSON 4
PMCID: PMC11705151  PMID: 39740896

Abstract

Background/Aim

Pharmacotherapy is vital in medicine, but inappropriate and inadequate use of medications significantly impacts global mortality and morbidity. Increased prescribing may indicate irrational use or prolonged illness, while decreased prescribing could suggest undertreatment, supply shortages, or the availability of safer and, more effective treatments. The COVID-19 pandemic disrupted health systems, potentially altering prescribing patterns. This study examined its impact on the prescribing patterns of common therapeutic categories and high-risk medicines in general practice in England.

Materials and Methods

Common therapeutic categories were identified from English General Practice prescription data, and high-risk medicines were identified by mapping the UK pharmacovigilance data onto the English prescribing data. A retrospective analysis compared monthly prescription data pre-pandemic, during the pandemic, and post-pandemic. Significant changes in the prescribing volumes of therapeutic categories and high-risk medicines were tracked to determine persistence, intensification, or diminution post-pandemic. Linear regression models analysed prescribing trends.

Results

Among 220 therapeutic categories, 16 experienced significant changes: 14 increased and two decreased during the pandemic. Of 78 high-risk medicines, six showed significant changes: two increased and three decreased. Only three therapeutic categories and two high-risk medicines returned to pre-pandemic levels.

Conclusion

Despite a reduction in general practice appointments during the pandemic, prescribing for several therapeutic categories and certain high-risk medicines surged, indicating increased treatment, prolonged illness or stockpiling. Post-pandemic downward trends suggest long-term under-treatment or reduced stockpiling. Continuous monitoring, strategic healthcare planning, and regulatory interventions are needed to optimise prescribing. Future research is needed to assess the long-term effects on disease management.

Keywords: COVID-19, pandemic, data linkage, pharmacovigilance, high-risk medicines, prescription patterns, time-trend analysis, time-varying analysis, General Practice, primary care, healthcare planning

Medications are crucial for public health to prevent and control health conditions. They can also serve as proxies for patient adherence, access to medications, clinical pathway disruptions and disease control during pandemics (1,2). While judicious use yields preventative and therapeutic benefits, inappropriate and insufficient use is a cause of concern (3). Decreased prescribing may indicate undertreatment, whilst increased prescribing may imply irrational use, treatment resistance, exacerbations or prolonged illnesses. Importantly, an increase in prescribing of high-risk medication, associated with serious and fatal adverse drug events (ADEs), is expected to impact hospitalisation and mortality rates.

The Coronavirus disease 2019 (COVID-19) pandemic, as well as the associated lockdowns and the vaccination campaign, have significantly disrupted healthcare systems and disease burden (4) and shifted focus away from managing long-term conditions in primary care (5,6). The United Kingdom documented its first case of COVID-19 on 31 January 2020 with three key lockdowns in March 2020, November 2020 and January 2021 (7). General practice (GP) appointments in England dropped by 30% in March 2020 (8). Analysing prescribing patterns quantifies COVID-19’s impact on health systems, aiding future pandemic preparations and ensuring adequate provisions and maintaining routine healthcare. While some studies documented the pandemic’s effect on certain therapeutic classes in the UK (1,2,9-22), no comprehensive studies have analysed all therapeutic categories and high-risk medications in general practice in England. This analysis examines COVID-19’s effects on monthly prescription patterns for common therapeutic chapters, sections and subsections as per the British National Formulary (BNF) (23), and high-risk medicines in general practice in England and assesses whether prescribing levels have returned to pre-pandemic states.

Materials and Methods

Identification of therapeutic chapters, sections and subsections for analysis. English General Practice prescription data, sourced from OpenPrescribing (24), was used for analysis, encompassing all 12 BNF therapeutic chapters, each focusing on a specific system of the body. The BNF therapeutic sections and subsections were selected for analysis based on clinical judgment and prescription volume. These sections within each chapter broadly group drugs by therapeutic use or condition treated, while the subsections provide more specific classifications, grouping drugs into pharmacological or therapeutic classes.

Identification of high-risk medicines. To identify high-risk medications in general practice, we mapped UK pharmacovigilance data on serious and fatal ADEs from the Yellow Card database (25) onto English General Practice prescription data from OpenPrescribing (24) as previously described (26). Forest plots of ADE rates per million items prescribed within each therapeutic subsection identified high-risk medications as those with top-ranked ADE rates and non-overlapping confidence intervals compared to other medications in their subsection.

Time-varying analysis of prescribing patterns. Data were divided into three sets: pre-pandemic, pandemic and post-pandemic. A retrospective analysis examined monthly prescription data through two analyses: 1) Comparing mean monthly prescriptions of therapeutic chapters, sections and subsections, as well as high-risk medicines between pandemic and pre-pandemic periods, and 2) Assessing if pandemic-related prescribing changes persisted, intensified, or diminished post-pandemic by comparing post-pandemic with pre-pandemic figures.

For the first analysis, regarding therapeutic chapters, sections, and subsections, trends from 11/2016 to 10/2021 compared 21 months during the pandemic (02/2020 to 10/2021) with the pre-pandemic period (11/2016 to 01/2020). Regarding high-risk medicines, trends from 01/2017 to 10/2021 compared 21 months during the pandemic (02/2020 to 10/2021) with the pre-pandemic period (01/2017 to 01/2020).

For the second analysis, regarding therapeutic chapters, sections, and subsections, trends compared the post-pandemic period (11/2021 to 07/2023) with the pre-pandemic period (11/2016 to 01/2020). Regarding high-risk medicines, trends compared the post-pandemic period (11/2021 to 11/2023) with the pre-pandemic period (01/2017 to 01/2020).

The timeframes for the prescription data in these two distinct analyses were determined by the availability of data on the OpenPrescribing platform at the time of each analysis, which provides data for only the most recent 60 months. However, this does not affect the validity of the results. Data management and pre-processing were performed in Microsoft Excel (Microsoft Corporation, Redmond, WA, USA). Statistical analyses of prescribing trends were conducted using Stata/SE 16.0 (StataCorp, College Station, TX, USA). Linear regression models with interaction terms were fitted using the ‘regress’ command. Bonferroni-corrected p-values were as follows: p=0.000227 for therapeutic sections and subsections, p=0.000617 for high-risk medicines, and p=0.00227 for return-to-pre-pandemic levels. Line graphs were used to visually verify significant changes, and the findings were presented with regression coefficients β, 95% confidence intervals (CI), and p-values.

Results

220 common therapeutic chapters, sections, and subsections identified along with 78 high-risk medicines. We identified 220 common therapeutic chapters, sections and subsections from English General Practice prescription data, including 12 chapters, 79 sections, and 129 subsections. Mapping UK pharmacovigilance data onto prescribing data as previously described (26), we identified 78 high-risk medicines.

Significant prescribing changes in 16 therapeutic sections and subsections, and 5 high-risk medicines. We analysed 4,931,702,489 items prescribed from 01/12/2016 to 01/11/2021. Overall, monthly prescribing volumes increased but were not statistically significant (β Coef. 49167, p=0.25). During the COVID-19 pandemic, a downward trend was observed but was also not significant (β Coef. -9276, p=0.94). The highest monthly volume in five years (89,885,464 items) was observed in March 2020 (Figure 1).

Figure 1.

Figure 1

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Time trends of the combined monthly prescription volumes of all the British National Formulary therapeutic chapters in general practice in England during pre-pandemic and pandemic periods.

Among 220 therapeutic chapters, sections and subsections, the 12 therapeutic chapters showed no significant changes in prescribing patterns between the pandemic and pre-pandemic periods. Of the therapeutic sections and subsections, 21 increased and 10 decreased significantly in monthly volumes. After excluding those with very low monthly prescribing volumes and H2-receptor antagonists due to Ranitidine’s withdrawal in October 2019 (27), 14 therapeutic sections and subsections increased and two decreased. Significant changes are listed in Table I, with trends in Figure 2 and detailed analyses in Table S1.

Table I. The British National Formulary therapeutic sections and subsections with significant prescribing changes during COVID-19 vs. pre-pandemic, and post-pandemic vs. pre-pandemic monthly volumes.

graphic file with name in_vivo-39-500-i0001.jpg

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*Statistically significant changes.

Figure 2.

Figure 2

Figure 2

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Time trends in monthly prescriptions of commonly prescribed the British National Formulary therapeutic sections and subsections with significant changes during pre-pandemic, pandemic, and post-pandemic periods. The shaded area represents the Covid-19 pandemic period with subsequent lockdowns. A) Non-steroidal anti-inflammatory drugs, B) Female sex hormones and their modulators, C) Sulfonamides and trimethoprim, D) Compound haemorrhoidal preparations with corticosteroid, E) Cephalosporins and other beta-lactams, F) Parenteral anticoagulants, G) Soothing haemorrhoidal preparations, H) Compound bronchodilator preparations, I) Emollient and barrier preparations, J) Drugs for soft-tissue disorders and topical pain relief, K) Drugs acting on the oropharynx, L) Local preparations for anal and rectal disorders, M) Drugs used in the treatment of obesity, N) Preparations for warts and calluses, O) Topical local anaesthetics and antipruritics, P) Drugs for dementia.

Of 78 high-risk medications, prescribing significantly increased for eight and decreased for nine when comparing the pandemic to pre-pandemic periods. After excluding medications with very low monthly prescribing volumes and Varenicline due to its gradual withdrawal in June 2021 (28), two high-risk medications increased and three decreased. Significant changes are listed in Table II, with trends in Figure 3 and detailed analyses in Table S2.

Table II. High-risk medicines with significant prescribing changes during COVID-19 vs. pre-pandemic, and post-pandemic vs. pre-pandemic monthly volumes.

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*Statistically significant changes.

Figure 3.

Figure 3

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Time trends in monthly prescriptions of high-risk medications with significant changes during pre-pandemic, pandemic, and post-pandemic periods. A) Ibuprofen, B) Mirabegron, C) Canagliflozin, D) Alogliptin, E) Ticagrelor.

Only three therapeutic sections and subsections, and two high-risk medications returned to pre-pandemic levels. After evaluating high-risk medications and therapeutic sections and subsections with significant monthly prescribing changes between the pandemic and pre-pandemic periods, we found that only three therapeutic sections and subsections, and two high-risk medications returned to pre-pandemic levels. For the 13 therapeutic sections and subsections that did not revert, the impact of the pandemic markedly intensified for three: female sex hormones and modulators increased fourfold, obesity treatment drugs rose by approximately 70%, and emollient and barrier preparations increased by roughly a third. In contrast, three sections and subsections experienced a decline post-pandemic: non-steroidal anti-inflammatory drugs (NSAIDs) decreased by more than a third, local anal and rectal preparations fell by nearly a third, and compound bronchodilators also dropped by about a third. Detailed results are in Table I. Among the three high-risk medications with prescribing levels that did not return to pre-pandemic levels, only alogliptin demonstrated a notable post-pandemic increase, by roughly 25% compared to the pandemic levels. Detailed results are in Table II.

Discussion

The COVID-19 pandemic disrupted health systems, affecting prescribing patterns. This time-varying analysis, the first and largest of its kind, evaluated the impact on common therapeutic areas at the chapter, section and subsection levels, as well as high-risk medicines in general practice in England. By comparing pandemic with pre-pandemic data, we endeavoured to assess disease control through medication utilisation. To our knowledge, none of the previous studies have examined COVID-19’s effects on prescription patterns for all common therapeutic chapters, sections and subsections or high-risk medicines in general practice in England, identified by mapping the UK pharmacovigilance data onto the English GP prescribing data.

Numerous studies have investigated the impact of COVID-19 on prescription patterns, often focusing on primary care outside of England (29-35), and particularly focusing on specific therapeutic categories, such as antibiotics (29,30,32), psychotropics (33) and statins (34). In the UK, studies have explored primary care prescription trends during the pandemic, focusing on particular drug classes like antibiotics (15-20), opioids (21,22), cardiovascular medications (2), anticoagulants (9), immune disorder medications (10), contraceptives (11,12), and asthma and chronic obstructive pulmonary disease (COPD) medicines (13,14). While almost all these studies only compared prescribing before and during the pandemic, our research offers a comprehensive analysis of prescription trends both during and after the pandemic, addressing key gaps in the existing literature.

We observed an overall reduction in the monthly prescribing volumes during the pandemic, consistent with reduced GP appointments (8), but this was not statistically significant. Some therapeutic sections, subsections and high-risk medications showed significant reductions, in line with the decrease in GP appointments, while others increased despite reduced disease detection during the pandemic. Persistent or intensified post-pandemic changes are particularly concerning. Downward trends suggest long-term under-treatment, supply shortages, or the availability of more effective or safer treatments on the market. In contrast, upward trends indicate a sustained surge in conditions, prolonged illness and treatment resistance. This latter trend for high-risk medications can be particularly worrisome compared to whole therapeutic areas due to potential increases in hospitalisation and mortality associated with these high-risk medications. However, some sections and subsections showed signs of recovery with diminished post-pandemic prescribing compared to pre-pandemic levels.

Multiple factors contribute to significant shifts in prescribing patterns. Prescribing changes are due to various factors, though exact causes remain unclear due to the use of aggregated data and an inability to account for medication indications. Reduced hospital admissions and elective procedures potentially lowered some prescriptions, like ticagrelor. However, not all antiplatelet drugs followed this trend. Additionally, during the COVID-19 pandemic, many patients as well as clinicians focused on more critical or life-threatening conditions, resulting in de-prioritisation of some conditions that do not require immediate treatment (36). One example is the reduced prescribing volumes of mirabegron, a medication mainly used for controlling symptoms of overactive bladder. Remote consultations increased prescription access and potentially led to medication stockpiling due to concerns about further lockdowns and supply shortages (37). Yet, this increase may indicate improved adherence or more new users. For example, lockdown-related activity changes likely increased musculoskeletal issues (38), explaining the rise in prescriptions for soft-tissue disorders and pain relief. COVID-19 complications and secondary infections likely increased sulfonamide, trimethoprim, cephalosporin, and beta-lactam use, suggesting more community-acquired pneumonia. This aligns with increased prescription of antibacterial drugs in the community in the UK during the pandemic (39). Nonetheless, some of the increase in antibacterial prescriptions may be linked to the distribution of rescue packs at the start of the pandemic (40). Respiratory symptoms and throat irritations from COVID-19 (41), due to high virus concentration in these areas (42), potentially raised demand for oropharyngeal drugs. It remains uncertain why most of the above-mentioned therapeutic sections and subsections have not returned to pre-pandemic levels.

Some prescribing levels returned to pre-pandemic norms. The pandemic led to a decline in dementia drug prescriptions, consistent with similar disruptions in prescribing trends observed in Europe and North America (43), raising concerns about potential under-treatment and missed diagnoses. Dementia, a major cause of morbidity and mortality (44), was significantly impacted by reduced healthcare access and lockdown measures as this vulnerable population relies heavily on caregiver support and regular medical check-ups. Conversely, parenteral anticoagulant prescriptions surged likely due to increased venous thromboembolism from COVID-19, with a potentially minor contribution from vaccine-related effects (45,46). Prescriptions for soft-tissue disorders and pain relief could be linked to musculoskeletal issues from altered activity levels during lockdowns. Despite these changes, these therapeutic sections and subsections have returned to pre-pandemic levels, easing concerns.

Pandemic’s impact on some prescriptions diminished but not recovered post-pandemic. Prescribing for some therapeutic sections and subsections has slightly improved post-pandemic, indicating potential recovery though not yet to pre-pandemic levels. Compound bronchodilator prescriptions, reduced during the pandemic due to fewer non-COVID respiratory infections (such as influenza and common colds) as a result of mask-wearing and enhanced hygiene practices, have slightly increased. NSAID prescriptions surged during the pandemic likely due to their use for managing COVID-19 symptoms and lockdown-exacerbated chronic conditions (47,48) despite initial concerns about their safety and lack of evidence regarding their effectiveness (49,50). These have dropped by 38% post-pandemic compared to pandemic figures. Local rectal preparations, which likely increased due to more digestive issues and haemorrhoids as a result of sedentary lifestyles and altered habits (51), dropped by 30% post-pandemic compared to the pandemic period. Hemorrhoidal preparation prescriptions, which rose due to sedentary lifestyles and drastic changes in eating and sleeping habits (52), have also shown slight improvement. Despite significant pandemic-induced changes, post-pandemic levels for the above therapeutic sections and subsections have shown slight improvement, suggesting a potential recovery.

Pandemic’s impact on some prescriptions intensified post-pandemic. Prescribing shifts intensified post-pandemic for some therapeutic sections, subsections and high-risk medicines, raising concerns. Prescribing of female sex hormones and modulators increased due to COVID-19-related menstrual disorders, lifestyle changes and stress-induced menstrual issues (53). More women may have sought hormonal therapies (54) for their potential protective effects against severe COVID-19 symptoms (55,56). Obesity treatment drugs, especially Orlistat, rose due to health awareness (57), weight gain (58), and remote consultations (59). Emollient and barrier prescriptions surged due to frequent hand sanitiser use and handwashing, leading to lingering stress-exacerbated skin conditions (60,61). Heightened stress worsened eczema and psoriasis (62,63), with easier prescription access through remote consultations (64). Alogliptin’s decline intensified post-pandemic, roughly by a third compared with the pandemic levels, likely due to a shift to newer alternatives like sodium-glucose cotransporter-2 (SGLT2) inhibitors and glucagon-like peptide-1 (GLP-1) receptor agonists, which are preferred over gliptins for patients with other comorbidities due to their broader benefits beyond glucose control (65).

Implications for practice and research. In addition to providing evidence of the pandemic’s impact on the management of health conditions, this study demonstrates that medication data can estimate pandemic-related health issues and overall disease burden. Medications, combined with diagnostic codes like SNOMED in GP records (66), can improve the efficiency of clinical investigations, particularly during healthcare disruptions such as pandemics (1). During such times, routine services are often reduced, leading to incomplete medical records and potential gaps in patient care. When diagnostic codes are missing or incomplete due to changes in healthcare provision, medication data can also be used to supplement the information, ensuring more accurate diagnoses and supporting treatment continuity. Additionally, analysing prescribing changes can help measure or model the impact of missed medications or not treating conditions. Moreover, ongoing surveillance of prescribing, particularly for high-risk medications, is essential, necessitating strategic planning and regulatory measures to support patients taking these agents during pandemics. Plans may include continuous monitoring, educational programmes, and support services such as extended roles for pharmacists in general practice. Furthermore, this study can enhance routine healthcare by ensuring equitable access to treatment and addressing medication utilisation more broadly. The pandemic revealed healthcare inequities, with fewer face-to-face appointments. Access to remote consultations varied by demographics, highlighting the need to identify and treat individuals who missed care to minimise the indirect costs of pandemics.

Study limitations. This study has a few limitations. First, mapping data from the Yellow Card database onto the English prescription database warrants caution due to the inherent limitations present in both databases (26), though this likely affects all medications equally. Second, the use of aggregated data limited our ability to analyse individual behaviours and actions such as patient adherence to prescribed medicines. Furthermore, the lack of disease incidence figures made it difficult to determine whether the observed changes in prescription numbers were due to a real shift in the incidence of particular medical conditions during the pandemic or a general hesitance to seek medical attention for assessment. Thus, patient-level data is required to more accurately estimate changes in prescribing, including new prescriptions and usage. Third, the dataset lacks patient demographic information, which made it impossible to derive conclusions about prescribing patterns among different patient groups or control for demographic biases. Fourth, attributing specific diseases to medications used for multiple indications was challenging. Fifth, multiple regression analyses with stringent corrections for multiple comparisons may have precluded the detection of some true positives. Smaller, more focused studies may identify significant results not detected in our broader analysis. Finally, a more rigorous approach than comparing monthly prescription volumes between the post-pandemic and pre-pandemic periods would be to model the expected post-pandemic prescription levels if the pandemic had not happened. Methods such as interrupted time series analysis (67) could help predict the trajectory of prescription volumes or estimate what prescribing trends would have looked like had the pandemic not occurred. This approach would accurately quantify how post-pandemic levels deviate from the pre-pandemic trend and would provide a more robust measure of the impact of the pandemic on prescribing patterns.

Conclusion

This is the first extensive analysis of COVID-19’s impact on prescribing patterns for common therapeutic chapters, sections, subsections and high-risk medicines in English general practice. Prescribing surged for some therapeutic sections, subsections and high-risk medicines, indicating increased treatment or stockpiling, while others showed downward trends, suggesting reduced disease management. Post-pandemic, some upward trends persisted or intensified, raising concerns about prolonged illness and treatment resistance, while persistent or intensified downward trends indicated long-term under-treatment. High-risk medication trends are particularly worrying due to potential rises in hospitalisation and mortality. This study highlights the need for ongoing monitoring, strategic planning and regulatory interventions to optimise prescribing during pandemics and improve routine healthcare. Additional research is needed to understand the reasons behind the prescribing trends observed in this study and explore the long-term effects on disease management.

Supplementary Material

Available at: https://doi.org/10.6084/m9.figshare.26981086.v3

Data Availability

All data relevant to the study are included in the article or uploaded as supplementary information.

Conflicts of Interest

The Authors declare that they have no conflicts of interest that are directly relevant to the content of this study.

Authors’ Contributions

KM generated the data, performed statistical analyses, created the graphs, interpreted the results and drafted the manuscript. LJ contributed to the analysis and provided expert interpretation of the data. KE, RD provided clinical expertise in interpreting the findings. FA reviewed the manuscript and provided general feedback.

Acknowledgements

This study received support from the University of Exeter Sanctuary Scholarship. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.

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