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General Practitioner-Related Factors Associated with Antibiotic Prescription in Community-Dwelling Adult Population
 
 
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Article

Patient Characteristics Associated with Repeat Antibiotic Prescribing Pre- and during the COVID-19 Pandemic: A Retrospective Nationwide Cohort Study of >19 Million Primary Care Records Using the OpenSAFELY Platform

by
Alex Orlek
1,
Eleanor Harvey
1,
Louis Fisher
2,
Amir Mehrkar
2,
Seb Bacon
2,
Ben Goldacre
2,
Brian MacKenna
2 and
Diane Ashiru-Oredope
1,*
1
HCAI, Fungal, AMR, AMU & Sepsis Division, UK Health Security Agency, London NW9 5EQ, UK
2
Bennett Institute for Applied Data Science, University of Oxford, Oxford OX2 6GG, UK
*
Author to whom correspondence should be addressed.
Pharmacoepidemiology 2023, 2(2), 168-187; https://doi.org/10.3390/pharma2020016
Submission received: 21 February 2023 / Revised: 30 May 2023 / Accepted: 1 June 2023 / Published: 8 June 2023
(This article belongs to the Special Issue Feature Papers of Pharmacoepidemiology)
Browse Figures
Versions Notes

Abstract

:
COVID-19 pandemic-related pressures on primary care may have driven the inappropriate continuation of antibiotic prescriptions. Yet, prescribing modality (repeat/non-repeat) has not previously been investigated in a pandemic context. With the approval of NHS England, we conducted a retrospective cohort study of >19 million English primary care patient records using the OpenSAFELY-TPP analytics platform. We analysed repeat/non-repeat prescribing frequency in monthly patient cohorts between January 2020 and 2022. In-depth analysis was conducted on January 2020 (“pre-pandemic”) and January 2021 (“pandemic”) cohorts (with a particular focus on repeat prescribing). Per-patient prescribing and clinical conditions were determined by searching primary care records using clinical codelists. Prescriptions in a 6-month lookback period were used to delineate repeat prescribing (≥3 prescriptions) and non-repeat prescribing (1–2 prescriptions). Associations between demographics (e.g., age, sex, ethnicity) and prescribing were explored using unadjusted risk ratios. The frequency of clinical conditions among prescribed patients was examined. Antibiotic prescribing declined from May 2020; non-repeat prescribing declined more strongly than repeat prescribing (maximum declines −26% vs. −11%, respectively). Older patients were at a higher risk of prescribing (especially repeat prescribing). Comorbidities were more common among repeat- vs. non-repeat-prescribed patients. In the pandemic cohort, the most common clinical conditions linked to repeat prescribing were COPD comorbidity and urinary tract infection. Our findings inform the ongoing development of stewardship interventions in England, targeting patient groups wherein there is a high prevalence of repeat prescribing.

1. Introduction

Antimicrobial resistance can be accelerated by the overuse of antimicrobials [1]; therefore, ensuring appropriate antimicrobial prescribing through antimicrobial stewardship is crucial. In England, in 2020, >80% of patient antibiotic prescriptions occurred in primary care, notably in the general practice (GP) setting, which accounted for 73% of patient prescriptions [2]. Hence, primary care is a key target for stewardship interventions. The COVID-19 pandemic has affected antibiotic prescribing and stewardship in primary care. Total antibiotic consumption in the GP setting showed a 9.4% decline between 2019 and 2020 in England (following a more gradual decline in prior years; 10.4% between 2016 and 2019). This decline was at least partly due to the reduced incidence of bacterial pathogens in 2020, resulting from factors such as reduced social mixing [2] and improved infection prevention behaviours (e.g., hand hygiene) [3]. Meanwhile, there is evidence to suggest that optimal primary care antibiotic stewardship behaviour has been disrupted during the pandemic. The pandemic led to a shift towards online consultation, which can increase diagnostic uncertainty, potentially leading to inappropriate prescribing [4]. For example, general practitioners reported a lower threshold for prescribing for respiratory infections [5], and increases in antibiotic prescribing in the dental sector were attributed to restricted access to dental care [2]. Additionally, antibiotic stewardship may have been de-prioritised due to pandemic-related pressures [6,7]. Therefore, there is a need to re-enforce antimicrobial stewardship in the COVID-19 pandemic era.
The UK’s national action plan on antimicrobial resistance outlines ambitions and actions for antimicrobial stewardship [8]. Regarding primary care, a proposed action is to enhance the role of pharmacists in reviewing the dose and duration of prescriptions, especially repeat prescriptions. Through the “Clinical Pharmacists in General Practice” scheme (established in England in 2019), pharmacists work in multidisciplinary primary care teams [9]; therefore, they are well placed to support the review of repeat antibiotic prescribing. Repeat prescribing encompasses long-term repeat prescribing for chronic conditions (e.g., prophylaxis for immunosuppressed patients), as well as shorter-term repeated prescribing for acute conditions that fail to dissipate after a single antibiotic course [10]. Repeat prescribing is clinically warranted in some cases, but judicious discontinuation of repeat prescriptions is an important aspect of optimal antibiotic stewardship. Bias towards maintaining the status quo may lead to repeat prescriptions being inappropriately continued [10]; “status-quo bias” may have been exacerbated during the COVID-19 pandemic, given time-constraints and the de-prioritisation of antibiotic stewardship. Hence, in a pandemic context, antibiotic stewardship in primary care may need to be re-enforced, and repeat antibiotic prescribing is an important focus for stewardship.
A greater understanding of the nature and scale of antibiotic prescribing in primary care can guide the development of stewardship interventions. Previous studies of antibiotic prescribing in England have often used aggregate data published by the national health service (NHS) [11,12]. These data do not include patient-level information (demographics, clinical conditions). OpenSAFELY-TPP is a new secure data platform that enables the near-real time analysis of primary care electronic health records, covering ~24 million people registered with GP practices in England (~40% of the total English population) [13], meaning that it is broadly representative of the English population. The platform can provide detailed information on patient demographics, clinical conditions, and prescriptions.
Here, we use the OpenSAFELY-TPP platform to investigate patterns of repeat and non-repeat antibiotic prescribing in primary care within the context of the COVID-19 pandemic. Total repeat/non-repeat prescribing was assessed from January 2020 through to January 2022. More detailed analysis (broken down by patient demographic characteristics, clinical conditions, and prescribed antibiotic class) was conducted on patient cohorts pre-pandemic (January 2020) and during the pandemic (January 2022). A key aim of this analysis was to highlight patient groups with the highest burden of repeat antibiotic prescribing in the COVID-19 pandemic era. This will inform the ongoing development of antibiotic stewardship interventions, supporting clinical pharmacists working in primary care to review repeat antibiotic prescriptions.

2. Results

2.1. Frequency of Antibiotic Prescribing (Repeat and Non-Repeat) through the COVID-19 Pandemic

Across monthly rolling patient cohorts from January 2020 to January 2022, the mean study cohort population was 19.58 million. The percentage of patients in monthly cohorts receiving repeat prescriptions ranged from 0.93 to 1.18%, while the percentage receiving non-repeat prescriptions ranged from 4.14 to 6.15% (Figure 1A). For both prescribing modes, peak prescribing occurred in the April 2020 cohort (patients registered with GP practices as of 1 April 2020), representing an increase from baseline (January 2020) of ~10% (Figure 1B). The percentage of patients prescribed antibiotics declined in May 2020, with non-repeat prescribing showing a more pronounced decline vs. repeat prescribing (maximum decline of −26.2% from baseline [non-repeat prescribing, November 2020] vs. −10.9% from baseline [repeat prescribing, August 2021]). For both prescribing modes, prescribing increased from ~August 2021, and by January 2022, prescribing was similar to baseline levels (Figure 1B).
Regarding the January 2020 (pre-pandemic) and January 2021 (pandemic) cohorts, which were analysed in more detail (see below), the rate of repeat prescribing declined by −7.27% in the pandemic vs. pre-pandemic cohort, while non-repeat prescribing showed a steeper decline of −24.46% (see Supplementary Results for details).

2.2. Demographics of Antibiotic Prescribing (Repeat and Non-Repeat) in Pre-Pandemic and Pandemic Cohorts

Table 1 shows the demographic characteristics of patients receiving repeat antibiotic prescriptions in pre-pandemic and pandemic cohorts (see Table S1 for non-repeat prescribed patient demographics). Figure 2 and Figure S1 visualise the percent change in the rate of repeat and non-repeat antibiotic prescribing in the pandemic cohort vs. pre-pandemic cohort (categorised according to patient demographics). Across most demographic categories, the percent change in repeat/non-repeat prescribing was negative and similar to the overall percent change vs. pre-pandemic (described above). However, repeat prescribing in young women (aged 18–29) increased 6.72% vs. pre-pandemic (Figure 2). Regarding care home residence status, the decline in non-repeat prescribing was smaller in the care home and nursing home categories (Figure 2). Patients categorised as residing in a “care or nursing home” showed a positive percent change In repeat prescribing. However, this category is small (n = 2505), represents uncertain assignment between care and nursing home, and the result is not reflected in the larger care home and nursing home unambiguous categories, which followed the overall negative trend in repeat prescribing. Percent change in antibiotic prescribing seemed to vary by ethnicity (Figure 2). Notably, compared with the overall trend, there was a substantially steeper reduction in non-repeat prescribing among patients of Chinese ethnicity (−40.23% vs. pre-pandemic). In contrast to the overall trend for repeat prescribing, there was positive percent change vs. pre-pandemic in the following mixed background ethnicities: White and Black African, White and Black Caribbean, and any other mixed background (Figure 2).
Risk ratios (RRs) were used to explore associations between demographic characteristics and repeat/non-repeat antibiotic prescribing (Figure 3, Tables S2 and S3). Overall trends were similar between pandemic/pre-pandemic cohorts. Since changes in prescribing rates are described above, the demographic associations described below are for the pandemic cohort only. Rates of repeat and non-repeat antibiotic prescribing were lower in men than in women across all age brackets (Table 1). The risk of antibiotic prescribing—especially repeat antibiotic prescribing—increased in older patients (Figure 3). For example, compared with women aged 18–29, women over 79 years of age had a 264% increased risk of repeat prescribing vs. a 42% increased risk of non-repeat prescribing (RR 3.64 CI = 3.57–3.72 vs. 1.42 CI = 1.40–1.43). Compared with White British patients, patients belonging to non-White ethnicities were less likely to receive antibiotic prescriptions, especially repeat prescriptions. Those categorised as being of Chinese ethnicity showed the strongest negative association with antibiotic prescribing (88% lower repeat prescribing [RR 0.12 CI = 0.11–0.14] and 79% lower non-repeat prescribing [RR 0.21 CI = 0.20–0.22]) (Figure 3). Care home residency status had a strong effect on prescribing. Residents in care/nursing homes were at higher risk of antibiotic prescribing, especially repeat prescribing, compared to residents of private homes (Figure 3). Compared to London, all other English regions had higher antibiotic prescribing, especially repeat prescribing (Figure S2). Patients from less deprived areas (higher IMD quintile) were less likely to receive antibiotic prescriptions compared with patients from more deprived areas; however, the effect sizes were very modest (Figure S2).
Table
Table 1. Demographic characteristics of primary care patients receiving repeat antibiotic prescriptions in pre-pandemic (January 2020) and pandemic (January 2021) cohorts.
Table 1. Demographic characteristics of primary care patients receiving repeat antibiotic prescriptions in pre-pandemic (January 2020) and pandemic (January 2021) cohorts.
CharacteristicJanuary 2020 (Pre-Pandemic) CohortJanuary 2021 (Pandemic) Cohort
Population
(n = 19,375,208
n Female = 9,756,346
n Male = 9,618,862)		Prescribed Repeat Antibiotics
(n = 206,865
n Female = 129,717
n Male = 77,148)		Prescribing per 1000Population
(n = 19,545,285
n Female = 9,835,177
n Male = 9,710,108)		Prescribed Repeat Antibiotics
(n = 193,517
n Female = 121,616
n Male = 71,901)		Prescribing per 1000Percent Change vs.
January 2020
Age (female patients)
 18–291,789,887 (9.24) (18.35)13,038 (6.30) (10.05)7.281,768,295 (9.05) (17.98)13,746 (7.10) (11.30)7.776.72
 30–391,683,962 (8.69) (17.26)11,029 (5.33) (8.50)6.551,707,559 (8.74) (17.36)11,156 (5.76) (9.17)6.53−0.25
 40–491,526,149 (7.88) (15.64)13,261 (6.41) (10.22)8.691,530,997 (7.83) (15.57)12,700 (6.56) (10.44)8.3−4.53
 50–591,635,345 (8.44) (16.76)19,576 (9.46) (15.09)11.971,654,637 (8.47) (16.82)18,363 (9.49) (15.10)11.1−7.29
 60–691,305,184 (6.74) (13.38)22,772 (11.01) (17.56)17.451,331,364 (6.81) (13.54)20,668 (10.68) (16.99)15.52−11.02
 70–791,090,670 (5.63) (11.18)27,359 (13.23) (21.09)25.081,116,918 (5.71) (11.36)24,437 (12.63) (20.09)21.88−12.78
 >79725,149 (3.74) (7.43)22,682 (10.96) (17.49)31.28725,407 (3.71) (7.38)20,546 (10.62) (16.89)28.32−9.45
Age (male patients)
 18–291,801,657 (9.30) (18.73)6764 (3.27) (8.77)3.751,787,078 (9.14) (18.40)6455 (3.34) (8.98)3.61−3.79
 30–391,732,184 (8.94) (18.01)4305 (2.08) (5.58)2.491,756,215 (8.99) (18.09)4086 (2.11) (5.68)2.33−6.39
 40–491,620,217 (8.36) (16.84)6224 (3.01) (8.07)3.841,629,425 (8.34) (16.78)5889 (3.04) (8.19)3.61−5.92
 50–591,686,327 (8.70) (17.53)11,459 (5.54) (14.85)6.81,705,162 (8.72) (17.56)10,919 (5.64) (15.19)6.4−5.76
 60–691,281,804 (6.62) (13.33)16,188 (7.83) (20.98)12.631,310,721 (6.71) (13.50)14,842 (7.67) (20.64)11.32−10.34
 70–79992,394 (5.12) (10.32)19,662 (9.50) (25.49)19.811,014,499 (5.19) (10.45)18,263 (9.44) (25.40)18−9.14
 >79504,279 (2.60) (5.24)12,546 (6.06) (16.26)24.88507,008 (2.59) (5.22)11,447 (5.92) (15.92)22.58−9.25
England region
 London1,370,757 (7.07)5582 (2.70)4.071,401,485 (7.17)5340 (2.76)3.81−6.43
 East4,473,416 (23.09)50,364 (24.35)11.264,524,344 (23.15)46,804 (24.19)10.34−8.11
 East Midlands3,346,011 (17.27)37,326 (18.04)11.163,368,011 (17.23)35,476 (18.33)10.53−5.58
 North East924,774 (4.77)11,482 (5.55)12.42928,030 (4.75)10,503 (5.43)11.32−8.85
 North West1,695,187 (8.75)23,100 (11.17)13.631,706,464 (8.73)22,020 (11.38)12.9−5.31
 South East1,316,373 (6.79)13,095 (6.33)9.951,323,206 (6.77)11,985 (6.19)9.06−8.95
 South West2,701,539 (13.94)29,560 (14.29)10.942,726,547 (13.95)27,402 (14.16)10.05−8.15
 West Midlands777,478 (4.01)7135 (3.45)9.18778,032 (3.98)6633 (3.43)8.53−7.1
 Yorkshire and The Humber2,760,064 (14.25)29,119 (14.08)10.552,778,667 (14.22)27,246 (14.08)9.81−7.06
 Missing9609 (0.05)102 (0.05)10.6210,499 (0.05)108 (0.06)10.29−3.09
Ethnicity
 British13,336,088 (68.83)180,353 (87.18)13.5213,424,038 (68.68)168,923 (87.29)12.58−6.95
 African237,664 (1.23)806 (0.39)3.39248,818 (1.27)799 (0.41)3.21−5.31
 Any other Asian
background		280,197 (1.45)1085 (0.52)3.87290,504 (1.49)991 (0.51)3.41−11.9
 Any other Black
background		88,421 (0.46)454 (0.22)5.1393,335 (0.48)441 (0.23)4.72−7.98
 Any other Mixed
background		90,785 (0.47)432 (0.21)4.7694,187 (0.48)462 (0.24)4.913.08
 Any other White
background		1,671,589 (8.63)8543 (4.13)5.111,718,196 (8.79)7836 (4.05)4.56−10.76
 Any other ethnic group304,982 (1.57)1252 (0.61)4.11320,839 (1.64)1202 (0.62)3.75−8.74
 Bangladeshi85,337 (0.44)445 (0.22)5.2189,091 (0.46)387 (0.20)4.34−16.7
 Caribbean105,798 (0.55)615 (0.30)5.81106,382 (0.54)618 (0.32)5.81−0.06
 Chinese133,388 (0.69)216 (0.10)1.62134,678 (0.69)211 (0.11)1.57−3.25
 Indian528,449 (2.73)2591 (1.25)4.9547,831 (2.80)2348 (1.21)4.29−12.58
 Irish102,020 (0.53)1523 (0.74)14.93102,812 (0.53)1497 (0.77)14.56−2.46
 Pakistani372,857 (1.92)2505 (1.21)6.72385,318 (1.97)2396 (1.24)6.22−7.44
 White and Asian45,869 (0.24)239 (0.12)5.2148,185 (0.25)238 (0.12)4.94−5.2
 White and Black African42,435 (0.22)166 (0.08)3.9144,412 (0.23)192 (0.10)4.3210.51
 White and Black Caribbean53,562 (0.28)330 (0.16)6.1655,520 (0.28)381 (0.20)6.8611.38
 Missing1,895,767 (9.78)5310 (2.57)2.81,841,139 (9.42)4595 (2.37)2.5−10.9
IMD quintile
 13,688,616 (19.04)41,591 (20.11)11.283,708,065 (18.97)39,089 (20.20)10.54−6.51
 23,806,589 (19.65)40,311 (19.49)10.593,827,052 (19.58)37,623 (19.44)9.83−7.17
 34,113,190 (21.23)44,300 (21.41)10.774,133,488 (21.15)41,411 (21.40)10.02−6.98
 43,861,551 (19.93)40,581 (19.62)10.513,883,672 (19.87)37,744 (19.50)9.72−7.52
 53,529,411 (18.22)36,074 (17.44)10.223,548,646 (18.16)33,424 (17.27)9.42−7.85
 Missing375,851 (1.94)4008 (1.94)10.66444,362 (2.27)4226 (2.18)9.51−10.82
Residence
 Private home19,102,126 (98.59)199,286 (96.34)10.4319,276,300 (98.62)186,730 (96.49)9.69−7.15
 Care home56,979 (0.29)2908 (1.41)51.0456,385 (0.29)2583 (1.33)45.81−10.24
 Care or nursing home2505 (0.01)117 (0.06)46.712322 (0.01)114 (0.06)49.15.11
 Nursing home48,843 (0.25)2678 (1.29)54.8347,447 (0.24)2389 (1.23)50.35−8.17
 Missing164,755 (0.85)1876 (0.91)11.39162,831 (0.83)1701 (0.88)10.45−8.26
Table shows N (%) across demographic categories for total population and total population prescribed repeat antibiotics in January 2020 and January 2021 cohorts. For age/sex strata, % is shown relative to total population, total female/male population, total repeat prescribed population, total female/male repeat prescribed population. The rate of antibiotic prescribing is calculated per 1000, and percent change in the rate of antibiotic prescribing is calculated for January 2021 vs. January 2020.

2.3. Patterns of Antibiotic Prescribing (Repeat and Non-Repeat) across Patient Clinical Conditions and Antibiotic Classes in Pre-Pandemic and Pandemic Cohorts

Figure 4A shows the relative frequency of clinical conditions (divided into comorbidities and indications) among patients prescribed repeat and non-repeat antibiotics in pre-pandemic and pandemic cohorts. The most frequent clinical conditions linked to repeat prescribing in the pandemic cohort were COPD (comorbidity), followed by urinary tract infection (indication), COPD exacerbation/lower respiratory tract infection (indication), splenectomy (comorbidity), skin & soft tissue infection (indication), and acne (indication) (Figure 4A). The relative frequency of indications was similar between repeat and non-repeat prescribed patient groups. Conversely, comorbidities (COPD, splenectomy, cancer immunosuppression, sickle cell disease) were more common among repeat prescribed patients. For example, in the pandemic cohort, COPD comorbidity was found in 10% of patients receiving non-repeat prescriptions compared with 22% of patients on repeat prescriptions. Taking all clinical conditions together, in pre-pandemic and pandemic cohorts, respectively, one or more of the clinical conditions was found in 49.8 and 43.6% of patients compared with 36.8 and 29.4% of patients prescribed non-repeat. Figure 4B shows percent change in patients with clinical conditions prescribed repeat/non-repeat antibiotics in the pandemic vs. the pre-pandemic cohort (note that the percent changes may reflect changes in prescribing behaviour and/or changes in incidence of clinical conditions). There were declines in repeat and non-repeat prescribing for patients with indications (observed across all indications). The most notable declines were the 56% and 72% reductions (repeat and non-repeat prescribing, respectively) among patients with COPD exacerbation/lower respiratory tract infection. Compared with prescribing for patients with indications, prescribing for patients with comorbidities generally declined less, and there were moderate increases in the number of patients with splenectomy, cancer immunosuppression, and sickle cell disease prescribed repeat antibiotics.
Figure 5A and Table S4 show the rate of repeat/non-repeat prescribing per antibiotic class (13 antibiotic classes, as defined by the British National Formulary [BNF]). The antibiotic classes used most frequently for repeat prescribing were tetracyclines, macrolides, sulfonamides and trimethoprim, urinary tract infection antibiotics (nitrofurantoin and methenamine hippurate), and penicillins. There were some differences in the profile of antibiotic classes for repeat vs. non-repeat prescribing, including increased relative frequency of urinary tract infection antibiotics but lower relative frequency of metronidazole, tinidazole, and ornidazole (Figure S3). Patterns of prescribing by antibiotic class varied by clinical condition. In contrast to the pattern for all patients, for patients with cancer immunosuppression, sickle cell disease and splenectomy comorbidities, penicillins predominated both repeat and non-repeat prescribing (Figure S4). Macrolides were the most frequently prescribed class among patients with otitis media. Sulfonamides and trimethoprim, followed by urinary tract infection antibiotics, were the most frequently prescribed classes linked to urinary tract infections (Figure S4). Doxycycline was the most frequently prescribed tetracycline sub-class, except for patients with acne, where lymecycline prescribing predominated (Figure S5). Figure 5B compares the frequency of repeat/non-repeat prescribed antibiotic classes in pandemic vs. pre-pandemic cohorts. There were strong declines in the non-repeat prescribing of the following antibiotic classes: tetracyclines; macrolides; urinary tract infection antibiotics; penicillins; antileprotic drugs; antituberculosis drugs; and aminoglycosides. Conversely, non-repeat quinolone prescribing increased by 16%. Frequencies of repeat prescribing across antibiotic classes generally showed relatively moderate positive/negative percent changes in the pandemic vs. pre-pandemic cohort; however, repeat prescribing of antituberculosis drugs showed a strong 27% decline vs. the pre-pandemic cohort.

3. Discussion

This large population-based study investigated patterns of antibiotic prescribing prior to and during the COVID-19 pandemic in English primary care. Antibiotic prescribing was assessed by prescribing modality (repeat and non-repeat prescribing), and prescribing patterns were analysed in relation to patient demographics and clinical conditions. Repeat prescribing is an important focus for antibiotic stewardship in the COVID-19 pandemic era because “status quo bias” in prescribing decisions may have been exacerbated during the pandemic due to time-constraints and the de-prioritisation of antibiotic stewardship [10]. This study reveals the burden of repeat antibiotic prescribing during the COVID-19 pandemic in England, and our findings have guided the ongoing development of primary care antibiotic stewardship interventions (discussed below).
Analysis of rolling monthly patient cohorts (patients registered with GP practices as of 1 January 2020 through to 1 January 2022) demonstrated that, for both repeat and non-repeat prescribing, the percentage of primary care patients receiving antibiotic prescriptions increased by ~10% following the initial onset of the pandemic (peaking in the April 2020 cohort, covering prescribing in a 6-month window through to 1 April 2020). A strict national COVID-19 lockdown was introduced in England on 23 March 2020 [14,15]. Repeat and non-repeat prescribing reduced following this lockdown, corroborating existing reports [2,12,16,17,18]. In England, most legal restrictions on social contact were removed on 19 July 2021 [14], after which we found that antibiotic prescribing increased towards pre-pandemic levels. The reduced prescribing observed in cohorts from 1 May 2020 to 1 August 2021 likely reflects a combination of factors, including reduced incidence of bacterial infections (given social distancing measures and improved infection prevention behaviours) and reduced access to/demand for primary care (as non-essential appointments were de-prioritised due to concerns around infection risk, or to “protect the National Health Service”) [16]. There was a more pronounced reduction in non-repeat prescribing compared with repeat prescribing. This suggests that, among patients receiving repeat prescriptions, repeat antibiotic prescribing rates were less influenced by COVID-19-related changes in primary care access/provision or infection transmission rates. A more in-depth analysis of pandemic (January 2021) and pre-pandemic (January 2020) patient cohorts can better inform interpretations. Specifically, our analysis of prescriptions according to demographic characteristics indicated that patients prescribed repeat antibiotics were more likely to be older and/or reside in care/nursing homes. These patients are also more likely to have age-related comorbidities such as COPD and cancer immunosuppression, which can be managed through long-term repeat prophylactic prescribing. The consistency of long-term repeat prescriptions for comorbidities plausibly explains (at least in part) the lower reduction in repeat vs. non-repeat prescribing rates during the pandemic. Indeed, our analysis of prescriptions according to clinical conditions demonstrated that levels of repeat prescribing for patients with some comorbidities, including cancer immunosuppression, showed a moderate increase in the pandemic vs. pre-pandemic cohorts, whereas there were declines in prescribing (especially non-repeat prescribing) for patients with indications (infections).
The most pronounced reduction observed in the pandemic vs. pre-pandemic cohort was for patients with COPD exacerbation/lower respiratory tract infection (56% and 72% reductions for repeat and non-repeat prescribing, respectively). This finding aligns with a previous study of primary care prescribing for respiratory tract infections in England, which found that prescriptions roughly halved when comparing prescribing rates during Winter 2020–2021 with the previous (pre-pandemic) Winter [11]. Changes in prescribing rates in pandemic vs. pre-pandemic cohorts also varied by patient residency and ethnicity. Specifically, there were steeper declines in non-repeat prescribing in private homes (when compared with care/nursing homes) and in patients of Chinese ethnicity (compared with other ethnicities). Overall, these patterns are likely to be driven by (variable) reductions in the incidence of infections, including bacterial respiratory infections. Supporting this, COVID-19 surveillance data showed that infection rates were lower in Chinese patients vs. all other recorded ethnicity categories [19]. Meanwhile, in care homes, COVID-19 infection risk was elevated due to the frequent close contact required for care and indoor setting [20]. Although COVID-19 infections alone may not have been a key driver of antibiotic prescribing (assuming sufficient community diagnostic testing in England) [21], COVID-19 infection risk is likely to be a proxy for bacterial respiratory infection risk.
Our findings highlight differences in antibiotic prescribing, providing evidence that requires further consideration in future work. This includes understanding the levels of unwarranted prescribing and assessment of health inequalities. More deprived areas were moderately more likely to receive repeat and non-repeat antibiotic prescriptions, potentially reflecting poorer health status/increased infection risk. There was lower antibiotic prescribing among non-White ethnic minorities, especially Chinese patients. While this may be partly explained by younger age among non-White ethnic minorities [22], the disparity may also reflect reduced engagement with primary care services (especially regarding Chinese ethnicity, which is not an outlier in terms of age profile compared with other non-White categories [22], yet shows lower prescribing vs. all other ethnicity categories). Comparing the pandemic and pre-pandemic cohorts, there were increases in repeat prescribing among mixed-ethnicity Black/Caribbean patients.
A key aim of our study was to inform antimicrobial stewardship, particularly in relation to repeat prescribing. The findings described above regarding rates of repeat prescribing highlight the importance of regular antibiotic reviews in older patients, care/nursing home residents, and patients with comorbidities. Regarding antibiotic classes, whereas tetracycline prescribing showed the highest overall frequency, for patients with immunosuppressive comorbidities (cancer immunosuppression, splenectomy, sickle cell disease), penicillins were the most prescribed antibiotic class. Penicillins are recommended as first-line chemoprophylactics for asplenic (prior splenectomy) and sickle cell disease patients [23]. While prophylaxis is required for asplenic patients, a stronger evidence base to guide prophylactic prescribing in adult sickle cell patients would be beneficial [24,25]. Our analysis of the relative frequency of antibiotic prescribing by clinical condition can help further target stewardship interventions for repeat prescribing. The most frequent clinical conditions linked to repeat prescribing in the pandemic cohort were COPD comorbidity and urinary tract infection. Although acne was not among the most common clinical conditions linked to repeat prescribing, repeat prescribing for acne has previously been criticised due to over-prescribing and alternative non-antibiotic treatments [26]. Based on these findings, primary care antimicrobial stewardship intervention toolkits targeting acne and COPD have been developed and published [27,28], and a toolkit targeting urinary tract infection is in development. In future, knowledge on health inequalities will be embedded into stewardship tool development.
A strength of this study is the use of OpenSAFELY-TPP data (representing ~40% of general practices), which provided a national-level understanding of the burden of antibiotic prescribing across patient-level demographics and clinical characteristics. A particularly novel aspect of this study was the distinction between repeat and non-repeat prescribing. This has enabled us to better target stewardship tools towards high-burden repeat prescribing. A limitation of the study, as with all large electronic health record database studies, is that not all clinical conditions are coded upon infection diagnosis/patient review or that they may be coded inaccurately. In addition, below 50% of patients prescribed repeat antibiotics were accounted for with the clinical conditions added to codelists; therefore, the burden of repeat prescribing across clinical conditions is incompletely understood. Various definitions of repeat prescribing exist, and different definitions may influence study results. Here, we opted for a simple pragmatic definition by using a somewhat arbitrary 6-month lookback window (without incorporating strict stipulations on dosage or duration between sequential prescriptions) [10]. Likewise, the definition of indications (based on the intersection of antibiotic prescribing and clinical condition within a lookback window) was somewhat arbitrary. Another study limitation is that our main analyses focused on only two cohorts (pre-pandemic/pandemic); therefore, changes in prescribing patterns across patient characteristics through the course of the pandemic cannot be fully captured. Finally, a key limitation is that our study is an observational study that forgoes adjustment for potential confounding. For example, the elevated risk of prescribing in regions outside London is likely to be explained, at least partly, by an older average patient age outside London. Other variables not included in our analysis are likely to be important contributing factors for antibiotic prescribing; for example, the (variable) incidence of bacterial infections. Further analyses using multivariable models to control confounding would better measure the patient demographic and clinical risk factors for repeat and non-repeat antibiotic prescribing. In the meantime, our study provides valuable initial insights into a novel research area (repeat prescribing in a COVID-19 pandemic context), informing the rapid development of antibiotic stewardship interventions.

4. Materials and Methods

4.1. OpenSAFELY-TPP Data Source and Study Design

We conducted a retrospective cohort study using English primary care electronic health record data retrieved from the OpenSAFELY-TPP data analytics platform. Overall, we aimed to investigate repeat/non-repeat antibiotic prescribing outcome variables in relation to demographic and clinical condition exposure variables. The OpenSAFELY framework uses a “study definition” script to query patient records; variables are defined in the study definition using in-built variable extractor functions; extracted tabular data (one row per patient) is saved to a secure server [29]. We used primary care records managed by The Phoenix Partnership (TPP) (a major primary care software provider in England). Outputs released from the secure server were subject to disclosure control (see below for details). Data were retrieved on two main patient cohorts comprising all patients registered at a GP practice as of 1 January 2020, and as of 1 January 2021. These cohorts represent periods before and during the COVID-19 pandemic, respectively (i.e., “pre-pandemic” and “pandemic” cohorts). Cohorts were restricted to patients in England known to be aged 18–120, male or female, and not deceased. As described below, antibiotic prescribing in cohorts was measured using a 6-month lookback window. Therefore, the measure of prescribing in the pandemic cohort covers a period from 1 July 2020 to 1 January 2021. In England, this period encompasses the initially reduced COVID-19-induced restrictions in July–August 2020, followed by increases in COVID-19 cases, the emergence of the Alpha variant (B.1.1.7), and a second national lockdown in November 2020 [14,15].
For the pre-pandemic and pandemic cohorts, in addition to age and sex, other categorical demographic exposure variables were retrieved for each patient, and are listed as follows: patient geography and deprivation (derived from GP practice address), ethnicity, and care home residency status. Patient geography was categorised into nine English regions—the Nomenclature of Territorial Units for Statistics 1 (NUTS 1) regions. Deprivation was measured as Index of Multiple Deprivation (IMD) quintile from 1 to 5 (most to least deprived). Ethnicity was divided into the 16 categories of the 2001 UK census [30] and ascertained from patient primary care records, if present. If not, ethnicity was ascertained from hospital Secondary Uses Service (SUS) data. Care home residency status was determined using a previously described address linkage method that matches GP registered addresses to care home addresses [31]. The method yields the following residency categories: care home, nursing home, either care home or nursing home, private home (neither care home nor nursing home). Missing data were handled as follows: There were no missing data for age and sex since the data were restricted on these variables, as described above. For all other demographic variables, missing or invalid data (e.g., IMD values outside valid bounds of 1–32,844) were included in downstream analysis as a “Missing” category.
Patient-level clinical variables (antibiotic prescribing and clinical conditions) were determined by searching primary care record event data using codelists. Clinical condition exposure variables were binary categorical (i.e., based on searching their primary care record, each patient either had or did not have a given condition). Patient clinical conditions were conceptually divided into comorbidities (chronic conditions predisposing to increased infection risk and subsequent antibiotic use) and indications (infections treated with antibiotics). The following comorbidities were investigated: chronic obstructive pulmonary disease (COPD), cancer immunosuppression, sick cell disease, and splenectomy (i.e., prior surgical spleen removal). Aside from cancer immunosuppression, both sickle cell disease and splenectomy are linked to immunosuppression. The spleen regulates immune responses, and sickle cell disease is associated with reduced spleen function. Sickle cell disease patients may also require surgical spleen removal (splenectomy) [32]. The following indications were investigated: acne, COPD exacerbation/lower-respiratory tract infection, dental infection, otitis media, skin and soft tissue infection, and urinary tract infection. A patient was considered to have a comorbidity if the respective comorbidity was recorded at any time during or before the cohort index date. An indication was defined by determining the date of first antibiotic prescription in a 6-month lookback window (see below for details) and then retrospectively considering significant events within 6 months of the date of initial prescription (+/− 3 months either side). If the respective indication was recorded within this window for a given patient, then the patient was considered to have the indication.
Antibiotic prescribing was assessed by searching primary care records using antibiotic codelists, covering 13 antibiotic classes, as defined by the British National Formulary (BNF) (https://openprescribing.net/bnf/0501, accessed on 16 May 2022). The number of antibiotic prescriptions within a 6-month lookback window prior to the cohort index dates was computed, yielding initial count variables, from which binary categorical repeat and non-repeat prescribing variables were derived. Specifically, per-class repeat and non-repeat prescribing was determined as follows: repeat antibiotic prescribing was defined as a patient receiving 3 or more prescriptions of a given antibiotic class in the 6-month lookback window; non-repeat prescribing was defined as 1–2 antibiotic prescriptions in a 6-month lookback window. Repeat and non-repeat prescribing was also determined for all antibiotics combined (all-class prescribing). In this case, repeat prescribing was defined non-stringently as 3 or more antibiotic prescriptions, irrespective of whether this involved consecutive prescriptions of the same antibiotic class. While a key focus of this study was repeat antibiotic prescribing, the rationale for distinguishing both repeat and non-repeat prescribing was to assist the interpretation of results. Besides assessing the number of antibiotic prescriptions in the 6-month lookback window, for all-class prescribing, the date of the first prescription within this window was also recorded and subsequently used to derive the clinical condition indication variables (see above). For a given patient, if the date of first prescription was missing (due to invalid date or absence of any antibiotic prescriptions), the indication was recorded as absent [33].
In addition to determining antibiotic prescribing for the pre-pandemic and pandemic cohorts, a monthly rolling measure of the frequency of repeat and non-repeat prescribing was calculated using patient cohorts from 1 January 2020 to 1 January 2022. For monthly rolling cohorts, data on patient demographics and clinical conditions were not retrieved (due to practical limitations regarding required computation time).

4.2. Statistics and Reproducibility

Using data from the OpenSAFELY-TPP platform, the frequency of repeat and non-repeat antibiotic prescribing was calculated as a rate per total study population (expressed per 1000 patients for all-class prescribing and per 100,000 patients for per-class prescribing). For the monthly rolling cohort analysis, repeat and non-repeat all-class prescribing from January 2020–January 2022 was visualised as monthly frequency and monthly percent change in frequency of prescribing vs. January 2020 (baseline). For the January 2020 (pre-pandemic) and January 2021 (pandemic) cohorts, both all-class and per-class prescribing rates and percent change were calculated. In addition, demographic and clinical condition exposures were investigated.
To investigate associations between demographic exposures and the frequency of all-class repeat and non-repeat antibiotic prescribing, unadjusted risk ratios (and corresponding 95% confidence intervals and chi-square p values) were calculated using the ‘oddsratio.wald’ function from the ‘epitools’ R package [34]. Adjusted analysis using regression modelling was not conducted. A Bonferroni-corrected alpha = 0.00024 (52 comparisons across 4 patient groups [repeat and non-repeat prescribed patients in January 2020 and 2021 cohorts]) was used to guide the interpretation of risk ratios. We also assessed the percent change in the frequency of repeat and non-repeat prescribing in pandemic vs. pre-pandemic cohorts—categorised according to demographic characteristics.
Patterns of antibiotic prescribing were explored across patient clinical conditions. Within each patient subgroup (repeat/non-repeat prescribed patients in pre-pandemic and pandemic cohorts), the relative frequency of clinical conditions was examined; the percent change in number of repeat/non-repeat prescribed patients with clinical conditions was compared in pandemic vs. pre-pandemic cohorts.
Initial data management and retrieval was carried out using Python. All analysis code is open-source (https://github.com/opensafely/LT-repeat-antimicrobial-prescribing, accessed on 5 June 2023), and codelists used to identify patient characteristics are freely available for inspection and re-use by anyone (https://github.com/opensafely/LT-repeat-antimicrobial-prescribing/tree/main/codelists, accessed on 5 June 2023). Downstream statistical analysis was conducted using R.

4.3. Data Security and Disclosure Control

All data were linked, stored, and analysed securely within the OpenSAFELY-TPP platform: https://opensafely.org, accessed on 5 June 2023. These data comprise pseudonymised data such as coded diagnoses, medications, and physiological parameters. No free text data are included. All code is shared openly for review and re-use under MIT open license https://github.com/opensafely/LT-repeat-antimicrobial-prescribing, accessed on 5 June 2023. Detailed pseudonymised patient data are potentially re-identifiable and therefore not shared. Results from our analysis included counts of patients receiving repeat/non-repeat prescriptions across antibiotic classes and clinical conditions. These data included small counts to reduce the risk of disclosure prior to data release; small counts (≤7) were redacted, and then counts were rounded to the nearest 5 [35].

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/pharma2020016/s1, Table S1: Demographic characteristics of primary care patients receiving non-repeat antibiotic prescriptions in pre-pandemic (January 2020) and pandemic (January 2021) cohorts; Table S2: Unadjusted risk ratios indicating associations between patient demographic characteristics and repeat prescribing in January 2020 (pre-pandemic) and January 2021 (pandemic) cohorts; Table S3: Unadjusted risk ratios indicating associations between patient demographic characteristics and non-repeat prescribing in January 2020 (pre-pandemic) and January 2021 (pandemic) cohorts; Table S4: Rate of repeat and non-repeat antibiotic prescribing per 100,000 patients, in January 2020 and January 2021 cohorts (4 patient groups), broken down by antibiotic class; Figure S1: Percentage change in the rate of repeat and non-repeat antibiotic prescribing among patients in the January 2021 (pandemic) cohort vs January 2020 (pre-pandemic) cohort, broken down by demographic characteristics; Figure S2: Log risk ratios and 95% confidence intervals showing unadjusted associations between patient demographic characteristics and frequency of repeat and non-repeat antibiotic prescribing in patients from the January 2020 (pre-pandemic) and January 2021 (pandemic) cohorts (4 patient groups); Figure S3: Relative frequency of patients prescribed antibiotic classes is shown as a percentage of total per class prescribing (sum of patients, across all classes), within each patient group; Figure S4: The relative frequency of prescribed antibiotic classes is shown as a percentage of total patients within each patient group, broken down by patient clinical condition. Patient groups represent repeat and non-repeat prescribing of a given antibiotic class in January 2020 and 2021 cohorts; Figure S5: The relative frequency of prescribed tetracycline sub-classes is shown as a percentage of total patients within each patient group, broken down by patient clinical condition. Patient groups represent repeat and non-repeat prescribing of a given antibiotic class in January 2020 and 2021 cohorts.

Author Contributions

Conceptualization, B.M. and D.A.-O.; Data curation, S.B.; Formal analysis, A.O.; Funding acquisition, B.G. and D.A.-O.; Investigation, A.O., E.H. and D.A.-O.; Methodology, A.O., E.H., B.M. and D.A.-O.; Project administration, A.O., E.H., L.F. and A.M.; Resources, S.B. and D.A.-O.; Software, A.O., L.F., S.B. and B.G.; Supervision, B.G., B.M. and D.A.-O.; Validation, L.F. and A.M.; Writing—original draft, A.O. and E.H.; Writing—review and editing, A.O., E.H., L.F., A.M., S.B., B.G., B.M. and D.A.-O. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by UK Health Security Agency. The views expressed are those of the authors and not necessarily those of the NIHR, NHS England, UK Health Security Agency, or the Department of Health and Social Care. This research used data assets made available as part of the Data and Connectivity National Core Study led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref MC_PC_20058). In addition, the OpenSAFELY Platform is supported by grants from the Wellcome Trust (222097/Z/20/Z); MRC (MR/V015757/1, MC_PC-20059, MR/W016729/1); NIHR (NIHR135559, COV-LT2-0073), and Health Data Research UK (HDRUK2021.000, 2021.0157).

Institutional Review Board Statement

NHS England is the data controller; TPP is the data processor; and the researchers on OpenSAFELY are acting with the approval of NHS England. This implementation of OpenSAFELY is hosted within the TPP environment, which is accredited to the ISO 27001 information security standard and is NHS IG Toolkit compliant [36,37]. Patient data have been pseudonymised for analysis, and linkage was performed using industry-standard cryptographic hashing techniques. All pseudonymised datasets transmitted for linkage onto OpenSAFELY are encrypted; access to the platform is only granted via a virtual private network (VPN) connection—restricted to a small group of researchers. The researchers hold contracts with NHS England, and only access the platform to initiate database queries and statistical models. All database activity is logged; only aggregate statistical outputs leave the platform environment following best practice for the anonymisation of results such as statistical disclosure control for low cell counts [38]. The OpenSAFELY research platform adheres to the obligations of the UK General Data Protection Regulation (GDPR) and the Data Protection Act 2018. In March 2020, the Secretary of State for Health and Social Care used powers under the UK Health Service (Control of Patient Information) Regulations 2002 (COPI) to require organisations to process confidential patient information for the purposes of protecting public health, providing healthcare services to the public and monitoring and managing the COVID-19 outbreak and incidents of exposure; this sets aside the requirement for patient consent [39]. Taken together, these provide the legal bases to link patient datasets on the OpenSAFELY platform. GP practices, from which the primary care data are obtained, are required to share relevant health information to support the public health response to the pandemic, and have been informed of the OpenSAFELY analytics platform. In accordance with the NHS Health Research Authority guidelines, this study does not require ethical approval as it focuses on service evaluation (and the further development of antimicrobial stewardship initiatives) [40].

Informed Consent Statement

Not applicable: The OpenSAFELY research platform adheres to the obligations of the UK General Data Protection Regulation (GDPR) and the Data Protection Act 2018. In March 2020, the Secretary of State for Health and Social Care, under the UK Health Service (Control of Patient Information) Regulations 2002 (COPI), passed legislation that meant that organisations werenow required to process confidential patient information for the purposes of protecting public health, providing healthcare services to the public, and monitoring and managing the outbreak of COVID-19 and incidents of exposure—setting aside the requirement for patient consent [39]. Taken together, these provide the legal bases to link patient datasets on the OpenSAFELY platform. GP practices, from which the primary care data are obtained, are required to share relevant health information to support the public health response to the pandemic, and have been informed of the OpenSAFELY analytics platform.

Data Availability Statement

All data relevant to the study are included in the article or have been uploaded as supplementary information. All code for data management and analyses in addition to the prespecified protocol are archived at: https://github.com/opensafely/LT-repeat-antimicrobial-prescribing. All codelists for identifying exposures, covariates, and outcomes are openly shared at https://codelists.opensafely.org, accessed on 5 June 2023. The platform is accessible via a virtual private network connection—restricted to a small group of researchers.

Acknowledgments

We are grateful for the generous support received from the TPP Technical Operations team, and from the information governance and database teams at NHS England/NHSX. We would also like to thank Naomi Fleming, Kieran Hand, Helen Kilminster, and David Ladenheim for their involvement in broader long term and repeated antibiotics project; Helen Kilminster and David Ladenheim for providing anonymised Primary Care Network data; and Shazia Patel for co-developing the antimicrobial stewardship booklets as a direct result of this study.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the study, in the design, collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication. Over the past five years, B.G. has received research funding from the Laura and John Arnold Foundation, the NHS National Institute for Health Research (NIHR), the NIHR School of Primary Care Research, the NIHR Oxford Biomedical Research Centre, the Mohn-Westlake Foundation, NIHR Applied Research Collaboration Oxford and Thames Valley, the Wellcome Trust, the Good Thinking Foundation, Health Data Research UK (HDRUK), the Health Foundation, and the World Health Organisation. He also receives personal income from speaking and writing for lay audiences on the misuse of science. B.M. is also employed by NHS England, working on medicinal policy, and is a clinical lead for primary care medicines data.

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Figure 1. Monthly measure of repeat and non-repeat antibiotic prescribing patterns from January 2020 to January 2022. (A) Percentage of study population prescribed antibiotics. (B) Percent change in study population prescribed antibiotics relative to baseline (January 2020). Black dotted lines indicate timepoints of key COVID-19 pandemic-related legal restrictions to social contact in England: the first national lockdown on 23 March 2020; and the removal of most legal restrictions to social contact on 19 July 2021.
Figure 1. Monthly measure of repeat and non-repeat antibiotic prescribing patterns from January 2020 to January 2022. (A) Percentage of study population prescribed antibiotics. (B) Percent change in study population prescribed antibiotics relative to baseline (January 2020). Black dotted lines indicate timepoints of key COVID-19 pandemic-related legal restrictions to social contact in England: the first national lockdown on 23 March 2020; and the removal of most legal restrictions to social contact on 19 July 2021.
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Figure 2. Percentage change in the rate of repeat and non-repeat antibiotic prescribing among patients in the January 2021 (pandemic) cohort vs. January 2020 (pre-pandemic) cohort—categorised by the following demographic characteristics: age and sex strata; care home residency status; ethnicity. The red and blue dashed lines indicate the overall percent change in the rate of repeat and non-repeat prescribing, respectively. See Figure S1 for an equivalent visualisation for the following demographic characteristics: index of multiple deprivation (IMD) quintile; England region.
Figure 2. Percentage change in the rate of repeat and non-repeat antibiotic prescribing among patients in the January 2021 (pandemic) cohort vs. January 2020 (pre-pandemic) cohort—categorised by the following demographic characteristics: age and sex strata; care home residency status; ethnicity. The red and blue dashed lines indicate the overall percent change in the rate of repeat and non-repeat prescribing, respectively. See Figure S1 for an equivalent visualisation for the following demographic characteristics: index of multiple deprivation (IMD) quintile; England region.
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Figure 3. Log risk ratios and 95% confidence intervals showing unadjusted associations between patient demographic characteristics and frequency of repeat and non-repeat antibiotic prescribing in patients from the January 2020 (pre-pandemic) and January 2021 (pandemic) cohorts (four patient groups). Log risk ratios are calculated relative to the reference factor levels for the following demographic characteristics: age/sex strata; ethnicity; care home residency status. Log risk ratios > 0 indicate higher risk of prescribing vs. the reference factor level (and vice versa).
Figure 3. Log risk ratios and 95% confidence intervals showing unadjusted associations between patient demographic characteristics and frequency of repeat and non-repeat antibiotic prescribing in patients from the January 2020 (pre-pandemic) and January 2021 (pandemic) cohorts (four patient groups). Log risk ratios are calculated relative to the reference factor levels for the following demographic characteristics: age/sex strata; ethnicity; care home residency status. Log risk ratios > 0 indicate higher risk of prescribing vs. the reference factor level (and vice versa).
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Figure 4. (A) Relative frequency of clinical conditions among patients prescribed repeat and non-repeat antibiotics in January 2020 and 2021 (four patient groups). Low frequencies (values < 1) are indicated above bars. (B) Percent change in frequency of clinical conditions among patients prescribed repeat and non-repeat antibiotics in January 2021 vs. January 2020. Clinical conditions can be delineated as indications (urinary tract infection, COPD exacerbation/lower respiratory tract infection, skin and soft tissue infection, acne, otitis media, dental infection) and co-morbidities (COPD, splenectomy, cancer immunosuppression, sickle cell disease). See methods for details. The ‘Any clinical condition’ category reflects patients with any of the defined indications and comorbidities. Note that patients in our analysis may be assigned multiple clinical conditions.
Figure 4. (A) Relative frequency of clinical conditions among patients prescribed repeat and non-repeat antibiotics in January 2020 and 2021 (four patient groups). Low frequencies (values < 1) are indicated above bars. (B) Percent change in frequency of clinical conditions among patients prescribed repeat and non-repeat antibiotics in January 2021 vs. January 2020. Clinical conditions can be delineated as indications (urinary tract infection, COPD exacerbation/lower respiratory tract infection, skin and soft tissue infection, acne, otitis media, dental infection) and co-morbidities (COPD, splenectomy, cancer immunosuppression, sickle cell disease). See methods for details. The ‘Any clinical condition’ category reflects patients with any of the defined indications and comorbidities. Note that patients in our analysis may be assigned multiple clinical conditions.
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Figure 5. (A) Rate of repeat and non-repeat antibiotic prescribing per 100,000 patients, in January 2020 and January 2021 cohorts (four patient groups)—categorised according to antibiotic class. Note that the y axis is on a log10 scale. Antibiotic classes are ordered by frequency of prescribing among the January 2021 repeat prescribed patient group (from most frequent [tetracyclines] to least frequent [aminoglycosides]). See Table S4 for an equivalent tabular presentation of per-class prescribing rates. (B) Percent change in frequency of repeat and non-repeat antibiotic prescribing in January 2021 vs. January 2020—categorised according to antibiotic class.
Figure 5. (A) Rate of repeat and non-repeat antibiotic prescribing per 100,000 patients, in January 2020 and January 2021 cohorts (four patient groups)—categorised according to antibiotic class. Note that the y axis is on a log10 scale. Antibiotic classes are ordered by frequency of prescribing among the January 2021 repeat prescribed patient group (from most frequent [tetracyclines] to least frequent [aminoglycosides]). See Table S4 for an equivalent tabular presentation of per-class prescribing rates. (B) Percent change in frequency of repeat and non-repeat antibiotic prescribing in January 2021 vs. January 2020—categorised according to antibiotic class.
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MDPI and ACS Style

Orlek, A.; Harvey, E.; Fisher, L.; Mehrkar, A.; Bacon, S.; Goldacre, B.; MacKenna, B.; Ashiru-Oredope, D. Patient Characteristics Associated with Repeat Antibiotic Prescribing Pre- and during the COVID-19 Pandemic: A Retrospective Nationwide Cohort Study of >19 Million Primary Care Records Using the OpenSAFELY Platform. Pharmacoepidemiology 2023, 2, 168-187. https://doi.org/10.3390/pharma2020016

AMA Style

Orlek A, Harvey E, Fisher L, Mehrkar A, Bacon S, Goldacre B, MacKenna B, Ashiru-Oredope D. Patient Characteristics Associated with Repeat Antibiotic Prescribing Pre- and during the COVID-19 Pandemic: A Retrospective Nationwide Cohort Study of >19 Million Primary Care Records Using the OpenSAFELY Platform. Pharmacoepidemiology. 2023; 2(2):168-187. https://doi.org/10.3390/pharma2020016

Chicago/Turabian Style

Orlek, Alex, Eleanor Harvey, Louis Fisher, Amir Mehrkar, Seb Bacon, Ben Goldacre, Brian MacKenna, and Diane Ashiru-Oredope. 2023. "Patient Characteristics Associated with Repeat Antibiotic Prescribing Pre- and during the COVID-19 Pandemic: A Retrospective Nationwide Cohort Study of >19 Million Primary Care Records Using the OpenSAFELY Platform" Pharmacoepidemiology 2, no. 2: 168-187. https://doi.org/10.3390/pharma2020016

APA Style

Orlek, A., Harvey, E., Fisher, L., Mehrkar, A., Bacon, S., Goldacre, B., MacKenna, B., & Ashiru-Oredope, D. (2023). Patient Characteristics Associated with Repeat Antibiotic Prescribing Pre- and during the COVID-19 Pandemic: A Retrospective Nationwide Cohort Study of >19 Million Primary Care Records Using the OpenSAFELY Platform. Pharmacoepidemiology, 2(2), 168-187. https://doi.org/10.3390/pharma2020016

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