Skip to main content

Main menu

  • HOME
  • ONLINE FIRST
  • CURRENT ISSUE
  • ALL ISSUES
  • AUTHORS & REVIEWERS
  • SUBSCRIBE
  • BJGP LIFE
  • MORE
    • About BJGP
    • Conference
    • Advertising
    • eLetters
    • Alerts
    • Video
    • Audio
    • Librarian information
    • Resilience
    • COVID-19 Clinical Solutions
  • RCGP
    • BJGP for RCGP members
    • BJGP Open
    • RCGP eLearning
    • InnovAIT Journal
    • Jobs and careers

User menu

  • Subscriptions
  • Alerts
  • Log in

Search

  • Advanced search
British Journal of General Practice
Intended for Healthcare Professionals
  • RCGP
    • BJGP for RCGP members
    • BJGP Open
    • RCGP eLearning
    • InnovAIT Journal
    • Jobs and careers
  • Subscriptions
  • Alerts
  • Log in
  • Follow bjgp on Twitter
  • Visit bjgp on Facebook
  • Blog
  • Listen to BJGP podcast
  • Subscribe BJGP on YouTube
Intended for Healthcare Professionals
British Journal of General Practice

Advanced Search

  • HOME
  • ONLINE FIRST
  • CURRENT ISSUE
  • ALL ISSUES
  • AUTHORS & REVIEWERS
  • SUBSCRIBE
  • BJGP LIFE
  • MORE
    • About BJGP
    • Conference
    • Advertising
    • eLetters
    • Alerts
    • Video
    • Audio
    • Librarian information
    • Resilience
    • COVID-19 Clinical Solutions
Research

Association of strong opioids and antibiotics prescribing with GP burnout: a retrospective cross-sectional study

Alexander Hodkinson, Salwa S Zghebi, Evangelos Kontopantelis, Christos Grigoroglou, Darren M Ashcroft, Mark Hann, Carolyn A Chew-Graham, Rupert A Payne, Paul Little, Simon de Lusignan, Anli Zhou, Aneez Esmail and Maria Panagioti
British Journal of General Practice 2023; 73 (733): e634-e643. DOI: https://doi.org/10.3399/BJGP.2022.0394
Alexander Hodkinson
NIHR School for Primary Care Research and NIHR Greater Manchester Patient Safety Translational Research Centre, University of Manchester, Manchester.
PhD, MSc
Roles: National Institute for Health and Care Research (NIHR) senior fellow
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Alexander Hodkinson
Salwa S Zghebi
NIHR School for Primary Care Research, University of Manchester, Manchester.
PhD
Roles: Presidential fellow
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Salwa S Zghebi
Evangelos Kontopantelis
NIHR School for Primary Care Research; Division of Informatics, University of Manchester, Manchester.
PhD, MSc
Roles: Professor of data science & health services research
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Evangelos Kontopantelis
Christos Grigoroglou
NIHR School for Primary Care Research; Manchester Centre for Health Economics, University of Manchester, Manchester.
PhD
Roles: Research fellow
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Christos Grigoroglou
Darren M Ashcroft
NIHR School for Primary Care Research; NIHR Greater Manchester Patient Safety Translational Research Centre; Centre for Pharmacoepidemiology and Drug Safety, University of Manchester, Manchester.
PhD
Roles: Professor of pharmacoepidemiology
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Darren M Ashcroft
Mark Hann
NIHR School for Primary Care Research, University of Manchester, Manchester.
PhD
Roles: Senior research fellow
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Mark Hann
Carolyn A Chew-Graham
School of Medicine, Keele University, Keele.
MD, PhD
Roles: Professor of general practice research
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Carolyn A Chew-Graham
Rupert A Payne
Exeter Collaboration for Academic Primary Care, University of Exeter Medical School, Exeter.
PhD, MRCGP, FRCPE, FBPhS
Roles: Professor of primary care & clinical pharmacology
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Rupert A Payne
Paul Little
Primary Care Research Centre, University of Southampton, Southampton.
MD, FMedSci
Roles: Professor in primary care research
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Paul Little
Simon de Lusignan
Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford; Royal College of General Practitioners Research and Surveillance Centre, London.
MSc, MD, FRCGP
Roles: Professor of primary care and clinical informatics
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Simon de Lusignan
Anli Zhou
NIHR School for Primary Care Research, University of Manchester, Manchester.
MBChB
Roles: NIHR doctoral fellow
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Anli Zhou
Aneez Esmail
NIHR School for Primary Care Research, University of Manchester, Manchester.
MD, PhD, LRCP MRCS
Roles: Professor (emeritus) of general practice
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Aneez Esmail
Maria Panagioti
NIHR School for Primary Care Research and NIHR Greater Manchester Patient Safety Translational Research Centre, University of Manchester, Manchester.
PhD, MSc
Roles: Senior lecturer
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Maria Panagioti
  • Article
  • Figures & Data
  • Info
  • eLetters
  • PDF
Loading

Abstract

Background Prescribing of strong opioids and antibiotics impacts patient safety, yet little is known about the effects GP wellness has on overprescribing of both medications in primary care.

Aim To examine associations between strong opioid and antibiotic prescribing and practice- weighted GP burnout and wellness.

Design and setting A retrospective cross-sectional study was undertaken using prescription data on strong opioids and antibiotics from the Oxford- Royal College of General Practitioners Research and Surveillance Centre linking to a GP wellbeing survey overlaying the same 4-month period from December 2019 to April 2020.

Method Patients prescribed strong opioids and antibiotics were the outcomes of interest.

Results Data for 40 227 patients (13 483 strong opioids and 26 744 antibiotics) were linked to 57 practices and 351 GPs. Greater strong opioid prescribing was associated with increased emotional exhaustion (incidence risk ratio [IRR] 1.19, 95% confidence interval [CI] = 1.10 to 1.24), depersonalisation (IRR 1.10, 95% CI = 1.01 to 1.16), job dissatisfaction (IRR 1.25, 95% CI = 1.19 to 1.32), diagnostic uncertainty (IRR 1.12, 95% CI = 1.08 to 1.19), and turnover intention (IRR 1.32, 95% CI = 1.27 to 1.37) in GPs. Greater antibiotic prescribing was associated with increased emotional exhaustion (IRR 1.19, 95% CI = 1.05 to 1.37), depersonalisation (IRR 1.24, 95% CI = 1.08 to 1.49), job dissatisfaction (IRR 1.11, 95% CI = 1.04 to 1.19), sickness–presenteeism (IRR 1.18, 95% CI = 1.11 to 1.25), and turnover intention (IRR 1.38, 95% CI = 1.31 to 1.45) in GPs. Increased strong opioid and antibiotic prescribing was also found in GPs working longer hours (IRR 3.95, 95% CI = 3.39 to 4.61; IRR 5.02, 95% CI = 4.07 to 6.19, respectively) and in practices in the north of England (1.96, 95% CI = 1.61 to 2.33; 1.56, 95% CI = 1.12 to 3.70, respectively).

Conclusion This study found higher rates of prescribing of strong opioids and antibiotics in practices with GPs with more burnout symptoms, greater job dissatisfaction, and turnover intentions; working longer hours; and in practices in the north of England serving more deprived populations.

  • antibiotics
  • burnout
  • primary care
  • hazardous prescribing
  • opioids
  • patient safety

INTRODUCTION

Opioids are commonly used for the treatment of pain and include medicines such as morphine, fentanyl, and tramadol. In England, prescribing of opioids between 2008 and 2018 increased by 34%, with >231 million prescriptions dispensed in primary care in 2018–2019 alone.1 Non- medical use, prolonged use, misuse, and use without medical supervision can lead to opioid dependence, other serious health problems, and death.2 Worldwide, in 2017, an estimated 53.4 million people were on opioids, with opioids making up two-thirds of deaths related to drug misuse.3

Antibiotic resistance is also a major challenge to health care. The era of modern medicine has depended on the effective control of communicable diseases, of which many are bacterial in their origin.4 Faced with a situation where novel antibiotic agents are in short supply, the need to conserve the existing ‘supply’ of antibiotics becomes ever clearer. Antimicrobial stewardship encompasses a wide range of processes and interventions that are designed to ensure that antibiotics are used in the most effective manner.5,6 However, Public Health England’s National Infection Service recently found that as many as 23% of all antibiotic prescriptions in general practices may have been inappropriate.7 Optimising opioid and antibiotic prescribing are highly important policy targets globally.

Many medication optimisation strategies focus on identifying and resolving potentially inappropriate prescribing, such as through pharmacist medication reviews and the use of information technology (for example, PINCER).8,9 However, practice characteristics and staff wellness factors might be equally as important in reducing overprescribing and preventing patient safety incidents as patient factors.10 There is increasing evidence internationally that the wellness of physicians including GPs is associated with poor quality of care outcomes including medication and prescription errors.11 Furthermore, a study among 232 practising GPs suggested that changes at both the practice and individual level would help to promote a healthier work environment for staff and patients, and improve patient safety generally.12 However, this evidence has been criticised because it is mostly based on self-reported quality of care and patient safety data by doctors.13 Moreover, this self-reported evidence has not focused on prescribing specifically.

View this table:
  • View inline
  • View popup

How this fits in

A key marker of healthcare staff wellness is burnout, which is defined as a work- related syndrome involving three key dimensions: emotional exhaustion; depersonalisation; and personal accomplishment.14

Closely related characteristics that associate with burnout include turnover intention (intention to leave job within 5 years) and job satisfaction.15 Importantly, physician wellness has been increasingly seen as an organisational quality indicator. Furthermore, GP wellness has been viewed as an organisational problem, and thus wellness measures could be analysed as a practice-rather than an individual-level characteristic of GPs.

The aim of this study was to assess the association of the volume and potentially hazardous prescribing of strong opioids and antibiotics as the outcome of interest with key characteristics of general practices (with a focus on GP burnout) as the key exposure. Prescription data were obtained from the UK Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) from December 2019 to April 2020, and the exposure burnout/wellbeing variables were surveyed across the same time period.

METHOD

Data source

A retrospective cross-sectional study was conducted involving prescription outcome data that was linked with GP wellbeing responses (exposure) from an online survey from December 2019 to April 2020 using the RSC. The prescription data and survey responses both covered the same 4-month period to ensure consistency when linking the two datasets in the cross- sectional study.

The RSC is an internationally renowned source of information holding pseudo- anonymised individual-level GP primary care data.16,17 It provides patient- level data including prescription records and information about diagnosis, which have been carefully curated into variables historically using the Read terminology and more recently the Systematised Nomenclature of Medicine (SNOMED) Clinical Terms (CT).18,19 The RSC sentinel data include the monitoring of upper and lower respiratory tract infections (URTIs and LRTIs, respectively), and the careful differentiation of new and ongoing episodes of care.20 RSC data also capture every prescription issued in primary care and have previously been used to conduct research on antibiotic use.21

The authors of the current study provided the RSC with coded product and medical concepts using similar strategies to those used in their earlier work involving opioids22 and antibiotics.23 A list of the Read codes, which constitute the current study’s inclusion criteria for products and medical conditions, are provided in Supplementary Table S1.

The GP survey involved 10 items and was intended to reach 350–400 GPs across 70 different practices. The distribution of the survey was done using random sampling in house and was sent online to participating GP practices through the RCGP RSC using the Survey Monkey platform. Each participating GP received a £20 payment to their GP practice. A copy of the full survey is provided in Supplementary Information S1.

Reporting in the study was undertaken in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.24

Study populations

The study included patients aged ≥18 years in the 4-month period with:

  • any indication of chronic pain (including post-operative pain);

  • prescribed strong opioids; and

  • any respiratory tract infection and prescribed antibiotics.

The full medical and product code list is provided in Supplementary Table S1.

Covariates

Survey and GP wellness scores. 

GP characteristics collected in the survey included practice identification code (including NHS region), age, sex, full- time equivalence (FTE), and seven key outcomes associated with GP wellness including emotional exhaustion (EE) and depersonalisation (DP) subscales of burnout,25 sickness-presenteeism,26 work– life balance,27 diagnostic uncertainty,28 job satisfaction,29 and intention to leave their job within 5 years (turnover intention).

The single-item measures for GP burnout with the highest factor loading on EE (‘feelings of being burnt out’) and DP (‘feelings of becoming callous towards people’) were used as the primary measures of burnout. Both items were initially measured on the ordinal scale of 1 (low) to 7 (high), and the other wellness factors were also measured on an ordinal scale.25

RSC record-linked primary care data

GP surveillance data include all prescriptions issued including the dosage, which were converted to count data based on the total number of tablets per patient. As the data were delivered in various ways (that is, tablets, capsules, ampoule, and patches), for consistency in this study each prescription was standardised to the single measure of mgs to allow for adjustment for potency in the modelling. Demographic patient data included baseline characteristics of age, sex, ethnicity (that is, White, Asian, Black, mixed, or other), consultation type (that is, clinical administration, electronic consultation, face to face, telephone, home visit, and unspecified), related comorbidities (that is, immunocompromised, asthma, and chronic respiratory disease), mental health symptoms/episodes (that is, anxiety, depression, mental health referral, obsessive–compulsive disorder [OCD], panic attack, post-traumatic stress disorder, and stress), smoking (that is, active, ex-smoker, non-smoker, or non-specific), drinking habit (alcoholic, hazardous, safe, and non-drinker), history of other available medication use (that is, hypnotic, antidepressant, and anxiolytic), and socioeconomic status measured using the English Index of Multiple Deprivation (IMD) 2015 quintiles.30

The IMD is an aggregate measure of relative deprivation across seven domains (income, employment, education and skills, health and disability, crime, barriers to housing and services, and living environment) using an area-based model at a low geography (average of 1500 people) and via practice/patient postcodes. Overall IMD is calculated as a weighted mean across the seven domains, with income and employment deprivation given the largest weight (22.5%), followed by health and disability and education and skills deprivation (13.5%), and the other three domains are given equal weights (9.3%).31

Statistical analysis

As some of the GP wellness scores were missing from the original survey, missing data were imputed using the R package ‘MICE: Multivariate Imputation by Chained Equations’.32 As it was not possible to directly link the individual GP wellness response data from the survey to the main RSC prescription data without attaining consent from the GPs, in this study the data were linked at the practice level. This meant a practice-weighted score for GP wellness was calculated for each practice. The practice-weighted scores were calculated for all of the nine items in the survey (GP age, FTE, EE, DP, sickness-presenteeism, work–life balance, diagnostic uncertainty, job satisfaction, and turnover intention) using the average weight function svyby in the survey package of R.33 The variable FTE was used as a predictor to improve approximation of the practice-weighted scores. The GP wellness variable was then included in the multilevel model described below.

Descriptive statistics described the characteristics of the patient population involved, and spearman rank correlations assessed associations across all the practice-weighted GP wellness scores.

The patient-level association of volume of prescribing (response variable) with the practice-weighted wellness scores (EE, DP, job satisfaction, sickness-presenteeism, diagnostic uncertainty, turnover intention, and work–life balance), practice (NHS region, average GP age, and FTE), and patient factors (sex, age, ethnicity, IMD, consultation type, comorbidities, symptoms/episodes, and history of other medication use related to condition) were examined by fitting a multilevel generalised linear model (GLM) with a negative binomial distribution for each medication independently.

A negative binomial model is favoured here because the prescription data were found to be overdispersed (that is, the magnitude of the variance exceeds the magnitude of the mean). The Poisson regression model often underestimates the standard errors with the presence of overdispersion.34 Thus, empirically, a negative binomial model gives more accurate estimates than Poisson regression in most cases.35,36 Data availability drove the decision as to which variables were to be included in the models. In both models a random effects intercept of practice ID was introduced, and the incidence risk ratio (IRR) and 95% confidence interval (CI) estimates were used to determine the association with increased prescribing. All P-values were two-sided, and variables with P-values <0.05 were regarded as significant in the model.37 Variance inflation factors (VIFs) were examined for multicollinearity, with scores <5 considered as moderately correlated and scores ≥5 considered highly correlated.38 If a VIF was violated, a sensitivity analysis removing highly correlated variables from the model was applied. All analysis was done in R (version 4.0.5), and the MASS package was used to fit the GLMs.39

RESULTS

The cross-sectional study of just over 4 months included 67 practices, of which 57 (85%) (involving 351 GPs) could be linked to the RSC primary care data for 40 227 patients who met the study inclusion criteria. The 10 (15%) practices excluded from the study involved GP registrars consulting at multiple practices at once and therefore they could not be consigned to one unique practice ID.

Descriptive patient and service characteristics

The median response rate of the GPs across the practices was 39% (range 12%–91%). In total, 13 483 (34%) users of strong opioids and 26 744 (66%) users of antibiotics were identified. The median age of strong opioid users was 65 (range 52–77) years, and for antibiotics it was 50 (24–70) years (Tables 1 and 2).

View this table:
  • View inline
  • View popup
Table 1.

Descriptive characteristics of the patients who were strong opioid users

View this table:
  • View inline
  • View popup
Table 2.

Descriptive characteristics of the patients who were antibiotic users

At least 62% and 57% of users of strong opioids and antibiotics, respectively, were female. Over 70% of the patients in both treatment groups were of White ethnicity, over 66% were based in a city/town, and 54% involved patients registered with an NHS practice in northern England. IMD quintile scores were classified >2 in over 60% of patients in both medication groups (Tables 1 and 2).

Nearly 30% of strong opioid users were classified as ‘hazardous’ alcohol drinkers, and 39% of antibiotic users were ex-smokers (Tables 1 and 2). At least 19% and 12% of antibiotic users had asthma and chronic respiratory disease, respectively (Table 2).

Characteristics of the practice-weighted GP wellness scores

The median number of GP responders per practice was 5 (interquartile range [IQR] 4). On average GPs reported EE a few times a week (median 3.3, IQR 1.5), experiences of DP once a week (median 3.7, IQR 1.3), sickness- presenteeism >5 times a year (median 3.3, IQR 0.5), find at least 11%–15% of patients difficult to diagnose (median 3.0, IQR 0.6), and were dissatisfied with their current work–life balance (median 3.2, IQR 1.2). On average GPs also reported a moderate likelihood of leaving direct patient care within 5 years (median 2.1, IQR 0.9) and were generally dissatisfied with their job (median 2.5, IQR 0.9) (data not shown).

Figure 1 provides the correlations of the practice-weighted GP burnout scores (EE and DP) against the other practice- weighted GP wellness factors. EE was strongly associated with increased DP (ρ = 0.7), job dissatisfaction (ρ = 0.7) and turnover intention (ρ = 0.6). The correlations between the other factors were low to medium (ρ = 0 to 0.5).

Figure 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 1.

Correlation plot of the 57 practice-weighted GP wellness scores.

Association of increased prescribing of strong opioids and antibiotics with GP wellness and other patient/practice factors

Strong opioids

Based on 13 483 (34%) users of strong opioids, increased prescribing was significantly associated with a practice-weighted higher risk of EE (IRR 1.19, 95% CI = 1.10 to 1.24), DP (IRR 1.10, 95% CI = 1.01 to 1.16), job dissatisfaction (IRR 1.25, 95% CI = 1.19 to 1.32), diagnostic uncertainty (IRR 1.12, 95% CI = 1.08 to 1.19), and turnover intention (IRR 1.32, 95% CI = 1.27 to 1.37) in GPs. Increased opioid prescribing was also found in practices with longer working hours (FTE) (IRR 3.95, 95% CI = 3.39 to 4.61) and in practices in the north of England (IRR 1.96, 95% CI = 1.61 to 2.33) compared with practices in the south (as the north of England is the reference group, IRR has been reversed; see Supplementary Table S2 for full results).

In terms of patient factors, strong opioid prescribing was significantly associated with male patients (IRR 1.18, 95% CI = 1.15 to 1.22), being older (IRR 1.02, 95% CI = 1.01 to 1.02), more deprived (IMD quintile 5, IRR 1.51, 95% CI = 1.22 to 1.89), and with alcoholism (IRR 1.13, 95% CI = 1.08 to 1.18) or ‘hazardous’ drinking status (IRR 1.09, 95% CI = 1.06 to 1.13). Reduced prescribing of strong opioids was found in Black (IRR 0.45, 95% CI = 0.24 to 0.82) and mixed (IRR 0.45, 95% CI = 0.21 to 0.97) ethnicity patients compared with White patients, and in patients with a higher number of depression episodes (IRR 0.94, 95% CI = 0.91 to 0.96), mental health referrals (IRR 0.79, 95% CI = 0.70 to 0.88), and OCD episodes (IRR 0.12, 95% CI = 0.02 to 0.64). The VIF scores were all <5, therefore no variables were removed.

Antibiotics

Based on 26 744 (66%) users of antibiotics, increased prescribing was significantly associated with a higher practice-weighted risk of EE (IRR 1.19, 95% CI = 1.05 to 1.37), DP (IRR 1.24, 95% CI = 1.08 to 1.49), job dissatisfaction (IRR 1.11, 95% CI = 1.04 to 1.19), sickness- presenteeism (IRR 1.18, 95% CI = 1.11 to 1.25), and turnover intention (IRR 1.38, 95% CI = 1.31 to 1.45) in GPs. Increased antibiotics prescribing was also found in practices with longer working hours (IRR 5.02, 95% CI = 4.07 to 6.19) and in practices in the north of England (IRR 1.56, 95% CI = 1.12 to 3.70) compared with practices in the south of England (as the north of England is the reference group, IRR has been reversed; see Supplementary Table S3).

With regards to patient factors, increased antibiotic prescribing was significantly associated with male patients (IRR 1.15, 95% CI = 1.11 to 1.19), being older (IRR 1.01, 95% CI = 1.01 to 1.02), and with higher deprivation (IMD quintile 5, IRR 1.16, 95% CI = 1.08 to 1.25). There was a significant reduction in antibiotic prescribing in Black patients (IRR 0.72, 95% CI = 0.62 to 0.82), those diagnosed with asthma (IRR 0.86, 95% CI = 0.82 to 0.90) or chronic respiratory disease (IRR 0.79, 95% CI = 0.75 to 0.83), and who were active (IRR 0.65, 95% CI = 0.62 to 0.69) or ex-smokers (IRR 0.79, 95% CI = 0.75 to 0.82). No multicollinearity was present in the model (see Supplementary Table S3).

DISCUSSION

Summary

In this large national cross-sectional study involving 57 practices comprising 351 GPs with 40 227 patients, it was found that prescribing of strong opioids over 4 months in 13 483 patients was greatest in GPs working in practices in the north of England, who worked longer hours, and who showed increased levels of practice-weighted burnout (EE and DP), job dissatisfaction, diagnostic uncertainty, and turnover intention. For antibiotic use in 26 744 patients over 4 months, it was found that there was increased prescribing in practices in the north of England, in GPs working longer hours, and those with increased levels of practice-weighted burnout (EE and DP), job dissatisfaction, sickness-presenteeism, and turnover intention.

Strengths and limitations

To the authors’ knowledge, this is the first study with use of a novel approach to link GP survey responses weighted at practice- level to GP patient surveillance records to investigate the relationship between the prescribing of strong opioids and antibiotics and GP wellness across practices in England.

The study has several limitations. First, it was not an experimental study design, meaning unmeasurable confounding for prescribing of both drugs is possible.

Second, by not being able to directly link the GP survey responses to the surveillance health records without the GPs consent meant it was necessary to calculate the practice-weighted scores for GP wellness factors. This in turn affected the ability to directly assess for potential clustering factors of the GPs with their prescribing characteristics. Furthermore, the accuracy for estimating the practice-weighted scores may have been impeded by the low response rate, which on average was 39% across practices. This may have led to overestimation or even an underestimation of the average practice burnout/wellbeing scores. However, this is still higher than the 12% response rate attained in the UK’s Tenth National GP work-life Survey in 2019.40 One consideration was to try to account for this low response rate in the study design by using imputation methods.41 However, there are significant complexities surrounding the best ways to impute the missing GP responses and how reliable this would be, given there was such limited demographic information about the GPs and practices themselves from the survey. Bayesian models using missing at random and missing not at random algorithms have been proven effective when imputing missing response data,42 but need to be properly tested in this environment.

Third, the decision not to apply a form of univariable regressions to observe how each covariate altered the treatment response and establish an order of importance for each of the GP wellbeing factors may have weakened the modelling. However, given the importance of each wellbeing factor the authors opted to include them all in the final model. In terms of the patient- level factors such as patient demographic characteristics and complications/symptoms, these variables were chosen based on input from the clinicians involved in the study. Practice- weighted wellness scores (EE, DP, job satisfaction, sickness-presenteeism, diagnostic uncertainty, turnover intention, and work– life balance) were selected based on existing frameworks that have studied the relationship between occupational distress in physicians and poor quality of patient care outcomes.43,44

Fourth, the study overlapped with the start of the COVID-19 pandemic, meaning some patients may have been subject to more relaxed medicine management, low morale, and predominantly remote care,45 which will have had some impact on antibiotic prescribing.46 This was not adjusted for in the analysis.

Fifth, the number of practices recruited in this study was based on available funding for the questionnaire collection, rather than a formal sample size calculation. However, the patient sample was not small, and the study did find statistically significant associations between the key variables of interest (GP wellbeing and overprescribing), and overprescribing of antibiotics and opioids. However, the authors of the current study strongly encourage larger studies to further investigate these associations, especially in a prospective research design.

Sixth, as detailed in the Method, dosage data were provided in different forms of delivery. Thus, the authors had to standardise the data to the unique measure based on mgs. However, as it was not possible to standardise up to 13% of the prescription data to mgs, these data therefore had to be removed from the cohort. Undertaking a sensitivity analysis was considered to adjust for the loss of these data, but because of the uncertainty on the dose provided to the patient the authors decided against this.

Finally, as a result of the relatively low number of general practices (n = 57), it was not possible to assess disparities between rural versus inner city/urban areas, which is important to understand from a UK policy perspective.

Comparisons with existing literature

The current findings are consistent with the fast-growing research evidence that shows that physician burnout may risk the quality of care provided to patients.43 To date, however, most of this evidence has been based on patient safety outcomes, self- reported by physicians. The current findings add to this body of evidence, demonstrating that GP burnout is associated with objectively reported overprescribing of strong opioids and antibiotics, by utilising novel linkages between a GP survey and patient data contained in a large health database.

A previous study has shown that primary care providers who overprescribed opioids to treat patients with chronic pain often exhibit signs of burnout and feel unable to help patients overcome their complex challenges.47 However, that study involved a very small sample of only 19 primary care clinicians and eight nurses, and they used a qualitative ethnographic approach that limited any quantification of the association between prescribing of opioids and burnout.

Furthermore, while inappropriate antibiotic use has been linked to the emergence of drug resistance, which contributes directly to increased medical costs,48 the impact of antibiotic overprescribing due to worsening GP wellness has not been formally assessed. One such effort49 had tried to assess the association between physician wellness (burnout and empathy) and antibiotic prescribing for RTIs in 36 primary care practices in Northeast Ohio, US. They found no association between physician wellness and antibiotic prescribing, but these findings might be more reflective of a lack of statistical power in their study sample.

Implications for research and practice

The findings from the current practice- level approach to burnout and quality of patient care have important policy implications. Policies are urgently needed to mitigate burnout in UK general practice, commissioned as practice-embedded workforce wellness programmes rather than external support services made available to individual members of the workforce (for example, GPs) who may experience burnout. Such practice-embedded workforce wellness programmes could produce further improvements to the mainstream category of medication safety improvement strategies, which focus mostly on identifying patients ‘at risk’ rather than workforce or general practices at risk.

Monitoring and understanding healthcare worker wellness requires conducting health- related surveys and surveillance, but the combining of these data with prescription (surveillance) electronic health records is more challenging as it requires consent to attribute the GPs who are responsible for prescribing the medication.50,51 Obtaining such consent is considered a controversial area for many physicians, and the authors are not aware of any such novel and successful efforts to date. If a large enough response rate can be achieved, then the association of wellness factors and prescription characteristics can be assessed with high reliability considering missing responses. The authors encourage similar innovative efforts to investigate this as a possible model in future research designs.

Acknowledgments

The authors thank the Oxford-RCGP RSC network member practices for providing the surveillance data and for distributing the GP surveys across English practices. The authors also thank the RSC team including John Briggs, Filipa Ferreira, and Ivelina Yonova for supporting any data- and system-related queries.

Notes

Funding

This work was funded by the National Institute for Health and Care Research Greater Manchester Patient Safety Translational Research Centre (NIHR GM PSTRC) (award number: PSTRC-2016-003). Alexander Hodkinson is funded by his NIHR fellowship. Carolyn A Chew- Graham is part funded by the NIHR West Midlands Applied Research Collaboration. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

Ethical approval

The project was reviewed by the University of Manchester’s research ethics committee before approval (Integrated Research Application System ID: 268 533).

Data

This study used pseudonymised patient- level data from the Oxford-Royal College of General Practitioner (RCGP) Research and Surveillance Centre (RSC). These data can be accessed for ethically approved research by applying via: https://orchid.phc.ox.ac.uk

Provenance

Freely submitted; externally peer reviewed.

Competing interests

Simon de Lusignan has received funding through his University from Astra-Zeneca, Eli-Lilly, GSK, MSD, NovoNordisk, Sanofi, Seqirus, and Takeda, and has been a member of advisory boards for Astra- Zeneca, Sanofi, and Seqirus. He is Director of the Oxford-RCGP RSC. Darren M Ashcroft reports research grants from AbbVie, Almirall, Celgene, Eli-Lilly, Janssen, Novartis, UCB, and the Leo Foundation. All other authors have declared no competing interests.

Discuss this article

Contribute and read comments about this article: bjgp.org/letters

  • Received July 29, 2022.
  • Revision requested September 26, 2022.
  • Accepted January 27, 2023.
  • © The Authors
http://creativecommons.org/licenses/by/4.0/

This article is Open Access: CC BY 4.0 licence (http://creativecommons.org/licences/by/4.0/).

REFERENCES

  1. 1.↵
    1. Nowakowska M,
    2. Zghebi SS,
    3. Perisi R,
    4. et al.
    (2021) Association of socioeconomic deprivation with opioid prescribing in primary care in England: a spatial analysis. J Epidemiol Community Health 75, 2, 128–136.
    OpenUrlAbstract/FREE Full Text
  2. 2.↵
    1. World Health Organization
    (2021) Opioid overdose. https://www.who.int/news-room/fact-sheets/detail/opioid-overdose (accessed 18 Apr 2023).
  3. 3.↵
    1. United Nations Office on Drugs and Crime
    (2019) World drug report 2019 Booklet 3: depressants, https://wdr.unodc.org/wdr2019/prelaunch/WDR19_Booklet_3_DEPRESSANTS.pdf (accessed 21 Apr 2023).
  4. 4.↵
    1. Public Health England, Department of Health
    (2015) Behaviour change and antibiotic prescribing in healthcare settings: literature review and behavioural analysis, https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/774129/Behaviour_Change_for_Antibiotic_Prescribing_-_FINAL.pdf (accessed 18 Apr 2023).
  5. 5.↵
    1. National Institute for Health and Care Excellence
    (2015) Antimicrobial stewardship: systems and processes for effective antimicrobial medicine use NG15, https://www.nice.org.uk/guidance/ng15 (accessed 18 Apr 2023).
  6. 6.↵
    1. Centre for Disease Control and Prevention
    (2021) Antibiotic prescribing and use: core elements of antibiotic stewardship. https://www.cdc.gov/antibiotic-use/core-elements/index.html (accessed 18 Apr 2023).
  7. 7.↵
    1. Smieszek T,
    2. Pouwels KB,
    3. Dolk FCK,
    4. et al.
    (2018) Potential for reducing inappropriate antibiotic prescribing in English primary care. J Antimicrob Chemother 73, suppl_2, ii36–ii43.
    OpenUrlCrossRefPubMed
  8. 8.↵
    1. Avery AJ,
    2. Rodgers S,
    3. Cantrill JA,
    4. et al.
    (2012) A pharmacist-led information technology intervention for medication errors (PINCER): a multicentre, cluster randomised, controlled trial and cost-effectiveness analysis. Lancet 379, 9823, 1310–1319.
    OpenUrlCrossRefPubMed
  9. 9.↵
    1. McCahon D,
    2. Denholm RE,
    3. Huntley AL,
    4. et al.
    (2021) Development of a model of medication review for use in clinical practice: Bristol medication review model. BMC Med 19, 1, 262.
    OpenUrl
  10. 10.↵
    1. Stocks SJ,
    2. Kontopantelis E,
    3. Akbarov A,
    4. et al.
    (2015) Examining variations in prescribing safety in UK general practice: cross sectional study using the Clinical Practice Research Datalink. BMJ 351, h5501.
    OpenUrlAbstract/FREE Full Text
  11. 11.↵
    1. Hall LH,
    2. Johnson J,
    3. Watt I,
    4. et al.
    (2016) Healthcare staff wellbeing, burnout, and patient safety: a systematic review. PLoS One 11, 7, e0159015.
    OpenUrlCrossRefPubMed
  12. 12.↵
    1. Hall LH,
    2. Johnson J,
    3. Watt I,
    4. O’Connor DB
    (2019) Association of GP wellbeing and burnout with patient safety in UK primary care: a cross-sectional survey. Br J Gen Pract, https://doi.org/10.3399/bjgp19X702713.
  13. 13.↵
    1. Tawfik DS,
    2. Scheid A,
    3. Profit J,
    4. et al.
    (2019) Evidence relating health care provider burnout and quality of care: a systematic review and meta-analysis. Ann Intern Med 171, 8, 555–567.
    OpenUrlCrossRefPubMed
  14. 14.↵
    1. Wright JD
    1. Maslach C
    (2015) Burnout, psychology of. in International Encyclopedia of the Social and Behavioral Sciences, ed Wright JD (Elsevier, Oxford), 2nd edn, 929–932.
  15. 15.↵
    1. Maslach C,
    2. Schaufeli WB,
    3. Leiter MP
    (2001) Job burnout. Annu Rev Psychol 52, 1, 397–422.
    OpenUrlCrossRefPubMed
  16. 16.↵
    1. Correa A,
    2. Hinton W,
    3. McGovern A,
    4. et al.
    (2016) Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC) sentinel network: a cohort profile. BMJ Open 6, 4, e011092.
    OpenUrlAbstract/FREE Full Text
  17. 17.↵
    1. de Lusignan S,
    2. Correa A,
    3. Smith GE,
    4. et al.
    (2017) RCGP Research and Surveillance Centre: 50 years’ surveillance of influenza, infections, and respiratory conditions. Br J Gen Pract, https://doi.org/10.3399/bjgp17X692645.
  18. 18.↵
    1. de Lusignan S
    (2005) Codes, classifications, terminologies and nomenclatures: definition, development and application in practice. Inform Prim Care 13, 1, 65–70.
    OpenUrlCrossRefPubMed
  19. 19.↵
    1. de Lusignan S,
    2. Liaw ST,
    3. Michalakidis G,
    4. et al.
    (2011) Defining datasets and creating data dictionaries for quality improvement and research in chronic disease using routinely collected data: an ontology-driven approach. Inform Prim Care 19, 3, 127–134.
    OpenUrlCrossRefPubMed
  20. 20.↵
    1. Smith N,
    2. Livina V,
    3. Byford R,
    4. et al.
    (2018) Automated differentiation of incident and prevalent cases in primary care computerised medical records (CMR). Stud Health Technol Inform 247, 151–155.
    OpenUrlPubMed
  21. 21.↵
    1. de Lusignan S,
    2. Joy M,
    3. Sherlock J,
    4. et al.
    (2021) PRINCIPLE trial demonstrates scope for in-pandemic improvement in primary care antibiotic stewardship: a retrospective sentinel network cohort study. BJGP Open, https://doi.org/10.3399/BJGPO.2021.0087.
  22. 22.↵
    1. Nowakowska M,
    2. Zghebi SS,
    3. Perisi R,
    4. et al.
    (2021) Association of socioeconomic deprivation with opioid prescribing in primary care in England: a spatial analysis. J Epidemiol Community Health 75, 2, 128–136.
    OpenUrlAbstract/FREE Full Text
  23. 23.↵
    1. Nowakowska M,
    2. van Staa T,
    3. Mölter A,
    4. et al.
    (2019) Antibiotic choice in UK general practice: rates and drivers of potentially inappropriate antibiotic prescribing. J Antimicrob Chemother 74, 11, 3371–3378.
    OpenUrlCrossRefPubMed
  24. 24.↵
    1. Elm Ev,
    2. Altman DG,
    3. Egger M,
    4. et al.
    (2007) Strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ 335, 7624, 806–808.
    OpenUrlFREE Full Text
  25. 25.↵
    1. West CP,
    2. Dyrbye LN,
    3. Sloan JA,
    4. et al.
    (2009) Single item measures of emotional exhaustion and depersonalization are useful for assessing burnout in medical professionals. J Gen Intern Med 24, 12, 1318–1321.
    OpenUrlCrossRefPubMed
  26. 26.↵
    1. Thun S,
    2. Fridner A,
    3. Minucci D,
    4. Løvseth LT
    (2014) Sickness present with signs of burnout: the relationship between burnout and sickness presenteeism among university hospital physicians in four European countries. Scand Psychol doi:10.15714/scandpsychol.1.e5.
    OpenUrlCrossRef
  27. 27.↵
    1. Shanafelt TD,
    2. Boone S,
    3. Tan L,
    4. et al.
    (2012) Burnout and satisfaction with work–life balance among US physicians relative to the general US population. Arch Intern Med 172, 18, 1377–1385.
    OpenUrlCrossRefPubMed
  28. 28.↵
    1. Sarkar U,
    2. Bonacum D,
    3. Strull W,
    4. et al.
    (2012) Challenges of making a diagnosis in the outpatient setting: a multi-site survey of primary care physicians. BMJ Qual Saf 21, 8, 641–648.
    OpenUrlAbstract/FREE Full Text
  29. 29.↵
    1. Neumann JL,
    2. Mau LW,
    3. Virani S,
    4. et al.
    (2018) Burnout, moral distress, work-life balance, and career satisfaction among hematopoietic cell transplantation professionals. Biol Blood Marrow Transplant 24, 4, 849–860.
    OpenUrlPubMed
  30. 30.↵
    1. Ministry of Housing, Communities and Local Government
    (2015) English indices of deprivation 2015. https://www.gov.uk/government/statistics/english-indices-of-deprivation-2015 (accessed 18 Apr 2023).
  31. 31.↵
    1. Kontopantelis E,
    2. Mamas MA,
    3. van Marwijk H,
    4. et al.
    (2018) Geographical epidemiology of health and overall deprivation in England, its changes and persistence from 2004 to 2015: a longitudinal spatial population study. J Epidemiol Community Health 72, 2, 140–147.
    OpenUrlAbstract/FREE Full Text
  32. 32.↵
    1. van Buuren S,
    2. Groothuis-Oudshoorn K
    , eds (2011) mice: multivariate imputation by chained equations in R. JSS 45, 3, 1–67.
    OpenUrl
  33. 33.↵
    1. Lumley T
    (2022) Analysis of complex survey samples, https://cran.r-project.org/web/packages/survey/survey.pdf (accessed 21 Apr 2023).
  34. 34.↵
    1. Du J,
    2. Park YT,
    3. Theera-Ampornpunt N,
    4. et al.
    (2012) The use of count data models in biomedical informatics evaluation research. JAMIA 19, 1, 39–44.
    OpenUrlCrossRefPubMed
  35. 35.↵
    1. Liu W,
    2. Cela J
    (2008) Count data models in SAS, http://www2.sas.com/proceedings/forum2008/371-2008.pdf (accessed 18 Apr 2023).
  36. 36.↵
    1. Hausman J,
    2. Hall BH,
    3. Griliches Z
    (1984) Econometric models for count data with an application to the patents – R & D relationship. J Econom 52, 4, 909–938.
    OpenUrl
  37. 37.↵
    1. Harrell FE Jr.
    (2015) Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis (Springer, Cham).
  38. 38.↵
    1. Everitt BS,
    2. Skrondal A
    (2010) The Cambridge dictionary of statistics (Cambridge University Press, Cambridge).
  39. 39.↵
    1. Venables WN,
    2. Ripley BD
    (2002) Modern applied statistics with S (Springer, New York, NY), 4th edn.
  40. 40.↵
    1. Walker B,
    2. Moss C,
    3. Gibson J,
    4. et al.
    (2019) Tenth National GP Worklife Survey 2019, https://prucomm.ac.uk/projects/current-projects/tenth-gp-worklife-survey.html (accessed 19 Apr 2023).
  41. 41.↵
    1. Hayati Rezvan P,
    2. Lee KJ,
    3. Simpson JA
    (2015) The rise of multiple imputation: a review of the reporting and implementation of the method in medical research. BMC Med Res Methodol 15, 30.
    OpenUrlCrossRefPubMed
  42. 42.↵
    1. Halme AS,
    2. Tannenbaum C
    (2018) Performance of a Bayesian approach for imputing missing data on the SF-12 health-related quality-of-life measure. Value Health 21, 12, 1406–1412.
    OpenUrl
  43. 43.↵
    1. Hodkinson A,
    2. Zhou A,
    3. Johnson J,
    4. et al.
    (2022) Associations of physician burnout with career engagement and quality of patient care: systematic review and meta-analysis. BMJ 378, e070442.
    OpenUrlAbstract/FREE Full Text
  44. 44.↵
    1. Welle D,
    2. Trockel MT,
    3. Hamidi MS,
    4. et al.
    (2020) Association of occupational distress and sleep-related impairment in physicians with unsolicited patient complaints. Mayo Clin Proc 95, 4, 719–726.
    OpenUrlCrossRefPubMed
  45. 45.↵
    1. British Medical Association
    (2022) COVID-19: toolkit for GPs and GP practices. https://www.bma.org.uk/advice-and-support/covid-19/gp-practices/covid-19-toolkit-for-gps-and-gp-practices (accessed 18 Apr 2023).
  46. 46.↵
    1. Gillies MB,
    2. Burgner DP,
    3. Ivancic L,
    4. et al.
    (2022) Changes in antibiotic prescribing following COVID-19 restrictions: Lessons for post-pandemic antibiotic stewardship. Br J Clin Pharmacol 88, 3, 1143–1151.
    OpenUrl
  47. 47.↵
    1. Webster F,
    2. Rice K,
    3. Katz J,
    4. et al.
    (2019) An ethnography of chronic pain management in primary care: the social organization of physicians’ work in the midst of the opioid crisis. PLoS One 14, 5, e0215148.
    OpenUrlCrossRef
  48. 48.↵
    1. Costelloe C,
    2. Metcalfe C,
    3. Lovering A,
    4. et al.
    (2010) Effect of antibiotic prescribing in primary care on antimicrobial resistance in individual patients: systematic review and meta-analysis. BMJ 340, c2096.
    OpenUrlAbstract/FREE Full Text
  49. 49.↵
    1. Sun BZ,
    2. Chaitoff A,
    3. Hu B,
    4. et al.
    (2017) Empathy, burnout, and antibiotic prescribing for acute respiratory infections: a cross-sectional primary care study in the US. Br J Gen Pract, https://doi.org/10.3399/bjgp17X691901.
  50. 50.↵
    1. de Lusignan S,
    2. Mold F,
    3. Sheikh A,
    4. et al.
    (2014) Patients’ online access to their electronic health records and linked online services: a systematic interpretative review. BMJ Open 4, 9, e006021.
    OpenUrlAbstract/FREE Full Text
  51. 51.↵
    1. Lindemann F,
    2. Rozsnyai Z,
    3. Zumbrunn B,
    4. et al.
    (2019) Assessing the mental wellbeing of next generation general practitioners: a cross-sectional survey. BJGP Open, https://doi.org/10.3399/bjgpopen19X101671.
Back to top
Previous ArticleNext Article

In this issue

British Journal of General Practice: 73 (733)
British Journal of General Practice
Vol. 73, Issue 733
August 2023
  • Table of Contents
  • Index by author
Download PDF
Download PowerPoint
Email Article

Thank you for recommending British Journal of General Practice.

NOTE: We only request your email address so that the person to whom you are recommending the page knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Association of strong opioids and antibiotics prescribing with GP burnout: a retrospective cross-sectional study
(Your Name) has forwarded a page to you from British Journal of General Practice
(Your Name) thought you would like to see this page from British Journal of General Practice.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Association of strong opioids and antibiotics prescribing with GP burnout: a retrospective cross-sectional study
Alexander Hodkinson, Salwa S Zghebi, Evangelos Kontopantelis, Christos Grigoroglou, Darren M Ashcroft, Mark Hann, Carolyn A Chew-Graham, Rupert A Payne, Paul Little, Simon de Lusignan, Anli Zhou, Aneez Esmail, Maria Panagioti
British Journal of General Practice 2023; 73 (733): e634-e643. DOI: 10.3399/BJGP.2022.0394

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Association of strong opioids and antibiotics prescribing with GP burnout: a retrospective cross-sectional study
Alexander Hodkinson, Salwa S Zghebi, Evangelos Kontopantelis, Christos Grigoroglou, Darren M Ashcroft, Mark Hann, Carolyn A Chew-Graham, Rupert A Payne, Paul Little, Simon de Lusignan, Anli Zhou, Aneez Esmail, Maria Panagioti
British Journal of General Practice 2023; 73 (733): e634-e643. DOI: 10.3399/BJGP.2022.0394
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One
  • Mendeley logo Mendeley

Jump to section

  • Top
  • Article
    • Abstract
    • INTRODUCTION
    • METHOD
    • RESULTS
    • DISCUSSION
    • Acknowledgments
    • Notes
    • REFERENCES
  • Figures & Data
  • Info
  • eLetters
  • PDF

Keywords

  • antibiotics
  • burnout
  • primary care
  • hazardous prescribing
  • opioids
  • patient safety

More in this TOC Section

  • General practice as a place to receive help for domestic abuse during the COVID-19 pandemic: a qualitative interview study in England and Wales
  • Understanding primary care perspectives on supporting women’s health needs: a qualitative study
  • Trends in consultations and prescribing for rheumatic and musculoskeletal diseases: an electronic primary care records study
Show more Research

Related Articles

Cited By...

Intended for Healthcare Professionals

BJGP Life

BJGP Open

 

Tweets by @BJGPjournal

 
 

British Journal of General Practice

NAVIGATE

  • Home
  • Current Issue
  • All Issues
  • Online First
  • Authors & reviewers

RCGP

  • BJGP for RCGP members
  • BJGP Open
  • RCGP eLearning
  • InnovAiT Journal
  • Jobs and careers

MY ACCOUNT

  • RCGP members' login
  • Subscriber login
  • Activate subscription
  • Terms and conditions

NEWS AND UPDATES

  • About BJGP
  • Alerts
  • RSS feeds
  • Facebook
  • Twitter

AUTHORS & REVIEWERS

  • Submit an article
  • Writing for BJGP: research
  • Writing for BJGP: other sections
  • BJGP editorial process & policies
  • BJGP ethical guidelines
  • Peer review for BJGP

CUSTOMER SERVICES

  • Advertising
  • Contact subscription agent
  • Copyright
  • Librarian information

CONTRIBUTE

  • BJGP Life
  • eLetters
  • Feedback

CONTACT US

BJGP Journal Office
RCGP
30 Euston Square
London NW1 2FB
Tel: +44 (0)20 3188 7400
Email: journal@rcgp.org.uk

British Journal of General Practice is an editorially-independent publication of the Royal College of General Practitioners
© 2023 British Journal of General Practice

Print ISSN: 0960-1643
Online ISSN: 1478-5242