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.
