Improving primary care antimicrobial stewardship by implementing a peer audit and feedback intervention in Cape Town community healthcare centres
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The increasing prevalence of antibiotic resistance is a major threat to public health. Primary care, where 80% of antibiotics are consumed, is a pivotal setting to direct antimicrobial stewardship (AMS) efforts. However, the ideal model to improve antibiotic prescribing in primary care in low-resource settings is not known. OBJECTIVE: To implement a multidisciplinary audit and feedback AMS intervention with the aim to improve appropriate antibiotic prescribing at primary care level. METHODS: The intervention was implemented and monitored in 10 primary care centres of the Cape Town metropole between July 2017 and June 2019. The primary and secondary outcome measures were monthly adherence to a bundle of antibiotic quality process measures and monthly antibiotic consumption, respectively. Multidisciplinary audit and feedback meetings were initiated and integrated into facility clinical meetings. Two Excel tools were utilised to automatically calculate facility audit scores and consumption. Once a month, 10 antibiotic prescriptions were randomly selected for a peer review audit by the team. The prescriptions were audited for adherence to a bundle of seven antibiotic process measures using the standard treatment guidelines (STG) and Essential Medicines List (EML) as standard. Concurrently, primary care pharmacists monitored monthly antibiotic consumption by calculating defined daily doses (DDDs) per 100 prescriptions dispensed. Adherence and consumption feedback were regularly provided to the facilities. Learning collaboratives involving representative multidisciplinary teams were held twice-yearly. Pre-, baseline and post-intervention periods were defined as 6 months before, first 6 months and last 6 months of the study, respectively. RESULTS: The mean overall adherence increased from 19% (baseline) to 47% (post intervention) (p<0.001). Of the 2 077 prescriptions analysed, 33.7% had an antibiotic prescribed inappropriately. No diagnosis had been captured in patient notes, and the antibiotic chosen was not according to the STG and EML in 30.1% and 31.7% of cases, respectively. Seasonal variation was observed in prescribing adherence, with significantly lower adherence in winter and spring months (adjusted odds ratio 0.60). A reduction of 12.9 DDDs between the pre- and post-intervention periods (p=0.0084) was documented, which represented a 19.3% decrease in antibiotic consumption. CONCLUSION: The study demonstrated that peer reviewed audit and feedback is an effective AMS intervention to improve antibiotic prescribing in primary care in a low-resource setting. The intervention, utilising existing resources and involving multidisciplinary engagement, may be incorporated into existing quality improvement processes at facility level, to ensure sustainable change.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it