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Randomized Controlled Trial
. 2023 Mar 1;183(3):213-220.
doi: 10.1001/jamainternmed.2022.6529.

Effect of Antibiotic Prescription Audit and Feedback on Antibiotic Prescribing in Primary Care: A Randomized Clinical Trial

Soheila Aghlmandi  1 , Florian S Halbeisen  1 , Ramon Saccilotto  2 , Pascal Godet  3 , Andri Signorell  4 , Simon Sigrist  5 , Dominik Glinz  1 , Giusi Moffa  6 , Andreas Zeller  7 , Andreas F Widmer  8 , Andreas Kronenberg  9 , Julia Bielicki  10   11 , Heiner C Bucher  1
Affiliations
Randomized Controlled Trial

Effect of Antibiotic Prescription Audit and Feedback on Antibiotic Prescribing in Primary Care: A Randomized Clinical Trial

Soheila Aghlmandi et al. JAMA Intern Med. .

Abstract

Importance: Antibiotics are commonly prescribed in primary care, increasing the risk of antimicrobial resistance in the population.

Objective: To investigate the effect of quarterly audit and feedback on antibiotic prescribing among primary care physicians in Switzerland with medium to high antibiotic prescription rates.

Design, setting, and participants: This pragmatic randomized clinical trial was conducted from January 1, 2018, to December 31, 2019, among 3426 registered primary care physicians and pediatricians in single or small practices in Switzerland who were among the top 75% prescribers of antibiotics. Intention-to-treat analysis was performed using analysis of covariance models and conducted from September 1, 2021, to January 31, 2022.

Interventions: Primary care physicians were randomized in a 1:1 fashion to undergo quarterly antibiotic prescribing audit and feedback with peer benchmarking vs no intervention for 2 years, with 2017 used as the baseline year. Anonymized patient-level claims data from 3 health insurers serving roughly 50% of insurees in Switzerland were used for audit and feedback. The intervention group also received evidence-based guidelines for respiratory tract and urinary tract infection management and community antibiotic resistance information. Physicians in the intervention group were blinded regarding the nature of the trial, and physicians in the control group were not informed of the trial.

Main outcomes and measures: The claims data used for audit and feedback were analyzed to assess outcomes. Primary outcome was the antibiotic prescribing rate per 100 consultations during the second year of the intervention. Secondary end points included overall antibiotic use in the first year and over 2 years, use of quinolones and oral cephalosporins, all-cause hospitalizations, and antibiotic use in 3 age groups.

Results: A total of 3426 physicians were randomized to the intervention (n = 1713) and control groups (n = 1713) serving 629 825 and 622 344 patients, respectively, with a total of 4 790 525 consultations in the baseline year of 2017. In the entire cohort, a 4.2% (95% CI, 3.9%-4.6%) relative increase in the antibiotic prescribing rate was noted during the second year of the intervention compared with 2017. In the intervention group, the median annual antibiotic prescribing rate per 100 consultations was 8.2 (IQR, 6.1-11.4) in the second year of the intervention and was 8.4 (IQR, 6.0-11.8) in the control group. Relative to the overall increase, a -0.1% (95% CI, -1.2% to 1.0%) lower antibiotic prescribing rate per 100 consultations was found in the intervention group compared with the control group. No relevant reductions in specific antibiotic prescribing rates were noted between groups except for quinolones in the second year of the intervention (-0.9% [95% CI, -1.5% to -0.4%]).

Conclusions and relevance: This randomized clinical trial found that quarterly personalized antibiotic prescribing audit and feedback with peer benchmarking did not reduce antibiotic prescribing among primary care physicians in Switzerland with medium to high antibiotic prescription rates.

Trial registration: ClinicalTrials.gov Identifier: NCT03379194.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Aghlmandi reported receiving grants from the Swiss National Science Foundation during the conduct of the study; and grants from the Swiss National Science Foundation outside the submitted work. Dr Saccilotto reported receiving personal fees from the Basel Institute for Clinical Epidemiology and Biostatistics as external consultant during the conduct of the study. Dr Glinz reported being employed by Roche Pharma (Schweiz) AG; data collection for this article was completed before his current employment. Dr Kronenberg reported being the head of the Swiss Federal Office of Public Health (SFOPH) Swiss Antibiotic Resistance Center and being financially supported by the SFOPH during the conduct of the study. Dr Bielicki reported being the project lead for the subproject “Swiss Antimicrobial Stewardship Programme” on behalf of Swissnoso, supported by the Federal Office of Public Health under the umbrella of the National Strategy against Antimicrobial Resistance. Dr Bucher reported receiving grants, support for traveling, consultancy fees, and honoraria from Gilead, Bristol Myers Squibb, ViiV Healthcare, Roche, and Pfizer; and serving as the president of the Association Contre le HIV et Autres Infections Transmissibles, for which he has received support for the Swiss HIV Cohort Study from ViiV Healthcare, Gilead, Bristol Myers Squibb, and MSD, outside the submitted work. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Flow Diagram of Disposition of Primary Care Physicians and Pediatricians (Intention-to-Treat Analysis)
Figure 2.
Figure 2.. Median Antibiotic Prescription Rates per Month
Error bars indicate IQRs.
Figure 3.
Figure 3.. Change in Prescription Rates per 100 Consultations by Physicians in the Intervention vs the Control Group in the Second Year of the Intervention (Intention-to-Treat Analysis) Relative to the Baseline Year 2017
The base model is treatment variable adjusted to baseline prescription rate and the multivariable model is adjusted to the predefined comorbidities.
Figure 4.
Figure 4.. Change in Prescription Rates per 100 Consultations by Physicians in the Intervention vs the Control Group in the First Year of the Intervention (Intention-to-Treat Analysis) Relative to the Baseline Year 2017
The base model is treatment variable adjusted to baseline prescription rate and the multivariable model is adjusted to the predefined comorbidities.
See this image and copyright information in PMC

Comment in

References

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