Reducing Unnecessary Antibiotic Prescriptions
Last registered on September 12, 2017

Pre-Trial

Trial Information
General Information
Title
Reducing Unnecessary Antibiotic Prescriptions
RCT ID
AEARCTR-0002420
Initial registration date
September 11, 2017
Last updated
September 12, 2017 4:06 PM EDT
Location(s)

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Primary Investigator
Affiliation
Harvard University
Other Primary Investigator(s)
Additional Trial Information
Status
In development
Start date
2017-06-06
End date
2018-06-30
Secondary IDs
Abstract
This study will evaluate the effectiveness of various behaviourally-informed approaches to reducing unnecessary prescriptions of antibiotics by General Practitioners (GPs).
External Link(s)
Registration Citation
Citation
Hiscox, Michael. 2017. "Reducing Unnecessary Antibiotic Prescriptions." AEA RCT Registry. September 12. https://www.socialscienceregistry.org/trials/2420/history/21347
Experimental Details
Interventions
Intervention(s)
Active interventions include sending letters about antimicrobial resistance and antibiotic prescriptions to GPs.
Intervention Start Date
2017-06-06
Intervention End Date
2017-06-30
Outcomes
Outcomes (end points)
The outcome measure will be the number of antibiotic prescriptions per 1000 GP visits, measured monthly for 12 months.
Outcomes (explanation)
Experimental Design
Experimental Design
Cluster randomised controlled trial with data collected monthly until the end of the intervention period (12-months).

Experimental Design Details
Not available
Randomization Method
Randomisation will occur at the practice level. Practices will be matched into quintuplets using an optimal greedy algorithm based on: number of GPs in the cluster, cluster average prescription rate over the previous 12 months and the cluster SEIFA score (a measure of socioeconomic advantage/disadvantage for the geographic area containing the practice).
Randomization Unit
Unit of randomisation will be practice.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
3,198
Sample size: planned number of observations
We have 12 data extraction/observation periods, one each month for 12 months, for approximately 6,500 GPs (units).
Sample size (or number of clusters) by treatment arms
We will have approximately 640 clusters in each of 5 groups. Each treatment arm will contain approximately 1,330 individual GPs.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
After adjusting the standard deviation for the variance explained by historical prescriptions rates (R2 = 0.25) and adjusting for clustering effects by applying the calculated design effect (DE = 1.28, ICC = 0.26, average cluster size = 2.07), a sample size of approximately 1,330 per group will provide 90% power at a 5% significance level to detect a 2.75% reduction in the antibiotic prescription rate (from 100.4 to 97.7 scripts per 1000 consults). This is a small standardised effect size (0.13).
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
BETA Ethics Committee
IRB Approval Date
2017-02-21
IRB Approval Number
BETA ETH 2017 – 012