CMO Letter to Reduce Inappropriate Antibiotic Prescribing Winter 2019/2020

Study Overview
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Brief Summary
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This trial aims to reduce inappropriate prescription of antibiotics and broad spectrum antibiotics by general practitioners (GPs) in England. Unnecessary prescriptions are defined as those that do not improve patient health outcomes. The intervention is to send GPs a letter from the Chief Medical Officer (CMO) that gives feedback on their practice's prescribing levels. There will be three intervention samples: practices whose prescribing in the past year was under the new target of 0.965 items per STAR-PU but who would exceed the target if they had a 5% increase in prescribing; trial compares prescribing of practices whose GPs receive a letter informing them that their practice's prescribing is just under the new target to that of practices that are not sent a letter Practices whose prescribing in the past year was above the new target but who not in the top 20% of prescribers; trial compares prescribing of practices whose GPs receive a letter informing them that their practice's prescribing exceeds the new target to practices who get a letter that includes a graph showing their prescribing relative to the target and to practices that are not sent a letter Practices that are currently in the top 20% of prescribers; trial compares effect on prescribing of a feedback letter with a social norms message (current standard practice for this group) to a letter informing GPs that their practice's prescribing exceeds the new target and to a letter with a social norms message, that includes a specific example of a case of patient harm caused by antimicrobial resistance.

Detailed Description
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The study will involve three trials, each conducted as non-blinded randomised controlled trial, with GP practices as the unit of randomisation. Trial 1 Targeting practices whose prescribing in the past year was under the new target but who would exceed the target if they had a 5% increase in prescribing Control: No letter Intervention: Letter informing them that their practice's prescribing is just under the new target (Letter A) Trial hypothesis: Sending a letter to GPs whose practices are just under the new prescribing target will reduce antibiotic prescribing Trial 2 Targeting practices whose prescribing in the past year was above the new target but who not in the top 20% of prescribers Control: No letter Intervention 1: Letter informing them that their practice's prescribing exceeds the new target (Letter B1) Intervention 2: Letter informing them that their practice's prescribing exceeds the new target with a graph representing prescribing relative to the target (Letter B2) Hypotheses: (i) Sending a letter to GPs whose practices missed the new prescribing target will reduce their prescribing; (ii) A letter with a graph will be more effective than a letter without a graph. Trial 3 Targeting practices that are currently in the top 20% of prescribers Control: Current standard practice, a social norms message, that their practice is in the top 20% of prescribers (Letter C1) Intervention 1: Letter informing them that their practice's prescribing exceeds the new target (Letter C2) Intervention 2: Social norms message, that they are in the top 20%, with a specific example of a case of patient harm caused by antimicrobial resistance (Letter C3) Hypotheses: (i) A letter with a social norms message and a specific example of a case where a patient came to harm will be more effective than a feedback letter without a specific example; (ii) A letter telling GPs that they missed the prescribing target will be no less effective than a letter with social norms feedback For each letter, there will be two versions, one for practices whose prescribing has increased by > 5% in the previous year, informing them of that their prescribing has increased since the previous year, and one for practices whose prescribing has not been increasing. The letters will signpost GPs to resources to help address patient demand for inappropriate antibiotic prescribing, recognising that many GPs feel that patients expect antibiotics and that GPs may find it difficult to have the necessary patient conversations, especially within a short consultation. As with previous letters, these letters will advise GPs of actions that they can take to reduce inappropriate prescribing, supporting them to have conversations with patients, and there will be TARGET leaflets enclosed. Power calculation All trials are powered to detect a 2% reduction in prescribing at a significance level of 0.05 with a power of 80%. Statistical analysis plan In order to test our hypotheses, the investigators will use a fixed effects panel regression model, with time trends accounting for seasonal effects, to estimate the effect of treatment status on prescribing. The investigators will also run ANCOVAs for each month separately and one covering the whole six months of the trial. Analysis will control for baseline prescribing rates and for whether practices got the version of the letter saying that their prescribing has been increasing.

Official Title
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A Letter From the CMO in England to Reduce Antibiotic Prescribing in General Practice: A Randomized Controlled Trial Comparing Monitoring and Feedback (With and Without Graphs), and Social Norms Feedback (Without and Without a Specific Case Study of Patient Harm)

Conditions
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Prescribing, Off-Label

Intervention / Treatment
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* Behavioral: Letter


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: • GP practices that prescribed more than 0.919 Antibacterial Items/STAR- PU (5% under the target of 0.965) for the twelve months April 2018 - March 2019 Exclusion Criteria: • Practices in the 99th percentile of prescribers
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Other
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| No Intervention: Just under target control<br>Practices whose prescribing in the past year was under the new target but who would exceed the target if they had a 5% increase in prescribing; no letter sent. | |
| Experimental: Just under target letter<br>Practices whose prescribing in the past year was under the new target but who would exceed the target if they had a 5% increase in prescribing: receive a letter informing of this. Randomization is stratified according to whether their prescribing had increased by > 5% compared to the previous year; those whose prescribing had increased had it mentioned in the letter | Behavioral: Letter<br>* Letters sent to GPs in relevant practices (prescribing data is by practice, so the practice is the unit of randomization)<br>|
| No Intervention: Over target control<br>Practices whose prescribing in the past year was above the new target but who were not in the top 20% of prescribers; no letter sent Intervention 1: Letter informing them that their practice's prescribing exceeds the new target (Letter B1) Intervention 2: Letter informing them that their practice's prescribing exceeds the new target with a graph representing prescribing relative to the target (Letter B2) | |
| Experimental: Over target letter<br>Practices whose prescribing in the past year was above the new target but who were not in the top 20% of prescribers; receive a letter informing them that their practice's prescribing exceeds the new target (Letter B1) Randomization is stratified according to whether their prescribing had increased by > 5% compared to the previous year; those whose prescribing had increased had it mentioned in the letter | Behavioral: Letter<br>* Letters sent to GPs in relevant practices (prescribing data is by practice, so the practice is the unit of randomization)<br>|
| Experimental: Over target letter with bar chart<br>Practices whose prescribing in the past year was above the new target but who were not in the top 20% of prescribers; receive a letter informing them that their practice's prescribing exceeds the new target, including a bar chart showing their prescribing compared to the target (Letter B1) Randomization is stratified according to whether their prescribing had increased by > 5% compared to the previous year; those whose prescribing had increased had it mentioned in the letter | Behavioral: Letter<br>* Letters sent to GPs in relevant practices (prescribing data is by practice, so the practice is the unit of randomization)<br>|
| Active Comparator: Top 20% feedback letter control<br>Targeting practices that are currently in the top 20% of prescribers; letters informing them of the percentile they are on--standard practice--(Letter C1) Randomization is stratified according to whether their prescribing had increased by > 5% compared to the previous year; those whose prescribing had increased had it mentioned in the letter | Behavioral: Letter<br>* Letters sent to GPs in relevant practices (prescribing data is by practice, so the practice is the unit of randomization)<br>|
| Experimental: Top 20% above target letter<br>Targeting practices that are currently in the top 20% of prescribers; letters informing them that their prescribing exceeds the new target (Letter C2) Randomization is stratified according to whether their prescribing had increased by > 5% compared to the previous year; those whose prescribing had increased had it mentioned in the letter. | Behavioral: Letter<br>* Letters sent to GPs in relevant practices (prescribing data is by practice, so the practice is the unit of randomization)<br>|
| Experimental: Top 20% feedback letter with specific example of patient harm<br>Targeting practices that are currently in the top 20% of prescribers • Control: Current standard practice, a social norms message, that their practice is in the top 20% of prescribers (Letter C1) Targeting practices that are currently in the top 20% of prescribers; letters informing them of the percentile they are on with a specific example of a case of patient harm caused by antimicrobial resistance (Letter C3) Randomization is stratified according to whether their prescribing had increased by > 5% compared to the previous year; those whose prescribing had increased had it mentioned in the letter. | Behavioral: Letter<br>* Letters sent to GPs in relevant practices (prescribing data is by practice, so the practice is the unit of randomization)<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Total antibiotic prescribing in September | antibiotic prescribing weighted by Specific Therapeutic group Age-sex Related Prescribing Unit (STAR-PU) | 1 month | 
| Total antibiotic prescribing in October | antibiotic prescribing weighted by Specific Therapeutic group Age-sex Related Prescribing Unit (STAR-PU) | 2 months | 
| Total antibiotic prescribing in November | antibiotic prescribing weighted by Specific Therapeutic group Age-sex Related Prescribing Unit (STAR-PU) | 3 months | 
| Total antibiotic prescribing in December | antibiotic prescribing weighted by Specific Therapeutic group Age-sex Related Prescribing Unit (STAR-PU) | 4 months | 
| Total antibiotic prescribing in January | antibiotic prescribing weighted by Specific Therapeutic group Age-sex Related Prescribing Unit (STAR-PU) | 5 months | 
| Total antibiotic prescribing in February | antibiotic prescribing weighted by Specific Therapeutic group Age-sex Related Prescribing Unit (STAR-PU) | 6 months | 
| Total antibiotic prescribing in from September-February | antibiotic prescribing weighted by Specific Therapeutic group Age-sex Related Prescribing Unit (STAR-PU) | 6 months | 
| Proportion of practices in each group whose prescribing was under the target | Whether antibiotic prescribing weighted by Specific Therapeutic group Age-sex Related Prescribing Unit (STAR-PU) for April 2019-March 2020 is under the NHS target of 0.965 items per STAR-PU | 8 months | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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antimicrobial resistance, social norms feedback, behavioural science, feedback and monitoring