Using behavioural insights to reduce unnecessary antibiotic prescriptions by New Zealand doctors
Last registered on March 06, 2020

Pre-Trial

Trial Information
General Information
Title
Using behavioural insights to reduce unnecessary antibiotic prescriptions by New Zealand doctors
RCT ID
AEARCTR-0005526
Initial registration date
March 05, 2020
Last updated
March 06, 2020 3:03 PM EST
Location(s)
Region
Primary Investigator
Affiliation
Behavioural Insights Team
Other Primary Investigator(s)
PI Affiliation
The Behavioural Insights Team
PI Affiliation
The Behavioural Insights Team
Additional Trial Information
Status
On going
Start date
2019-08-19
End date
2020-04-30
Secondary IDs
Abstract
Antimicrobial resistance (AMR) is a global concern, which the World Health Organisation has named one of the biggest threats to global health, food security and development today. The over-prescription of antibiotics is a key driver of AMR. Unnecessary prescriptions are especially problematic in New Zealand, which had the 7th highest community prescription rate in the OECD in 2015.

The Behavioural Insights Team's previous work in the UK has shown that a letter with personalised feedback to practices can reduce prescriptions of unnecessary antibiotics, and a recent replication in Australia by the Behavioural Economics Team of the Australian Government was even more effective by targeting individual General Practitioners (GPs).

The current trial is being conducted by BIT, and draws on this prior work to tailor it to the New Zealand context. The trial aims to test whether personalised feedback to New Zealand GPs, in the form of a letter, can reduce prescriptions of unnecessary antibiotics. The trial will also test the impact on prescribing to Māori and Pacific patients, who tend to experience worse health outcomes.
External Link(s)
Registration Citation
Citation
Chappell, Nathan, Alex Gyani and Sarah Hayward. 2020. "Using behavioural insights to reduce unnecessary antibiotic prescriptions by New Zealand doctors ." AEA RCT Registry. March 06. https://doi.org/10.1257/rct.5526-1.0.
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Experimental Details
Interventions
Intervention(s)
This trial contains two trial arms: the treatment group (who receive the letter intervention), and the control (who receive no letter intervention).

The trial launched on 19 August 2019, with the one-off letters sent out, and prescribing behaviour will be analysed from September - December 2019.

All high-prescribing GPs in the trial are stratified by area (District Health Board) before randomly allocating each GP to either the treatment group or the control group.
Intervention Start Date
2019-08-19
Intervention End Date
2019-12-31
Primary Outcomes
Primary Outcomes (end points)
There are two primary outcomes:

1. Do the intervention letters reduce the prescribing rate of high-prescribing GPs? The prescribing rate is measured as the number of patients prescribed antibiotics per 100 patients prescribed any medicine, and is taken as the monthly average from September - December 2019.

2. Do the intervention letters reduce the absolute number of antibiotics prescribed? This is measured as the number of antibiotic scripts dispensed from the doctors' prescribing, and is taken as a monthly average from September - December 2020.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
The secondary outcomes are listed below:

1. Do the intervention letters reduce prescribing to Māori patients, to Pacific patients, and to non-Māori and non-Pacific patients? This will be measured as the prescribing rate (as defined in the first primary outcome measure) to Māori patients, to Pacific patients, and to non-Māori and non-Pacific patients.

2. Do the intervention letters change prescribing of paracetamol? This will be measured as the number of people prescribed paracetamol scripts per 100 prescribed any medicine.
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The intervention will be evaluated with a two-armed randomised controlled trial (RCT), with randomisation at the GP level and stratification by District Health Board (area).
Experimental Design Details
Randomization Method
Randomisation in this trial is done using the 'stratified' function within the 'splitstackshape' package in R, to randomise GPs into the treatment or control arm within each stratum,
Randomization Unit
Randomisation is at the GP level.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
This trial is not clustered.
Sample size: planned number of observations
There are 1,234 high-prescribing GPs in our sample, of whom 617 are sent the intervention letter.
Sample size (or number of clusters) by treatment arms
There are 617 GPs per trial arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We conduct power calculations to describe the relationship between the effect size, sample size, significance level and statistical power. This helps us to understand how many interventions we can test, and the minimum difference in the outcome measure that we can expect to be able to identify between arms. We have described only the power calculation for our primary outcome measure - a GP’s antibiotic prescribing rate. Below we set out some assumptions which shape the calculations: Significance level: 0.05 Power: 0.80 Number of participants: between 600 and 1,100 high-prescribing GPs in total, half of whom will receive the letter Baseline rate: Average AB rate 17.2 among high prescribers Number of treatment arms: 1 If our total sample size is 1,100 GPs, we can detect an effect size of around 0.75 (a 4% decrease in the AB rate). We believe we are powered to detect realistic effect sizes. For example, the most effective intervention in the Australia trial reduced prescribing rates by 12.3% and was also targeted at individual GPs.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
New Zealand Ethics Committee
IRB Approval Date
2019-07-26
IRB Approval Number
NZEC Application 2019_34
Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)
REPORTS & OTHER MATERIALS