Using behavioural insights to reduce unnecessary antibiotic prescriptions by New Zealand doctors

Last registered on March 06, 2020

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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

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

March 06, 2020, 3:03 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country

New Zealand

Region

Primary Investigator

Name

Alex Gyani

Affiliation

Behavioural Insights Team

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Nathan Chappell

PI Affiliation

The Behavioural Insights Team

Contact Investigator

PI Name

Sarah Hayward

PI Affiliation

The Behavioural Insights Team

Contact Investigator

Additional Trial Information

Status

On going

Start date

2019-08-19

End date

2020-04-30

Keywords

Health

Additional Keywords

Antimicrobial resistance

JEL code(s)

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.5526-1.1

Sponsors & Partners

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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 (Hidden)

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

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?

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.

Supporting Documents and Materials

IRB

Institutional Review Boards (IRBs)

IRB Name

New Zealand Ethics Committee

IRB Approval Date

2019-07-26

IRB Approval Number

NZEC Application 2019_34

Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?

Yes

Intervention Completion Date

September 29, 2019, 12:00 +00:00

Data Collection Complete

Yes

Data Collection Completion Date

December 30, 2019, 12:00 +00:00

Final Sample Size: Number of Clusters (Unit of Randomization)

1,260

Was attrition correlated with treatment status?

Final Sample Size: Total Number of Observations

1,260

Final Sample Size (or Number of Clusters) by Treatment Arms

Treatment: 602 Control: 612

Data Publication

Is public data available?

Program Files

Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials

Description

The results are published in the New Zealand Medical Journal

Citation

Chappell, N., Gerard, C., Gyani, A., Hamblin, R., McKree, R., Lawrence, A., ... & White, J. (2021). Using a randomised controlled trial to test the effectiveness of social norms feedback to reduce antibiotic prescribing without increasing inequities. The New Zealand Medical Journal (Online), 134(1544), 13-6.

URL

https://www.proquest.com/openview/89ffc7ac34717e71c8ea06f63941a93e/1?pq-origsite=gscholar&cbl=1056335