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. 2021 Mar 31:12:608971.
doi: 10.3389/fphar.2021.608971. eCollection 2021.

Changing Patient and Public Beliefs About Antimicrobials and Antimicrobial Resistance (AMR) Using a Brief Digital Intervention

Amy Hai Yan Chan  1   2 , Rob Horne  1 , Helen Lycett  3 , Eva Raebel  3 , Jordi Guitart  3 , Emilie Wildman  1   3 , Karen Ang  1   2   3
Affiliations

Changing Patient and Public Beliefs About Antimicrobials and Antimicrobial Resistance (AMR) Using a Brief Digital Intervention

Amy Hai Yan Chan et al. Front Pharmacol. .

Abstract

Background: A key driver of antimicrobial resistance (AMR) is patient demand for unnecessary antibiotics, which is driven by patients' beliefs about antibiotics and AMR. Few interventions have targeted beliefs to reduce inappropriate demand. Objective: To examine whether a brief, online algorithm-based intervention can change beliefs that may lead to inappropriate antibiotic demand (i.e. perceptions of antibiotic necessity and lack of concern about antibiotic harm). Design: Pre- and post-intervention study. Participants: Participants were 18 years or older, and residing in the United Kingdom, who self-selected to participate via Amazon mTurk, an online survey plaform, and via research networks. Intervention: Participants were presented with a hypothetical situation of cold and flu symptoms, then exposed to the intervention. The online intervention comprised: 1) a profiling tool identifying individual beliefs (antibiotic necessity, concerns, and knowledge) driving inappropriate antibiotic demand; 2) messages designed to change beliefs and knowledge (i.e. reduce antibiotic necessity, and increase antibiotic concerns and knowledge), and 3) an algorithm linking specific messages to specific beliefs and knowledge. Main measures: The profiling tool was repeated immediately after the intervention and compared with baseline scores to assess change in beliefs. A paired samples t-test was used to determine intervention effect. Key Results: A total of 100 respondents completed the study. A significant change in beliefs relating to inappropriate demand was observed after the intervention, with a reduction in beliefs about antibiotic necessity (t = 7.254; p < 0.0001), an increase in antibiotic concerns (t = -7.214; p < 0.0001), and increases in antibiotic and AMR knowledge (t = -4.651; p < 0.0001). Conclusion: This study is the first to demonstrate that patient beliefs about antibiotics and AMR associated with inappropriate demand can be changed by a brief, tailored online intervention. This has implications for the design of future interventions to reduce unnecessary antimicrobial use.

Keywords: antibiotic; antimicrobial resist ance; behavioural science; beliefs about medicines; digital intervention; online intervention; pragmatic intervention; treatment beliefs.

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Conflict of interest statement

AC reports consultancy fees from Janssen-Cilag, and the UCL-Business spin-out company Spoonful of Sugar Ltd., outside the submitted work. AC has also received grants from Health Research Council, Asthma UK, A+ Charitable Trust, Chorus, Oakley Mental Health Foundation, outside the submitted work. RH reports fees from Medical Innovation Academic Consortium (CASMI), AbbVie, Amgen, Biogen, Idec, Gilead Sciences, GlaxoSmithKline, Janssen, Pfizer, Roche, Shire Pharmaceuticals, MSD, Astellas, AstraZeneca, DRSU, Novartis, Universit€atsklinikum Hamburg-Eppendorf, and Teva Pharmaceuticals, outside the submitted work. RH is also the Founder and Director of UCL-Business spin-out company Spoonful of Sugar Ltd. ER, JG, EW, KA and HL were employees of the UCL-Business spin-out company Spoonful of Sugar Ltd., at the time of study conduct. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Magnitude of change in Necessity beliefs (difference in total score for the Necessity beliefs subscale). Positive effect of the intervention is shown in purple (i.e. reduction in antibiotic necessity), and negative effect in green.
FIGURE 2
FIGURE 2
Magnitude of change in Concerns (difference in total score for the concerns subscale). Positive effect of the intervention is shown in purple (i.e. increase in concerns), and negative effect in green.
FIGURE 3
FIGURE 3
Magnitude of change in perceptions (difference in total score for the perceptions subscale). Positive effect of the intervention is shown in purple (i.e. increase in accuracy of perceptions of antibiotics and AMR).
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