ArticleApplied health economics and health policy2025
Contextual Factors that Influence Antibiotic Prescribing: A Discrete Choice Experiment of GP Registrars.
Article in Applied health economics and health policy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Estimation of 'spectrum value': A discrete choice experiment to investigate the willingness-to-pay for narrow-spectrum based on other antimicrobial characteristics.Applied health economics and health policy · 2026Article
- Physician Antibiotic Prescribing Preferences in Community Acquired Pneumonia and Complicated Urinary Tract Infections: A National Discrete Choice Experiment.Open forum infectious diseases · 2026Article
- Article
- Similarities in the consumption trajectory of antibacterial drugs in the outpatient care sector in Germany from 1986 to 2022: identification of shared patterns, correlation analysis of prescribed defined daily dose and assessment of underlying influences.Naunyn-Schmiedeberg's archives of pharmacology · 2025Article
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Authors and funding
9 authors.
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Abstract
introductionAntimicrobial resistance is a global emergency related to overprescribing of antibiotics. Few studies have explored how prescribing behaviours may change as the consequence of changing resistance. Understanding how contextual factors influence antibiotic prescribing will facilitate improved communication strategies to promote appropriate antibiotic prescribing. We aimed to develop and conduct a discrete choice experiment (DCE) to measure how contextual factors influence intended antibiotic prescribing of general practitioner (GP) registrars.
methodsFactors included as attributes in the DCE were level of antibiotic resistance, requirement for an authority to prescribe, existence of a Practice Incentives Program (PIP) for low prescribing and supervisor support for low prescribing. The survey was administered in an online format for GP registrars undergoing training between 2020 and 2021. Regression analysis using a conditional logit model with interaction effects was used on the basis of the assumptions of independence of irrelevant alternatives, independence of error terms and no preference heterogeneity.
resultsIn total, 617 unique respondents answered at least one choice set question. Respondents showed significant preference for avoiding prescribing antibiotics when antibiotic resistance was 25-35% or 40-60% compared with 5-8%. There was also a significant preference for avoiding prescribing when an authority to prescribe was required, or when there was supervisory support of low antibiotic prescribing. In the main effects analysis, respondents were significantly less likely to choose a prescribing option if there was a PIP; however, when interaction effects were included in the regression analysis there was a significant interaction between PIP and resistance rates, but the preference weights for PIP was no longer significant.
conclusionsKnowledge about community resistance impacts the stated intention of GP registrars to prescribe antibiotics. The use of the DCE may have made it possible to determine factors influencing prescribing that would not be detected using other survey methods. These findings provide guidance for producing, explaining and communicating issues regarding antibiotic prescribing to GP registrars.
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