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A comparative assessment of action plans on antimicrobial resistance from OECD and G20 countries using natural language processing

Author

Listed:
  • Özçelik, Ece A
  • Doucet, Cédric
  • Kang, Hyunjin
  • Levy, Noémie
  • Feldhaus, Isabelle
  • Hashiguchi, Tiago Cravo Oliveira
  • Lerouge, Aliénor
  • Cecchini, Michele

Abstract

Following the launch of the Global Action Plan on antimicrobial resistance (AMR-GAP) in 2015, most OECD and G20 countries developed their own national action plans (AMR-NAPs). This is the first paper that deploys natural language processing (NLP) techniques to systematically measure and compare the extent to which AMR-NAPs from 21 OECD and G20 countries align with the AMR-GAP in terms of the strategic objectives and interventions. We quantify the extent of alignment based on two NLP metrics: term-frequency (TF) and term-frequency-inverse document frequency (TF-IDF). Quantifying TF allows us to compare the relative prominence of strategic objectives and interventions, whereas quantifying TF-IDF enables us to identify interventions that occur more frequently in each AMR-NAP. Similar to the AMR-GAP, in our sample, terms associated with optimizing antimicrobial use in human and animal health have the highest frequency (TF = 0. 287), whereas terms linked to raising AMR awareness and education have the lowest frequency (TF = 0.066). Substantial cross-country variation exists in the distribution of interventions that are distinctly frequent in each AMR-NAP. We also report new evidence on the selected policy design and monitoring and evaluation features of these documents. Our results suggest a high degree of congruence between the AMR-GAP and AMR-NAPs, with notable diversity in the spate of interventions that OECD and G20 countries discuss in their action plans.

Suggested Citation

  • Özçelik, Ece A & Doucet, Cédric & Kang, Hyunjin & Levy, Noémie & Feldhaus, Isabelle & Hashiguchi, Tiago Cravo Oliveira & Lerouge, Aliénor & Cecchini, Michele, 2022. "A comparative assessment of action plans on antimicrobial resistance from OECD and G20 countries using natural language processing," Health Policy, Elsevier, vol. 126(6), pages 522-533.
    • Handle: RePEc:eee:hepoli:v:126:y:2022:i:6:p:522-533
    DOI: 10.1016/j.healthpol.2022.03.011
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    References listed on IDEAS

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    1. Susan Rogers Van Katwyk & Jeremy M Grimshaw & Miriam Nkangu & Ranjana Nagi & Marc Mendelson & Monica Taljaard & Steven J Hoffman, 2019. "Government policy interventions to reduce human antimicrobial use: A systematic review and evidence map," PLOS Medicine, Public Library of Science, vol. 16(6), pages 1-17, June.
    2. De Rosis, Sabina & Lopreite, Milena & Puliga, Michelangelo & Vainieri, Milena, 2021. "The early weeks of the Italian Covid-19 outbreak: sentiment insights from a Twitter analysis," Health Policy, Elsevier, vol. 125(8), pages 987-994.
    3. Amy Rudge & Kristen Foley & Belinda Lunnay & Emma R. Miller & Samantha Batchelor & Paul R. Ward, 2021. "How Are the Links between Alcohol Consumption and Breast Cancer Portrayed in Australian Newspapers?: A Paired Thematic and Framing Media Analysis," IJERPH, MDPI, vol. 18(14), pages 1-18, July.
    4. Giorgia Sulis & Pierrick Adam & Vaidehi Nafade & Genevieve Gore & Benjamin Daniels & Amrita Daftary & Jishnu Das & Sumanth Gandra & Madhukar Pai, 2020. "Antibiotic prescription practices in primary care in low- and middle-income countries: A systematic review and meta-analysis," PLOS Medicine, Public Library of Science, vol. 17(6), pages 1-20, June.
    5. Arora, Vishal S. & McKee, Martin & Stuckler, David, 2019. "Google Trends: Opportunities and limitations in health and health policy research," Health Policy, Elsevier, vol. 123(3), pages 338-341.
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    • G20 - Financial Economics - - Financial Institutions and Services - - - General

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