Quantifying the primary and secondary effects of antimicrobial resistance on surgery patients: Methods and data sources for empirical estimation in England
Antimicrobial resistance (AMR) may negatively impact surgery patients through reducing the efficacy of treatment of surgical site infections, also known as the “primary effects” of AMR. Previous estimates of the burden of AMR have largely ignored the potential “secondary effects,” such as changes in...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.803943/full |
| _version_ | 1811339327818432512 |
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| author | Nichola R. Naylor Nichola R. Naylor Nichola R. Naylor Stephanie Evans Koen B. Pouwels Koen B. Pouwels Rachael Troughton Theresa Lamagni Berit Muller-Pebody Gwenan M. Knight Rifat Atun Rifat Atun Julie V. Robotham Julie V. Robotham |
| author_facet | Nichola R. Naylor Nichola R. Naylor Nichola R. Naylor Stephanie Evans Koen B. Pouwels Koen B. Pouwels Rachael Troughton Theresa Lamagni Berit Muller-Pebody Gwenan M. Knight Rifat Atun Rifat Atun Julie V. Robotham Julie V. Robotham |
| author_sort | Nichola R. Naylor |
| collection | DOAJ |
| description | Antimicrobial resistance (AMR) may negatively impact surgery patients through reducing the efficacy of treatment of surgical site infections, also known as the “primary effects” of AMR. Previous estimates of the burden of AMR have largely ignored the potential “secondary effects,” such as changes in surgical care pathways due to AMR, such as different infection prevention procedures or reduced access to surgical procedures altogether, with literature providing limited quantifications of this potential burden. Former conceptual models and approaches for quantifying such impacts are available, though they are often high-level and difficult to utilize in practice. We therefore expand on this earlier work to incorporate heterogeneity in antimicrobial usage, AMR, and causative organisms, providing a detailed decision-tree-Markov-hybrid conceptual model to estimate the burden of AMR on surgery patients. We collate available data sources in England and describe how routinely collected data could be used to parameterise such a model, providing a useful repository of data systems for future health economic evaluations. The wealth of national-level data available for England provides a case study in describing how current surveillance and administrative data capture systems could be used in the estimation of transition probability and cost parameters. However, it is recommended that such data are utilized in combination with expert opinion (for scope and scenario definitions) to robustly estimate both the primary and secondary effects of AMR over time. Though we focus on England, this discussion is useful in other settings with established and/or developing infectious diseases surveillance systems that feed into AMR National Action Plans. |
| first_indexed | 2024-04-13T18:24:45Z |
| format | Article |
| id | doaj.art-804c1d3ee61a46a3b30305dce0872f34 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-04-13T18:24:45Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-804c1d3ee61a46a3b30305dce0872f342022-12-22T02:35:19ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-08-011010.3389/fpubh.2022.803943803943Quantifying the primary and secondary effects of antimicrobial resistance on surgery patients: Methods and data sources for empirical estimation in EnglandNichola R. Naylor0Nichola R. Naylor1Nichola R. Naylor2Stephanie Evans3Koen B. Pouwels4Koen B. Pouwels5Rachael Troughton6Theresa Lamagni7Berit Muller-Pebody8Gwenan M. Knight9Rifat Atun10Rifat Atun11Julie V. Robotham12Julie V. Robotham13The National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College London, London, United KingdomDepartment of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, Antimicrobial Resistance (AMR) Centre, London School of Hygiene and Tropical Medicine, London, United KingdomHealthcare Associated Infection, Fungal, Antimicrobial Resistance, Antimicrobial Usage and Sepsis division, United Kingdom Health Security Agency, London, United KingdomHealthcare Associated Infection, Fungal, Antimicrobial Resistance, Antimicrobial Usage and Sepsis division, United Kingdom Health Security Agency, London, United KingdomNuffield Department of Population Health, Health Economics Research Centre, University of Oxford, Oxford, United KingdomThe National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, United KingdomThe National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College London, London, United KingdomHealthcare Associated Infection, Fungal, Antimicrobial Resistance, Antimicrobial Usage and Sepsis division, United Kingdom Health Security Agency, London, United KingdomHealthcare Associated Infection, Fungal, Antimicrobial Resistance, Antimicrobial Usage and Sepsis division, United Kingdom Health Security Agency, London, United KingdomDepartment of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, Antimicrobial Resistance (AMR) Centre, London School of Hygiene and Tropical Medicine, London, United KingdomDepartment of Global Health and Population, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, United StatesDepartment of Global Health and Social Medicine, Harvard Medical School, Harvard University, Boston, MA, United StatesThe National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infection and Antimicrobial Resistance at Imperial College London, London, United KingdomHealthcare Associated Infection, Fungal, Antimicrobial Resistance, Antimicrobial Usage and Sepsis division, United Kingdom Health Security Agency, London, United KingdomAntimicrobial resistance (AMR) may negatively impact surgery patients through reducing the efficacy of treatment of surgical site infections, also known as the “primary effects” of AMR. Previous estimates of the burden of AMR have largely ignored the potential “secondary effects,” such as changes in surgical care pathways due to AMR, such as different infection prevention procedures or reduced access to surgical procedures altogether, with literature providing limited quantifications of this potential burden. Former conceptual models and approaches for quantifying such impacts are available, though they are often high-level and difficult to utilize in practice. We therefore expand on this earlier work to incorporate heterogeneity in antimicrobial usage, AMR, and causative organisms, providing a detailed decision-tree-Markov-hybrid conceptual model to estimate the burden of AMR on surgery patients. We collate available data sources in England and describe how routinely collected data could be used to parameterise such a model, providing a useful repository of data systems for future health economic evaluations. The wealth of national-level data available for England provides a case study in describing how current surveillance and administrative data capture systems could be used in the estimation of transition probability and cost parameters. However, it is recommended that such data are utilized in combination with expert opinion (for scope and scenario definitions) to robustly estimate both the primary and secondary effects of AMR over time. Though we focus on England, this discussion is useful in other settings with established and/or developing infectious diseases surveillance systems that feed into AMR National Action Plans.https://www.frontiersin.org/articles/10.3389/fpubh.2022.803943/fullantimicrobial resistancesecondary effectssurgical site infectionsurgeryburden |
| spellingShingle | Nichola R. Naylor Nichola R. Naylor Nichola R. Naylor Stephanie Evans Koen B. Pouwels Koen B. Pouwels Rachael Troughton Theresa Lamagni Berit Muller-Pebody Gwenan M. Knight Rifat Atun Rifat Atun Julie V. Robotham Julie V. Robotham Quantifying the primary and secondary effects of antimicrobial resistance on surgery patients: Methods and data sources for empirical estimation in England Frontiers in Public Health antimicrobial resistance secondary effects surgical site infection surgery burden |
| title | Quantifying the primary and secondary effects of antimicrobial resistance on surgery patients: Methods and data sources for empirical estimation in England |
| title_full | Quantifying the primary and secondary effects of antimicrobial resistance on surgery patients: Methods and data sources for empirical estimation in England |
| title_fullStr | Quantifying the primary and secondary effects of antimicrobial resistance on surgery patients: Methods and data sources for empirical estimation in England |
| title_full_unstemmed | Quantifying the primary and secondary effects of antimicrobial resistance on surgery patients: Methods and data sources for empirical estimation in England |
| title_short | Quantifying the primary and secondary effects of antimicrobial resistance on surgery patients: Methods and data sources for empirical estimation in England |
| title_sort | quantifying the primary and secondary effects of antimicrobial resistance on surgery patients methods and data sources for empirical estimation in england |
| topic | antimicrobial resistance secondary effects surgical site infection surgery burden |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.803943/full |
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