Improving antimicrobial prescription in primary care: a multi – dimensional approach to antimicrobial resistance Martijn Sijbom
IMPROVING ANTIMICROBIAL PRESCRIPTION IN PRIMARY CARE a multi – dimensional approach to antimicrobial resistance Martijn Sijbom
Improving antimicrobial prescription in primary care: a multi – dimensional approach to antimicrobial resistance Martijn Sijbom, 2024 Department of Public Health and Primary Care of the Leiden University Medical Center / Health Campus The Hague ISBN: 978-94-6506-190-0 Cover design: Suze van der Velde Lay-out and printing: Ridderprint | www.ridderprint.nl Copyright © 2024 Martijn Sijbom, The Hague, The Netherlands This thesis is protected by international copyright law. All rights reserved. No part of this thesis may be reproduced, stored, or transmitted in any form or by any means without prior permission of the author, or when applicable, of the publishers of the scientific paper. The study in chapter 5 was part of a project on improving antibiotic allergy registrations, which was funded by the Dutch Ministry of Health, Welfare and Sport (Reference number 327952). The other studies were internally funded by the LUMC.
Improving antimicrobial prescription in primary care a multi – dimensional approach to antimicrobial resistance Proefschrift ter verkrijging van de graad van doctor aan de Universiteit Leiden op gezag van rector magnificus prof. dr. ir. H. Bijl volgens besluit van het college voor promoties te verdedigen op donderdag 24 oktober 2024 klokke 16:00 uur door Martijn Sijbom Geboren te Emmen in 1977
Promotoren Prof. dr. M.E. Numans Prof. dr. M.G.J. de Boer Copromotor Dr. M. Boelens Promotiecommissie Prof. dr. L.G. Visser Prof. dr. J.G. van der Bom Prof. dr. M.L. van Driel (The University of Queensland) Dr. C. van Nieuwkoop Prof. dr. T.J.M. Verheij (Universitair Medisch Centrum Utrecht)
CONTENTS Chapter 1 Introduction 8 Chapter 2 Trends in antibiotic selection pressure generated in primary care and their association with sentinel antibiotic resistance patterns in Europe 20 Chapter 3 Determinants of inappropriate antibiotic prescription in primary care in developed countries with general practitioners as gatekeepers: a systematic review and construction of a framework 46 Chapter 4 Comparing antibiotic prescriptions in primary care between SARS-CoV-2 and influenza: a retrospective observational study 94 Chapter 5 Cues to improve antibiotic-allergy registration: A mixed-method study 114 Chapter 6 Routine data registries as a basis to analyse and improve the quality of antimicrobial prescription in Primary Care 142 Chapter 7 Discussion 184 English summary 204 Nederlandse samenvatting 212 Curriculum vitae 220 Literatuur lijst 222 Nawoord 226
Chapter 1
Introduction
10 Chapter 1 Antimicrobial resistance Antimicrobial resistance (AMR) has become a major global health threat over the past few decades, and its prevalence continues to increase worldwide (1). AMR is defined as any adaptation by a pathogen that renders an antimicrobial ineffective. Morbidity, mortality and healthcare costs attributable to AMR are increasing worldwide, as affected patients generally require longer and more frequent hospital admissions and more complex treatment (2). Studies have demonstrated that AMR-related mortality in Europe is higher than mortality due to human immunodeficiency virus, tuberculosis and influenza combined (3, 4). While it is a natural phenomenon for bacteria to become non-susceptible to antimicrobials, the (over)use of antimicrobials has accelerated this process and is now the major driver of AMR (5). Use of antimicrobials worldwide has increased to such an extent that we can now speak in terms of an AMR pandemic or silent or slow pandemic. The AMR pandemic exhibits similarities with the tragedy of the commons concept (6-9), a phenomenon whereby common resources that are unprotected by formal regulation tend to be depleted through unrestricted individual use. If users of such resources act to maximize their self-interest and do not coordinate with others to maximize the overall common good, the result may be exhaustion or even permanent destruction of the resource if the number of and demand from users exceeds availability (10). This concept is to a certain extent applicable to the development of AMR, as antimicrobials are widely available, easily accessible and available in some pharmacies without a physician prescription, factors that together result in often uncontrolled overuse. From a broader perspective, AMR is the basis of a classic example of a conflict between personal versus common interest, and between current versus future generations. For the individual patient, use of antimicrobials can be easy and helpful and is unlikely to cause side effects. However, in the long term other patients will suffer from infections caused by resistant bacteria (11). The high prevalence of AMR has resulted in many antimicrobials becoming less and less effective, which in turn leads to increased prescribing of broad-spectrum antibiotics by physicians. In countries with a high prevalence of AMR, physicians often assume drug-resistant micro organisms are at play when treating bacterial infections. This further encourages the prescribing of broad-spectrum antimicrobials, often supported by guidelines advising this course. This relatively uncontrolled spiral of increasing prescription of more and broader spectrum antimicrobials will eventually reach a tipping point beyond which few antimicrobials remain suitable for empirical use. This
11 Introduction 1 process may ultimately lead to a post-antimicrobial era, in which few or no currently available antimicrobials remain effective and infections once again become a major cause of morbidity and mortality. Antimicrobial prescribing The discovery of antimicrobials was a major medical breakthrough and heralded a new era of effective treatment of bacterial infections (12). Before the discovery and use of antibiotics in clinical care, infections that are now considered minor were a leading cause of death. Use of antimicrobial treatment and prophylaxis is nowadays an indispensable routine medical treatment in primary and hospital care. Antimicrobial prescribing is part of routine medical care in primary care. General practitioners prescribe antimicrobial drugs daily to patients with an acute presumed or confirmed infection. Pneumonia and cellulitis, which could potentially evolve into life-threatening infections, can be managed effectively and relatively simply in a primary care setting with antimicrobial treatment. Antimicrobial prescribing in primary care is, in general, empiric for the whole duration of the treatment. Cultures are not routinely obtained, except in case of treatment failure or a complicated or recurrent urinary tract infection (UTI). The initially prescribed antimicrobial is not altered during an infection, except in case of treatment failure or when culture results show that bacteria are susceptible for a narrower spectrum antimicrobial than initially prescribed. This empirical approach makes the selection of an appropriate antimicrobial even more important. Choosing an antimicrobial with a spectrum too broad can lead to preventable AMR, while a too narrow-spectrum antimicrobial may not be effective against a particular bacterial infection. In hospital care antimicrobial medication is currently essential in many treatments, even if no actual infection is present, such as in the protocollary prevention of infection during an operation. In general, antimicrobial prescribing starts empirically with the treatment of an infection and a specific antimicrobial drug is chosen based on expected causative bacteria and the type and location of the presumed infection (13). Infections in patients admitted to the hospital are usually severe and these patients are at additional risk of complications. Hence, in hospital care initial treatment has to be effective to prevent further deterioration, usually resulting in the choice of a broad-spectrum antimicrobial effective against nearly all causative bacteria, often including less susceptible strains or species. As part of hospital treatment, cultures are routinely obtained, so when antimicrobial stewardship is practiced, antimicrobials
12 Chapter 1 can be de-escalated during treatment based on the clinical course and the outcome of cultures, aiming for an antimicrobial with the narrowest spectrum possible. One health approach The One health approach is often used in the context of AMR. The One health approach recognizes that the health of humans, domestic and wild animals, plants, and the wider environment (including ecosystems) are closely linked and interdependent, sharing not only the same environment but also many infectious diseases (14, 15). Although the interdependence of humans, animals and nature has been acknowledged for centuries, the relatively new One health approach goes further by encompassing the health of the environment, humans and animals. It promotes the idea that, with ever-increasing human population growth, accompanied by climate change, pollution and depletion of the earth's resources, health disciplines and other fields must collaborate to ensure the future health and well-being of humans, animals and the environment (15, 16). Antimicrobial selection pressure is an essential factor in the development of AMR and is defined as the extent to which the use of antimicrobials enhances the selective process, increasing the prevalence of resistant microorganisms (17). When applying the One health approach to antimicrobial selection pressure, antimicrobial use in all domains (hospital care, veterinary care, primary care or industrial use) contributes to overall antimicrobial selection pressure, regardless of the specific domain where the antimicrobial was used. It is still unclear to what extent each domain contributes to overall antimicrobial selection pressure. Although various aspects of antimicrobial prescribing differ between primary and hospital care, both domains contribute to the risk of AMR through antimicrobial prescription. It could be argued that the impact of primary care on AMR is lower compared to hospital care, one element of which is the general view that antimicrobial prescriptions in primary care are mainly short-term, narrow-spectrum penicillins. Another is that even if a patient is a carrier of resistant bacteria, the risk of contaminating other patients is low outside of hospital. By contrast, in hospital care antimicrobial prescriptions are more often broad-spectrum antimicrobials, sometimes used for long periods. Resistant bacteria from admitted patients are more easily transferred to other patients. Nonetheless, around 80-90% of antimicrobial prescriptions for human use are estimated to originate from primary care in European countries (18). While this likely has a substantial effect on antimicrobial selection
13 Introduction 1 pressure, the relative impact of each domain on antimicrobial selection pressure or the size of their role under a “One health” approach has been insufficiently studied. Decisions regarding antimicrobial prescribing in primary care The decision to prescribe an antimicrobial is or should be primarily based on the expected effectiveness of an antimicrobial drug in curing the patient with a particular infection, caused by a particular micro organism or group of micro organisms. In other words, use of an antimicrobial drug will prevent morbidity and mortality by changing the course of the infection. However, during our daily work in primary care many general practitioners (GPs), including myself, experience situations that are often not so clear and straightforward. Uncertainty about the diagnosis or severity of the disease, the expected course of disease and the risk of complications are daily challenges in primary care. In this context, reliance on antimicrobial medication might not be effective in reducing symptoms and preventing morbidity and/or mortality. Determinants from several interacting domains (e.g., society, primary care practice, physician, patient) influence the decision to prescribe antimicrobial medication, an example of which is the presence of a comorbidity. Physicians tend to prescribe an antimicrobial more often if comorbidity is present, even though this is not a guideline recommendation for many infections. Physicians assume that a comorbidity will increase the risk of complications and that antimicrobial treatment will lower this risk. Indeed, many of the determinants that influence prescription behaviour have already been identified (19). However, information regarding associations between social-economic and primary care practice determinants is still lacking. A better understanding of socialeconomic determinants (such as those associated with immigrant groups),primary care practice determinants and as well as how these factors interact, is needed to understand and improve antimicrobial prescribing in primary care. Once the decision has been taken to prescribe an antibiotic, the next step is to choose the specific antimicrobial drug. This choice is based primarily on the site and severity of the infection, expected causative bacteria, presence of comorbidities and contraindications such as antibiotic allergies. Based on these criteria, recommendations in international guidelines advise a first choice antimicrobial, which generally has a narrow spectrum and few side effects (20-22). A second choice antimicrobial is recommended if the first choice antimicrobial conflicts with a registered antibiotic allergy or in case of treatment failure. To effectively treat unexpected causative or resistant bacteria the second choice antimicrobial has a broader spectrum, which can
14 Chapter 1 potentially induce development of AMR. In addition, second choice antimicrobials - in general - tend to cause more side effects (23-26). Although adequate registration of antimicrobial allergies is essential to prevent rare but potentially life-threatening reactions upon re-exposure, up to 90% of antibiotic allergy registrations are incorrect (27-29) and lead to many avoidable broad-spectrum antimicrobial prescriptions. Understanding the reasons for incorrect antibiotic allergy registrations would assist general practitioners (GP) in improving these registrations. This in turn would help reduce prescribing of second choice antimicrobials, lowering or avoiding consequent adverse effects and development of AMR. Novel viral respiratory tract infections Novel viral respiratory tract infections (RTI), such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), have emerged in recent years and others are expected to emerge over the coming decades (30). Novel viral RTIs tend to change the antimicrobial prescription behaviour of physicians. Initially, little is known about effective treatment, morbidity and mortality. Due to this uncertainty, physicians sometimes prescribe antimicrobials hoping to change the course of the infection and prevent complications such as a bacterial superinfection, pneumonia or hospital admission (31, 32). Therefore, close surveillance of antimicrobial use and prescription behaviour is needed during a pandemic. Antimicrobial stewardship To prevent further increase of AMR, antimicrobial stewardship (AMS) initiatives have been designed and implemented. In brief, AMS is a coherent set of actions which promote the responsible use of antimicrobials. This definition can be applied to actions at the individual level as well as the national and global level, and spans human health, animal health and the environment (1). These actions are coordinated through an antimicrobial stewardship (AMS) programme, which is an organizational or systemwide health care strategy to promote appropriate use of antimicrobials through the implementation of evidence-based interventions. The One health approach is incorporated in AMS programs. the World Health Organisation has made decreasing AMR a priority and has promoted the development and implementation of AMS programmes on a national level (14). Worldwide implementation of AMS programs has started, but not all countries are making progress at the same speed (18).
15 Introduction 1 Antimicrobial resistance in The Netherlands In The Netherlands, the prevalence of AMR has increased only modestly over the past decade. Current prevalence is considered problematic but is not yet seen as a threat (33), as attributable mortality due to resistant infections is still limited in The Netherlands (34). However, vigilance is needed as many neighbouring European countries are already experiencing increasing and even problematic levels of AMR (35). Resistant pathogens can be easily transported to The Netherlands due to extensive travel by Dutch inhabitants and visitors. To prepare for this pandemic the Dutch government has set up a structure consisting of ten regional care networks, tasked with organizing and implementing AMS programs, which are coordinated and supported by the National Institute for Public Health and the Environment (RIVM). The Dutch Working Party on Antibiotic Policy (SWAB) has formulated several guidelines on AMS. The aim is to stop further spread of highly resistant micro organisms and to decrease AMR (36). The two main focus areas are hygiene measurements and prudent use of antimicrobials, while in primary care the focus is on improving the quality of antimicrobial prescribing. All major stakeholders (municipal health services, elderly care, primary care and hospital care) are involved in this network. Role of Dutch primary care The number of antimicrobial prescriptions originating from primary care in The Netherlands is much lower compared to other European countries (18). For example in 2022, GPs in Dutch primary care prescribed 9.1 defined daily doses (DDD) of antimicrobials per 1000 patients, compared with 21.9 prescribed by primary care physicians in Italy (18). Dutch GPs are, in general, cautious when prescribing antimicrobials and Dutch primary care guidelines have restraining recommendations for prescribing antimicrobials (21). Therefore, one could postulate that there is limited room for improvement in antimicrobial prescribing in the Netherlands. However, Dutch studies have found antimicrobial overprescribing rates of 40 to 50% for RTIs (37, 38), although information about potential improvements for other types of infections is limited at present. Aim This thesis focuses on the quality and quantity of antimicrobial drug prescription in primary care, exploring the background and determinants that influence it. The aim of this thesis was therefore to examine the impact and quality of antimicrobial prescribing
16 Chapter 1 and to which extent the quality of antimicrobial prescribing can be improved. With this approach we hope to find starting points from which to restrain currently increasing AMR. Quality of antimicrobial prescribing is defined by two elements in this thesis: 1. an antimicrobial is only prescribed when effective in treating symptoms and preventing complications, morbidity or mortality 2. an appropriate antimicrobial is prescribed for the type, location and severity of the infection, with the narrowest spectrum possible. Outline of the thesis Five different studies, described in chapters 2-6, address the aims of this thesis, with each study examining a distinct dimension of AMR in primary care. The impact of antimicrobial prescriptions originating in primary care on antimicrobial selection pressure and consequent AMR was examined in chapter 2. This opensource data study used publicly available data from the European Centre of Disease Prevention and Control (ECDC) and inventoried types and volumes of antimicrobials prescribed by primary care physicians in European countries. Antimicrobial pressure was calculated using a proxy indicator, the Antibiotic Spectrum Index (ASI), which we correlated with a country’s AMR. Different elements of antimicrobial prescribing in primary care were examined in chapter 3. The goal of this systematic literature review was to provide a framework of determinants of inappropriate antimicrobial prescribing in primary care in developed countries where GPs acts as a gatekeeper. Our observational cohort study in chapter 4 explored the influence of SARSCoV-2 infections on the numbers of antimicrobial prescriptions in primary care. The proportion of antimicrobial prescriptions for patients during a COVID-19 infection was compared with the proportion of antimicrobial prescriptions for patients during an influenza or influenza-like infection in other years. The association between antimicrobial prescriptions and risk factors for an adverse course of a SARS-CoV-2 infections was examined. In a mixed method study that included semi-structured interviews and a file analysis (chapter 5), we explored the details of incorrect antibiotic allergy registrations and what might be improved in the registration of antimicrobial allergies. The results show
17 Introduction 1 how and to what extent the quality of antibiotic allergy registrations can be improved. In a retrospective observational cohort study, described in chapter 6, we used and combined large health care registries for the purpose of evaluation of antimicrobial use in primary care. The aim was to determine the number of appropriate and inappropriate antimicrobial prescriptions in primary care over a period of 10 years, which patient groups and determinants are associated with appropriate antimicrobial prescribing, and the degree to which antimicrobial prescribing in primary care might be improved. Finally, the main results of all studies are summarized and critically appraised in chapter 7, and recommendations on how to incorporate the results of this thesis in AMS interventions are provided.
18 Chapter 1 References 1. Worldwide country situation analysis: response to antimicrobial resistance. 2015 [World Health Organisation report]. Available from: https://www.who.int/drugresistance/documents/situationanalysis/en/. 2. Poudel AN, Zhu S, Cooper N, Little P, Tarrant C, Hickman M, et al. The economic burden of antibiotic resistance: A systematic review and meta-analysis. PloS one. 2023;18(5):e0285170. 3. Cassini A, Högberg LD, Plachouras D, Quattrocchi A, Hoxha A, Simonsen GS, et al. Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area in 2015: a population-level modelling analysis. The Lancet Infectious diseases. 2019;19(1):56-66. 4. Stewardson AJ, Allignol A, Beyersmann J, Graves N, Schumacher M, Meyer R, et al. The health and economic burden of bloodstream infections caused by antimicrobial-susceptible and non-susceptible Enterobacteriaceae and Staphylococcus aureus in European hospitals, 2010 and 2011: a multicentre retrospective cohort study. Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin. 2016;21(33). 5. Bell BG, Schellevis F, Stobberingh E, Goossens H, Pringle M. A systematic review and meta-analysis of the effects of antibiotic consumption on antibiotic resistance. BMC infectious diseases. 2014;14:13. 6. Hardin G. The tragedy of the commons. The population problem has no technical solution; it requires a fundamental extension in morality. Science. 1968;162(3859):1243-8. 7. Baquero F, Campos J. The tragedy of the commons in antimicrobial chemotherapy. Rev Esp Quimioter. 2003;16(1):11-3. 8. Hollis A, Maybarduk P. Antibiotic Resistance Is a Tragedy of the Commons That Necessitates Global Cooperation. J Law Med Ethics. 2015;43 Suppl 3:33-7. 9. Halaby A. Multidrug-resistant bacteria in clinical practice. Amsterdam: Vrije Universiteit Amsterdam; 2018. 10. Lloyd W. W. F. Lloyd on the Checks to Population. Population and Development Review. 1980;6(3):473-96. 11. Vandenbroucke-Grauls CM. Oud en nieuw. Ned Tijdschr Med Microbiol 2018;26(4). 12. Fleming A. On the Antibacterial Action of Cultures of a Penicillium, with Special Reference to their Use in the Isolation of B. influenzæ. Br J Exp Pathol. 1929;10(3):226-36. 13. Dutch General Practitioners Society Primary Care Guidelines. [Available from: www.nhg.org. 14. One health 2022 [World Health Organization report]. Available from: https://www.who.int/news-room/ questions-and-answers/item/one-health. 15. Zinsstag J, Meisser A, Schelling E, Bonfoh B, Tanner M. From 'two medicines' to 'One Health' and beyond. Onderstepoort J Vet Res. 2012;79(2):492. 16. One health Commission. What is One Health? 2018 [Available from: https://www.onehealthcommission.org/ en/why_one_health/what_is_one_health/. 17. Hughes D, Andersson DI. Evolutionary Trajectories to Antibiotic Resistance. Annu Rev Microbiol. 2017;71:579-96. 18. Antimicrobial consumption in Europe 2023 [European Centre for Disease Prevention and Control report]. Available from: https://ecdc.europa.eu/en/antimicrobial-consumption/surveillance-and-disease-data/database. 19. Rose J, Crosbie M, Stewart A. A qualitative literature review exploring the drivers influencing antibiotic overprescribing by GPs in primary care and recommendations to reduce unnecessary prescribing. Perspect Public Health. 2021;141(1):19-27. 20. Van Pinxteren B, Knottnerus BJ, Geerlings SE, Visser HS, Klinkhamer S, Van der Weele GM, et al. NHG-Standaard Urineweginfecties (derde herziening). Huisarts Wet. 2013;56(6):270-80. 21. Workinggroup. Dutch Society of General Practitioners guideline: Acute coughing 2011 [Dutch Society of General Practitioners guideline]. Available from: https://richtlijnen.nhg.org/standaarden/acuut-hoesten. 22. National Institute for Health Care and Excellence (NICE): Antimicrobial stewardship: systems and processes for effective antimicrobial medicine use. 2015. 23. Su T, Broekhuizen BDL, Verheij TJM, Rockmann H. The impact of penicillin allergy labels on antibiotic and healthcare use in primary care: a retrospective cohort study. Clinical and translational allergy. 2017;7:18.
19 Introduction 1 24. Borch JE, Andersen KE, Bindslev-Jensen C. The Prevalence of Suspected and Challenge-Verified Penicillin Allergy in a University Hospital Population. 2006;98(4):357-62. 25. Shah NS, Ridgway JP, Pettit N, Fahrenbach J, Robicsek A. Documenting Penicillin Allergy: The Impact of Inconsistency. PloS one. 2016;11(3):e0150514. 26. Li M, Krishna MT, Razaq S, Pillay D. A real-time prospective evaluation of clinical pharmaco-economic impact of diagnostic label of ‘penicillin allergy’ in a UK teaching hospital. Journal of clinical pathology. 2014;67(12):108892. 27. Salden OA, Rockmann H, Verheij TJ, Broekhuizen BD. Diagnosis of allergy against beta-lactams in primary care: prevalence and diagnostic criteria. Family practice. 2015;32(3):257-62. 28. Salkind AR, Cuddy PG, Foxworth JW. The rational clinical examination. Is this patient allergic to penicillin? An evidence-based analysis of the likelihood of penicillin allergy. Jama. 2001;285(19):2498-505. 29. Trubiano JA, Adkinson NF, Phillips EJ. Penicillin Allergy Is Not Necessarily Forever. Jama. 2017;318(1):82-3. 30. Zumla A, Hui DS, Al-Tawfiq JA, Gautret P, McCloskey B, Memish ZA. Emerging respiratory tract infections. The Lancet Infectious Diseases. 2014;14(10):910-1. 31. Rothberg MB, Haessler SD, Brown RB. Complications of viral influenza. Am J Med. 2008;121(4):258-64. 32. Falsey AR, Becker KL, Swinburne AJ, Nylen ES, Formica MA, Hennessey PA, et al. Bacterial complications of respiratory tract viral illness: a comprehensive evaluation. J Infect Dis. 2013;208(3):432-41. 33. National Institute for Public health and the Enivronment (RIVM); Dutch Working Party on Antibiotic Policy (SWAB) NethMap 2022. Consumption of antimicrobial agents and antimicrobial resistance among medically important bacteria in the Netherlands in 2021. 2022. 34. Rottier WC, Deelen JWT, Caruana G, Buiting AGM, Dorigo-Zetsma JW, Kluytmans J, et al. Attributable mortality of antibiotic resistance in gram-negative infections in the Netherlands: a parallel matched cohort study. Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases. 2020. 35. Antimicrobial resistance In Europe 2023 [Report by European Centre for Disease Prevention and Control ]. Available from: https://ecdc.europa.eu/en/antimicrobial-resistance. 36. Ministerie van Volksgezondheid. Antibioticaresistentie in de zorg [Available from: https://www.rijksoverheid. nl/onderwerpen/antibioticaresistentie/antibioticaresistentie-in-de-gezondheidszorg. 37. van der Velden AW, Kuyvenhoven MM, Verheij TJ. Improving antibiotic prescribing quality by an intervention embedded in the primary care practice accreditation: the ARTI4 randomized trial. The Journal of antimicrobial chemotherapy. 2016;71(1):257-63. 38. Dekker ARJ, Verheij TJM, van der Velden AW. Inappropriate antibiotic prescription for respiratory tract indications: most prominent in adult patients. Family practice. 2015;32(4):401-7.
Chapter 2
Trends in antibiotic selection pressure generated in primary care and their association with sentinel antibiotic resistance patterns in Europe Martijn Sijbom, Frederike L. Büchner, Nicholas H. Saadah, Mattijs E. Numans and Mark G.J. De Boer. J Antimicrob Chemother 2023; 78: 1245–1252
22 Chapter 2 Abstract Objectives We studied trends in antibiotic prescribing by primary care and assessed the associations between generated antibiotic selection pressure (ASP) and the prevalence of sentinel drug-resistant micro organisms (SDRMs). Methods The volume of antibiotic prescribing in primary and hospital care expressed in DDD/1000 inhabitants per day and the prevalences of SDRMs in European countries where GPs act as gatekeepers were obtained from the European Centre for Disease Control ESAC-NET. Associations were tested between (i) DDD and (ii) the Antibiotic Spectrum Index (ASI) as a proxy indicator for ASP, and the prevalences of three SDRMs: MRSA, MDR Escherichia coli and Streptococcus pneumoniae resistant to macrolides. Results Fourteen European countries were included. Italy, Poland and Spain had the highest prevalence of SDRMs and prescribed the highest volume of antibiotics in primary care (average 17 DDD per 1000 inhabitants per day), approximately twice that of countries with the lowest volumes. Moreover, the ASIs of these high antibiotic volume countries were approximately three times higher than those of the low-volume countries. Cumulative ASI showed the strongest association with a country’s prevalence of SDRMs. The cumulative ASI generated from primary care was about four to five times higher than the cumulative ASI generated by hospital care. Conclusions Prevalences of SDRMs are associated with the volume of antimicrobial prescribing and in particular broad-spectrum antibiotics in European countries where GPs act as gatekeepers. The impact of ASP generated from primary care on increasing antimicrobial resistance may be much larger than currently assumed.
23 Trends in antibiotic selection pressure generated in primary care and their association with sentinel antibiotic resistance patterns in Europe 2 Introduction Antimicrobial resistance (AMR) is increasing worldwide and represents a major threat to global healthcare (1). The major driver of the rise in AMR is the use of antibiotics (2). Worldwide, efforts are now being undertaken to decrease antibiotic prescribing and consequently reduce the rate of AMR development (1). Given that GPs are responsible for the majority of antibiotic prescriptions in a country, they potentially have an important role to play in reducing AMR (3). However, the extent to which antibiotic prescribing in primary care contributes to increasing AMR is still unclear (4). For varied reasons, not all GPs consider their antibiotic prescribing practices to be part of the process eventually leading to increasing AMR (5,6). Part of the process leading to AMR is referred to as ‘antibiotic selection pressure’ (ASP), defined as the extent to which the use of antibiotics enhances the selective process increasing the growth of resistant microorganisms (7). According to the One Health concept, all antibiotic prescriptions contribute to ASP (8). The relative contribution to the ASP of an antibiotic most likely depends on the dosage, duration of use, and type and spectrum of an antibiotic. The aim of this study was to inventory types and volumes of antibiotics prescribed by primary care practitioners in European countries where they act as gatekeepers. Importantly, this study investigates the correlation between a country’s AMR and the overall level of antibiotic prescribing, and resultant antibiotic pressure, in that country. Testing associations between prescription data and the AMR levels in a country provides insight into the role primary care has compared with hospital care in increasing AMR. Methods In this study, we collected and analysed open source data on the volume of antibiotic prescriptions and on the prevalence of three drug-resistant micro organisms. The volume of antibiotic prescriptions was used to calculate ASP. The volume of antibiotic prescriptions and ASP were then correlated to the prevalence of a sentinel drug-resistant micro organism (SDRM). The study was performed according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidance for reporting observational studies (9), and the STROBE-AMS recommendations for reporting epidemiological studies of AMR and informing improvement in antimicrobial stewardship (S1) (10)
24 Chapter 2 Country selection We analysed data on antibiotic prescriptions from European countries because they collect and report their data in a standardized format through the European Centre for Disease Prevention and Control (ECDC) (11). For a country to be included in the study, GPs had to act as a ‘gatekeeper’ in the healthcare system, defined as a compulsory GP referral to access most types of specialist care except in case of emergency (S2) (12). These countries generally have lower levels of antibiotic prescriptions (13). Data extraction Antibiotic prescriptions The volume of antibiotic prescriptions per country was extracted from the ECDC open source antimicrobial consumption database (ESAC-NET) on 15 March 2022 (11). The volumes were represented in DDD per 1000 inhabitants per day for the years 2011 through 2020. DDD is defined as the assumed average maintenance dose per day for a drug used for its main indication in adults (14). To translate absolute volumes of prescribed antibiotics to a value representing the ASP in a country, we calculate and present the Antibiotic Spectrum Index (ASI) as a proxy indicator for ASP (15). The ASI incorporates the volume of used antibiotics and their activity against micro organisms, expressing these through an index number representing the spectrum of micro organisms that are susceptible to that drug (S3a). The ASI assigns numerical values for an antibiotic that has activity against 1 or more of 13 categories of pathogens, with lower values indicating narrow-spectrum agents and higher values broader-spectrum agents. The ECDC website does not provide data on individual antibiotics, instead providing information per Anatomical Therapeutic Chemical Classification System (ATC) fourthlevel chemical subgroup. Antibiotics in a subgroup are effective against the same micro organisms and have an equal index number (15). Only antibiotics in ATC subgroups macrolides and quinolones have different index numbers. Hence, a mean ASI had to be calculated for these subgroups. For antibiotics lacking a reported ASI, one was calculated using the method proposed by Gerber et al. on the basis of their activity against microorganisms (15). In total, 13 antibiotics were not indexed in the ASI (S3b) and were indexed instead by our research group. The ATC subgroup J01RA, combinations of antibacterials, was excluded from the ASI analysis because it was not possible to calculate an average. The cumulative ASI per ATC subgroup was calculated by multiplying the volume of antibiotic prescriptions in DDD per 1000 inhabitants by the ASI number for that
25 Trends in antibiotic selection pressure generated in primary care and their association with sentinel antibiotic resistance patterns in Europe 2 subgroup. The cumulative ASI (i.e. cumulative antibiotic spectrum index per 1000 inhabitants) in a country was calculated by adding up the ASIs of each subgroup. For each country, this was calculated for (i) primary care, (ii) hospital care and (iii) primary and hospital care combined (i.e. the combined cumulative ASI). AMR of sentinel micro organisms AMR surveillance systems can use a set of drug-resistant micro organisms rather than a complete overview of micro organisms to monitor trends in AMR (16). This approach was taken and three so-called SDRMs relevant for primary care were selected: Staphylococcus aureus, Escherichia coli and Streptococcus pneumoniae are often used to monitor AMR (16). MRSA was used because S. aureus is the leading cause of skin and soft tissue infections. From the order Enterobacterales, E. coli resistant to third-generation cephalosporins and fluoroquinolones and aminoglycosides was selected, because E. coli is the leading pathogen causing urinary tract infections. S. pneumoniae is the most common cause of community-acquired bacterial pneumonia and was considered resistant if non-susceptible to macrolides. We chose to select nonsusceptibility to macrolides instead of resistance to penicillin. Macrolides are regularly second-choice antibiotics for the treatment of community-acquired pneumonia in primary care guidelines, making it a reserved antibiotic only used where other antibiotics are not effective or administrable (17). Country-level prevalences of the three SDRMs were obtained from the ECDC open source database, Surveillance Atlas Antimicrobial resistance, on 2 March 2022 for the years 2011–2020 (11). The ECDC uses the EUCAST guidelines for detecting and reporting specific resistant micro organisms. Treatment of infections in primary care is most often empirical, and obtaining cultures is therefore not part of standard care and not always feasible due to practical reasons. Anticipating a lack of SDRM cultures available from primary care, we combined primary and hospital care data to characterize AMR in each country because, according to the One Health concept, all antibiotic prescriptions contribute to ASP and eventually to AMR (8). Descriptive statistics were used to describe and compare antibiotic volumes between countries and periods, as well as the trends in the volume of antibiotic prescriptions, and the prevalences of SDRMs. The combined cumulative ASI and combined DDD were plotted against the prevalence of each SDRM per country for the year 2020, because it is the most recent year with available data. Univariate linear regression was used to calculate associations between (i) ASI and (ii) DDD and each SDRM prevalence.
26 Chapter 2 Results Statistical analysis Fourteen European countries (Denmark, Estonia, Finland, Ireland, Italy, Latvia, Lithuania, The Netherlands, Norway, Poland, Slovenia, Spain, Sweden and the UK) were identified in which the GPs act as gatekeepers and from which data on antibiotic prescriptions and SDRMs could be obtained. Volumes of antibiotic use in primary care and hospital care The volume of antibiotic prescriptions in primary care decreased over the course of our observation period (2011–2020) in seven countries (Denmark, Finland, Italy, The Netherlands, Norway, Sweden and the UK—see Figure 1 ). Ireland, Italy, Poland and Spain had the highest volumes of antibiotic prescriptions in primary care in 2020, with DDDs between 16 and 17 per 1000 inhabitants per day. The volume of antibiotic prescriptions was in all countries at its lowest in the year 2020. The proportion of antibiotic prescriptions in hospital care compared with the total volume of antibiotic prescriptions ranged from a low of 7.4% in Poland to a high of 16.6% in Latvia. Prevalence of resistant micro organisms MDR E. coli was the SDRM with the lowest prevalence in most countries (Figure 2). The prevalence ranged from 1.2% (Norway) to 14.6% (Italy). The prevalence of MRSA was stable over the period 2011–2020 in most countries. Four countries (Ireland, Italy, Poland and Spain) had a prevalence above 10% for MRSA. The prevalence decreased over the observation period only in Ireland and the UK. Macrolide-resistant S. pneumoniae had the highest prevalence of the three SDRMs, with seven countries reporting a mean prevalence above 10% during the period 2011–2020. Patterns of antimicrobial selection pressure The cumulative primary care ASI in Italy and Spain was about three times higher than in the Netherlands and Sweden, whereas the volume of antibiotic prescribing in primary care in DDD was twice as high in Italy and Spain as The Netherlands and Sweden (Figure 3). Tetracyclines and penicillin were the largest contributors to the cumulative primary care ASI in all countries, respectively ranging from 3.6% (Italy) to 39.8% (Sweden) and from 22.9% (Norway) to 50.7% (Spain). Within the penicillin antibiotic group, penicillin combinations (ATC code J01CR) (e.g. amoxicillin/clavulanate) were the largest contributor to the cumulative primary care ASI in eight countries.
27 Trends in antibiotic selection pressure generated in primary care and their association with sentinel antibiotic resistance patterns in Europe 2 The contribution of primary care to the cumulative combined ASI (primary and hospital care) ranged from 80.4% (Finland) to 91.1% (Spain) (Figure 4). Association of ASP and AMR in a country The combined volumes of antibiotic prescribing in primary and hospital care, expressed both as DDD and the combined cumulative ASI, are shown plotted against the prevalence of the three SDRMs in Figure 5, and the standardized coefficients of association (beta) are presented in S4. The betas representing associations between SDRMs and combined cumulative ASI were all higher than those representing associations between SDRMs and combined total DDD. Discussion We studied the trends in volume of antibiotic prescribing in primary care, the prevalences of SDRMs, and the ASP using proxy indicators ASI and DDD in European countries where GPs act as gatekeepers. The volumes of antibiotic prescriptions in primary care and the prevalences of SDRMs varied significantly between countries. DDD and ASI were associated with SDRM prevalence. Primary care was a larger contributor to ASP than hospital care. Total number of antibiotic prescriptions We found a large variation in volume of antibiotic prescriptions between countries in primary care. This may be due to cultural effects on the prescription of antibiotics. Borg and Camilleri showed a high association between a high degree of uncertainty avoidance and the prescribing of more broad-spectrum antibiotics (18), and FletcherLartey et al. showed uncertainty avoidance to be associated with inappropriate antibiotic prescribing (5). Italy, Poland and Spain had high uncertainty avoidance scores (19). In 2020, the volume of antibiotic prescriptions in primary care was lower in all countries than in preceding years. This is likely due to the trend of decreasing antibiotic prescriptions and the severe acute respiratory syndrome coronavirus-2 pandemic. During the pandemic, there were fewer non-coronaviral disease respiratory tract infections (20), leading subsequently to fewer antibiotic prescriptions.
28 Chapter 2 SDRMs The percentage of invasive isolates with MRSA declined in both Ireland and the UK between 2011 and 2020. The decline in Ireland and the UK is likely a result of the introduction of guidelines on the prevention and control of MRSA in 2007 and of multiple interventions including hygiene protocols and mandatory reporting of MRSA, respectively (21,22). For all three SDRMs, Italy, Poland and Spain have the highest prevalences among the countries in our study. These three countries also have a higher volume of antibiotic prescribing as expressed in DDD, and a higher ASP as represented by ASI. The higher prevalence of an SDRM is a likely consequence of the high volume of antibiotic prescribing and will lead to prescribing of more broad-spectrum antibiotics. Physicians often assume drug-resistant micro organisms are at play when treating bacterial infections in locations where drug-resistant micro organisms are known to be an issue. This encourages prescribing broad-spectrum antibiotics, often supported by guidelines advising this course. The resulting evolutionary pressure on the microbiome leads to increased selection of antimicrobial resistance. This vicious circle of prescribing more and broader spectrum antibiotics can lead to a point of no return when few antibiotics suitable for empirical use remain. Proxy indicators of ASP The levels of DDD and ASI varied between countries. Primary care practitioners in Italy and Spain prescribed twice the volume of antibiotics compared with their colleagues in Denmark, The Netherlands and Sweden, but the cumulative ASI was three times higher in Italy and Spain. Furthermore, the DDD in Spain and Italy was comparable to those of Ireland and Poland for the year 2020, whereas the ASI in 2020 was 1.5 times higher in Spain and Italy. These differences may be largely explained by the very high number of prescriptions for penicillin combinations and quinolones in Italy and Spain in primary care. Both groups are broad-spectrum antibiotics and have high ASIs of 6 and 8, respectively. The cumulative ASI seems to correlate better with the prevalence of a SDRM than does total antibiotic consumption expressed in DDD, as illustrated by data from Ireland and Italy. The DDD of Italy was only slightly higher than that of Ireland, but the prevalence of the selected SDRMs in Italy was significantly higher (Figure 2). Further, the ASI in Italy was much higher than that of Ireland and more strongly correlated with the prevalence of an SDRM (Figure 5 and S4). MRSA and S. pneumoniae showed the strongest associations with ASI, with standardized coefficients of 0.94 and 0.91,
29 Trends in antibiotic selection pressure generated in primary care and their association with sentinel antibiotic resistance patterns in Europe 2 respectively. Particularly relevant for primary care is the strong association with S. pneumoniae because this is a very common cause of respiratory tract infections in primary care, even more so than skin infections caused by S. aureus (23). Comparison with existing literature Although ASI has been examined in institutes such as hospitals and nursing homes (24–30), we found no studies exploring this at a national level. The studies who examined ASI in hospitals and nursing homes showed that ASI gives additional insight into antibiotic prescribing patterns compared with other proxy indicators such as DDD or days of therapy, and may be useful for internal and external comparisons of institutions (24,28,29). Monitoring antibiotic consumption combined with surveillance of resistant micro organisms is advised as part of the One Health strategy (31). Most healthcare systems still use DDD as the only measure to represent the volume of antibiotic use. Strengths and limitations A strength of our study is using absolute volumes of antibiotic prescriptions in primary and hospital care when calculating the proxy indicator cumulative ASI. The proxy indicator is in this way a better representation of the ASP in a country than, for example, weighted mean volumes. The applied method of calculating the ASP is relatively simple, which makes it easily implemented in almost every country or region as a proxy indicator. A limitation of this study is that some of the prescribed antibiotics may not be directly related to increasing resistance found in a specific SDRM. However, exposure to antibiotics in general is sufficient to generate community-acquired resistant infections in members of the same community. Further, the cumulative ASI is a proxy indicator representing the level of implementation of antimicrobial stewardship and the prevalence of already existing AMR in a country. The ratio between antimicrobial stewardship and already existing AMR contributing to ASI is not deducible from our study. We used only three specific SDRMs in our study. Although using other SDRMs may lead to slightly different results, the expected trend would be similar. Because only European countries in which GPs act as gatekeepers were included in this study, the results may be less generalizable to countries with differently organized healthcare systems.
30 Chapter 2 Conclusions We found substantial variation in both the volume of antibiotic prescriptions in primary care and the prevalence of SDRMs between countries. There is, however, a clear association between the volume of antibiotic prescribing and the prevalence of SDRMs. Approximately 90% of the ASP expressed in the ASI originated from primary care, which is even more associated with the prevalence of SDRMs, compared with the volume of antibiotic prescribing. This emphasizes that the role of primary care in the development of AMR may be much larger than previously assumed by some GPs. This is an important insight, because some GPs may believe that antibiotic prescribing in their practice does not contribute to the development of AMR, but that instead AMR is driven by antibiotic prescriptions in hospitals or those used in veterinary care. The societal and medical impacts of this phenomenon warrant further investigation into mechanisms for improvement and implementation of antibiotic stewardship in primary care.
31 Trends in antibiotic selection pressure generated in primary care and their association with sentinel antibiotic resistance patterns in Europe 2 Figures Figure 1. Volume of antibiotic prescription in Defined Daily Doses per 1000 inhabitans per day from primary care *Spain saw a strong ostensible increase in prescription from 2016 onwards. However, this was due to the reporting of only reimbursement data until 2015, whereas figures from 2016 on were based on sales data (11) #Data from primary care in the United Kingdom for the year 2020 was missing in the open source database of the ECDC. Figure 2 Figure 2a. Meticillin-resistant Staphyloccocus aureus: percentage resistant isloates
32 Chapter 2 Figure 2b. E.coli, multidrugresistant*, percentage resistant isloates Figure 2c. S.pneumoniae non-susceptible to macrolides, percentage resistant isolates *Data from primary care in the United Kingdom for the year 2020 was missing in the open source database of the ECDC.
33 Trends in antibiotic selection pressure generated in primary care and their association with sentinel antibiotic resistance patterns in Europe 2 Figure 3. Antibiotic spectrum index for primary care *Spain saw a strong ostensible increase in prescription from 2016 onwards. However, this was due to the reporting of only reimbursement data until 2015, whereas figures from 2016 on were based on sales data (11). Figure 4. Antibiotic Spectrum Index for primary care and hospital care for the year 2020 *United Kingdom is not included due to missing data on the year 2020.
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