<br><div> <head profile="http://www.w3.org/1999/xhtml/vocab"> <!--[if IE]><![endif]--> <meta http-equiv="Content-Type" content="text/html; charset=utf-8"/> <link rel="dns-prefetch" href="//pnas-movie.glencoesoftware.com"/> <link rel="dns-prefetch" href="//scholar.google.com"/> <link rel="dns-prefetch" href="//cdnjs.cloudflare.com"/> <link rel="dns-prefetch" href="//stats.g.doubleclick.net"/> <link rel="dns-prefetch" href="//www.google-analytics.com"/> <link rel="dns-prefetch" href="//www.google.com"/> <meta name="viewport" content="initial-scale=1, maximum-scale=1, width=device-width, user-scalable=yes"/> <link rel="shortcut icon" href="https://www.pnas.org/sites/default/files/images/favicon.ico" type="image/vnd.microsoft.icon"/> <link rel="canonical" href="https://www.pnas.org/content/early/2020/10/30/2013694117"/> <meta name="Generator" content="Drupal 7 (http://drupal.org)"/> <link rel="alternate" type="application/pdf" title="Full Text (PDF)" href="http://www.pnas.org/content/early/2020/10/30/2013694117.full.pdf"/> <link rel="alternate" type="text/plain" title="Full Text (Plain)" href="http://www.pnas.org/content/early/2020/10/30/2013694117.full.txt"/> <link rel="alternate" type="application/vnd.ms-powerpoint" title="Powerpoint" href="http://www.pnas.org/content/early/2020/10/30/2013694117.ppt"/> <meta name="type" content="article"/> <meta name="category" content="research-article"/> <meta name="HW.identifier" content="/pnas/early/2020/10/30/2013694117.atom"/> <meta name="HW.pisa" content="pnas;2013694117v1"/> <meta name="DC.Format" content="text/html"/> <meta name="DC.Language" content="en"/> <meta name="DC.Title" content="The role of “spillover” in antibiotic resistance"/> <meta name="DC.Identifier" content="10.1073/pnas.2013694117"/> <meta name="DC.Date" content="2020-11-02"/> <meta name="DC.Publisher" content="National Academy of Sciences"/> <meta name="DC.Rights" content="Copyright © 2020 the Author(s). Published by PNAS.. https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND)."/> <meta name="DC.AccessRights" content="open-access"/> <meta name="DC.Description" content="Antibiotic resistance can spread from person to person, a phenomenon exemplified by the rapid global spread of novel resistance determinants. Antibiotic resistance can also “spill over” between family members or between a hospital and its surrounding community, such that antibiotic use in one population selects for resistance that is transmitted into the other population. In theory, antibiotic use in one population could spill over into its neighbors, making it difficult to understand to what degree a population’s own behavior, such as its antibiotic use, determines its level of resistance. Here we use theoretical modeling and observational data to quantify spillover, finding that even modest interactions between populations can lead to substantial sharing of resistance levels. Antibiotic use is a key driver of antibiotic resistance. Understanding the quantitative association between antibiotic use and resulting resistance is important for predicting future rates of antibiotic resistance and for designing antibiotic stewardship policy. However, the use–resistance association is complicated by “spillover,” in which one population’s level of antibiotic use affects another population’s level of resistance via the transmission of bacteria between those populations. Spillover is known to have effects at the level of families and hospitals, but it is unclear if spillover is relevant at larger scales. We used mathematical modeling and analysis of observational data to address this question. First, we used dynamical models of antibiotic resistance to predict the effects of spillover. Whereas populations completely isolated from one another do not experience any spillover, we found that if even 1% of interactions are between populations, then spillover may have large consequences: The effect of a change in antibiotic use in one population on antibiotic resistance in that population could be reduced by as much as 50%. Then, we quantified spillover in observational antibiotic use and resistance data from US states and European countries for three pathogen–antibiotic combinations, finding that increased interactions between populations were associated with smaller differences in antibiotic resistance between those populations. Thus, spillover may have an important impact at the level of states and countries, which has ramifications for predicting the future of antibiotic resistance, designing antibiotic resistance stewardship policy, and interpreting stewardship interventions. Data and code to reproduce results have been deposited in Zenodo (DOI: 10.5281/zenodo.3909812)."/> <meta name="DC.Contributor" content="Scott W. Olesen"/> <meta name="DC.Contributor" content="Marc Lipsitch"/> <meta name="DC.Contributor" content="Yonatan H. Grad"/> <meta name="article:published_time" content="2020-11-02"/> <meta name="article:section" content="Biological Sciences"/> <meta name="citation_title" content="The role of “spillover” in antibiotic resistance"/> <meta name="citation_abstract" lang="en" content="<p>Antibiotic use is a key driver of antibiotic resistance. Understanding the quantitative association between antibiotic use and resulting resistance is important for predicting future rates of antibiotic resistance and for designing antibiotic stewardship policy. However, the use–resistance association is complicated by “spillover,” in which one population’s level of antibiotic use affects another population’s level of resistance via the transmission of bacteria between those populations. Spillover is known to have effects at the level of families and hospitals, but it is unclear if spillover is relevant at larger scales. We used mathematical modeling and analysis of observational data to address this question. First, we used dynamical models of antibiotic resistance to predict the effects of spillover. Whereas populations completely isolated from one another do not experience any spillover, we found that if even 1% of interactions are between populations, then spillover may have large consequences: The effect of a change in antibiotic use in one population on antibiotic resistance in that population could be reduced by as much as 50%. Then, we quantified spillover in observational antibiotic use and resistance data from US states and European countries for three pathogen–antibiotic combinations, finding that increased interactions between populations were associated with smaller differences in antibiotic resistance between those populations. Thus, spillover may have an important impact at the level of states and countries, which has ramifications for predicting the future of antibiotic resistance, designing antibiotic resistance stewardship policy, and interpreting stewardship interventions.</p>"/> <meta name="citation_abstract" lang="en" scheme="executive-summary" content="<h3>Significance</h3> <p>Antibiotic resistance can spread from person to person, a phenomenon exemplified by the rapid global spread of novel resistance determinants. Antibiotic resistance can also “spill over” between family members or between a hospital and its surrounding community, such that antibiotic use in one population selects for resistance that is transmitted into the other population. In theory, antibiotic use in one population could spill over into its neighbors, making it difficult to understand to what degree a population’s own behavior, such as its antibiotic use, determines its level of resistance. Here we use theoretical modeling and observational data to quantify spillover, finding that even modest interactions between populations can lead to substantial sharing of resistance levels.</p>"/> <meta name="citation_journal_title" content="Proceedings of the National Academy of Sciences"/> <meta name="citation_publisher" content="National Academy of Sciences"/> <meta name="citation_publication_date" content="2020/11/02"/> <meta name="citation_mjid" content="pnas;2013694117v1"/> <meta name="citation_id" content="2013694117v1"/> <meta name="citation_public_url" content="https://www.pnas.org/content/early/2020/10/30/2013694117"/> <meta name="citation_abstract_html_url" content="https://www.pnas.org/content/early/2020/10/30/2013694117.abstract"/> <meta name="citation_full_html_url" content="https://www.pnas.org/content/early/2020/10/30/2013694117.full"/> <meta name="citation_pdf_url" content="https://www.pnas.org/content/pnas/early/2020/10/30/2013694117.full.pdf"/> <meta name="citation_issn" content="0027-8424"/> <meta name="citation_issn" content="1091-6490"/> <meta name="citation_journal_abbrev" content="PNAS"/> <meta name="citation_doi" content="10.1073/pnas.2013694117"/> <meta name="citation_pmid" content="33139558"/> <meta name="citation_num_pages" content="6"/> <meta name="citation_article_type" content="Research Article"/> <meta name="citation_section" content="Biological Sciences"/> <meta name="citation_access" content="all"/> <meta name="citation_author" content="Scott W. 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O’Brien;citation_title=Antimicrobial resistance following mass azithromycin distribution for trachoma: A systematic review;citation_pages=E14-E25;citation_volume=19;citation_year=2019"/> <meta name="citation_fulltext_world_readable" content=""/> <meta name="twitter:title" content="The role of “spillover” in antibiotic resistance"/> <meta name="twitter:card" content="summary_large_image"/> <meta name="twitter:image" content="https://www.pnas.org/content/early/2020/10/30/2013694117/F1.large.jpg"/> <meta name="twitter:description" content="Antibiotic resistance can spread from person to person, a phenomenon exemplified by the rapid global spread of novel resistance determinants. Antibiotic resistance can also “spill over” between family members or between a hospital and its surrounding community, such that antibiotic use in one population selects for resistance that is transmitted into the other population. In theory, antibiotic use in one population could spill over into its neighbors, making it difficult to understand to what degree a population’s own behavior, such as its antibiotic use, determines its level of resistance. Here we use theoretical modeling and observational data to quantify spillover, finding that even modest interactions between populations can lead to substantial sharing of resistance levels. Antibiotic use is a key driver of antibiotic resistance. Understanding the quantitative association between antibiotic use and resulting resistance is important for predicting future rates of antibiotic resistance and for designing antibiotic stewardship policy. However, the use–resistance association is complicated by “spillover,” in which one population’s level of antibiotic use affects another population’s level of resistance via the transmission of bacteria between those populations. Spillover is known to have effects at the level of families and hospitals, but it is unclear if spillover is relevant at larger scales. We used mathematical modeling and analysis of observational data to address this question. First, we used dynamical models of antibiotic resistance to predict the effects of spillover. Whereas populations completely isolated from one another do not experience any spillover, we found that if even 1% of interactions are between populations, then spillover may have large consequences: The effect of a change in antibiotic use in one population on antibiotic resistance in that population could be reduced by as much as 50%. Then, we quantified spillover in observational antibiotic use and resistance data from US states and European countries for three pathogen–antibiotic combinations, finding that increased interactions between populations were associated with smaller differences in antibiotic resistance between those populations. Thus, spillover may have an important impact at the level of states and countries, which has ramifications for predicting the future of antibiotic resistance, designing antibiotic resistance stewardship policy, and interpreting stewardship interventions. Data and code to reproduce results have been deposited in Zenodo (DOI: 10.5281/zenodo.3909812)."/> <meta name="og-title" property="og:title" content="The role of “spillover” in antibiotic resistance"/> <meta name="og-url" property="og:url" content="https://www.pnas.org/content/early/2020/10/30/2013694117"/> <meta name="og-site-name" property="og:site_name" content="PNAS"/> <meta name="og-description" property="og:description" content="Antibiotic resistance can spread from person to person, a phenomenon exemplified by the rapid global spread of novel resistance determinants. Antibiotic resistance can also “spill over” between family members or between a hospital and its surrounding community, such that antibiotic use in one population selects for resistance that is transmitted into the other population. In theory, antibiotic use in one population could spill over into its neighbors, making it difficult to understand to what degree a population’s own behavior, such as its antibiotic use, determines its level of resistance. Here we use theoretical modeling and observational data to quantify spillover, finding that even modest interactions between populations can lead to substantial sharing of resistance levels. Antibiotic use is a key driver of antibiotic resistance. Understanding the quantitative association between antibiotic use and resulting resistance is important for predicting future rates of antibiotic resistance and for designing antibiotic stewardship policy. However, the use–resistance association is complicated by “spillover,” in which one population’s level of antibiotic use affects another population’s level of resistance via the transmission of bacteria between those populations. Spillover is known to have effects at the level of families and hospitals, but it is unclear if spillover is relevant at larger scales. We used mathematical modeling and analysis of observational data to address this question. First, we used dynamical models of antibiotic resistance to predict the effects of spillover. Whereas populations completely isolated from one another do not experience any spillover, we found that if even 1% of interactions are between populations, then spillover may have large consequences: The effect of a change in antibiotic use in one population on antibiotic resistance in that population could be reduced by as much as 50%. Then, we quantified spillover in observational antibiotic use and resistance data from US states and European countries for three pathogen–antibiotic combinations, finding that increased interactions between populations were associated with smaller differences in antibiotic resistance between those populations. Thus, spillover may have an important impact at the level of states and countries, which has ramifications for predicting the future of antibiotic resistance, designing antibiotic resistance stewardship policy, and interpreting stewardship interventions. Data and code to reproduce results have been deposited in Zenodo (DOI: 10.5281/zenodo.3909812)."/> <meta name="og-type" property="og:type" content="article"/> <meta name="og-image" property="og:image" content="https://www.pnas.org/content/early/2020/10/30/2013694117/F1.large.jpg"/> <meta name="format-detection" content="telephone=no"/> <title>The role of “spillover” in antibiotic resistance | PNAS</title> <link type="text/css" rel="stylesheet" href="https://www.pnas.org/sites/default/files/advagg_css/css__zUOsaJT2-txr78Pro2jqp5HkKYs7CY9FbkfBemQXYlw__kiQOVz2drquY4Y9F8gINaFiOXWD3R3YCpizYXeeITSI__UzrqD_h8OKuy3iJpIppwSN48bmlTjo5U9K9rTivOfV4.css" media="all"/> <link type="text/css" rel="stylesheet" href="https://www.pnas.org/sites/all/modules/highwire/highwire/highwire.style.highwire.css?qjakvd" media="all"/> <link type="text/css" rel="stylesheet" href="https://www.pnas.org/sites/default/files/advagg_css/css__IBZ9mHpn46TRp2BimuZ9LV8gzg5ve38Vt8Ur3OYiiIE__NtZxs4y1UkEH1BmzA2899OuPWrL4Vyfw35jNwF7gpGQ__UzrqD_h8OKuy3iJpIppwSN48bmlTjo5U9K9rTivOfV4.css" media="all"/> <link type="text/css" rel="stylesheet" href="//cdn.jsdelivr.net/qtip2/2.2.1/jquery.qtip.min.css" media="all"/> <link type="text/css" rel="stylesheet" href="https://www.pnas.org/sites/default/files/advagg_css/css__ft6ZYtGw6GnVtGDFL-yy7Nv98janBucWkHy-_6LIcNY__X6SoMpUVch0hUZhU5GhhA43N8gkW5jGOyL-zhqU413o__UzrqD_h8OKuy3iJpIppwSN48bmlTjo5U9K9rTivOfV4.css" media="all"/> <link type="text/css" rel="stylesheet" href="//pnas-movie.glencoesoftware.com/static/video-js.min.css" media="all"/> <link type="text/css" rel="stylesheet" href="https://www.pnas.org/sites/default/files/advagg_css/css__7CYEtfDxMu3CEBZ53TQrEfMpO7duR_XdIuTLye9W7Cc__CzzPXev2zJRWVry4kyDAKuuERTizWaGzrN8gCp2auaE__UzrqD_h8OKuy3iJpIppwSN48bmlTjo5U9K9rTivOfV4.css" media="all"/> <link type="text/css" rel="stylesheet" href="https://www.pnas.org/sites/default/files/advagg_css/css__jkm-npsOhZrLPJlxKJmRyMM29PIIEzp30AtQP4akkdQ__uXTXACSh1RnKlXg12UTZBUuOuNiPkD7tiXoiDXEGHWk__UzrqD_h8OKuy3iJpIppwSN48bmlTjo5U9K9rTivOfV4.css" media="all"/> <!--[if lt IE 9]><![endif]--> <!--[if lt IE 10]><![endif]--> </head> <body id="wp_automatic_ReadabilityBody"> <!-- Google Tag Manager --> <noscript><iframe src="http://www.googletagmanager.com/ns.html?id=GTM-NDVQMQ4" height="0" width="0" style="display:none;visibility:hidden"></iframe></noscript> <!-- End Google Tag Manager --> <div class="page" id="page"> <!-- /.section-header --> <section role="main" class="section section-content" id="section-content"> <div class="container-fluid zone-wrapper zone-content-wrapper"> <div class="zone zone-content row"> <div class="region region-content col-narrow-22 col-narrow-offset-1"> <div id="block-panels-mini-whats-new-in" class="block block-panels-mini"> <div class="content"> <div class="panel-display panel-1col clearfix" id="mini-panel-whats_new_in"> <div class="panel-panel panel-col"> <div> <div class="panel-pane pane-snippet pane-pnas-physical-sciences wni-dropdown"> <h3 class="pane-title"><span class="pane-title-text">Physical Sciences</span></h3> <div class="pane-content"> <div class="pnas-physical-sciences" id="pnas-physical-sciences"> <div class="snippet-content"> <h4>Featured Portals</h4> <h4>Articles by Topic</h4> </div> </div> </div> </div> <div class="panel-pane pane-snippet pane-pnas-social-sciences wni-dropdown"> <h3 class="pane-title"><span class="pane-title-text">Social Sciences</span></h3> <div class="pane-content"> <div class="pnas-social-sciences" id="pnas-social-sciences"> <div class="snippet-content"> <h4>Featured Portals</h4> <h4>Articles by Topic</h4> </div> </div> </div> </div> <div class="panel-pane pane-snippet pane-pnas-biological-sciences wni-dropdown"> <h3 class="pane-title"><span class="pane-title-text">Biological Sciences</span></h3> <div class="pane-content"> <div class="pnas-biological-sciences" id="pnas-biological-sciences"> <div class="snippet-content"> <h4>Featured Portals</h4> <h4>Articles by Topic</h4> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div><!-- /.zone-content --> </div><!-- /.zone-content-wrapper --> </section> <!-- /.section-content --> <!-- /.section-footer --> </div> <!-- /.page --> </body> </div>
