Does locally relevant, real-time infection epidemiological data improve clinician management and antimicrobial prescribing in primary care? A systematic review
Identifieur interne : 000F23 ( Pmc/Corpus ); précédent : 000F22; suivant : 000F24Does locally relevant, real-time infection epidemiological data improve clinician management and antimicrobial prescribing in primary care? A systematic review
Auteurs : Isabel Lane ; Ashley Bryce ; Suzanne M. Ingle ; Alastair D. HaySource :
- Family Practice [ 0263-2136 ] ; 2018.
Abstract
Antimicrobial resistance is a significant threat to public health. Diagnostic uncertainty is a key driver of antimicrobial prescribing. We sought to determine whether locally relevant, real-time syndromic or microbiological infection epidemiology can improve prescribing by reducing diagnostic uncertainty.
Eligible studies investigated effects on primary care prescribing for common infections in Organisation For Economic Co-Operation And Development countries. We searched Medline, Embase, Cumulative index to nursing and allied health literature, Web of Science, grey literature sources, thesis databases and trial registries.
We identified 9548 reports, of which 17 were eligible, reporting 12 studies, of which 3 reported relevant outcomes. The first (observational) showed antibacterial prescribing for upper respiratory infections reduced from 26.4% to 8.6% (
There is promising evidence that epidemiological syndromic and microbiological data can reduce primary care antimicrobial prescribing. Future research should use randomized designs of behaviourally informed interventions, investigate costs and harms, and establish mechanisms of behaviour change.
CRD42016038871.
Url:
DOI: 10.1093/fampra/cmy008
PubMed: 29529261
PubMed Central: 6142716
Links to Exploration step
PMC:6142716Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Does locally relevant, real-time infection epidemiological data improve clinician management and antimicrobial prescribing in primary care? A systematic review</title><author><name sortKey="Lane, Isabel" sort="Lane, Isabel" uniqKey="Lane I" first="Isabel" last="Lane">Isabel Lane</name><affiliation><nlm:aff id="AF0001">National Institute for Health Research School for Primary Care Research, Centre for Academic Primary Care, University of Bristol, Bristol, UK</nlm:aff></affiliation></author><author><name sortKey="Bryce, Ashley" sort="Bryce, Ashley" uniqKey="Bryce A" first="Ashley" last="Bryce">Ashley Bryce</name><affiliation><nlm:aff id="AF0001">National Institute for Health Research School for Primary Care Research, Centre for Academic Primary Care, University of Bristol, Bristol, UK</nlm:aff></affiliation></author><author><name sortKey="Ingle, Suzanne M" sort="Ingle, Suzanne M" uniqKey="Ingle S" first="Suzanne M" last="Ingle">Suzanne M. Ingle</name><affiliation><nlm:aff id="AF0002">The National Institute for Health Research Health Protection Research Unit in Evaluation of Interventions at University of Bristol, Bristol, UK</nlm:aff></affiliation></author><author><name sortKey="Hay, Alastair D" sort="Hay, Alastair D" uniqKey="Hay A" first="Alastair D" last="Hay">Alastair D. Hay</name><affiliation><nlm:aff id="AF0001">National Institute for Health Research School for Primary Care Research, Centre for Academic Primary Care, University of Bristol, Bristol, UK</nlm:aff></affiliation><affiliation><nlm:aff id="AF0002">The National Institute for Health Research Health Protection Research Unit in Evaluation of Interventions at University of Bristol, Bristol, UK</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">29529261</idno><idno type="pmc">6142716</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6142716</idno><idno type="RBID">PMC:6142716</idno><idno type="doi">10.1093/fampra/cmy008</idno><date when="2018">2018</date><idno type="wicri:Area/Pmc/Corpus">000F23</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000F23</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Does locally relevant, real-time infection epidemiological data improve clinician management and antimicrobial prescribing in primary care? A systematic review</title><author><name sortKey="Lane, Isabel" sort="Lane, Isabel" uniqKey="Lane I" first="Isabel" last="Lane">Isabel Lane</name><affiliation><nlm:aff id="AF0001">National Institute for Health Research School for Primary Care Research, Centre for Academic Primary Care, University of Bristol, Bristol, UK</nlm:aff></affiliation></author><author><name sortKey="Bryce, Ashley" sort="Bryce, Ashley" uniqKey="Bryce A" first="Ashley" last="Bryce">Ashley Bryce</name><affiliation><nlm:aff id="AF0001">National Institute for Health Research School for Primary Care Research, Centre for Academic Primary Care, University of Bristol, Bristol, UK</nlm:aff></affiliation></author><author><name sortKey="Ingle, Suzanne M" sort="Ingle, Suzanne M" uniqKey="Ingle S" first="Suzanne M" last="Ingle">Suzanne M. Ingle</name><affiliation><nlm:aff id="AF0002">The National Institute for Health Research Health Protection Research Unit in Evaluation of Interventions at University of Bristol, Bristol, UK</nlm:aff></affiliation></author><author><name sortKey="Hay, Alastair D" sort="Hay, Alastair D" uniqKey="Hay A" first="Alastair D" last="Hay">Alastair D. Hay</name><affiliation><nlm:aff id="AF0001">National Institute for Health Research School for Primary Care Research, Centre for Academic Primary Care, University of Bristol, Bristol, UK</nlm:aff></affiliation><affiliation><nlm:aff id="AF0002">The National Institute for Health Research Health Protection Research Unit in Evaluation of Interventions at University of Bristol, Bristol, UK</nlm:aff></affiliation></author></analytic><series><title level="j">Family Practice</title><idno type="ISSN">0263-2136</idno><idno type="eISSN">1460-2229</idno><imprint><date when="2018">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><title>Abstract</title><sec id="s1"><title>Purpose</title><p>Antimicrobial resistance is a significant threat to public health. Diagnostic uncertainty is a key driver of antimicrobial prescribing. We sought to determine whether locally relevant, real-time syndromic or microbiological infection epidemiology can improve prescribing by reducing diagnostic uncertainty.</p></sec><sec id="s2"><title>Methods</title><p>Eligible studies investigated effects on primary care prescribing for common infections in Organisation For Economic Co-Operation And Development countries. We searched Medline, Embase, Cumulative index to nursing and allied health literature, Web of Science, grey literature sources, thesis databases and trial registries.</p></sec><sec id="s3"><title>Results</title><p>We identified 9548 reports, of which 17 were eligible, reporting 12 studies, of which 3 reported relevant outcomes. The first (observational) showed antibacterial prescribing for upper respiratory infections reduced from 26.4% to 8.6% (<italic>P</italic> = 0.01). The second (observational) showed antibacterial prescribing reduced during influenza pandemic compared with seasonal influenza periods [odds ratio (OR) 0.72 (95% CI, 0.68 to 0.77), <italic>P</italic> < 0.001], while antiviral prescribing increased [OR 6.43 (95% CI, 5.02 to 8.25), <italic>P</italic> < 0.001]. The likelihood of prescribing also decreased as the number of infection cases a physician saw increased in the previous week [OR 0.57 (95% CI, 0.51 to 0.63), <italic>P</italic> < 0.001 for ≥12 versus ≤1 patient). The third (randomized-controlled trial) showed an absolute reduction in antibacterial prescribing of 5.1% during a period of moderate influenza activity (<italic>P</italic> < 0.05). We did not find measures of diagnostic certainty, harms or costs.</p></sec><sec id="s4"><title>Conclusion</title><p>There is promising evidence that epidemiological syndromic and microbiological data can reduce primary care antimicrobial prescribing. Future research should use randomized designs of behaviourally informed interventions, investigate costs and harms, and establish mechanisms of behaviour change.</p></sec><sec id="s5"><title>PROSPERO registration</title><p>CRD42016038871.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Costelloe, C" uniqKey="Costelloe C">C Costelloe</name></author><author><name sortKey="Metcalfe, C" uniqKey="Metcalfe C">C Metcalfe</name></author><author><name sortKey="Lovering, A" uniqKey="Lovering A">A Lovering</name></author><author><name sortKey="Mant, D" uniqKey="Mant D">D Mant</name></author><author><name sortKey="Hay, Ad" uniqKey="Hay A">AD Hay</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bryce, A" uniqKey="Bryce A">A Bryce</name></author><author><name sortKey="Hay, Ad" uniqKey="Hay A">AD Hay</name></author><author><name sortKey="Lane, If" 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Fam Pract</journal-id><journal-id journal-id-type="iso-abbrev">Fam Pract</journal-id><journal-id journal-id-type="publisher-id">famprj</journal-id><journal-title-group><journal-title>Family Practice</journal-title></journal-title-group><issn pub-type="ppub">0263-2136</issn><issn pub-type="epub">1460-2229</issn><publisher><publisher-name>Oxford University Press</publisher-name><publisher-loc>UK</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">29529261</article-id><article-id pub-id-type="pmc">6142716</article-id><article-id pub-id-type="doi">10.1093/fampra/cmy008</article-id><article-id pub-id-type="publisher-id">cmy008</article-id><article-categories><subj-group subj-group-type="heading"><subject>Systematic Reviews</subject></subj-group></article-categories><title-group><article-title>Does locally relevant, real-time infection epidemiological data improve clinician management and antimicrobial prescribing in primary care? A systematic review</article-title></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="false">http://orcid.org/0000-0002-6996-6351</contrib-id><name><surname>Lane</surname><given-names>Isabel</given-names></name><xref ref-type="aff" rid="AF0001">1</xref><xref ref-type="corresp" rid="c1"></xref><pmc-comment>il7266@bristol.ac.uk </pmc-comment>
</contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid" authenticated="false">http://orcid.org/0000-0002-6657-1514</contrib-id><name><surname>Bryce</surname><given-names>Ashley</given-names></name><xref ref-type="aff" rid="AF0001">1</xref></contrib><contrib contrib-type="author"><name><surname>Ingle</surname><given-names>Suzanne M</given-names></name><xref ref-type="aff" rid="AF0002">2</xref></contrib><contrib contrib-type="author"><name><surname>Hay</surname><given-names>Alastair D</given-names></name><xref ref-type="aff" rid="AF0001">1</xref><xref ref-type="aff" rid="AF0002">2</xref></contrib></contrib-group><aff id="AF0001"><label>1</label>National Institute for Health Research School for Primary Care Research, Centre for Academic Primary Care, University of Bristol, Bristol, UK</aff><aff id="AF0002"><label>2</label>The National Institute for Health Research Health Protection Research Unit in Evaluation of Interventions at University of Bristol, Bristol, UK</aff><author-notes><corresp id="c1">Corresponding to Isabel Lane, Centre for Academic Primary Care, School of Social and Community Medicine, University of Bristol, Office G.06d, Canynge Hall, 39 Whatley Road, Bristol BS8 2PS, UK; E-mail: <email>il7266@bristol.ac.uk</email></corresp></author-notes><pub-date pub-type="collection"><month>10</month><year>2018</year></pub-date><pub-date pub-type="epub" iso-8601-date="2018-02-26"><day>26</day><month>2</month><year>2018</year></pub-date><pub-date pub-type="pmc-release"><day>26</day><month>2</month><year>2018</year></pub-date><pmc-comment> PMC Release delay is 0 months and 0 days and was based on the
<volume>35</volume><issue>5</issue><fpage>542</fpage><lpage>550</lpage><permissions><copyright-statement>© The Author(s) 2018. Published by Oxford University Press.</copyright-statement><copyright-year>2018</copyright-year><license license-type="cc-by-nc" xlink:href="http://creativecommons.org/licenses/by-nc/4.0/"><license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by-nc/4.0/">http://creativecommons.org/licenses/by-nc/4.0/</ext-link>), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com</license-p></license></permissions><self-uri xlink:href="cmy008.pdf"></self-uri><abstract><title>Abstract</title><sec id="s1"><title>Purpose</title><p>Antimicrobial resistance is a significant threat to public health. Diagnostic uncertainty is a key driver of antimicrobial prescribing. We sought to determine whether locally relevant, real-time syndromic or microbiological infection epidemiology can improve prescribing by reducing diagnostic uncertainty.</p></sec><sec id="s2"><title>Methods</title><p>Eligible studies investigated effects on primary care prescribing for common infections in Organisation For Economic Co-Operation And Development countries. We searched Medline, Embase, Cumulative index to nursing and allied health literature, Web of Science, grey literature sources, thesis databases and trial registries.</p></sec><sec id="s3"><title>Results</title><p>We identified 9548 reports, of which 17 were eligible, reporting 12 studies, of which 3 reported relevant outcomes. The first (observational) showed antibacterial prescribing for upper respiratory infections reduced from 26.4% to 8.6% (<italic>P</italic> = 0.01). The second (observational) showed antibacterial prescribing reduced during influenza pandemic compared with seasonal influenza periods [odds ratio (OR) 0.72 (95% CI, 0.68 to 0.77), <italic>P</italic> < 0.001], while antiviral prescribing increased [OR 6.43 (95% CI, 5.02 to 8.25), <italic>P</italic> < 0.001]. The likelihood of prescribing also decreased as the number of infection cases a physician saw increased in the previous week [OR 0.57 (95% CI, 0.51 to 0.63), <italic>P</italic> < 0.001 for ≥12 versus ≤1 patient). The third (randomized-controlled trial) showed an absolute reduction in antibacterial prescribing of 5.1% during a period of moderate influenza activity (<italic>P</italic> < 0.05). We did not find measures of diagnostic certainty, harms or costs.</p></sec><sec id="s4"><title>Conclusion</title><p>There is promising evidence that epidemiological syndromic and microbiological data can reduce primary care antimicrobial prescribing. Future research should use randomized designs of behaviourally informed interventions, investigate costs and harms, and establish mechanisms of behaviour change.</p></sec><sec id="s5"><title>PROSPERO registration</title><p>CRD42016038871.</p></sec></abstract><kwd-group><kwd>Antibacterial agents</kwd><kwd>general practice</kwd><kwd>infection</kwd><kwd>population surveillance</kwd><kwd>primary health care</kwd><kwd>public health surveillance</kwd></kwd-group><funding-group><award-group award-type="grant"><funding-source><named-content content-type="funder-name">National Institute for Health Research</named-content><named-content content-type="funder-identifier">10.13039/501100000272</named-content></funding-source></award-group></funding-group><counts><page-count count="10"></page-count></counts></article-meta></front><body><sec id="s6"><title>Introduction</title><list list-type="simple"><list-item><p>(i) Antimicrobial resistance is a serious international health threat;</p></list-item><list-item><p>(ii) Diagnostic uncertainty is a key driver of antimicrobial prescribing for common infections in primary care;</p></list-item><list-item><p>(iii) Our systematic review found two observational and one experimental study, showing that real-time, locally relevant, syndromic and microbiological epidemiological data can reduce antibacterial prescribing and</p></list-item><list-item><p>(iv) Future research should use randomized designs of behaviourally informed interventions, investigate costs and harms, and establish mechanisms of behaviour change.</p></list-item></list><p>Antimicrobial resistance (AMR) has been described as one of the greatest challenges to modern day public health (<xref rid="CIT0001" ref-type="bibr">1</xref>). The over and misuse of antimicrobials are recognized as drivers of AMR, with high levels of poorly targeted antimicrobials are prescribed in the community and 74% of all antibacterial prescribing occurring in general practice (<xref rid="CIT0002" ref-type="bibr">2</xref>). The routine use of antibacterials in primary care has been shown to be directly linked to AMR (<xref rid="CIT0003" ref-type="bibr">3</xref>,<xref rid="CIT0004" ref-type="bibr">4</xref>), and the majority of patients presenting to primary care with an uncomplicated respiratory tract infection in the UK still receive an antibacterial prescription (<xref rid="CIT0005" ref-type="bibr">5</xref>).</p><p>Clinician uncertainty has been identified as a driver for prescribing antimicrobials in primary care and, therefore, a potential target for interventions looking to affect behaviour change of clinicians (<xref rid="CIT0006" ref-type="bibr">6</xref>,<xref rid="CIT0007" ref-type="bibr">7</xref>). Furthermore, consideration is required to determine how interventions can address this uncertainty, ensure continued safe management and appropriate prescribing of antimicrobials in situations where they are still required. Horwood <italic>et al</italic>. (<xref rid="CIT0008" ref-type="bibr">8</xref>) suggest that additional support is needed for clinicians in their decision-making and interventions that seek to tackle this uncertainty in order to change clinician behaviour are more likely to affect a measurable change.</p><p>Improving antibacterial prescribing and reducing AMR are complex problems, requiring complex, multifaceted solutions. This systematic review evaluates one element of what could be a multifaceted approach to reduce clinician uncertainty, improve diagnostic accuracy and reduce antibacterial prescribing. We sought to determine whether locally relevant, real-time syndromic or microbiological infection epidemiology could reduce diagnostic uncertainty and improve antibacterial prescribing. We also sought to describe the theoretical framework of sources contributing to surveillance systems and describe their breadth, purpose, data sources and intended recipients.</p></sec><sec sec-type="methods" id="s7"><title>Methods</title><p>The review protocol was written following preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines (<xref rid="CIT0009" ref-type="bibr">9</xref>) and registered with PROSPERO (No.: CRD42016038871).</p><sec id="s8"><title>Search strategy</title><p>The search strategy (<xref ref-type="supplementary-material" rid="sup1">Supplementary Table S1</xref>) was designed to identify studies investigating the effect on primary care clinician management of common infections in Organisation for Economic Co-operation and Development (OECD) member countries (<xref rid="CIT0010" ref-type="bibr">10</xref>) where the intervention includes dissemination of real-time, population-based data on locally relevant microbes or syndromic presentations.</p><p>Databases searched were Medline, Embase, Cumulative index to nursing and allied health literature (CINAHL) and Web of Science from database inception to April 2016. Medical subject headings (MeSH) terms and text word searches were combined to produce a comprehensive search strategy covering the following four key areas: ‘common infection’, ‘primary care’, ‘population-based surveillance’ and ‘dissemination of information’. Grey literature sources including WHO website and dissertation and thesis registries, including Ethos and Proquest, were searched. Trial registries were also searched including US trial registry (clinicaltrials.gov), European Union (EU) clinical trial registry, International Standard Registered Clinical/Social Study Number (ISRCTN) register and meta-register of controlled trials and the health research authority (HRA) register. Searches were conducted for records in any language. Full-text papers were subject to citation searches.</p></sec><sec id="s9"><title>Study selection</title><p>Eligible studies were those investigating effects on primary care clinician management of common (respiratory, gastrointestinal, urinary and skin) infections in OECD member countries where the intervention included dissemination of real-time, population-based data on locally relevant microbes or syndromic presentations. Eligibility was assessed based on a hierarchy of factors: first, records were initially assessed for meeting the criteria of a common infection, and surveillance systems for conditions such as human immunodeficiency virus (HIV), tuberculosis and malaria were excluded (see <xref ref-type="supplementary-material" rid="sup1">Supplementary Table S2</xref> for examples of excluded conditions); second, they were checked for being conducted in an OECD member country; third, studies were assessed for being conducted in a primary care setting and finally that they disseminated information to primary care clinicians, which was locally relevant (provision of data to clinicians more specific than national-level data) and in real time (provision of data to clinicians at least quarterly or more frequently).</p><p>One reviewer (IL) undertook initial title screening. At the next stage, the title and abstract screen was undertaken by one reviewer (IL) with a random 10% sample checked by a second reviewer (AB). A kappa statistic of 0.69 demonstrated substantial agreement between reviewers (<xref rid="CIT0011" ref-type="bibr">11</xref>). Full-text records were assessed by two reviewers (IL and AB), and any disagreements resolved by discussion or, if needed, consultation with a third reviewer (ADH).</p></sec><sec id="s10"><title>Data extraction and quality assessment</title><p>The following data were double extracted by two reviewers using a purpose-designed spreadsheet: author; year of publication; journal, study design; study country; OECD status; study setting; recruitment and details of participants; description of intervention; source and scope of surveillance data; mode and frequency of intervention dissemination; use of comparator group. Primary outcomes of interest to the review were antibacterial prescribing rates, secondary care referral rates and any harms attributable to the intervention. The secondary outcomes were types of antimicrobials or adherence to guidelines, consultation rates, costs and clinician diagnostic certainty.</p><p>We used the Cochrane Risk of Bias (ROB) tool 2.0 (<xref rid="CIT0012" ref-type="bibr">12</xref>) to assess the quality of studies using a randomized controlled trial methodology, and we used the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool to assess study quality for non-randomized intervention studies (<xref rid="CIT0013" ref-type="bibr">13</xref>).</p></sec><sec id="s11"><title>Data synthesis and analysis</title><p>Through familiarization with the literature during the screening process, we planned to better understand and summarize the range of surveillance systems in use to be able to describe existing surveillance systems within the scope of this review. We conducted a narrative synthesis of the eligible studies and planned to conduct a meta-analysis if appropriate.</p></sec></sec><sec id="s12"><title>Results</title><p>We identified 9548 records through database and additional searches (<xref ref-type="fig" rid="F1">Fig. 1</xref>). Of these, 1693 were duplicates, 4018 were excluded on the basis of the title and 3799 were excluded following second title and abstract screen leaving 38 records to be retrieved in full text from the database and additional searches. A further 10 records were identified from reference lists, and an additional 2 records identified through contacting experts. We obtained these 50 records in full text; of which, 33 did not meet our eligibility criteria. Of these 33 records, 5 were regarding conditions not considered common infections in primary care, 3 were not within OECD countries, 10 were not in a primary care setting, 11 did not disseminate any information and 4 were not locally relevant. Of the 17 records eligible for inclusion, several records report on the same study leaving 12 eligible studies for inclusion (<xref rid="CIT0014" ref-type="bibr">14–25</xref>) with the remaining 5 records providing supporting material (<xref rid="CIT0026" ref-type="bibr">26–30</xref>).</p><fig fig-type="figure" id="F1" orientation="portrait" position="float"><label>Figure 1.</label><caption><p>Systematic review flow chart (<xref rid="CIT0009" ref-type="bibr">9</xref>). Infection surveillance in primary care. Years covered by review 1946–2016.</p></caption><graphic xlink:href="cmy00801"></graphic></fig><p>Of the 12 eligible studies, the principle records for each study included 10 publications in peer-reviewed journals (<xref rid="CIT0014" ref-type="bibr">14–21</xref>,<xref rid="CIT0023" ref-type="bibr">23</xref>,<xref rid="CIT0024" ref-type="bibr">24</xref>) and two conferences proceedings (<xref rid="CIT0022" ref-type="bibr">22</xref>,<xref rid="CIT0025" ref-type="bibr">25</xref>).</p><sec id="s13"><title>Surveillance systems</title><p><xref ref-type="fig" rid="F2">Figure 2</xref> shows the wide variety of surveillance system purposes (including AMR, surveillance, bioterrorism and food safety, as well as infection surveillance), data sources and intended recipients.</p><fig fig-type="figure" id="F2" orientation="portrait" position="float"><label>Figure 2.</label><caption><p>Schema of surveillance systems by purpose, data source and intended recipient. Infection surveillance in primary care. Years covered by review 1946–2016. OTC, over-the-counter; OOH, out-of-hours.</p></caption><graphic xlink:href="cmy00802"></graphic></fig></sec><sec id="s14"><title>Study characteristics</title><p>Of the 12 eligible studies, one was a prospective cluster randomized controlled trial (<xref rid="CIT0025" ref-type="bibr">25</xref>) and 11 were observational studies of a variety of designs, including cohort studies (<xref rid="CIT0014" ref-type="bibr">14</xref>,<xref rid="CIT0016" ref-type="bibr">16</xref>,<xref rid="CIT0019" ref-type="bibr">19</xref>,<xref rid="CIT0022" ref-type="bibr">22</xref>,<xref rid="CIT0023" ref-type="bibr">23</xref>), programme descriptions (<xref rid="CIT0015" ref-type="bibr">15</xref>,<xref rid="CIT0018" ref-type="bibr">18</xref>,<xref rid="CIT0020" ref-type="bibr">20</xref>,<xref rid="CIT0021" ref-type="bibr">21</xref>,<xref rid="CIT0024" ref-type="bibr">24</xref>) and a pilot study (<xref rid="CIT0017" ref-type="bibr">17</xref>). Eight of the studies took place in the USA (<xref rid="CIT0014" ref-type="bibr">14–16</xref>,<xref rid="CIT0018" ref-type="bibr">18</xref>,<xref rid="CIT0019" ref-type="bibr">19</xref>,<xref rid="CIT0021" ref-type="bibr">21</xref>,<xref rid="CIT0023" ref-type="bibr">23</xref>,<xref rid="CIT0025" ref-type="bibr">25</xref>), one in Canada (<xref rid="CIT0024" ref-type="bibr">24</xref>), one in New Zealand (<xref rid="CIT0017" ref-type="bibr">17</xref>), and two in Europe (Spain (<xref rid="CIT0020" ref-type="bibr">20</xref>) and Norway (<xref rid="CIT0022" ref-type="bibr">22</xref>)).</p></sec><sec id="s15"><title>Study participants, interventions and outcomes</title><p>The level of detail provided on study participants varied and included general practitioners, primary care providers, family practice residents, urgent care clinics and community clinics. Four studies provided insufficient detail to clearly define participants (<xref rid="T1" ref-type="table">Table 1</xref> and <xref ref-type="supplementary-material" rid="sup1">Supplementary Table S3</xref>) (<xref rid="CIT0015" ref-type="bibr">15</xref>,<xref rid="CIT0018" ref-type="bibr">18</xref>,<xref rid="CIT0020" ref-type="bibr">20</xref>,<xref rid="CIT0021" ref-type="bibr">21</xref>).</p><table-wrap id="T1" orientation="portrait" position="float"><label>Table 1.</label><caption><p>Study characteristics of studies reporting on primary or secondary outcomes of interest. Infection surveillance in primary care. Years covered by review 1946–2016</p></caption><table frame="vsides" rules="groups"><thead><tr><th align="left" rowspan="2" colspan="1">Author and year of publication</th><th rowspan="2" colspan="1">Country</th><th rowspan="2" colspan="1">Design</th><th align="left" rowspan="1" colspan="1">Participants</th><th colspan="3" rowspan="1">Intervention</th><th rowspan="2" colspan="1">Comparison</th></tr><tr><th rowspan="1" colspan="1">Recipients of intervention</th><th rowspan="1" colspan="1">Intervention details</th><th rowspan="1" colspan="1">Microbiological (M) or syndromic (S)</th><th rowspan="1" colspan="1">Mode and frequency</th></tr></thead><tbody><tr><td rowspan="1" colspan="1">Temte (<xref rid="CIT0014" ref-type="bibr">14</xref>), 1999</td><td rowspan="1" colspan="1">USA</td><td rowspan="1" colspan="1">Cohort with historical control</td><td rowspan="1" colspan="1">Family practice residents starting in 1992 for 3 years (<italic>n</italic> = 14)</td><td rowspan="1" colspan="1">Educational and surveillance program delivered over 3 years. Summary report of compiled results of viral cultures and other clinical specimens sent to eight different surveillance sites. Information provided a report specific to the site as well as regional, state and national trends</td><td rowspan="1" colspan="1">M: Respiratory viral culture results (respiratory).</td><td rowspan="1" colspan="1">Biweekly fax</td><td rowspan="1" colspan="1">Family practice residents’ pre-intervention (<italic>n</italic> = 8)</td></tr><tr><td rowspan="1" colspan="1">Hebert (<xref rid="CIT0023" ref-type="bibr">23</xref>), 2012</td><td rowspan="1" colspan="1">USA</td><td rowspan="1" colspan="1">Retrospective cohort</td><td rowspan="1" colspan="1">69 physicians in 26 practices. Exposed group—7789 patient visits during the pandemic period versus 20512 visits during the non-pandemic period</td><td rowspan="1" colspan="1">Study to examine the association between contextual factors and antimicrobial prescribing for a FRI. Effect of pandemic period—heavy media coverage, public anxiety, regular updates to physicians on management guidelines, epidemiological data and vaccine information</td><td rowspan="1" colspan="1">S: FRI</td><td rowspan="1" colspan="1">Alerts and information received during a pandemic period</td><td rowspan="1" colspan="1">Seasonal (non-pandemic) period used as control</td></tr><tr><td rowspan="1" colspan="1">Shah (<xref rid="CIT0025" ref-type="bibr">25</xref>), 2014 (Supporting material (<xref rid="CIT0029" ref-type="bibr">29</xref>))</td><td rowspan="1" colspan="1">USA</td><td rowspan="1" colspan="1">Prospective cluster RCT</td><td rowspan="1" colspan="1">27 GP practices (cluster randomized)</td><td rowspan="1" colspan="1">Syndromic heat map generated from data collected daily from EHRs to provide GPs with a point of care clinical decision support tool available via the EHR to GPs that generates a syndromic heat map for ILI, pertussis, GAS and paediatric asthma</td><td rowspan="1" colspan="1">M and S: ILI, pertussis, GAS and paediatric asthma</td><td rowspan="1" colspan="1">On demand via the electronic health record</td><td rowspan="1" colspan="1">27 GP practices (cluster randomized)</td></tr></tbody></table><table-wrap-foot><fn id="fn-01"><p>GAS, Group A Streptococcus; ILI, influenza-like illness; EHR, electronic health records.</p></fn></table-wrap-foot></table-wrap><p>The interventions were wide-ranging and heterogeneous. Five studies disseminated syndromic data to clinicians (<xref rid="CIT0017" ref-type="bibr">17</xref>,<xref rid="CIT0018" ref-type="bibr">18</xref>,<xref rid="CIT0021" ref-type="bibr">21</xref>,<xref rid="CIT0023" ref-type="bibr">23–25</xref>), two disseminated microbiological data (<xref rid="CIT0014" ref-type="bibr">14</xref>,<xref rid="CIT0019" ref-type="bibr">19</xref>) and five disseminated a mixture of microbiological and syndromic data (<xref rid="CIT0015" ref-type="bibr">15</xref>,<xref rid="CIT0016" ref-type="bibr">16</xref>,<xref rid="CIT0020" ref-type="bibr">20</xref>,<xref rid="CIT0022" ref-type="bibr">22</xref>) (<xref rid="T1" ref-type="table">Table 1</xref> and <xref ref-type="supplementary-material" rid="sup1">Supplementary Table S3</xref>). The majority of studies disseminated infection surveillance information via websites, electronic databases and emails. One study disseminated information through biweekly faxes (<xref rid="CIT0014" ref-type="bibr">14</xref>), one via a tool embedded in the electronic health record to be available on demand (<xref rid="CIT0025" ref-type="bibr">25</xref>) and one study allowed users to define how they wanted to receive the data from a range of options (<xref rid="CIT0018" ref-type="bibr">18</xref>) (<xref rid="T1" ref-type="table">Table 1</xref> and <xref ref-type="supplementary-material" rid="sup1">Supplementary Table S3</xref>). The majority of the studies provided information on a daily or weekly basis (<xref rid="T1" ref-type="table">Table 1</xref> and <xref ref-type="supplementary-material" rid="sup1">Supplementary Table S3</xref>). The studies included in this review presented the use of surveillance information for a range of different types of infections, including respiratory, gastrointestinal and skin infections (<xref rid="T1" ref-type="table">Table 1</xref> and <xref ref-type="supplementary-material" rid="sup1">Supplementary Table S3</xref>). None of the studies described the use of a behavioural change model to design or implement their intervention.</p><p>A comparison group was described by three studies (<xref rid="CIT0014" ref-type="bibr">14</xref>,<xref rid="CIT0023" ref-type="bibr">23</xref>,<xref rid="CIT0025" ref-type="bibr">25</xref>), and one additional study defined their planned comparison group (<xref rid="CIT0022" ref-type="bibr">22</xref>) (<xref rid="T1" ref-type="table">Table 1</xref> and <xref ref-type="supplementary-material" rid="sup1">Supplementary Table S3</xref>).</p><p>Three studies reported on antibacterial prescribing rates (<xref rid="CIT0014" ref-type="bibr">14</xref>,<xref rid="CIT0023" ref-type="bibr">23</xref>,<xref rid="CIT0025" ref-type="bibr">25</xref>) (primary outcome), and two studies reported on antiviral prescribing rates (<xref rid="CIT0023" ref-type="bibr">23</xref>,<xref rid="CIT0025" ref-type="bibr">25</xref>) (secondary outcome; <xref rid="T2" ref-type="table">Table 2</xref>). The three studies reporting on outcomes of interest to this review all focussed on respiratory infections (<xref rid="CIT0014" ref-type="bibr">14</xref>,<xref rid="CIT0023" ref-type="bibr">23</xref>,<xref rid="CIT0025" ref-type="bibr">25</xref>). No outcomes were reported on secondary care referral rates, harms, consultation rates, costs or diagnostic certainty (<xref ref-type="supplementary-material" rid="sup1">Supplementary Table S4</xref>).</p><table-wrap id="T2" orientation="portrait" position="float"><label>Table 2.</label><caption><p>Planned or reported primary or secondary outcomes of interest to the review. Infection surveillance in primary care. Years covered by review 1946–2016</p></caption><graphic xlink:href="cmy008t2"></graphic></table-wrap><sec id="s16"><title>Antibacterial prescribing rates (<xref rid="T2" ref-type="table">Table 2</xref>)</title><p>The three studies that reported on antibacterial prescribing rates varied in the study design and included a cohort study with a historical control group (<xref rid="CIT0014" ref-type="bibr">14</xref>), a retrospective cohort study (<xref rid="CIT0023" ref-type="bibr">23</xref>) and a prospective cluster randomized controlled trial (<xref rid="CIT0025" ref-type="bibr">25</xref>).</p><p>A reduction in antibacterial prescribing was seen following a 3-year educational and surveillance programme delivered by Temte <italic>et al.</italic> (<xref rid="CIT0014" ref-type="bibr">14</xref>) to family practice residents with prescribing falling from 26.4% to 8.6% (<italic>P</italic> = 0.01) for upper respiratory infections. A reduction in antibacterial prescribing was reported by Hebert <italic>et al.</italic> (<xref rid="CIT0023" ref-type="bibr">23</xref>) during a pandemic influenza period when compared with seasonal influenza periods: [odds ratio (OR) 0.72 (95% CI, 0.68 to 0.77), <italic>P</italic> < 0.001]. They also demonstrated that the likelihood of prescribing an antibacterial decreased as the number of febrile respiratory illness (FRI) cases that a physician had seen in the previous week increased—if 12+ patients were seen in the preceding week compared with 0–1 patients, antibacterial prescribing reduced [OR 0.57 (95% CI, 0.51 to 0.63), <italic>P</italic> < 0.001] (<xref rid="CIT0023" ref-type="bibr">23</xref>). Shah <italic>et al.</italic> (<xref rid="CIT0025" ref-type="bibr">25</xref>) reported a reduction in antibacterial prescribing following the introduction of an intervention providing clinicians with a syndromic heat map of influenza activity—they measured an absolute reduction in antibacterial prescribing of 5.1% during a period of moderate influenza activity (<italic>P</italic> < 0.05).</p></sec><sec id="s17"><title>Antiviral prescribing rates (<xref rid="T2" ref-type="table">Table 2</xref>)</title><p>Hebert <italic>et al.</italic> (<xref rid="CIT0023" ref-type="bibr">23</xref>) described an increase in antiviral prescribing rates during a pandemic influenza period when compared with seasonal influenza periods: [OR 6.43 (95% CI, 5.02 to 8.25), <italic>P</italic> < 0.001). They also demonstrated that the likelihood of prescribing an antiviral agent increased as the number of FRI cases that a physician had seen in the previous week increased—if 12+ patients were seen in the preceding week compared with 0–1 patients, antiviral prescribing increased [OR 4.25 (95% CI, 3.42 to 5.28) <italic>P</italic> < 0.001) (<xref rid="CIT0023" ref-type="bibr">23</xref>). Shah <italic>et al.</italic> (<xref rid="CIT0025" ref-type="bibr">25</xref>) reported an absolute 1.6% increase in antiviral prescriptions for influenza-like illness (ILI) visits during a high ILI activity (<italic>P</italic>-value not <0.05 but actual numerical value not reported).</p></sec></sec><sec id="s18"><title>Data synthesis and quality assessment</title><p>Further quantitative synthesis was not possible due to lack of numerical outcomes reported and high levels of heterogeneity between the studies. Due to the challenges of implementing complex interventions in a wide variety of clinical settings, there were numerous methodological challenges leaving studies open to relatively high levels of risk of bias (<xref rid="T3" ref-type="table">Tables 3</xref> and <xref rid="T4" ref-type="table">4</xref>).</p><table-wrap id="T3" orientation="portrait" position="float"><label>Table 3.</label><caption><p>Quality assessment for non-randomized intervention studies using ROBINS-I tool (<xref rid="CIT0013" ref-type="bibr">13</xref>). Infection surveillance in primary care. Years covered by review 1946–2016</p></caption><graphic xlink:href="cmy008t3"></graphic></table-wrap><table-wrap id="T4" orientation="portrait" position="float"><label>Table 4.</label><caption><p>Quality assessment for randomized intervention studies using Cochrane Risk Of Bias tool 2.0 (<xref rid="CIT0012" ref-type="bibr">12</xref>). Infection surveillance in primary care. Years covered by review 1946–2016</p></caption><graphic xlink:href="cmy008t4"></graphic></table-wrap></sec></sec><sec id="s19"><title>Discussion</title><sec id="s20"><title>Summary of main findings</title><p>This review demonstrates the wide variety of surveillance systems and data sources that could support primary care antimicrobial decision-making. We found few had been evaluated, but those that had shown promising, albeit methodologically weak, evidence that providing locally relevant, real-time epidemiological information improved antimicrobial prescribing in primary care.</p></sec><sec id="s21"><title>Strengths and limitations</title><p>We conducted a novel, rigorous, comprehensive review for evidence to support one potential solution to an internationally recognized public health problem. The small number of included studies prevented us from assessing the effects of publication bias (no studies were identified reporting an increase in antibacterial prescribing). Although the review set out to consider surveillance of any common infection relevant to primary care, the three studies included that reported on outcomes of interest to this review (<xref rid="CIT0014" ref-type="bibr">14</xref>,<xref rid="CIT0023" ref-type="bibr">23</xref>,<xref rid="CIT0025" ref-type="bibr">25</xref>) focussed primarily on respiratory infections. It is important to recognize this limitation of the review and to take this into account when considering surveillance of non-respiratory infections. Even within respiratory infections, due to the low number of studies eligible for inclusion in the review, there are likely to be differences in application of surveillance data for different types of respiratory infection. For example, for influenza, outcomes from the use of surveillance information as a clinical decision support tool may vary depending on whether the clinician is in a locality experiencing expected seasonal activity of the virus, an epidemic or a pandemic.</p></sec><sec id="s22"><title>Results in context with other studies</title><p>Most existing surveillance literature focuses on two elements: first, how to optimize accurate, timely data (microbiological, syndromic, absenteeism, over-the-counter sales) completion (<xref rid="CIT0031" ref-type="bibr">31–33</xref>) and second, distributing analyzed surveillance data to health departments and public health officials for outbreak detection and health service preparedness. As we have demonstrated, surveillance data are rarely targeted towards primary care clinicians with a view to changing clinical management. Qualitative research suggests that GPs are not even aware of what surveillance data is publically available and do not currently access this type of information in their clinical practice to support their diagnostic reasoning (<xref rid="CIT0034" ref-type="bibr">34</xref>).</p></sec><sec id="s23"><title>Clinical and research implications</title><p>Even though only a small number of studies have demonstrated a trend towards reduced antibacterial prescribing with increased access to surveillance data for primary care clinicians, the evidence is not yet sufficiently mature to be used in routine practice, and significant investment would be required to make existing data sources ready for intervention. That said, even small improvements in the primary care use of antibacterials could have significant implications for reducing AMR (<xref rid="CIT0003" ref-type="bibr">3</xref>,<xref rid="CIT0004" ref-type="bibr">4</xref>) and patient demand for primary care consultations (<xref rid="CIT0035" ref-type="bibr">35</xref>). To maximize the potential benefits of this type of intervention, future research needs to, first, establish what information, and using which delivery method, would be most valued by clinicians and to, second, assess effects using adequately powered, randomized studies of interventions underpinned by robust behaviour change methods.</p></sec></sec><sec id="s24"><title>Conclusions</title><p>There is promising evidence that syndromic and microbiological epidemiological data can influence the use of antibacterials in primary care. Future research should use behaviourally informed interventions, tested using prospective randomized designs; and establish the mechanisms of behaviour change.</p></sec><sec sec-type="supplementary-material" id="s25"><title>Supplementary material</title><p>Supplementary data is available at <italic>Family Practice</italic> online.</p><supplementary-material content-type="local-data" id="sup1"><label>Supplementary Materials</label><media xlink:href="cmy008_suppl_suppl_material.docx"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><sec id="s26"><title>Declaration</title><p>Funding: This paper presents independent research funded by the National Institute for Health Research School for Primary Care Research (NIHR SPCR). SMI and ADH are supported by the NIHR Health Protection Research Unit in Evaluation of Interventions. 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