Evidence map›Paper›PMID 29237496›Full record

ArticleSystematic reviews2017

Government policy interventions to reduce human antimicrobial use: protocol for a systematic review and meta-analysis.

Susan Rogers Van Katwyk, Jeremy M Grimshaw, Marc Mendelson, Monica Taljaard, Steven J Hoffman

Abstract read
In one paragraph

Article in Systematic reviews, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Carbapenem-ResistantMicroorganisms · 2023
    Article
  6. Article
  7. Governing the Global Antimicrobial Commons: Introduction to Special Issue.Health care analysis : HCA : journal of health philosophy and policy · 2023
    Article
  8. How Antimicrobial Resistance Is Linked to Climate Change: An Overview of Two Intertwined Global Challenges.International journal of environmental research and public health · 2023
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Susan Rogers Van KatwykSchool of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada. Susan.Rogers.Van.Katwyk@uOttawa.ca.ORCID http://orcid.org/0000-0002-1758-8635
Jeremy M GrimshawClinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
Marc MendelsonDivision of Infectious Diseases and HIV Medicine, Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa.
Monica TaljaardSchool of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada.
Steven J HoffmanGlobal Strategy Lab, Dahdaleh Institute for Global Health Research, Faculty of Health and Osgoode Hall Law School, York University, Ottawa, ON, Canada.

Funding

Public Health Agency of Canada N/A (Contract Funding)Research Council of Norway GLOBVAC Project #234608
6 · The paper itself

Abstract

backgroundAntimicrobial resistance (AMR) is a recognized threat to global public health. Increasing AMR and a dry pipeline of novel antimicrobial drugs have put AMR in the international spotlight. One strategy to combat AMR is to reduce antimicrobial drug consumption. Governments around the world have been experimenting with different policy interventions, such as regulating where antimicrobials can be sold, restricting the use of last-resort antimicrobials, funding AMR stewardship programs, and launching public awareness campaigns. To inform future action, governments should have access to synthesized data on the effectiveness of large-scale AMR interventions. This planned systematic review will (1) identify and describe previously evaluated government policy interventions to reduce human antimicrobial use and (2) estimate the effectiveness of these different strategies.

methodsAn electronic search strategy has been developed in consultation with two research librarians. Seven databases (MEDLINE, CINAHL, EMBASE, CENTRAL, PAIS Index, Web of Science, and PubMed excluding MEDLINE) will be searched, and additional studies will be identified using several gray literature search strategies. To be included, a study must (1) clearly describe the government policy and (2) use a rigorous design to quantitatively measure the impact of the policy on human antibiotic use. The intervention of interest is any policy intervention enacted by a government or government agency in any country to change human antimicrobial use. Two independent reviewers will screen for eligibility using criteria defined a priori. Data will be extracted with Covidence software using a customized extraction form. If sufficient data exists, a meta-analysis by intervention type will be conducted as part of the effectiveness review. However, if there are too few studies or if the interventions are too heterogeneous, data will be tabulated and a narrative synthesis strategy will be used. DISCUSSION: This evidence synthesis is intended for use by policymakers, public health practitioners, and researchers to inform future government policies aiming to address antimicrobial resistance. This review will also identify gaps in the evidence about the effectiveness of different policy interventions to inform future research priorities. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42017067514 .

Indexed as

GovernmentPolicyPublic Health PracticeAnti-Bacterial AgentsDrug Resistance, BacterialDrug Utilization ReviewHumansMeta-Analysis as TopicSystematic Reviews as TopicAnti-Bacterial AgentsAMRAntimicrobial resistanceGovernmentPolicyProtocolSystematic review

Identifiers

PMID29237496
PMCPMC5729233

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.