Evidence map›Paper›PMID 31185011›Full record

SynthesisPLoS medicine2019

Government policy interventions to reduce human antimicrobial use: A systematic review and evidence map.

Susan Rogers Van Katwyk, Jeremy M Grimshaw, Miriam Nkangu, Ranjana Nagi, Marc Mendelson, Monica Taljaard, Steven J Hoffman

Abstract readSystematic Review
In one paragraph

Synthesis in PLoS medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed, 8 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

63 citing papers in PubMed, 8 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Pooled it
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Article

3 more citing papers are in PubMed but not listed here.

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

7 authors.

Susan Rogers Van KatwykSchool of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada.ORCID 0000-0002-1758-8635
Jeremy M GrimshawClinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.ORCID 0000-0001-8015-8243
Miriam NkanguSchool of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada.
Ranjana NagiGlobal Strategy Lab, Dahdaleh Institute for Global Health Research, Faculty of Health and Osgoode Hall Law School, York University, Toronto, Ontario, 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, Ontario, Canada.
Steven J HoffmanGlobal Strategy Lab, Dahdaleh Institute for Global Health Research, Faculty of Health and Osgoode Hall Law School, York University, Toronto, Ontario, Canada.ORCID 0000-0002-2064-3711

Funding

CIHR
6 · The paper itself

Abstract

backgroundGrowing political attention to antimicrobial resistance (AMR) offers a rare opportunity for achieving meaningful action. Many governments have developed national AMR action plans, but most have not yet implemented policy interventions to reduce antimicrobial overuse. A systematic evidence map can support governments in making evidence-informed decisions about implementing programs to reduce AMR, by identifying, describing, and assessing the full range of evaluated government policy options to reduce antimicrobial use in humans. METHODS AND

findingsSeven databases were searched from inception to January 28, 2019, (MEDLINE, CINAHL, EMBASE, PAIS Index, Cochrane Central Register of Controlled Trials, Web of Science, and PubMed). We identified studies that (1) clearly described a government policy intervention aimed at reducing human antimicrobial use, and (2) applied a quantitative design to measure the impact. We found 69 unique evaluations of government policy interventions carried out across 4 of the 6 WHO regions. These evaluations included randomized controlled trials (n = 4), non-randomized controlled trials (n = 3), controlled before-and-after designs (n = 7), interrupted time series designs (n = 25), uncontrolled before-and-after designs (n = 18), descriptive designs (n = 10), and cohort designs (n = 2). From these we identified 17 unique policy options for governments to reduce the human use of antimicrobials. Many studies evaluated public awareness campaigns (n = 17) and antimicrobial guidelines (n = 13); however, others offered different policy options such as professional regulation, restricted reimbursement, pay for performance, and prescription requirements. Identifying these policies can inform the development of future policies and evaluations in different contexts and health systems. Limitations of our study include the possible omission of unpublished initiatives, and that policies not evaluated with respect to antimicrobial use have not been captured in this review.

conclusionsTo our knowledge this is the first study to provide policy makers with synthesized evidence on specific government policy interventions addressing AMR. In the future, governments should ensure that AMR policy interventions are evaluated using rigorous study designs and that study results are published. PROTOCOL REGISTRATION: PROSPERO CRD42017067514.

Indexed as

Anti-Bacterial AgentsAnti-Infective AgentsDrug Resistance, MicrobialEvidence-Based MedicineHealth PolicyHumansRandomized Controlled Trials as TopicAnti-Bacterial AgentsAnti-Infective Agents

Identifiers

PMID31185011
PMCPMC6559631

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.