Government policy interventions to reduce human antimicrobial use: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Antimicrobial 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. METHODS: An 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 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.101 | 0.126 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.023 | 0.024 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.097 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".