The socioeconomic burden of antibiotic resistance in conflict-affected settings and refugee hosting countries: a systematic scoping review
Bibliographic record
Abstract
Abstract Background Antibiotic resistance (ABR) is a major global threat. Armed and protracted conflicts act as multipliers of infection and ABR, thus leading to increased healthcare and societal costs. We aimed to understand and describe the socioeconomic burden of ABR in conflict-affected settings and refugee hosting countries by conducting a systematic scoping review. Methods A systematic search of PubMed, Medline (Ovid), Embase, Web of Science, SCOPUS and Open Grey databases was conducted to identify all relevant human studies published between January 1990 and August 2019. An updated search was also conducted in April 2020 using Medline/Ovid. Independent screenings of titles/abstracts followed by full texts were performed using pre-defined criteria. The Newcastle-Ottawa Scale was used to assess study quality. Data extraction and analysis were based on the PICOS framework and following the PRISMA-ScR guideline. Results The search yielded 8 studies (7 publications), most of which were single-country, mono-center and retrospective studies. The studies were conducted in Lebanon (n = 3), Iraq (n = 2), Jordan (n = 1), Palestine (n = 1) and Yemen (n = 1). Most of the studies did not have a primary aim to assess the socioeconomic impact of ABR and were small studies with limited statistical power that could not demonstrate significant associations. The included studies lacked sufficient information for the accurate evaluation of the cost incurred by antibiotic resistant infections in conflict-affected countries. Conclusion This review highlights the scarcity of research on the socioeconomic burden of ABR on general populations in conflict-affected settings and on refugees and migrants in host countries, and lists recommendations for consideration in future studies. Further studies are needed to understand the cost of ABR in these settings to develop and implement adaptable policies.
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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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".