Global, regional and national incidence and causes of needlestick injuries: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Needlestick injuries (NSIs) are one of the most serious occupational hazards for healthcare workers (HCWs). Aims: The aim of this study was to evaluate the incidence and causes of NSIs globally. Methods: A systematic review and meta-analysis of data from January 2000 to May 2020 collected from Scopus, PubMed, Embase, Web of Science, and Google Scholar. The Newcastle-Ottawa Scale was used to assess the quality of the included articles. The data obtained were analysed by R version 3/5/0, and 113 articles were retrieved. Results: There were 113 studies with a total of 525 798 HCWs. The incidence of NSIs was 43%. Africa had the highest rate of these injuries of 51%, and the World Health Organization (WHO) African Region had the highest incidence among WHO regions of 52%. Women were more frequently affected by NSIs than men. Hepatitis C virus infection was the disease most commonly transmitted via NSIs (21%). The highest rates of NSIs according to causes, devices, hospital locations, occupations and procedures were for recapping of needles, needles, general wards, nurses and waste disposal, respectively. Conclusion: The incidence of NSIs is gradually decreasing. The findings of this study can contribute to improving the decision-making process for reducing NSIs in HCWs.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.025 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".