Bacterial contamination of Healthcare workers’ mobile phones in Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Mobile phones are potential reservoirs for pathogens and sources of healthcare-associated illnesses. More microbes can be found on a mobile phone than on a man’s lavatory seat, the sole of a shoe, or a door handle. When examining patients, frequent handling of mobile phones can spread bacteria and provide a suitable breeding environment for numerous microorganisms. Nevertheless, evidence of bacterial contamination of mobile phones among healthcare workers in Africa was not conclusive. Thus, this meta-analysis and systematic review was conducted to estimate the pooled prevalence of bacterial contamination of mobile phones used by healthcare workers and the most frequent bacterial isolates in Africa. Methods We systematically retrieved relevant studies using PubMed/MEDLINE, Scopus, POPLINE, HINARI, Science Direct, Cochrane Library databases, and Google Scholar from 2009 to 2021 publication year. We included observational studies that reported the prevalence of bacterial contamination of mobile phones among healthcare workers. Two independent authors assessed the quality of the studies. The DerSimonian-random Laird’s effect model was used to calculate effect estimates for the pooled prevalence of bacterial contamination in mobile phones, as well as a 95% confidence interval (CI). Results Among 3882 retrieved studies, 23 eligible articles with a total sample size of 2,623 study participants were included in the meta-analysis. The pooled prevalence of mobile phones bacterial contamination among healthcare workers was 83.9% (95% CI: 80.6, 87.2%; I 2 = 98%, p-value < 0.001). The most dominant type of bacteria isolated in this review was coagulase-negative staphylococci (CONS) which accounted for 44.5% of the pooled contamination rate of mobile phones used by healthcare workers, followed by Staphylococcus aureus (32.3%), and Escherichia coli (8.4%). Conclusion The review indicated that the contamination with a different bacterial isolate of mobile phones used by health care workers was high. The most dominant bacterial isolates were Coagulase-negative staphylococci, Staphylococcus aureus , and Escherichia coli . Hence, these findings would have implications for policymakers and resource allocation for preventive measures initiatives.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.044 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".