A systematic review and meta-analysis on prevalence of gastrointestinal helminthic infections in rodents of Iran: An emphasis on zoonotic aspects
Bibliographic record
Abstract
Rodents are the largest group of mammals that adapt to different ecosystems and may act as the potential reservoirs of significant pathogens including gastrointestinal (GI) helminths. Rodent-borne parasitic pathogens have been and remain a great concern for animal and human health. The aim of this systematic review and meta-analysis is thus to clarify and better understand the pooled prevalence of GI helminthic infections and the associated risk factors in rodents in Iran. Multiple databases (PubMed, Scopus, Science Direct, Web of Science, Google Scholar, SID, Magiran and Irandoc) were searched for relevant literature published up to March 2022. A random-effects meta-analysis model was applied to estimate the pooled prevalence with 95% confidence interval. Moreover, heterogeneity among studies was evaluated using the Cochran's Q test and the I 2 -statistic. Out of the 5438 publications searched, 28 articles (30 datasets) were ultimately eligible for inclusion in the study. Thus, 3,649 captured rodents belonging to 6 families, 20 genera, and 35 species were examined for GI helminths in Iran. Then, 54 helminth species were identified in the present research, including 33 nematodes, 16 cestodes, 4 trematods, and 1 acanthocephalan. The prevalence rate of GI parasitic infections was 56% (95% CI: 50-63%). Hymenolepis diminuta , Syphacia obvelata and Rodentolepis nana were the most common helminthic infections (13%, 9%, and 8%, respectively). Moreover, 11 potential zoonotic helminths were found. There was no significant difference in pooled prevalence between male and female rodents (P=0.40). Considering geographical areas, northern and eastern provinces had the highest prevalence of GI helminthic infections among rodents. The prevalence of GI worms in Iranian rodents was as high as 56% with 11 zoonotic helminths. Therefore, it is suggested to observe the health of the environment, destroy the biological nests of rodents, avoid half-finished constructions, repair and improve streams and sidewalks, organize and collect garbage, and carry out biological and chemical control to handle the population of rodents. Increasing the awareness of local people about the harmful effects of rodents and the ways of transmission and prevention of rodent-borne intestinal worms transmitted to humans should be prioritized in health decisions.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".