Evidence map›Paper›PMID 36506009›Full record

SynthesisFrontiers in cellular and infection microbiology2022

Global prevalence and risk factors of

Xiao-Man Li, Hong-Li Geng, Yong-Jie Wei, Wei-Lan Yan, Jing Liu, Xin-Yu Wei, Miao Zhang, Xiang-Yu Wang, Xiao-Xuan Zhang, Gang Liu

Open access · goldAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Frontiers in cellular and infection microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 pooled it
2.5field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 14 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Molecular surveillance and genetic diversity ofFrontiers in veterinary science · 2026
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  7. Global epidemiology and species/genotype distribution ofFood and waterborne parasitology · 2024
    Review
  8. Prevalence ofVeterinary world · 2024
    Article
  9. Article
  10. Global prevalence ofFrontiers in veterinary science · 2024
    Review
  11. Article
  12. Epidemiology ofMicroorganisms · 2023
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 1 country.

Xiao-Man LiCollege of Veterinary Medicine, Qingdao Agricultural University, Qingdao, Shandong, China.
Hong-Li GengCollege of Veterinary Medicine, Qingdao Agricultural University, Qingdao, Shandong, China.
Yong-Jie WeiCollege of Veterinary Medicine, Qingdao Agricultural University, Qingdao, Shandong, China.
Wei-Lan YanCollege of Veterinary Medicine, Qingdao Agricultural University, Qingdao, Shandong, China.
Jing LiuCollege of Life Science, Changchun Sci-Tech University, Shuangyang, Jilin, China.
Xin-Yu WeiCollege of Animal Science and Veterinary Medicine, Heilongjiang Bayi Agricultural University, Daqing, Heilongjiang, China.
Miao ZhangCollege of Veterinary Medicine, Qingdao Agricultural University, Qingdao, Shandong, China.
Xiang-Yu WangCollege of Veterinary Medicine, Qingdao Agricultural University, Qingdao, Shandong, China.
Xiao-Xuan ZhangCollege of Veterinary Medicine, Qingdao Agricultural University, Qingdao, Shandong, China.
Gang LiuCollege of Veterinary Medicine, Qingdao Agricultural University, Qingdao, Shandong, China.
Qingdao Agricultural University · CNChangchun University · CNHeilongjiang Bayi Agricultural University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intoduction: Cryptosporidiosis is a zoonotic disease caused by Cryptosporidium infection with the main symptom of diarrhea. The present study performed a metaanalysis to determine the global prevalence of Cryptosporidium in Equus animals. Methods: Data collection was carried out using Chinese National Knowledge Infrastructure (CNKI), VIP Chinese journal database (VIP), WanFang Data, PubMed, and ScienceDirect databases, with 35 articles published before 2021 being included in this systematic analysis. This study analyzed the research data through subgroup analysis and univariate regression analysis to reveal the factors leading to high prevalence. We applied a random effects model (REM) to the metadata. Results: The total prevalence rate of Cryptosporidium in Equus was estimated to be 7.59% from the selected articles. The prevalence of Cryptosporidium in female Equus was 2.60%. The prevalence of Cryptosporidium in Equus under 1-year-old was 11.06%, which was higher than that of Equus over 1-year-old (2.52%). In the experimental method groups, the positive rate detected by microscopy was the highest (10.52%). The highest Cryptosporidium prevalence was found in scale breeding Equus (7.86%). The horses had the lowest Cryptosporidium prevalence (7.32%) among host groups. C. muris was the most frequently detected genotype in the samples (53.55%). In the groups of geographical factors, the prevalence rate of Cryptosporidium in Equus was higher in regions with low altitude (6.88%), rainy (15.63%), humid (22.69%), and tropical climates (16.46%). Discussion: The search strategy use of five databases might have caused the omission of some researches. This metaanalysis systematically presented the global prevalence and potential risk factors of Cryptosporidium infection in Equus. The farmers should strengthen the management of young and female Equus animals, improve water filtration systems, reduce stocking densities, and harmless treatment of livestock manure.

Indexed as

CryptosporidiosisCryptosporidiumAnimalsFemaleHorsesPrevalenceRisk FactorsZoonosesCryptosporidiumEquusmeta-analysisprevalencezoonotic diseases

Identifiers

PMID36506009
PMCPMC9732577
OpenAlexW4310040015

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.