Evidence map›Paper›PMID 38440384›Full record

SynthesisFrontiers in veterinary science2024

Prevalence of

Dong Li, Dan-Ni Li, Xin-Yue Liu, Yu-Hao Song, Xue-Tong Liu, Siddique Sehrish, Yu-Xin Jia, Ying Zong, Jian-Ming Li, Kun Shi and 6 more

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in veterinary science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. 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

16 authors.

Dong Li *College of Chinese Medicine Materials, Jilin Agricultural University, Changchun, China.
Dan-Ni Li *College of Animal Science and Technology, Jilin Agricultural University, Changchun, China.
Xin-Yue Liu *College of Animal Science and Technology, Jilin Agricultural University, Changchun, China.
Yu-Hao SongCollege of Animal Science and Technology, Jilin Agricultural University, Changchun, China.
Xue-Tong LiuCollege of Animal Science and Technology, Jilin Agricultural University, Changchun, China.
Siddique SehrishCollege of Animal Science and Technology, Jilin Agricultural University, Changchun, China.
Yu-Xin JiaCollege of Animal Science and Technology, Jilin Agricultural University, Changchun, China.
Ying ZongCollege of Chinese Medicine Materials, Jilin Agricultural University, Changchun, China.
Jian-Ming LiCollege of Chinese Medicine Materials, Jilin Agricultural University, Changchun, China.
Kun ShiCollege of Chinese Medicine Materials, Jilin Agricultural University, Changchun, China.
Xue LengCollege of Chinese Medicine Materials, Jilin Agricultural University, Changchun, China.
Fei LiuCollege of Animal Science and Technology, Jilin Agricultural University, Changchun, China.
Nai-Chao DiaoCollege of Chinese Medicine Materials, Jilin Agricultural University, Changchun, China.
Fan-Li ZengCollege of Chinese Medicine Materials, Jilin Agricultural University, Changchun, China.
Qing-Long GongCollege of Animal Science and Technology, Jilin Agricultural University, Changchun, China.
Rui DuCollege of Chinese Medicine Materials, Jilin Agricultural University, Changchun, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Deer tuberculosis is a chronic zoonotic infectious disease, despite the existence of socio-economic and zoonotic risk factors, but at present, there has been no systematic review of deer tuberculosis prevalence in mainland China. The aim of this meta-analysis was to estimate the overall prevalence of deer TB in mainland China and to assess possible associations between potential risk factors and the prevalence of deer tuberculosis. Methodology: This study was searched in six databases in Chinese and English, respectively (1981 to December 2023). Four authors independently reviewed the titles and abstracts of all retrieved articles to establish the inclusion exclusion criteria. Using the meta-analysis package estimated the combined effects. Cochran's Q-statistic was used to analyze heterogeneity. Funnel plots (symmetry) and used the Egger's test identifying publication bias. Trim-and-fill analysis methods were used for validation and sensitivity analysis. we also performed subgroup and meta-regression analyses. Results: In this study, we obtained 4,400 studies, 20 cross-sectional studies were screened and conducted a systematic review and meta-analysis. Results show: The overall prevalence of tuberculosis in deer in mainland China was 16.1% (95% confidence interval (CI):10.5 24.6; (Deer tuberculosis infected 5,367 out of 22,215 deer in mainland China) 5,367/22215; 1981 to 2023). The prevalence in Central China was the highest 17.5% (95% CI:14.0-21.9; 63/362), and among provinces, the prevalence in Heilongjiang was the highest at 26.5% (95% CI:13.2-53.0; 1557/4291). Conclusion: Deer tuberculosis is still present in some areas of China. Assessing the association between risk factors and the prevalence of deer tuberculosis showed that reasonable and scientific-based breeding methods, a suitable breeding environment, and rapid and accurate detection methods could effectively reduce the prevalence of deer tuberculosis. In addition, in the management and operation of the breeding base, improving the scientific feed nutrition standards and establishing comprehensive standards for disease prevention, immunization, quarantine, treatment, and disinfection according to the breeding varieties and scale, are suggested as ways to reduce the prevalence of deer tuberculosis.

Indexed as

deermainland Chinameta-analysisMycobacterium bovisprevalence

Identifiers

PMID38440384
PMCPMC10909987

What OpenQuestion holds

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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.