Evidence map›Paper›PMID 34095074›Full record

ArticleFrontiers in public health2021

The Impact of COVID-19 Interventions on Influenza and Mycobacterium Tuberculosis Infection.

Yiman Geng, Gang Li, Leiliang Zhang

Abstract read
In one paragraph

Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
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

3 authors.

Yiman GengDepartment of Clinical Laboratory, Henan Provincial People's Hospital, Peolple's Hospital of Zhengzhou University, Zhengzhou, China.
Gang LiDepartment of Clinical Laboratory, Henan Provincial People's Hospital, Peolple's Hospital of Zhengzhou University, Zhengzhou, China.
Leiliang ZhangInstitute of Basic Medicine, The First Affiliated Hospital of Shandong First Medical University, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A series of public health interventions have been implemented to prevent the transmission of SARS-CoV-2 in China. However, the effect of non-pharmaceutical interventions to COVID-19 on the incidence of the influenza virus and Mycobacterium tuberculosis infections is not clear. In current study, we analyzed surveillance data on influenza and Mycobacterium tuberculosis from Henan Provincial People's Hospital in Zhengzhou, Henan province, China from 2019 to 2020. The monthly positive test rate for influenza and Mycobacterium tuberculosis to estimate transmissibility changes was calculated. The positive detection rate of influenza A declined significantly during the implementation of inventions in 2020, from a total positive rate of 17.69% in 2019 to 5.77% in 2020. Similarly, a 2.15% reduction in positive detective rate was seen for influenza B, from a total positive rate of 5.15% in 2019 to 3% in 2020. The positive rate curve of Mycobacterium tuberculosis measured by x-pert in 2020 remained above the curve in 2019 from March to June, and August, showing the rising trend under these precautions. Our study suggests that non-pharmaceutical public health interventions likely reduced influenza transmission significantly and have less effect on Mycobacterium tuberculosis transmission in 2020.

Indexed as

COVID-19Influenza, HumanTuberculosisChinaHumansSARS-CoV-2COVID-19influenzaMycobacterium tuberculosisnon-pharmaceutical interventionsSARS-CoV-2

Identifiers

PMID34095074
PMCPMC8175651

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.