Evidence map›Paper›PMID 36513958›Full record

SynthesisJournal of evidence-based medicine2022

Infection rate and factors affecting close contacts of COVID-19 cases: A systematic review.

Yunxuan Li, Jing Tan, Suoyi Tan, Yilong Zhou, Bin Sai, Bitao Dai, Xin Lu

Open access · hybridAbstract readSystematic ReviewMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Journal of evidence-based medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.5field-weighted citation impact, top 19% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. 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

7 authors at 2 institutions in 2 countries.

Yunxuan LiCollege of Systems Engineering, National University of Defense Technology, Changsha, China.
Jing TanChinese Evidence-Based Medicine Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
Suoyi TanCollege of Systems Engineering, National University of Defense Technology, Changsha, China.
Yilong ZhouCollege of Systems Engineering, National University of Defense Technology, Changsha, China.
Bin SaiCollege of Systems Engineering, National University of Defense Technology, Changsha, China.
Bitao DaiCollege of Systems Engineering, National University of Defense Technology, Changsha, China.
Xin LuCollege of Systems Engineering, National University of Defense Technology, Changsha, China.ORCID https://orcid.org/0000-0002-3547-6493
National University of Defense Technology · CNSichuan University · CN

Funding

Hunan Science and Technology Plan Project 2020JJ4673Hunan Science and Technology Plan Project 2020JJ5679Hunan Science and Technology Plan Project 2020TP1013Innovation Team Project of Colleges in Guangdong Province 2020KCXTD040National Nature Science Foundation of China 71790615National Nature Science Foundation of China 72001211National Nature Science Foundation of China 72025405National Nature Science Foundation of China 72088101National Nature Science Foundation of China 91846301Shenzhen Basic Research Project for Development of Science and Technology 202008291726500001Shenzhen Basic Research Project for Development of Science and Technology JCYJ20200109141218676
6 · The paper itself

Abstract

objectiveContact tracing plays an essential role in mitigating the impact of an epidemic. During the COVID-19 pandemic, studies of those who have been in close contact with confirmed cases offer critical insights to understand the epidemiological characteristics of SARS-CoV-2 better. This study conducts a meta-analysis of existing studies' infection rates and affecting factors.

methodsWe searched PubMed, Web of Science and CNKI from the inception to April 30 2022 to identify systematic reviews. Two reviewers independently extracted the data and assessed risk of bias. Meta-analyses were conducted to calculate pooled estimates by using Stata/SE 15.1 software.

resultsThere were 47 studies in the meta-analysis. Among COVID-19 close contacts, older age (RR = 1.94, 95% CI: 1.70, 2.21), contacts in households (RR = 2.83, 95% CI: 2.20, 3.65), and people in close contact with symptomatic infections (RR = 3.62, 95% CI: 1.88, 6.96) were associated with higher infection rates.

conclusionOn average, each primary infection corresponded to 5.8 close contacts. Among COVID-19 close contacts, older age and contacts in households were associated with higher infection rates, and people in close contact with symptomatic infections had three times higher risk of infection compared to people in close contact with asymptomatic infections. In general, there are significantly more studies from China about close contacts, and the infection rate among close contacts was lower compared to other countries.

Indexed as

COVID-19ChinaContact TracingHumansPandemicsSARS-CoV-2close contactsCOVID-19infection ratemeta-analysisSARS-CoV-2

Identifiers

PMID36513958
PMCPMC9877962
OpenAlexW4311313462

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.