Evidence map›Paper›PMID 39597575›Full record

ArticleMicroorganisms2024

Underestimated Incidence Rate of Pertussis in the Community: Results from Active Population-Based Surveillance in Yiwu, China.

Hanying Dai, Hanqing He, Juan Xu, Yao Zhu, Tao Fu, Bohan Chen, Jie Li, Yuan Gao, Aiping Qin, Maojun Zhang and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Microorganisms, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Trial
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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

11 authors.

Hanying DaiNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Hanqing HeZhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310000, China.
Juan XuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Yao ZhuZhejiang Provincial Center for Disease Control and Prevention, Hangzhou 310000, China.
Tao FuYiwu District Center for Disease Control and Prevention, Jinhua 321000, China.
Bohan ChenNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Jie LiNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Yuan GaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Aiping QinNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Maojun ZhangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Zhujun ShaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China.ORCID 0000-0001-5216-6537

Funding

Enhancement of Infectious Disease Surveillance and Control Technological Capabilities 102393240020020000003Operation of Public Health Emergency Response Mechanisms - Infectious Disease Control 102393220020020000029Sanofi No. PER00082the National Science Foundation of China No. 81973106
6 · The paper itself

Abstract

backgroundThe resurgence of pertussis in China underscores the urgency of active surveillance to complement the passive surveillance system.

methodsActive surveillance for pertussis was conducted from 1 June 2021 to 31 May 2022, at Yiwu, Zhejiang province of China. Patients with suspected pertussis were further confirmed as pertussis cases by PCR and culture. The incidence rate of pertussis in the community was estimated.

resultsThe overall estimated incidence of pertussis was 108.3 per 100,000 (95% CrI: 91.7-126.4). Children aged 4-5 years had the highest incidence (1154.3 per 100,000 [95% CrI: 817.4-1553.5]), followed by infants aged 1 year (836.1 per 100,000 [95% CrI: 434.0-1308.8]). Infants aged 0-4 months had the highest hospitalization rate among the pertussis patients (>50.0%). Although the incidence was low in elderly aged ≥ 60 years, the hospitalization rate was rather high (6.7%).

conclusionActive surveillance in this study revealed a higher burden of pertussis in Yiwu, China, compared to passive surveillance. Children aged 4-5 years are the dominant population group at risk of pertussis. Infants aged ≤ 4 months are the most vulnerable pertussis patients that require hospitalization treatment. Our results highlight the urgency of large-scale active surveillance of pertussis in China.

Indexed as

active surveillanceChinahospitalization rateincidencepertussis

Identifiers

PMID39597575
PMCPMC11596255

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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.