Evidence map›Paper›PMID 40822318›Full record

ArticleOne health (Amsterdam, Netherlands)2025

Foodborne disease outbreaks before and during the COVID-19 pandemic in Jiangsu Province, China.

Qianrang Zhu, Wenqi Zong, Yucheng Wu, Ping Fu, Yue Dai

Erratum issuedAbstract read
In one paragraph

Article in One health (Amsterdam, Netherlands), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Qianrang ZhuJiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
Wenqi ZongJiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
Yucheng WuJiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
Ping FuNational Health Commission Key Laboratory of Food Safety Risk Assessment, China National Center for Food Safety Risk Assessment, Beijing, China.
Yue DaiJiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Foodborne diseases pose a significant global health issue affecting society and families. This study evaluates the impact of the COVID-19 pandemic on the epidemiology and causes of foodborne disease outbreaks (FBDOs) in Jiangsu Province, China. Methods: Data were obtained from the Foodborne Disease Outbreaks Surveillance System, as reported by the Centers for Disease Control and Prevention (CDC) at all levels in Jiangsu Province from 2017 to 2022. A retrospective analysis examined the epidemiological characteristics and associated factors of FBDOs before (2017 to 2019) and during (2020 to 2022) the COVID-19 pandemic. Results: FBDOs decreased from 470 to 338, and cases dropped from 6470 to 4344, representing relative reductions of 28.1 % and 32.9 %, respectively, during the COVID-19 period. While FBDOs are typically frequent from May to October, their numbers have markedly decreased during the pandemic. Ten prefecture-level cities reported fewer outbreaks and cases, whereas three prefecture-level cities saw increases. Conclusions: During the pandemic, there was a significant decrease in FBDOs and cases, along with shifts in their causes, food sources, and locations. Changes in dietary composition and hygiene behavior due to non-pharmacological interventions after COVID-19 onset likely influenced these trends. These insights can assist the government in developing evidence-based policies to reduce foodborne diseases and improve public health post-epidemic.

Indexed as

COVID-19Foodborne diseaseFood safetyVibrio parahaemolyticus

Identifiers

PMID40822318
PMCPMC12354779

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.