Evidence map›Paper›PMID 39350134›Full record

ArticleBMC public health2024

Epidemiological changes of scarlet fever before, during and after the COVID-19 pandemic in Chongqing, China: a 19-year surveillance and prediction study.

Rui Wu, Yu Xiong, Ju Wang, Baisong Li, Lin Yang, Han Zhao, Jule Yang, Tao Yin, Jun Sun, Li Qi and 6 more

Abstract read
In one paragraph

Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

16 authors.

Rui Wu *Chongqing Center for Disease Control and Prevention, No. 187 Tongxing North Road, Beibei district, Chongqing Municipality, China.
Yu Xiong *Chongqing Center for Disease Control and Prevention, No. 187 Tongxing North Road, Beibei district, Chongqing Municipality, China.
Ju WangChongqing Center for Disease Control and Prevention, No. 187 Tongxing North Road, Beibei district, Chongqing Municipality, China.
Baisong LiChongqing Center for Disease Control and Prevention, No. 187 Tongxing North Road, Beibei district, Chongqing Municipality, China.
Lin YangChongqing Center for Disease Control and Prevention, No. 187 Tongxing North Road, Beibei district, Chongqing Municipality, China.
Han ZhaoChongqing Center for Disease Control and Prevention, No. 187 Tongxing North Road, Beibei district, Chongqing Municipality, China.
Jule YangChongqing Center for Disease Control and Prevention, No. 187 Tongxing North Road, Beibei district, Chongqing Municipality, China.
Tao YinChongqing Center for Disease Control and Prevention, No. 187 Tongxing North Road, Beibei district, Chongqing Municipality, China.
Jun SunChongqing Center for Disease Control and Prevention, No. 187 Tongxing North Road, Beibei district, Chongqing Municipality, China.
Li QiChongqing Center for Disease Control and Prevention, No. 187 Tongxing North Road, Beibei district, Chongqing Municipality, China.
Jiang LongChongqing Center for Disease Control and Prevention, No. 187 Tongxing North Road, Beibei district, Chongqing Municipality, China.
Qin LiChongqing Center for Disease Control and Prevention, No. 187 Tongxing North Road, Beibei district, Chongqing Municipality, China.
Xiaoni ZhongSchool of Public Health and Management, Chongqing Medical University, No. 1 Yixueyuan Road, Yuzhong district, Chongqing Municipality, China.
Wenge TangChongqing Center for Disease Control and Prevention, No. 187 Tongxing North Road, Beibei district, Chongqing Municipality, China. wengetangcq@163.com.
Yaokai ChenChongqing Public Health Medical Center, No. 109 Baoyu Road, Shapingba district, Chongqing Municipality, China. yaokaichen@hotmail.com.
Kun SuChongqing Center for Disease Control and Prevention, No. 187 Tongxing North Road, Beibei district, Chongqing Municipality, China. sukun325@163.com.

Funding

Scientific Research Project of Chongqing Talent Plan cstc2022ycjh-bgzxm0251the Key Research and Development Project in the Health Field of Chongqing CSTC2021jscx-gksb-N0003
6 · The paper itself

Abstract

backgroundThis study aimed to investigate the epidemiological changes in scarlet fever before, during and after the COVID-19 pandemic (2005-2023) and predict the incidence of the disease in 2024 and 2025 in Chongqing Municipality, Southwest China.

methodsDescriptive analysis was used to summarize the characteristics of the scarlet fever epidemic. Spatial autocorrelation analysis was utilized to explore the distribution pattern of the disease, and the seasonal autoregressive integrated moving average (SARIMA) model was constructed to predict its incidence in 2024 and 2025.

resultsBetween 2005 and 2023, 9,593 scarlet fever cases were reported in Chongqing, which resulted in an annual average incidence of 1.6694 per 100,000 people. Children aged 3-7 were the primary victims of this disease, with the highest average incidence found among children aged 6 (5.0002 per 100,000 people). Kindergarten children were the dominant infected population, accounting for as much as 54.32% of cases, followed by students (34.09%). The incidence for the male was 1.51 times greater than that for the female. The monthly distribution of the incidence showed a bimodal pattern, with one peak occurring between April and June and another in November or December. The spatial autocorrelation analysis revealed that scarlet fever cases were markedly clustered; the areas with higher incidence were mainly concentrated in Chongqing's urban areas and its adjacent districts, and gradually spreading to remote areas after 2020. The incidence of scarlet fever increased by 106.54% and 39.33% in the post-upsurge period (2015-2019) and the dynamic zero-COVID period (2020-2022), respectively, compared to the pre-upsurge period (2005-2014) (P < 0.001). During the dynamic zero-COVID period, the incidence of scarlet fever decreased by 68.61%, 25.66%, and 10.59% (P < 0.001) in 2020, 2021, and 2022, respectively, compared to the predicted incidence. In 2023, after the dynamic zero-COVID period, the reported cases decreased to 1.5168 per 100,000 people unexpectedly instead of increasing. The cases of scarlet fever are predicted to increase in 2024 (675 cases) and 2025 (705 cases).

conclusionsChildren aged 3-7 years are the most affected population, particularly males, and kindergartens and primary schools serving as transmission hotspots. It is predicted that the high incidence of scarlet fever in Chongqing will persist in 2024 and 2025, and the outer districts (counties) beyond urban zone would bear the brunt of the impact. Therefore, imminent public health planning and resource allocation should be focused within those areas.

Indexed as

COVID-19Scarlet FeverAdolescentAdultChildChild, PreschoolChinaFemaleForecastingHumansIncidenceInfantMaleMiddle AgedPandemicsYoung AdultCOVID-19EpidemiologySARIMA modelScarlet fever

Identifiers

PMID39350134
PMCPMC11443759

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LicenceCC BY-NC-ND
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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.