Evidence map›Paper›PMID 40533066›Full record

ArticleJMIR public health and surveillance2025

Changes in Epidemiological Characteristics of Varicella and Breakthrough Cases in Ningbo, China, From 2010 to 2023: Surveillance Study.

Xingqiang Pan, Yan Zhang, Xuefei Zhao, Dandan Zhang

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

4 authors.

Xingqiang PanNingbo Center for Disease Control and Prevention, No. 1166 Fanjiangan Road, Haishu District, Ningbo, 315000, China, 86 057487680154.ORCID 0009-0004-5093-2399
Yan ZhangNingbo Center for Disease Control and Prevention, No. 1166 Fanjiangan Road, Haishu District, Ningbo, 315000, China, 86 057487680154.ORCID 0000-0002-6126-1744
Xuefei ZhaoNingbo Center for Disease Control and Prevention, No. 1166 Fanjiangan Road, Haishu District, Ningbo, 315000, China, 86 057487680154.ORCID 0009-0005-9107-0681
Dandan ZhangNingbo Center for Disease Control and Prevention, No. 1166 Fanjiangan Road, Haishu District, Ningbo, 315000, China, 86 057487680154.ORCID 0009-0000-6263-8964

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Varicella is a prevalent respiratory infectious disease. Continuous monitoring is essential to understand evolving epidemiological patterns, particularly given the impact of vaccination and recent nonpharmacological interventions. Objective: This study aims to monitor the epidemiological characteristics of varicella and the changes in breakthrough cases to inform adjustments in immunization strategies and enhance prevention efforts. Methods: From 2010 to 2023, varicella incidence was monitored using active (2010-2011) and passive (2012-2023) surveillance methods. Data were obtained from the Chinese Center for Disease Prevention and Control's information system and Ningbo's Immunization Information System. The study period was divided into four intervals to analyze trends. A birth cohort (2009-2013) was established to examine breakthrough cases. A recurrent neural network model was constructed for deep learning analysis of incidence trends and the impact of nonpharmaceutical interventions. Results: Between 2010 and 2023, a total of 70,163 varicella cases were reported in Ningbo. Seasonal distribution indicated two incidence troughs before 2020 and only one from 2020 to 2023. The predominant age of onset was 10-14 years, accounting for 23.93% (16,795/70,163) of cases. From 2010 to 2013, the highest incidence was among children aged 5-9 years; from 2014 to 2019, it shifted to those aged 10-14 years; and from 2020 to 2023, it was primarily among individuals aged 15-19 years. Following the introduction of a second vaccine dose (2014-2019), incidence among children younger than 10 years of age decreased, notably by 59.54% in those aged 1-4 years. Conversely, incidence among individuals aged 10 years and older increased, particularly by 123.78% in the 15-19 years age group, with a significant upward trend (Ptrend<.001). From 2020 to 2023, although incidence rates increased across age groups 15 years and older, the rise was modest. The average annual incidence rate of breakthrough cases after one vaccine dose was 83.40/100,000 (range, 51.21-119.50/100,000), significantly higher than the 24.80/100,000 (range, 17.67-32.90/100,000) observed after two doses. However, the incidence of breakthrough cases after the first dose declined following the implementation of the 2-dose program. The median time from vaccination to breakthrough case occurrence was 27 (IQR 17.50-48) months. The recurrent neural network model demonstrated high accuracy (mean squared error, 49.96) and indicated that implementation of emergency response and community lockdown measures in early 2020 correlated with a divergence between predicted and actual case numbers, suggesting an impact of nonpharmaceutical interventions on varicella transmission. Conclusions: The significant shifts in varicella epidemiology between 2010 and 2023 highlight the importance of continuous monitoring and proactive immunization adjustments. We recommend enhanced varicella surveillance focusing on adult populations, and a targeted increase in 2-dose vaccine coverage, particularly in high-risk environments such as high schools and universities.

Indexed as

ChickenpoxPopulation SurveillanceAdolescentAdultChickenpox VaccineChildChild, PreschoolChinaFemaleHumansIncidenceInfantMaleMiddle AgedSeasonsYoung AdultChickenpox Vaccinebreakthrough casedisease surveillanceepidemiologyinfectiousmachine learningmonitoringnonpharmacological interventionsvaccinevaricella

Identifiers

PMID40533066
PMCPMC12195626

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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.