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CORRECTION article

Front. Public Health, 09 July 2025

Sec. Infectious Diseases: Epidemiology and Prevention

Volume 13 - 2025 | https://doi.org/10.3389/fpubh.2025.1650142

Correction: Long-term trends in rubella incidence across various regions and age groups in China, 2004–2021

This article is a correction to:

  1. The long-term trends in rubella incidence across various regions and age groups in China from 2004 to 2021 #

    1. Read original article
Yongjian Su,&#x;Yongjian Su1,2†Zhengqin Su&#x;Zhengqin Su1†Zixiu Huang&#x;Zixiu Huang3†Shan Yang Shan Yang4*Zhongyou Li Zhongyou Li1*Hai Li Hai Li1*
  • 1School of Public Health and Management, Guangxi University of Chinese Medicine, Nanning, China
  • 2Ruikang Hospital Affiliated with Guangxi University of Chinese Medicine, Nanning, China
  • 3College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
  • 4School of Innovation and Entrepreneurship, Guangxi University of Traditional Chinese Medicine, Nanning, China

A Correction on
Long-term trends in rubella incidence across various regions and age groups in China, 2004–2021

by Su, Y., Su, Z., Huang, Z., Yang, S., Li, Z., and Li, H. (2025). Front. Public Health. 13:1566999. doi: 10.3389/fpubh.2025.1566999

The reference for [15] was erroneously written as “Zhu Z, Wang HL, Zhang Y. Analysis of serum IgM antibody and viral nucleic acid detection results in measles and rubella cases in China from 2014 to 2023. Chinese J Prev Med. (2024) 8:1318–23. doi: 10.3760/cma.j.cn112150-20240306-00191”. It should be “Zhu Z, Wang HL, Zhang Y. Analysis of serum IgM antibody and viral nucleic acid detection results in measles and rubella cases in China from 2014 to 2023. Chin J Prev Med. (2024) 8:1318–23. doi: 10.3760/cma.j.cn112150-20240306-00191”.

There was a mistake in Figures 1, 4, 5, 6, 7 and 8 as published. To ensure academic rigor, we have rechecked the data and performed additional analyses using the latest procedures, providing more accurate data and higher-resolution figures. The corrected Figures 1, 4, 5, 6, 7 and 8 appear below.

Figure 1
Bar and line graph showing the number of cases and incidence rates from 2004 to 2021. Cases peak in 2008 with over 120,000, while incidence also peaks then. Both metrics decrease significantly after 2008, with a minor rise in 2019.

Figure 1. The annual number of cases and incidence of rubella in China from 2004 to 2021.

Figure 4
Line graph of crude rates per 100,000 in Mainland China from 2004 to 2021, with observed data points shown as red diamonds. A blue line represents a rising trend with an annual percent change (APC) of 46.58 from 2004 to the joinpoint in 2008. After 2008, a green line shows a declining trend with an APC of -18.41 until 2021.

Figure 4. The long-term trend of rubella incidence in China from 2004 to 2021.

Figure 5
Map of China showing average annual incidence rates per 100,000 people for different regions. Tibet has the highest rate at 8.63, shown in dark purple. Other high-incidence regions include Ningxia at 7.72 and Liaoning at 7.13. Lower rates are highlighted in lighter colors. Beijing is marked with a red star, showing a rate of 2.66. A color scale indicates ranges of incidence rates from light to dark purple.

Figure 5. Distribution map of rubella incidence in different regions of China from 2004 to 2021.

Figure 6
Map of China highlighting regional differences in AAPC percentages. Dark green regions, including Sinkiang, Jilin, Ningxia, Zhejiang, and Tianjian, show significant decreases from -18.75 to -21.81. Beijing is marked with a star. A legend indicates color-coded AAPC percentage ranges.

Figure 6. Distribution of rubella incidence with decreasing trends in five regions of China from 2004 to 2021.

Figure 7
Bar and line graph showing the number of cases and average annual incidence per 100,000 across different age groups. Green bars represent case numbers peaking at ages ten to fourteen. The red line indicates incidence, highest at ages ten to fourteen, then declining.

Figure 7. The number of cases and the average annual incidence of rubella in different age groups from 2004 to 2021.

Figure 8
Bar chart showing Average Annual Percent Change (AAPC) in various age groups, with significant values marked by asterisks.For statistically significant results, negative values appear for ages zero to nine, fifty to fifty-four, and seventy to seventy-nine. The remaining fourteen age groups trends may seem negatively or positively value, but there are considered statistically non-significant.

Figure 8. Distribution of the long-term trends of rubella incidence in different age groups from 2004 to 2021.

There was a mistake in Tables 1 and 2 as published. To ensure academic rigor, we have rechecked the data and performed additional analyses using the latest procedures, providing more accurate data. The corrected Tables 1 and 2 appear below.

Table 1
www.frontiersin.org

Table 1. The long-term trends of rubella incidence in the whole country and 31 regions from 2004 to 2021.

Table 2
www.frontiersin.org

Table 2. The long-term trends of rubella incidence in 26 age groups from 2004 to 2021.

A correction has been made to Section 2.2 Research methods, 2.2.1 Basic statistical description, paragraph 1. The text “(incidence = number of cases/number of exposed population *100,000%)” has been changed to “(incidence = number of cases/number of exposed population *100,000).”

The original version of this article has been updated.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: rubella, incidence, descriptive study, joinpoint regression model, China

Citation: Su Y, Su Z, Huang Z, Yang S, Li Z and Li H (2025) Correction: Long-term trends in rubella incidence across various regions and age groups in China, 2004–2021. Front. Public Health 13:1650142. doi: 10.3389/fpubh.2025.1650142

Received: 19 June 2025; Accepted: 23 June 2025;
Published: 09 July 2025.

Edited and reviewed by: Marc Jean Struelens, Université libre de Bruxelles, Belgium

Copyright © 2025 Su, Su, Huang, Yang, Li and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Shan Yang, MTQwMjY1MEBxcS5jb20=; Zhongyou Li, NTgyODkzMTU2QHFxLmNvbQ==; Hai Li, bGloYWk2NjA5QGhvdG1haWwuY29t

†These authors share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.