This paper is in the following e-collection/theme issue:

Statistical Methods for Surveillance and Population Health (188) Obstetrics, Gynecology, and Reproductive Medicine (329) Public (e)Health, Digital Epidemiology and Public Health Informatics (1008) Longitudinal and Cohort Studies in Public Health (411) Cancer Epidemiology, Cancer Surveillance and Infodemiology (113) Cancer Prognosis Models and Machine Learning (67) HIV/AIDS/STI Prevention and Care (1043)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46360, first published .
Projected Time for the Elimination of Cervical Cancer Under Various Intervention Scenarios: Age-Period-Cohort Macrosimulation Study

Projected Time for the Elimination of Cervical Cancer Under Various Intervention Scenarios: Age-Period-Cohort Macrosimulation Study

Projected Time for the Elimination of Cervical Cancer Under Various Intervention Scenarios: Age-Period-Cohort Macrosimulation Study

Authors of this article:

Yi-Chu Chen1 Author Orcid Image ;   Yun-Yuan Chen2 Author Orcid Image ;   Shih-Yung Su3 Author Orcid Image ;   Jing-Rong Jhuang4 Author Orcid Image ;   Chun-Ju Chiang1, 5 Author Orcid Image ;   Ya-Wen Yang5 Author Orcid Image ;   Li-Ju Lin6 Author Orcid Image ;   Chao-Chun Wu6 Author Orcid Image ;   Wen-Chung Lee1, 5, 7 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

Journals

  1. Yuan Y, Liu C, Guo M, Xin Z, Chen G, Yang Y, Zheng J, Zang B, Yang J. Exploring Cancer Incidence Trends by Age and Sex Among 14.14 Million Individuals in China From 2007 to 2021: Population-Based Study. JMIR Public Health and Surveillance 2024;10:e55657 View
  2. Wu Y, Chen Y, Jhuang J, Chiang C, Lee W. Emerging trends in cervical cancer incidence among younger Taiwanese generations: an urban–rural comparison. Annals of Medicine 2025;57(1) View
  3. Lu Z, Dong B, Cai H, Tian T, Wang J, Fu L, Wang B, Zhang W, Lin S, Tuo X, Wang J, Yang T, Huang X, Zheng Z, Xue H, Xu S, Liu S, Sun P, Zou H. Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study. JMIR Public Health and Surveillance 2025;11:e67840 View