A birth cohort analysis of the incidence of papillary thyroid cancer in the United States, 1973-2004.

作者信息

Zhu Cairong, Zheng Tongzhang, Kilfoy Briseis A, Han Xuesong, Ma Shuangge, Ba Yue, Bai Yana, Wang Rong, Zhu Yong, Zhang Yawei

机构信息

Yale University School of Public Health, New Haven, Connecticut 06520, USA.

出版信息

Thyroid. 2009 Oct;19(10):1061-6. doi: 10.1089/thy.2008.0342.

Abstract

BACKGROUND

The incidence of papillary thyroid cancer has been reported to be increasing during the past three decades, with a 65-126% increase between 1975 and 2004. The reason for the increase is currently unknown. This study examined the incidence pattern of papillary thyroid cancer in the United States, and evaluated the components of birth cohort (defined based on year of birth), time period, and age as determinants of the observed time trend of the disease.

METHODS

Using the data from the National Cancer Institute's Surveillance, Epidemiology, and End Results program for 1973-2004, we conducted both univariate analysis and age-period-cohort modeling to evaluate birth cohort patterns and evaluate age, period, and cohort effects on incidence trends over time.

RESULTS

The increasing incidence showed a clear birth cohort pattern for both men and women. The results from age-period-cohort modeling showed that, while period effect appeared to have had an impact on the observed incidence trends, birth cohort effect may also explain part of the increasing trend in papillary thyroid carcinoma during the study period, especially among women.

CONCLUSION

While a period effect that is likely due to advancements in diagnostic techniques and increased medical detection of small thyroid nodules may explain some of the observed increase in the incidence, we speculate that birth cohort-related changes in environmental exposures (such as increased exposure to diagnostic X-rays and polybrominated diphenyl ethers) have also contributed to the observed increase in papillary thyroid cancer during the past decades.

摘要

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索