Authors
Jiadi Li, Zide Chen
Published in
Frontiers in public health. Volume 14. Pages 1869275. Epub Jul 30, 2026.
Abstract
To characterize temporal trends and sex-specific differences in mortality attributed to thyroid disease as the underlying cause among U.S. adults aged ≥65 years from 1999 to 2023 and to project mortality trajectories through 2030.
We used the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) Multiple Cause of Death database to identify deaths among adults aged ≥65 years in which thyroid disease (International Classification of Diseases, 10th Revision codes E00-E07) was recorded as the underlying cause. Age-adjusted mortality rates (AAMRs) were calculated using the 2000 U.S. standard population. Temporal trends from 1999 to 2023 were evaluated using Joinpoint regression and log-linear models to estimate annual and average annual percent changes (APC/AAPC) and the estimated annual percentage change (EAPC). Autoregressive integrated moving average (ARIMA) and generalized additive models (GAM) were fitted to AAMR data from 1999 to 2020 to generate exploratory projections for 2021-2030.
A total of 41,660 thyroid disease-attributed deaths were identified. The overall AAMR decreased from 4.690 per 100,000 population in 1999 to 3.482 per 100,000 in 2023. The AAPC was -1.22%, and the EAPC was -1.43%, indicating a sustained decline over the study period. Women consistently had higher AAMRs than men, whereas men experienced a steeper decline in recent years. The ARIMA model projected a generally stable but gradually decreasing mortality trajectory through 2030. In contrast, the GAM captured nonlinear variation and suggested that the decline may attenuate or plateau during certain periods.
Mortality attributed to thyroid disease among older adults in the United States declined over the past 25 years, although a measurable burden and persistent sex differences remain. These findings support continued surveillance and targeted thyroid assessment and management among high-risk older adults. The forecasting results should be interpreted as exploratory, model-based scenarios that may help inform long-term public health planning rather than as precise predictions of future mortality.
PMID:
42598127
Bibliographic data and abstract were imported from PubMed on 14 Aug 2026.
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