Evidence map›Paper›PMID 40535374›Full record

ArticleCureus2025

Demographic and Temporal Trends in Thyroid Cancer Incidence in the United States From 1999 to 2020: An Analysis of Age, Gender, and Race.

Daaniya Tariq, Onuralp Kafali, Jorge Cabrera Macias, Nithin Karnan, Min Ji Kim

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Daaniya TariqDepartment of Internal Medicine, Shaheed Suhrawardy Medical College and Hospital, Dhaka, BGD.
Onuralp KafaliDepartment of Internal Medicine, Duzce University Medical School, Duzce, TUR.
Jorge Cabrera MaciasSchool of Medicine, University of Guayaquil, Guayaquil, ECU.
Nithin KarnanDepartment of Internal Medicine, K.A.P. Viswanatham Government Medical College, Tiruchirappalli, IND.
Min Ji KimDepartment of Internal Medicine, Tashkent Pediatric Medical Institute, Tashkent, UZB.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThyroid cancer is a growing concern in public health and has noticeable disparities in incidence among demographic subgroups and regions. This study analyzed the incidence of thyroid cancer from 1999 to 2020 using the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC-WONDER) database, focusing on age, race, and gender. Additionally, the study sought to identify geographical disparities in thyroid cancer incidence and understand temporal trends over the study period.

methodsThe study extracted thyroid cancer incidences from the CDC-WONDER database from 1999 to 2020 by age, race, and gender demographics. Crude incidence rates were calculated based on a 100,000 population, and temporal trends were identified to peak incidence periods, using Excel (Microsoft Corporation, Redmond, WA) and RStudio version 4.3.1 (Posit Software, Boston, MA).

resultsThe data included a total population of 6,722,531,004, with 837,105 (0.0124%) diagnosed cases of thyroid cancer between 1999 and 2020. The crude rate was highest in the 65-74-year age group with a rate of 22.5 per 100,000, in females at 18.4 per 100,000, and in the White race with a rate of 13.2 per 100,000, while California had the highest statewise distribution, exceeding 75,000 per 100,000 population. From temporal trends, there is a general increasing trend in the incidence of thyroid cancer, with the crude incidence rate for the period under study amounting to 12.5 per 100,000 population, and a peak observed in 2015.

conclusionThe findings point to significant demographic and geographic disparities in the incidence of thyroid cancer across the United States. Such an insight should be crucial for designing directed public health interventions and well-conceived screening protocols for high-risk subgroups. Geographic disparities further aid in resource mobilization and health service planning. This study emphasizes the importance of good cancer surveillance and continuous monitoring of public health strategies, which would provide early detection to attain better patient outcomes.

Indexed as

agecdc-wondercrude incidence ratesdemographic trendsepidemiologygenderpublic healthracethyroid cancerunited states

Identifiers

PMID40535374
PMCPMC12174759

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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.