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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Oncol.</journal-id>
<journal-title-group>
<journal-title>Frontiers in Oncology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Oncol.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2234-943X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fonc.2026.1634710</article-id>
<article-version article-version-type="Version of Record" vocab="NISO-RP-8-2008"/>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Original Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Trends and projections of global testicular cancer burden from 1990 to 2035</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name><surname>Mao</surname><given-names>Changkun</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/2988357/overview"/>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Formal analysis" vocab-term-identifier="https://credit.niso.org/contributor-roles/formal-analysis/">Formal analysis</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Data curation" vocab-term-identifier="https://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; original draft" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing &#x2013; original draft</role>
</contrib>
<contrib contrib-type="author">
<name><surname>Tao</surname><given-names>Chengpin</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; original draft" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing &#x2013; original draft</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Data curation" vocab-term-identifier="https://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
</contrib>
<contrib contrib-type="author">
<name><surname>Yang</surname><given-names>Chao</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; original draft" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing &#x2013; original draft</role>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Shen</surname><given-names>Jian</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>*</sup></xref>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="conceptualization" vocab-term-identifier="https://credit.niso.org/contributor-roles/conceptualization/">Conceptualization</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; review &amp; editing" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing &#x2013; review &amp; editing</role>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Li</surname><given-names>Guangyuan</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>*</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1948360/overview"/>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing &#x2013; review &amp; editing" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing &#x2013; review &amp; editing</role>
<role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="conceptualization" vocab-term-identifier="https://credit.niso.org/contributor-roles/conceptualization/">Conceptualization</role>
</contrib>
</contrib-group>
<aff id="aff1"><label>1</label><institution>Department of Urology, Lu&#x2019;an Municipal People&#x2019;s Hospital, Lu&#x2019;an Hospital of Anhui Medical University</institution>, <city>Lu&#x2019;an</city>,&#xa0;<country country="cn">China</country></aff>
<aff id="aff2"><label>2</label><institution>Department of Urology, Anhui Provincial Children&#x2019;s Hospital</institution>, <city>Hefei</city>,&#xa0;<country country="cn">China</country></aff>
<aff id="aff3"><label>3</label><institution>Department of Urology, Children&#x2019;s Hospital of Fudan University</institution>, <city>Shanghai</city>,&#xa0;<country country="cn">China</country></aff>
<author-notes>
<corresp id="c001"><label>*</label>Correspondence: Jian Shen, <email xlink:href="mailto:shenjian_79@hotmail.com">shenjian_79@hotmail.com</email>; Guangyuan Li, <email xlink:href="mailto:liguangyuanc@163.com">liguangyuanc@163.com</email></corresp>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-03-04">
<day>04</day>
<month>03</month>
<year>2026</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2026</year>
</pub-date>
<volume>16</volume>
<elocation-id>1634710</elocation-id>
<history>
<date date-type="received">
<day>25</day>
<month>05</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>17</day>
<month>02</month>
<year>2026</year>
</date>
<date date-type="rev-recd">
<day>14</day>
<month>02</month>
<year>2026</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2026 Mao, Tao, Yang, Shen and Li.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Mao, Tao, Yang, Shen and Li</copyright-holder>
<license>
<ali:license_ref start_date="2026-03-04">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. 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.</license-p>
</license>
</permissions>
<abstract>
<sec>
<title>Background</title>
<p>Testicular cancer (TC) is the most common malignancy in young men, with incidence increasing globally, especially in high-income countries. Although survival has improved due to advances in diagnosis and treatment, disparities in TC burden remain. This study analyzes global, regional, and national trends in TC incidence, mortality, and disability-adjusted life years (DALYs) from 1990 to 2021, and projects future trends to 2035.</p>
</sec>
<sec>
<title>Methods</title>
<p>Data were obtained from the Global Burden of Disease (GBD) 2021 database. Incidence, mortality, and DALY rates per 100,000 population were calculated with 95% uncertainty intervals (UIs). Trend analysis used Joinpoint regression and estimated annual percentage change (EAPC). Decomposition analysis identified drivers of burden changes. A Bayesian age-period-cohort (BAPC) model projected future burden.</p>
</sec>
<sec>
<title>Results</title>
<p>In 2021, there were 91,507 TC cases, 11,388 deaths, and 560,921 DALYs globally. From 1990 to 2021, cases rose by 136%, deaths by 49%, and DALYs by 44%. Incidence increased from 1.45 to 2.31 per 100,000. The middle socio-demographic index (SDI) region showed the highest EAPCs for incidence (4.34%), mortality (1.07%), and DALYs (0.92%). The Caribbean had the fastest-growing incidence (EAPC = 5.71%). Nationally, the U.S. had the most cases (11,845), Monaco the highest incidence (32.89/100,000), and Qatar the steepest rise (EAPC = 10.25%). By 2035, incidence is projected to rise further, while mortality and DALY rates may decline.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>The global burden of TC has increased markedly since 1990, especially in middle-SDI regions and the Caribbean. Although some areas have seen improvements, rising incidence highlights the need for targeted prevention and optimized care strategies.</p>
</sec>
</abstract>
<kwd-group>
<kwd>age-period-cohort</kwd>
<kwd>Bayesian</kwd>
<kwd>epidemiology</kwd>
<kwd>Global Burden of Disease</kwd>
<kwd>testicular cancer</kwd>
<kwd>trend analysis</kwd>
</kwd-group>
<funding-group>
<award-group id="gs1">
<funding-source id="sp1">
<institution-wrap>
<institution>Anhui Province Key Laboratory of Medical Physics and Technology</institution>
<institution-id institution-id-type="doi" vocab="open-funder-registry" vocab-identifier="10.13039/open_funder_registry">10.13039/501100019396</institution-id>
</institution-wrap>
</funding-source>
</award-group>
<funding-statement>The author(s) declared that financial support was received for this work and/or its publication. This study was supported by the Anhui Provincial Health Research Program (AHWJ2024Aa20225).</funding-statement>
</funding-group>
<counts>
<fig-count count="8"/>
<table-count count="1"/>
<equation-count count="0"/>
<ref-count count="22"/>
<page-count count="14"/>
<word-count count="5697"/>
</counts>
<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Cancer Epidemiology and Prevention</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>Testicular cancer (TC), though relatively uncommon, represents the most frequent malignancy among adolescent and young adult (AYA) males (15&#x2013;40 years) globally (<xref ref-type="bibr" rid="B1">1</xref>). Although its prognosis is generally favorable, survivors face significant challenges, including an increased risk of infertility, sexual dysfunction, and other side effects of treatment (<xref ref-type="bibr" rid="B2">2</xref>). While comprising less than 1% of all male cancers, its incidence has been steadily rising, particularly in high-income countries, contrasting with lower rates in most Asian and African regions (<xref ref-type="bibr" rid="B3">3</xref>). Advances in diagnosis and platinum-based chemotherapy have significantly improved survival rates; however, the overall disease burden&#x2014;encompassing incidence, mortality, and disability-adjusted life years (DALYs)&#x2014;continues to escalate, posing considerable challenges for healthcare systems and policy development (<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B4">4</xref>).</p>
<p>Previous epidemiological research over recent decades has consistently documented an increasing TC incidence and marked spatiotemporal disparities across nations (<xref ref-type="bibr" rid="B5">5</xref>). Nonetheless, a comprehensive, up-to-date assessment of the global, regional, and national TC burden, especially one that dynamically analyzes trends in incidence, mortality, and DALYs while also forecasting future trajectories using the latest available data, remains a critical need. Existing studies often lack the granularity or the predictive component necessary to fully inform targeted public health interventions. This study addresses this gap by leveraging the Global Burden of Disease (GBD) 2021 database to systematically evaluate TC burden across 204 countries and territories from 1990 to 2021. Furthermore, employing a Bayesian Age-Period-Cohort (BAPC) model, we project these trends to 2035. Our objective is to furnish robust, evidence-based insights to guide the formulation and prioritization of global TC prevention and control strategies, thereby mitigating its growing public health impact, particularly in identified high-risk or rapidly changing regions.</p>
</sec>
<sec id="s2">
<label>2</label>
<title>Methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Data source and case definition</title>
<p>Data were extracted from the Global Burden of Disease (GBD) Study 2021, coordinated by the Institute for Health Metrics and Evaluation (IHME) (<xref ref-type="bibr" rid="B6">6</xref>). GBD 2021 provides systematic estimates of health loss for 371 diseases and injuries and 88 risk factors across 204 countries/territories and 21 GBD regions (<xref ref-type="bibr" rid="B6">6</xref>). Detailed GBD 2021 methodology and results are publicly available (<ext-link ext-link-type="uri" xlink:href="https://vizhub.healthdata.org/gbd-results/">https://vizhub.healthdata.org/gbd-results/</ext-link>).</p>
<p>Testicular cancer (TC) cases were identified using International Classification of Diseases (ICD) codes: ICD-10 (C62-C62.9, D29.2-D29.8, D40.1-D40.8) and ICD-9 (186-186.9, 222.0, 222.3, 236.4) (<xref ref-type="bibr" rid="B6">6</xref>). We retrieved annual TC incidence, mortality, and disability-adjusted life years (DALYs) for individuals aged 15 years and older from 1990 to 2021, as GBD data for TC in younger age groups were unavailable. Age subgroups (e.g., 15-19, 20-24, &#x2026;, &#x2265;75 years) were analyzed.</p>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Socio-demographic index</title>
<p>The SDI, a composite measure ranging from 0 (least developed) to 1 (most developed), was used to stratify countries and regions. It is based on per capita income, average years of education, and total fertility rate (<xref ref-type="bibr" rid="B7">7</xref>). GBD 2021 categorizes locations into five SDI quintiles: low, low-middle, middle, high-middle, and high (<xref ref-type="bibr" rid="B7">7</xref>). Further details on SDI calculation are provided in the <xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Materials</bold></xref>.</p>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Statistical analysis</title>
<p>We calculated TC incidence, mortality, and DALY rates per 100,000 population with 95% uncertainty intervals (UIs). To quantify temporal trends from 1990 to 2021, the estimated annual percentage change (EAPC) with its 95% confidence interval (CI) was calculated using a log-transformed linear regression model (ln(rate) = &#x3b1; + &#x3b2; &#xd7; year + &#x3f5;). An EAPC &gt; 0 indicates an increasing trend, EAPC &lt; 0 a decreasing trend, and EAPC &#x2248; 0 a stable trend. Joinpoint regression analysis (Joinpoint Regression Program, Version 4.9.1.0; National Cancer Institute) was used to identify significant changes in trends over time, calculating the annual percentage change (APC) for each segment and the average annual percentage change (AAPC) over the entire period.</p>
<p>Decomposition analysis, using the Das Gupta method, was performed to attribute changes in the absolute number of TC incident cases, deaths, and DALYs between 1990 and 2021 to three factors: population growth, population aging (changes in age structure), and epidemiological changes (changes in age-specific rates). Details of this method are available in prior GBD publications (<xref ref-type="bibr" rid="B6">6</xref>).</p>
<p>Future trends in TC incidence, deaths, and DALYs, along with their age-standardized rates, were projected up to 2035 using a Bayesian age-period-cohort (BAPC) model. This model, implemented using the BAPC R package, incorporates age, period, and cohort effects and utilizes Integrated Nested Laplace Approximation (INLA) for efficient computation, leveraging GBD 2021 population projections. All statistical analyses were performed using R software (version 4.3.3). Results were visualized using tables and maps. A two-sided p-value &lt; 0.05 was considered statistically significant.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Results</title>
<sec id="s3_1">
<label>3.1</label>
<title>Global burden and temporal trends of testicular cancer</title>
<p>Globally, the absolute number of TC incident cases surged by 135.64% (95% UI: 123.26-149.59), from 38,833 in 1990 to 91,507 in 2021. Concurrently, the incidence rate escalated by 59.84% (95% UI: 51.44-69.30), from 1.45 to 2.31 per 100,000 population (<xref ref-type="table" rid="T1"><bold>Table&#xa0;1</bold></xref>, <xref ref-type="fig" rid="f1"><bold>Figures&#xa0;1A, B</bold></xref>). Joinpoint analysis identified the most rapid increase in incidence rate between 2001 and 2019 (APC = 1.74%, 95% CI: 1.62-1.86) (<xref ref-type="fig" rid="f2"><bold>Figure&#xa0;2A</bold></xref>). The absolute number of deaths increased by 49.48% (95% UI: 36.41-63.63) to 11,388 in 2021. However, the mortality rate demonstrated a marginal overall increase of 1.40% (95% UI: -7.47-10.99) over the study period (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table&#xa0;S4</bold></xref>, <xref ref-type="fig" rid="f2"><bold>Figures&#xa0;2B</bold></xref>, <xref ref-type="fig" rid="f1"><bold>1C, D</bold></xref>). Similarly, DALYs rose by 43.89% (95% UI: 31.27-57.22) to 560,921 in 2021, while the DALY rate experienced a slight decrease of 2.39% (95% UI: -10.95-6.64) (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table&#xa0;S2</bold></xref>, <xref ref-type="fig" rid="f2"><bold>Figures&#xa0;2C</bold></xref>, <xref ref-type="fig" rid="f1"><bold>1E, F</bold></xref>).</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Incidence of testicular cancer between 1990 and 2021 at the global and regional level.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left"/>
<th valign="middle" colspan="7" align="left">Rate per 100 000 (95% UI)</th>
</tr>
<tr>
<th valign="middle" align="left"/>
<th valign="middle" colspan="2" align="left">1990</th>
<th valign="middle" colspan="2" align="left">2021</th>
<th valign="middle" colspan="3" align="left">1990-2021</th>
</tr>
<tr>
<th valign="middle" align="left">Location</th>
<th valign="middle" align="left">Incident cases</th>
<th valign="middle" align="left">Incident rate</th>
<th valign="middle" align="left">Incident cases</th>
<th valign="middle" align="left">Incident rate</th>
<th valign="middle" align="left">Cases change<sup>b</sup></th>
<th valign="middle" align="left">Rate change<sup>b</sup></th>
<th valign="middle" align="left">EAPC<sup>a</sup></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">Global</td>
<td valign="middle" align="left">38833.49 (37572.49, 40260.79)</td>
<td valign="middle" align="left">1.45 (1.40, 1.50)</td>
<td valign="middle" align="left">91507.38 (87965.92, 95709.72)</td>
<td valign="middle" align="left">2.31 (2.22, 2.42)</td>
<td valign="middle" align="left">135.64 (123.26, 149.59)</td>
<td valign="middle" align="left">59.84 (51.44, 69.30)</td>
<td valign="middle" align="left">1.65 (1.58, 1.72)</td>
</tr>
<tr>
<th valign="middle" colspan="8" align="left">SDI</th>
</tr>
<tr>
<td valign="middle" align="left">Low SDI</td>
<td valign="middle" align="left">609.62 (436.72, 784.06)</td>
<td valign="middle" align="left">0.24 (0.17, 0.31)</td>
<td valign="middle" align="left">2199.93 (1796.68, 2609.39)</td>
<td valign="middle" align="left">0.39 (0.32, 0.47)</td>
<td valign="middle" align="left">260.87 (188.30, 360.87)</td>
<td valign="middle" align="left">62.86 (30.11, 108.00)</td>
<td valign="middle" align="left">1.47 (1.20, 1.74)</td>
</tr>
<tr>
<td valign="middle" align="left">Low-middle SDI</td>
<td valign="middle" align="left">1990.02 (1665.84, 2338.13)</td>
<td valign="middle" align="left">0.34 (0.28, 0.40)</td>
<td valign="middle" align="left">7520.04 (6729.04, 8437.75)</td>
<td valign="middle" align="left">0.78 (0.70, 0.87)</td>
<td valign="middle" align="left">277.89 (204.48, 379.72)</td>
<td valign="middle" align="left">131.36 (86.42, 193.70)</td>
<td valign="middle" align="left">2.87 (2.69, 3.06)</td>
</tr>
<tr>
<td valign="middle" align="left">Middle SDI</td>
<td valign="middle" align="left">4189.41 (3960.79, 4443.02)</td>
<td valign="middle" align="left">0.48 (0.45, 0.51)</td>
<td valign="middle" align="left">20757.02 (19265.04, 22330.50)</td>
<td valign="middle" align="left">1.68 (1.56, 1.81)</td>
<td valign="middle" align="left">395.46 (350.44, 442.16)</td>
<td valign="middle" align="left">252.38 (220.36, 285.59)</td>
<td valign="middle" align="left">4.34 (4.20, 4.48)</td>
</tr>
<tr>
<td valign="middle" align="left">High-middle SDI</td>
<td valign="middle" align="left">9724.91 (9159.69, 10359.35)</td>
<td valign="middle" align="left">1.84 (1.73, 1.96)</td>
<td valign="middle" align="left">26679.38 (24613.76, 28913.10)</td>
<td valign="middle" align="left">4.09 (3.77, 4.43)</td>
<td valign="middle" align="left">174.34 (144.66, 203.20)</td>
<td valign="middle" align="left">122.50 (98.43, 145.91)</td>
<td valign="middle" align="left">2.83 (2.71, 2.95)</td>
</tr>
<tr>
<td valign="middle" align="left">High SDI</td>
<td valign="middle" align="left">22263.58 (21595.73, 22992.35)</td>
<td valign="middle" align="left">5.14 (4.98, 5.31)</td>
<td valign="middle" align="left">34233.78 (32897.38, 35658.51)</td>
<td valign="middle" align="left">6.27 (6.03, 6.53)</td>
<td valign="middle" align="left">53.77 (46.64, 61.55)</td>
<td valign="middle" align="left">22.12 (16.47, 28.30)</td>
<td valign="middle" align="left">0.72 (0.55, 0.89)</td>
</tr>
<tr>
<th valign="middle" colspan="8" align="left">Regions</th>
</tr>
<tr>
<td valign="middle" align="left">Andean Latin America</td>
<td valign="middle" align="left">138.33 (102.68, 180.94)</td>
<td valign="middle" align="left">0.73 (0.54, 0.96)</td>
<td valign="middle" align="left">958.52 (728.76, 1235.76)</td>
<td valign="middle" align="left">2.89 (2.20, 3.73)</td>
<td valign="middle" align="left">592.93 (373.06, 936.51)</td>
<td valign="middle" align="left">294.95 (169.63, 490.79)</td>
<td valign="middle" align="left">4.68 (4.10, 5.27)</td>
</tr>
<tr>
<td valign="middle" align="left">Australasia</td>
<td valign="middle" align="left">739.65 (660.44, 831.07)</td>
<td valign="middle" align="left">7.34 (6.56, 8.25)</td>
<td valign="middle" align="left">1262.00 (1089.18, 1443.75)</td>
<td valign="middle" align="left">8.23 (7.11, 9.42)</td>
<td valign="middle" align="left">70.62 (42.11, 100.30)</td>
<td valign="middle" align="left">12.12 (-6.62, 31.62)</td>
<td valign="middle" align="left">0.11 (-0.24, 0.47)</td>
</tr>
<tr>
<td valign="middle" align="left">Caribbean</td>
<td valign="middle" align="left">32.78 (29.20, 36.78)</td>
<td valign="middle" align="left">0.19 (0.17, 0.21)</td>
<td valign="middle" align="left">315.64 (265.93, 369.00)</td>
<td valign="middle" align="left">1.34 (1.13, 1.57)</td>
<td valign="middle" align="left">862.85 (681.74, 1084.23)</td>
<td valign="middle" align="left">614.78 (480.33, 779.13)</td>
<td valign="middle" align="left">5.71 (4.50, 6.93)</td>
</tr>
<tr>
<td valign="middle" align="left">Central Asia</td>
<td valign="middle" align="left">305.10 (254.06, 373.65)</td>
<td valign="middle" align="left">0.90 (0.75, 1.10)</td>
<td valign="middle" align="left">305.10 (254.06, 373.65)</td>
<td valign="middle" align="left">1.70 (1.44, 2.04)</td>
<td valign="middle" align="left">164.46 (102.25, 242.96)</td>
<td valign="middle" align="left">88.80 (44.38, 144.84)</td>
<td valign="middle" align="left">2.02 (1.62, 2.41)</td>
</tr>
<tr>
<td valign="middle" align="left">Central Europe</td>
<td valign="middle" align="left">2597.05 (2425.10, 2840.76)</td>
<td valign="middle" align="left">4.24 (3.96, 4.64)</td>
<td valign="middle" align="left">5100.45 (4614.65, 5688.74)</td>
<td valign="middle" align="left">9.08 (8.22, 10.13)</td>
<td valign="middle" align="left">96.39 (74.48, 121.36)</td>
<td valign="middle" align="left">114.22 (90.32, 141.45)</td>
<td valign="middle" align="left">2.88 (2.69, 3.07)</td>
</tr>
<tr>
<td valign="middle" align="left">Central Latin America</td>
<td valign="middle" align="left">937.37 (893.22, 980.34)</td>
<td valign="middle" align="left">1.15 (1.10, 1.21)</td>
<td valign="middle" align="left">6322.08 (5745.31, 6925.56)</td>
<td valign="middle" align="left">5.13 (4.66, 5.62)</td>
<td valign="middle" align="left">574.45 (504.04, 646.54)</td>
<td valign="middle" align="left">343.93 (297.59, 391.38)</td>
<td valign="middle" align="left">5.14 (4.96, 5.33)</td>
</tr>
<tr>
<td valign="middle" align="left">Central Sub-Saharan Africa</td>
<td valign="middle" align="left">52.78(36.76,70.06)</td>
<td valign="middle" align="left">0.19(0.13,0.26)</td>
<td valign="middle" align="left">224.12(154.00,311.24)</td>
<td valign="middle" align="left">0.33(0.23,0.46)</td>
<td valign="middle" align="left">324.65(185.86,518.98)</td>
<td valign="middle" align="left">69.22(13.92,146.67)</td>
<td valign="middle" align="left">1.76(1.46,2.07)</td>
</tr>
<tr>
<td valign="middle" align="left">East Asia</td>
<td valign="middle" align="left">1996.35 (1669.71, 2341.07)</td>
<td valign="middle" align="left">0.32 (0.27, 0.37)</td>
<td valign="middle" align="left">7089.73 (5597.16, 9024.83)</td>
<td valign="middle" align="left">0.94 (0.74, 1.20)</td>
<td valign="middle" align="left">255.13 (160.07, 377.49)</td>
<td valign="middle" align="left">195.77 (116.60, 297.67)</td>
<td valign="middle" align="left">3.41 (3.20, 3.63)</td>
</tr>
<tr>
<td valign="middle" align="left">Eastern Europe</td>
<td valign="middle" align="left">2373.13(2183.45,2546.77)</td>
<td valign="middle" align="left">2.24(2.06,2.41)</td>
<td valign="middle" align="left">4538.16(4150.89,4897.50)</td>
<td valign="middle" align="left">4.72(4.32,5.09)</td>
<td valign="middle" align="left">91.23(69.31,116.80)</td>
<td valign="middle" align="left">110.45(86.33,138.59)</td>
<td valign="middle" align="left">2.26(1.98,2.53)</td>
</tr>
<tr>
<td valign="middle" align="left">Eastern Sub-Saharan Africa</td>
<td valign="middle" align="left">219.20(155.08,283.17)</td>
<td valign="middle" align="left">0.23(0.16,0.30)</td>
<td valign="middle" align="left">1067.40(839.18,1323.32)</td>
<td valign="middle" align="left">0.51(0.40,0.63)</td>
<td valign="middle" align="left">386.96(268.43,554.78)</td>
<td valign="middle" align="left">118.16(65.06,193.34)</td>
<td valign="middle" align="left">2.57(2.25,2.88)</td>
</tr>
<tr>
<td valign="middle" align="left">High-income Asia Pacific</td>
<td valign="middle" align="left">2392.32(2132.80,2701.63)</td>
<td valign="middle" align="left">2.79(2.49,3.15)</td>
<td valign="middle" align="left">2862.05(2619.33,3130.41)</td>
<td valign="middle" align="left">3.14(2.87,3.43)</td>
<td valign="middle" align="left">19.63(3.48,38.36)</td>
<td valign="middle" align="left">12.43(-2.76,30.02)</td>
<td valign="middle" align="left">0.12(-0.43,0.68)</td>
</tr>
<tr>
<td valign="middle" align="left">High-income North America</td>
<td valign="middle" align="left">7762.94(7494.19,8046.56)</td>
<td valign="middle" align="left">5.64(5.45,5.85)</td>
<td valign="middle" align="left">13696.00(13111.72,14330.17)</td>
<td valign="middle" align="left">7.53(7.20,7.87)</td>
<td valign="middle" align="left">76.43(67.22,85.72)</td>
<td valign="middle" align="left">33.37(26.40,40.39)</td>
<td valign="middle" align="left">0.94(0.84,1.04)</td>
</tr>
<tr>
<td valign="middle" align="left">North Africa and Middle East</td>
<td valign="middle" align="left">1709.82(1334.94,2164.81)</td>
<td valign="middle" align="left">0.98(0.77,1.25)</td>
<td valign="middle" align="left">12009.08(10179.89,14045.69)</td>
<td valign="middle" align="left">3.71(3.15,4.34)</td>
<td valign="middle" align="left">602.36(423.16,844.01)</td>
<td valign="middle" align="left">277.35(181.07,407.19)</td>
<td valign="middle" align="left">4.95(4.62,5.29)</td>
</tr>
<tr>
<td valign="middle" align="left">Oceania</td>
<td valign="middle" align="left">6.66(4.81,8.85)</td>
<td valign="middle" align="left">0.20(0.14,0.26)</td>
<td valign="middle" align="left">16.23(12.95,20.40)</td>
<td valign="middle" align="left">0.23(0.18,0.28)</td>
<td valign="middle" align="left">143.70(79.92,221.75)</td>
<td valign="middle" align="left">14.96(-15.12,51.78)</td>
<td valign="middle" align="left">0.49(0.28,0.71)</td>
</tr>
<tr>
<td valign="middle" align="left">South Asia</td>
<td valign="middle" align="left">2475.63(2042.23,2924.54)</td>
<td valign="middle" align="left">0.44(0.36,0.51)</td>
<td valign="middle" align="left">8348.89(7202.42,9574.41)</td>
<td valign="middle" align="left">0.89(0.77,1.02)</td>
<td valign="middle" align="left">237.24(162.63,338.05)</td>
<td valign="middle" align="left">103.68(58.61,164.56)</td>
<td valign="middle" align="left">2.34(2.02,2.65)</td>
</tr>
<tr>
<td valign="middle" align="left">Southeast Asia</td>
<td valign="middle" align="left">782.98(690.70,898.61)</td>
<td valign="middle" align="left">0.34(0.30,0.39)</td>
<td valign="middle" align="left">3181.29(2620.61,3811.10)</td>
<td valign="middle" align="left">0.91(0.75,1.09)</td>
<td valign="middle" align="left">306.31(225.75,390.02)</td>
<td valign="middle" align="left">168.68(115.41,224.03)</td>
<td valign="middle" align="left">3.09(3.01,3.17)</td>
</tr>
<tr>
<td valign="middle" align="left">Southern Latin America</td>
<td valign="middle" align="left">858.17(736.35,1001.49)</td>
<td valign="middle" align="left">3.54(3.04,4.13)</td>
<td valign="middle" align="left">3566.05(3111.04,4086.60)</td>
<td valign="middle" align="left">10.78(9.41,12.36)</td>
<td valign="middle" align="left">315.54(248.39,406.28)</td>
<td valign="middle" align="left">204.80(155.55,271.36)</td>
<td valign="middle" align="left">3.78(3.42,4.14)</td>
</tr>
<tr>
<td valign="middle" align="left">Southern Sub-Saharan Africa</td>
<td valign="middle" align="left">109.91(90.18,128.64)</td>
<td valign="middle" align="left">0.43(0.35,0.51)</td>
<td valign="middle" align="left">308.42(266.28,355.96)</td>
<td valign="middle" align="left">0.79(0.68,0.91)</td>
<td valign="middle" align="left">180.61(129.32,254.32)</td>
<td valign="middle" align="left">81.98(48.71,129.78)</td>
<td valign="middle" align="left">2.11(1.92,2.29)</td>
</tr>
<tr>
<td valign="middle" align="left">Tropical Latin America</td>
<td valign="middle" align="left">545.24(510.09,586.26)</td>
<td valign="middle" align="left">0.72(0.68,0.78)</td>
<td valign="middle" align="left">2906.86(2645.15,3161.16)</td>
<td valign="middle" align="left">2.61(2.38,2.84)</td>
<td valign="middle" align="left">433.13(379.16,494.08)</td>
<td valign="middle" align="left">261.74(225.12,303.09)</td>
<td valign="middle" align="left">4.42(4.32,4.53)</td>
</tr>
<tr>
<td valign="middle" align="left">Western Europe</td>
<td valign="middle" align="left">12684.73 (12105.15, 13313.37)</td>
<td valign="middle" align="left">6.78 (6.47, 7.11)</td>
<td valign="middle" align="left">16570.80 (15453.79, 17835.56)</td>
<td valign="middle" align="left">7.71 (7.19, 8.30)</td>
<td valign="middle" align="left">30.64 (20.22, 42.63)</td>
<td valign="middle" align="left">13.78 (4.71, 24.23)</td>
<td valign="middle" align="left">0.73 (0.46, 1.00)</td>
</tr>
<tr>
<td valign="middle" align="left">Western Sub-Saharan Africa</td>
<td valign="middle" align="left">113.35(91.52,135.81)</td>
<td valign="middle" align="left">0.12(0.10,0.14)</td>
<td valign="middle" align="left">356.72(275.74,444.81)</td>
<td valign="middle" align="left">0.15(0.12,0.19)</td>
<td valign="middle" align="left">214.71(145.14,310.16)</td>
<td valign="middle" align="left">26.38(-1.56,64.71)</td>
<td valign="middle" align="left">0.41(0.21,0.61)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>EAPC, estimated annual percentage change; SDI, sociodemographic Index; UI, uncertainty interval.</p></fn>
<fn>
<p>a EAPC is expressed as 95% confidence interval. b Change shows the percentage change.</p></fn>
</table-wrap-foot>
</table-wrap>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Trends in testicular cancer from 1990 to 2021 at the global level and across five SDI regions. <bold>(A)</bold> Number of incidence cases. <bold>(B)</bold> Incidence rate. <bold>(C)</bold> Number of death cases. <bold>(D)</bold> Mortality rate. <bold>(E)</bold> Number of DALYs. <bold>(F)</bold> DALYs rate.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-16-1634710-g001.tif">
<alt-text content-type="machine-generated">Six line graphs labeled A&#x2013;F illustrate global trends in testicular cancer epidemiological measures from 1990 to 2021. Panels A, C, and E display the absolute numbers of incidence, deaths, and disability-adjusted life years (DALYs), respectively. Panels B, D, and F present the corresponding rates. Each graph includes multiple colored lines representing global estimates and five socio-demographic index (SDI) categories: high, high-middle, middle, low-middle, and low SDI regions. The horizontal axis shows calendar years from 1990 to 2021, and the vertical axis represents counts or rates depending on the panel. The figures depict temporal trends and variation across SDI groups over the study period.</alt-text>
</graphic></fig>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>APC and AAPC in the global incidence rate, mortality rate, and DALYs rate of testicular cancer from 1990 to 2021. <bold>(A)</bold> Incidence rate. <bold>(B)</bold> Mortality rate. <bold>(C)</bold> DALYs rate.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-16-1634710-g002.tif">
<alt-text content-type="machine-generated">Three line charts labeled A, B, and C display trends from 1990 to 2021, each with segmented trend lines indicating different periods and annual percentage changes (APC) with confidence intervals. Chart A shows incidence increasing until 2019, then declining, with overall annual average percent change (AAPC) slightly positive. Chart B depicts death rates declining, then increasing after 2009, followed by a slight decline, with overall AAPC stable. Chart C presents disability-adjusted life years (DALYs) rates declining, then rising after 2009, with overall AAPC close to zero. Axis labels for each chart are time (x-axis) and corresponding measure (y-axis).</alt-text>
</graphic></fig>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Stark regional and national disparities in TC burden</title>
<p>Significant heterogeneity in TC burden and its temporal trends was evident across SDI regions, GBD regions, and individual nations (<xref ref-type="table" rid="T1"><bold>Table&#xa0;1</bold></xref>, <xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Tables&#xa0;S2</bold></xref>, <xref ref-type="supplementary-material" rid="SM1"><bold>S4</bold></xref>, <xref ref-type="fig" rid="f3"><bold>Figures&#xa0;3</bold></xref>&#x2013;<xref ref-type="fig" rid="f5"><bold>5</bold></xref>).</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Testicular cancer incidence in 204 countries and territories in 2021. <bold>(A)</bold> Number of incidence cases. <bold>(B)</bold> Incidence rate. <bold>(C)</bold> EAPC in incidence rate from 1990 to 2021.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-16-1634710-g003.tif">
<alt-text content-type="machine-generated">Three panels of colored world maps display global data: Panel A shows raw incidence cases by country, Panel B shows incidence rates, and Panel C shows estimated annual percent change (EAPC) in incidence. Insets highlight regional details for the Caribbean and Central America, Persian Gulf, Balkan Peninsula, Southeast Asia, West Africa, Eastern Mediterranean, and Northern Europe. Color gradients in each panel represent varying levels, with legends provided for interpretation.</alt-text>
</graphic></fig>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>EAPC changes in testicular cancer incidence rate, mortality rate, and DALYs rate from 1990 to 2021 at the global level, across five SDI regions, and in 21 GBD regions. <bold>(A)</bold> Incidence rate. <bold>(B)</bold> Mortality rate. <bold>(C)</bold> DALYs rate.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-16-1634710-g004.tif">
<alt-text content-type="machine-generated">Bar graph with three panels labeled A, B, and C, each showing regions on the x-axis and EAPC values on the y-axis, with most regions displaying positive EAPC values for Caribbean and Latin American regions and negative or near-zero values for high-income regions and Australasia; error bars denote statistical uncertainty.</alt-text>
</graphic></fig>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>The relationship between testicular cancer incidence rate, mortality rate, and DALYs rate with SDI. <bold>(A)</bold> Global and regional testicular cancer incidence rates from 1990 to 2021. <bold>(B)</bold> Testicular cancer incidence rates in 204 countries in 2021. <bold>(C)</bold> Global and regional testicular cancer mortality rates from 1990 to 2021. <bold>(D)</bold> Testicular cancer mortality rates in 204 countries in 2021. <bold>(E)</bold> Global and regional testicular cancer DALYs rates from 1990 to 2021. <bold>(F)</bold> Testicular cancer DALYs rates in 204 countries in 2021.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-16-1634710-g005.tif">
<alt-text content-type="machine-generated">Six panels labeled A&#x2013;F illustrate the relationship between testicular cancer burden and the socio-demographic index (SDI). Panels A, C, and E display line graphs showing global and regional trends in incidence rates, mortality rates, and DALYs rates from 1990 to 2021, respectively, with separate colored lines representing different SDI regions. Panels B, D, and F present scatter plots of incidence, mortality, and DALYs rates across 204 countries in 2021 plotted against SDI values. Each point represents a country, and fitted trend lines indicate the overall association between SDI and the corresponding rate. Axes are labeled according to rate type and SDI values.</alt-text>
</graphic></fig>
<p>Incidence Hotspots and Fastest Increases: Middle-SDI regions bore the brunt of the increasing TC burden, exhibiting the highest EAPC in incidence rate (4.34%, 95% CI: 4.20-4.48). Among GBD regions, the Caribbean displayed the most accelerated rise in incidence rate (EAPC = 5.71%, 95% CI: 4.50-6.93). Nationally, while the USA recorded the highest absolute number of cases in 2021 (11,845), Monaco registered the highest incidence rate (32.89 per 100,000). Qatar demonstrated the most dramatic surge in incidence rate from 1990 to 2021 (EAPC = 10.25, 95% CI: 8.74-11.79) (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table&#xa0;S1</bold></xref>, <xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3</bold></xref>).</p>
<p>Divergent Mortality and DALY Trends: High-SDI regions achieved notable reductions in mortality rate (EAPC = -1.11%) and DALY rate (EAPC = -1.24%). In stark contrast, middle-SDI regions faced the most substantial increases in both mortality rate (EAPC = 1.07%) and DALY rate (EAPC = 0.92%). The Caribbean also led GBD regions in mortality rate (EAPC = 4.30%) and DALY rate (EAPC = 3.65%) escalations. India reported the highest absolute number of TC deaths and DALYs in 2021. Georgia and Chile presented the highest mortality rate and DALY rate at the national level, respectively (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Tables&#xa0;S3</bold></xref>, <xref ref-type="supplementary-material" rid="SM1"><bold>S5</bold></xref>, <xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Figures&#xa0;S1</bold></xref>, <xref ref-type="supplementary-material" rid="SM1"><bold>S2</bold></xref>, <xref ref-type="supplementary-material" rid="SM1"><bold>S4</bold></xref>).</p>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>Age-specific patterns: a young man&#x2019;s disease</title>
<p>TC incidence rose across all analyzed age groups between 1990 and 2021, with the 20&#x2013;24 year age cohort experiencing the largest percentage increase (68.77%). The incidence peak in 2021 was observed in the 25&#x2013;29 year age group (ASIR 5.58 per 100,000) (<xref ref-type="fig" rid="f6"><bold>Figure&#xa0;6A</bold></xref>, <xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Figure&#xa0;3A</bold></xref>, <xref ref-type="supplementary-material" rid="SM1"><bold>S5B</bold></xref>). Mortality rates generally declined across most age groups, with the exception of a slight increase in the 20&#x2013;24 year cohort (3.03%). The highest mortality burden consistently remained among men aged &#x2265;75 years (<xref ref-type="fig" rid="f6"><bold>Figure&#xa0;6B</bold></xref>, <xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Figure&#xa0;S3B</bold></xref>, <xref ref-type="supplementary-material" rid="SM1"><bold>S5D</bold></xref>). DALY patterns mirrored these age-specific trends (<xref ref-type="fig" rid="f6"><bold>Figure&#xa0;6C</bold></xref>, <xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Figure&#xa0;S3C</bold></xref>, <xref ref-type="supplementary-material" rid="SM1"><bold>S5F</bold></xref>).</p>
<fig id="f6" position="float">
<label>Figure&#xa0;6</label>
<caption>
<p>Age-specific percentage of testicular cancer incidence rate, mortality rate, and DALYs rate in 2021. <bold>(A)</bold> Incidence rate. <bold>(B)</bold> Mortality rate. <bold>(C)</bold> DALYs rate.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-16-1634710-g006.tif">
<alt-text content-type="machine-generated">Panel A, B, and C each display a horizontal stacked bar chart comparing the age distribution (covering 15&#x2013;19 to 75+ years) of three different global health metrics for 2021 across various world regions and sociodemographic index groupings, with each age group color-coded. Panel labels A, B, and C distinguish the charts, and a color key on the right clarifies age group assignments. Each bar is segmented by percentage proportions, with region names labeled along the vertical axis and proportions along the horizontal axis.</alt-text>
</graphic></fig>
</sec>
<sec id="s3_4">
<label>3.4</label>
<title>Drivers of burden change: population dynamics and epidemiological shifts</title>
<p>Decomposition analysis revealed that, globally, epidemiological changes (contributing 47.79%) and population growth (45.34%) were the predominant drivers of the increase in absolute TC incident cases from 1990 to 2021. Population aging played a lesser role (6.87%). For mortality and DALYs, population growth was a key factor increasing absolute numbers, whereas epidemiological shifts (likely reflecting improved survival due to treatment advances) contributed to a decrease in age-standardized rates in many regions (<xref ref-type="fig" rid="f7"><bold>Figure&#xa0;7</bold></xref>, <xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Tables S6-S8</bold></xref>).</p>
<fig id="f7" position="float">
<label>Figure&#xa0;7</label>
<caption>
<p>Combined effects of population aging, population growth, and epidemiological changes on testicular cancer from 1990 to 2021 at the global level, across five SDI regions, and in 21 GBD regions. The black dots represent the total change contributed by the three components. A positive value for each component indicates a positive contribution, while a negative value indicates a negative contribution. <bold>(A)</bold> Number of incidence cases. <bold>(B)</bold> Number of death cases. <bold>(C)</bold> Number of DALYs cases.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-16-1634710-g007.tif">
<alt-text content-type="machine-generated">Grouped horizontal bar charts comparing the effects of aging, epidemiological change, and population on male cancer incidence, deaths, and disability-adjusted life years (DALYs) globally and by region. Each panel: A shows incidence, B shows deaths, and C shows DALYs, with bars for high, middle, low sociodemographic index (SDI) and regional breakdowns. Bars are color-coded: red for aging, light blue for epidemiological change, and green for population, with incidence, deaths, and DALYs as horizontal axes. Black dots are overlaid to indicate additional comparisons.</alt-text>
</graphic></fig>
</sec>
<sec id="s3_5">
<label>3.5</label>
<title>Future outlook: projections to 2035</title>
<p>The BAPC model forecasts a continued escalation in the global number of TC incident cases, projected to reach 126,020 (95% UI: 109,145-142,894) by 2035, with the age-standardized incidence rate (ASIR) anticipated to rise to 4.05 per 100,000. Absolute deaths and DALYs are also projected to increase. However, age-standardized mortality rate (ASMR) and age-standardized DALY rate (ASDR) are forecasted to exhibit a slight downward trend globally by 2035 (<xref ref-type="fig" rid="f8"><bold>Figure&#xa0;8</bold></xref>), suggesting ongoing, albeit modest, improvements in TC management relative to population changes and incidence increases.</p>
<fig id="f8" position="float">
<label>Figure&#xa0;8</label>
<caption>
<p>BAPC model projections of testicular cancer incidence, deaths, and DALYs along with their corresponding age-standardized rates through 2035. <bold>(A)</bold> Number of incidence cases. <bold>(B)</bold> Age-standardized incidence rate. <bold>(C)</bold> Number of death cases. <bold>(D)</bold> Age-standardized mortality rate. <bold>(E)</bold> Number of DALYs. <bold>(F)</bold> Age-standardized DALYs rate.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-16-1634710-g008.tif">
<alt-text content-type="machine-generated">Six panels labeled A&#x2013;F present Bayesian age&#x2013;period&#x2013;cohort (BAPC) model projections of testicular cancer burden through 2035. Panels A, C, and E show projected numbers of incidence cases, deaths, and disability-adjusted life years (DALYs), respectively. Panels B, D, and F display the corresponding age-standardized incidence, mortality, and DALYs rates. Each panel includes a solid line representing observed data and a dashed extension indicating projected estimates beyond the most recent observed year. Shaded areas around the projection lines represent uncertainty intervals that widen over time. The horizontal axis shows calendar years, extending into the future projection period, and the vertical axis represents counts or age-standardized rates depending on the panel.</alt-text>
</graphic></fig>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<sec id="s4_1">
<label>4.1</label>
<title>Global trends of testicular cancer</title>
<p>TC is the most common malignancy among young men aged 15 to 40 years. Considering the critical role of this age group in population growth, effective control of TC is not only essential for patient health but may also have implications for future population trends. Therefore, this study comprehensively analyzed the global epidemiological patterns of TC from 1990 to 2021 and projected trends to 2035, providing data support for the development of targeted prevention and control strategies.</p>
<p>From a global perspective, the incidence of TC increased steadily from 1990 to 2021, with a particularly pronounced rise between 2001 and 2019 (APC = 1.74%). This trend may be associated with the combined effects of demographic changes, improvements in healthcare systems, and established etiological risk factors. Global population growth and the expansion of the young adult male population&#x2014;the age group at highest risk for TC&#x2014;have contributed to the increasing number of incident cases, as demonstrated by our decomposition analysis (<xref ref-type="bibr" rid="B3">3</xref>). Concurrently, advances in diagnostic capacity, including the widespread use of imaging modalities and serum biomarkers (AFP, hCG, and LDH), have improved case ascertainment and early diagnosis (<xref ref-type="bibr" rid="B8">8</xref>). In addition to healthcare-related factors, well-established etiological determinants such as cryptorchidism, genetic susceptibility, and <italic>in utero</italic> hormonal influences remain central to TC development and may partly explain long-term incidence patterns (<xref ref-type="bibr" rid="B9">9</xref>). Lifestyle-related factors, including obesity, smoking, and alcohol consumption, have also been associated with increased TC risk (<xref ref-type="bibr" rid="B10">10</xref>). Environmental exposures, particularly endocrine-disrupting chemicals (EDCs) such as plasticizers and pesticides, have been proposed as potential contributors by interfering with endocrine development during fetal life, thereby increasing the risk of developing TC (<xref ref-type="bibr" rid="B11">11</xref>, <xref ref-type="bibr" rid="B12">12</xref>).</p>
<p>Over the past three decades, TC mortality has shown an overall pattern of initial decline followed by a modest increase, with a peak in 2019. Although the absolute number of TC-related deaths increased by 49.5%, the age-standardized mortality rate rose only slightly (1.4%; EAPC = 0.01%), while the DALYs rate decreased by 2.4%, reflecting substantial improvements in disease management. Advances in treatment, particularly the widespread use of platinum-based chemotherapy (e.g., cisplatin), have markedly improved survival in advanced TC (<xref ref-type="bibr" rid="B13">13</xref>). In addition, multidisciplinary treatment strategies, including surgery (such as retroperitoneal lymph node dissection) and radiotherapy, have further contributed to mortality reduction (<xref ref-type="bibr" rid="B14">14</xref>). Mortality trends differed across age groups, with declines observed in most groups except those aged 20&#x2013;24 years, and the greatest reduction seen among individuals aged 60&#x2013;64 years. This pattern may partly reflect lower exposure to TC-specific risk factors at older ages and the influence of competing causes of death, which can affect mortality attribution (<xref ref-type="bibr" rid="B15">15</xref>). The mortality peak observed in 2019 may be associated with delayed diagnosis, suboptimal treatment adherence, and unequal access to healthcare services (<xref ref-type="bibr" rid="B16">16</xref>). Regional disparities in medical resource availability, particularly in low- and middle-income countries, likely further contribute to fluctuations in TC mortality (<xref ref-type="bibr" rid="B17">17</xref>).</p>
</sec>
<sec id="s4_2">
<label>4.2</label>
<title>Regional disparities by SDI and GBD regions</title>
<p>From the perspectives of SDI and GBD regions, marked regional disparities in TC incidence and mortality were observed over the past three decades. High-SDI regions, including Western Europe and North America, accounted for the largest number of TC cases (34,234 in 2021), but exhibited relatively modest growth (53.8%) compared with middle-SDI regions (252.4%; EAPC = 4.34%) and the Caribbean (614.8%; EAPC = 5.71%). These differences likely reflect the combined effects of genetic susceptibility, healthcare capacity, and socioeconomic development. Racial and ethnic variation contributes substantially to incidence differences. In the United States, non-Hispanic White men have the highest TC incidence (6.63 per 100,000 men per year), approximately five times that of non-Hispanic Black men (1.27 per 100,000 men per year) (<xref ref-type="bibr" rid="B11">11</xref>). Genetic susceptibility and family history are well-established TC risk factors, and population-level differences in genetic background may partly explain these patterns. In high-income regions, more complete cancer registries and widespread use of ultrasound and serum biomarkers have improved case detection and early diagnosis, contributing to higher reported incidence (<xref ref-type="bibr" rid="B3">3</xref>). In contrast, the rapid increase in incidence observed in middle-SDI regions and the Caribbean is more likely related to changing risk profiles accompanying urbanization and socioeconomic transition. Shifts toward &#x201c;Westernized&#x201d; lifestyles, including dietary changes, increased alcohol consumption, and tobacco or marijuana use, have been associated with elevated TC risk (<xref ref-type="bibr" rid="B11">11</xref>, <xref ref-type="bibr" rid="B18">18</xref>). Environmental exposures, such as endocrine-disrupting chemicals, have also been proposed as potential contributors, although current evidence remains limited (<xref ref-type="bibr" rid="B11">11</xref>). Substantial regional differences were also evident in TC mortality and DALYs. High-SDI and upper-middle-SDI regions experienced significant declines in mortality, particularly in high-SDI regions (EAPC = &#x2212;1.11%), reflecting early detection, higher public health awareness, and access to effective treatments, including platinum-based chemotherapy and multidisciplinary care (<xref ref-type="bibr" rid="B19">19</xref>). In contrast, mortality continued to rise in low- and middle-SDI regions (middle-SDI regions: EAPC = 1.07%), likely due to delayed diagnosis, limited diagnostic and treatment capacity, and barriers to healthcare access (<xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B21">21</xref>). The Caribbean region showed the greatest increase in mortality (EAPC = 4.30%), underscoring severe constraints in healthcare resources. Consistent patterns were observed for DALYs, with reductions in high-SDI regions (EAPC = &#x2212;1.24%) and increases in middle-SDI regions (EAPC = 0.92%). Differences in healthcare infrastructure, diagnostic availability, and cancer registry completeness likely contribute to cross-national variation and should be considered when interpreting regional comparisons.</p>
</sec>
<sec id="s4_3">
<label>4.3</label>
<title>National-level patterns and implications</title>
<p>At the national level, TC incidence, mortality, and DALYs varied substantially, reflecting differences in population size, healthcare capacity, and disease management. Countries with the highest TC burden were mainly the United States, China, Turkey, and India. In 2021, the United States reported the largest number of TC cases worldwide (11,845 cases), likely related to its large population, comprehensive cancer registry systems, and high diagnostic coverage (<xref ref-type="bibr" rid="B4">4</xref>). The high prevalence of obesity among men in the United States may further contribute to TC risk (<xref ref-type="bibr" rid="B19">19</xref>). Several countries, including Qatar, Belize, Ecuador, and Grenada, showed particularly rapid increases in incidence. These trends may be associated with recent socioeconomic transitions, population mobility, and changes in lifestyle and environmental exposures. In terms of mortality, India recorded the highest number of TC deaths in 2021 (1,823 deaths), which may reflect its large population base, regional disparities in healthcare access, and a higher proportion of late-stage diagnoses (<xref ref-type="bibr" rid="B20">20</xref>). Notably, Belize experienced marked increases in both mortality and DALYs (EAPC: 6.97 and 6.92, respectively), indicating substantial gaps in TC surveillance, early detection, and treatment capacity. Overall, national-level heterogeneity highlights the strong influence of socioeconomic conditions, healthcare resource allocation, and health awareness on TC burden. These findings underscore the need for context-specific prevention, early detection, and treatment strategies to reduce disparities and effectively mitigate TC burden across countries.</p>
</sec>
<sec id="s4_4">
<label>4.4</label>
<title>Decomposition analysis and future projections</title>
<p>Our decomposition analysis highlights population growth and epidemiological changes as the primary drivers of the increasing global burden of TC. The contribution of epidemiological changes likely reflects shifts in environmental exposures, genetic susceptibility, and lifestyle factors within populations (<xref ref-type="bibr" rid="B21">21</xref>). In contrast, population aging appears to mitigate TC burden in some regions, particularly in high-income countries, as TC incidence predominantly peaks among young men aged 15&#x2013;40 years, while older age groups exhibit substantially lower incidence rates (<xref ref-type="bibr" rid="B4">4</xref>). Overall, global TC burden trends result from the combined effects of population growth, demographic transitions, improvements in diagnostic capacity and healthcare access, changes in established and emerging risk factors, and variability in cancer registry completeness across regions. These findings provide an evidence base for policymakers to prioritize early detection, expand access to standardized treatment, and strengthen cancer surveillance systems, especially in countries with high TC-related mortality and DALYs.</p>
<p>The BAPC model projects that, although the global incidence, mortality, and DALYs of TC will continue to increase through 2035, age-standardized mortality (ASMR) and DALYs rates (ASDR) are expected to decline. This divergence suggests that the increasing absolute number of deaths and DALYs is largely driven by population growth and population aging, whereas declining age-standardized rates likely reflect improvements in early detection and treatment outcomes, a pattern commonly observed in global cancer epidemiology (<xref ref-type="bibr" rid="B22">22</xref>). Declining ASMR and ASDR suggest continued improvements in survival, likely driven by advances in platinum-based chemotherapy, surgical techniques, and radiotherapy (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B14">14</xref>). Given that TC is a highly treatable malignancy, timely diagnosis and standardized management remain key determinants of favorable prognosis (<xref ref-type="bibr" rid="B13">13</xref>). While these trends indicate progress in disease control, substantial challenges persist in low- and middle-SDI regions, where limited diagnostic capacity, delayed presentation, and constrained healthcare resources may sustain a disproportionate burden of TC. Future global control efforts should therefore prioritize strengthening early detection and treatment infrastructure in these high-risk settings.</p>
</sec>
<sec id="s4_5">
<label>4.5</label>
<title>Limitations</title>
<p>This study has several limitations that should be acknowledged. First, the quality and completeness of GBD data vary across countries and regions, particularly in low- and middle-SDI settings where cancer registration systems remain underdeveloped. This may result in underreporting or misclassification, potentially affecting the accuracy of incidence and mortality estimates. In addition, GBD estimates are partially derived from statistical modeling rather than purely registry-based observations, which may introduce uncertainty and should be considered when interpreting cross-national comparisons. Second, this analysis did not explicitly incorporate individual-level risk factors, such as environmental exposures, lifestyle behaviors, genetic predisposition, or ethnicity, which limits the ability to fully explain the observed epidemiological patterns of TC. Third, although the BAPC model is widely used for trend projection, its estimates are subject to uncertainty, especially in regions undergoing rapid social, economic, or healthcare transitions, where future trends may deviate from model assumptions. In addition, disruptions caused by the COVID-19 pandemic may have affected cancer diagnosis, treatment, and reporting in some regions, potentially influencing estimates in the most recent years of the study period. Moreover, long-term projections are inherently uncertain, and future structural changes in healthcare systems or unexpected external events may lead to deviations from model-based estimates.</p>
</sec>
</sec>
<sec id="s5" sec-type="conclusions">
<label>5</label>
<title>Conclusion</title>
<p>The global burden of TC, especially its incidence, has significantly and persistently increased from 1990 to 2021, with projections indicating a continued rise by 2035. Middle-SDI regions and specific areas like the Caribbean are emerging as new frontiers with rapidly escalating TC burden, demanding urgent, targeted attention. While treatment advancements have led to relative improvements in mortality and DALY rates in some areas, the growing absolute number of cases poses a substantial and ongoing challenge to global health systems. Tailored public health strategies emphasizing early detection, equitable access to high-quality care, and comprehensive survivorship programs are critical to stem the rising tide of TC and alleviate its impact on young men worldwide.</p>
</sec>
</body>
<back>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Material</bold></xref>. Further inquiries can be directed to the corresponding authors.</p></sec>
<sec id="s7" sec-type="ethics-statement">
<title>Ethics statement</title>
<p>For the GBD study, the Institutional Review Board (IRB) of the University of Washington reviewed and approved the waiver of informed consent (<ext-link ext-link-type="uri" xlink:href="https://www.healthdata.org/research-analysis/gbd">https://www.healthdata.org/research-analysis/gbd</ext-link>). The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants&#x2019; legal guardians/next of kin in accordance with the national legislation and institutional requirements.</p></sec>
<sec id="s8" sec-type="author-contributions">
<title>Author contributions</title>
<p>CM: Formal analysis, Data curation, Writing &#x2013; original draft. CT: Writing &#x2013; original draft, Data curation. CY: Writing &#x2013; original draft. JS: Conceptualization, Writing &#x2013; review &amp; editing. GL: Writing &#x2013; review &amp; editing, Conceptualization.</p></sec>
<ack>
<title>Acknowledgments</title>
<p>We sincerely thank Jingding Medical Tech for their support, authorization, and technical assistance with the JD_GBDR software, which greatly facilitated data processing and analysis in this study.</p>
</ack>
<sec id="s10" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec>
<sec id="s11" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declared that generative AI was not used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p></sec>
<sec id="s12" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>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.</p></sec>
<sec id="s13" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fonc.2026.1634710/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fonc.2026.1634710/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="DataSheet1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/></sec>
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<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3214750">Andrea Graziani</ext-link>, University of Padua, Italy</p></fn>
<fn id="n2" fn-type="custom" custom-type="reviewed-by">
<p>Reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/854001">Giovanni Rosti</ext-link>, San Matteo Hospital Foundation (IRCCS), Italy</p>
<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3263731">Patricia Rioja</ext-link>, Instituto Nacional de Enfermedades Neopl&#xe1;sicas (INEN), Peru</p></fn>
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<fn-group>
<fn fn-type="abbr" id="abbrev1">
<label>Abbreviations:</label>
<p>TC, Testicular Cancer; GBD, Global Burden of Disease; DALYs, Disability-Adjusted Life Years; SDI, Socio-demographic Index; EAPC, Estimated Annual Percentage Change; APC, Annual Percentage Change; AAPC, Average Annual Percentage Change; BAPC, Bayesian Age-Period-Cohort; ASIR, Age-Standardized Incidence Rate; ASMR, Age-Standardized Mortality Rate; ASDR, Age-Standardized DALYs Rate; UI, Uncertainty Interval; CI, Confidence interval; EDCs, Endocrine-Disrupting Chemicals; AFP, Alpha-Fetoprotein; hCG, Human Chorionic Gonadotropin; LDH, Lactate Dehydrogenase; AYA, Adolescent and Young Adult.</p>
</fn>
</fn-group>
</back>
</article>