Evidence map›Paper›PMID 42837657›Full record

ArticleJMIR public health and surveillance2026

Temporal Trends in Lung Cancer and Histological Subtype Incidence and Bayesian Projections to 2030 in China: Population-Based Quantitative Study.

Xing Su, Jing Li, Xiaowei Hu

Abstract read
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Article in JMIR public health and surveillance, 2026. 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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Xing SuXihu District Center for Disease Control and Prevention, Hangzhou, Zhejiang, China.ORCID https://orcid.org/0009-0005-9973-9538
Jing LiXihu District Center for Disease Control and Prevention, Hangzhou, Zhejiang, China.ORCID https://orcid.org/0009-0004-9780-3164
Xiaowei HuXihu District Center for Disease Control and Prevention, Hangzhou, Zhejiang, China.ORCID https://orcid.org/0009-0002-5718-8457

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung cancer is the leading cause of cancer-related death globally, with a significant burden in China, which accounts for more than one-third of the world's new cases and 40% of related mortalities. Despite evidence that low-dose computed tomography screening can reduce mortality, its limited uptake in China means that most patients are diagnosed at an advanced stage, contributing to a low 5-year survival rate compared to high-income countries. Although overall incidence trends have been well documented, there is a scarcity of population-based data on the temporal trends of different histological subtypes of lung cancer in China.

objectiveThis study aimed to analyze population-based cancer registry data to assess lung cancer incidence trends by histological subtype and sex from 2001 to 2020 and to forecast future incidence rates through 2030 in Xihu District, Hangzhou, Zhejiang Province, eastern China.

methodsThe study used population-based surveillance data from the Xihu Cancer Registry from 2001 to 2020. Statistical analyses were conducted to assess the burden of each tumor subtype, including the average annual percentage change (AAPC) to evaluate temporal trends and the Bayesian age-period-cohort (BAPC) model to forecast incidence rates through 2030.

resultsLung cancer incidence rates were generally low among individuals aged <45 years but escalated markedly with age, reaching a peak of 567.6 per 100,000 person-years in among those aged 80 to 84 years. Notably, a significant rise in incidence was observed among those aged 30 to 44 years in recent years. Sex-based differences in histological distribution were pronounced. Adenocarcinoma accounted for 66.65% (2322/3484) of female cases compared to 37.1% (1795/4838) of male cases, while squamous-cell carcinoma was substantially less common in female cases (86/3484, 2.47% compared to 829/4838, 17.13% in male cases). Among male cases, the age-standardized incidence rates (ASIRs) of adenocarcinoma increased from 1.39 to 42.18 per 100,000 between 2001 and 2020; among female cases, the corresponding increase was even more pronounced, rising from 2.81 to 67.37 per 100,000. Joinpoint regression analysis demonstrated a consistent long-term increase in adenocarcinoma (AAPC=35.1, 95% CI 14.2-50.2), with peak acceleration observed from 2005 to 2012 (annual percentage change [APC]=76.7, 95% CI 45.2-124.2), and a steady increase in large-cell and other specified carcinomas (AAPC=32.7, 95% CI 12.5-45.7), while unspecified carcinoma showed a long-term decline (AAPC=-3.1, 95% CI -14.2 to -1.1). BAPC projections indicated that adenocarcinoma ASIR will continue to surge between 2020 and 2030, emerging as the dominant histological subtype, while unspecified carcinoma, squamous-cell carcinoma, and small-cell lung cancer are anticipated to decline modestly.

conclusionsThe increase in lung cancer incidence, particularly among female participants, underscores the need for effective prevention and control strategies, including targeted interventions to reduce smoking.

Indexed as

Lung NeoplasmsPopulation SurveillanceAdultAgedAged, 80 and overBayes TheoremChinaFemaleForecastingHumansIncidenceMaleMiddle AgedRegistriesage-period-cohorthistological subtypelung cancerpredictiontrend

Identifiers

PMID42837657
PMCPMC13684115

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