Evidence map›Paper›PMID 39698306›Full record

ArticleInternational journal of public health2024

Trends in the Disease Burden and Risk Factors of Women's Cancers in China From 1990 to 2019.

Wei Ning, Jinnan Liu, Yongbo Lu, Bin Zhu, Wei-Hong Zhang, Ying Mao

Abstract read
In one paragraph

Article in International journal of public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Wei Ning *School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China.
Jinnan Liu *School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China.
Yongbo LuSchool of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China.
Bin ZhuSchool of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, China.
Wei-Hong ZhangInternational Centre for Reproductive Health, Ghent University, Ghent, Belgium.
Ying MaoSchool of Public Policy and Administration, Xi'an Jiaotong University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To examine age-specific trends and risk factors in the burden of women's cancers (WCs) in China from 1990 to 2019 to inform strategies. Methods: Data were sourced from the Global Burden of Disease 2019 and World Population Prospects 2019. Time trends, age differences, and key factors for breast, cervical, and ovarian cancers (BC, CC, and OC) were analyzed based on age-standardized incidence rate (ASIR) and disability-adjusted life years (DALYs) rate. Results: ASIRs for BC and CC increased over the study period, with a slower growth rate for CC after 2005, likely due to targeted HPV prevention. OC showed the highest ASIR and DALY increases, indicating a growing concern. Peak ASIR for BC and CC was in women aged 50-55, while OC showed a higher burden in women aged 70-79. Lower DALYs in women born after 1985 suggest improved healthcare access. Conclusion: This study highlights significant trends in cancer burden among Chinese women, driven by age and reproductive health policies. Future efforts should enhance screening, health literacy, and age-targeted risk reduction for specific cancer types.

Indexed as

Breast NeoplasmsAdultAgedAge FactorsChinaCost of IllnessDisability-Adjusted Life YearsFemaleGlobal Burden of DiseaseHumansIncidenceMiddle AgedOvarian NeoplasmsRisk FactorsUterine Cervical NeoplasmsYoung Adultbreast cancercervical cancerChinadisease burdenovarian cancerrisk factorstime trends

Identifiers

PMID39698306
PMCPMC11652174

What OpenQuestion holds

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Registered trials

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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.