Evidence map›Paper›PMID 38809949›Full record

ArticlePloS one2024

Trends in cancer mortality among the elderly in China, 2005-2035.

Meiyan Wang, Xunli Su, Yunhua Hu, Jian Yang

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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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

5 citing papers in PubMed.

  1. Trial
  2. Article
  3. Global burden of MASLD-IBD comorbidity from 1990 to 2021 and trend prediction to 2050.International journal of surgery (London, England) · 2026
    Article
  4. Article
  5. Article
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

4 authors.

Meiyan WangShihezi University School of Medicine, Shihezi, Xinjiang, China.ORCID 0000-0001-8131-4025
Xunli SuShihezi University School of Medicine, Shihezi, Xinjiang, China.
Yunhua HuShihezi University School of Medicine, Shihezi, Xinjiang, China.
Jian YangThe First Affiliated Hospital, School of Medicine, Shihezi University, Shihezi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the appearance and aggravation of the aging society, cancer has become one of the major problems that threaten the life and health of Chinese residents seriously.

objectiveTo explore the cancer epidemiological characteristics among the elderly in China from 2005 to 2016, and to provide strategies for cancer prevention and treatment.

methodsStratified analysis was conducted on the cancer mortality data of the elderly aged ≥60 years in China, which were selected from the Chinese Cancer Registry Annual Report. Joinpoint regression model was used to calculted average annual percentage change (AAPC) to estimate the time trends. Age-period-cohort (APC) model was used to explore the age, period and birth cohort effect on the risk of cancer death. Bayesian age-period-cohort (BAPC) model was used to predict trends in cancer mortality among elderly by gender and region to 2035.

results2005-2016, cancer mortality in the elderly in China showed a decreasing trend (AAPC = -1.2%, P<0.001). Cancer mortality in rural areas was higher than that in urban areas, but the urban-rural difference gradually narrowed (t = 6.1, P<0.01). The APC model showed that cancer mortality increased with age. The relative risk (RR) for the period effect decreased. RR was lower for the later- born cohort than that for the earlier-born cohort in rural areas. Lung cancer mortality ranked first in both male and female, and showed an increasing trend among female in the 60-64, 80-84 and ≥85 age groups (AAPC60-64 = 1.0%, AAPC80-84 = 0.8%, AAPC≥85 = 2.0%, all P<0.05). By 2035, cancer mortality for the elderly was predicted to decline nationally, by sex and in rural areas, while rising in urban areas.

conclusionCancer mortality in the elderly in China showed a decreasing trend from 2005 to 2016, but it was still higher than the world average. Early cancer screening is important, especially in the elderly male and in rural areas.

Indexed as

NeoplasmsAgedAged, 80 and overBayes TheoremChinaFemaleHumansMaleMiddle AgedMortalityRegistriesRural PopulationUrban Population

Identifiers

PMID38809949
PMCPMC11135761

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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.