Evidence map›Paper›PMID 36330120›Full record

ArticleFrontiers in public health2022

Trends of cancer incidence among Chinese older adults from 2005 to 2016: A log-linear regression and age-period-cohort analysis.

Hong Guo, Kangqian Lin, Kaiyue Yang, Zhenrong Ma, Miao Cao, Yunhua Hu, Yizhong Yan

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.8field-weighted citation impact, top 7% of its field
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

7 citing papers in PubMed, 19 citations in OpenAlex.

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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

7 authors at 1 institution in 1 country.

Hong GuoDepartment of Public Health, Shihezi University School of Medicine, Xinjiang, China.
Kangqian LinDepartment of Public Health, Shihezi University School of Medicine, Xinjiang, China.
Kaiyue YangDepartment of Public Health, Shihezi University School of Medicine, Xinjiang, China.
Zhenrong MaDepartment of Public Health, Shihezi University School of Medicine, Xinjiang, China.
Miao CaoDepartment of Public Health, Shihezi University School of Medicine, Xinjiang, China.
Yunhua HuDepartment of Public Health, Shihezi University School of Medicine, Xinjiang, China.
Yizhong YanDepartment of Public Health, Shihezi University School of Medicine, Xinjiang, China.
Shihezi University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: To study the corresponding strategies for controlling cancer in older adults aged 60 and above in China, a comprehensive assessment of disease burden is required. Therefore, we will introduce the cancer epidemiological characteristics of older adults in China over a recent 12 year period. Methods: The age-period-cohort model was constructed using the cancer incidence data from the Chinese Cancer Registry Annual Report published in 2008-2019. The annual change percentage (APC) was estimated by log-linear regression to reflect the time trend. The data from the GLOBOCAN 2020 database was selected for worldwide comparative analysis. Results: The cancer incidence in older adults aged 60 and above in China showed a decreasing trend (APC = -0.73%, Conclusions: The cancer incidence in Chinese older adults aged 60 and above showed a decreasing trend, but it was still at a relatively high level. The key targets of prevention and treatment should be males, urban areas, younger people, older adults aged 60-69, lung, gastrointestinal, and breast cancers in the future.

Indexed as

Breast NeoplasmsAgedChinaCohort StudiesFemaleHumansIncidenceLinear ModelsMaleMiddle Agedagedage-period-cohort modelcancerepidemiologylog-linear regression

Identifiers

PMID36330120
PMCPMC9623261
OpenAlexW4306685666

What OpenQuestion holds

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LicenceCC BY
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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.