Evidence map›Paper›PMID 32073760›Full record

ArticleCancer medicine2020

Distinctive lung cancer incidence trends among men and women attributable to the period effect in Shanghai: An analysis spanning 42 years.

Li Xie, Ying Qian, Yishan Liu, Yixuan Li, Sinong Jia, Herbert Yu, Chunfang Wang, Biyun Qian, Pingping Bao

Open access · goldAbstract read
In one paragraph

Article in Cancer medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
0.9field-weighted citation impact, top 24% 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

12 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Review
  3. Natural Compounds for Preventing Age-Related Diseases and Cancers.International journal of molecular sciences · 2024
    Review
  4. Secular-Trend Analysis of the Incidence Rate of Lung Squamous Cell Carcinoma in Taiwan.International journal of environmental research and public health · 2023
    Article
  5. Article
  6. 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

9 authors at 3 institutions in 2 countries.

Li XieHongqiao International Institute of Medicine, Shanghai Tong Ren Hospital and Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.ORCID 0000-0003-1892-2217
Ying QianHongqiao International Institute of Medicine, Shanghai Tong Ren Hospital and Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Yishan LiuHongqiao International Institute of Medicine, Shanghai Tong Ren Hospital and Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Yixuan LiHongqiao International Institute of Medicine, Shanghai Tong Ren Hospital and Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Sinong JiaHongqiao International Institute of Medicine, Shanghai Tong Ren Hospital and Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Herbert YuCancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA.
Chunfang WangShanghai Municipal Center for Disease Control and Prevention, Shanghai, People's Republic of China.
Biyun QianHongqiao International Institute of Medicine, Shanghai Tong Ren Hospital and Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Pingping BaoShanghai Municipal Center for Disease Control and Prevention, Shanghai, People's Republic of China.
Shanghai Jiao Tong University · CNShanghai Municipal Center For Disease Control Prevention · CNUniversity of Hawaiʻi at Mānoa · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMany previous studies reported secular trend of lung cancer incidence and mortality, but little is known about the possible reasons for these trends.

methodsData were obtained from Shanghai Cancer Registry. Age-standardized rates were calculated and average annual percent changes (AAPCs) were evaluated by Joinpoint regression. Age, period, and birth cohort effects were assessed by age-period-cohort models.

resultsFrom 1973 to 2010, compared with long-time slowly increasing trend in women, male lung cancer incidence had significantly decreased between 2001 and 2009. After that lung cancer incidence rising sharply in women (AAPC = 14.13%, 95%CI: 2.68%-26.86%, P = .016) and similar rising trends without statistical significance in men (AAPC = 2.96, 95%CI: -2.47%-8.69%, P = .281) between 2010 and 2014. Age-period cohort model showed the different patterns of period effects for lung cancer incidence between men and women. The period effects for lung cancer incidence showed rising effect for women, whereas there was decline effect for lung cancer incidence for men. On the other hand, the model showed a significant period effect in both genders with a similar fashion in mortality, yielding steady falling trends during the entire study period.

conclusionsThe distinctive patterns of lung cancer incidence between men and women may be attributable to significant period effects, which reflected the changes in public health policies or diagnostic practices and highlighted the urgent of continued monitoring of gender-specific risk factors for lung cancer incidence.

Indexed as

AdolescentAdultAgedAged, 80 and overAge FactorsChildChild, PreschoolChinaFemaleHumansIncidenceInfantInfant, NewbornLung NeoplasmsMaleMiddle Agedage-period-cohort modelgender disparityincidenceLung cancermortality

Identifiers

PMID32073760
PMCPMC7163103
OpenAlexW3008146233

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.