Evidence map›Paper›PMID 31046731›Full record

ArticleBMC public health2019

Age-standardized expected years of life lost: quantification of cancer severity.

Yueh Wang, Chun-Ju Chiang, Wen-Chung Lee

Abstract read
In one paragraph

Article in BMC public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

3 authors.

Yueh WangTaiwan Cancer Registry, Rm 506, No. 17, Xuzhou Rd., Taipei, 100, Taiwan.
Chun-Ju ChiangTaiwan Cancer Registry, Rm 506, No. 17, Xuzhou Rd., Taipei, 100, Taiwan.
Wen-Chung LeeTaiwan Cancer Registry, Rm 506, No. 17, Xuzhou Rd., Taipei, 100, Taiwan. wenchung@ntu.edu.tw.ORCID http://orcid.org/0000-0003-3171-7672

Funding

Ministry of Education (MOE) in Taiwan NTU-107L9003Ministry of Health and Welfare in Taiwan A1051011Ministry of Science and Technology in Taiwan MOST 104-2314-B-002-118-MY3Ministry of Science and Technology in Taiwan MOST 105-2314-B-002-049-MY3Ministry of Science and Technology in Taiwan MOST 107-3017-F-002-003
6 · The paper itself

Abstract

backgroundThe critical implications of the expected years of life lost (EYLL) index of cancer for health policy assessments have been largely overlooked. We advocate to standardize life lost indices.

methodsUsing the Taiwan Cancer Registry database as an example, we calculated the EYLL and the age-standardized EYLL to facilitate comparisons among cancer types (a total of 903,935 patients from the database). The International Cancer Survival Standard was used for calculating age-standardized EYLL.

resultsPancreatic cancer is the most severe cancer in Taiwan, with the greatest age-standardized EYLL for the men (15.6 years) and women (18.0 years) as well as for the men and women combined (16.6 years). Negative correlations were observed between unstandardized EYLL of cancer and mean age at diagnosis.

conclusionsThe unstandardized EYLL represents an overall assessment of disease burden, whereas the age-standardized EYLL is a suitable measure of disease severity. We suggest that both measures be incorporated into routine annual reports of cancer statistics alongside the usual incidence and mortality rates and their age-standardized counterparts.

Indexed as

Life ExpectancyRegistriesAdultAgedCancer SurvivorsDatabases, FactualFemaleFollow-Up StudiesHumansIncidenceMaleMiddle AgedNeoplasm InvasivenessPancreatic NeoplasmsSurvival AnalysisTaiwanAge-standardizationBurden of diseaseCancerExpected years of life lost

Identifiers

PMID31046731
PMCPMC6498471

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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.