Evidence map›Paper›PMID 37641011›Full record

ArticleBMC public health2023

Epidemiological characteristics of leukemia in China, 2005-2017: a log-linear regression and age-period-cohort analysis.

Kangqian Lin, Huaimiao Jia, Miao Cao, Tongtong Xu, Zuhai Chen, Xi Song, Yingfang Miao, Teng Yao, Chenxian Dong, Jianjiang Shao and 3 more

Abstract read
In one paragraph

Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

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  17. Metabolism of asparagine in the physiological state and cancer.Cell communication and signaling : CCS · 2024
    Review
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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

13 authors.

Kangqian LinDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Huaimiao JiaShihezi Center for Disease Control and Prevention, Shihezi, Xinjiang, China.
Miao CaoDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Tongtong XuDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Zuhai ChenDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Xi SongDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Yingfang MiaoDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Teng YaoDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Chenxian DongDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Jianjiang ShaoDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Heng GuoDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China. shzuguoheng@foxmail.com.
Yunhua HuDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China. huyunhua1019@sina.com.
Yizhong YanDepartment of Preventive Medicine, School of Medicine, Shihezi University, Shihezi, Xinjiang, China. erniu19880215@sina.com.ORCID http://orcid.org/0000-0003-1957-5421

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLeukemia is a threat to human health, and there are relatively few studies on the incidence, mortality and disease burden analysis of leukemia in China. This study aimed to analyze the incidence and mortality rates of leukemia in China from 2005 to 2017 and estimate their age-period-cohort effects, it is an important prerequisite for effective prevention and control of leukemia.

methodsLeukemia incidence and mortality data from 2005 to 2017 were collected from the Chinese Cancer Registry Annual Report. Joinpoint regression model was used to estimate the average annual percentage change (AAPC) and annual percentage change (APC) response time trend. Age-period-cohort model was constructed to analyze the effects of age, period and cohort.

resultsThe age-standardized incidence rate of leukemia was 4.54/100,000 from 2005 to 2017, showed an increasing trend with AAPC of 1.9% (95% CI: 1.3%, 2.5%). The age-standardized mortality rate was 2.91/100,000, showed an increasing trend from 2005 to 2012 with APC of 2.1% (95%CI: 0.4%, 3.9%) and then a decreasing trend from 2012 to 2017 with APC of -2.5% (95%CI: -5.3%, 0.3%). The age-standardized incidence (mortality) rates of leukemia were not only higher in males than that in females, but also increased more rapidly. The incidence of leukemia in rural areas was lower than in urban areas, but the AAPC was 2.2 times higher than urban areas. Children aged 0-4 years were at higher risk of leukemia. The risk of leukemia incidence and mortality increased with age. The period effect of leukemia mortality risk showed a decreasing trend, while the cohort effect showed an increasing and then decreasing trend with the turning point of 1955-1959.

conclusionsThe age-standardized incidence rate of leukemia in China showed an increasing trend from 2005 to 2017, while the age-standardized mortality rate increased first and then decreased in 2012 as a turning point. Differences existed by gender and region. The risk of leukemia incidence and mortality increased accordingly with age. The risk of mortality due to leukemia gradually decreased from 2005 to 2017. Leukemia remains a public health problem that requires continuous attention.

Indexed as

LeukemiaChinaFemaleHumansLinear ModelsMalePublic HealthAge-period-cohort modelIncidenceJoinpoint regression modelLeukemiaMortality

Identifiers

PMID37641011
PMCPMC10464264

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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.