Evidence map›Paper›PMID 39170811›Full record

ArticleInternational journal of public health2024

Evolution of Esophageal Cancer Incidence Patterns in Hong Kong, 1992-2021: An Age-Period-Cohort and Decomposition Analysis.

Lijun Wang, Jianqiang Du, Haifeng Sun

Abstract read
In one paragraph

Article in International journal of public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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.

Lijun WangDepartment of Orthopaedics, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Jianqiang DuKey Laboratory of Biomedical Information Engineering, Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
Haifeng SunThird Department of Medical Oncology, Shaanxi Provincial Cancer Hospital Affiliated to Medical College of Xi'an Jiaotong University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To elucidate the historical trends, underlying causes and future projections of esophageal cancer incidence in Hong Kong. Methods: Utilizing the Age-Period-Cohort (APC) model, we analyzed data from the Hong Kong Cancer Registry (1992-2021) and United Nations World Population Prospects 2022 Revision. Age-standardized incidence rates were computed, and APC models evaluated age, period, and cohort effects. Bayesian APC modeling, coupled with decomposition analysis, projected future trends and identified factors influencing incidence. Results: Between 1992 and 2021, both crude and age-standardized incidence rates of esophageal cancer witnessed significant declines. Net drifts exhibited pronounced downward trends for both sexes, with local drift diminishing across all age groups. Period and cohort rate ratios displayed a consistent monotonic decline for both sexes. Projections indicate a continued decline in esophageal cancer incidence. Population decomposition analysis revealed that epidemiological changes offset the increase in esophageal cancer cases due to population growth and aging. Conclusion: The declining trend of esophageal cancer in Hong Kong is influenced by a combination of age, period, and cohort. Sustaining and enhancing these positive trends requires continuous efforts in public health interventions.

Indexed as

Esophageal NeoplasmsRegistriesAdultAgedAged, 80 and overAge DistributionAge FactorsBayes TheoremCohort StudiesFemaleHong KongHumansIncidenceMaleMiddle AgedYoung Adultcohort effectdemographic factorsesophageal cancerincidenceperiod analysis

Identifiers

PMID39170811
PMCPMC11335483

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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.