Evidence map›Paper›PMID 36831350›Full record

ArticleCancers2023

Geographic Variation and Risk Factor Association of Early Versus Late Onset Colorectal Cancer.

Weichuan Dong, Uriel Kim, Johnie Rose, Richard S Hoehn, Matthew Kucmanic, Kirsten Eom, Shu Li, Nathan A Berger, Siran M Koroukian

Abstract read
In one paragraph

Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

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

  1. Pooled it
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  15. Review
  16. Impact of Mir196a-2 Genotypes on Colorectal Cancer Risk in Taiwan.International journal of molecular sciences · 2023
    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.

Weichuan DongPopulation Cancer Analytics Shared Resource and Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA.ORCID 0000-0002-3981-658X
Uriel KimPopulation Cancer Analytics Shared Resource and Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA.ORCID 0000-0001-9910-0682
Johnie RosePopulation Cancer Analytics Shared Resource and Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA.
Richard S HoehnDepartment of Surgery, Division of Surgical Oncology, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA.
Matthew KucmanicDepartment of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA 52242, USA.
Kirsten EomMetroHealth Cancer Center, Cleveland, OH 44109, USA.ORCID 0000-0003-3809-2978
Shu LiSchool of Digital Sciences, Kent State University, Kent, OH 44240, USA.
Nathan A BergerCase Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA.ORCID 0000-0001-7086-9885
Siran M KoroukianPopulation Cancer Analytics Shared Resource and Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA.

Funding

TUMOR METABOLISM PROGRAMP30CA043703 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI Amar Desai · 1987 to 2026
$142.3M
NCI NIH HHS P30 CA043703
6 · The paper itself

Abstract

The proportion of patients diagnosed with colorectal cancer (CRC) at age < 50 (early-onset CRC, or EOCRC) has steadily increased over the past three decades relative to the proportion of patients diagnosed at age ≥ 50 (late-onset CRC, or LOCRC), despite the reduction in CRC incidence overall. An important gap in the literature is whether EOCRC shares the same community-level risk factors as LOCRC. Thus, we sought to (1) identify disparities in the incidence rates of EOCRC and LOCRC using geospatial analysis and (2) compare the importance of community-level risk factors (racial/ethnic, health status, behavioral, clinical care, physical environmental, and socioeconomic status risk factors) in the prediction of EOCRC and LOCRC incidence rates using a random forest machine learning approach. The incidence data came from the Surveillance, Epidemiology, and End Results program (years 2000-2019). The geospatial analysis revealed large geographic variations in EOCRC and LOCRC incidence rates. For example, some regions had relatively low LOCRC and high EOCRC rates (e.g., Georgia and eastern Texas) while others had relatively high LOCRC and low EOCRC rates (e.g., Iowa and New Jersey). The random forest analysis revealed that the importance of community-level risk factors most predictive of EOCRC versus LOCRC incidence rates differed meaningfully. For example, diabetes prevalence was the most important risk factor in predicting EOCRC incidence rate, but it was a less important risk factor of LOCRC incidence rate; physical inactivity was the most important risk factor in predicting LOCRC incidence rate, but it was the fourth most important predictor for EOCRC incidence rate. Thus, our community-level analysis demonstrates the geographic variation in EOCRC burden and the distinctive set of risk factors most predictive of EOCRC.

Indexed as

colorectal cancerearly-onsetgeographic information systemmachine learningrandom forestregionalizationrisk factor

Identifiers

PMID36831350
PMCPMC9954005

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.