ArticleCancers2023
Geographic Variation and Risk Factor Association of Early Versus Late Onset Colorectal Cancer.
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.
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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.
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.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Techniques Used for the Identification of Sociodemographic Factors Associated With Cancer: Systematic Literature Review.Journal of medical Internet research · 2026Pooled it
- Temporal Trends in Colorectal Cancer Associated With Type 2 Diabetes Mellitus in U.S. Adults, 1999-2023: A CDC Wonder Database Analysis-A Population-Based Cross-Sectional Study.Health science reports · 2026Article
- Lifestyle and metabolic determinants of early- and late-onset colorectal cancer: insights from a tertiary medical center in Alabama.Cancer causes & control : CCC · 2026Article
- Beyond screening: neighborhood-level factors associated with colorectal cancer stage at diagnosis.Cancer causes & control : CCC · 2025Article
- Global, regional, and national burden of early-onset and late-onset colorectal cancer attributable to high body-mass index from 1990 to 2021: a trend analysis and forecasts up to 2040 based on the global burden of disease study 2021.BMC gastroenterology · 2025Article
- Early-onset colorectal cancer as an emerging disease of metabolic dysregulation.Nature reviews. Endocrinology · 2025Review
- Article
- Analyzing Geospatial and Socioeconomic Disparities in Breast Cancer Screening Among Populations in the United States: Machine Learning Approach.JMIR cancer · 2025Article
- Trend and age-period-cohort analysis of colorectal cancer burden attributable to low whole grains intake: a global, regional, and national analysis with predictive modeling (1990-2046).Frontiers in nutrition · 2025Article
- Development and validation of machine learning models for young-onset colorectal cancer risk stratification.NPJ precision oncology · 2024Article
- Trends in Cancer Incidence and Mortality in US Adolescents and Young Adults, 2016-2021.Cancers · 2024Article
- Geographical Variations in Early Onset Colorectal Cancer in the United States between 2001 and 2020.Cancers · 2024Article
- County-level socio-environmental factors and obesity prevalence in the United States.Diabetes, obesity & metabolism · 2024Article
- Article
- Obesity and early-onset colorectal cancer risk: emerging clinical evidence and biological mechanisms.Frontiers in oncology · 2024Review
- Impact of Mir196a-2 Genotypes on Colorectal Cancer Risk in Taiwan.International journal of molecular sciences · 2023Article
- A Comparative Observational Study of Late-Onset Colorectal Cancer Patients in a Tertiary Surgery Unit.Current health sciences journalArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
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.
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