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

Author:

Dong Weichuan1ORCID,Kim Uriel123ORCID,Rose Johnie134,Hoehn Richard S.5,Kucmanic Matthew6,Eom Kirsten7ORCID,Li Shu8,Berger Nathan A.49ORCID,Koroukian Siran M.134

Affiliation:

1. Population Cancer Analytics Shared Resource and Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA

2. Kellogg School of Management, Northwestern University, Evanston, IL 60208, USA

3. Center for Community Health Integration, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA

4. Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA

5. Department of Surgery, Division of Surgical Oncology, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA

6. Department of Geographical and Sustainability Sciences, University of Iowa, Iowa City, IA 52242, USA

7. MetroHealth Cancer Center, Cleveland, OH 44109, USA

8. School of Digital Sciences, Kent State University, Kent, OH 44240, USA

9. Center for Science, Health and Society, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA

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.

Funder

National Cancer Institute Case Comprehensive Cancer Center

Publisher

MDPI AG

Subject

Cancer Research,Oncology

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