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Article

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

by
Weichuan Dong
1,*,
Uriel Kim
1,2,3,
Johnie Rose
1,3,4,
Richard S. Hoehn
5,
Matthew Kucmanic
6,
Kirsten Eom
7,
Shu Li
8,
Nathan A. Berger
4,9 and
Siran M. Koroukian
1,3,4
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
*
Author to whom correspondence should be addressed.
Cancers 2023, 15(4), 1006; https://doi.org/10.3390/cancers15041006
Submission received: 7 January 2023 / Revised: 31 January 2023 / Accepted: 2 February 2023 / Published: 4 February 2023
(This article belongs to the Special Issue Early Onset Colorectal Cancer: Epidemiology and Etiology)
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Abstract

:

Simple Summary

While the incidence of late-onset colorectal cancer (LOCRC) has steadily decreased, the incidence of early-onset colorectal cancer (EOCRC) has continued to increase in the US. This study aims to uncover geographic disparities in EOCRC and understand how risk factors between EOCRC and LOCRC differ. The geographic analysis revealed regions with relatively low LOCRC rates and high EOCRC rates, identifying regions with a disproportionate burden of EOCRC. We then evaluated and compared community-level risk factors associated with incidence rates of EOCRC and LOCRC using the random forest machine learning method. The analysis identified a set of risk factors most predictive of EOCRC and LOCRC, such as diabetes prevalence and physical inactivity, but the importance of these risk factors varied between EOCRC and LORC. Collectively, these findings can help facilitate future studies that further uncover actionable interventions to reduce EOCRC and guide where targeted interventions to reduce EOCRC burden should be deployed.

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.

1. Introduction

Colorectal cancer (CRC) incidence in the US has declined steadily since the mid-1990s, owing to substantial drops in new cases among individuals aged 50 years and older (late-onset CRC, or LOCRC) [1,2]. This trend is likely attributable to improved screening rates in this age group. However, diagnosis of CRC in individuals younger than 50 years old (early-onset CRC, or EOCRC) has nearly doubled over the same period and continues to increase by approximately 2% annually [1]. Thus, uncovering risk factors for EOCRC, especially those that may be unique to early-onset disease versus late-onset disease, can inform efforts that facilitate the prevention and detection of this growing cancer subtype.
Researchers have identified multiple shared risk factors for EOCRC and LOCRC, including sedentary behaviors, Western-style diets, smoking, and diabetes [3,4,5]. Studies suggest that EOCRC may have distinct clinical and molecular phenotypes compared with LOCRC. This may be attributable to differences in demographic and genetic factors, or potentially modifiable behavioral and environmental factors [6]. Studies also have linked CRC risk with social determinants of health [7], which are often measured by area-level characteristics of individuals’ residences as a surrogate. However, the extent to which these factors are associated with EOCRC, and whether they differ from those associated with LOCRC, remains unclear.
Since incidence rates in many sparsely populated counties are often unreliable due to the small number of cases in those areas, it is challenging to conduct population and geographic studies on EOCRC. To our knowledge, no study has conducted a comprehensive area-level analysis comparing demographic, health status, behavioral, clinical care, environmental, and socioeconomic risk factors between EOCRC and LOCRC. This cross-sectional study applied a geographic method (Max-p-regions) to address the data scarcity problem at the county level, followed by comparing the differences in the geographic distributions of the incidence rates of EOCRC and LOCRC. Finally, the study evaluated and compared risk factor importance in the prediction of EOCRC and LOCRC incidence rates.

2. Materials and Methods

2.1. Study Population and Data Sources

The study included individuals diagnosed with CRC before 50 years of age (EOCRC) and at 50 years or older (LOCRC) between 2000 and 2019 from the Surveillance, Epidemiology, and End Results (SEER) database [8]. Data in the SEER program cover 48% of the U.S. population with 97% completeness within the SEER regions [9]. Geographic regions included in this study cover the U.S. states of California, Connecticut, Georgia, Hawaii, Idaho, Illinois, Iowa, Kentucky, Louisiana, Massachusetts, New Jersey, New Mexico, New York, Texas, Utah, and Washington (Seattle–Puget Sound), with a total of 1085 counties. All data were obtained at the county level and were aggregated into a larger geographic unit for analysis as necessary using the max-p-regions method described in further detail below.

2.2. Variables of Interest

The outcomes of interest were the age-adjusted EOCRC incidence rate and age-adjusted LOCRC incidence rate per 100,000 persons. The SEER*STAT software was used to query and extract cancer incidence data and patient characteristics. Predictor variables for the random forest models were obtained from the County Health Rankings and Roadmaps (CHR) [10] and the Health Resources and Services Administration—Area Health Resources Files (AHRF) [11] to include the relevant domains of population race/ethnicity, health status, health behaviors, clinical care, physical environment, and socioeconomic status (Table 1).

2.3. Analytic Approach

2.3.1. Composite Counties

To protect patient confidentiality and mitigate unstable cancer incidence rates, counties with fewer than 16 EOCRC or LOCRC cases are typically suppressed. However, since over one-third of counties had fewer than 16 EOCRC or LOCRC cases over the study period, excluding these counties would likely introduce bias by excluding patients from less populated areas. To address this issue, we used a regionalization method called Max-p-Regions [12] to aggregate adjacent, socio-demographically similar counties into the smallest geographic area based on the threshold constraint of at least 16 EOCRC or LOCRC cases and called the newly created area composite county (CC). The county-level area deprivation index (ADI), an index of social deprivation calculated from 17 census variables [13], was used to determine which counties should be grouped together considering their socio-demographical similarity. Next, the age-adjusted incidence rates of EOCRC and LOCRC were calculated at the CC-level. To account for differences in population size of CCs, county-level predictor variables were weighted at the CC-level based on the relative population of the grouped counties.

2.3.2. Random Forest and Variable Importance

Random forest analysis was used to evaluate variable importance (VI) of risk factors in predicting EOCRC incidence rate and LOCRC incidence rate, respectively. Random forest is a tree-based, nonlinear, nonparametric machine learning method that creates and aggregates an ensemble of trees for prediction using random variable selection and bootstrap sampling [14]. In each regression tree of the random forest, every predictor is “competing” with all other predictors during the selection of a split node. The results of the random forest analysis are highly stable and replicable because they are based on the average output of all its regression trees [14]. Compared with traditional linear regression models, random forest can evaluate a large number of predictors without concerns about correlation among them, as demonstrated in a previous study [15].
VI is a measure of node impurity, which reflects the extent to which stratification by a given variable minimizes the variance of responses within resultant subgroups in the regression trees of the random forest. Generally, the frequency of a predictor serving as the splits of trees in the random forest determines its VI. The VI of each predictor was then ordered from highest to lowest, and was also classified into high, median high, median low, and low using the Jenks natural breaks classification method, a data classification method that reduces the variance within classes and maximizes the variance between classes [16].
We created 20,000 trees with all variables from Table 1 included as predictors in the random forest analyses. The number of variables randomly sampled as candidates at each tree split was set to 5. R statistical software (version 4.2.1) and the package “randomForest” were used for the random forest analyses.
Because the random forest is a nonlinear tree-based method and cannot determine the direction of association between the outcome and predictors, we used the correlation coefficient to represent the direction of association as a surrogate (i.e., positive/negative, or +/−) in the VI plots.

3. Results

3.1. Patient Characteristics of EOCRC and LOCRC

Table 2 shows the patient characteristics of EOCRC and LOCRC in the study area. Our study included 136,065 and 1,141,775 individuals who were newly diagnosed with invasive EOCRC and LOCRC, respectively, in the 1085 SEER counties. The overall EOCRC incidence rate and LOCRC incidence rate were 6.9 and 134.7 per 100,000 persons, respectively. Of those, 47.7% of EOCRC and 48.4% of LOCRC patients were women. Non-Hispanic White was the most common race/ethnicity group for both EOCRC and LOCRC patients. Compared to LOCRC, there were more EOCRC patients who were Non-Hispanic Black (13.9% vs. 11.2%), Non-Hispanic Asian or Pacific Islander (7.5% vs. 5.5%), and Hispanic (18.5% vs. 10.4%), while there were fewer EOCRC patients who were Non-Hispanic White (58.7% vs. 72.2%). The number of EOCRC cases increased substantially every 5 years, with a total increase of 31.6% from 2000–2004 to 2015–2019, while the number of LOCRC cases decreased by 10.1% over the same period. Regarding tumor site, there were more EOCRC patients who had tumors in the rectum or rectosigmoid junction compared to LOCRC patients (38.4% vs. 27.9%).

3.2. Geographic Distribution of EOCRC and LOCRC

The max-p-regions method created 709 CCs from the 1085 counties. The medians (IQR) of CC-level EOCRC and LOCRC incidence rates were 7.4 (6.6–8.5) and 142.0 (129.4–156.9) per 100,000 persons, respectively. Figure 1 shows the geographic distributions of the CC-level incidence rates of EOCRC and LOCRC by quartile in the SEER regions. Generally, states in the West (California, Washington (Seattle–Puget Sound), Idaho, Utah, and New Mexico) had lower EOCRC and LOCRC incidence rates than those in the Midwest (Iowa and Illinois) or the South (Kentucky, Louisiana, and Georgia). Kentucky, Louisiana, and Southern Illinois had the highest incidence rates of both EOCRC and LOCRC. Notably, Georgia and eastern Texas had relatively higher EOCRC incidence rates and lower LOCRC incidence rates compared to other areas. In contrast, Iowa, New Jersey, northern Illinois, and upstate New York had relatively lower EOCRC incidence rates and higher LOCRC incidence rates compared to other areas.

3.3. Importance of Risk Factors in Predicting EOCRC and LOCRC

Figure 2 shows the VI of risk factors from the random forest analyses in predicting EOCRC incidence rate and LOCRC incidence rate. The most important variable is set to 100%, and the VI of the rest of the variables is scaled relative to the most important variable. The position of the horizontal lines (+/−) indicates the direction of association determined by a linear correlation coefficient between the predictor and the outcome. We observed that the direction of association was consistent between the EOCRC and the LOCRC models for all risk factors. Figure 3 presents the differences in the rankings of the variables with the ten highest VI between the EOCRC and the LOCRC models.
For race/ethnicity, the Hispanic population predictor had a high VI (ranked second) in the EOCRC model and medium-high VI (ranked second) in the LOCRC model, with a negative association with both outcomes, meaning that both of these diseases were less likely to be in the Hispanic population. The Non-Hispanic Black predictor was positively associated with both EOCRC and LOCR, but their VI was low for both outcomes (20th and 10th, respectively). The Non-Hispanic White predictor was also positively associated with both EOCRC and LOCRC, and their VI was medium-low for both outcomes (11th for EOCRC and sixth for LOCRC).
For health status risk factors, diabetes prevalence had the highest VI (first) for EOCRC, and it had a medium-low VI (seventh) for LOCRC. Low birthweight was relatively more important in the EOCRC compared to that in the LOCRC model (sixth vs. 16th).
For health behavioral risk factors, both physical inactivity and smoking were positively associated with EOCRC and LOCRC, with physical inactivity ranked fourth for EOCRC and first for LOCRC, and smoking ranked third for both EOCRC and LOCRC. Adult obesity was ranked fifth (medium-low VI) in both models. Excessive drinking was negatively associated with EOCRC and LOCRC and had a medium-low VI (12th) for EOCRC and a low VI for LOCRC (eighth). Other health behavioral risk factors, including access to recreational facilities, food insecurity, insufficient sleep, and teen birth, had a low VI for both EOCRC and LOCRC outcomes.
For clinical care risk factors, the preventable hospital stays predictor was positively associated with both outcomes with a medium-low VI, but its rank of VI increased from 10th for EOCRC to fourth for LOCRC. The predictor of primary care physicians was negatively associated with both outcomes with its VI ranked 10th for EOCRC and 27th for LOCRC. Community health centers and health care cost had a low VI for both EOCRC and LOCRC, despite their positive associations with both outcomes.
Regarding physical environment of the composite counties, air pollution, driving alone to work (indicators of the transit system and physical inactivity), and rural population all had a positive association with both EOCRC and LOCRC outcomes. However, air pollution and driving alone to work had a low VI for both outcomes, and rural population had a medium-low VI (seventh) for EOCRC and a low VI (11th) for LOCRC.

4. Discussion

Using geographic and random forest methods, this cross-sectional study explored the geographic distribution and risk factor association of EOCRC incidence rate and LOCRC incidence rate across the US at the community level. We found substantial geographic disparities in the risk of EOCRC and LOCRC. Certain regions also had a relatively low LOCRC risk and high EOCRC risk (e.g., Georgia and eastern Texas) while other areas presented relatively high LOCRC risk and low EOCRC risk (e.g., Iowa and New Jersey). The random forest analyses revealed that certain risk factors, especially diabetes prevalence and physical inactivity, had different degrees of importance in predicting the incidence rate of EOCRC and the incidence rate of LOCRC.
Diabetes is a known risk factor for CRC [17]. For example, studies from the Netherlands and Sweden found that diabetes was associated with an increased risk of CRC for patients at a younger age compared to their older counterparts [18,19]. Reinforcing these previous studies, our study found that diabetes prevalence was the top important variable in predicting the incidence rate of EOCRC, but not a high-importance variable in predicting the incidence rate of LOCRC. An interesting geographic region of investigation for diabetes and its relationship with EOCRC is the “Diabetes Belt”, defined by researchers as a region comprising 644 counties in the American South [20] (and includes the states of Georgia, Kentucky, and Louisiana in our study area). Previous research has also noted that people in the Diabetes Belt were more likely to be non-Hispanic Black, have a sedentary lifestyle, be obese, and have a smaller fitness/recreation facility density than in the rest of the US [20,21]. In our geographic analysis, we found that in Georgia, a state in the Diabetes Belt and whose diabetes prevalence was among the highest, most composite counties were in the top quartile of EOCRC incidence rates but were in lower quartiles of LOCRC incident rates (Figure 1), suggesting that diabetes has a stronger effect on EOCRC versus LOCRC. In recent years, the prevalence of type 2 diabetes in adolescents and young adults has dramatically increased [22,23], and increases have been disproportionally observed among African Americans [23,24]. Thus, future studies should continue to investigate the role of place, race, ethnicity, diabetes, and EOCRC.
Our models identified physical inactivity as having the highest VI for LOCRC and a medium-high VI for EOCRC, consistent with numerous studies suggesting that physical inactivity or sedentary lifestyle is a substantial risk factor for CRC [3,5,25]. Physical inactivity is also associated with other chronic conditions, such as obesity and diabetes [26,27], which may also increase the risk of CRC. In recent years, sedentary-based work practices and passive behaviors such as smartphone use have increased among young adults, especially in the COVID-19 era [28,29]. A growing body of literature has also linked sedentary lifestyle to an increased risk of EOCRC [30,31,32]. Our study provides further evidence suggesting that increasing physical activities may be an effective and actionable risk reduction strategy for CRC for people of all ages.
As a risk factor strongly associated with diabetes and physical inactivity, adult obesity had a relatively low VI in predicting either EOCRC incidence rate or LOCRC incidence rate. Despite CRC being one of the obesity-associated cancers [33,34], associations between obesity and EOCRC have not been consistently shown in earlier studies [3,4,35].
Regarding other health behavioral risk factors, smoking was the third important predictor in both the EOCRC and LOCRC models in our study, which supports earlier findings on smoking being a risk factor for CRC [3,4,5]. Interestingly, excessive drinking was negatively associated with EOCRC and LOCRC in our study, though it should be noted that the risk factor had a relatively low VI. Alcohol consumption, especially heavy drinking, has been associated with increased CRC risk in previous studies [36,37,38]. This inconsistency may be because excessive drinking is confounded by factors such as the rurality of residence and diet. In another study, moderate alcohol consumption was associated with a reduced CRC risk in populations with greater adherence to a Mediterranean diet [39]. The lack of consistency warrants further investigation into the effect of alcohol consumption on the risk of EOCRC and LOCRC.
There were substantial differences in the risk of EOCRC and LOCRC among racial/ethnic groups. Recent epidemiological data show that the incidence of CRC for Hispanics was 25% and 12% lower than that for Non-Hispanic Blacks and Non-Hispanic Whites in the US, respectively [1]. However, this difference narrowed for patients older than 50, as suggested by a population-based study of patients from California [40]. Despite the negative association between Hispanic population and CRC risk from our study, our data in Table 2 show that the proportion of Hispanic patients with EOCRC was much higher than those with LOCRC (18.5% vs. 10.4%). This might be attributable to changes in lifestyle between the US-born Hispanic population versus first-generation Hispanic immigrants; not only are US-born Hispanics younger on average compared to first-generation Hispanic immigrants [41], but they also report lower intake of fruits and vegetables [42], higher rates of smoking [43], higher rates of physical inactivity [44], and higher rates of obesity [45]. Previous studies have also shown that Hispanics born in the US have a higher risk of colorectal cancer death compared to their foreign-born counterparts [46] and even Non-Hispanic Whites [47]. Thus, as the proportion of US-born Hispanics increases and the proportion of foreign-born Hispanics decreases [48], the association between Hispanic ethnicity and CRC burden may evolve [49].
Finally, compared to race/ethnicity, health status, and health behaviors, risk factors related to clinical care, physical environment, and socioeconomic status did not show high VI in predicting either EOCRC or LOCRC incidence rate. However, these risk factors might still influence the risk of EOCRC and LOCRC through their impact on other risk factors such as smoking, physical inactivity, obesity, and diabetes, as suggested by their directions of association in Figure 2. Future studies should investigate the causal pathways between these risk factors and CRC.
Our findings should be interpreted in light of the following limitations. First, we were not able to consider temporal variations in either the outcomes or the predictors during the 20-year study period. Second, this community-level study did not consider individual-level characteristics in the analyses. However, many of the associations identified from our study are consistent with previous individual-level studies on risk factors of EOCRC and LOCRC. Finally, we could not consider spatially varying associations [50] between the risk factors and EOCRC and LOCRC due to the relatively small number of geographic units (composite counties) and non-contiguous regions in the study area.
We note that this random forest approach is data-driven and results are stable and reproducible based on the data inputs. Future studies using new additional data may impact the results of EOCRC and LOCRC. Also, the focus here is not to discover causal pathways for EOCRC or LOCRC, noting that causality cannot be established from this study, given its cross-sectional nature. In addition, the results obtained from data-driven approaches should be interpreted with caution, and the plausibility of the emerging associations should be determined in light of the relevant body of knowledge. Nevertheless, evaluating the association between community-level risk factors and outcomes is an essential first step toward establishing causality.

5. Conclusions

This community-level study evaluated and compared associations between various risk factors and EOCRC and LOCRC using novel geographic and machine learning approaches. The geographic analysis revealed regions with an elevated risk of EOCRC but a lower risk of LOCRC, identifying regions with a disproportionate burden of EOCRC. The machine learning analysis identified risk factor profiles specific to EOCRC and LOCRC. Collectively, these findings can help facilitate future studies that further uncover actionable interventions to reduce EOCRC and guide where targeted interventions to reduce EOCRC burden should be deployed.

Author Contributions

Conceptualization, W.D., U.K., J.R., N.A.B. and S.M.K.; methodology, W.D., U.K., J.R., M.K., S.L., N.A.B. and S.M.K.; validation, W.D.; investigation, W.D.; resources, N.A.B. and S.M.K.; data curation, W.D. and U.K.; writing—original draft preparation, W.D.; writing—review and editing, All Authors; visualization, W.D., U.K., J.R. and S.L.; supervision, N.A.B. and S.M.K.; funding acquisition, J.R. and S.M.K. We confirm the originality of content. W.D. had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Cancer Institute Case Comprehensive Cancer Center (P30 CA043703).

Institutional Review Board Statement

Ethical review and approval were waived for this study due to the fact that this study did not constitute human participant research.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data can be shared upon request.

Conflicts of Interest

Weichuan Dong and Siran M. Koroukian reported receiving grants from American Cancer Society (RWIA-20-111-02 RWIA) and by contracts from Cleveland Clinic Foundation, including a subcontract from Celgene Corporation. Siran M. Koroukian was also supported by grants from the Centers for Disease Control and Prevention, U48 DP005030-05S1 and U48 DP006404-03S7; National Institutes of Health (R15 NR017792, UH3-DE025487, and R01 AG074946-01) and American Cancer Society (132678-RSGI-19-213-01-CPHPS). Uriel Kim is supported by grants from the National Institute of General Medical Sciences (5T32GM007250), National Center for Advancing Translational Sciences (5TL1TR002549), and the PhRMA Foundation (PDHO18). Johnie Rose reported receiving grants from NIH/National Cancer Institute during the conduct of the study; holding stock in Vinya Intelligence Inc outside the submitted work; and having a patent issued for US 270,799 B2 “In-home remote monitoring systems and methods for predicting health status decline”. No other disclosures were reported.

References

  1. Siegel, R.L.; Miller, K.D.; Goding Sauer, A.; Fedewa, S.A.; Butterly, L.F.; Anderson, J.C.; Cercek, A.; Smith, R.A.; Jemal, A. Colorectal cancer statistics, 2020. CA Cancer J. Clin. 2020, 70, 145–164. [Google Scholar] [CrossRef]
  2. Wang, W.; Chen, W.; Lin, J.; Shen, Q.; Zhou, X.; Lin, C. Incidence and characteristics of young-onset colorectal cancer in the United States: An analysis of SEER data collected from 1988 to 2013. Clin. Res. Hepatol. Gastroenterol. 2019, 43, 208–215. [Google Scholar] [CrossRef]
  3. Archambault, A.N.; Lin, Y.; Jeon, J.; Harrison, T.A.; Bishop, D.T.; Brenner, H.; Casey, G.; Chan, A.T.; Chang-Claude, J.; Figueiredo, J.C.; et al. Nongenetic Determinants of Risk for Early-Onset Colorectal Cancer. JNCI Cancer Spectr. 2021, 5, pkab029. [Google Scholar] [CrossRef]
  4. Hayes, R.B. Advances in Understanding Early-Onset Colorectal Cancer. Cancer Epidemiol. Biomark. Prev. 2021, 30, 1775–1777. [Google Scholar] [CrossRef]
  5. Sinicrope, F.A. Increasing Incidence of Early-Onset Colorectal Cancer. N. Engl. J. Med. 2022, 386, 1547–1558. [Google Scholar] [CrossRef] [PubMed]
  6. Mauri, G.; Sartore-Bianchi, A.; Russo, A.G.; Marsoni, S.; Bardelli, A.; Siena, S. Early-onset colorectal cancer in young individuals. Mol. Oncol. 2019, 13, 109–131. [Google Scholar] [CrossRef] [PubMed]
  7. Doubeni, C.A.; Laiyemo, A.O.; Major, J.M.; Schootman, M.; Lian, M.; Park, Y.; Graubard, B.I.; Hollenbeck, A.R.; Sinha, R. Socioeconomic status and the risk of colorectal cancer: An analysis of more than a half million adults in the National Institutes of Health-AARP Diet and Health Study. Cancer 2012, 118, 3636–3644. [Google Scholar] [CrossRef] [PubMed]
  8. Surveillance, Epidemiology, and End Results (SEER) Program, SEER*Stat Database: Incidence—SEER Research Plus Limited-Field Data, 22 Registries, November 2021 Sub (2000–2019)—Linked To County Attributes—Total U.S. 1969-2020 Counties, National Cancer Institute, DCCPS, Surveillance Research Program, released April 2022, based on the November 2021 Submission. Available online: www.seer.cancer.gov (accessed on 28 November 2022).
  9. Surveillance, Epidemiology, and End Results (SEER) Program. Overview of the SEER Program. Available online: https://seer.cancer.gov/about/overview.html (accessed on 27 January 2023).
  10. County Health Rankings & Roadmaps. Available online: https://www.countyhealthrankings.org (accessed on 27 January 2023).
  11. Health Resources and Services Administration—Area Health Resources Files. Available online: https://data.hrsa.gov/topics/health-workforce/ahrf (accessed on 27 January 2023).
  12. Duque, J.C.; Anselin, L.; Rey, S.J. The Max-P-Regions Problem*. J. Reg. Sci. 2012, 52, 397–419. [Google Scholar] [CrossRef]
  13. Singh, G.K. Area deprivation and widening inequalities in US mortality, 1969–1998. Am. J. Public Health 2003, 93, 1137–1143. [Google Scholar] [CrossRef]
  14. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef] [Green Version]
  15. Dong, W.; Bensken, W.P.; Kim, U.; Rose, J.; Berger, N.A.; Koroukian, S.M. Phenotype Discovery and Geographic Disparities of Late-Stage Breast Cancer Diagnosis across U.S. Counties: A Machine Learning Approach. Cancer Epidemiol. Biomark. Prev. 2022, 31, 66–76. [Google Scholar] [CrossRef]
  16. Jenks, G.F. The data model concept in statistical mapping. Int. Yearb. Cartogr. 1967, 7, 186–190. [Google Scholar]
  17. Ma, Y.; Yang, W.; Song, M.; Smith-Warner, S.A.; Yang, J.; Li, Y.; Ma, W.; Hu, Y.; Ogino, S.; Hu, F.B.; et al. Type 2 diabetes and risk of colorectal cancer in two large U.S. prospective cohorts. Br. J. Cancer 2018, 119, 1436–1442. [Google Scholar] [CrossRef] [PubMed]
  18. de Kort, S.; Masclee, A.A.; Sanduleanu, S.; Weijenberg, M.P.; van Herk-Sukel, M.P.; Oldenhof, N.J.; van den Bergh, J.P.; Haak, H.R.; Janssen-Heijnen, M.L. Higher risk of colorectal cancer in patients with newly diagnosed diabetes mellitus before the age of colorectal cancer screening initiation. Sci. Rep. 2017, 7, 46527. [Google Scholar] [CrossRef]
  19. Ali Khan, U.; Fallah, M.; Tian, Y.; Sundquist, K.; Sundquist, J.; Brenner, H.; Kharazmi, E. Personal History of Diabetes as Important as Family History of Colorectal Cancer for Risk of Colorectal Cancer: A Nationwide Cohort Study. Am. J. Gastroenterol. 2020, 115, 1103–1109. [Google Scholar] [CrossRef] [PubMed]
  20. Barker, L.E.; Kirtland, K.A.; Gregg, E.W.; Geiss, L.S.; Thompson, T.J. Geographic distribution of diagnosed diabetes in the U.S.: A diabetes belt. Am. J. Prev. Med. 2011, 40, 434–439. [Google Scholar] [CrossRef]
  21. Myers, C.A.; Slack, T.; Broyles, S.T.; Heymsfield, S.B.; Church, T.S.; Martin, C.K. Diabetes prevalence is associated with different community factors in the diabetes belt versus the rest of the United States. Obesity 2017, 25, 452–459. [Google Scholar] [CrossRef]
  22. Lascar, N.; Brown, J.; Pattison, H.; Barnett, A.H.; Bailey, C.J.; Bellary, S. Type 2 diabetes in adolescents and young adults. Lancet Diabetes Endocrinol. 2018, 6, 69–80. [Google Scholar] [CrossRef] [PubMed]
  23. Virani, S.S.; Alonso, A.; Aparicio, H.J.; Benjamin, E.J.; Bittencourt, M.S.; Callaway, C.W.; Carson, A.P.; Chamberlain, A.M.; Cheng, S.; Delling, F.N.; et al. Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association. Circulation 2021, 143, e254–e743. [Google Scholar] [CrossRef]
  24. Wang, M.C.; Shah, N.S.; Carnethon, M.R.; O’Brien, M.J.; Khan, S.S. Age at Diagnosis of Diabetes by Race and Ethnicity in the United States From 2011 to 2018. JAMA Intern. Med. 2021, 181, 1537–1539. [Google Scholar] [CrossRef] [PubMed]
  25. Slattery, M.L.; Potter, J.; Caan, B.; Edwards, S.; Coates, A.; Ma, K.N.; Berry, T.D. Energy balance and colon cancer—beyond physical activity. Cancer Res. 1997, 57, 75–80. Available online: https://aacrjournals.org/cancerres/article/57/1/75/503178/Energy-Balance-and-Colon-Cancer-beyond-Physical (accessed on 27 January 2023). [PubMed]
  26. Hu, F.B.; Li, T.Y.; Colditz, G.A.; Willett, W.C.; Manson, J.E. Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women. JAMA 2003, 289, 1785–1791. [Google Scholar] [CrossRef]
  27. Qi, Q.; Li, Y.; Chomistek, A.K.; Kang, J.H.; Curhan, G.C.; Pasquale, L.R.; Willett, W.C.; Rimm, E.B.; Hu, F.B.; Qi, L. Television watching, leisure time physical activity, and the genetic predisposition in relation to body mass index in women and men. Circulation 2012, 126, 1821–1827. [Google Scholar] [CrossRef] [PubMed]
  28. Ricci, F.; Izzicupo, P.; Moscucci, F.; Sciomer, S.; Maffei, S.; Di Baldassarre, A.; Mattioli, A.V.; Gallina, S. Recommendations for Physical Inactivity and Sedentary Behavior During the Coronavirus Disease (COVID-19) Pandemic. Front. Public Health 2020, 8, 199. [Google Scholar] [CrossRef] [PubMed]
  29. Wilms, P.; Schröder, J.; Reer, R.; Scheit, L. The Impact of “Home Office” Work on Physical Activity and Sedentary Behavior during the COVID-19 Pandemic: A Systematic Review. Int. J. Environ. Res. Public Health 2022, 19, 12344. [Google Scholar] [CrossRef]
  30. Nguyen, L.H.; Liu, P.H.; Zheng, X.; Keum, N.; Zong, X.; Li, X.; Wu, K.; Fuchs, C.S.; Ogino, S.; Ng, K.; et al. Sedentary Behaviors, TV Viewing Time, and Risk of Young-Onset Colorectal Cancer. JNCI Cancer Spectr. 2018, 2, pky073. [Google Scholar] [CrossRef] [PubMed]
  31. Rogers, C.R.; Moore, J.X.; Qeadan, F.; Gu, L.Y.; Huntington, M.S.; Holowatyj, A.N. Examining factors underlying geographic disparities in early-onset colorectal cancer survival among men in the United States. Am. J. Cancer Res. 2020, 10, 1592–1607. Available online: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7269786 (accessed on 27 January 2023).
  32. Schmid, D.; Leitzmann, M.F. Television viewing and time spent sedentary in relation to cancer risk: A meta-analysis. J. Natl. Cancer Inst. 2014, 106, dju098. [Google Scholar] [CrossRef]
  33. Steele, C.B.; Thomas, C.C.; Henley, S.J.; Massetti, G.M.; Galuska, D.A.; Agurs-Collins, T.; Puckett, M.; Richardson, L.C. Vital Signs: Trends in Incidence of Cancers Associated with Overweight and Obesity—United States, 2005–2014. MMWR Morb. Mortal. Wkly. Rep. 2017, 66, 1052–1058. [Google Scholar] [CrossRef]
  34. Koroukian, S.M.; Dong, W.; Berger, N.A. Changes in Age Distribution of Obesity-Associated Cancers. JAMA Netw. Open 2019, 2, e199261. [Google Scholar] [CrossRef]
  35. Liu, P.H.; Wu, K.; Ng, K.; Zauber, A.G.; Nguyen, L.H.; Song, M.; He, X.; Fuchs, C.S.; Ogino, S.; Willett, W.C.; et al. Association of Obesity With Risk of Early-Onset Colorectal Cancer Among Women. JAMA Oncol. 2019, 5, 37–44. [Google Scholar] [CrossRef] [PubMed]
  36. Fedirko, V.; Tramacere, I.; Bagnardi, V.; Rota, M.; Scotti, L.; Islami, F.; Negri, E.; Straif, K.; Romieu, I.; La Vecchia, C.; et al. Alcohol drinking and colorectal cancer risk: An overall and dose-response meta-analysis of published studies. Ann. Oncol. 2011, 22, 1958–1972. [Google Scholar] [CrossRef]
  37. Moskal, A.; Norat, T.; Ferrari, P.; Riboli, E. Alcohol intake and colorectal cancer risk: A dose-response meta-analysis of published cohort studies. Int. J. Cancer 2007, 120, 664–671. [Google Scholar] [CrossRef]
  38. Pedersen, A.; Johansen, C.; Grønbaek, M. Relations between amount and type of alcohol and colon and rectal cancer in a Danish population based cohort study. Gut 2003, 52, 861–867. [Google Scholar] [CrossRef] [PubMed]
  39. Klarich, D.S.; Brasser, S.M.; Hong, M.Y. Moderate Alcohol Consumption and Colorectal Cancer Risk. Alcohol Clin. Exp. Res. 2015, 39, 1280–1291. [Google Scholar] [CrossRef]
  40. Ellis, L.; Abrahão, R.; McKinley, M.; Yang, J.; Somsouk, M.; Marchand, L.L.; Cheng, I.; Gomez, S.L.; Shariff-Marco, S. Colorectal Cancer Incidence Trends by Age, Stage, and Racial/Ethnic Group in California, 1990–2014. Cancer Epidemiol. Biomark. Prev. 2018, 27, 1011–1018. [Google Scholar] [CrossRef] [PubMed]
  41. Patten, E. The Nation’s Latino Population Is Defined by Its Youth. Pew Research Center 2016. Available online: https://www.pewresearch.org/hispanic/2016/04/20/the-nations-latino-population-is-defined-by-its-youth/ (accessed on 27 January 2023).
  42. Bermudez, O.I.; Falcon, L.M.; Tucker, K.L. Intake and food sources of macronutrients among older Hispanic adults: Association with ethnicity, acculturation, and length of residence in the United States. J. Am. Diet. Assoc. 2000, 100, 665–673. [Google Scholar] [CrossRef] [PubMed]
  43. Pulvers, K.; Cupertino, A.P.; Scheuermann, T.S.; Cox, L.S.; Ho, Y.Y.; Nollen, N.L.; Cuellar, R.; Ahluwalia, J.S. Daily and Nondaily Smoking Varies by Acculturation among English-Speaking, US Latino Men and Women. Ethn. Dis. 2018, 28, 105–114. [Google Scholar] [CrossRef]
  44. Joseph, R.P.; Benitez, T.J.; Ainsworth, B.E.; Todd, M.; Keller, C. Acculturation and Physical Activity among Latinas Enrolled in a 12-Month Walking Intervention. West. J. Nurs. Res. 2018, 40, 942–960. [Google Scholar] [CrossRef]
  45. Lara, M.; Gamboa, C.; Kahramanian, M.I.; Morales, L.S.; Hayes Bautista, D.E. Acculturation and Latino health in the United States: A review of the literature and its sociopolitical context. Annu. Rev. Public Health 2005, 26, 367–397. [Google Scholar] [CrossRef]
  46. Chen, H.; Wu, A.H.; Wang, S.; Bookstein, A.; Le Marchand, L.; Wilkens, L.R.; Haiman, C.A.; Cheng, I.; Monroe, K.R.; Setiawan, V.W. Cancer Mortality Patterns by Birthplace and Generation Status of Mexican Latinos: The Multiethnic Cohort. J. Natl. Cancer Inst. 2022, 114, 959–968. [Google Scholar] [CrossRef] [PubMed]
  47. Pinheiro, P.S.; Callahan, K.E.; Gomez, S.L.; Marcos-Gragera, R.; Cobb, T.R.; Roca-Barcelo, A.; Ramirez, A.G. High cancer mortality for US-born Latinos: Evidence from California and Texas. BMC Cancer 2017, 17, 478. [Google Scholar] [CrossRef] [PubMed]
  48. Gonzalez-Barrera, A. More Mexicans Leaving Than Coming to the US. Pew Research Center 2015. Available online: https://www.pewresearch.org/hispanic/2015/11/19/more-mexicans-leaving-than-coming-to-the-u-s/ (accessed on 27 January 2023).
  49. Pinheiro, P.S.; Callahan, K.E.; Stern, M.C.; de Vries, E. Migration from Mexico to the United States: A high-speed cancer transition. Int. J. Cancer 2018, 142, 477–488. [Google Scholar] [CrossRef]
  50. Dong, W.; Bensken, W.P.; Kim, U.; Rose, J.; Fan, Q.; Schiltz, N.K.; Berger, N.A.; Koroukian, S.M. Variation in and Factors Associated With US County-Level Cancer Mortality, 2008–2019. JAMA Netw. Open 2022, 5, e2230925. [Google Scholar] [CrossRef] [PubMed]
Cancers 15 01006 g001a 550Cancers 15 01006 g001b 550
Figure 1. Geographic distribution of (A) EOCRC incidence rate, and (B) LOCRC incidence rate. Notes: Incidence rates are age-adjusted and classified by quartile. Data for states in white are not available in SEER.
Figure 1. Geographic distribution of (A) EOCRC incidence rate, and (B) LOCRC incidence rate. Notes: Incidence rates are age-adjusted and classified by quartile. Data for states in white are not available in SEER.
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Figure 2. Variable importance and direction of association of risk factors for (A) EOCRC incidence rate, and (B) LOCRC incidence rate. Notes: (1) The most important variable is set to 100%. The importance of the rest of the variables is scaled relative to the most important variable. (2) Direction of association was determined by a linear correlation coefficient. (3) Categories of variable importance (i.e., high to low) was classified by the Jenks natural breaks method.
Figure 2. Variable importance and direction of association of risk factors for (A) EOCRC incidence rate, and (B) LOCRC incidence rate. Notes: (1) The most important variable is set to 100%. The importance of the rest of the variables is scaled relative to the most important variable. (2) Direction of association was determined by a linear correlation coefficient. (3) Categories of variable importance (i.e., high to low) was classified by the Jenks natural breaks method.
Cancers 15 01006 g002
Cancers 15 01006 g003 550
Figure 3. Comparisons of the top ten risk factors based on variable importance between EOCRC and LOCRC models.
Figure 3. Comparisons of the top ten risk factors based on variable importance between EOCRC and LOCRC models.
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Table
Table 1. County-level explanatory variables used in random forest analyses.
Table 1. County-level explanatory variables used in random forest analyses.
VariableDescriptionYearOriginal Source
Ethnicity/Race
Non-Hispanic BlackPercentage of Non-Hispanic African American population2011Census—PE
Non-Hispanic WhitePercentage of Non-Hispanic White population
HispanicPercentage of population identifying as Hispanics
Health Status
DiabetesPercentage of adults (age 20+) with diagnosed diabetes (age-adjusted)2009CDC—DIA
Low birthweightPercentage of live births with low birthweight (<2500 g)2004–2010CDC—NCHS
Poor or fair healthPercentage of adults reporting fair or poor health (age-adjusted)2014CDC—BRFSS
Health Behaviors
Adult obesityPercentage of the people (age 18+) that report a body mass index ≥ 30 (age-adjusted)2009CDC—DIA
Physical inactivityPercentage of adults (age 18+) reporting no leisure-time physical activity (age-adjusted)2009
Excessive drinkingPercentage of adults reporting binge or heavy drinking (age-adjusted)2014CDC—BRFSS
Insufficient sleepPercentage of adults who report fewer than 7 h of sleep on average (age-adjusted)2014
SmokingPercentage of adults who are current smokers (age-adjusted)2014
Access to recreational facilitiesNumber of recreational facilities (engaged in fitness and recreational sports) per 100,000 persons2010USDA FEA and Census CBP
Food insecurityPercentage of population who lack adequate access to food2013MMP
Teen birthNumber of births per 1000 females ages 15–192004–2010CDC—NCHS
Clinical Care
Uninsured adultsPercentage of adults under age 65 without health insurance2010Census—SAHIE
Health care costsPer capita spending of Medicare enrollees2009DAHC
Preventable hospital staysRate of hospital stays for ambulatory-care sensitive conditions per 100,000 Medicare enrollees2010
Primary care physicians Primary care physicians in patient care per 100,000 persons2010HRSA
Community health centersCommunity health centers per 100,000 persons2013
Physical Environment
Air pollutionTotal days when the fine particulate matter (PM2.5) exceeded the 24 h primary standard established by the US EPA.2008CDC WONDER
Driving alone to work Percentage of the workforce that drives alone to work (indicators of the transit system and physical inactivity)2007–2011Census—ACS
Rural populationPercentage of people living in rural areas2010Census—PE
Socioeconomic Status
Area deprivation indexA composite measure of 17 census variables designed to describe social deprivation2008–2012Census—ACS
Median household incomeThe income where half of households in a county earn more and half of households earn less2011Census—SAIPE
PovertyPercentage of people in poverty2010
Receipt of SNAP benefitsPercentage of people who were food stamp recipients2010Census—SNAP
Some collegePercentage of people (age 25–44) with some post-secondary education2007–2011Census—ACS
Female-headed householdsPercentage of female-headed households2010Census (Decennial)
UnemploymentPercentage of population (age 16+) unemployed but seeking work2011BLS
Violent crimeNumber of reported violent crime offenses per 100,000 persons2008–2010FBI—UCR
Abbreviations: ACS: American Community Survey; BLS: Bureau of Labor Statistics; BRFSS: Behavioral Risk Factor Surveillance System; CBP: County Business Patterns; CDC: Centers for Disease Control and Prevention; DAHC: Dartmouth Atlas of Health Care; DIA: Diabetes Interactive Atlas; FEA: Food Environment Atlas; HRSA: Health Resources and Services Administration; NCHS: National Center for Health Statistics; PE: Population Estimates; SAHIE: Small Area Health Insurance Estimates; SAIPE: Small Area Income and Poverty Estimates; SEER: Surveillance, Epidemiology, and End Results; SNAP: Supplemental Nutrition Assistance Program; UCR: Uniform Crime Reporting; USDA: U.S. Department of Agriculture. Note: Area Deprivation Index was obtained using the ‘sociome’ R package; Community Health Centers, Poverty, Receipt of SNAP benefits, and Female-headed households were obtained from HRSA—Area Health Resources Files; All other variables were obtained from County Health Rankings and Roadmaps.
Table
Table 2. Patient characteristics of EOCRC and LOCRC in the study area.
Table 2. Patient characteristics of EOCRC and LOCRC in the study area.
EOCRC
(Age < 50)		LOCRC
(Age 50+)		p-Value
N136,0651,141,775
Incidence rate *6.9134.7
Sex (%) <0.0001
Male71,127 (52.3)588,623 (51.6)
Female64,938 (47.7)553,152 (48.4)
Race/Ethnicity (%) <0.0001
Non-Hispanic White79,807 (58.7)824,210 (72.2)
Non-Hispanic Black18,953 (13.9)127,910 (11.2)
Non-Hispanic American Indian/Alaska Native711 (0.5)3632 (0.3)
Non-Hispanic Asian or Pacific Islander10,252 (7.5)62,769 (5.5)
Hispanic (All Races)25,240 (18.5)118,234 (10.4)
Non-Hispanic Unknown Race1102 (0.8)5020 (0.4)
Year of Diagnosis (%) <0.0001
2000–200429,908 (22.0)306,441 (26.8)
2005–200932,582 (23.9)287,722 (25.2)
2010–201434,203 (25.1)271,980 (23.8)
2015–201939,372 (28.9)275,632 (24.1)
Tumor Site (%) <0.0001
Colon excluding Sigmoid Colon55,063 (40.6)606,845 (53.1)
Sigmoid Colon28,722 (21.1)216,986 (19.0)
Rectum and Rectosigmoid Junction52,280 (38.4)317,944 (27.9)
* Age-adjusted incidence rate per 100,000 persons. Abbreviations. EOCRC: early-onset colorectal cancer; LOCRC: late-onset colorectal cancer; NOS: not otherwise specified.
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MDPI and ACS Style

Dong, W.; Kim, U.; Rose, J.; Hoehn, R.S.; Kucmanic, M.; Eom, K.; Li, S.; Berger, N.A.; Koroukian, S.M. Geographic Variation and Risk Factor Association of Early Versus Late Onset Colorectal Cancer. Cancers 2023, 15, 1006. https://doi.org/10.3390/cancers15041006

AMA Style

Dong W, Kim U, Rose J, Hoehn RS, Kucmanic M, Eom K, Li S, Berger NA, Koroukian SM. Geographic Variation and Risk Factor Association of Early Versus Late Onset Colorectal Cancer. Cancers. 2023; 15(4):1006. https://doi.org/10.3390/cancers15041006

Chicago/Turabian Style

Dong, Weichuan, Uriel Kim, Johnie Rose, Richard S. Hoehn, Matthew Kucmanic, Kirsten Eom, Shu Li, Nathan A. Berger, and Siran M. Koroukian. 2023. "Geographic Variation and Risk Factor Association of Early Versus Late Onset Colorectal Cancer" Cancers 15, no. 4: 1006. https://doi.org/10.3390/cancers15041006

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