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

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.

Keywords: colorectal cancer; early-onset; geographic information system; machine learning; random forest; regionalization; risk factor.

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.

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 3

Comparisons of the top ten risk factors based on variable importance between EOCRC and LOCRC models.