Analysis of Minnesota colon and rectum cancer point patterns with spatial and nonspatial covariate information

TL;DR

This study models colon and rectum cancer point patterns in Minnesota using spatial and individual covariates, revealing differences in risk factors and suggesting targeted screening strategies.

Contribution

It extends spatial point pattern analysis with covariates in a log Gaussian Cox process to analyze colon and rectum cancers separately.

Findings

Higher colon cancer risk in inner Twin Cities and southern/western exurbs.

Significant age and stage differences between colon and rectum cancers.

Smoothed maps of cancer intensity highlight areas for targeted screening.

Abstract

Colon and rectum cancer share many risk factors, and are often tabulated together as ``colorectal cancer'' in published summaries. However, recent work indicating that exercise, diet, and family history may have differential impacts on the two cancers encourages analyzing them separately, so that corresponding public health interventions can be more efficiently targeted. We analyze colon and rectum cancer data from the Minnesota Cancer Surveillance System from 1998--2002 over the 16-county Twin Cities (Minneapolis--St. Paul) metro and exurban area. The data consist of two marked point patterns, meaning that any statistical model must account for randomness in the observed locations, and expected positive association between the two cancer patterns. Our model extends marked spatial point pattern analysis in the context of a log Gaussian Cox process to accommodate spatially referenced covariates (local poverty rate and location within the metro area), individual-level risk factors (patient age and cancer stage), and related interactions. We obtain smoothed maps of marginal log-relative intensity surfaces for colon and rectum cancer, and uncover significant age and stage differences between the two groups. This encourages more aggressive colon cancer screening in the inner Twin Cities and their southern and western exurbs, where our model indicates higher colon cancer relative intensity.