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

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.

💡 Research Summary

This paper presents a joint spatial analysis of colon and rectum cancer incidence in the Twin Cities metropolitan and surrounding exurban area of Minnesota, using data from the Minnesota Cancer Surveillance System for the years 1998‑2002. The authors treat the two cancers as a pair of marked point patterns: each observed case has a geographic coordinate (latitude‑longitude) and a mark consisting of individual‑level covariates (patient age and cancer stage). Because the two cancers share many environmental and lifestyle risk factors, the authors explicitly model the expected positive dependence between the two point processes.

Methodologically, the study extends the log‑Gaussian Cox process (LGCP) framework to accommodate both spatially referenced covariates (census‑derived poverty rate and a binary indicator of whether a location lies within the core metropolitan area) and individual‑level risk factors. The intensity function for cancer type i (i = colon, rectum) at location s is specified as

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