{Reference Type}: Journal Article
{Title}: A National Study of Colorectal Cancer Survivorship Disparities: A Latent Class Analysis Using SEER (Surveillance, Epidemiology, and End Results) Registries.
{Author}: Montiel Ishino FA;Odame EA;Villalobos K;Liu X;Salmeron B;Mamudu H;Williams F;
{Journal}: Front Public Health
{Volume}: 9
{Issue}: 0
{Year}: 2021
{Factor}: 6.461
{DOI}: 10.3389/fpubh.2021.628022
{Abstract}: Introduction: Long-standing disparities in colorectal cancer (CRC) outcomes and survival between Whites and Blacks have been observed. A person-centered approach using latent class analysis (LCA) is a novel methodology to assess and address CRC health disparities. LCA can overcome statistical challenges from subgroup analyses that would normally impede variable-centered analyses like regression. Aim was to identify risk profiles and differences in malignant CRC survivorship outcomes. Methods: We conducted an LCA on the Surveillance, Epidemiology, and End Results data from 1975 to 2016 for adults ≥18 (N = 525,245). Sociodemographics used were age, sex/gender, marital status, race, and ethnicity (Hispanic/Latinos) and stage at diagnosis. To select the best fitting model, we employed a comparative approach comparing sample-size adjusted BIC and entropy; which indicates a good separation of classes. Results: A four-class solution with an entropy of 0.72 was identified as: lowest survivorship, medium-low, medium-high, and highest survivorship. The lowest survivorship class (26% of sample) with a mean survival rate of 53 months had the highest conditional probabilities of being 76-85 years-old at diagnosis, female, widowed, and non-Hispanic White, with a high likelihood with localized staging. The highest survivorship class (53% of sample) with a mean survival rate of 92 months had the highest likelihood of being married, male with localized staging, and a high likelihood of being non-Hispanic White. Conclusion: The use of a person-centered measure with population-based cancer registries data can help better detect cancer risk subgroups that may otherwise be overlooked.