A National Study of Colorectal Cancer Survivorship Disparities: A Latent Class Analysis Using SEER (Surveillance, Epidemiology, and End Results) Registries.

Autor:	Montiel Ishino FA; Division of Intramural Research, National Institute on Minority Health and Health Disparities, Bethesda, MD, United States., Odame EA; Department of Environmental Health Sciences, University of Alabama at Birmingham, Birmingham, AL, United States., Villalobos K; Division of Intramural Research, National Institute on Minority Health and Health Disparities, Bethesda, MD, United States., Liu X; Division of Intramural Research, National Institute on Minority Health and Health Disparities, Bethesda, MD, United States., Salmeron B; Division of Intramural Research, National Institute on Minority Health and Health Disparities, Bethesda, MD, United States., Mamudu H; Department of Health Services Management and Policy, College of Public Health, East Tennessee State University, Johnson City, TN, United States., Williams F; Division of Intramural Research, National Institute on Minority Health and Health Disparities, Bethesda, MD, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in public health [Front Public Health] 2021 Feb 25; Vol. 9, pp. 628022. Date of Electronic Publication: 2021 Feb 25 (Print Publication: 2021).
DOI:	10.3389/fpubh.2021.628022
Abstrakt:	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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer SG declared a shared affiliation, with no collaboration, with several of the authors, FM, KV, XL, BS, and FW to the handling editor at the time of review. (Copyright © 2021 Montiel Ishino, Odame, Villalobos, Liu, Salmeron, Mamudu and Williams.)
Databáze:	MEDLINE
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