JOURNAL ARTICLE
Health-related quality of life by race, ethnicity, and country of origin among cancer survivors.
Published In: JNCI: Journal of the National Cancer Institute, 2023, v. 115, n. 3. P. 258 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Reeve, Bryce B; Graves, Kristi D; Lin, Li; Potosky, Arnold L; Ahn, Jaeil; Henke, Debra M; Pan, Wei; Fall-Dickson, Jane M 3 of 3
Abstract
This article focuses on a population-based study examining how health-related quality of life (HRQOL) among U.S. cancer survivors varies by self-reported race, ethnicity, and country of origin, adjusting for clinical and social determinants of health. Using data from 5,366 survivors across four Surveillance, Epidemiology, and End Results (SEER) cancer registries, the study identified four distinct HRQOL clusters—high, average, low, and very low—based on Patient-Reported Outcomes Measurement Information System (PROMIS) domains. Findings revealed that Caribbean, American Indian and Alaska Native, Cuban, Dominican, and Puerto Rican survivors were disproportionately represented in the very low HRQOL cluster compared to non-Hispanic White survivors, even after controlling for socioeconomic and clinical factors. The study underscores the importance of including detailed racial, ethnic, and country-of-origin data in HRQOL research to better identify at-risk groups and inform targeted interventions for improving survivorship outcomes.
Additional Information
- Source:JNCI: Journal of the National Cancer Institute. 2023/03, Vol. 115, Issue 3, p258
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2023
- ISSN:0027-8874
- DOI:10.1093/jnci/djac230
- Accession Number:162327242
- Copyright Statement:Copyright of JNCI: Journal of the National Cancer Institute is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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