Data gaps and opportunities for modeling cancer health equity.

Autor:	Trentham-Dietz A; Department of Population Health Sciences and Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA., Corley DA; Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA., Del Vecchio NJ; Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA., Greenlee RT; Marshfield Clinic Research Institute, Marshfield, WI, USA., Haas JS; Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA., Hubbard RA; Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Hughes AE; Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA., Kim JJ; Department of Health Policy and Management, Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA, USA., Kobrin S; Healthcare Delivery Research Program, Division of Cancer Control & Population Sciences, National Cancer Institute, National Institutes of Health, Rockville, MD, USA., Li CI; Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA., Meza R; Department of Integrative Oncology, British Columbia (BC) Cancer Research Institute, Vancouver, BC, Canada., Neslund-Dudas CM; Department of Public Health Sciences and Henry Ford Cancer, Henry Ford Health, Detroit, MI, USA., Tiro JA; Department of Public Health Sciences, University of Chicago Biological Sciences Division, and University of Chicago Medicine Comprehensive Cancer Center, Chicago, IL, USA.
Jazyk:	angličtina
Zdroj:	Journal of the National Cancer Institute. Monographs [J Natl Cancer Inst Monogr] 2023 Nov 08; Vol. 2023 (62), pp. 246-254.
DOI:	10.1093/jncimonographs/lgad025
Abstrakt:	Population models of cancer reflect the overall US population by drawing on numerous existing data resources for parameter inputs and calibration targets. Models require data inputs that are appropriately representative, collected in a harmonized manner, have minimal missing or inaccurate values, and reflect adequate sample sizes. Data resource priorities for population modeling to support cancer health equity include increasing the availability of data that 1) arise from uninsured and underinsured individuals and those traditionally not included in health-care delivery studies, 2) reflect relevant exposures for groups historically and intentionally excluded across the full cancer control continuum, 3) disaggregate categories (race, ethnicity, socioeconomic status, gender, sexual orientation, etc.) and their intersections that conceal important variation in health outcomes, 4) identify specific populations of interest in clinical databases whose health outcomes have been understudied, 5) enhance health records through expanded data elements and linkage with other data types (eg, patient surveys, provider and/or facility level information, neighborhood data), 6) decrease missing and misclassified data from historically underrecognized populations, and 7) capture potential measures or effects of systemic racism and corresponding intervenable targets for change. (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)
Databáze:	MEDLINE
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