Year: 2023
Type: article
Abstract: 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.
Source: JNCI Monographs
Institutions: University of Wisconsin–Madison, University of Wisconsin Carbone Cancer Center, Kaiser Permanente, Kaiser Permanente Northern California, Fred Hutch Cancer Center, Marshfield Clinic Research Foundation, Marshfield Clinic Research Institute, Massachusetts General Hospital, University of Pennsylvania, The University of Texas Southwestern Medical Center, Harvard University, National Institutes of Health, National Cancer Institute, Henry Ford Health, University of Chicago, University of Chicago Medicine Comprehensive Cancer Center
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
Awards: Multi-level Optimization of the Cervical Cancer Screening Process in Diverse Settings & Populations (METRICS), Interventions to Promote Equity Across the Cancer Care Continuum, G168207875, Comparative Modeling of Precision Breast Cancer Control Across the Translational Continuum, Coordinating Center for Population-based Research to Optimize Cancer Screening (PROSPR) (U24), Optimizing Colorectal Cancer Screening PREcision and Outcomes in CommunIty-baSEd Populations (PRECISE), Fred Hutchinson Breast Cancer Clinical Validation Center, Comparative Modeling to Inform Cervical Cancer Control Policies, Center for Research to Optimize Precision Lung Cancer Screening in Diverse Populations, UW Comprehensive Cancer Center Support, Advancing Risk-based Breast Cancer Screening and Surveillance in Community Practice, Comparative Modeling of Lung Cancer Prevention, Early Detection and Treatment Interventions
OpenAlex ID: W4388556587