TY - JOUR
T1 - Data gaps and opportunities for modeling cancer health equity
AU - Trentham-Dietz, Amy
AU - Corley, Douglas A.
AU - Del Vecchio, Natalie J.
AU - Greenlee, Robert T.
AU - Haas, Jennifer S.
AU - Hubbard, Rebecca A.
AU - Hughes, Amy E.
AU - Kim, Jane J.
AU - Kobrin, Sarah
AU - Li, Christopher I.
AU - Meza, Rafael
AU - Neslund-Dudas, Christine M.
AU - Tiro, Jasmin A.
N1 - Publisher Copyright:
© 2023 Oxford University Press. All rights reserved.
PY - 2023/11/1
Y1 - 2023/11/1
N2 - 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.
AB - 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.
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U2 - 10.1093/jncimonographs/lgad025
DO - 10.1093/jncimonographs/lgad025
M3 - Article
C2 - 37947335
AN - SCOPUS:85176451947
SN - 1052-6773
VL - 2023
SP - 246
EP - 254
JO - Journal of the National Cancer Institute - Monographs
JF - Journal of the National Cancer Institute - Monographs
IS - 62
ER -