A health equity framework to support the next generation of cancer population simulation models

JNCI Monographs, Год журнала: 2023, Номер 2023(62), С. 255 - 264

Опубликована: Ноя. 1, 2023

Abstract Over the past 2 decades, population simulation modeling has evolved as an effective public health tool for surveillance of cancer trends and estimation impact screening treatment strategies on incidence mortality, including documentation persistent inequities. The goal this research was to provide a framework support next generation models identify leverage points in control continuum accelerate achievement equity care minoritized populations. In our framework, systemic racism is conceptualized root cause inequity upstream influence acting subsequent downstream events, which ultimately exert physiological effects mortality competing comorbidities. To date, most investigating racial have used individual-level race variables. Individual-level proxy exposure racism, not biological construct. However, single-level variables are suboptimal proxies multilevel systems, policies, practices that perpetuate inequity. We recommend future designed capture relationships between outcomes replace or extend with measures structural, interpersonal, internalized racism. Models should investigate actionable levers, such changes care, education, economic structures policies increase reductions health-care–based interpersonal This integrated approach could novel approaches, make explicit different highlight data gaps interactions model components mirroring how factors act real world, inform we collect equity, generate results policy.

Язык: Английский

Data gaps and opportunities for modeling cancer health equity

JNCI Monographs, Год журнала: 2023, Номер 2023(62), С. 246 - 254

Опубликована: Ноя. 1, 2023

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 that are appropriately representative, collected in a harmonized manner, have minimal missing or inaccurate values, adequate sample sizes. Data resource priorities modeling to support health equity include increasing availability 1) arise from uninsured underinsured individuals those traditionally not included health-care delivery studies, 2) relevant exposures groups historically intentionally excluded across full control continuum, 3) disaggregate categories (race, ethnicity, socioeconomic status, gender, sexual orientation, etc.) their intersections conceal important variation outcomes, 4) identify specific populations interest clinical databases whose outcomes been understudied, 5) enhance records through expanded elements linkage with other types (eg, patient surveys, provider and/or facility level information, neighborhood data), 6) decrease misclassified underrecognized populations, 7) capture potential measures effects systemic racism corresponding intervenable targets change.

Язык: Английский

Using simulation modeling to guide policy to reduce disparities and achieve equity in cancer outcomes: state of the science and a road map for the future

JNCI Monographs, Год журнала: 2023, Номер 2023(62), С. 159 - 166

Опубликована: Ноя. 1, 2023

Язык: Английский