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

Chapman C, Jayasekera J, Dash C, Sheppard V, Mandelblatt J. A health equity framework to support the next generation of cancer population simulation models. Journal of The National Cancer Institute. Monographs. 2023 Nov 8; 2023(62):255-264.

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Abstract:

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