Computer Models May Help Reduce Cancer Disparities

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Sophisticated computer models may help reduce health disparities in incidence and mortality for patients with major cancer types, according to a collection of new studies published in a special issue of the Journal of the National Cancer Institute


Given the high costs and long time periods needed to obtain results from clinical trials focused on interventions to prevent and treat many cancer types, simulating cancer outcomes in diverse populations with sophisticated computer modeling tools has been recommended by the National Academy of Sciences and other institutions as a high-quality alternative assessment method. To that end, the Cancer Intervention and Surveillance Modeling Network (CISNET) has been funded by the National Cancer Institute since the year 2000 to advance modeling science for cancer.

“Modeling the effects of race on cancer outcomes will require gathering important data to capture the systemic effects of racism, which is a pervasive element in persisting inequities in cancer outcomes,” stressed Jeanne Mandelblatt, MD, MPH, Professor of Oncology and Medicine at the Georgetown University School of Medicine and Director of the Georgetown Lombardi Institute for Cancer and Aging Research.

New Findings

In studies using CISNET, researchers used computer modeling to synthesize data and quantify the contributions of different aspects of cancer care to the impact on cancer disparities and mortality rates between the overall U.S. population compared with Black patients. The studies considered an antiracism framework aiming to identify new strategies to advance health equity among Black patients and other underrepresented groups in medical research. Notably, the researchers were able to confirm that CISNET-modeled incidence and mortality rates closely matched observed statistical trends for cancer over time, lending validation to the modeling approach. 

The researchers addressed a single research question designed to identify leverage points that could be targeted to increase equity in the burden of cancer among Black patients. The resulting body of research illustrated how population simulation modeling may be applied to address critical public health issues. Taken together, the results of the modeling analyses included several patterns:

  • In cancer types with widely used screening procedures but persistently large gaps in racial incidence, better access to screening by racial minorities could play a larger role in helping reduce those disparities. 
  • High-quality therapies—in accordance with treatment recommendations from professional organizations with treatment guidelines delivered promptly after diagnosis, with all planned cycles completed and no or minimal dose reductions—could have a very positive impact on mortality disparities between Black patients and the overall U.S. population, especially as new and more effective therapeutic options evolve.


“We have framed systemic racism as the root cause of inequity that can exert significant effects on cancer incidence and mortality and competing comorbidities. Because of these research efforts, we hope that we have provided a framework to support the next generation of cancer population simulation models,” emphasized Dr. Mandelblatt.

“We have long since recognized the impact of discrimination on health and well-being in Black communities,” underscored Lucille Adams-Campbell, PhD, Professor of Oncology and Associate Director of Minority Health and Health Disparities Research at the Georgetown Lombardi Institute for Cancer and Aging Research. “We hope that future models can better capture relationships between systemic racism and cancer outcomes and replace or extend single-level race variables with measures that capture structural, interpersonal, and internalized racism,” she concluded.

Disclosure: This research was supported by grants from the National Cancer Institute and the National Institute on Aging.

The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.