Lung Cancer Epidemiology and Prevention in Black Americans: Insights from Computational Models
Creators: name: Skolnick, Sarah
Publisher: name: My University
Year: 2024
Subjects: subject: lung cancer
subject: racial/ethnic disparities
subject: Public Health
subject: Health Sciences

description: Lung cancer is the leading cause of cancer death for both men and women in the United States, leading to over 125,000 deaths every year. As with many health outcomes, Black Americans face a disproportionate burden of lung cancer mortality. Black Americans smoke fewer cigarettes than White Americans but have higher lung cancer mortality. The reasons for these disparities are not well understood. Thus, the aim of this dissertation is to use mathematical modeling techniques to illuminate the mechanisms of this paradox and investigate interventions to address these disparities. In Chapter 2, we used a biologically based carcinogenesis model, the two-stage clonal expansion model, fitted to data from an ethnically diverse cohort, the Multi-ethnic Cohort (MEC), to investigate the impact of smoking and non-smoking related factors on lung cancer initiation, promotion, malignant conversion and incidence in the Black and White American populations. Ultimately, we found that while smoking plays an important role in the disparities observed for Black Americans, there is a significant and under-recognized background (non-smoking related) risk that also substantially contributes to these disparities. In Chapter 3, we developed the first lung cancer natural history model for the US Black population to study the relative impacts of smoking history, incidence, histology, stage, and survival on lung cancer mortality. To do this, we integrated the model from Chapter 2 into an existing validated lung cancer natural history model for the US population, and then, adapted the other components of this natural history model to the Black population, such as histology distribution and survival. We found that, currently, the largest contributor to disparities in lung cancer mortality for Black Americans is incidence. Histology, stage, and survival had minimal influence on disparities in mortality, but this may change as screening and treatment improves. Finally, in Chapter 4, we used the natural history model from Chapter 3 and a version of the natural history model for White Americans to determine efficient screening interventions for the Black and the White US populations. These analyses can help decision-makers plan high-impact interventions and optimize screening recommendations to best fit these populations. We found that current recommendations are efficient for White Americans, but not optimal for Black Americans. In contrast, screening strategies that were more inclusive (starting at earlier ages and requiring fewer pack-years) were more efficient for Black Americans compared to White Americans. The work in this dissertation demonstrates the invaluable use of mathematical modeling for understanding disease epidemiology and risks across populations and racial disparities, and for evaluating the potential impacts of cancer control strategies, helping guide public health policy decision-making.