Opportunities, challenges, and future directions for simulation modeling the effects of structural racism on cancer mortality in the United States: a scoping review.

PURPOSE: Structural racism could contribute to racial and ethnic disparities in cancer mortality via its broad effects on housing, economic opportunities, and health care. However, there has been limited focus on incorporating structural racism into simulation models designed to identify practice and policy strategies to support health equity. We reviewed studies evaluating structural racism and cancer mortality disparities to highlight opportunities, challenges, and future directions to capture this broad concept in simulation modeling research. METHODS: We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Review Extension guidelines. Articles published between 2018 and 2023 were searched including terms related to race, ethnicity, cancer-specific and all-cause mortality, and structural racism. We included studies evaluating the effects of structural racism on racial and ethnic disparities in cancer mortality in the United States. RESULTS: A total of 8345 articles were identified, and 183 articles were included. Studies used different measures, data sources, and methods. For example, in 20 studies, racial residential segregation, one component of structural racism, was measured by indices of dissimilarity, concentration at the extremes, redlining, or isolation. Data sources included cancer registries, claims, or institutional data linked to area-level metrics from the US census or historical mortgage data. Segregation was associated with worse survival. Nine studies were location specific, and the segregation measures were developed for Black, Hispanic, and White residents. CONCLUSIONS: A range of measures and data sources are available to capture the effects of structural racism. We provide a set of recommendations for best practices for modelers to consider when incorporating the effects of structural racism into simulation models.

Inability to get needed health care during the COVID-19 pandemic among a nationally representative, diverse population of U.S. adults with and without chronic conditions.

BACKGROUND: Delays in health care have been observed in the U.S. during the COVID-19 pandemic; however, the prevalence of inability to get needed care and potential disparities in health care access have yet to be assessed. METHODS: We conducted a nationally representative, online survey of 5,500 American Indian/Alaska Native, Asian, Black/African American, Latino (English- and Spanish-speaking), Native Hawaiian/Pacific Islander, White, and multiracial adults between 12/2020-2/2021 (baseline) and 8/16/2021-9/9/2021 (6-month follow-up). Participants were asked "Since the start of the pandemic, was there any time when you did not get medical care that you needed?" Those who responded "Yes" were asked about the type of care and the reason for not receiving care. Poisson regression was used to estimate the association between sociodemographics and inability to receive needed care; all analyses were stratified by chronic condition status. Chronic conditions included: chronic obstructive pulmonary disease (COPD), heart conditions, type 2 diabetes, chronic kidney disease or on dialysis, sickle cell disease, cancer, and immunocompromised state (weakened immune system). RESULTS: Overall, 20.0% of participants at baseline and 22.7% at follow-up reported not getting needed care. The most common reasons for being unable to get needed care included fear of COVID-19 (baseline: 44.1%; follow-up: 47.2%) and doctors canceled appointment (baseline: 25.3%; follow-up: 14.1%). Routine care (baseline: 59.9%; follow-up: 62.6%) and chronic care management (baseline: 31.5%; follow-up: 30.1%) were the most often reported types of delayed care. Fair/poor self-reported physical health was significantly associated with being unable to get needed care despite chronic condition status (≥ 1 chronic condition: aPR = 1.36, 95%CI = 1.04-1.78); no chronic conditions: aPR = 1.52, 95% CI = 1.28-1.80). The likelihood of inability to get needed care differed in some instances by race/ethnicity, age, and insurance status. For example, uninsured adults were more likely to not get needed care (≥ 1 chronic condition: aPR = 1.76, 95%CI = 1.17-2.66); no chronic conditions: aPR = 1.25, 95% CI = 1.00-1.56). CONCLUSIONS: Overall, about one fifth of participants reported being unable to receive needed care at baseline and follow-up. Delays in receiving needed medical care may exacerbate existing conditions and perpetuate existing health disparities among vulnerable populations who were more likely to have not received needed health care during the pandemic.