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

Jinani Jayasekera; Safa El Kefi; Jessica R Fernandez; Kaitlyn M Wojcik; Jennifer M P Woo; Adaora Ezeani; Jennifer L Ish; Manami Bhattacharya; Kemi Ogunsina; Che-Jung Chang; Camryn M Cohen; Stephanie Ponce; Dalya Kamil; Julia Zhang; Randy Le; Amrita L Ramanathan; Gisela Butera; Christina Chapman; Shakira J Grant; Marquita W Lewis-Thames; Chiranjeev Dash; Traci N Bethea; Allana T Forde

doi:10.1093/jncimonographs/lgad020

. 2023 Nov 8;2023(62):231–245. doi: 10.1093/jncimonographs/lgad020

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

Jinani Jayasekera ^1,^✉, Safa El Kefi ², Jessica R Fernandez ³, Kaitlyn M Wojcik ⁴, Jennifer M P Woo ⁵, Adaora Ezeani ⁶, Jennifer L Ish ⁷, Manami Bhattacharya ⁸, Kemi Ogunsina ⁹, Che-Jung Chang ¹⁰, Camryn M Cohen ¹¹, Stephanie Ponce ¹², Dalya Kamil ¹³, Julia Zhang ^14,¹⁵, Randy Le ¹⁶, Amrita L Ramanathan ¹⁷, Gisela Butera ¹⁸, Christina Chapman ¹⁹, Shakira J Grant ²⁰, Marquita W Lewis-Thames ²¹, Chiranjeev Dash ²², Traci N Bethea ²³, Allana T Forde ²⁴

¹ Division of Intramural Research at the National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA

² NYU Langone Health, New York University, New York, NY, USA

³ Division of Intramural Research at the National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA

⁴ Division of Intramural Research at the National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA

⁵ Epidemiology Branch at the National Institute of Environmental Health Sciences at the National Institutes of Health, Bethesda, MD, USA

⁶ Health Behaviors Research Branch of the Behavioral Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA

⁷ Epidemiology Branch at the National Institute of Environmental Health Sciences at the National Institutes of Health, Bethesda, MD, USA

⁸ Cancer Prevention Fellowship Program, Division of Cancer Prevention, and the Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA

⁹ Epidemiology Branch at the National Institute of Environmental Health Sciences at the National Institutes of Health, Bethesda, MD, USA

¹⁰ Epidemiology Branch at the National Institute of Environmental Health Sciences at the National Institutes of Health, Bethesda, MD, USA

¹¹ Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA

¹² Division of Intramural Research at the National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA

¹³ Division of Intramural Research at the National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA

¹⁴ Division of Intramural Research at the National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA

¹⁵ Sophomore at Williams College, Williamstown, MA, USA

¹⁶ Division of Intramural Research at the National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA

¹⁷ Diabetes, Endocrinology, & Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA

¹⁸ Office of Research Services, National Institutes of Health Library, Bethesda, MD, USA

¹⁹ Department of Radiation Oncology, Baylor College of Medicine, and the Center for Innovations in Quality, Effectiveness, and Safety in the Department of Medicine, Baylor College of Medicine and the Houston Veterans Affairs, Houston, TX, USA

²⁰ Department of Medicine, Division of Hematology, University of North Carolina, Chapel Hill, NC, USA

²¹ Department of Medical Social Science, Center for Community Health at Northwestern University Feinberg School of Medicine, Chicago, IL, USA

²² Office of Minority Health and Health Disparities Research at the Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA

²³ Office of Minority Health and Health Disparities Research at the Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA

²⁴ Division of Intramural Research at the National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA

^✉

Correspondence to: Jinani Jayasekera, PhD, Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research at the National Institute on Minority Health and Health Disparities, National Institutes of Health, 2 White Flint North (2WFN), Bethesda, MD 20892, USA (e-mail: jinani.jayasekera@nih.gov).

Roles

Jinani Jayasekera: PhD, Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing - original draft, Writing - review & editing

Safa El Kefi: PhD, Writing - review & editing

Jessica R Fernandez: PhD, Writing - original draft, Writing - review & editing

Kaitlyn M Wojcik: MPH, Conceptualization, Data curation, Formal analysis, Writing - original draft

Jennifer M P Woo: PhD, MPH, Data curation, Formal analysis, Writing - original draft

Adaora Ezeani: MD, MPH, Data curation, Writing - review & editing

Jennifer L Ish: PhD, MS, Data curation, Writing - original draft, Writing - review & editing

Manami Bhattacharya: PhD, MS, Data curation, Writing - original draft, Writing - review & editing

Kemi Ogunsina: MD, PhD, Data curation, Writing - original draft, Writing - review & editing

Che-Jung Chang: MS, PhD, Data curation, Writing - original draft, Writing - review & editing

Camryn M Cohen: BS, Data curation, Formal analysis, Writing - original draft, Writing - review & editing

Stephanie Ponce: BA, Data curation, Formal analysis, Writing - original draft, Writing - review & editing

Dalya Kamil: BSc, Data curation, Formal analysis, Writing - original draft, Writing - review & editing

Julia Zhang: Data curation, Formal analysis, Methodology

Randy Le: BS, Data curation, Formal analysis, Methodology

Amrita L Ramanathan: BA, Data curation, Formal analysis, Writing - original draft, Writing - review & editing

Christina Chapman: MD, Writing - review & editing

Shakira J Grant: MBBS, Funding acquisition, Resources

Marquita W Lewis-Thames: MPH, MS, PhD, Funding acquisition, Resources

Chiranjeev Dash: MBBS, PhD, Resources

Traci N Bethea: MPA, PhD, Funding acquisition, Resources, Methodology, Validation, Writing - review & editing

Allana T Forde: PhD, MPH, Writing - review & editing

PMCID: PMC10637025 PMID: 37947336

Abstract

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.

Simulation modeling is a powerful computational tool that could be used to inform the development of cancer care policies, guidelines, clinical trials, and web-based personalized clinical decision tools (1-5). In this context, the simulation models developed by the National Cancer Institute Cancer Intervention and Surveillance Modeling Network have provided data to inform cancer screening policies and guidelines in the United States (1,2,6-8).

National Cancer Institute Cancer Intervention and Surveillance Modeling Network models comparing model results across racial groups acknowledge that race is a sociopolitical construct that reflects the impact of racism on cancer outcomes. Currently, the models capture the effects of racism via its race-specific input parameters on cancer incidence, screening and treatment dissemination, stage distributions, survival, and competing mortality (9-11). The models assume that the underlying effects of structural racism are reflected in the racial differences in cancer burden (eg, incidence) and care delivery (eg, screening and treatment). Although individual and structural racism are conceptualized as the root cause of racial disparities (12), the models have not yet incorporated measures of structural racism or its direct impact on cancer processes and outcomes (13). Simulation modeling structural racism could potentially provide useful data to inform policies and practices that could eliminate structural racism and promote equity in cancer care. However, prerequisite to such efforts, the models would require information on measurement and real-world data linking structural racism to disease processes captured in the models, including its effects on cancer mortality.

The definition of structural racism has evolved over the last decade (14-19). A review published by Bailey et al. (18) provides a contemporary definition for investigating the impact of structural racism on cancer outcomes (20). Structural racism is “the totality of ways in which societies foster racial discrimination through mutually reinforcing systems of housing, education, employment, earnings, benefits, credit, media, health care, and criminal justice” (18). Although, structural racism has been reviewed in the literature (17,21), there have been no systematic or scoping reviews on structural racism in the context of simulation modeling research. We addressed this gap by conducting a scoping review of peer-reviewed articles evaluating the impact of structural racism on cancer mortality–related racial and ethnic disparities in the United States. The overall findings were used to provide recommendations for best practices to incorporate the effects of structural racism into simulation models. This study is intended to highlight the opportunities, challenges, and future directions for the development of novel simulation models that will inform equitable cancer care policies and guidelines in the United States.

Methods

Our scoping review was conducted following a standard methodological framework (22-24). A scoping review was considered for this study as our goal was to scope a body of literature, clarify concepts and measures, and provide recommendations for best practices to incorporate the effects of structural racism into simulation modeling research (25). The standard methodological framework (22-24) provides 6 stages to guide scoping review processes including specifying the research question; identifying relevant literature; selecting studies; data mapping; summarizing, synthesizing, and reporting the results; and including expert consultation. The review was carried out in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (26) (Supplementary Table 1, available online), and the study was registered in Open Science Framework (doi: 10.17605/OSF.IO/SP8TH) (27). Since we included a review of published articles and study-level results, this study did not require submission for institutional review board approval or exemption.

The following definitions of race, ethnicity, structural racism, and cancer mortality were used to develop the search strategy for this scoping review. Race is a social construct rooted in the historical, social, political, and economic contexts (28). Ethnicity refers to the social and cultural characteristics, backgrounds, or experiences shared by a group of people (29). We identified self-reported Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian or other Pacific Islander, White, Middle Eastern or North African, and Hispanic or Latino/a racial and ethnic groups according to the Office of Management and Budget standards (30). Structural racism is defined above (18). Cancer mortality included death due to cancer and/or all-cause mortality in individuals diagnosed with cancer (31).

Guided by these definitions, we searched for studies evaluating the impact of structural racism and its dimensions (ie, housing, education, employment, earnings [income], benefits, credit, health care, media, and criminal justice) on racial and ethnic disparities in cancer mortality. A search strategy was developed using an iterative approach, with 3 rounds of preliminary searches, and refinement of the search strategy based on initial search results by a trained librarian (GB) at the National Institutes of Health. Each preliminary search was performed in the PubMed–MEDLINE database constrained to English-only language restrictions and research published between January 1, 2018, and January 1, 2023, to inform current policies considering modern cancer care. The comprehensive search strategy included a combination of keywords, synonyms, medical subject headings, and Emtree terms related to structural racism, race, ethnicity, and cancer mortality. Three researchers were consulted to gauge the suitability of the search terms (Supplementary Table 2, available online). The searches were performed across the following 6 widely used databases: MEDLINE via PubMed (National Library of Medicine), PsycINFO (American Psychological Association), Embase (Elsevier), Scopus (Elsevier), Web of Science Core Collection (Clarivate Analytics), and Cochrane CENTRAL (Wiley & Sons). Additional references were extracted from reviews, systematic reviews, and meta-analyses. Duplicates were omitted.

Eligibility criteria

Eligibility criteria were based on preliminary searches and included primary empirical research studies published between January 2018 and January 2023; involving human participants, samples, and/or datasets; specific to the US population or setting; in any type of cancer (except skin cancer) and cancer mortality; and with the terms race and ethnicity and structural racism and/or its dimensions in the title or abstract. Further details are provided in Supplementary Table 3 (available online).

Data screening, extraction, and synthesis

The review was conducted in Covidence, a web-based publication screening software platform. Titles and abstracts of the articles were screened independently by 3 researchers (JJ, KW, JW). Any discrepancies in the inclusion or exclusion decision were resolved through discussion. A team of extractors independently performed full-text screening of articles.

Data extraction was conducted independently using an online form, which was pilot tested and customized to support the review by ensuring charting consistency and inter-reviewer reliability. The data extraction template included the title, purpose, research design, target population and setting, inclusion and exclusion criteria, start and end date, site and type(s) of cancer, data sources, primary outcome, race and ethnicity categories, individual characteristics, variables used to capture structural racism and/or its dimensions, statistical methods, results, limitations of the study, funding source(s), and conflicts of interest. We also extracted the definitions and conceptual frameworks used to guide the research question(s) and variable selection in the studies. Data were summarized descriptively in tabular form and text using a narrative approach.

Results

A total of 8345 records were identified through the search. After removing duplicate or ineligible articles, 1038 articles underwent full text review, and 183 articles were included in the final review (Figure 1). Most studies were designed to evaluate mortality disparities across White (26%), Black (25%), Hispanic (19%), or Asian or Pacific Islander (18%) groups, whereas relatively fewer studies focused on American Indian or Alaska Native groups (3%) and we found no studies on Middle Eastern or North African groups (Supplementary Table 4, available online).

Figure 1. — Article identification process.

* *Structural racism* was defined as the totality of ways in which societies foster racial discrimination through mutually reinforcing systems of housing, education, employment, earnings, benefits, credit, media, health care, and criminal justice (18).

Definitions, theories, and conceptual frameworks

A total of 40 studies mentioned theories and conceptual frameworks to guide the research question(s) and variable selection (Table 1). Intersectionality Theory describing the interconnected nature of social identities and/or positions was used to explore the interactions between race and ethnicity and structural factors (n = 2) (32-36). Eco-social Theory of Disease Distribution (37) (n = 3) (38-40) and Urban Theory (41) (n = 1) (42) were used for variable selection. For example, the variables for Concentrated Disadvantage Index (42) was selected based on Urban Theory (41) regarding the overconcentration of Black individuals, children, and female-headed families in poor neighborhoods.

Table 1.

Summary of definitions, theories, and conceptual frameworks mentioned in the studies included in the scoping review

Concept	Description
Structural racism	“The ways in which mutually reinforcing systems of housing, education, employment, earnings, benefits, credit, media, health care, and criminal justices are root causes of health disparities” (18). “Structural racism refers to a system in which public policies, institutional practices, cultural representations, and other norms work to perpetuate racial group inequity” (83).
Racial residential segregation	Segregation refers to the physical separation of the races in residential contexts (61). Williams et al. highlights that “segregation was imposed by legislation, supported by major economic institutions, enshrined in the housing policies of the federal government, enforced by the judicial system, and legitimized by the ideology of white supremacy that was advocated by churches and other cultural institutions. These institutional policies combined with the efforts of vigilant neighborhood organizations, discrimination on the part of real estate agents and home sellers, and restrictive covenants to limit the housing options of Black Americans to the least desirable residential areas” (61,84-86). Racially segregated areas in the United States are primarily minoritized neighborhoods with persistent poverty, low home equity, and stagnant social mobility (46,87).
Housing discrimination	Housing discrimination limits high-quality housing availability to individuals of a given race (18,88). Housing discrimination can take several forms, including biased mortgage lending, discrimination in renting, and racial segregation (55).
Intersectionality	Intersectionality theory posits that dimensions of structural inequality, such as racism and classism, are mutually constitutive (ie, intersectional), therefore, social groups at the nexus of more than 1 marginalized identity experience unique forms of disadvantage (34-36).
Eco-social Theory of Disease Distribution	Health disparities in various disease outcomes are a product of social, ecological, political, and historical exposures across multiple levels, including neighborhoods (37,44,89,90).
Urban Theory	The Urban Theory observes the economic, social, and physical barriers of urban settings seen in economically disadvantaged communities (41).
Berkman framework on social integration and health	This theoretical framework serves to identify social networks contributing to breast cancer survival. Social networks are explored in continuum, with each stage acting as a precursor to the next—macro social context, network structure, and mobilized network resources, respectively (91,92).
A framework proposed by Kirtane and Lee (93)	Racial disparities are driven by biological factors, individual factors, health behaviors, and structural barriers (93,94).
National Institute on Minority Health and Health Disparities Framework	Conceptualizes various levels of influence at the individual, interpersonal, community, and societal levels with biological, behavioral, sociocultural and environmental, and health-care system domains of influence over the course of one’s lifetime to better understand the factors that influence health disparities in minority populations (95).
Social inequalities along the childhood cancer continuum	A framework model developed to study the impact of equity in childhood cancer by connecting structural determinants of health inequality in childhood cancer outcomes with social determinants of childhood cancer (96).

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In our sample of studies, structural racism was commonly (n = 20) measured using racial residential segregation (39,40,43-60). Racial residential segregation is defined as the physical separation of the racial groups in residential contexts (61). Segregation could lead to potentially worse socioeconomic and physical conditions for minoritized groups at the individual, household, neighborhood, and community levels (45,62,63).

Measures

The 183 studies evaluating the impact of structural racism on cancer mortality used 150 measures of structural racism, including socioeconomic (housing, education, employment, and income; n = 113), health care and benefits (n = 34); combined socioeconomic and health care (n = 2); and criminal justice (n = 1) dimensions. Accordingly, 114 measures were related to social and economic policy, 34 measures were related to health-care policy, and 2 measures were related to a combination of social, economic, and health policies (12). The measures, single or composite, descriptions, structural racism dimensions, individual or geographic unit of analysis, and data sources are provided in Supplementary Table 5 (available online). Composite measures were created using structural equation modeling or principal component analysis.

There was variation in the measures used to capture structural racism. For example, racial residential segregation was measured using the Dissimilarity Index (n = 11); Index of Concentration at the Extremes (n = 6); Redlining Index (n = 2); Isolation Index (n = 2); historical, georeferenced Homeowners Loan Corporation grading data (n = 1); and a composite measure of structural racism incorporating tract-level racial segregation, disadvantage, and affluence (n = 1) (Supplementary Table 5, available online) (39,40,43-60). Index of Dissimilarity measured segregation by assessing the heterogeneity of 2 racial groups within a geographical area (45-49,51,53,57). The Index of Concentration at the Extremes is a measure of spatial social polarization quantifying extremes of deprived and privileged social groups, and it consists of 3 measures encompassing economic segregation (income-based Index of Concentration at the Extremes), racial segregation (race-based Index of Concentration at the Extremes), and racialized economic segregation (race income–based Index of Concentration at the Extremes) (64). Overall, these measures of segregation and structural racism consisted of area-level variables belonging to the housing, income, education, employment, and/or credit dimensions (39,40,43-60). Health-care (eg, health care insurance, facilities) dimensions were not directly reflected in the measures of racial residential segregation.

The health-care (and benefits) dimension(s) included individual-level (eg, insurance status, travel distance), facility-level (eg, facility type, accreditation), and area-level (eg, county-level screening rate, state Medicaid expansion) measures. At least 2 studies considered composite measures combining socioeconomic and health-care dimensions (eg, California Healthy Places Index) (65,66). In our sample of studies, there were no composite measures capturing all the dimensions of structural racism in a single variable. Further, we did not find measures of structural racism specific to American Indian or Alaska Native, Native Hawaiian, or other Pacific Islander, or Middle Eastern or North African groups (eg, isolation scores for Middle Eastern or North African).

Data sources and unit of analysis

Data sources included cancer registry, hospital, or survey data commonly linked to US census data for area-level characteristics. Data were collected at the individual, facility, census tract, block, city or town, and county levels.

Analytic approaches

In the 183 studies, we identified 6 distinct methods used to quantify the effects of structural racism (its dimensions) on racial and ethnic disparities in cancer mortality: 1) racial and ethnic disparities in a single location (eg, rural hospital, urban city); 2) comparison of subgroups defined by race and ethnicity and a structural factor (eg, rural Asian vs urban White individuals); 3) effects of sequential adjustment of structural factors (with other demographic and clinical variables) on racial and ethnic disparities in multivariable models; 4) structural factors as potential mediators of racial and ethnic disparities in mortality; 5) interactions between structural factors and race and ethnicity and effect modification; and 6) geospatial analyses to identify structural factors associated with area-level race or ethnicity-specific mortality rates.

Impact of structural racism on racial and ethnic disparities in cancer mortality

The results varied by cancer site, data sources, methods, geographic unit of analysis, location, racial and ethnic comparators, structural racism measure(s), and other covariates included in the analysis (Supplementary Table 6, available online). We illustrate the variation in results using residential segregation as an example (Table 2) (39,40,43-60).

Table 2.

Summary of studies evaluating the impact of racial residential segregation (one component of structural racism) on racial and ethnic disparities in cancer mortality

Manuscript title	Disease site	Data source	Variables used to measure racial segregation	Statistical methods	Primary findings
Residential racial segregation and disparities in breast cancer presentation, treatment, and survival (48)	Breast	SEER and US Census American Community Survey	County-level Index of Dissimilarity^a	Multivariable Cox proportional hazards model	In adjusted models: Black patients had a 29% increased hazard of overall stage–specific mortality (HR = 1.29, 95% CI = 1.04 to 1.60) with increasing Index of Dissimilarity. White patients had no statistically significant relationship with Index of Dissimilarity and death (HR = 1.13, 95% CI = 0.97 to 1.33).
Structural racism and breast cancer-specific survival: impact of economic and racial residential segregation (60)	Breast	Cancer registries across 2 hospital systems: NCI-designated cancer center and a partner safety-net hospital in South Florida	Census tract–level income and race-based Index of Concentration at the Extremes^b	Multilevel Cox models	In the fully adjusted models stratified by race and ethnicity: Non-Hispanic White residents in low-income and racially segregated neighborhoods showed increased hazard of breast cancer–specific death compared with non-Hispanic White residents living in high-income neighborhoods (HR_Income = 2.40, 95% CI = 1.40 to 4.13; HR_NHB= 2.13, 95% CI = 1.16 to 3.92; HR_Hispanic = 2.24, 95% CI = 1.35 to 2.70; HR_IncomeNHB = 4.61, 95% CI = 2.69 to 7.85; HR_{IncomeHispanic} = 3.08, 95% CI = 1.85 to 5.09). Non-Hispanic Black residents in low-income neighborhoods had an increased hazard of death (HR = 2.22, 95% CI = 1.10 to 4.50). Hispanic residents showed no statistically significant association between any form of segregation and breast cancer mortality.
Racialized economic segregation and breast cancer mortality among women in Maryland (52)	Breast	Maryland Cancer Registry and US Census	Census tract–level Index of Concentration at the Extremes^b (income, race, and race and income-based Index of Concentration at the Extremes)	Age- and race-adjusted Cox models (accounting for clustering at the census tract–level)	Results varied by the measure of Index of Concentration at the Extremes, race (Black and White), age (younger than 60 years; 60 years and older), prognostic factors, and time since diagnosis.
The impact of neighborhood economic and racial inequalities on the spatial variation of breast cancer survival in New Jersey (43)	Breast	New Jersey State Cancer Registry and US Census	Census tract–level Index of Concentration at the Extremes^b (income, race, and race and income-based Index of Concentration at the Extremes)	Nonlinear geo additive Bayesian models	The percentage of geographic disparity based on spatial variance decreased from 89.7% in the null model to 24.7% in the model including age, stage, subtype, race and ethnicity, marital status, and insurance type and further reduced to 15.3% when income based Index of Concentration at the Extremes was included in the models.
Mortgage lending bias and breast cancer survival among older women in the United States (54)	Breast	SEER–Medicare and Home Mortgage Disclosure Act data	Redlining Index^c identifies regions within the same metropolitan statistical areas (within SEER) containing properties that are less likely to secure mortgages.	Multivariable Cox proportional hazards model	No linear relationship between Redlining Index and mortality. The adjusted relationship between race and all-cause and breast cancer mortality with Redlining Index was not statistically significant. Women living in more redlined areas experienced poorer all-cause and breast cancer–specific survival, with the most pronounced effect among women without comorbid conditions.
Association between residence in historically redlined districts indicative of structural racism and racial and ethnic disparities in breast cancer outcomes (59)	Breast	New Jersey State Cancer Registry and historical, georeferenced Homeowners Loan Corporation grading data	HOLC grades: best, hazardous, still desirable, definitely declining	Multivariable cox proportional hazards model	The associations between HOLC grade varied by race and ethnicity. Compared with residence in HOLC redlined areas, residence in HOLC areas graded “best” was associated with lower hazard of breast cancer–specific death (HR = 0.48, 95% CI = 0.35 to 0.65) but only among non-Latina White women.
Structural racism is a mediator of disparities in acute myeloid leukemia outcomes (58)	Blood	Patient data from 6 academic cancer centers in the metropolitan Chicago region and American Community Survey	Structural racism^d at the census tract–level	Mediation analysis with discrete-time models	Adjustment for census tract–level structural racism (in addition to age, sex, and hospital) reduced the disparities for leukemia death in: Hispanic patients (vs non-Hispanic White patients) from a hazard ratio of 1.20 (95% CI = 0.83 to 1.72) to 0.96 (95% CI = 0.65 to 1.43). Non-Hispanic Black patients (vs non-Hispanic White patients) from a hazard ratio of 1.61 (95% CI = 1.15 to 2.26) to 1.04 (95% CI = 0.65 to 1.66).
The impact of racial residential segregation on colorectal cancer outcomes and treatment (49)	Colorectal	SEER and US Census American Community Survey	County-level Index of Dissimilarity^a	Multivariable Cox proportional hazards model	Black patients experienced a 43% increase in 5-year cancer-specific mortality with increasing Index of Dissimilarity (HR = 1.43, 95% CI = 1.17 to 1.74). White patients had no statistically significant relationship with Index of Dissimilarity (HR = 0.99, 95% CI = 0.87 to 1.11).
Racial residential segregation and colorectal cancer mortality in the Mississippi Delta region (50)	Colorectal	SEER and US Census Bureau	Racial residential segregation for White and Black residents in each Delta county using the multilevel index of dissimilarity (97)	Mixed linear regression models	County-level colorectal cancer mortality rates among Black and White residents in Delta region counties varied by rurality and White–Black residential segregation measured by multilevel index of dissimilarity.
Ecological study of variability in the relationship between liver cancer mortality and racial residential segregation (56)	Liver	Wisconsin Department of Health Statistics’ Vital Records Service and US Census	Census tract–level Racial Residential Segregation^e measured using local Black and Hispanic isolation scores (98)	Geospatial analysis of 5 metropolitan statistical areas in Wisconsin	Higher Black isolation was associated with increased likelihood of a tract being in a high-mortality cluster and decreased likelihood of being in a low-mortality cluster (driven by 2 of 5 metropolitan statistical areas). No clear exposure–response relationship for Hispanic isolation.
The role of racial segregation in treatment and outcomes among patients with hepatocellular carcinoma (47)	Liver	SEER and US Census American Community Survey	Index of Dissimilarity^a calculated within 100 most populous participating SEER counties	Kaplan–Meier method and competing risk models	5-year cancer-specific (overall and by stage) and overall survival by Index of Dissimilarity for Black and White patients.
The impact of residential racial segregation on non-small cell lung cancer treatment and outcomes (57)	Lung	SEER and US Census American Community Survey	County-level racial Index of Dissimilarity^a	Competing risk models	In the adjusted models: Black patients in the highest quartile of Index of Dissimilarity had 5% increased hazards (ie, 5-year cancer-specific death) compared with White patients in the highest quartile of Index of Dissimilarity (HR = 1.05, 95% CI = 1.03 to 1.08) and 8% increased hazards compared with White patients in the lowest quartile of Index of Dissimilarity (HR = 1.08, 95% CI = 1.05 to 1.12;). Black patients in the lowest quartile of Index of Dissimilarity had 4% increased hazards of death compared with White patients in the lowest quartile of Index of Dissimilarity (HR = 1.04, SE = 1.02, 95% CI = 1.01 to 1.08). White patients in the highest quartile of Index of Dissimilarity had 3% increased hazards of death compared with White patients in the lowest quartile of Index of Dissimilarity (HR = 1.03, 95% CI = 1.01 to 1.05).
Effects of racial residential segregation on oral squamous cell carcinoma prognosis and survival (51)	Oral	SEER and US Census American Community Survey	County-level Index of Dissimilarity^a	Multivariate Cox proportional hazards model	Extreme Index of Dissimilarity score was associated with increased overall death in Black individuals (HR = 1.42, 95% CI = 1.19 to 1.70). No association with Index of Dissimilarity in White individuals (HR = 1.01, 95% CI = 0.96 to 1.07).
Residential segregation and overall survival of women with epithelial ovarian cancer (44)	Ovarian	Florida Cancer Data System and US Census American Community Survey	Census-defined place-based Index of Concentration at the Extremes^b: Economic segregation (income-based Index of Concentration at the Extremes) Racial segregation (race-based Index of Concentration at the Extremes for non-Hispanic Black and Hispanic populations) Racialized economic segregation (race income–based Index of Concentration at the Extremes)	Multilevel Cox models	Living in economically marginalized areas was associated with statistically significantly increased (all-cause) death after controlling for individual-level factors: Economic segregation for marginalized vs privileged neighborhoods (HR = 1.15, 95% CI = 1.06 to 1.25). Racial segregation for more concentrated non-Hispanic Black segregation (HR = 1.12, 95% CI = 1.02 to 1.22), and more Hispanic segregation (HR = 1.02, 95% CI = 0.93-1.11) vs concentrated non-Hispanic White neighborhoods. Racialized economic segregation for non-Hispanic Black low-income neighborhoods (HR = 1.21, 95% CI = 1.12 to 1.32) and low-income Hispanic segregated neighborhoods (HR = 1.12, 95% CI = 1.03 to 1.22) vs non-Hispanic White high-income neighborhoods. Fully adjusted race-stratified models showed that Hispanic women had an increased hazard of death in all forms of segregation.
The impact of residential segregation on pancreatic cancer diagnosis, treatment, and mortality (53)	Pancreas	SEER and US Census	County-level racial Index of Dissimilarity^a was calculated from the census block–level estimates of the Black and White population within the county.	Kaplan–Meier method	Kaplan–Meier estimates of overall survival and stage-specific survival (up to 12 mo) stratified by race for the lowest and highest quartiles of segregation.
Socioeconomic mediation of racial segregation in pancreatic cancer treatment and outcome disparities (45)	Pancreas	SEER and US Census	County-level Index of Dissimilarity^a	Mediation analysis using generalized structural equation modeling	Black patients in the highest levels of Index of Dissimilarity reported an 8% increase in overall death (HR = 1.08, 95% CI = 1.03 to 1.14) compared with White patients in the lowest level of Index of Dissimilarity.
The impact of racial residential segregation on prostate cancer diagnosis and treatment (46)	Prostate	SEER and US Census American Community Survey	Index of Dissimilarity^a calculated within 100 most populous participating SEER counties	Cumulative hazards and Fine–Gray competing risk models	10-year overall and cancer-specific cumulative hazards for overall and by stage for Black and White patients in the highest and the lowest quartiles of Index of Dissimilarity.
Trends in mortality among Black and White men with prostate cancer in Massachusetts and Pennsylvania: race and neighborhood socioeconomic position (40)	Prostate	Massachusetts and Pennsylvania cancer registries and US Census	Neighborhood socioeconomic position, a summary score generated using census tract–level measures of % poverty, median family income, % households with female head, % with income from interest or rent, % White, % Black, % unemployment, % male unemployment, % receiving welfare assistance, and the joint race and income-based Index of Concentration at the Extremes^b	Multivariable Cox proportional hazards model	Disparities associated with neighborhood socioeconomic position were either stagnant or widened across all endpoints (ie, all-cause, prostate cancer, and cardiovascular mortality) among men with prostate cancer from 2000 to 2015.
Using the index of concentration at the extremes at multiple geographical levels to monitor health inequities in an era of growing spatial social polarization: Massachusetts, USA (2010-14) (39)	Cancer (all-site)	Massachusetts Vital Statistics	Census tract and city or town level Index of Concentration at the Extremes^b (income, race, and race and income-based Index of Concentration at the Extremes)	Multilevel Poisson models for age-standardized mortality rates	Cancer (all-site) mortality rate ratios for Index of Concentration at the Extremes measures (income, race, and race and income-based) overall and by census tract, city, and town level, gender, and race and ethnicity.
Housing discrimination and racial cancer disparities among the 100 largest US metropolitan areas (55)	Cancer (all-site)	CDC WONDER and NCHS and US Census and SEER and Home Mortgage Disclosure Act data	Redlining Index^c (Metropolitan Statistical Area); Dissimilarity Index (Black) (census tract); Isolation Index (Black)^f (census tract)	Geographic patterns and correlations	Correlations between structural racism measures and Black-to-White cancer mortality (rate ratios) disparities by gender.

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A validated measure of segregation. It assesses the heterogeneity of 2 racial groups within a geographical area with values ranging from zero (complete integration) to 1 (complete segregation) and represents the proportion of people within that area who would need to move to achieve complete integration. CDC WONDER = Centers for Disease Control and Prevention Wide-ranging ONline Data for Epidemiologic Research; CI = confidence interval; HOLC = Homeowners Loan Corporation; HR = hazard ratio; NCHS = National Center for Health Statistics; NCI = National Cancer Institute; SEER = Surveillance Epidemiology and End Results; NHB = non-Hispanic Black.

A measure that quantifies the extent to which persons in a specified area are concentrated into the top vs bottom extremes of a specified social distribution. The Index of Concentration at the Extremes was initially a measure of economic residential segregation and was expanded to racial or ethnic and racialized economic segregation to capture the extremes of high-income White communities and low-income Black or Hispanic communities. As a result, Index of Concentration at the Extremes captures both racial or ethnic and economic elements of residential segregation at the same time. Economic segregation was defined as the difference in the number of persons living in households earning no more than $25 000 compared with the number of persons living in households earning $100 000 or more as a proportion of the total population with household income information; racial segregation was defined as the difference in the number of Black or Hispanic persons compared with the number of White persons as a proportion of the total persons with race and ethnicity information; and racialized economic segregation was defined as difference in the number of Black or Hispanic persons in households earning no more than $25 000 compared with White persons in households earning $100 000 or more as a proportion of the total persons with income and race or ethnicity information.

Measures the odds ratio of denial of a mortgage application based on the property location, comparing local properties with all properties within the same metropolitan statistical areas. This index identifies regions within metropolitan statistical areas containing properties that are less likely to secure mortgages. The index was estimated using logistic regression modeling in an adaptive spatial filtering framework based on Home Mortgage Disclosure Act data.

A composite variable incorporating racial segregation, disadvantage, and affluence. Racial segregation measured by the proportion of individuals who were non-Hispanic Black and proportion of individuals who were non-Hispanic White; disadvantage was measured by the proportion of families with incomes below the poverty line, proportion of families receiving public assistance, proportion of adults who were unemployed, and proportion of households that were female-headed households with children; and affluence was defined by the proportion of families with incomes higher than $75 000, proportion of adults with at least a college education, and proportion of adults employed in professional or managerial occupations.

Measures how much racial and ethnic demographics in a sub area (census-tract) deviate from the expected case that all racial and ethnic groups are equally distributed across a region.

The percentage of same-group population in the geographic location where the average member of a racial and ethnic group lives.

High residential segregation measured by the Index of Dissimilarity was associated with worse survival in Black residents with pancreatic, breast, colorectal, oral, lung, liver, and prostate cancer (n = 7) (45-49,51,57) but not among White residents with breast, colorectal, or oral cancer (n = 3) (48,49,51). Homeowners Loan Corporation grading data in New Jersey showed that residence in areas graded as “best” was associated with lower breast cancer–specific mortality among non-Latina White women (hazard ratio [HR] = 0.48, 95% confidence interval [CI] = 0.35 to 0.65) (59). In fully adjusted race-stratified models, Hispanic women with epithelial ovarian cancer showed increased hazard of overall mortality in neighborhoods with extreme economic segregation (HR = 1.41, 95% CI = 1.18 to 1.69) and racialized economic segregation (for non-Hispanic Black HR = 1.42, 95% CI = 1.10 to 1.82; for Hispanic HR = 1.34, 95% CI = 1.03 to 1.74) (44). Another study found segregation measured by Index of Concentration at the Extremes was associated with worse breast cancer survival in White and Black but not in Hispanic residents (60). Adjustment for the tract-level composite measure of structural racism (in addition to age, sex, and diagnosing hospital) reduced the cancer mortality disparity for Hispanic patients from a hazard ratio of 1.20 (95% CI = 0.83 to 1.72) to 0.96 (95% CI = 0.65 to 1.43) and from 1.61 (95% CI = 1.15to 2.26) to 1.04 (95% CI = 0.65 to 1.66) for non-Hispanic Black patients with acute myeloid leukemia (58). Approximately half of the studies (n = 9) were location specific, and the segregation measures were developed for Black, Hispanic, and White residents.

Discussion

Simulation modeling is a methodological tool used to inform cancer care policies and guidelines; therefore, incorporating the impact of structural racism into simulation modeling research could potentially help advance equity in cancer care. The 183 studies included in this review showed wide variation in the measures, data sources, methods, and results. We found 150 measures belonging to 8 dimensions of structural racism including housing, education, employment, income, credit, benefits, health care, and criminal justice. The segregation measures captured area-level socioeconomic and credit dimensions of structural racism.

A previous systematic review conducted by Groos et al. (17) in 2018, summarizing 20 articles evaluating the impact of structural racism on the health and well-being of individuals also noted the heterogeneity in the measurement of structural racism. This variation may arise from the complexity as well as the multidimensional and multilevel impact of structural racism on individual health (17,67). Recent studies have provided practical guidance and recommendations for measuring and incorporating the effects of structural racism in health-care research (20,67,68). These recommendations may also be useful for simulation modeling research. For example, a recent report by the Assistant Secretary for Planning and Evaluation (68) provided a broad overview of existing measures and methods for considering area-level structural inequalities. Further, studies also recommend the use of index measures, developing measures targeted to capture structural racism in other racial and ethnic groups, and use of psychometric evaluations to test measures for relevance over historical eras and life course (20).

Strengths and limitations of the scoping review

Our study was developed and reviewed by a multidisciplinary research team. The search strategy included 6 electronic bibliographic databases to ensure a broad search of the literature. The inclusion and exclusion criteria, as well as the data extraction forms were pilot tested and revised prior to the implementation of the study. However, our results should be reviewed within the context of the limitations of this study. This review did not include studies outside of the United States and articles published in languages other than English. Therefore, our findings may not reflect global patterns of the role of structural racism (or its equivalent) in other countries. We also did not include studies published prior to January 2018 as our goal was to inform model development considering modern cancer care. Moreover, the systematic review by Groos et al. (17) included studies published up to 2017, albeit the goals were different to the current study. We limited the scope of this study to review the effects of structural racism on cancer mortality disparities. Although this approach helped us identify the next steps needed for modeling, the downstream impact of structural racism on cancer mortality could also be studied based on its impact on cancer natural history, screening and timely diagnosis, and treatment separately.

Simulation modeling the effects of structural racism

Simulation models are developed using input parameters that capture the life course of individuals from birth to death including the natural history of cancer and risk factor exposures (eg, smoking) (6). Screening performance and dissemination parameters are overlaid on the life course and natural history for early detection of precancerous lesions or preclinical cancers, whereas treatment efficacy and dissemination parameters are used to alter postdiagnosis survival rates. The effects of structural racism could be incorporated into these models via its impact on natural history, screening dissemination, and treatment efficacy and/or dissemination. The data gaps report by Trentham-Dietz et al. (13) highlights the need for data on the effects of structural racism, adequate sample size for precise parameter estimation, and multidisciplinary collaboration for content knowledge and lived experiences of those affected by structural racism. In our study, we identified several opportunities, challenges, and future directions to support the incorporation of structural racism into population models.

Opportunities

The input parameters for structural racism could be developed using publicly available data sources outlined in Supplementary Table 5 (available online) (eg, US Census). Ideally, such parameters should be developed in collaboration with the scientists previously involved in creating the original measure. The PhenX Toolkit (69) Social Determinants of Health Project (70) provides a collection of publicly available measurement protocols to facilitate collaboration and combination of study data to increase statistical power.

Structural equation modeling techniques could potentially be used to develop novel composite measures that capture the multiple dimensions related to structural racism (71,72). These measures may provide additional insight into the mechanisms underlying population health inequities. Further, US Census data have been available for several decades, which allows modelers to capture the variation of structural factors over time in specific geographic locations. Moreover, several advanced methods have been developed to geocode patient locations and link individual data to broader area-level characteristics (73). There are a growing number of variables for income, housing, education, employment, credit, benefits, and health-care characteristics that fall within the definition of or are influenced by structural racism. This, combined with novel statistical methods to develop new composite measures, provides novel opportunities to explore the incorporation of structural racism into simulation modeling research.

Challenges

The variation of measures and methods evaluating the effects of structural racism poses a challenge in summarizing results across studies for input parameter development. The use of area-level characteristics (eg, zip code–level median income) derived from US Census data as proxies for individual-level characteristics (eg, income) could lead to ecological fallacy (74,75). Ecological fallacy refers to the incorrect assumption that the associations observed at the aggregated level are necessarily the same at the individual level (76). Studies have shown that the effects from aggregate data could be incorrect in magnitude and direction at the individual level (74-76). Also as noted in the data gaps report (13), other challenges include limited generalizability of data to inform national-level policies, standardization of data collected over long periods of time, data missingness, and lack of structural measures specific to other minoritized populations (eg, American Indian, Alaska Native), which could be necessary to capture modern trends in cancer incidence and mortality in the United States (77,78).

Future directions for modeling

To our knowledge, currently, there are no simulation models that directly model the effects of structural racism on cancer outcomes. Current models consider race as a proxy for the effects of racism. Therefore, based on our findings, we provided recommendations for best practices to incorporate the effects of structural racism into cancer simulation models (Table 3). Our recommendations were developed considering the components provided in the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making (ISPOR-SMDM) Modeling Good Practices Task Force recommendations (79).

Table 3.

Recommendations for best practices to incorporate the effects of structural racism into simulation models

Simulation model components	Recommendations for best practices
Model conceptualization	Consult early and often with health equity experts (and other stakeholders) to ensure that the goals and model development represent the impact of structural racism on cancer processes. Evaluate the incorporation of expert and stakeholder feedback. Develop an explicit process a priori to incorporate the impact of structural racism into model structure ensuring that the model reflects current knowledge. The conceptualization of the effects of structural racism should not be driven by data availability. Develop clear written statements of the decision problem, objectives, and scope to ensure accessibility to the multidisciplinary stakeholders.
Measures of structural racism	Develop measures for structural racism in consultation with health equity experts and other stakeholders considering the decision problem, cancer type, target population, historical era, and context. Evaluate and consider the variation of the impact of the selected measures of structural racism by race and ethnicity (eg, the effects of racial residential segregation). Carefully consider the variation of the meaning and interpretation of a given measure (eg, income) based on the use of single (eg, income) vs composite measures (eg, a socioeconomic status index) and individual-level (eg, individual income) vs area-level (eg, neighborhood median income) variables. The use of single vs composite measures should be guided by the decision problem and context. Composite measures could capture the multiple dimensions of structural racism, and single measure may provide actionable policy levers. Evaluate the impact of ecological fallacy (75) when using area-level measures. Input parameter development for area-level effects should account for clustering using appropriate statistical methods (eg, multilevel modeling, latent variable analysis). Consider the effects of the structural measure(s) along the life course of individuals and its varying effects at different historical time points in collaboration with health equity scholars. Ensure that the meaning and definitions of variables remain consistent when multiple data sources are merged to develop input parameters that could track the effects of structural measures over time. Evaluate observational data, meta-analysis, and other sources of data used for input parameter development for quality and methodological rigor.
Incorporation of the effects of structural racism into simulation modeling	Consider modeling the effects of structural racism on physiological systems (eg, allostatic load, inflammation, oxidative stress, DNA damage) that contribute to cancer when data are available. Use age-period cohort models stratified by the structural measure(s) considering the role of history and birth cohort effects of structural racism. Include the effects of structural racism on the likelihood (probability) of receiving screening. These effects could be incorporated via screening dissemination models stratified by the structural factor(s). Include the effects of structural factors on diagnostic follow-up (eg, time to biopsy) and treatment delays. These could be incorporated using the effects of structural racism on the likelihood (probability) of receiving treatment via treatment dissemination models stratified by the structural factor(s). Use real-world treatment effectiveness for addressing social, economic, and health-care policy–relevant issues. Treatment efficacy could be adjusted using a decrement that quantifies the effects of structural racism. The decrements could be derived using meta-analyses or observational data.
Uncertainty	Clearly explain the sources of uncertainty related to the measure of structural racism. Describe the impact of uncertainty in the measure of structural racism on model output. Provide results for a range of values (eg, confidence interval) pertaining to the measure(s) of structural racism. Evaluate the impact of using different data sources on the effects of structural racism.
Transparency and validation	Provide documentation on the input parameter and model development in culturally relevant and appropriate lay language. Make input parameters, methods, and programs accessible to all stakeholders. Clearly outline the assumptions associated with the incorporation of structural racism. Use an independent data source representative of the target population for model validation.

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The effects of structural racism in minoritized communities have been studied for decades by researchers belonging to different fields and disciplines (eg, historians). Therefore, it is important to collaborate with multidisciplinary health equity researchers and partner with minoritized communities when developing new definitions, guidelines, and measures to model the effects of structural racism. Further, modeling should consider the impact of using individual-level (eg, income) vs area-level variables (eg, neighborhood median income) of structural factors. Survey data collected at the individual level (when available) could be less prone to ecological fallacy (75). Modeling should consider the effects of structural racism along the life course of individuals and across different historical eras.

Currently, there is no single data source that could provide all the relevant individual, clinical, and structural measures needed to comprehensively study disparities in cancer mortality. Modeling could combine information from different data sources and provide a virtual laboratory to better understand the effects of structural racism and other factors on racial and ethnic disparities in cancer mortality. Recent studies have shown that targeted interventions to increase screening among minoritized populations; timely resolution of abnormal findings; initiation and completion of therapy; and patient navigation could potentially help eliminate disparities in colorectal cancer mortality (80-82). In this context, simulation modeling could be used as a tool to study the downstream effects of structural racism along the life course and design hypothetical targeted interventions to eliminate disparities in cancer outcomes. Our study provides recommendations for best practices to consider when developing these simulation models.

Supplementary Material

lgad020_Supplementary_Data

Click here for additional data file.^{(1.3MB, docx)}

Acknowledgments

The study funders had no role in the design of the study; the collection, analysis, or interpretation of the data; the writing of the manuscript; or the decision to submit the manuscript for publication.

Opinions and comments expressed in this paper belong to the authors and do not necessarily reflect those of the US government, Department of Health and Human Services, National Institutes of Health, or the National Institute on Minority Health and Health Disparities.

The contents and views in this manuscript are those of the authors and should not be construed to represent the views of the National Institutes of Health.

Contributor Information

Jinani Jayasekera, Division of Intramural Research at the National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA.

Safa El Kefi, NYU Langone Health, New York University, New York, NY, USA.

Jessica R Fernandez, Division of Intramural Research at the National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA.

Kaitlyn M Wojcik, Division of Intramural Research at the National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA.

Jennifer M P Woo, Epidemiology Branch at the National Institute of Environmental Health Sciences at the National Institutes of Health, Bethesda, MD, USA.

Adaora Ezeani, Health Behaviors Research Branch of the Behavioral Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA.

Jennifer L Ish, Epidemiology Branch at the National Institute of Environmental Health Sciences at the National Institutes of Health, Bethesda, MD, USA.

Manami Bhattacharya, Cancer Prevention Fellowship Program, Division of Cancer Prevention, and the Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA.

Kemi Ogunsina, Epidemiology Branch at the National Institute of Environmental Health Sciences at the National Institutes of Health, Bethesda, MD, USA.

Che-Jung Chang, Epidemiology Branch at the National Institute of Environmental Health Sciences at the National Institutes of Health, Bethesda, MD, USA.

Camryn M Cohen, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA.

Stephanie Ponce, Division of Intramural Research at the National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA.

Dalya Kamil, Division of Intramural Research at the National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA.

Julia Zhang, Division of Intramural Research at the National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA; Sophomore at Williams College, Williamstown, MA, USA.

Randy Le, Division of Intramural Research at the National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA.

Amrita L Ramanathan, Diabetes, Endocrinology, & Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA.

Gisela Butera, Office of Research Services, National Institutes of Health Library, Bethesda, MD, USA.

Christina Chapman, Department of Radiation Oncology, Baylor College of Medicine, and the Center for Innovations in Quality, Effectiveness, and Safety in the Department of Medicine, Baylor College of Medicine and the Houston Veterans Affairs, Houston, TX, USA.

Shakira J Grant, Department of Medicine, Division of Hematology, University of North Carolina, Chapel Hill, NC, USA.

Marquita W Lewis-Thames, Department of Medical Social Science, Center for Community Health at Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Chiranjeev Dash, Office of Minority Health and Health Disparities Research at the Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA.

Traci N Bethea, Office of Minority Health and Health Disparities Research at the Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA.

Allana T Forde, Division of Intramural Research at the National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA.

Data Availability

All the studies summarized in this scoping review are available in the article and in its online supplementary material.

Author contributions

Jinani Jayasekera, PhD, MS (Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Supervision; Validation; Writing—original draft; Writing—review & editing), Chiranjeev Dash, MBBS, PhD (Resources), Marquita Lewis-Thames, MPH, MS, PHD (Funding acquisition; Resources), Shakira Grant, MBBS (Funding acquisition; Resources), Christina Chapman, MD (Writing—review & editing), Gisela Bethea, MEd, MLIS (Methodology; Validation; Writing—review & editing), Amrita Ramanathan, BA (Data curation; Formal analysis; Writing—original draft; Writing—review & editing), Randy Le, BS (Data curation; Formal analysis; Methodology), Julia Zhang, BA (Data curation; Formal analysis; Methodology), Dalya Kamil, BSc (Data curation; Formal analysis; Writing—original draft; Writing—review & editing), Traci Bethea, MPA, PhD (Funding acquisition; Resources), Stephanie Ponce, BA (Data curation; Formal analysis; Writing—original draft; Writing—review & editing), Che-Jung Chang, PhD, MS (Data curation; Writing—original draft; Writing—review & editing), Kemi Ogunsina, MD, PhD (Data curation; Writing—original draft; Writing—review & editing), Manami Bhattacharya, PhD, MS (Data curation; Writing—original draft; Writing—review & editing), Jennifer Ish, PhD, MS (Data curation; Writing—original draft; Writing—review & editing), Adaora Ezeani, MD, MPH (Data curation; Writing—review & editing), Jennifer Woo, PhD, MPH (Data curation; Formal analysis; Writing—original draft), Safa El Kefi, PhD (Writing—review & editing), Kaitlyn Wojcik, MPH (Conceptualization; Data curation; Formal analysis; Writing—original draft), Jessica Fernandez, PhD (Writing—original draft; Writing—review & editing), Camryn Cohen, BS (Data curation; Formal analysis; Writing—original draft; Writing—review & editing), and Allana Forde, PhD, MPH (Writing—review & editing).

Funding

Jinani Jayasekera and Allana T. Forde were supported by the Division of Intramural Research at the National Institute on Minority Health and Health Disparities of the National Institutes of Health, and the National Institutes of Health Distinguished Scholars Program (Grant Number: N/A). Jessica R. Fernandez was supported by the Division of Intramural Research at the National Institute on Minority Health and Health Disparities of the National Institutes of Health (Grant Number: N/A). Traci Bethea is supported in part by the National Cancer Institute (Grant Number: K01CA212056). Kemi Ogunsina, Jennifer M.P. Woo, Che-Jung Chang, and Jennifer L. Ish are supported by the Intramural Research Program at the National Institutes of Health, National Institute of Environmental Health Sciences (Grant Number: N/A). Adaora Ezeani was supported by the Intramural Continuing Umbrella of Research Experiences (iCURE) program at the National Cancer Institute. Amrita L. Ramanathan was supported in part by the Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), Washington, DC. The work of Camryn M. Cohen was supported by the intramural research program, Division of Cancer Epidemiology and Genetics, National Cancer Institute. Marquita Lewis-Thames was supported by a grant from the National Cancer Institute (Grant Number: K01CA262342), Northwestern University Clinical and Translational Sciences Institute grant (Grant Numbers: NUCATS; UL1TR001422, PI: D’Aquilla), National Institutes of Health’s National Institute on Aging (Grant Number: P30AG059988), and funds from the Northwestern University Center for Community Health (Grant Number: N/A). Shakira Grant is supported by a National Cancer Institute grant (Grant Number: 5-K12-CA120780-13, PI: William Kim) and a National Institute on Aging grant (Grant Number: 1 R03 AG074030-01). This work is supported in part by the National Institutes of Health under National Cancer Institute Grant U01 CA199218.

Monograph sponsorship

This article appears as part of the monograph “Reducing Disparities to Achieve Cancer Health Equity: Using Simulation Modeling to Inform Policy and Practice Change,” sponsored by the National Cancer Institute, National Institutes of Health ([Comparative Modeling of Precision Breast Cancer Control Across the Translational Continuum; 3 U01 CA253911-03S2]).

Conflicts of Interest

None.

References

1. Mandelblatt JS, Stout NK, Schechter CB, et al. Collaborative modeling of the benefits and harms associated with different U.S. breast cancer screening strategies. Ann Intern Med. 2016;164(4):215-225. [DOI] [PMC free article] [PubMed] [Google Scholar]
2. Meza R, Jeon J, Toumazis I, et al. Evaluation of the benefits and harms of lung cancer screening with low-dose computed tomography: modeling study for the US Preventive Services Task Force. JAMA. 2021;325(10):988-997. [DOI] [PMC free article] [PubMed] [Google Scholar]
3. Jayasekera J, Sparano JA, O’Neill S, et al. Development and validation of a simulation model-based clinical decision tool: identifying patients where 21-gene recurrence score testing may change decisions. J Clin Oncol. 2021;39(26):2893-2902. [DOI] [PMC free article] [PubMed] [Google Scholar]
4. Jayasekera J, Zhao A, Schechter C, et al. Reassessing the benefits and harms of risk-reducing medication considering the persistent risk of breast cancer mortality in estrogen receptor-positive breast cancer. J Clin Oncol 2023;41(4):859-870. [DOI] [PMC free article] [PubMed] [Google Scholar]
5. Jayasekera J, Li Y, Schechter CB, et al. ; for the CISNET-BOLD Collaborative Group. Simulation modeling of cancer clinical trials: application to omitting radiotherapy in low-risk breast cancer. J Natl Cancer Inst. 2018;110(12):1360-1369. [DOI] [PMC free article] [PubMed] [Google Scholar]
6. Trentham-Dietz A, Alagoz O, Chapman C, et al. ; for the Breast Working Group of the Cancer Intervention and Surveillance Modeling Network (CISNET). Reflecting on 20 years of breast cancer modeling in CISNET: recommendations for future cancer systems modeling efforts. PLoS Comput Biol. 2021;17(6):e1009020. [DOI] [PMC free article] [PubMed] [Google Scholar]
7. Knudsen AB, Rutter CM, Peterse EFP, et al. Colorectal cancer screening: an updated modeling study for the US Preventive Services Task Force. JAMA. 2021;325(19):1998-2011. [DOI] [PMC free article] [PubMed] [Google Scholar]
8. Kulasingam SL, Havrilesky LJ, Ghebre R, et al. Screening for cervical cancer: a modeling study for the US Preventive Services Task Force. J Low Genit Tract Dis. 2013;17(2):193-202. [DOI] [PMC free article] [PubMed] [Google Scholar]
9. Chapman CH, Schechter CB, Cadham CJ, et al. Identifying equitable screening mammography strategies for Black women in the United States using simulation modeling. Ann Intern Med. 2021;174(12):1637-1646. [DOI] [PMC free article] [PubMed] [Google Scholar]
10. Chang Y, Schechter CB, van Ravesteyn NT, et al. Collaborative modeling of the impact of obesity on race-specific breast cancer incidence and mortality. Breast Cancer Res Treat. 2012;136(3):823-835. [DOI] [PMC free article] [PubMed] [Google Scholar]
11. Han SS, Chow E, Ten Haaf K, et al. Disparities of national lung cancer screening guidelines in the US population. J Natl Cancer Inst. 2020;112(11):1136-1142. [DOI] [PMC free article] [PubMed] [Google Scholar]
12. Chapman C, Jayasekera J, Dash C, et al. A health equity framework to support the next generation of cancer population simulation models. J Natl Cancer Inst Monogr. 2023;2023(62):255–264. [DOI] [PMC free article] [PubMed] [Google Scholar]
13. Trentham-Dietz AC, Del Vecchio N, Greenlee RT, et al. DA Data gaps and opportunities for modeling cancer health equity. J Natl Cancer Inst Monogr. [DOI] [PMC free article] [PubMed] [Google Scholar]
14. Gee GC, Ford CL.. Structural racism and health inequities: old issues, new directions. Du Bois Rev. 2011;8(1):115-132. [DOI] [PMC free article] [PubMed] [Google Scholar]
15. Williams DR, Lawrence JA, Davis BA.. Racism and health: evidence and needed research. Annu Rev Public Health. 2019;40(1):105-125. [DOI] [PMC free article] [PubMed] [Google Scholar]
16. Bailey ZD, Feldman JM, Bassett MT.. How structural racism works—racist policies as a root cause of U.S. racial health inequities. N Engl J Med 2021;384(8):768-773. [DOI] [PMC free article] [PubMed] [Google Scholar]
17. Groos M, Wallace M, Hardeman R, et al. Measuring inequity: a systematic review of methods used to quantify structural racism. J Health Dispar Res Pract. 2018;11(2):13. [Google Scholar]
18. Bailey ZD, Krieger N, Agenor M, et al. Structural racism and health inequities in the USA: evidence and interventions. Lancet. 2017;389(10077):1453-1463. [DOI] [PubMed] [Google Scholar]
19. Jones CP. Levels of racism: a theoretic framework and a gardener’s tale. Am J Public Health. 2000;90(8):1212-1215. [DOI] [PMC free article] [PubMed] [Google Scholar]
20. Dean LT, Thorpe RJ Jr. What structural racism is (or is not) and how to measure it: clarity for public health and medical researchers. Am J Epidemiol. 2022;191(9):1521-1526. [DOI] [PMC free article] [PubMed] [Google Scholar]
21. National Academies of Sciences, Engineering, Medicine. Structural Racism and Rigorous Models of Social Inequity: Proceedings of a Workshop. Washington, DC: National Academies Press; 2022. [PubMed] [Google Scholar]
22. Arksey H, O’Malley L.. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19-32. [Google Scholar]
23. Levac D, Colquhoun H, O’Brien KK.. Scoping studies: advancing the methodology. Implement Sci. 2010;5(1):69. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Peters MDJ, Godfrey C, McInerney P, Munn Z, Tricco AC, Khalil, H. Chapter 11: Scoping reviews (2020 version). In: Aromataris E, Munn Z, eds. Joanna Briggs Institute (JBI) Manual for Evidence Synthesis. South Australia: JBI; 2020. https://synthesismanual.jbi.global. 10.46658/JBIMES-20-12. Accessed January 21, 2023. [DOI] [Google Scholar]
25. Munn Z, Peters MDJ, Stern C, et al. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol. 2018;18(1):143. [DOI] [PMC free article] [PubMed] [Google Scholar]
26. Tricco AC, Lillie E, Zarin W, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467-473. [DOI] [PubMed] [Google Scholar]
27. Jayasekera J, Wojcik K, Kamil DH, et al. Opportunities, challenges, and future directions for modeling the impact of structural racism on cancer mortality in the U.S.: A scoping review. doi: 10.17605/OSF.IO/JR2QS. [DOI] [PMC free article] [PubMed]
28. Smedley A, Smedley BD.. Race as biology is fiction, racism as a social problem is real: anthropological and historical perspectives on the social construction of race. Am Psychol. 2005;60(1):16-26. [DOI] [PubMed] [Google Scholar]
29.National Cancer Institute. National Cancer Institute Dictionary of Cancer Terms. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/ethnicity. Accessed March 24, 2023.
30. United States Census Bureau. About the topic of race. United States Census Bureau.https://www.census.gov/topics/population/race/about.html. Accessed March 11, 2023.
31. Hudis CA, Barlow WE, Costantino JP, et al. Proposal for standardized definitions for efficacy end points in adjuvant breast cancer trials: the STEEP system. J Clin Oncol. 2007;25(15):2127-2132. [DOI] [PubMed] [Google Scholar]
32. Castañeda-Avila MA, Jesdale BM, Beccia A, et al. Differences in survival among multiple myeloma patients in the United States SEER population by neighborhood socioeconomic status and race/ethnicity. Cancer Causes Control. 2021;32(9):1021-1028. [DOI] [PMC free article] [PubMed] [Google Scholar]
33. Swords DS, Bednarski BK, Messick CA, et al. Quality and location of the surgical episode mediate a large proportion of socioeconomic-based survival disparities in patients with resected stage I-III colon cancer. Ann Surg Oncol. 2022;29(1):706-716. [DOI] [PubMed] [Google Scholar]
34. Crenshaw KW. Demarginalizing the intersection of race and sex: a Black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. University of Chicago Legal Forum1989:139–167.
35. Collins PH. Black Feminist Thought: Knowledge, Consciousness, and the Politics of Empowerment. New York: Routledge; 1990. [Google Scholar]
36. Bauer GR. Incorporating intersectionality theory into population health research methodology: challenges and the potential to advance health equity. Soc Sci Med. 2014;110:10-17. [DOI] [PubMed] [Google Scholar]
37. Krieger N. Epidemiology and the web of causation: has anyone seen the spider? Soc Sci Med. 1994;39(7):887-903. [DOI] [PubMed] [Google Scholar]
38. Westrick A, Schlumbrecht M, Hlaing WM, et al. Racial and ethnic disparities in the overall survival of women with epithelial ovarian cancer in Florida, 2001-2015. Cancer Causes Control. 2020;31(4):333-340. [DOI] [PubMed] [Google Scholar]
39. Krieger N, Kim R, Feldman J, et al. Using the Index of Concentration at the Extremes at multiple geographical levels to monitor health inequities in an era of growing spatial social polarization: Massachusetts, USA (2010-14). Int J Epidemiol. 2018;47(3):788-819. [DOI] [PubMed] [Google Scholar]
40. Iyer HS, Gomez SL, Chen JT, et al. Trends in mortality among Black and White men with prostate cancer in Massachusetts and Pennsylvania: race and neighborhood socioeconomic position. Cancer. 2021;127(14):2525-2534. [DOI] [PMC free article] [PubMed] [Google Scholar]
41. Sampson RJ, Raudenbush SW, Earls F.. Neighborhoods and violent crime: a multilevel study of collective efficacy. Science. 1997;277(5328):918-924. [DOI] [PubMed] [Google Scholar]
42. Hossain F, Danos D, Prakash O, et al. Neighborhood social determinants of triple negative breast cancer. Front Public Health. 2019;7:18. [DOI] [PMC free article] [PubMed] [Google Scholar]
43. Wiese D, Stroup AM, Crosbie A, et al. The impact of neighborhood economic and racial inequalities on the spatial variation of breast cancer survival in New Jersey. Cancer Epidemiol Biomarkers Prev. 2019;28(12):1958-1967. [DOI] [PubMed] [Google Scholar]
44. Westrick AC, Bailey ZD, Schlumbrecht M, et al. Residential segregation and overall survival of women with epithelial ovarian cancer. Cancer. 2020;126(16):3698-3707. [DOI] [PubMed] [Google Scholar]
45. Poulson MR, Papageorge MV, LaRaja AS, et al. Socioeconomic mediation of racial segregation in pancreatic cancer treatment and outcome disparities. Ann Surg. 2023;278(2):246-252. [DOI] [PubMed] [Google Scholar]
46. Poulson MR, Helrich SA, Kenzik KM, et al. The impact of racial residential segregation on prostate cancer diagnosis and treatment. BJU Int. 2021;127(6):636-644. [DOI] [PubMed] [Google Scholar]
47. Poulson MR, Blanco BA, Geary AD, et al. The role of racial segregation in treatment and outcomes among patients with hepatocellular carcinoma. HPB (Oxford). 2021;23(6):854-860. [DOI] [PMC free article] [PubMed] [Google Scholar]
48. Poulson MR, Beaulieu-Jones BR, Kenzik KM, et al. Residential racial segregation and disparities in breast cancer presentation, treatment, and survival. Ann Surg. 2021;273(1):3-9. [DOI] [PubMed] [Google Scholar]
49. Poulson M, Cornell E, Madiedo A,. et al. The Impact of Racial Residential Segregation on Colorectal Cancer Outcomes and Treatment. Ann Surg. 2021;273(6):1023-1030. 10.1097/SLA.0000000000004653 33234793 [DOI] [PubMed] [Google Scholar]
50. Kruse-Diehr AJ, McDaniel JT, Lewis-Thames MW, et al. Racial residential segregation and colorectal cancer mortality in the Mississippi delta region. Prev Chronic Dis. 2021;18:E14. [DOI] [PMC free article] [PubMed] [Google Scholar]
51. Harris JA, Hunter WP, Ji YD, et al. Effects of racial residential segregation on oral squamous cell carcinoma prognosis and survival. Oral Oncol. 2021;119:105364. [DOI] [PubMed] [Google Scholar]
52. Connor AE, Kaur M, Dibble KE, et al. Racialized economic segregation and breast cancer mortality among women in Maryland. Cancer Epidemiol Biomarkers Prev. 2022;31(2):413-421. [DOI] [PMC free article] [PubMed] [Google Scholar]
53. Blanco BA, Poulson M, Kenzik KM, et al. The impact of residential segregation on pancreatic cancer diagnosis, treatment, and mortality. Ann Surg Oncol. 2021;28(6):3147-3155. [DOI] [PubMed] [Google Scholar]
54. Beyer KMM, Zhou Y, Laud PW, et al. Mortgage lending bias and breast cancer survival among older women in the United States. J Clin Oncol. 2021;39(25):2749-2757. [DOI] [PMC free article] [PubMed] [Google Scholar]
55. Beyer KMM, Laud PW, Zhou Y, et al. Housing discrimination and racial cancer disparities among the 100 largest US metropolitan areas. Cancer. 2019;125(21):3818-3827. [DOI] [PMC free article] [PubMed] [Google Scholar]
56. Bemanian A, Cassidy LD, FraserR, et al. Ecological study of variability in the relationship between liver cancer mortality and racial residential segregation. IJERPH. 2021;18(18):9732. [DOI] [PMC free article] [PubMed] [Google Scholar]
57. Annesi CA, Poulson MR, Mak KS, et al. The impact of residential racial segregation on non-small cell lung cancer treatment and outcomes. Ann Thorac Surg. 2022;113(4):1291-1298. [DOI] [PubMed] [Google Scholar]
58. Abraham IE, Rauscher GH, Patel AA, et al. Structural racism is a mediator of disparities in acute myeloid leukemia outcomes. Blood. 2022;139(14):2212-2226. [DOI] [PMC free article] [PubMed] [Google Scholar]
59. Plascak JJ, Beyer K, Xu X, et al. Association between residence in historically redlined districts indicative of structural racism and racial and ethnic disparities in breast cancer outcomes. JAMA Netw Open. 2022;5(7):e2220908. [DOI] [PMC free article] [PubMed] [Google Scholar]
60. Goel N, Westrick AC, Bailey ZD, et al. Structural racism and breast cancer-specific survival: impact of economic and racial residential segregation. Ann Surg. 2022;275(4):776-783. [DOI] [PMC free article] [PubMed] [Google Scholar]
61. Williams DR, Collins C.. Racial residential segregation: a fundamental cause of racial disparities in health. Public Health Rep. 2001;116(5):404-416. [DOI] [PMC free article] [PubMed] [Google Scholar]
62. Krimmel J. Persistence of prejudice: estimating the long-term effects of redlining. SocArXiv. 2018. doi: 10.31235/osf.io/jdmq9. Accessed January 30, 2033. [DOI] [Google Scholar]
63. Aaronson D, Hartley D, Mazumder B.. The effects of the 1930s HOLC “redlining” maps. Am Econ J Econ Pol. 2021;13(4):355-392. [Google Scholar]
64. Krieger N, Waterman PD, Spasojevic J, et al. Public health monitoring of privilege and deprivation with the index of concentration at the extremes. Am J Public Health. 2016;106(2):256-263. [DOI] [PMC free article] [PubMed] [Google Scholar]
65. Yu Q, Yu M, Zou J, et al. Multilevel mediation analysis on time-to-event outcomes: exploring racial/ethnic disparities in breast cancer survival in California. Res Methods Med Health Sci. 2021;2(4):157-167. [DOI] [PMC free article] [PubMed] [Google Scholar]
66. Silber JH, Rosenbaum PR, Ross RN, et al. Disparities in breast cancer survival by socioeconomic status despite Medicare and Medicaid insurance. Milbank Q. 2018;96(4):706-754. [DOI] [PMC free article] [PubMed] [Google Scholar]
67. Adkins-Jackson PB, Chantarat T, Bailey ZD, et al. Measuring structural racism: a guide for epidemiologists and other health researchers. Am J Epidemiol. 2022;191(4):539-547. [DOI] [PMC free article] [PubMed] [Google Scholar]
68. Assistant Secretary for Planning and Evaluation (ASPE). Landscape of Area-Level Deprivation Measures and Other Approaches to Account for Social Risk and Social Determinants of Health in Health Care Payments. 2022. https://aspe.hhs.gov/sites/default/files/documents/ce8cdc5da7d1b92314eab263a06efd03/Area-Level-SDOH-Indices-Report.pdf. Accessed May 22, 2023.
69. Hamilton CM, Strader LC, Pratt JG, et al. The PhenX Toolkit: get the most from your measures. Am J Epidemiol. 2011;174(3):253-260. [DOI] [PMC free article] [PubMed] [Google Scholar]
70. Social Determinants of Health Collections. National Institutes of Health. https://www.phenxtoolkit.org/collections/view/6. Accessed March 20, 2023.
71. Chantarat T, Van Riper DC, Hardeman RR.. The intricacy of structural racism measurement: a pilot development of a latent-class multidimensional measure. EClinicalMedicine. 2021;40:101092. [DOI] [PMC free article] [PubMed] [Google Scholar]
72. Jayasekera J, Onukwugha E, Cadham C, et al. An ecological approach to monitor geographic disparities in cancer outcomes. PLoS One. 2019;14(6):e0218712. [DOI] [PMC free article] [PubMed] [Google Scholar]
73. Villanueva C, Chang J, Ziogas A, et al. Ambient air pollution and ovarian cancer survival in California. Gynecol Oncol. 2021;163(1):155-161. [DOI] [PMC free article] [PubMed] [Google Scholar]
74. Robinson WS. Ecological correlations and the behavior of individuals. Am Sociol Review. 1950;15(3):351-357. [Google Scholar]
75. Shih Y-CT, Bradley C, Yabroff KR.. Ecological and individualistic fallacies in health disparities research. J Natl Cancer Inst. 2023;115(5):488-491. [DOI] [PMC free article] [PubMed] [Google Scholar]
76. Jargowsky PA. Ecological fallacy. In: Kempf-Leonard K, ed. Encyclopedia of Social Measurement. New York: Elsevier; 2005, 715-722. [Google Scholar]
77. Miller KD, Ortiz AP, Pinheiro PS, et al. Cancer statistics for the US Hispanic/Latino population, 2021. CA Cancer J Clin. 2021;71(6):466-487. [DOI] [PubMed] [Google Scholar]
78. Kratzer TB, Jemal A, Miller KD, et al. Cancer statistics for American Indian and Alaska Native individuals, 2022: including increasing disparities in early onset colorectal cancer. CA Cancer J Clin. 2023;73(2):120-146. [DOI] [PubMed] [Google Scholar]
79. Caro JJ, Briggs AH, Siebert U, et al. ; for the ISPOR-SMDM Modeling Good Research Practices Task Force. Modeling good research practices–overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force–1. Value Health. 2012;15(6):796-803. [DOI] [PubMed] [Google Scholar]
80. Grubbs SS, Polite BN, Carney J Jr, et al. Eliminating racial disparities in colorectal cancer in the real world: it took a village. Del Med J. 2014;86(10):301-305. [PubMed] [Google Scholar]
81. Rhoads KF, Patel MI, Ma Y, et al. How do integrated health care systems address racial and ethnic disparities in colon cancer? J Clin Oncol. 2015;33(8):854-860. [DOI] [PMC free article] [PubMed] [Google Scholar]
82. Doubeni CA, Corley DA, Zhao W, et al. Association between improved colorectal screening and racial disparities. N Engl J Med. 2022;386(8):796-798. [DOI] [PMC free article] [PubMed] [Google Scholar]
83. Aspen Institute Roundtable on Community Change. Structural Racism and Community Building. Aspen Institute Roundtable on Community Change. 2004. https://assets.aspeninstitute.org/content/uploads/files/content/docs/rcc/aspen_structural_racism2.pdf. Accessed March 01, 2023.
84. Cell JW. The Highest Stage of White Supremacy. Cambridge: Cambridge University Press; 1982. [Google Scholar]
85. Douglas S, Massey D.. American Apartheid: Segregation and the Making of the Underclass. London, UK: Harvard University Press; 1993. [Google Scholar]
86. Jaynes GD, Williams RM Jr.. A Common Destiny: Blacks and American Society. Washington, DC, US: National Academy Press; 1989. [Google Scholar]
87. Mitchell B, Franco J.. HOLC “Redlining” Maps: The Persistent Structure of Segregation and Economic Inequality. Washington, DC: National Community Reinvestment Coalition; 2018. https://ncrc.org/holc/. Accessed March 01, 2023.
88. Lukachko A, Hatzenbuehler ML, Keyes KM.. Structural racism and myocardial infarction in the United States. Soc Sci Med. 2014;103:42-50. [DOI] [PMC free article] [PubMed] [Google Scholar]
89. Kramer MR. Race, place, and space: ecosocial theory and spatiotemporal patterns of pregnancy outcomes. In: Howell FM, Porter JR, Matthews SA, eds. Recapturing Space: New Middle-Range Theory in Spatial Demography. Cham, Switzerland: Springer International Publishing; 2016:275-299. [Google Scholar]
90. El-Sayed AM, Galea S.. Epidemiology and the people’s health: by Nancy Krieger. Am J Epidemiol. 2012;175(8):854-856. [Google Scholar]
91. Vijayasiri G, Molina Y, Chukwudozie IB, et al. Racial disparities in breast cancer survival: the mediating effects of macro-social context and social network factors. J Health Dispar Res Pract. 2018;11(3):6. [PMC free article] [PubMed] [Google Scholar]
92. Berkman LF, Glass T, Brissette I, et al. From social integration to health: Durkheim in the new millennium. Soc Sci Med. 2000;51(6):843-857. [DOI] [PubMed] [Google Scholar]
93. Kirtane K, Lee SJ.. Racial and ethnic disparities in hematologic malignancies. Blood. 2017;130(15):1699-1705. [DOI] [PMC free article] [PubMed] [Google Scholar]
94. Vaughn JL, Spies D, Xavier AC, et al. Racial disparities in the survival of patients with indolent non-Hodgkin lymphomas in the United States. Am J Hematol. 2021;96(7):816-822. [DOI] [PubMed] [Google Scholar]
95. National Institute on Minority Health and Health Disparities. NIMHD research framework. 2017. https://nimhd.nih.gov/researchFramework. Accessed June 22, 2023.
96. Erdmann F, Feychting M, Mogensen H, et al. Social inequalities along the childhood cancer continuum: an overview of evidence and a conceptual framework to identify underlying mechanisms and pathways. Front Public Health. 2019;7:84. [DOI] [PMC free article] [PubMed] [Google Scholar]
97. Harris RJ, Owen DA.. Implementing a multilevel index of dissimilarity in R with a case study of the changing scales of residential ethnic segregation in England and Wales. Environ Plan B Urban Anal City Sci. 2018;45(6):1003-1021. [Google Scholar]
98. Bemanian A, Beyer KM.. Measures matter: the local exposure/isolation (LEx/Is) metrics and relationships between local-level segregation and breast cancer survival. Cancer Epidemiol Biomarkers Prev. 2017;26(4):516-524. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

lgad020_Supplementary_Data

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Data Availability Statement

All the studies summarized in this scoping review are available in the article and in its online supplementary material.

[lgad020-B1] 1. Mandelblatt JS, Stout NK, Schechter CB, et al. Collaborative modeling of the benefits and harms associated with different U.S. breast cancer screening strategies. Ann Intern Med. 2016;164(4):215-225. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B2] 2. Meza R, Jeon J, Toumazis I, et al. Evaluation of the benefits and harms of lung cancer screening with low-dose computed tomography: modeling study for the US Preventive Services Task Force. JAMA. 2021;325(10):988-997. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B3] 3. Jayasekera J, Sparano JA, O’Neill S, et al. Development and validation of a simulation model-based clinical decision tool: identifying patients where 21-gene recurrence score testing may change decisions. J Clin Oncol. 2021;39(26):2893-2902. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B4] 4. Jayasekera J, Zhao A, Schechter C, et al. Reassessing the benefits and harms of risk-reducing medication considering the persistent risk of breast cancer mortality in estrogen receptor-positive breast cancer. J Clin Oncol 2023;41(4):859-870. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B5] 5. Jayasekera J, Li Y, Schechter CB, et al. ; for the CISNET-BOLD Collaborative Group. Simulation modeling of cancer clinical trials: application to omitting radiotherapy in low-risk breast cancer. J Natl Cancer Inst. 2018;110(12):1360-1369. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B6] 6. Trentham-Dietz A, Alagoz O, Chapman C, et al. ; for the Breast Working Group of the Cancer Intervention and Surveillance Modeling Network (CISNET). Reflecting on 20 years of breast cancer modeling in CISNET: recommendations for future cancer systems modeling efforts. PLoS Comput Biol. 2021;17(6):e1009020. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B7] 7. Knudsen AB, Rutter CM, Peterse EFP, et al. Colorectal cancer screening: an updated modeling study for the US Preventive Services Task Force. JAMA. 2021;325(19):1998-2011. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B8] 8. Kulasingam SL, Havrilesky LJ, Ghebre R, et al. Screening for cervical cancer: a modeling study for the US Preventive Services Task Force. J Low Genit Tract Dis. 2013;17(2):193-202. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B9] 9. Chapman CH, Schechter CB, Cadham CJ, et al. Identifying equitable screening mammography strategies for Black women in the United States using simulation modeling. Ann Intern Med. 2021;174(12):1637-1646. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B10] 10. Chang Y, Schechter CB, van Ravesteyn NT, et al. Collaborative modeling of the impact of obesity on race-specific breast cancer incidence and mortality. Breast Cancer Res Treat. 2012;136(3):823-835. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B11] 11. Han SS, Chow E, Ten Haaf K, et al. Disparities of national lung cancer screening guidelines in the US population. J Natl Cancer Inst. 2020;112(11):1136-1142. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B12] 12. Chapman C, Jayasekera J, Dash C, et al. A health equity framework to support the next generation of cancer population simulation models. J Natl Cancer Inst Monogr. 2023;2023(62):255–264. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B13] 13. Trentham-Dietz AC, Del Vecchio N, Greenlee RT, et al. DA Data gaps and opportunities for modeling cancer health equity. J Natl Cancer Inst Monogr. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B14] 14. Gee GC, Ford CL.. Structural racism and health inequities: old issues, new directions. Du Bois Rev. 2011;8(1):115-132. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B15] 15. Williams DR, Lawrence JA, Davis BA.. Racism and health: evidence and needed research. Annu Rev Public Health. 2019;40(1):105-125. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B16] 16. Bailey ZD, Feldman JM, Bassett MT.. How structural racism works—racist policies as a root cause of U.S. racial health inequities. N Engl J Med 2021;384(8):768-773. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B17] 17. Groos M, Wallace M, Hardeman R, et al. Measuring inequity: a systematic review of methods used to quantify structural racism. J Health Dispar Res Pract. 2018;11(2):13. [Google Scholar]

[lgad020-B18] 18. Bailey ZD, Krieger N, Agenor M, et al. Structural racism and health inequities in the USA: evidence and interventions. Lancet. 2017;389(10077):1453-1463. [DOI] [PubMed] [Google Scholar]

[lgad020-B19] 19. Jones CP. Levels of racism: a theoretic framework and a gardener’s tale. Am J Public Health. 2000;90(8):1212-1215. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B20] 20. Dean LT, Thorpe RJ Jr. What structural racism is (or is not) and how to measure it: clarity for public health and medical researchers. Am J Epidemiol. 2022;191(9):1521-1526. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B21] 21. National Academies of Sciences, Engineering, Medicine. Structural Racism and Rigorous Models of Social Inequity: Proceedings of a Workshop. Washington, DC: National Academies Press; 2022. [PubMed] [Google Scholar]

[lgad020-B22] 22. Arksey H, O’Malley L.. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19-32. [Google Scholar]

[lgad020-B23] 23. Levac D, Colquhoun H, O’Brien KK.. Scoping studies: advancing the methodology. Implement Sci. 2010;5(1):69. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B24] 24.Peters MDJ, Godfrey C, McInerney P, Munn Z, Tricco AC, Khalil, H. Chapter 11: Scoping reviews (2020 version). In: Aromataris E, Munn Z, eds. Joanna Briggs Institute (JBI) Manual for Evidence Synthesis. South Australia: JBI; 2020. https://synthesismanual.jbi.global. 10.46658/JBIMES-20-12. Accessed January 21, 2023. [DOI] [Google Scholar]

[lgad020-B25] 25. Munn Z, Peters MDJ, Stern C, et al. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol. 2018;18(1):143. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B26] 26. Tricco AC, Lillie E, Zarin W, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467-473. [DOI] [PubMed] [Google Scholar]

[lgad020-B27] 27. Jayasekera J, Wojcik K, Kamil DH, et al. Opportunities, challenges, and future directions for modeling the impact of structural racism on cancer mortality in the U.S.: A scoping review. doi: 10.17605/OSF.IO/JR2QS. [DOI] [PMC free article] [PubMed]

[lgad020-B28] 28. Smedley A, Smedley BD.. Race as biology is fiction, racism as a social problem is real: anthropological and historical perspectives on the social construction of race. Am Psychol. 2005;60(1):16-26. [DOI] [PubMed] [Google Scholar]

[lgad020-B29] 29.National Cancer Institute. National Cancer Institute Dictionary of Cancer Terms. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/ethnicity. Accessed March 24, 2023.

[lgad020-B30] 30. United States Census Bureau. About the topic of race. United States Census Bureau.https://www.census.gov/topics/population/race/about.html. Accessed March 11, 2023.

[lgad020-B31] 31. Hudis CA, Barlow WE, Costantino JP, et al. Proposal for standardized definitions for efficacy end points in adjuvant breast cancer trials: the STEEP system. J Clin Oncol. 2007;25(15):2127-2132. [DOI] [PubMed] [Google Scholar]

[lgad020-B32] 32. Castañeda-Avila MA, Jesdale BM, Beccia A, et al. Differences in survival among multiple myeloma patients in the United States SEER population by neighborhood socioeconomic status and race/ethnicity. Cancer Causes Control. 2021;32(9):1021-1028. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B33] 33. Swords DS, Bednarski BK, Messick CA, et al. Quality and location of the surgical episode mediate a large proportion of socioeconomic-based survival disparities in patients with resected stage I-III colon cancer. Ann Surg Oncol. 2022;29(1):706-716. [DOI] [PubMed] [Google Scholar]

[lgad020-B34] 34. Crenshaw KW. Demarginalizing the intersection of race and sex: a Black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. University of Chicago Legal Forum1989:139–167.

[lgad020-B35] 35. Collins PH. Black Feminist Thought: Knowledge, Consciousness, and the Politics of Empowerment. New York: Routledge; 1990. [Google Scholar]

[lgad020-B36] 36. Bauer GR. Incorporating intersectionality theory into population health research methodology: challenges and the potential to advance health equity. Soc Sci Med. 2014;110:10-17. [DOI] [PubMed] [Google Scholar]

[lgad020-B37] 37. Krieger N. Epidemiology and the web of causation: has anyone seen the spider? Soc Sci Med. 1994;39(7):887-903. [DOI] [PubMed] [Google Scholar]

[lgad020-B38] 38. Westrick A, Schlumbrecht M, Hlaing WM, et al. Racial and ethnic disparities in the overall survival of women with epithelial ovarian cancer in Florida, 2001-2015. Cancer Causes Control. 2020;31(4):333-340. [DOI] [PubMed] [Google Scholar]

[lgad020-B39] 39. Krieger N, Kim R, Feldman J, et al. Using the Index of Concentration at the Extremes at multiple geographical levels to monitor health inequities in an era of growing spatial social polarization: Massachusetts, USA (2010-14). Int J Epidemiol. 2018;47(3):788-819. [DOI] [PubMed] [Google Scholar]

[lgad020-B40] 40. Iyer HS, Gomez SL, Chen JT, et al. Trends in mortality among Black and White men with prostate cancer in Massachusetts and Pennsylvania: race and neighborhood socioeconomic position. Cancer. 2021;127(14):2525-2534. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B41] 41. Sampson RJ, Raudenbush SW, Earls F.. Neighborhoods and violent crime: a multilevel study of collective efficacy. Science. 1997;277(5328):918-924. [DOI] [PubMed] [Google Scholar]

[lgad020-B42] 42. Hossain F, Danos D, Prakash O, et al. Neighborhood social determinants of triple negative breast cancer. Front Public Health. 2019;7:18. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B43] 43. Wiese D, Stroup AM, Crosbie A, et al. The impact of neighborhood economic and racial inequalities on the spatial variation of breast cancer survival in New Jersey. Cancer Epidemiol Biomarkers Prev. 2019;28(12):1958-1967. [DOI] [PubMed] [Google Scholar]

[lgad020-B44] 44. Westrick AC, Bailey ZD, Schlumbrecht M, et al. Residential segregation and overall survival of women with epithelial ovarian cancer. Cancer. 2020;126(16):3698-3707. [DOI] [PubMed] [Google Scholar]

[lgad020-B45] 45. Poulson MR, Papageorge MV, LaRaja AS, et al. Socioeconomic mediation of racial segregation in pancreatic cancer treatment and outcome disparities. Ann Surg. 2023;278(2):246-252. [DOI] [PubMed] [Google Scholar]

[lgad020-B46] 46. Poulson MR, Helrich SA, Kenzik KM, et al. The impact of racial residential segregation on prostate cancer diagnosis and treatment. BJU Int. 2021;127(6):636-644. [DOI] [PubMed] [Google Scholar]

[lgad020-B47] 47. Poulson MR, Blanco BA, Geary AD, et al. The role of racial segregation in treatment and outcomes among patients with hepatocellular carcinoma. HPB (Oxford). 2021;23(6):854-860. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B48] 48. Poulson MR, Beaulieu-Jones BR, Kenzik KM, et al. Residential racial segregation and disparities in breast cancer presentation, treatment, and survival. Ann Surg. 2021;273(1):3-9. [DOI] [PubMed] [Google Scholar]

[lgad020-B49] 49. Poulson M, Cornell E, Madiedo A,. et al. The Impact of Racial Residential Segregation on Colorectal Cancer Outcomes and Treatment. Ann Surg. 2021;273(6):1023-1030. 10.1097/SLA.0000000000004653 33234793 [DOI] [PubMed] [Google Scholar]

[lgad020-B50] 50. Kruse-Diehr AJ, McDaniel JT, Lewis-Thames MW, et al. Racial residential segregation and colorectal cancer mortality in the Mississippi delta region. Prev Chronic Dis. 2021;18:E14. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B51] 51. Harris JA, Hunter WP, Ji YD, et al. Effects of racial residential segregation on oral squamous cell carcinoma prognosis and survival. Oral Oncol. 2021;119:105364. [DOI] [PubMed] [Google Scholar]

[lgad020-B52] 52. Connor AE, Kaur M, Dibble KE, et al. Racialized economic segregation and breast cancer mortality among women in Maryland. Cancer Epidemiol Biomarkers Prev. 2022;31(2):413-421. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B53] 53. Blanco BA, Poulson M, Kenzik KM, et al. The impact of residential segregation on pancreatic cancer diagnosis, treatment, and mortality. Ann Surg Oncol. 2021;28(6):3147-3155. [DOI] [PubMed] [Google Scholar]

[lgad020-B54] 54. Beyer KMM, Zhou Y, Laud PW, et al. Mortgage lending bias and breast cancer survival among older women in the United States. J Clin Oncol. 2021;39(25):2749-2757. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B55] 55. Beyer KMM, Laud PW, Zhou Y, et al. Housing discrimination and racial cancer disparities among the 100 largest US metropolitan areas. Cancer. 2019;125(21):3818-3827. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B56] 56. Bemanian A, Cassidy LD, FraserR, et al. Ecological study of variability in the relationship between liver cancer mortality and racial residential segregation. IJERPH. 2021;18(18):9732. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B57] 57. Annesi CA, Poulson MR, Mak KS, et al. The impact of residential racial segregation on non-small cell lung cancer treatment and outcomes. Ann Thorac Surg. 2022;113(4):1291-1298. [DOI] [PubMed] [Google Scholar]

[lgad020-B58] 58. Abraham IE, Rauscher GH, Patel AA, et al. Structural racism is a mediator of disparities in acute myeloid leukemia outcomes. Blood. 2022;139(14):2212-2226. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B59] 59. Plascak JJ, Beyer K, Xu X, et al. Association between residence in historically redlined districts indicative of structural racism and racial and ethnic disparities in breast cancer outcomes. JAMA Netw Open. 2022;5(7):e2220908. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B60] 60. Goel N, Westrick AC, Bailey ZD, et al. Structural racism and breast cancer-specific survival: impact of economic and racial residential segregation. Ann Surg. 2022;275(4):776-783. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B61] 61. Williams DR, Collins C.. Racial residential segregation: a fundamental cause of racial disparities in health. Public Health Rep. 2001;116(5):404-416. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B62] 62. Krimmel J. Persistence of prejudice: estimating the long-term effects of redlining. SocArXiv. 2018. doi: 10.31235/osf.io/jdmq9. Accessed January 30, 2033. [DOI] [Google Scholar]

[lgad020-B63] 63. Aaronson D, Hartley D, Mazumder B.. The effects of the 1930s HOLC “redlining” maps. Am Econ J Econ Pol. 2021;13(4):355-392. [Google Scholar]

[lgad020-B64] 64. Krieger N, Waterman PD, Spasojevic J, et al. Public health monitoring of privilege and deprivation with the index of concentration at the extremes. Am J Public Health. 2016;106(2):256-263. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B65] 65. Yu Q, Yu M, Zou J, et al. Multilevel mediation analysis on time-to-event outcomes: exploring racial/ethnic disparities in breast cancer survival in California. Res Methods Med Health Sci. 2021;2(4):157-167. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B66] 66. Silber JH, Rosenbaum PR, Ross RN, et al. Disparities in breast cancer survival by socioeconomic status despite Medicare and Medicaid insurance. Milbank Q. 2018;96(4):706-754. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B67] 67. Adkins-Jackson PB, Chantarat T, Bailey ZD, et al. Measuring structural racism: a guide for epidemiologists and other health researchers. Am J Epidemiol. 2022;191(4):539-547. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B68] 68. Assistant Secretary for Planning and Evaluation (ASPE). Landscape of Area-Level Deprivation Measures and Other Approaches to Account for Social Risk and Social Determinants of Health in Health Care Payments. 2022. https://aspe.hhs.gov/sites/default/files/documents/ce8cdc5da7d1b92314eab263a06efd03/Area-Level-SDOH-Indices-Report.pdf. Accessed May 22, 2023.

[lgad020-B69] 69. Hamilton CM, Strader LC, Pratt JG, et al. The PhenX Toolkit: get the most from your measures. Am J Epidemiol. 2011;174(3):253-260. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B70] 70. Social Determinants of Health Collections. National Institutes of Health. https://www.phenxtoolkit.org/collections/view/6. Accessed March 20, 2023.

[lgad020-B71] 71. Chantarat T, Van Riper DC, Hardeman RR.. The intricacy of structural racism measurement: a pilot development of a latent-class multidimensional measure. EClinicalMedicine. 2021;40:101092. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B72] 72. Jayasekera J, Onukwugha E, Cadham C, et al. An ecological approach to monitor geographic disparities in cancer outcomes. PLoS One. 2019;14(6):e0218712. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B73] 73. Villanueva C, Chang J, Ziogas A, et al. Ambient air pollution and ovarian cancer survival in California. Gynecol Oncol. 2021;163(1):155-161. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B74] 74. Robinson WS. Ecological correlations and the behavior of individuals. Am Sociol Review. 1950;15(3):351-357. [Google Scholar]

[lgad020-B75] 75. Shih Y-CT, Bradley C, Yabroff KR.. Ecological and individualistic fallacies in health disparities research. J Natl Cancer Inst. 2023;115(5):488-491. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B76] 76. Jargowsky PA. Ecological fallacy. In: Kempf-Leonard K, ed. Encyclopedia of Social Measurement. New York: Elsevier; 2005, 715-722. [Google Scholar]

[lgad020-B77] 77. Miller KD, Ortiz AP, Pinheiro PS, et al. Cancer statistics for the US Hispanic/Latino population, 2021. CA Cancer J Clin. 2021;71(6):466-487. [DOI] [PubMed] [Google Scholar]

[lgad020-B78] 78. Kratzer TB, Jemal A, Miller KD, et al. Cancer statistics for American Indian and Alaska Native individuals, 2022: including increasing disparities in early onset colorectal cancer. CA Cancer J Clin. 2023;73(2):120-146. [DOI] [PubMed] [Google Scholar]

[lgad020-B79] 79. Caro JJ, Briggs AH, Siebert U, et al. ; for the ISPOR-SMDM Modeling Good Research Practices Task Force. Modeling good research practices–overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force–1. Value Health. 2012;15(6):796-803. [DOI] [PubMed] [Google Scholar]

[lgad020-B80] 80. Grubbs SS, Polite BN, Carney J Jr, et al. Eliminating racial disparities in colorectal cancer in the real world: it took a village. Del Med J. 2014;86(10):301-305. [PubMed] [Google Scholar]

[lgad020-B81] 81. Rhoads KF, Patel MI, Ma Y, et al. How do integrated health care systems address racial and ethnic disparities in colon cancer? J Clin Oncol. 2015;33(8):854-860. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B82] 82. Doubeni CA, Corley DA, Zhao W, et al. Association between improved colorectal screening and racial disparities. N Engl J Med. 2022;386(8):796-798. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B83] 83. Aspen Institute Roundtable on Community Change. Structural Racism and Community Building. Aspen Institute Roundtable on Community Change. 2004. https://assets.aspeninstitute.org/content/uploads/files/content/docs/rcc/aspen_structural_racism2.pdf. Accessed March 01, 2023.

[lgad020-B84] 84. Cell JW. The Highest Stage of White Supremacy. Cambridge: Cambridge University Press; 1982. [Google Scholar]

[lgad020-B85] 85. Douglas S, Massey D.. American Apartheid: Segregation and the Making of the Underclass. London, UK: Harvard University Press; 1993. [Google Scholar]

[lgad020-B86] 86. Jaynes GD, Williams RM Jr.. A Common Destiny: Blacks and American Society. Washington, DC, US: National Academy Press; 1989. [Google Scholar]

[lgad020-B87] 87. Mitchell B, Franco J.. HOLC “Redlining” Maps: The Persistent Structure of Segregation and Economic Inequality. Washington, DC: National Community Reinvestment Coalition; 2018. https://ncrc.org/holc/. Accessed March 01, 2023.

[lgad020-B88] 88. Lukachko A, Hatzenbuehler ML, Keyes KM.. Structural racism and myocardial infarction in the United States. Soc Sci Med. 2014;103:42-50. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B89] 89. Kramer MR. Race, place, and space: ecosocial theory and spatiotemporal patterns of pregnancy outcomes. In: Howell FM, Porter JR, Matthews SA, eds. Recapturing Space: New Middle-Range Theory in Spatial Demography. Cham, Switzerland: Springer International Publishing; 2016:275-299. [Google Scholar]

[lgad020-B90] 90. El-Sayed AM, Galea S.. Epidemiology and the people’s health: by Nancy Krieger. Am J Epidemiol. 2012;175(8):854-856. [Google Scholar]

[lgad020-B91] 91. Vijayasiri G, Molina Y, Chukwudozie IB, et al. Racial disparities in breast cancer survival: the mediating effects of macro-social context and social network factors. J Health Dispar Res Pract. 2018;11(3):6. [PMC free article] [PubMed] [Google Scholar]

[lgad020-B92] 92. Berkman LF, Glass T, Brissette I, et al. From social integration to health: Durkheim in the new millennium. Soc Sci Med. 2000;51(6):843-857. [DOI] [PubMed] [Google Scholar]

[lgad020-B93] 93. Kirtane K, Lee SJ.. Racial and ethnic disparities in hematologic malignancies. Blood. 2017;130(15):1699-1705. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B94] 94. Vaughn JL, Spies D, Xavier AC, et al. Racial disparities in the survival of patients with indolent non-Hodgkin lymphomas in the United States. Am J Hematol. 2021;96(7):816-822. [DOI] [PubMed] [Google Scholar]

[lgad020-B95] 95. National Institute on Minority Health and Health Disparities. NIMHD research framework. 2017. https://nimhd.nih.gov/researchFramework. Accessed June 22, 2023.

[lgad020-B96] 96. Erdmann F, Feychting M, Mogensen H, et al. Social inequalities along the childhood cancer continuum: an overview of evidence and a conceptual framework to identify underlying mechanisms and pathways. Front Public Health. 2019;7:84. [DOI] [PMC free article] [PubMed] [Google Scholar]

[lgad020-B97] 97. Harris RJ, Owen DA.. Implementing a multilevel index of dissimilarity in R with a case study of the changing scales of residential ethnic segregation in England and Wales. Environ Plan B Urban Anal City Sci. 2018;45(6):1003-1021. [Google Scholar]

[lgad020-B98] 98. Bemanian A, Beyer KM.. Measures matter: the local exposure/isolation (LEx/Is) metrics and relationships between local-level segregation and breast cancer survival. Cancer Epidemiol Biomarkers Prev. 2017;26(4):516-524. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

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

Jinani Jayasekera, PhD

Safa El Kefi, PhD

Jessica R Fernandez, PhD

Kaitlyn M Wojcik, MPH

Jennifer M P Woo, PhD, MPH

Adaora Ezeani, MD, MPH

Jennifer L Ish, PhD, MS

Manami Bhattacharya, PhD, MS

Kemi Ogunsina, MD, PhD

Che-Jung Chang, MS, PhD

Camryn M Cohen, BS

Stephanie Ponce, BA

Dalya Kamil, BSc

Julia Zhang

Randy Le, BS

Amrita L Ramanathan, BA

Gisela Butera, MED, MLIS

Christina Chapman, MD

Shakira J Grant, MBBS

Marquita W Lewis-Thames, MPH, MS, PhD

Chiranjeev Dash, MBBS, PhD

Traci N Bethea, MPA, PhD

Allana T Forde, PhD, MPH

Roles

Abstract

Purpose

Methods

Results

Conclusions

Methods

Eligibility criteria

Data screening, extraction, and synthesis

Results

Figure 1.

Definitions, theories, and conceptual frameworks

Table 1.

Measures

Data sources and unit of analysis

Analytic approaches

Impact of structural racism on racial and ethnic disparities in cancer mortality

Table 2.

Discussion

Strengths and limitations of the scoping review

Simulation modeling the effects of structural racism

Opportunities

Challenges

Future directions for modeling

Table 3.

Supplementary Material

Acknowledgments

Contributor Information

Data Availability

Author contributions

Funding

Monograph sponsorship

Conflicts of Interest

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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