American Community Survey Research Articles

Asian American Diversity and Representation in the Health Care Workforce, 2007 to 2022

Asian American individuals comprise over 40 ethnoracial groups but are regularly aggregated into 1 category within health workforce analysis, thus obscuring substantial inequities in representation. To describe trends in Asian American diversity across the 4 most populous US health professions (physicians, registered nurses, nursing assistants, and home health aides) and to characterize subgroup representation within professions. Serial cross-sectional study from American Community Survey (ACS) population estimates of people reporting health profession occupations from 2007 to 2022. The ACS samples US residents every day, with approximately 3.5 million surveyed each year. Based on their sampling methods, the ACS then produces estimates of the entire US population for 1-year, 3-year, and 5-year periods. Data were analyzed from April to August 2024. For each year and subgroup, the proportions of the US population, the profession, and Asian American individuals within the profession were calculated. The representation quotient (RQ) of each subgroup was then calculated, defined as the proportion of the subgroup within the profession of interest divided by the proportion of the subgroup within the US population. Trends were examined over the 15-year period. Over the 15-year period, Indian Americans composed the largest percentage of Asian American physicians (mean [SD], 40.6% [1.6%]), followed by Chinese Americans (mean [SD], 18.9% [1.4%]). Pakistani and Indian Americans had the highest relative representation (mean [SD] RQ, 8.9 [0.9] and 7.8 [0.9], respectively). Conversely, Cambodian and Hmong Americans remained largely underrepresented (mean [SD] RQ, 0.2 [0.2] for both). Filipinx Americans accounted for more than half of Asian American registered nurses and nursing assistants, with high relative representation (mean [SD] RQ, 5.6 (0.3) and 2.9 [0.4], respectively). Bangladeshi and Chinese American relative representation were high among home health aides (mean [SD] RQ, 4.1 [1.5] and 2.7 [0.5], respectively). Asian American individuals accounted for an estimated 22% of physicians (approximately 260 693 respondents), 10% of registered nurses (approximately 420 418 respondents), 4.8% of nursing assistants (approximately 93 913 respondents), and 8.3% of home health aides (approximately 60 968 respondents) in 2022. By examining disaggregated data, this study found persistent inequities among Asian American subgroups in the health workforce. Reducing Asian American populations to a single racialized group erases subgroup differences rooted in histories of racism, colonialism, and xenophobia; enables false narratives of Asian American overrepresentation and success; and hampers progress in advancing health justice.

Eight Decades of Educational Assortative Mating: A Research Note

Recent social and economic trends in the United States, including increasing economic inequality, women's growing educational advantage, and the rise of online dating, have ambiguous implications for patterns of educational homogamy. In this research note, we examine changes in educational assortative mating in the United States over the last eight decades (1940 to 2020) using the U.S. decennial censuses and the American Community Survey, extending and expanding earlier work by Schwartz and Mare. We find that the rise in educational homogamy noted by Schwartz and Mare has not continued. Increases in educational homogamy stalled around 1990 and began reversing in the 2000s. We find a growing tendency for marriages to cross educational boundaries, but a college degree remains the strongest dividing line to intermarriage. A key trend explaining this new pattern is women's increasing tendency to marry men with less education than themselves. If not for this trend, homogamy would have continued increasing until the early 2010s. We also show substantial heterogeneity by race, ethnicity, and nativity and among same- versus different-sex couples.

Filling the Treatment Gap: Geographic Expansion of Buprenorphine Providers Across the U.S.

ObjectiveWith opioid-related deaths reaching epidemic levels, gaining a better understanding of access to treatment for opioid use disorder (OUD) is critical. Buprenorphine is an effective medication for the treatment of opioid addiction. The analysis is critical post-X-waiver elimination which extended the ability to prescribe buprenorphine for the treatment of OUD to all practitioners with DEA Schedules II-V on their DEA Registration. The primary purpose of the analysis was to explore the geographic patterns of substance use treatment and establish a geographical baseline to assess the removal of the X-Waiver in the future. Data and MethodsWe assessed the expansion of buprenorphine providers across the data from all US counties up to December 31, 2022. We used all certified buprenorphine providers' data from the database of the buprenorphine waiver notification system (BWNS). We used county-level population data from the US Census of American Community Survey (ACS) of 2021 and the CDC's Drug-related mortality data. We implemented spatial scan statistics to identify the spatial clusters using SaTScan. ResultsThe results from our analysis show that Doctor of Medicine/Doctor of Osteopathic Medicine (MD/DO) have the highest numbers of certified providers at 8134 (65.08%) in 2018 and 14525 (57.87%) in 2022. Our analysis shows that the distribution of buprenorphine providers across the counties of the United States was significantly clustered. Higher clusters with RR of more than 1 (p-value <0.001) were found in the states of Washington, Oregon, Northern California, and the Western borders of Montana. Similar clusters of counties with RR more than 1 (p-value <0.001) were found in the northeastern states of Maine, Vermont, and New Hampshire. ConclusionsFrom our analysis, it is evident that buprenorphine-certified providers are clustered in areas of higher drug-related mortality filling a treatment gap. The elimination of the X-waiver will be a significant step towards increasing access to MOUD and this analysis may be used as a geographical baseline to assess in the future.

Are new pediatric trauma centers located close to the high-risk populations? A geolocation study.

Higher rates of injury occur in children who live in low socioeconomic areas. Since 2010, the number of verified Level I and Level II pediatric trauma centers (PTCs) has doubled. The purpose of this study is to look at the location of new verified PTC in relation to children living in high-risk areas. Historical and recent data about verified Level I and Level II PTCs were obtained from the American College of Surgeons (ACS), the American Trauma Society, and State Data where available. Census data were obtained from the US Census Bureau's American Community Survey for 2010 and 2020 for children younger than 18 years. The pediatric population was stratified by (1) poverty threshold and (2) living within 30 miles of PTC. The census data and trauma center locations were geocoded using the ArcGIS Pro program. Data analysis was performed at the census tract level. A total of 55 Level I or Level II ACS-verified PTCs were in the United States in 2010 and 116 in 2020. In 2010, there were 14.5 million children (19.6%) below the poverty threshold and 12.9 million (17.8%) in 2020. In 2010, 23 states did not have an ACS-verified PTC either in the state or in a neighboring state within 30 miles. By 2020, only nine states did not have an ACS-verified PTC or a center in a neighboring state within 30 miles. When state level data were included, this dropped to four. The 19 states who had new verified PTCs covered between 14% and 21% of children below the poverty level. The percentage of children below the poverty threshold and less than 30-mile access to a Level I or Level II PTC in 2010 was 74.56% compared with children above poverty and less than 30-mile access, which was 70.34% (p < 0.0001). In 2020, children below poverty without 30-mile access had been reduced to 52% and 45.54% for children above poverty level (p < 0.0001). Thus, there was a greater increase in 30-mile access for children above the poverty level than for those below the poverty level (24.8% vs. 22.6%, p < 0.001). On a national level, despite doubling the number of PTC, it has not improved 30-mile access for high-risk children. Alternatively, 19 states now have better access. Retrospective Cohort Study, Level III.

Migrants from a Different Shore: Earnings and Economic Assimilation of Immigrants from China in the United States

AbstractUsing data from 1980, 1990, and 2000 U.S. censuses, as well as the 2010 and 2019 American Community Surveys and the 1993–2019 National Survey of College Graduates, we investigate the performance of Chinese immigrants in the U.S. labor market over the past 40 years since China initiated its economic reforms and open-door policy in 1978. The results indicate that by 1990, Chinese immigrants’ earnings surpassed those of immigrants from other countries, and by 2010, they exceeded the earnings of U.S.-born workers. Our Oaxaca-Blinder and Quantile decomposition analyses suggest that a significant portion of the earnings advantage held by Chinese immigrants, compared to other immigrant groups and U.S.-born workers over time, can be attributed to differences in observable characteristics, with education being the most crucial factor, both at the mean and across the earnings distribution. By employing national surveys that provide data on college graduates, we demonstrate that attaining the highest degree earned in the U.S. is associated with higher earnings for Chinese immigrants compared to all other immigrants. Furthermore, the difference in returns to U.S.-earned highest degrees can account for this earnings advantage. (JEL J31, J61, J24)

Closing Gaps or Holding Steady? The Affordable Care Act, Medicaid Expansion, & Racial Disparities in Coverage, 2010-2021.

The Affordable Care Act's (ACA) Medicaid expansion produced major gains in coverage. However, findings on racial and ethnic disparities are mixed and may depend on how disparities are measured. This study examines both absolute and relative changes in uninsurance from 2010-2021 by race and ethnicity, stratified by Medicaid expansion status. The sample contained all respondents under age 65 (N = 30,339,104) from the American Community Survey, 2010-2021. Absolute and relative differences in uninsurance, compared to White Non-Hispanic individuals, were calculated for Hispanic; Black; Asian-American, Pacific Islander and Native Hawaiian (AANHPI); American Indian and Alaska Native (AIAN); and multiracial individuals. States were stratified into ever-expanded vs. non-expansion status. After the ACA, three patterns of coverage disparities emerge. For Hispanic and Black individuals, relative to White individuals, absolute disparities in uninsurance declined but relative disparities were largely unchanged, in both expansion and non-expansion states. For AANHPI individuals, disparities were eliminated entirely in both expansion and non-expansion states. For AIAN individuals, disparities declined in absolute terms but grew in relative terms, particularly in expansion states. All groups experienced coverage gains post-ACA, but with heterogeneity in changes in disparities. Focused interventions are needed to improve coverage rates for Black, Hispanic, and AIAN individuals.

Assessing Electronic Health Records for Describing Racial and Ethnic Health Disparities: A Research Note.

The use of data derived from electronic health records (EHRs) to describe racial and ethnic health disparities is increasingly common, but there are challenges. While the number of patients covered by EHRs can be quite large, such patients may not be representative of a source population. One way to evaluate the extent of this limitation is by linking EHRs to an external source, in this case with the American Community Survey (ACS). Relying on a stratified random sample of about 200,000 patient records from a large, public, integrated health delivery system in North Carolina (2016-2019), we assess linkages to restricted ACS microdata (2001-2017) by race and ethnicity to understand the strengths and weaknesses of EHR-derived data for describing disparities. The results in this research note suggest that Black-White comparisons will benefit from standard adjustments (e.g., weighting procedures) but that misestimation of health disparities may arise for Hispanic patients because of differential coverage rates for this group.

The impact of digital inequities on salivary gland cancer disparities in the United States.

Technology and internet access have become increasingly integrated into healthcare as the primary platform for health-related information and provider-patient communication. Disparities in access to digital resources exist in the United States and have been shown to impact health outcomes in various head and neck malignancies. Our objective is to evaluate the associations of digital inequity on health outcomes in patients with salivary gland cancer (SGC). The Digital Inequity Index (DII) was developed using 17 census-tract level variables obtained from the American Community Survey and Federal Communications Commission. Variables were categorized as digital infrastructure or sociodemographic (e.g., non-digital) and scored based on relative rankings across all US counties. Scores were assigned to patients from the Surveillance-Epidemiology-End Results (SEER) database diagnosed with SGC between 2013 and 2017 based on county-of-residence. Regressions were performed between DII score and outcomes of surveillance time, survival time, tumor stage at time of diagnosis, and treatment modality. Among 9306 SGC-patients, increased digital inequity was associated with advanced-staging at presentation (OR: 1.04, 95% CI: 1.01-1.07, p = 0.033), increased odds of chemotherapy receipt (OR: 1.05, CI: 1.01-1.10, p = 0.010), and decreased odds of surgical intervention (OR: 0.94, 95% CI: 0.91-0.98, p = 0.003) after accounting for traditional sociodemographic factors. Increased digital inequity was also associated with decreased surveillance time and survival periods. Digital inequity significantly and independently associates with negative health and treatment outcomes in SGC patients, highlighting the importance of directed efforts to address these seldom-investigated drivers of health disparities.

Elderly care in an aging population: The impact of parental care needs on adult children

Abstract One of the challenges of population aging is the rising demand of elder care. Adult children fill a substantial portion of this care need. To understand its implication on their labor market choices and welfare outcomes, we build a simple static model where households can spend time and money producing care. We calibrate the model using data from the American Community Survey, the Health and Retirement Study, and National Health and Aging Trends Study/National Study of Caregiving to match moments in the labor market and caregiving patterns. With the calibrated model, we consider a few government programs under a projected aging population structure. Our results show that care subsidy and Medicaid expansion both cause a shift from informal care to formal care, relieving adult children from care burdens and thus improving their welfare. Caregiver allowance appears to have little effects on caregiving behaviors, which leads to minimal welfare improvement.

Pediatric Firearm Deaths and State Policies on Social, Emotional, and Academic Development

Objective: To determine whether state-level SEAD policies are associated with pediatric firearm homicides and suicides. Summary Background Data: Firearm deaths continue to rise among United States (US) children. School-based efforts to promote social, emotional, and academic development (SEAD) may be a means to reduce such deaths, but state-level policies vary and the effect on firearm deaths is unknown. Methods: We used Education Trust data regarding state-level SEAD policies, averaging 6 domains to create a cumulative score and investigating each domain individually. The primary outcomes were pediatric firearm homicides and suicides, using covariates from the American Community Survey and State Firearm Laws database. We mapped SEAD policies and pediatric death rates. Poisson regression was used to investigate associations between SEAD policies and pediatric firearm deaths. Results: Annual statewide pediatric firearm deaths ranged from 0.85 to 7.81 per 100,000; homicides from 0.64 to 5.69, and suicides from 0.21 to 4.75. Univariate analyses demonstrated associations between SEAD scores and both homicides (P=0.003) and suicides (P=0.032), but these were nonsignificant after adjustment. Professional Development and Engagement policies were associated with lower rates of pediatric firearm homicides after adjustment (IRR=0.33, P=0.004 and IRR=0.46, P=0.014, respectively). There was no significant association between any domain and pediatric firearm suicide. Conclusion: Professional Development and Student, Family and Community Engagement policies are associated with lower rates of pediatric firearm homicides, however, there was no significant association between pediatric firearm deaths and summary SEAD measures after adjustment. Certain SEAD policies may be helpful in decreasing pediatric firearm deaths.

Proximity to cancer rehabilitation and exercise oncology by geography, race, and socioeconomic status.

Cancer rehabilitation and exercise oncology (CR/EO) have documented benefits for people living with and beyond cancer. The authors examined proximity to CR/EO programs across the United States with respect to population density, race and ethnicity, socioeconomic status, and cancer incidence and mortality rates. This cross-sectional study was conducted in 2022-2023. Online searches were initiated to identify CR/EO programs. Geocoding was used to obtain latitudinal and longitudinal geospatial coordinates. Demographic data were abstracted from the 2020 5-year American Community Survey. Cancer incidence and mortality data were obtained from the Centers for Disease Control and Prevention. US 2013 Rural-Urban Continuum Code (RUCC) classification was used to define counties as either urban (RUCC 1-3) or rural (RUCC 4-9). Multivariable logistic regression was used to evaluate the association between being far from a program and census-tract level factors. In total, 2133 CR/EO programs were identified nationwide. The distance from a program increased with decreasing population density: rural tracts were 17.68±0.24 miles farther from a program compared with urban tracts (p<.001). Program proximity decreased as the neighborhood deprivation index increased (p<.001). Exercise oncology programs were less common than cancer rehabilitation programs in tracts with a larger proportion of minority residents (p<.001). Prior research has documented that underrepresented populations have worse cancer-related symptoms and higher cancer mortality. Herein, the authors document their findings that these same populations are less likely to have proximity to CR/EO programs, which are associated with improved cancer-related symptoms and cancer mortality outcomes. To realize the positive outcomes from CR/EO programming, efforts must focus on supporting expanded programming and sustainable payment for these services.

Abstract C181: Toward breast health equity: Insights into mammography access across Virginia’s social and structural landscape

Abstract Background: Breast cancer (BC) disparities across race, socioeconomic status, and location, stress the need for equitable access to screening. In Virginia (VA), BC is a major concern, with Petersburg City having the highest mortality rate nationwide. Understanding social and structural factors’ impact on mammography accessibility may provide insights on effectively serving screening-eligible women in VA, particularly those facing greater BC burden. Also, analyzing data at the census tract level is vital for accurate accessibility assessment, mitigating county-level data skewing and disparity concealment. Objective: Examine how social and structural factors (e.g., demographics, transportation, area deprivation) influence mammography facility and unit distribution in VA. Methods: We utilized a comprehensive approach to compile and analyze data at the census tract level. We employed Poisson regression models to predict the number of mammography facilities and units within each tract. The predictor variables included the number of bus stops (VA Transit Routes &amp; Stops GTFS), percentage of Black residents (American Community Survey), area deprivation index (ADI) scores (Neighborhood Atlas®), and vehicle ownership rates (American Community Survey), with the logarithm of the population of females aged 40-74 used as an offset to account for population size. Separate models were built for VA, our catchment area, urban areas within our catchment, and rural areas within our catchment. We exponentiated the model coefficients to obtain relative risks, indicating the multiplicative effect of each predictor on the outcome variable. Results: Study findings highlighted statistically significant disparities in the distribution of mammography facilities and units within VA. Higher ADI scores, indicating greater socioeconomic deprivation, were linked to fewer mammography units. This association was notable in our rural catchment areas (RR = 0.5492), indicating a 45.08% reduction in mammography units, in our catchment area overall (RR = 0.8653), a 13.47% decrease, and in urban catchment areas (RR = 0.8800), a 12.00% decline. Similarly, an increase in the percentage of Black residents was consistently associated with a decrease in the number of mammography units, with a relative risk of 0.8828 within our catchment area, indicating a 11.72% decrease, 0.8793 in urban areas within our catchment, signifying a 12.07% decrease, and 0.9391 statewide, showing a 6.09% decrease. Conclusions: These findings stress the need for targeted resource allocation to enhance mammography access. For instance, findings suggest a specific need for increased mammography units in rural areas and areas with higher percentages of Black women, like Petersburg (67.5% to 94.9% Black population depending on the tract), specifically within our catchment. Implications from these findings provide a unique opportunity to leverage the expertise of residents, community leaders and local representatives to identify where additional resources can be allocated. Citation Format: Bianca D. Owens, Jinlei Zhao, Katherine Y. Tossas. Toward breast health equity: Insights into mammography access across Virginia’s social and structural landscape [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C181.

Abstract C181: Toward breast health equity: Insights into mammography access across Virginia’s social and structural landscape

Abstract Background: Breast cancer (BC) disparities across race, socioeconomic status, and location, stress the need for equitable access to screening. In Virginia (VA), BC is a major concern, with Petersburg City having the highest mortality rate nationwide. Understanding social and structural factors’ impact on mammography accessibility may provide insights on effectively serving screening-eligible women in VA, particularly those facing greater BC burden. Also, analyzing data at the census tract level is vital for accurate accessibility assessment, mitigating county-level data skewing and disparity concealment. Objective: Examine how social and structural factors (e.g., demographics, transportation, area deprivation) influence mammography facility and unit distribution in VA. Methods: We utilized a comprehensive approach to compile and analyze data at the census tract level. We employed Poisson regression models to predict the number of mammography facilities and units within each tract. The predictor variables included the number of bus stops (VA Transit Routes &amp; Stops GTFS), percentage of Black residents (American Community Survey), area deprivation index (ADI) scores (Neighborhood Atlas®), and vehicle ownership rates (American Community Survey), with the logarithm of the population of females aged 40-74 used as an offset to account for population size. Separate models were built for VA, our catchment area, urban areas within our catchment, and rural areas within our catchment. We exponentiated the model coefficients to obtain relative risks, indicating the multiplicative effect of each predictor on the outcome variable. Results: Study findings highlighted statistically significant disparities in the distribution of mammography facilities and units within VA. Higher ADI scores, indicating greater socioeconomic deprivation, were linked to fewer mammography units. This association was notable in our rural catchment areas (RR = 0.5492), indicating a 45.08% reduction in mammography units, in our catchment area overall (RR = 0.8653), a 13.47% decrease, and in urban catchment areas (RR = 0.8800), a 12.00% decline. Similarly, an increase in the percentage of Black residents was consistently associated with a decrease in the number of mammography units, with a relative risk of 0.8828 within our catchment area, indicating a 11.72% decrease, 0.8793 in urban areas within our catchment, signifying a 12.07% decrease, and 0.9391 statewide, showing a 6.09% decrease. Conclusions: These findings stress the need for targeted resource allocation to enhance mammography access. For instance, findings suggest a specific need for increased mammography units in rural areas and areas with higher percentages of Black women, like Petersburg (67.5% to 94.9% Black population depending on the tract), specifically within our catchment. Implications from these findings provide a unique opportunity to leverage the expertise of residents, community leaders and local representatives to identify where additional resources can be allocated. Citation Format: Bianca D. Owens, Jinlei Zhao, Katherine Y. Tossas. Toward breast health equity: Insights into mammography access across Virginia’s social and structural landscape [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C181.

Abstract A054 Liver cancer mortality by structural disadvantage and race: An analysis of U.S. area-level factors

Abstract Introduction: Racial/ethnic and sex disparities in liver cancer (LC) mortality are well established; yet little is known about the contributions of county-level social risks on these disparities. Methods: We used U.S. national death certificate data from the National Center for Health Statistics (2018-2022) for LC mortality. County-level estimates of income inequality (ratio of household income at the 80th and 20th percentile), and median household income were obtained using the American Community Survey. Severe housing problems (percentage of households with 1 of 4 housing problems: overcrowding, high housing costs, lack of kitchen facilities, or lack of plumbing facilities) were abstracted from the U.S. Department of Housing and Urban Development (HUD) and U.S. Census Bureau. We estimated age-adjusted mortality rates (MR) and MR rate ratios (aRR) across metrics of county-level social risk grouped in quartiles overall and by race/ethnicity. Results: There were 101,088 liver cancer (LC) deaths from 2018-2022 (MR=6.16 per 100,000). Areas of highest county-level income inequality had 31% higher LC mortality compared to areas within the lowest quartile (aRR=1.31). LC MRs were 45% higher in counties with the lowest vs. highest median household income (aRR=1.45). Men and women in the highest quartile of severe housing problems had 29% and 22% higher LC MR (aRR=1.29 and 1.22, respectively), than those in the lowest quartiles. Men and women in the lowest quartile of median household income had 43% and 49% higher LC mortality than those in the highest median income quartiles (aRR=1.43 and 1.49, respectively). Within the highest quartile of income inequality, American Indian/Alaskan Native (AI/AN) persons had the highest LC mortality rates (MR=7.07), followed by Hispanic (MR=6.75), Black (MR=6.48), and Asian (MR=5.83) individuals. Similarly, in the highest quartile of severe housing problems, AI/AN persons had the highest LC mortality rate (MR 7.74), while Hispanic persons had 58% higher MR (MR 7.01), followed by Black (MR=6.19), Asian (MR=5.85) and White (MR=4.07) individuals. In the lowest median household income quartile, compared to White persons (MR=5.91), AI/AN persons had the highest MR (MR=8.86), and Hispanic persons had a 70% increase in mortality (MR=8.48). Among counties with the highest income inequality, compared to White persons (aRR=1.15), Black persons had an 80% increase in mortality (aRR=1.43). For those in the most concentrated quartile of severe housing problems, compared to White persons (aRR=1.07), AI/AN persons had an 80% increase in mortality (MR=1.34).Conclusion: All county-level indicators of highest social risk were associated with greater liver cancer mortality, but do not appear to fully account for racial and ethnic disparities in mortality. Therefore, place- based interventions are needed to address the role of county-level economic attributes in liver cancer mortality disparities. Citation Format: Jennifer K. McGee-Avila, Wayne R. Lawrence, Natalie Joe, Cameron B. Haas, Meredith S Shiels. Liver cancer mortality by structural disadvantage and race: An analysis of U.S. area-level factors [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A054.

Abstract A047: Neighborhood disadvantage and multiple myeloma risk among Black women living in the United States

Abstract Introduction: Multiple myeloma (MM) is a rare cancer of the plasma cells. Black men and women are at a greater than 2-fold increased risk of MM as compared to other racial/ethnic groups. In 2021, Black women living in the United States (U.S.) were diagnosed with MM at a rate of 13.9 per 100,000 women, and incidence has increased by more than 30% since 2000. Factors associated with the neighborhood in which Black women live and work may be one area to consider to disentangle this racial disparity in MM incidence and reduce the burden of disease for Black women. Methods: We evaluated the association between two neighborhood-level indices and MM within the Black Women’s Health Study, a longitudinal cohort of 59,000 self- identified Black women ages 21-69 at enrollment in 1995. Participant addresses at baseline were geocoded and linked with U.S. Census Bureau and American Community Survey data. We conducted a factor analysis to a create neighborhood concentrated disadvantage (nDIS) score, which included 6 variables: percentage of individuals below the poverty line, percentage of individuals on public assistance, percentage of female headed households, percentage unemployed, percentage of individuals ≤18 years, and percentage of Black residents. A neighborhood socioeconomic status (nSES) score included median household income, median housing value, percentage of households receiving interest, dividend or net rental income, percentage of adults aged ≥25 years who completed college, percentage of employed persons aged ≥16 years in occupations classified as managerial, executive or professional, and percentage of families with children that are not headed by a single female. We used Cox proportional hazards regression to calculate hazard ratios (HRs) and 95% confidence intervals (CI) for the association of quartiles of nSES and nDIS in relation to MM risk. Multivariable models were adjusted for age, body mass index, educational attainment, alcohol consumption, physical activity, and cigarette smoking. Results: During follow-up through 2021, we identified 276 incident cases of MM. Multivariable-adjusted HRs were 1.43 (95% CI: 1.00, 2.03) for women living in the highest quartile of neighborhood disadvantage relative to the lowest quartile, and 1.23 (95% CI: 0.86, 1.76) for women living in neighborhoods in the lowest quartiles of nSES compared to the highest. Conclusions: Black women in the U.S. living in neighborhoods of high concentrated disadvantage have an increased risk for MM compared to women who live in more advantaged communities, even after adjustment for individual SES as measured by educational attainment. Future studies to further tease apart specific neighborhood-level factors (e.g., walkability, chronic stress, environmental pollution, opportunities to engage in healthy behaviors) that might influence risk of MM would be beneficial. Ultimately, community reinvestment may offer an opportunity to reduce the increasing burden of MM among Black women. Citation Format: Etienne X. Holder, Julie R. Palmer, Kimberly A. Bertrand. Neighborhood disadvantage and multiple myeloma risk among Black women living in the United States [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A047.

Abstract A037: Understanding the role of neighborhood deprivation in racial and urban-rural disparities in triple negative breast cancer

Abstract Background: Triple negative breast cancer (TNBC) accounts for 15% of all breast cancers and is one of the most aggressive disease subtypes. Disparities in TNBC exist, where US Black women and women living in urban environments are more likely to be diagnosed with TNBC than their White or rural counterparts. Reasons for these disparities are not completely understood. Social determinants of health, such as the neighborhood environment, have been implicated as potential contributors to breast cancer disparities. To improve our understanding of disparities in TNBC, we examined neighborhood deprivation in relation to breast cancer subtype, according to race and rurality. Methods: We identified 40,095 non-Hispanic Black and White women diagnosed with invasive breast cancer in 2010-2017 in the Georgia Cancer Registry. Neighborhood deprivation was measured by the Neighborhood Deprivation Index (NDI), a composite measure of neighborhood poverty, income, housing, unemployment, occupation, and education derived through principal component analysis using 2011-2015 block group-level American Community Survey data. Georgia Department of Public Health data were used to measure county-level rurality. We estimated case-only multivariable odds ratios (OR) and 95% confidence intervals (CI) for the separate associations between NDI, in quintiles, and each breast cancer subtype (TNBC, ERBB2+, or luminal B vs. luminal A breast cancer), overall and according to race and rurality. Results: Of the 40,095 non-Hispanic Black (31.5%) and White (68.5%) women, 5,328 were diagnosed with TNBC, 1,926 with ERBB2+, 4,958 with luminal B, and 27,883 with luminal A breast cancer. Compared to living in the least deprived neighborhoods, living in the most deprived neighborhoods was associated with a 33% (OR= 1.33, 95% CI 1.19-1.48) increased odds of TNBC vs. luminal A breast cancer. Race and rurality did not modify the association. However, estimates were less pronounced among non-Hispanic Black (OR=1.18, 95% CI 1.00-1.39) compared to non-Hispanic White (OR=1.40, 95% CI 1.18- 1.65) women and among women living in urban (OR=1.33, 95% CI 1.18-1.50) compared to rural (OR=1.52, 95% CI 1.15-2.03) counties. Neighborhood deprivation was associated with an increase in ERBB2+ breast cancer (OR=1.19, 95% CI 1.00-1.40), which was confined to non- Hispanic White women (OR=1.29, 95% CI 1.00-1.66) and women living in urban counties (OR=1.25, 95% CI 1.04-1.52). The OR for luminal B breast cancer was 1.08 (95% CI 0.96- 1.21). Although race did not modify this relationship, an association was observed only among women living in urban counties (OR=1.14, 95% CI 1.00-1.29). Conclusions: In this population of Georgia breast cancer patients, neighborhood deprivation was associated with an increase in TNBC, regardless of race or rurality. The weaker associations among non-Hispanic Black women and women living in urban counties suggests that other factors, in addition to neighborhood deprivation, may play an important role in TNBC development among Black women and women residing in urban environments. Citation Format: Lauren E. Barber, Jasmine M. Miller-Kleinhenz, Maret L. Maliniak, Leah Moubadder, Jeffrey M. Switchenko, Lauren E. McCullough. Understanding the role of neighborhood deprivation in racial and urban-rural disparities in triple negative breast cancer [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A037.

Abstract A040: Effects of food environment, income inequality, and racial segregation on obesity- related cancer mortality

Abstract BACKGROUND: Rural areas experience 7-12% higher risk of obesity-related cancer mortality compared to their urban counterparts. Complex interactions between social and structural factors in rural areas, combined with limited access to healthy foods and lower income, warrant further study to understand drivers of this disparity. Methods: County-level rates of age-adjusted obesity-related cancer mortality per 100,000 (2009-2020 were obtained from the Centers for Disease Control and Prevention’s Wide-Ranging ONline Data for Epidemiologic Research database. Based on data from the Food Environment Atlas, we calculated food swamp using the modified Retail Food Environment Index (mRFEI) as the ratio of fast-food restaurants and convenience stores to grocery and specialized food stores, supercenters, and farmers markets. Food swamp scores were then divided into quartiles. Next, we used Global G and local Gi statistics to identify the presence of spatial autocorrelation in food swamp and subsequently calculated a spatial moving average (SMA) across counties. Measures of economic segregation, racial segregation, and racialized economic segregation were calculated using the Index of Concentration at the Extremes (ICE) using population and demographic estimates from the 2005- 2009 American Community Survey. Larger values reflect concentrated extremes of disadvantage. Poisson regression was used to assess the impact of food swamp, urban-rural status, economic segregation, racial segregation, and racialized economic segregation on rates of obesity-related cancer mortality. Results: Among the 3014 counties included, median food swamp score was 3.6 (interquartile range=2.6-4.9), and counties in the 2nd, 3rd, and 4th quartiles for food swamp had 2%, 4%, and 6% higher rates (respectively) of obesity-related cancer mortality than those in the 1st quartile. Incidence rate ratios for the association of food swamp quartile with obesity-related cancer mortality were 1-3% smaller after control for food swamp SMA and for racialized economic Urban-rural differences in obesity-related cancer mortality varied with all ICE measures. Rural counties had 5% higher rates of obesity-related cancer mortality (IRR=1.05, 95%CI=1.04-1.06), even after controlling for food swamp and food swamp SMA, but there was no longer an association after controlling for economic segregation (IRR=0.99, 95%CI=0.98-1.00). Rural counties had 5% higher rates of obesity-related cancer mortality (IRR=1.05, 95%CI=1.04-1.07) when controlling for racial segregation and 2% higher rates when controlling for racialized economic segregation (IRR=1.02, 95%CI=1.01-1.03). Thus, we stratified our models by urban-rural status and found that each ICE measure had larger effects on obesity-related cancer mortality in rural counties than in urban. CONCLUSION: While food swamp is important, the socioeconomic environment of a county has a larger impact on obesity- related cancer mortality. Individuals may have access to healthy food stores, but whether they can afford the healthy food is more important. Citation Format: Elizabeth S. Davis, Sara Myers, Jeffrey A. Franks, Michael R. Poulson, Julie R. Palmer, Kelly M. Kenzik. Effects of food environment, income inequality, and racial segregation on obesity- related cancer mortality [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A040.

Abstract A055: Neighborhood Deprivation and Oncotype DX Recurrence Scores among Black and White Women: the interplay between race, place, and tumor biology

Abstract Background: Breast cancer (BC) mortality disparities have persisted between non-Hispanic Black (NHB) and non-Hispanic White (NHW) women, even those diagnosed with prognostically favorable BC. The Oncotype Dx Recurrence score (ODX) test is a gene expression panel that can be used to identify estrogen receptor (ER)-positive BC with aggressive tumor biology. Previous studies have found that Black women are more likely to have higher ODX scores than White women and socioeconomic factors (e.g., area-level income, urbanicity) are associated with higher ODX scores. We explored race-stratified associations of neighborhood deprivation and ODX to help elucidate the interplay between race, place, and tumor biology. Methods: Neighborhood deprivation was examined in relation to ODX scores among 7,100 NHB and NHW women diagnosed with ER-positive BC (stage I–IIIA and ≤3 lymph nodes) between 2010–2017, followed through 2022, and identified by the Georgia Cancer Registry. The neighborhood deprivation index (NDI) was used to characterize neighborhood deprivation. Principal component analysis of block group-level American Community Survey data representing six domains (poverty, income, occupation, housing, employment, and education) was used to derive the NDI composite score. NDI was assessed in quartiles and linked to patient residence at the time of diagnosis. Three cut-points were used for ODX scores (low &amp;lt;18, intermediate 18–30, high ≥31). Prevalence ratios (PR) and 95% confidence intervals (CI) were calculated to determine the prevalence of ODX scores based on NDI by race/ethnicity. Results: This study included 1651 NHB and 5449 NHW women. There were 4152 (58.5%) with a low ODX Score and 514 (7.2%) with a high ODX score; 2773 (39.1%) resided in the least deprived neighborhoods (NDI quartile 1) and 928 (13.1%) resided in the most deprived neighborhood (NDI quartile 4). Living in the most deprived neighborhood was associated with an increased prevalence of high ODX scores in models adjusted for age, race, and stage (PR=1.23, 95% CI 0.97–1.62). Stratification by race showed associations were stronger among NHW women (PR=1.41, 95% CI 1.02–1.95) but null among NHB women (PR= 0.91, 95% CI 0.59-1.39). Overall, NHB women were more likely to have a high ODX compared to NHW women. The highest prevalence of a high ODX was observed among NHB women living in the least deprived neighborhoods (PR=1.92, 95% CI 1.34–2.77). Conclusions: Findings from this study suggest that living in a deprived neighborhood increases the prevalence of high ODX among NHW women but not NHB women. NHB women in the least deprived neighborhoods had the greatest prevalence of high ODX scores. The intersection between race, neighborhood, and tumor biology needs more nuanced investigation of the social-structural drivers of disparate BC outcomes. Citation Format: Jasmine M. Miller-Kleinhenz, Lauren E. Barber, Lindsay J. Collin, Maret L. Maliniak, Demetria J. Smith-Graziani, Lauren E. McCollough. Neighborhood Deprivation and Oncotype DX Recurrence Scores among Black and White Women: the interplay between race, place, and tumor biology [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A055.

Disparities in Spatial Access to Sleep Health Care in the United States: A Population-Based Geospatial Analysis

ObjectivesTo examine spatial access to sleep health care in the United States and investigate associations with demographic and socioeconomic characteristics, thereby identifying high-risk communities with limited spatial access to sleep health service. DesignA cross-sectional population-based geospatial analysis. Settings and ParticipantsResidents in US Census tracts across the 48 contiguous states, Alaska, and Hawaii. MethodsThe 2020 American Community Survey 5-year estimates, 2010 rural-urban commuting area codes, 2020 Area Deprivation Index, and sleep care provider locations from the National Provider Identifier file were used to assess the spatial access and related demographic/socioeconomic characteristics. Spatial access was measured by spatial access ratio using enhanced 2-step floating catchment area methods. The associations were investigated using logistic regression analysis and multivariate linear regression analysis. ResultsA total of 45.8 million residents experienced low spatial access to sleep health care. Spatial access decreased in rural and high Area Deprivation Index areas, and in areas characterized by higher population with uninsured status, vehicle unavailability, internet unavailability, cognitive difficulties, and hearing difficulties. With a 10% increase in the percentage of the racial minority (non-white) population, metropolitan census tracts experienced an increase in spatial access (3.268%), whereas micropolitan (−1.526%) and rural (−4.493%) areas experienced a decrease in spatial access. Similar findings were observed within the ethnic minority (Hispanic or Latino) population. Conclusions and ImplicationsDisparities exist in spatial access to sleep health care across the United States, especially for disadvantaged individuals. Racial/ethnic minorities exhibit contrasting spatial access patterns in urban and rural areas, with those in rural areas facing more challenges in spatial access to sleep health care.

Abstract B135: Association of residential economic and racial segregation with metastatic disease at diagnosis in pediatric patients with solid tumors

Abstract Background: Disparities in pediatric cancer outcomes by race and ethnicity may be influenced by neighborhood characteristics, particularly residential economic and racial segregation. Prior to a new cancer diagnosis, pediatric patients are vulnerable to structural barriers embedded in their neighborhoods. In particular, residential economic and racial segregation may impede access to initial care for early cancer detection, increasing the likelihood of presenting with distant metastatic disease at diagnosis that can translate to excess morbidity and mortality. This study assesses the association of residential economic and racial segregation as measured by the Index of Concentration at the Extremes (ICE) with the likelihood of presenting with distant metastatic disease (as compared to local or locoregional disease) among children newly diagnosed with cancer. Methods: We studied a cohort of pediatric patients diagnosed with solid tumors at Children’s Healthcare of Atlanta between 2010 and 2016. Patients’ ZIP codes were linked to the ICE, which was constructed at the ZIP code level using American Community Survey 2012-2016 five-year estimates. The ICE was categorized into tertile 1 (most deprived) through tertile 3 (most privileged). Multivariable logistic regression models were used to assess the association between residential economic and racial segregation, defined by tertiles of the ICE, and the likelihood of presenting with distant metastatic disease, as determined by initial cancer staging. All models adjusted for patient- and disease-level covariates, including age, sex at birth, health insurance type, and cancer type. Results: Among 829 patients identified in our analytic sample, 27.6% had distant metastatic disease at diagnosis. Living in the most deprived (ICE tertile 1) versus the most privileged (ICE tertiles 2/3) neighborhoods was significantly associated with a higher likelihood of presenting with distant metastatic disease at diagnosis (adjusted odds ratio [OR] = 1.46; 95% confidence interval [CI] = 1.03 to 2.07; p=0.03). Additionally, non-Hispanic Black patients living in the most deprived neighborhoods were more likely to have distant metastatic disease at diagnosis compared to non-Hispanic White patients living in the most privileged neighborhoods (adjusted OR = 1.83; 95% CI = 1.15 to 2.92; p=0.002). Conclusion: This study provides new insights into how residential economic and racial segregation is associated with metastatic disease at diagnosis among children presenting with a new solid tumor. Our findings highlight the need for geographically targeted interventions to improve community care, resource allocation and investment, and public policies aimed at supporting this vulnerable patient population. Citation Format: Alexandra Cathcart, Sharon M. Castellino, Lu Zhang, Karen Wasilewski-Masker, Andrew L. Hong, Heeju Sohn, Xu Ji. Association of residential economic and racial segregation with metastatic disease at diagnosis in pediatric patients with solid tumors [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B135.