Development of Neighborhood Trajectories Employing Historic Redlining and the Area Deprivation Index.

The role of historic residential redlining on health inequities is intertwined with policy changes made before and after the 1930s that influence current neighborhood characteristics and shape ongoing structural racism in the United States (U.S.). We developed Neighborhood Trajectories which combine historic redlining data and the current neighborhood socioeconomic characteristics as a novel approach to studying structural racism. Home Owners' Loan Corporation (HOLC) neighborhoods for the entire U.S. were used to map the HOLC grades to the 2020 U.S. Census block group polygons based on the percentage of HOLC areas in each block group. Each block group was also assigned an Area Deprivation Index (ADI) from the Neighborhood Atlas®. To evaluate changes in neighborhoods from historic HOLC grades to present degree of deprivation, we aggregated block groups into "Neighborhood Trajectories" using historic HOLC grades and current ADI. The Neighborhood Trajectories are "Advantage Stable"; "Advantage Reduced"; "Disadvantage Reduced"; and "Disadvantage Stable." Neighborhood Trajectories were established for 13.3% (32,152) of the block groups in the U.S., encompassing 38,005,799 people. Overall, the Disadvantage-Reduced trajectory had the largest population (16,307,217 people). However, the largest percentage of non-Hispanic/Latino Black residents (34%) fell in the Advantage-Reduced trajectory, while the largest percentage of Non-Hispanic/Latino White residents (60%) fell in the Advantage-Stable trajectory. The development of the Neighborhood Trajectories affords a more nuanced mechanism to investigate dynamic processes from historic policy, socioeconomic development, and ongoing marginalization. This adaptable methodology may enable investigation of ongoing sociopolitical processes including gentrification of neighborhoods (Disadvantage-Reduced trajectory) and "White flight" (Advantage Reduced trajectory).

Measuring Local-Area Racial Segregation for Medicare Hospital Admissions.

Importance: Considerable racial segregation exists in US hospitals that cannot be explained by where patients live. Approaches to measuring such segregation are limited. Objective: To measure how and where sorting of older Black patients to different hospitals occurs within the same health care market. Design, Setting, and Participants: This retrospective cross-sectional study used 2019 Medicare claims data linked to geographic data. Hospital zip code markets were based on driving time. The local hospital segregation (LHS) index was defined as the difference between the racial composition of a hospital's admissions and the racial composition of the hospital's market. Assessed admissions were among US Medicare fee-for-service enrollees aged 65 or older living in the 48 contiguous states with at least 1 hospitalization in 2019 at a hospital with at least 200 hospitalizations. Data were analyzed from November 2022 to January 2024. Exposure: Degree of residential segregation, ownership status, region, teaching hospital designation, and disproportionate share hospital status. Main Outcomes and Measures: The LHS index by hospital and a regional LHS index by hospital referral region. Results: In the sample of 1991 acute care hospitals, 4 870 252 patients (mean [SD] age, 77.7 [8.3] years; 2 822 006 [56.0%] female) were treated, including 11 435 American Indian or Alaska Native patients (0.2%), 129 376 Asian patients (2.6%), 597 564 Black patients (11.9%), 395 397 Hispanic patients (7.8), and 3 818 371 White patients (75.8%). In the sample, half of hospitalizations among Black patients occurred at 235 hospitals (11.8% of all hospitals); 878 hospitals (34.4%) exhibited a negative LHS score (ie, admitted fewer Black patients relative to their market area) while 1113 hospitals (45.0%) exhibited a positive LHS (ie, admitted more Black patients relative to their market area); of all hospitals, 79.4% exhibited racial admission patterns significantly different from their market. Hospital-level LHS was positively associated with government hospital status (coefficient, 0.24; 95% CI, 0.10 to 0.38), while New York, New York; Chicago, Illinois; and Detroit, Michigan, hospital referral regions exhibited the highest regional LHS measures, with hospital referral region LHS scores of 0.12, 0.16, and 0.21, respectively. Conclusions and Relevance: In this cross-sectional study, a novel measure of LHS was developed to quantify the extent to which hospitals were admitting a representative proportion of Black patients relative to their market areas. A better understanding of hospital choice within neighborhoods would help to reduce racial inequities in health outcomes.

Development of Neighborhood Trajectories employing Historic Redlining and the Area Deprivation Index.

The role of historic residential redlining on health disparities is intertwined with policy changes made before and after the 1930s that influence current neighborhood characteristics and shape ongoing structural racism in the United States. We developed Neighborhood Trajectories which combine historic redlining data and the current neighborhood socioeconomic characteristics as a novel approach to studying structural racism. Home Owners Loan Corporation (HOLC) neighborhoods for the entire U.S. were used to map the HOLC grades to the 2020 U.S. Census block group polygons based on the percentage of HOLC areas in each block group. Each block group was also assigned an Area Deprivation Index (ADI) from the Neighborhood Atlas®. To evaluate changes in neighborhoods from historic HOLC grades to present degree of deprivation, we aggregated block groups into "Neighborhood Trajectories" using historic HOLC grades and current ADI. The Neighborhood Trajectories are "Advantage Stable"; "Advantage Reduced"; "Disadvantage Reduced"; and "Disadvantage Stable." Neighborhood Trajectories were established for 13.3% (32,152) of the block groups in the U.S., encompassing 38,005,799 people. Overall, the Disadvantage-Reduced trajectory had the largest population (16,307,217 people). However, the largest percentage of Non-Hispanic/Latino Black residents (34%) fell in the Advantage-Reduced trajectory, while the largest percentage of Non-Hispanic/Latino White residents (60%) fell in the Advantage-Stable trajectory. The development of the Neighborhood Trajectories affords a more nuanced mechanism to investigate dynamic processes from historic policy, socioeconomic development, and ongoing marginalization. This adaptable methodology may enable investigation of ongoing sociopolitical processes including gentrification of neighborhoods (Disadvantage-Reduced trajectory) and "White flight" (Advantage Reduced trajectory).

Trends in Health Care Access/Experiences: Differential Gains across Sexuality and Sex Intersections before and after Marriage Equality.

BACKGROUND: Sexual minority adults experience several health care access inequities compared to their heterosexual peers; such inequities may be affected by LGBTQ+ legislation, such as the 2015 national marriage equality ruling. METHODS: Using population-based data (n = 28,463) from the Association of American Medical Colleges biannual Consumer Survey of Health Care Access, we calculated trend ratios (TR) for indicators of health care access (e.g., insurance coverage, delaying or forgoing care due to cost) and satisfaction (e.g., general satisfaction, being mistreated due to sexual orientation) from 2013 to 2018 across sexuality and sex. We also tested for changes in trends related to the 2015 marriage equality ruling using interrupted time series trend interactions (TRInt). RESULTS: The largest increases in access were observed in gay men (TR = 2.42, 95% CI 1.28, 4.57). Bisexual men had decreases in access over this period (TR = 0.47, 95% CI 0.22, 0.99). Only gay men had a significant increase in the health care access trend after U.S. national marriage equality (TRInt = 5.59, 95% CI 2.00, 9.18), while other sexual minority groups did not. CONCLUSIONS: We found that trends in health care access and satisfaction varied significantly across sexualities and sex. Our findings highlight important disparities in how federal marriage equality has benefited sexual minority groups.

Disparities in Mental Health Care Access Among Persons Differing in Sexual Identity: Nationally Representative Findings.

OBJECTIVE: The authors sought to describe disparities in three outcomes: self-reported mental health, need for mental health care, and barriers to care at the intersection of sexual identity and sex. METHODS: Data from the 2015 Association of American Medical Colleges Consumer Survey of Health Care Access (N=5,932) were analyzed in regression analyses to estimate relationships among sex, sexual identity, and all three outcomes. RESULTS: Compared with heterosexual men, bisexual women reported the poorest mental health (adjusted prevalence ratio [APR]=0.42, 95% confidence interval [CI]=0.35-0.51) and the greatest number of barriers to care (APR=2.29, 95% CI=1.77-2.97), whereas gay-lesbian women reported the most frequent need for care (APR=1.67, 95% CI=1.28-2.18). CONCLUSIONS: The findings support existing knowledge on health inequities among sexual minority groups and situate these disparities in the context of unequal access to behavioral and mental health care. As such, addressing barriers to care is paramount in efforts to address sexual orientation-related disparities in behavioral and mental health.

Social Stability and Unmet Health Care Needs in a Community-Based Sample of Women Who Use Drugs.

OBJECTIVE: To examine the relationship between social stability and access to healthcare services among a community-based sample of adult female drug users. METHODS: We developed a measure of social stability and examined its relationship to health care access. Data came from a cross-sectional sample of female drug users (N = 538) in Oakland, CA who were interviewed between September 2014 and August 2015. We categorized women as having low (1-5), medium (6-10), or high (11-16) social stability based on the tertile of the index sample distribution. We then used ordered logistic regression to examine the relationship between social stability and self-reported access to mental health services and medical care. RESULTS: Compared with women in the low stability group, those with high stability experienced a 58% decline in the odds of needed but unmet mental health services [AOR: 0.42; 95% C.I.: 0.26, 0.69] and a 68% decline in the odds of unmet medical care [AOR: 0.32; 95% C.I.: 0.19, 0.54] after adjusting for confounders. The coefficients we observed reduced in size at higher levels of the stability index suggesting a positive association between social experiences and access to healthcare services. CONCLUSION: Women who use drugs are at increased risk of adverse health outcomes and often experience high levels of unmet healthcare needs. Our study highlights the importance of addressing social determinants of health and suggests that improving social factors such as housing stability and personal safety may support access to healthcare among female drug users.

Differences in Health Care Access and Satisfaction Across Intersections of Race/Ethnicity and Sexual Identity.

PURPOSE: Racial/ethnic and sexual minorities experience numerous health disparities compared with their White and heterosexual counterparts, which may be exacerbated when these social identities intersect. The authors tested for differences in health care access and satisfaction across intersections of sexual identity and race/ethnicity. METHOD: A cross-sectional secondary data analysis of the 2012-2018 waves of the Association of American Medical Colleges biannual online Consumer Survey of Health Care Access was conducted. This survey captures a national sample of U.S. adults who reported needing health care in the past 12 months. The analytic sample included 29,628 participants. Sixteen possible combinations of sexual identity and race/ethnicity were examined. Health care access and satisfaction were measured with 10 items and an index created from these items. Cumulative prevalence ratios (PRs) for the index and PRs across sexual identity, both individually and in combination with race/ethnicity, for each health care access and satisfaction item were generated. RESULTS: Compared with White heterosexuals, all other groups had significantly more barriers to care before adjustment. The greatest barriers were observed among non-Hispanic Asian/Pacific Islander/Hawaiian gay/lesbian (unadjusted PR = 3.08; 95% confidence interval [CI]: 2.45, 3.88; adjusted PR = 2.01; 95% CI: 1.59, 2.53), non-Hispanic Black bisexual (unadjusted PR = 2.73; 95% CI: 2.28, 3.27; adjusted PR = 1.83; 95% CI: 1.52, 2.20), non-Hispanic Black other sexual identity (unadjusted PR = 2.27; 95% CI: 1.69, 3.06; adjusted PR = 2.07; 95% CI: 1.53, 2.78), and Hispanic/Latino other sexual identity (unadjusted PR = 2.06; 95% CI: 1.60, 2.65; adjusted PR = 1.39; 95% CI: 1.08, 1.79) participants. CONCLUSIONS: Persons of both racial/ethnic and sexual minority status generally had less health care access and satisfaction than White heterosexuals. An intersectional perspective is critical to achieving equity in quality health care access.

Disparities in Health Care Access and Utilization at the Intersections of Urbanicity and Sexual Identity in California.

Purpose: The aim was to examine differences in health care access at the intersections of urbanicity and sexual identity in California. Methods: We used the 2014-2017 Adult California Health Interview Survey paired with the sexual orientation special use research file to create dummy groups representing each dimension of urbanicity and sexual identity to compare access to health care outcomes. We calculated unadjusted proportions and estimated adjusted odds ratios of each dimension relative to urban heterosexual people using logistic regressions. Results: Relative to urban heterosexual people, urban gay/lesbian people had 1.651 odds of using the emergency room (ER). Urban bisexual people had 1.429 odds of being uninsured, 1.575 odds of delaying prescriptions, and 1.907 odds of using the ER. Rural bisexual people experienced similar access barriers having 1.904 odds of uninsurance and 2.571 odds of using the ER. Conclusions: Our study findings demonstrated disparate access to health care across sexual orientation and rurality. The findings are consistent with literature that suggests urban and rural sexual minority people experience health care differently and demonstrate that bisexual people experience health care differently than gay/lesbian people. These findings warrant further study to examine how social identities, such as race/ethnicity, interact with sexual orientation to determine health care access. Furthermore, these findings demonstrate the need to emphasize the health care access needs of sexual minority people in both rural and urban areas to eliminate health care access disparities.