scholarly journals Racial/Ethnic Disparities in the Lifetime Risk of Chlamydia trachomatis Diagnosis and Adverse Reproductive Health Outcomes Among Women in King County, Washington

2018 ◽  
Vol 67 (4) ◽  
pp. 593-599 ◽  
Author(s):  
Laura C Chambers ◽  
Christine M Khosropour ◽  
David A Katz ◽  
Julia C Dombrowski ◽  
Lisa E Manhart ◽  
...  
Keyword(s):  
Chlamydia Trachomatis ◽  
Reproductive Health ◽  
Health Outcomes ◽  
Ethnic Disparities ◽  
Lifetime Risk ◽  
King County ◽  
Racial Ethnic

Racial, ethnic, and socioeconomic disparities in treatment outcomes in patients (pts) with diffuse large B-cell lymphoma (DLBCL): A U.S. real-world study using a de-identified electronic health record (EHR)-derived database.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18514-e18514
Author(s):  
Yanling Jin ◽  
Jia Li ◽  
Yong Mun ◽  
Anthony Masaquel ◽  
Sylvia Hu ◽  
...  
Keyword(s):  
Health Outcomes ◽  
Real World ◽  
Eastern Cooperative Oncology Group ◽  
Ethnic Disparities ◽  
B Cell Lymphoma ◽  
Treatment Patterns ◽  
Disease Stage ◽  
Real World Data ◽  
Baseline Characteristics ◽  
Racial Ethnic

e18514 Background: DLBCL, an aggressive disease, is the most common subtype of non-Hodgkin lymphoma. Few studies have addressed socioeconomic and racial/ethnic disparities in treatment patterns and health outcomes for pts with DLBCL. We present a retrospective cohort study, leveraging real-world data from a nationwide database, to investigate these disparities. Methods: Pts with DLBCL treated with first-line (1L) therapy within 90 days of diagnosis were selected from the nationwide Flatiron Health EHR-derived de-identified database from January 2011 to May 2020. During the study, the de-identified data originated from approximately 280 US cancer clinics (̃800 sites of care). Pts’ baseline characteristics, treatment patterns, overall survival (OS), time to next therapy or death to any cause (TTNTD) were compared between race groups (non-Hispanic White [W], non-Hispanic African American [AA], Hispanic or Latino [H], non-Hispanic Asian [A]) and socioeconomic groups (Medicaid without Commercial [Medicaid] vs Commercial without Medicaid [Commercial]). Baseline characteristics were compared using Fisher’s exact, chi-squared or t-tests. Time to event endpoints were compared using Cox models adjusting baseline characteristics. Results: In total, 4,648 pts with DLBCL (82% W, 7% AA, 8% H, 3% A) were included. Compared with other race groups, W pts were older (mean age: 67 vs 60, 62, 62 [W vs AA, H, A]), had a higher proportion of pts with Eastern Cooperative Oncology Group score ≥2 (8% vs 5%, 4%, 4%), and fewer pts with Medicaid insurance (1.7% vs 5%, 6%, 3%). Across race groups, 1L treatments received were similar; 82% had R-CHOP. There were no significant differences in OS (P = 0.278; HR [AA, H, A vs W]: 0.87, 0.85, 0.84) and TTNTD (P = 0.158; HR: 0.89, 0.88, 1.19). There were statistically significant differences in time from diagnosis to treatment (P < 0.0001; HR: 0.83, 0.79, 1.12), although the magnitude of the median differences were relatively small (22, 24, 25, 19 days [W, AA, H, A]). In pts aged < 65, commercially insured pts had less advanced disease (Group Stage IV: 28% vs 59%), better OS (HR [95% CI]: 0.50 [0.31–0.81], P = 0.005) and later TTNTD (HR: 0.70 [0.48–1.03], P = 0.067) compared with Medicaid insured pts. In pts aged ≥65, commercially insured pts had similar disease stage, OS (HR: 1.09 [0.65–1.84], P = 0.756) and TTNTD (HR: 0.94 [0.61–1.44], P = 0.763) compared with Medicaid insured pts. Insurance was not a significant factor for time from diagnosis to treatment for pts aged < 65 (HR: 1.05 [0.80–1.37], P = 0.727) and ≥65 (HR: 1.05 [0.78–1.42], P = 0.742). Conclusions: In this analysis of over 4,500 pts with DLBCL treated in the real-world, access to commercial insurance was associated with health outcomes in pts under 65 years of age, possibly due to earlier diagnosis; race was not a significant factor.

scholarly journals Exacerbating Inequalities: Social Networks, Racial/Ethnic Disparities, and the COVID-19 Pandemic in the United States

2020 ◽  
Author(s):  
Gertrude R Gauthier ◽  
Jeffrey A Smith ◽  
Catherine García ◽  
Marc A Garcia ◽  
Patricia A Thomas
Keyword(s):  
Mental Health ◽  
Older Adults ◽  
Social Networks ◽  
Social Network ◽  
Health Outcomes ◽  
Ethnic Disparities ◽  
The United States ◽  
Mental Health Outcomes ◽  
Physical And Mental Health ◽  
Racial Ethnic

Abstract Objectives The disruption and contraction of older adults’ social networks are among the less discussed consequences of the COVID-19 pandemic. Our objective was to provide an evidence-based commentary on racial/ethnic disparities in social network resources and draw attention to the ways in which disasters differentially affect social networks, with meaningful insight for the ongoing pandemic. Methods We draw upon prior research on social networks and past natural disasters to identify major areas of network inequality. Attention is given to how pre-pandemic racial/ethnic network disparities are exacerbated during the current crisis, with implications for physical and mental health outcomes. Results Evidence from the literature shows a robust association between strong social networks and physical and mental health outcomes. During times of crisis, access to social networks for older adults is disrupted, particularly for marginalized groups. We document pre-pandemic disparities in social networks resources and offer insight for examining the impact of COVID-19 on disrupting social networks among older adults. Discussion Importantly, racial/ethnic disparities in social networks both prior to and as a result of the pandemic intensify existing inequalities and demonstrate the necessity of better understanding social network inequalities for marginalized older adults, particularly in the context of the COVID-19 health crisis.

scholarly journals Racial and Ethnic Disparities in Health Outcomes Among Long-Term Survivors of Childhood Cancer: A Scoping Review

2021 ◽  
Vol 9 ◽  
Author(s):  
Tegan J. Reeves ◽  
Taylor J. Mathis ◽  
Hailey E. Bauer ◽  
Melissa M. Hudson ◽  
Leslie L. Robison ◽  
...  
Keyword(s):  
Health Disparities ◽  
Health Outcomes ◽  
Childhood Cancer ◽  
Ethnic Disparities ◽  
Childhood Cancer Survivors ◽  
Individual Level ◽  
Family Level ◽  
Race Ethnicity ◽  
Racial Ethnic ◽  
Neighborhood Level

The five-year survival rate of childhood cancer has increased substantially over the past 50 yr; however, racial/ethnic disparities in health outcomes of survival have not been systematically reviewed. This scoping review summarized health disparities between racial/ethnic minorities (specifically non-Hispanic Black and Hispanic) and non-Hispanic White childhood cancer survivors, and elucidated factors that may explain disparities in health outcomes. We used the terms “race”, “ethnicity”, “childhood cancer”, “pediatric cancer”, and “survivor” to search the title and abstract for the articles published in PubMed and Scopus from inception to February 2021. After removing duplicates, 189 articles were screened, and 23 empirical articles were included in this review study. All study populations were from North America, and the mean distribution of race/ethnicity was 6.9% for non-Hispanic Black and 4.5% for Hispanic. Health outcomes were categorized as healthcare utilization, patient-reported outcomes, chronic health conditions, and survival status. We found robust evidence of racial/ethnic disparities over four domains of health outcomes. However, health disparities were explained by clinical factors (e.g., diagnosis, treatment), demographic (e.g., age, sex), individual-level socioeconomic status (SES; e.g., educational attainment, personal income, health insurance coverage), family-level SES (e.g., family income, parent educational attainment), neighborhood-level SES (e.g., geographic location), and lifestyle health risk (e.g., cardiovascular risk) in some but not all articles. We discuss the importance of collecting comprehensive social determinants of racial/ethnic disparities inclusive of individual-level, family-level, and neighborhood-level SES. We suggest integrating these variables into healthcare systems (e.g., electronic health records), and utilizing information technology and analytics to better understand the disparity gap for racial/ethnic minorities of childhood cancer survivors. Furthermore, we suggest national and local efforts to close the gap through improving health insurance access, education and transportation aid, racial-culture-specific social learning interventions, and diversity informed training.

Impact of socio-economic status and race on emergency hospital admission outcomes: Analysis from hospital admissions between 2001 and 2012

2021 ◽  
pp. 095148482110121
Author(s):  
Thomas Davidson ◽  
Farhaan Mirza ◽  
Mirza M Baig
Keyword(s):  
Length Of Stay ◽  
Health Outcomes ◽  
Death Rate ◽  
Hospital Admissions ◽  
Racial Bias ◽  
Ethnic Disparities ◽  
Emergency Admission ◽  
The United States ◽  
Emergency Hospital ◽  
Racial Ethnic

Socio-economic and racial/ethnic disparities in healthcare quality have been the point of huge discussion and debate. There is currently a public debate over healthcare legislation in the United States to eliminate the disparities in healthcare. We reviewed the literature and critically examined standard socio-economic and racial/ethnic measurement approaches. As a result of the literature review, we identified and discussed the limitations in existing quality assessment for identifying and addressing these disparities. The aim of this research was to investigate the difference between health outcomes based on patients’ ability to pay and ethnic status during a single emergency admission. We conducted a multifactorial analysis using the 11-year admissions data from a single hospital to test the bias in short-term health outcomes for length of stay and death rate, based on ‘payment type’ and ‘race’, for emergency hospital admissions. Inconclusive findings for racial bias in outcomes may be influenced by different insurance and demographic profiles by race. As a result, we found that the Self-Pay (no insurance) category has the shortest statistically significant length of stay. While the differences between Medicare, Private and Government are not significant, Self-Pay was significantly shorter. That ‘Whites’ have more Medicare (older) patients than ‘Blacks’ might possibly lead to a longer length of stay and higher death rate for the group.

scholarly journals Do racial and ethnic disparities in following stay-at-home orders influence COVID-19 health outcomes? A mediation analysis approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259803
Author(s):  
Songhua Hu ◽  
Weiyu Luo ◽  
Aref Darzi ◽  
Yixuan Pan ◽  
Guangchen Zhao ◽  
...  
Keyword(s):  
Health Outcomes ◽  
Infection Rate ◽  
Ethnic Differences ◽  
Human Mobility ◽  
Ethnic Disparities ◽  
Case Fatality ◽  
Equation Modeling ◽  
Case Fatality Ratio ◽  
Racial Ethnic ◽  
At Home

Racial/ethnic disparities are among the top-selective underlying determinants associated with the disproportional impact of the COVID-19 pandemic on human mobility and health outcomes. This study jointly examined county-level racial/ethnic differences in compliance with stay-at-home orders and COVID-19 health outcomes during 2020, leveraging two-year geo-tracking data of mobile devices across ~4.4 million point-of-interests (POIs) in the contiguous United States. Through a set of structural equation modeling, this study quantified how racial/ethnic differences in following stay-at-home orders could mediate COVID-19 health outcomes, controlling for state effects, socioeconomics, demographics, occupation, and partisanship. Results showed that counties with higher Asian populations decreased most in their travel, both in terms of reducing their overall POIs’ visiting and increasing their staying home percentage. Moreover, counties with higher White populations experienced the lowest infection rate, while counties with higher African American populations presented the highest case-fatality ratio. Additionally, control variables, particularly partisanship, median household income, percentage of elders, and urbanization, significantly accounted for the county differences in human mobility and COVID-19 health outcomes. Mediation analyses further revealed that human mobility only statistically influenced infection rate but not case-fatality ratio, and such mediation effects varied substantially among racial/ethnic compositions. Last, robustness check of racial gradient at census block group level documented consistent associations but greater magnitude. Taken together, these findings suggest that US residents’ responses to COVID-19 are subject to an entrenched and consequential racial/ethnic divide.

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