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Depression is common in patients with type 2 diabetes and associated with poor diabetes-related outcomes. We evaluated the factors associated with antidepressant use in a low-income, racially and ethnically diverse sample of patients with type 2 diabetes. We performed a cross-sectional study of baseline data from participants in a cluster randomized trial evaluating a health literacy intervention for diabetes care in safety net clinics. Depressive symptoms were measured by the Center for Epidemiological Studies Depression Scale (CES-D); antidepressant use was abstracted from medication lists. Multivariable mixed effects logistic regression was used to evaluate the relationship between antidepressant use and race/ethnicity adjusting for depressive symptoms, age, gender, income, and health literacy. Of 403 participants, 58% were non-Hispanic White, 18% were non-Hispanic Black, and 24% were Hispanic. Median age was 51 years old; 60% were female, 52% of participants had a positive screen for depression, and 18% were on antidepressants. Black and Hispanic participants were significantly less likely to be on an antidepressant compared with white participants, adjusted odds ratios 0.31(95% CI: 0.12 to 0.80) and 0.26 (95% CI: 0.10 to 0.74), respectively. In this vulnerable population with type 2 diabetes, we found a high prevalence of depressive symptoms, and a small proportion of participants were on an antidepressant. Black and Hispanic participants were significantly less likely to be treated with an antidepressant. Our findings suggest depression may be inadequately treated in low-income, uninsured patients with type 2 diabetes, especially racial and ethnic minorities.

We use the UK Household Longitudinal Study and compare pre-COVID-19 pandemic (2017-2019) and during-COVID-19 pandemic data (April 2020) for the same group of individuals to assess and quantify changes in mental health as measured by changes in the GHQ-12 (General Health Questionnaire), among ethnic groups in the UK. We confirm the previously documented average deterioration in mental health for the whole sample of individuals interviewed before and during the COVID-19 pandemic. In addition, we find that the average increase in mental distress varies by ethnicity and gender. Both women –regardless of their ethnicity– and Black, Asian, and minority ethnic (BAME) men experienced a higher average increase in mental distress than White British men, so that the gender gap in mental health increases only among White British individuals. These ethnic-gender specific changes in mental health persist after controlling for demographic and socioeconomic characteristics. Finally, we find some evidence that, among men, Bangladeshi, Indian and Pakistani individuals have experienced the highest average increase in mental distress with respect to White British men.

Using unique linked data, we examine income inequality and mobility across racial and ethnic groups in the United States. Our data encompass the universe of income tax filers in the United States for the period 2000-2014, matched with individual-level race and ethnicity information from multiple censuses and American Community Survey data. We document both income inequality and mobility trends over the period. We find significant stratification in terms of average incomes by racial/ethnic group and distinct differences in within-group income inequality. The groups with the highest incomes—whites and Asians—also have the highest levels of within-group inequality and the lowest levels of within-group mobility. The reverse is true for the lowest-income groups: blacks, American Indians, and Hispanics have lower within-group inequality and immobility. On the other hand, low-income groups are also highly immobile in terms of overall, rather than within-group, mobility. These same groups also have a higher probability of experiencing downward mobility compared with whites and Asians. We also find that within-group income inequality increased for all groups between 2000 and 2014, and the increase was especially large for whites. The picture that emerges from our analysis is of a rigid income structure, with mainly whites and Asians positioned at the top and blacks, American Indians, and Hispanics confined to the bottom.

Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data were collected by the National Center for Health Statistics. In 2017, 1,688,780 new cancer cases and 600,920 cancer deaths are projected to occur in the United States. For all sites combined, the cancer incidence rate is 20% higher in men than in women, while the cancer death rate is 40% higher. However, sex disparities vary by cancer type. For example, thyroid cancer incidence rates are 3-fold higher in women than in men (21 vs 7 per 100,000 population), despite equivalent death rates (0.5 per 100,000 population), largely reflecting sex differences in the \"epidemic of diagnosis.\" Over the past decade of available data, the overall cancer incidence rate (2004-2013) was stable in women and declined by approximately 2% annually in men, while the cancer death rate (2005-2014) declined by about 1.5% annually in both men and women. From 1991 to 2014, the overall cancer death rate dropped 25%, translating to approximately 2,143,200 fewer cancer deaths than would have been expected if death rates had remained at their peak. Although the cancer death rate was 15% higher in blacks than in whites in 2014, increasing access to care as a result of the Patient Protection and Affordable Care Act may expedite the narrowing racial gap; from 2010 to 2015, the proportion of blacks who were uninsured halved, from 21% to 11%, as it did for Hispanics (31% to 16%). Gains in coverage for traditionally underserved Americans will facilitate the broader application of existing cancer control knowledge across every segment of the population.

A person's racial or ethnic self-identification can change over time and across contexts, which is a component of population change not usually considered in studies that use race and ethnicity as variables. To facilitate incorporation of this aspect of population change, we show patterns and directions of individual-level race and Hispanic response change throughout the United States and among all federally recognized race/ethnic groups. We use internal U.S. Census Bureau data from the 2000 and 2010 censuses in which responses have been linked at the individual level (N = 162 million). Approximately 9.8 million people (6.1 %) in our data have a different race and/or Hispanic-origin response in 2010 than they did in 2000. Race response change was especially common among those reported as American Indian, Alaska Native, Native Hawaiian, Other Pacific Islander, in a multiple-race response group, or Hispanic. People reported as non-Hispanic white, black, or Asian in 2000 usually had the same response in 2010 (3 %, 6 %, and 9 % of responses changed, respectively). Hispanic/non-Hispanic ethnicity responses were also usually consistent (13 % and 1 %, respectively, changed). We found a variety of response change patterns, which we detail. In many race/Hispanic response groups, we see population churn in the form of large countervailing flows of response changes that are hidden in cross-sectional data. We find that response changes happen across ages, sexes, regions, and response modes, with interesting variation across racial/ethnic categories. Researchers should address the implications of race and Hispanic-origin response change when designing analyses and interpreting results.

Objectives. To examine 47 years of US urban and rural mortality trends at the county level, controlling for effects of education, income, poverty, and race. Methods. We obtained (1) Centers for Disease Control and Prevention WONDER (Wide-ranging ONline Data for Epidemiologic Research) data (1970–2016) on 104 million deaths; (2) US Census data on education, poverty, and race; and (3) Bureau of Economic Analysis data on income. We calculated ordinary least square regression models, including interaction models, for each year. We graphed standardized parameter estimates for 47 years. Results. Rural–urban mortality disparities increased from the mid-1980s through 2016. We found education, race, and rurality to be strong predictors; we found strong interactions between percentage poverty and percentage rural, indicating that the largest penalty was in high-poverty, rural counties. Conclusions. The rural–urban mortality disparity was persistent, growing, and large when compared to other place-based disparities. The penalty had evolved into a high-poverty, rural penalty that rivaled the effects of education and exceeded the effects of race by 2016. Public Health Implications. Targeting public health programs that focus on high-poverty, rural locales is a promising strategy for addressing disparities in mortality.

abstract We used data from the Medical Expenditure Panel Survey to explore potential explanations for racial/ethnic disparities in coronavirus disease 2019 (COVID-19) hospitalizations and mortality. Black adults in every age group were more likely than White adults to have health risks associated with severe COVID-19 illness. However, Whites were older, on average, than Blacks. Thus, when all factors were considered, Whites tended to be at higher overall risk compared with Blacks, with Asians and Hispanics having much lower overall levels of risk compared with either Whites or Blacks. We explored additional explanations for COVID19 disparities-namely, differences in job characteristics and how they interact with household composition. Blacks at high risk for severe illness were 1.6 times as likely as Whites to live in households containing health-sector workers. Among Hispanic adults at high risk for severe illness, 64.5 percent lived in households with at least one worker who was unable to work from home, versus 56.5 percent among Black adults and only 46.6 percent among White adults.

Abstract Context Minority young adults (YA) currently represent the largest growing population with type 1 diabetes (T1D) and experience very poor outcomes. Modifiable drivers of disparities need to be identified, but are not well-studied. Objective To describe racial-ethnic disparities among YA with T1D and identify drivers of glycemic disparity other than socioeconomic status (SES). Design Cross-sectional multicenter collection of patient and chart-reported variables, including SES, social determinants of health, and diabetes-specific factors, with comparison between non-Hispanic White, non-Hispanic Black, and Hispanic YA and multilevel modeling to identify variables that account for glycemic disparity apart from SES. Setting Six diabetes centers across the United States. Participants A total of 300 YA with T1D (18-28 years: 33% non-Hispanic White, 32% non-Hispanic Black, and 34% Hispanic). Main Outcome Racial-ethnic disparity in HbA1c levels. Results Non-Hispanic Black and Hispanic YA had lower SES, higher HbA1c levels, and much lower diabetes technology use than non-Hispanic White YA (P < 0.001). Non-Hispanic Black YA differed from Hispanic, reporting higher diabetes distress and lower self-management (P < 0.001). After accounting for SES, differences in HbA1c levels disappeared between non-Hispanic White and Hispanic YA, whereas they remained for non-Hispanic Black YA (+ 2.26% [24 mmol/mol], P < 0.001). Diabetes technology use, diabetes distress, and disease self-management accounted for a significant portion of the remaining non-Hispanic Black–White glycemic disparity. Conclusion This study demonstrated large racial-ethnic inequity in YA with T1D, especially among non-Hispanic Black participants. Our findings reveal key opportunities for clinicians to potentially mitigate glycemic disparity in minority YA by promoting diabetes technology use, connecting with social programs, and tailoring support for disease self-management and diabetes distress to account for social contextual factors.

Background Despite renewed interest in studying the safety and efficacy of psychedelic-assisted psychotherapy for the treatment of psychological disorders, the enrollment of racially diverse participants and the unique presentation of psychopathology in this population has not been a focus of this potentially ground-breaking area of research. In 1993, the United States National Institutes of Health issued a mandate that funded research must include participants of color and proposals must include methods for achieving diverse samples. Methods A methodological search of psychedelic studies from 1993 to 2017 was conducted to evaluate ethnoracial differences in inclusion and effective methods of recruiting peopple of color. Results Of the 18 studies that met full criteria ( n  = 282 participants), 82.3% of the participants were non-Hispanic White, 2.5% were African-American, 2.1% were of Latino origin, 1.8% were of Asian origin, 4.6% were of indigenous origin, 4.6% were of mixed race, 1.8% identified their race as “other,” and the ethnicity of 8.2% of participants was unknown. There were no significant differences in recruitment methodologies between those studies that had higher (> 20%) rates of inclusion. Conclusions As minorities are greatly underrepresented in psychedelic medicine studies, reported treatment outcomes may not generalize to all ethnic and cultural groups. Inclusion of minorities in futures studies and improved recruitment strategies are necessary to better understand the efficacy of psychedelic-assisted psychotherapy in people of color and provide all with equal opportunities for involvement in this potentially promising treatment paradigm.

The coronavirus disease 2019 (COVID-19) pandemic markedly changed human mobility patterns, necessitating epidemiological models that can capture the effects of these changes in mobility on the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 1 . Here we introduce a metapopulation susceptible–exposed–infectious–removed (SEIR) model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in ten of the largest US metropolitan areas. Our mobility networks are derived from mobile phone data and map the hourly movements of 98 million people from neighbourhoods (or census block groups) to points of interest such as restaurants and religious establishments, connecting 56,945 census block groups to 552,758 points of interest with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in the behaviour of the population over time. Our model predicts that a small minority of ‘superspreader’ points of interest account for a large majority of the infections, and that restricting the maximum occupancy at each point of interest is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups 2 – 8 solely as the result of differences in mobility: we find that disadvantaged groups have not been able to reduce their mobility as sharply, and that the points of interest that they visit are more crowded and are therefore associated with higher risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more-effective and equitable policy responses to COVID-19. An epidemiological model that integrates fine-grained mobility networks illuminates mobility-related mechanisms that contribute to higher infection rates among disadvantaged socioeconomic and racial groups, and finds that restricting maximum occupancy at locations is especially effective for curbing infections.