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Abstract Sleep deficiencies, which include insufficient or long sleep duration, poor sleep quality, and irregular timing of sleep, are disproportionately distributed among populations that experience health disparities in the United States. Sleep deficiencies are associated with a wide range of suboptimal health outcomes, high-risk health behaviors, and poorer overall functioning and well-being. This report focuses on sleep health disparities (SHDs), which is a term defined as differences in one or more dimensions of sleep health on a consistent basis that adversely affect designated disadvantaged populations. SHDs appear to share many of the same determinants and causal pathways observed for health outcomes with well-known disparities. There also appears to be common behavioral and biological mechanisms that connect sleep with poorer health outcomes, suggesting a link between SHDs and other health disparities observed within these designated populations. In 2018, the National Institute on Minority Health and Health Disparities, the National Heart, Lung, and Blood Institute, and the Office of Behavioral and Social Sciences Research convened a workshop with experts in sleep, circadian rhythms, and health disparities to identify research gaps, challenges, and opportunities to better understand and advance research to address SHDs. The major strategy to address SHDs is to promote integration between health disparity causal pathways and sleep and circadian-related mechanisms in research approaches and study designs. Additional strategies include developing a comprehensive, integrative conceptual model, building transdisciplinary training and research infrastructure, and designing as well as testing multilevel, multifactorial interventions to address SHDs.

Better understanding of variations in neighborhood disadvantage could lead to improved insight into the sociobiologic mechanisms that underlie health disparities, which could, in turn, facilitate the development of improved therapeutics and interventions.

On January 2020, the WHO Director General declared that the outbreak constitutes a Public Health Emergency of International Concern. The world has faced a worldwide spread crisis and is still dealing with it. The present paper represents a white paper concerning the tough lessons we have learned from the COVID-19 pandemic. Thus, an international and heterogenous multidisciplinary panel of very differentiated people would like to share global experiences and lessons with all interested and especially those responsible for future healthcare decision making. With the present paper, international and heterogenous multidisciplinary panel of very differentiated people would like to share global experiences and lessons with all interested and especially those responsible for future healthcare decision making.

The proportion of people in the United States who are members of at least two ethnic groups is projected to increase to 10% by the year 2050. This makes addressing health disparities and health inequities in minority populations increasingly more difficult. Minority populations, including those who classify themselves as African American and Hispanic, are using mobile phones to access health information via the internet more frequently than those who classify themselves as white, providing unique opportunities for those in public health and health education to reach these traditionally underserved populations using mobile health (mHealth) interventions. The objective of this review was to assess studies conducted in the United States that have used mHealth tools and strategies to develop and implement interventions in underserved populations. This review also examines the ways in which mHealth strategies are being employed in public health interventions to these priority population groups, as mobile phone capabilities include text messaging, mobile apps, internet access, emails, video streaming, social media, instant messaging, and more. A systematic literature review was conducted using key search phrases, the matrix method, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart diagram to identify key studies conducted between the years of 2009-2016 in the United States. These studies were reviewed for their use of mHealth interventions in historically underserved and minority populations. A total of 16,270 articles were initially identified using key search phrases in three databases. Titles were reviewed and articles not meeting criteria were excluded, leaving 156 articles for further review. After additional review for relevance and inclusion criteria, 16 articles were qualified and analyzed. mHealth is a promising area of development for public health and health education. While successful research has been done using text messaging (short message service, SMS) and other mHealth strategies, there is a need for more research using mobile phones and tablet applications. This literature review demonstrates mHealth technology has the ability to increase prevention and health education in health disparate communities and concludes that more specified research is needed.

Individuals who are minoritized as a result of race, sexual identity, gender, or socioeconomic status experience a higher prevalence of many diseases. Understanding the biological processes that cause and maintain these socially driven health inequities is essential for addressing them. The gut microbiome is strongly shaped by host environments and affects host metabolic, immune, and neuroendocrine functions, making it an important pathway by which differences in experiences caused by social, political, and economic forces could contribute to health inequities. Nevertheless, few studies have directly integrated the gut microbiome into investigations of health inequities. Here, we argue that accounting for host–gut microbe interactions will improve understanding and management of health inequities, and that health policy must begin to consider the microbiome as an important pathway linking environments to population health.

Box 1 Social distancing measures Advising the whole population to self-isolate at home if they or their family have symptoms Bans on social gatherings (including mass gatherings) Stopping flights and public transport Closure of “non-essential” workplaces (beyond the health and social care sector, utilities, and the food chain) with continued working from home for those that can Closure of schools, colleges, and universities Prohibition of all “non-essential” population movement Limiting contact for special populations (eg, care homes, prisons) The health benefits of social distancing measures are obvious, with a slower spread of infection reducing the risk that health services will be overwhelmed. Box 2 Groups at particular risk from responses to covid-19 Older people—highest direct risk of severe covid-19, more likely to live alone, less likely to use online communications, at risk of social isolation Young people—affected by disrupted education at critical time; in longer term most at risk of poor employment and associated health outcomes in economic downturn Women—more likely to be carers, likely to lose income if need to provide childcare during school closures, potential for increase in family violence for some People of East Asian ethnicity—may be at increased risk of discrimination and harassment because the pandemic is associated with China People with mental health problems—may be at greater risk from social isolation People who use substances or in recovery—risk of relapse or withdrawal People with a disability—affected by disrupted support services People with reduced communication abilities (eg, learning disabilities, limited literacy or English language ability)—may not receive key governmental communications Homeless people—may be unable to self-isolate or affected by disrupted support services People in criminal justice system—difficulty of isolation in prison setting, loss of contact with family Undocumented migrants—may have no access to or be reluctant to engage with health services Workers on precarious contracts or self-employed—high risk of adverse effects from loss of work and no income People on low income—effects will be particularly severe as they already have poorer health and are more likely to be in insecure work without financial reserves People in institutions (care homes, special needs facilities, prisons, migrant detention centres, cruise liners)—as these institutions may act as amplifiers Table 1 Health effects of social distancing measures and actions to mitigate them Mechanism Summary of effects Summary of mitigations Economic effects In the UK, 3.5 million additional people are expected to need universal credit (which includes unemployment payments) as a result of the pandemic.3 The growth of the informal, gig economy in some countries has created a large group of people who are especially vulnerable as they do not get sick pay, are on zero hours contracts, or are self-employed.4 They can easily lose all their income, and even if this is only temporary they often lack the safety net of savings. Unemployment has large negative effects on both physical and mental health,7 with a meta-analysis reporting a 76% increase in all-cause mortality in people

During the pandemic, the overall mental health of the US population declined. Given higher rates of COVID-19 infections and deaths experienced by communities of color along with greater exposure to pandemic-related stressors (e.g., unemployment, food insecurity), we expect that the decline in mental health during the pandemic was more pronounced among Black, Hispanic, and Asian adults, with these groups also having less access to mental health services. We examine two nationally representative US surveys: the 2019 National Household Interview Survey (NHIS; N  = 30,368) and the 2020–2021 Household Pulse Survey (HPS; N  = 1,677,238). We find mental health of Black, Hispanic, and Asian respondents worsened relative to White respondents during the pandemic, with significant increases in depression and anxiety among racialized minorities compared to Whites. There is also evidence of especially high mental health burden for Black adults around the murder of George Floyd by police and for Asian adults around the murder of six Asian women in Atlanta. White respondents are most likely to receive professional mental health care before and during the pandemic, and Black, Hispanic, and Asian respondents demonstrate higher levels of unmet mental health care needs during the pandemic than White respondents. Our results indicate that within the current environment, White adults are at a large and systemic advantage buffering them from unexpected crises—like the COVID-19 pandemic. Without targeted interventions, the long-term social consequences of the pandemic and other co-occurring events (e.g., death of Black and Hispanic people by police) will likely include widening mental health disparities between racial/ethnic groups.

The field of oncology is at the forefront of advances in artificial intelligence (AI) in health care, providing an opportunity to examine the early integration of these technologies in clinical research and patient care. Hope that AI will revolutionize health care delivery and improve clinical outcomes has been accompanied by concerns about the impact of these technologies on health equity. We aimed to conduct a scoping review of the literature to address the question, \"What are the current and potential impacts of AI technologies on health equity in oncology?\" Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases from January 2000 to August 2021 for records engaging with key concepts of AI, health equity, and oncology. We included all English-language articles that engaged with the 3 key concepts. Articles were analyzed qualitatively for themes pertaining to the influence of AI on health equity in oncology. Of the 14,011 records, 133 (0.95%) identified from our review were included. We identified 3 general themes in the literature: the use of AI to reduce health care disparities (58/133, 43.6%), concerns surrounding AI technologies and bias (16/133, 12.1%), and the use of AI to examine biological and social determinants of health (55/133, 41.4%). A total of 3% (4/133) of articles focused on many of these themes. Our scoping review revealed 3 main themes on the impact of AI on health equity in oncology, which relate to AI's ability to help address health disparities, its potential to mitigate or exacerbate bias, and its capability to help elucidate determinants of health. Gaps in the literature included a lack of discussion of ethical challenges with the application of AI technologies in low- and middle-income countries, lack of discussion of problems of bias in AI algorithms, and a lack of justification for the use of AI technologies over traditional statistical methods to address specific research questions in oncology. Our review highlights a need to address these gaps to ensure a more equitable integration of AI in cancer research and clinical practice. The limitations of our study include its exploratory nature, its focus on oncology as opposed to all health care sectors, and its analysis of solely English-language articles.

The contributors to this latest volume of Research in the Sociology of Health Care investigate macro-level system issues and micro-level issues involving the socially disadvantaged and underserved. Looking specifically at the factors impacting on health and health care differentials, this book is an examination of the health and health care issues of both patients and providers of care in the United States and around the globe. Chapters focus on linkages to policy, population concerns and patients and providers of care as ways to meet health care needs.

As national HIV prevention goals aim to increase the proportion of persons living with HIV, determining existing disparities in retention in care will allow for targeted intervention. The purpose of this systematic review was to identify existing disparities in retention in care. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) 2015 guided this systematic review. Electronic databases, including PubMed/MEDLINE, CINAHL, Sociological Collection, PsychInfo, and Cab Direct/Global Health, were systematically searched and twenty studies were included. This review identified disparities in retention in care that have been documented by race, gender, age, HIV exposure, incarceration history, place of birth, and U.S. geographic location. Research is necessary to further identify existing disparities in retention in care and to better understand determinants of health disparities. Additionally, interventions must be tailored to meet the needs of health disparate populations and should be assessed to determine their effectiveness in reducing health disparities.