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Online political discussion is a growing form of political behavior and plays an important role in political deliberation in the new media age. This article examines "daily talk" as a type of political deliberation, and emphasizes two overlooked factors that influence online political discussion: direct government–citizen interactions and perceptions of the importance of new media for online political discussion. It also examines the moderation effects of perceived importance of new media on group communication and government–citizen interaction. Survey data analysis from the 2008 Civic Engagement survey from the Pew Internet & American Life Project (n = 2,251) reveals that citizens' interactions with both members of their political group and government officials have positive influences on the frequency of online political discussion. Meanwhile, the association between online political discussion and online group communication becomes stronger when one perceives that new media are less important as source of political information. The theoretical and policy implications of the findings are discussed.

This study refreshes the communication mediation model by integrating impacts of individual psychological traits (civic motivations and political efficacy) with the relationships between Chinese netizens' media news uses, civic expression/discussion and civic engagement in the model. The results of an online survey (N=490) indicated that new media and conventional media have indirect effects on civic engagement through different mediators. Specifically, reading news from the newspapers has a negative impact on motivations driven by emotion, but directly spurs political efficacy, civic discussion and engagement. By contrast, watching TV news encourages civic discussion, while browsing news online increases the likelihood of participatory behaviours, driven by emotions of anger or sadness. Pressure from social networks is positively related to civic engagement. Motivations of civic duties, emotion and political efficacy are positively related to online civic expression and discussion with social networks about public affairs, both of which are strongly associated with participatory behaviours. (China/GIGA)

Do social media help individuals without organisational memberships to engage more in politics or do they only facilitate political participation for those already involved? We examine how social media use and organisational membership jointly affect participation. Comparative surveys in Hong Kong and Taipei reveal that information sharing and virtual political engagement on social media mobilised users to engage in collective political actions. The influence of social media on individual-based participation is conditional on organisational membership, as reflected by the number of organisations joined. Organisational membership moderates the relationship between social media use and political behaviours differently in Hong Kong and Taipei.

Recent increasing rates of COVID-19 cases, hospitalizations, and deaths among non-Hispanic Whites have led to declining rate ratios at a time of continuing high burden of COVID-19 in American Indian/ Alaska Native, Asian/Pacific Islander, African American, and Hispanic/Latino populations. The use of all epidemiological tools, including rate ratios and actual rates per 100,000 population, provides a more comprehensive assessment of the magnitude and trends of racial and ethnic disparities in COVID-19.Ethn Dis. 2022;32(2):109-112; doi:10.18865/ed.32.2.109

The Disparities Elimination through Coordinated Interventions to Prevent and Control Heart and Lung Disease Risk (DECIPHeR) research program, supported by the National Heart, Lung, and Blood Institute (NHLBI), focuses on developing and testing sustainable interventions to reduce heart and lung disease disparities. This perspective piece reflects on lessons learned during the planning phase (UG3) and outlines the accomplishments of the DECIPHeR Alliance. The article emphasizes the importance of a biphasic (UG3/UH3) funding mechanism, technical assistance, and collaborative subcommittees in achieving success. As DECIPHeR enters phase 2 (UH3), the article anticipates rigorously planned studies addressing social determinants of health and emphasizes the need for effective implementation strategies and equitable research frameworks. The Alliance's contributions, such as the IM4Equity framework, offer novel approaches to community-engaged health equity and implementation science research. The article explores future opportunities, including dissemination strategies, community engagement, and collaboration with diverse partners, to maximize DECIPHeR's impact on health disparities beyond cardiovascular and pulmonary health.

<p class="Default">The gap in educational attainment separating underrepresented minorities from Whites and Asians remains wide. Such a gap has sig­nificant impact on workforce diversity and in­clusion among cross-cutting Biomedical Data Science (BDS) research, which presents great opportunities as well as major challenges for addressing health disparities. This article pro­vides a brief description of the newly estab­lished National Institutes of Health Big Data to Knowledge (BD2K) diversity initiatives at four universities: California State University, Monterey Bay; Fisk University; University of Puerto Rico, Río Piedras Campus; and California State University, Fullerton. We em­phasize three main barriers to BDS careers (ie, preparation, exposure, and access to resources) experienced among those pioneer programs and recommendations for possible solutions (ie, early and proactive mentoring, enriched research experience, and data sci­ence curriculum development). The diversity disparities in BDS demonstrate the need for educators, researchers, and funding agencies to support evidence-based practices that will lead to the diversification of the BDS workforce. <em></em></p><p class="Default"><em>Ethn Dis. </em>2017;27(2):107-116; doi:10.18865/ed.27.2.107.</p>

<p>Achieving health equity requires that every person has the opportunity to attain their full health potential and no one is disadvantaged from achieving this potential because of social position or other socially determined circumstances. Inequity experienced by populations of lower socioeconomic status is reflected in differences in health status and mortality rates, as well as in the distribution of disease, disability and illness across these population groups. This article gives an overview of the health inequities literature associated with heart, lung, blood and sleep (HLBS) disorders. We present an ecological framework that provides a theoretical foundation to study late-stage T4 translation research that studies implementation strategies for proven effective interventions to address health inequities. <em>Ethn Dis. </em>2016;26(3):387-394; doi:10.18865/ ed.26.3.387 </p>

Cover -- Title Page -- Copyright Page -- Contents -- List of Contributors -- Foreword -- Acknowledgements -- Chapter 1 Definitions, Principles, and Concepts for Minority Health and Health Disparities Research -- 1.1 Introduction -- 1.2 NIMHD Mission -- 1.3 Definitions and Concepts of Minority Health and Health Disparities -- 1.3.1 Racial/Ethnic Minority Populations -- 1.3.2 Minority Health and Minority Health Research -- 1.3.3 Health Disparities and Health Disparities Research -- 1.3.4 Is It Minority Health or Health Disparities? -- 1.3.5 Standardized Measures of Minority Health- and Health Disparities-Related Constructs -- 1.4 The NIMHD Research Framework: Health Determinants in Action -- 1.5 Inclusion of Diverse Participants in Clinical Research -- 1.6 Conclusions -- 1.7 Key Points -- Disclaimer -- References -- Chapter 2 Getting Under the Skin: Pathways and Processes that Link Social and Biological Determinants of Disease -- 2.1 Introduction -- 2.2 Allostasis and Allostatic Load -- 2.3 The HPA Axis -- 2.3.1 How We Feed: The Role of the Hypothalamus in Pathways Controlling Feeding and Nutrition -- 2.3.2 How We Sleep: Light-Day Cycle, Circadian Clock, and Hypothalamic Linkages to Metabolic Control and Sleep -- 2.3.3 How We Feel: Stress and the Role of HPA Axis in Memory and Mood -- 2.4 Anticipatory Biology and Behavior: The Embedding of Exposures Across the Life Course -- 2.4.1 Studies of Stress and Allostatic Load Across the Life Course -- 2.5 Sleep -- 2.5.1 Sleep Health Disparities and Allostatic Load -- 2.5.2 Sleep Health Disparities and Genetics -- 2.5.3 Methodologies in Sleep Research -- 2.6 How We Feed: Nutrition and Nutrition-related Health Disparities -- 2.7 How We Feel: Mood and Depression -- 2.8 Summary -- 2.9 Key Points -- Disclaimer -- References -- Chapter 3 Racial/Ethnic, Socioeconomic, and Other Social Determinants.

Introduction/PurposePredictive models incorporating relevant clinical and social features can provide meaningful insights into complex interrelated mechanisms of cardiovascular disease (CVD) risk and progression and the influence of environmental exposures on adverse outcomes. The purpose of this targeted review (2018–2019) was to examine the extent to which present-day advanced analytics, artificial intelligence, and machine learning models include relevant variables to address potential biases that inform care, treatment, resource allocation, and management of patients with CVD.MethodsPubMed literature was searched using the prespecified inclusion and exclusion criteria to identify and critically evaluate primary studies published in English that reported on predictive models for CVD, associated risks, progression, and outcomes in the general adult population in North America. Studies were then assessed for inclusion of relevant social variables in the model construction. Two independent reviewers screened articles for eligibility. Primary and secondary independent reviewers extracted information from each full-text article for analysis. Disagreements were resolved with a third reviewer and iterative screening rounds to establish consensus. Cohen's kappa was used to determine interrater reliability.ResultsThe review yielded 533 unique records where 35 met the inclusion criteria. Studies used advanced statistical and machine learning methods to predict CVD risk (10, 29%), mortality (19, 54%), survival (7, 20%), complication (10, 29%), disease progression (6, 17%), functional outcomes (4, 11%), and disposition (2, 6%). Most studies incorporated age (34, 97%), sex (34, 97%), comorbid conditions (32, 91%), and behavioral risk factor (28, 80%) variables. Race or ethnicity (23, 66%) and social variables, such as education (3, 9%) were less frequently observed.ConclusionsPredictive models should adjust for race and social predictor variables, where relevant, to improve model accuracy and to inform more equitable interventions and decision making.

<p class="Default">Addressing minority health and health disparities has been a missing piece of the puzzle in Big Data science. This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged popula­tions over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most impor­tantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention devel­opment. However, the promise of Big Data needs to be considered in light of significant challenges that threaten to widen health dis­parities. Care must be taken to incorporate diverse populations to realize the potential benefits. Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for re­ducing health disparities but without active engagement, it risks further widening them.</p><p class="Default"><em>Ethn.Dis;</em>2017;27(2):95-106; doi:10.18865/ed.27.2.95.</p>