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Equitable health benefit design is central to addressing the health inequities of individuals with commercial health insurance in the United States. To do so, employers and other plan sponsors must take action to identify and address unmet health and well-being priorities among racialized groups and low-income workers. These historically underrepresented subpopulations will also benefit from more equitable approaches to healthcare benefits design that recognize and meaningfully address access and affordability concerns. Targeted appropriately, these actions have the potential to foster greater employee engagement and productivity, leading to enhanced business performance.

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.