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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.

Definitions, Principles, and Concepts for Minority Health and Health Disparities Research -- Getting Under the Skin : Pathways and Processes that Link Social and Biological Determinants of Disease -- Racial/Ethnic, Socioeconomic, and Other Social Determinants -- Behavioral Determinants in Population Health and Health Disparities Research -- Sociocultural Environments and Health Disparities Research : Frameworks, Methods and Promising Directions -- Physical Environment, and Minority Health and Health Disparities Research -- Genome-wide Genetic Approaches to Metabolic and Inflammatory Health Disparities -- Biologic Factors and Molecular Determinants in Inflammatory and Metabolic Diseases -- Insights into the Genomic Landscape of African Ancestry Populations : Implications for Health and Disease Disparities -- Applying Self-report Measures in Minority Health and Health Disparities Research -- Conducting Community-Based Participatory Research with Minority Communities to Reduce Health Disparities -- Racial/Ethnic Health and Healthcare Disparities Measurement : The Application of the Principles and Methods of Causal Inference -- Small Area Estimation and Small Area Analysis for Minority Health and Health Disparities -- Applications of Big Data Science and Analytic Techniques for Health Disparities Research -- Complex Systems Science -- Improving Equity in Health Care through Multilevel Interventions -- Using Implementation Science to Move from Knowledge of Disparities to Achievement of Equity -- Health Care and Public Policy (HCPP) : Challenges and Opportunities for Research Addressing Disparities in Access to High Quality Care -- Health Communication as a Mediator of Health and Healthcare Disparities -- Comparative Effectiveness Research in Health Disparity Populations -- The Role of Electronic Health Records and Health Information Technology in Addressing Health Disparities -- Precision Medicine and Health Disparities -- Recruitment, Inclusion, and Diversity in Clinical Trials -- Sexual and Gender Minority Health Disparities : Concepts, Methods, and Future Directions -- Workforce Diversity and Capacity Building to Address Health Disparities.

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.