Social determinants of health (SDoH), which encompass the economic, social, environmental, and psychosocial factors that influence health, play a significant role in the development of cardiovascular disease (CVD) risk factors as well as CVD morbidity and mortality. The COVID-19 pandemic and the current social justice movement sparked by the death of George Floyd have laid bare long-existing health inequities in our society driven by SDoH. Despite a recent focus on these structural drivers of health disparities, the impact of SDoH on cardiovascular health and CVD outcomes remains understudied and incompletely understood. To further investigate the mechanisms connecting SDoH and CVD, and ultimately design targeted and effective interventions, it is important to foster interdisciplinary efforts that incorporate translational, epidemiological, and clinical research in examining SDoH-CVD relationships. This review aims to facilitate research coordination and intervention development by providing an evidence-based framework for SDoH rooted in the lived experiences of marginalized populations. Our framework highlights critical structural/socioeconomic, environmental, and psychosocial factors most strongly associated with CVD and explores several of the underlying biologic mechanisms connecting SDoH to CVD pathogenesis, including excess stress hormones, inflammation, immune cell function, and cellular aging. We present landmark studies and recent findings about SDoH in our framework, with careful consideration of the constructs and measures utilized. Finally, we provide a roadmap for future SDoH research focused on individual, clinical, and policy approaches directed towards developing multilevel community-engaged interventions to promote cardiovascular health.

Sleep disturbance and depression are commonly encountered in primary care. In sickle cell disease, depression is associated with pain, poor treatment compliance, and lower quality of life. The prevalence of sleep disturbance and its effect upon quality of life in adults with sickle cell disease is unknown. The goal of this study was to determine the prevalence of sleep disturbance and if it is associated with pain and depression in sickle cell disease. Three hundred twenty eight adults with sickle cell disease enrolled on the Bethesda Sickle Cell Cohort Study were assessed using the Pittsburgh Sleep Quality Index and Beck Depression Inventory II screening measures as a cross-sectional survey. Scores greater than 5 (Pittsburgh Sleep Quality Index) and 16 (Beck Depression Inventory II) defined sleep disturbance and depression, respectively. Clinical and laboratory parameters were also assessed. The mean Pittsburgh Sleep Quality Index score was 8.4 (SD ± 4.2) indicating a 71.2% prevalence of sleep disturbance. The mean Beck Depression Inventory II score was 8.0 (SD ± 8.9). Sixty five (20.6%) participants had a score indicating depression, and half of these (10.0%) had thoughts of suicide. Both Pittsburgh Sleep Quality Index and Beck Depression Inventory II scores were significantly correlated (p < .001). The number of days with mild/moderate pain (p = .001) and a history of headaches (p = .005) were independently associated with depression by multivariate regression analysis. Patients with sleep disturbance were older (p = .002), had higher body mass index (p = .011), had more days of pain (p = .003) and more frequent severe acute painful events (emergency room visits and hospitalizations) during the previous 12 months (p < .001). More than 70 percent of adults with sickle cell disease had sleep disturbance, while 21 percent showed evidence of clinical depression. Sleep disturbance and depression were correlated, and were most common among those with more frequent pain. Providers caring for adults with sickle cell disease and frequent pain should consider screening for these common co-morbidities. Additional study is needed to confirm these findings and to determine if treatments for pain, depression or sleep disturbances will improve quality of life measures in this patient population. ClinicalTrials.gov identifier: NCT00011648 .

Background Data collection on race and ethnicity is critical in the assessment of racial disparities related to health. Studies comparing clinical and administrative data show discrepancies in race documentation and attribution. Methods Self-reported data from two studies were compared to demographics in the electronic health record (EHR) extracted from the Biomedical Translational Research Information System (BTRIS) repository. McNemar and Bhapkar analyses were conducted to quantify the agreement of ethnicity and race between self-reported and EHR data. Pearson’s chi-square tests were used to explore the relationship between acculturation, length of time in the USA, country of residence, and how individuals self-reported their race. Results The sample (n = 280) was predominantly female (52.1 %), with a mean age of 47 (SD ± 13.74), mean years in the USA were 12.8 (SD ± 11.67) and the majority were born outside of the USA. (55.6 %). Those who self-identified as Hispanic (n = 208) scored a mean of 5.5 (SD ± 3.07) on the short acculturation scale (SAS) that ranges 4 to 20; lower scores indicate less acculturation. A significant difference was found between the way race is reported in the electronic medical record and self-reported data among those people who identified as Hispanic, with significant differences in the white (p < 0.0001) and other (p < 0.0001) categories. Conclusions The misclassification of race is most frequent in those individuals who self-identified as Hispanic. As the Hispanic population in the USA continues to grow, understanding the factors that affect the way that individuals from this heterogeneous population self-report race may provide important guidance in tailoring care to address health disparities.