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.

Aim-This paper is a report of the effectiveness of a structured multifaceted mentorship programme designed to implement evidence-based practice in a clinical research intensive environment.Background-Barriers to implementing evidence-based practice are well-documented in the literature. Evidence-based practice is associated with higher quality care and better patient outcomes than care that is steeped in tradition. However, the integration of evidence-based practice implementation into daily clinical practice remains inconsistent, and the chasm between research and bedside practice remains substantial.

Adverse childhood experiences (ACEs) are associated with numerous risk behaviors and mental health outcomes among youth. This study examines the relationship between the number of types of exposures to ACEs and risk behaviors and mental health outcomes among reservation-based Native Americans. In 2011, data were collected from Native American (N = 288; 15-24 years of age) tribal members from a remote plains reservation using an anonymous web-based questionnaire. We analyzed the relationship between six ACEs, emotional, physical, and sexual abuse, physical and emotional neglect, witness to intimate partner violence, for those <18 years, and included historical loss associated symptoms, and perceived discrimination for those <19 years; and four risk behavior/mental health outcomes: post-traumatic stress disorder (PTSD) symptoms, depression symptoms, poly-drug use, and suicide attempt. Seventy-eight percent of the sample reported at least one ACE and 40 % reported at least two. The cumulative impact of the ACEs were significant (p < .001) for the four outcomes with each additional ACE increasing the odds of suicide attempt (37 %), poly-drug use (51 %), PTSD symptoms (55 %), and depression symptoms (57 %). To address these findings culturally appropriate childhood and adolescent interventions for reservation-based populations must be developed, tested and evaluated longitudinally.

Nurses are knowledgeable regarding the importance of health-promoting activities such as healthy eating, physical activity, stress management, sleep hygiene, and maintaining healthy relationships. However, this knowledge may not translate into nurses’ own self-care. Nurses may not follow recommended guidelines for physical activity and proper nutrition. Long hours, work overload, and shift work associated with nursing practice can be stressful and contribute to job dissatisfaction, burnout, and health consequences such as obesity and sleep disturbances. The purpose of this article is to provide an overview of research examining nurses’ participation in health-promoting behaviors, including intrinsic and extrinsic factors that may influence nurses’ participation in these activities. This article also provides recommendations for perioperative nurse leaders regarding strategies to incorporate into the nursing workplace to improve the health of the staff nurses by increasing health-promoting behaviors.

ObjectivesThere is much speculation on which hypervariable region provides the highest bacterial specificity in 16S rRNA sequencing. The optimum solution to prevent bias and to obtain a comprehensive view of complex bacterial communities would be to sequence the entire 16S rRNA gene; however, this is not possible with second generation standard library design and short-read next-generation sequencing technology.MethodsThis paper examines a new process using seven hypervariable or V regions of the 16S rRNA (six amplicons: V2, V3, V4, V6-7, V8, and V9) processed simultaneously on the Ion Torrent Personal Genome Machine (Life Technologies, Grand Island, NY). Four mock samples were amplified using the 16S Ion Metagenomics Kit™ (Life Technologies) and their sequencing data is subjected to a novel analytical pipeline.ResultsResults are presented at family and genus level. The Kullback-Leibler divergence (DKL), a measure of the departure of the computed from the nominal bacterial distribution in the mock samples, was used to infer which region performed best at the family and genus levels. Three different hypervariable regions, V2, V4, and V6-7, produced the lowest divergence compared to the known mock sample. The V9 region gave the highest (worst) average DKL while the V4 gave the lowest (best) average DKL. In addition to having a high DKL, the V9 region in both the forward and reverse directions performed the worst finding only 17% and 53% of the known family level and 12% and 47% of the genus level bacteria, while results from the forward and reverse V4 region identified all 17 family level bacteria.ConclusionsThe results of our analysis have shown that our sequencing methods using 6 hypervariable regions of the 16S rRNA and subsequent analysis is valid. This method also allowed for the assessment of how well each of the variable regions might perform simultaneously. Our findings will provide the basis for future work intended to assess microbial abundance at different time points throughout a clinical protocol.