BACKGROUND: Medication adherence (MA) is critical to successful chronic disease management. It is not clear how social determinants of health (SDH) impact MA. We conducted a systematic review and meta-analysis to summarize the evidence on the relationship between SDH and MA. METHODS: We conducted a systematic review of the literature using a Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) format. A literature search was performed using three databases: PubMed, Scopus, and Cochrane Clinical Trials Register in December of 2018. Included studies were completed in the USA, included adults aged 18 years and older, measured at least one social determinant of health, and medication adherence was the primary outcome measure. Data from included full texts were independently extracted using a standardized data extraction form. We then conducted a meta-analysis and pooled the odds ratios from the included studies for each social determinant as well as for all SDH factors collectively. RESULTS: A total of 3137 unduplicated abstracts were identified from our database searches. A total of 173 were selected for full text review after evaluating the abstract. A total of 29 articles were included for this systematic review. Economic-related SDH factors and MA were mostly commonly examined. The meta-analysis revealed a significant relationship between food insecurity (aOR = 0.56; 95% CI 0.42-0.7), housing instability (aOR = 0.64; 95% CI 0.44-0.93), and social determinants overall (aOR = 0.75; 95% CI 0.65-0.88) and medication adherence. DISCUSSION: Food insecurity and housing instability most consistently impacted medication adherence. Although included studies were heterogenous and varied widely in SDH and MA measurements, adverse social determinants overall were significantly associated with lower MA. The relationship between SDH and MA warrants more attention and research by health care providers and policymakers.

Social factors, such as social cognition skills (SCS) and social determinants of health (SDH), may be vital for mental health, even when compared with classical psycho-physical predictors (demographic, physical, psychiatric, and cognitive factors). Although major risk factors for psychiatric disorders have been previously assessed, the relative weight of SCS and SDH in relation to classical psycho-physical predictors in predicting symptoms of mental disorders remains largely unknown. In this study, we implemented multiple structural equation models (SEM) from a randomized sample assessed in the Colombian National Mental Health Survey of 2015 (CNMHS, n = 2947, females: 1348) to evaluate the role of SCS, SDH, and psycho-physical factors (totaling 17 variables) as predictors of mental illness symptoms (anxiety, depression, and other psychiatric symptoms). Specifically, we assessed the structural equation modeling of (a) SCS (emotion recognition and empathy skills); (b) SDH (including the experience of social adversities and social protective factors); (c) and classical psycho-physical factors, including psychiatric antecedents, physical-somatic factors (chronic diseases), and cognitive factors (executive functioning). Results revealed that the emotion recognition skills, social adverse factors, antecedents of psychiatric disorders and chronic diseases, and cognitive functioning were the best predictors of symptoms of mental illness. Moreover, SCS, particularly emotion recognition skills, and SDH (experiences of social adversities, familial, and social support networks) reached higher predictive values of symptoms than classical psycho-physical factors. Our study provides unprecedented evidence on the impact of social factors in predicting symptoms of mental illness and highlights the relevance of these factors to track early states of disease.

Point-of-care ultrasound (POCUS) improves both the sensitivity and specificity with which clinicians can make a variety of diagnoses at the bedside from abdominal aortic aneurysm to kidney stones. In outpatient clinics, urgent care centers, and emergency departments, where ultrasound imaging may be delayed by hours or even days, the use of POCUS can be very helpful. We believe that POCUS facilitates both the triage of patients and provides diagnostic information quickly. We hope to advance the use of POCUS in the primary care setting and have reviewed six sonographic topics where we believe ultrasound can be of immense assistance to the physician in the outpatient setting.

We use data from the Young Lives surveys, in Ethiopia, India, Peru, and Vietnam, to highlight an underappreciated phenomenon—that a substantial proportion of students in developing countries are the first in their families to go to school. We term these students first‐generation learners (FGLs). We both propose a simple “static” definition of FGL status—where a child’s parents have no education attainment—and utilize the panel dynamics of the Young Lives data set to look at a “dynamic” definition—where the child, in any given round of data collection, is enrolled at a level which his or her parents did not reach. We show descriptive statistics on the scale of this problem across the four countries. We find strong, consistent patterns of relative educational deprivation for FGLs. We tentatively explore the pathways through which FGL status may affect outcomes and find possible explanations through an inability to support with homework and lower aspirations. We look at how becoming an FGL affects the probability of being in school using child fixed‐effects estimations and find it increases the vulnerability of children to drop out.