Describing characteristics of adults with and without congenital heart defects hospitalized with COVID-19.

BACKGROUND: We sought to describe patient characteristics in adults with and without congenital heart defects (CHDs) during hospitalization for COVID-19. METHODS: We analyzed data collected by Optum®, a nationally representative database of electronic medical records, for 369 adults with CHDs and 41,578 without CHDs hospitalized for COVID-19 between January 1, 2020, and December 10, 2020. We used Poisson regression to describe and compare epidemiologic characteristics, heart-related conditions, and severe outcomes between these two groups. RESULTS: The distributions of many epidemiologic characteristics were similar between the two groups, but patients with CHDs were significantly more likely to be current or former smokers compared to patients without CHDs (risk ratio [RR]: 1.5, 95% confidence interval [CI]: 1.2, 1.8). Patients with CHDs were also significantly more likely to have heart failure, stroke, acute arrhythmia, myocardial injury, acute pulmonary hypertension, venous thromboembolism, and obesity documented at the time of the COVID-19 hospitalization (RR range: 1.5-4.7) but not respiratory failure. Patients with CHDs (7 days) had a significantly longer median length of stay than those without CHDs (5 days; p < .001) and were significantly more likely to have an intensive care unit (ICU) admission (RR: 1.6, 95 CI: 1.2-1.9). CONCLUSIONS: Our description of patients among a large population improves our understanding of the clinical course of COVID-19 among adults with CHDs. Adults with CHD appear to be at greater risk for more severe CHD, including greater risk of ICU admission and longer length of hospital stays.

Epidemiology of Antibiotic Use and Drivers of Cross-Border Procurement in a Mexican American Border Community.

Background: The U.S.-Mexico Border is an area of opportunity for improved health care access; however, gaps remain as to how and where U.S. border residents, particularly those who are underinsured, obtain care. Antibiotics are one of the most common reported drivers of cross-border healthcare access and a medication of particular concern since indiscriminate or inappropriate use is associated with antimicrobial resistance. In addition, many studies assessing preferences for Mexican pharmaceuticals and healthcare in U.S. border residents were done prior to 2010 when many prescription medications, including antibiotics, were available over the counter in Mexico. Methods: Data used in this study were collected during the baseline examination of an ongoing longitudinal cohort study in Starr Country, Texas, one of 14 counties on the Texas-Mexico border. Participants self-reported the name, date of use, and the source country of each antibiotic used in the past 12 months. Logistic regression was used to determine social, cultural, and clinical features associated with cross-border procurement of antibiotics. Results: Over 10% of the study cohort reported using antibiotics in the past 30 days with over 60% of all rounds used in the past 12 months sourced from Mexico. A lack of health insurance and generation score, a measure of acculturation, were the strongest predictors of cross-border procurement of antibiotics. Conclusions: Factors previously associated with cross-border acquisition of antibiotics are still present despite changes in 2010 to prescription drug regulations in Mexico. These results may be used to inform future public health initiatives to provide culturally sensitive education about responsible antibiotic stewardship and to address barriers to U.S. healthcare and pharmaceutical access in medically underserved, impoverished U.S.-Mexico border communities.

Modelo predictivo PLS-SEM sobre intención de abandono académico universitario durante la COVID-19

Uno de los sectores donde mayor impacto ha tenido la pandemia de COVID-19 ha sido el educativo. De una manera precipitada y sin apenas tiempo para reaccionar, se ha tenido que llevar a cabo una interrupción de la normalidad académica y transitar a una modalidad de enseñanza virtual. No solo el profesorado, que ha tenido que adaptarse y modificar los procesos de enseñanza-aprendizaje, sino también el alumnado se ha visto afectado por este cambio de rumbo drástico que se ha producido en la educación superior. En una fase clave del curso, con unas condiciones sociales y familiares no siempre favorables, con falta de recursos y con la distancia impuesta por las medidas de alarma, los estudiantes se han visto sometidos a una presión que ha puesto en riesgo la continuidad en los estudios. METODO El estudio realizado con una muestra de 475 estudiantes de diferentes titulaciones de grado de la Universidad de La Laguna (España), tuvo como objetivo validar un modelo predictivo sobre la intención de abandono, mediante un modelo de ecuaciones estructurales. Concretamente, se analizó el valor predictivo que el modelo de enseñanza virtual, el agotamiento académico y las expectativas de autoeficacia tenían en la intención de abandono del alumnado universitario. RESULTADOS Los resultados pusieron de manifiesto que el modelo resultante era válido para predecir la variable de intención de abandono de los estudios. DISCUSIÓN Los datos obtenidos pueden ayudar a prevenir en el futuro situaciones de riesgo de abandono, mediante la puesta en práctica de programas de orientación, información, apoyo académico y seguimiento al alumnado.Alternate abstract: One of the sectors where the COVID-19 pandemic has had the greatest impact has been education. In a hasty way and with little time to react, an interruption of academic normality had to be carried out and a virtual teaching modality had to be transitioned. Not only the teachers, who have had to adapt and modify the teaching-learning processes, but also the students have been affected by this drastic change of direction that has occurred in higher education. In a key phase of the course, with social and family conditions not always favorable, with lack of resources and with the distance imposed by alarm measures, students have been subjected to pressure that has put the continuity of studies. METHOD The study carried out with a sample of 475 students from different undergraduate degrees from the University of La Laguna (Spain), aimed to validate a predictive model on the intention to abandon, using a structural equation model. Specifically, the predictive value that the virtual teaching model, academic exhaustion and expectations of self-efficacy had in the intention of dropping out of university students was analyzed. RESULTS The results showed that the resulting model was valid to predict the variable of intention to abandon the studies. DISCUSSION The data obtained can help prevent situations of risk of abandonment in the future, through the implementation of guidance, information, academic support and student monitoring programs.

Leveraging a health information exchange for analyses of COVID-19 outcomes including an example application using smoking history and mortality.

Understanding sociodemographic, behavioral, clinical, and laboratory risk factors in patients diagnosed with COVID-19 is critically important, and requires building large and diverse COVID-19 cohorts with both retrospective information and prospective follow-up. A large Health Information Exchange (HIE) in Southeast Texas, which assembles and shares electronic health information among providers to facilitate patient care, was leveraged to identify COVID-19 patients, create a cohort, and identify risk factors for both favorable and unfavorable outcomes. The initial sample consists of 8,874 COVID-19 patients ascertained from the pandemic's onset to June 12th, 2020 and was created for the analyses shown here. We gathered demographic, lifestyle, laboratory, and clinical data from patient's encounters across the healthcare system. Tobacco use history was examined as a potential risk factor for COVID-19 fatality along with age, gender, race/ethnicity, body mass index (BMI), and number of comorbidities. Of the 8,874 patients included in the cohort, 475 died from COVID-19. Of the 5,356 patients who had information on history of tobacco use, over 26% were current or former tobacco users. Multivariable logistic regression showed that the odds of COVID-19 fatality increased among those who were older (odds ratio = 1.07, 95% CI 1.06, 1.08), male (1.91, 95% CI 1.58, 2.31), and had a history of tobacco use (2.45, 95% CI 1.93, 3.11). History of tobacco use remained significantly associated (1.65, 95% CI 1.27, 2.13) with COVID-19 fatality after adjusting for age, gender, and race/ethnicity. This effort demonstrates the impact of having an HIE to rapidly identify a cohort, aggregate sociodemographic, behavioral, clinical and laboratory data across disparate healthcare providers electronic health record (HER) systems, and follow the cohort over time. These HIE capabilities enable clinical specialists and epidemiologists to conduct outcomes analyses during the current COVID-19 pandemic and beyond. Tobacco use appears to be an important risk factor for COVID-19 related death.