Faculty Bibliography

STUDY OBJECTIVE/OBJECTIVE:Input from adolescents and healthcare providers is needed to develop electronic tools that can support patient-centered sexual and reproductive (SRH) care. This study explores facilitators and barriers to patient-centered communication in the context of developing an electronic appointment planning tool to promote SRH communication in clinic settings. DESIGN/METHODS:In-depth interviews were conducted to explore what constitutes adolescent-friendly SRH care and communication, as well as on the design of the appointment planning tool. Interviews were coded iteratively, and analyzed using the software Atlas.TI v8. SETTING/METHODS:An adolescent primary care clinic, and a pediatric and adolescent gynecology clinic. PARTICIPANTS/METHODS:Adolescent girls (N=32; ages 14-18) and providers who care for adolescent girls (N=10). MAIN OUTCOME MEASURES/METHODS:Thematic analyses explored facilitators/barriers to SRH communication and care and preferences for the tool. RESULTS:Facilitators identified by adolescents and providers included: direct patient/provider communication; adolescent-driven decision-making regarding care and contraceptive choice; supplementing clinic visits with electronic resources; and holistic care addressing physical, mental, and social needs. Barriers identified by participants included: limited time for appointments; limited adolescent autonomy in appointments; and poor continuity of care when adolescents cannot see the same provider. Given the complexity of issues raised, adolescents and providers were interested in developing an appointment planning tool to guide communication during appointments, and contributed input on its design. The resulting Appointment Planning Tool app pilot is in progress. CONCLUSIONS:Qualitative interviews with adolescents and providers offer critical insights for the development and implementation of mobile health (mHealth) tools that can foster patient-centered care.

Maintaining influenza vaccination at high coverage has the potential to prevent a proportion of COVID-19 morbidity and mortality. We examined whether flu-vaccination is associated with severe corona virus disease 2019 (COVID-19) disease, as measured by intensive care unit (ICU)-admission, ventilator-use, and mortality. Other outcome measures included hospital length of stay and total ICU days. Our findings showed that flu-vaccination was associated with a significantly reduced likelihood of an ICU admission especially among aged <65 and non-obese patients. Public health promotion of flu-vaccination may help mitigate the overwhelming demand for critical COVID-19 care pending the large-scale availability of COVID-19 vaccines.

INTRODUCTION/BACKGROUND:The aim of this review article is to compare the outcomes of newer non-thermal endovenous ablation techniques to thermal ablation techniques for the treatment of symptomatic venous insufficiency. EVIDENCE ACQUISITION/METHODS:Three independent reviewers screened PubMed® and EMBASE® databases to identify relevant studies. A total of 1173 articles were identified from database search that met our inclusion criteria. Two articles were identified through reference search. Removal of duplicates from our original search yielded 695 articles. We then screened these articles and assessed 173 full-text articles for eligibility. Subsequent to exclusion, 11 full-text articles were selected for final inclusion. EVIDENCE SYNTHESIS/RESULTS:The non-thermal techniques are similar to thermal techniques in terms of a high technical success rate, closure rate at 12 months, change in Venous Clinical Severity Score and change in quality of life after procedure. However, the length of procedure is shorter for non-thermal modalities and patient comfort is improved with lower pain scores. Return to work may also be earlier after non-thermal ablation. The rates of bruising, phlebitis and paresthesia are higher after thermal ablation. CONCLUSIONS:The non-thermal modalities are safe and effective in treating venous reflux and have shown improved patient comfort and shorter length of procedure which may make them favorable for use compared to the thermal modalities.

INTRODUCTION/BACKGROUND:Cardiovascular disease is the leading cause of death worldwide, and cardiovascular disease burden is increasing in low-resource settings and for lower socioeconomic groups. Machine learning algorithms are being developed rapidly and incorporated into clinical practice for cardiovascular disease prediction and treatment decisions. Significant opportunities for reducing death and disability from cardiovascular disease worldwide lie with accounting for the social determinants of cardiovascular outcomes. This study reviews how social determinants of health are being included in machine learning algorithms to inform best practices for the development of algorithms that account for social determinants. METHODS:A systematic review using 5 databases was conducted in 2020. English language articles from any location published from inception to April 10, 2020, which reported on the use of machine learning for cardiovascular disease prediction that incorporated social determinants of health, were included. RESULTS:Most studies that compared machine learning algorithms and regression showed increased performance of machine learning, and most studies that compared performance with or without social determinants of health showed increased performance with them. The most frequently included social determinants of health variables were gender, race/ethnicity, marital status, occupation, and income. Studies were largely from North America, Europe, and China, limiting the diversity of the included populations and variance in social determinants of health. DISCUSSION/CONCLUSIONS:Given their flexibility, machine learning approaches may provide an opportunity to incorporate the complex nature of social determinants of health. The limited variety of sources and data in the reviewed studies emphasize that there is an opportunity to include more social determinants of health variables, especially environmental ones, that are known to impact cardiovascular disease risk and that recording such data in electronic databases will enable their use.

[Figure: see text].

BACKGROUND/UNASSIGNED:Emergency department (ED) patients commonly experience both substance use and homelessness, and social relationships impact each in varied ways not fully captured by existing quantitative research. This qualitative study examines how social relationships can precipitate or ameliorate homelessness and the connection (if any) between substance use and social relationships among ED patients experiencing homelessness. METHODS/UNASSIGNED:As part of a broader study to develop ED-based homelessness prevention interventions, we conducted in-depth interviews with 25 ED patients who used alcohol or drugs and had recently become homeless. We asked patients about the relationship between their substance use and homelessness. Interviews were recorded, transcribed, and coded line-by-line by investigators. Final codes formed the basis for thematic analysis through consensus discussions. RESULTS/UNASSIGNED:Social relationships emerged as focal points for understanding the four major themes related to the intersection of homelessness and substance use: (1) Substance use can create strain in relationships; (2) Help is there until it's not; (3) Social relationships can create challenges contributing to substance use; and (4) Reciprocal relationship of substance use and isolation. Sub-themes were also identified and described. CONCLUSIONS/UNASSIGNED:The association between substance use and homelessness is multifaceted and social relationships are a complex factor linking the two. Social relationships are often critical for homelessness prevention, but they are impacted by and reciprocally affect substance use. ED-based substance use interventions should consider the high prevalence of homelessness and the impact of social relationships on the interaction between homelessness and substance use.

BACKGROUND/UNASSIGNED:The SUSIT significantly increased delivery of BI for drug use by PCPs during routine primary care encounters.

BACKGROUND:Harnessing health-related data posted on social media in real time can offer insights into how the pandemic impacts the mental health and general well-being of individuals and populations over time. OBJECTIVE:This study aimed to obtain information on symptoms and medical conditions self-reported by non-Twitter social media users during the COVID-19 pandemic, to determine how discussion of these symptoms and medical conditions changed over time, and to identify correlations between frequency of the top 5 commonly mentioned symptoms post and daily COVID-19 statistics (new cases, new deaths, new active cases, and new recovered cases) in the United States. METHODS:We used natural language processing (NLP) algorithms to identify symptom- and medical condition-related topics being discussed on social media between June 14 and December 13, 2020. The sample posts were geotagged by NetBase, a third-party data provider. We calculated the positive predictive value and sensitivity to validate the classification of posts. We also assessed the frequency of health-related discussions on social media over time during the study period, and used Pearson correlation coefficients to identify statistically significant correlations between the frequency of the 5 most commonly mentioned symptoms and fluctuation of daily US COVID-19 statistics. RESULTS:Within a total of 9,807,813 posts (nearly 70% were sourced from the United States), we identified a discussion of 120 symptom-related topics and 1542 medical condition-related topics. Our classification of the health-related posts had a positive predictive value of over 80% and an average classification rate of 92% sensitivity. The 5 most commonly mentioned symptoms on social media during the study period were anxiety (in 201,303 posts or 12.2% of the total posts mentioning symptoms), generalized pain (189,673, 11.5%), weight loss (95,793, 5.8%), fatigue (91,252, 5.5%), and coughing (86,235, 5.2%). The 5 most discussed medical conditions were COVID-19 (in 5,420,276 posts or 66.4% of the total posts mentioning medical conditions), unspecified infectious disease (469,356, 5.8%), influenza (270,166, 3.3%), unspecified disorders of the central nervous system (253,407, 3.1%), and depression (151,752, 1.9%). Changes in posts in the frequency of anxiety, generalized pain, and weight loss were significant but negatively correlated with daily new COVID-19 cases in the United States (r=-0.49, r=-0.46, and r=-0.39, respectively; P<.05). Posts on the frequency of anxiety, generalized pain, weight loss, fatigue, and the changes in fatigue positively and significantly correlated with daily changes in both new deaths and new active cases in the United States (r ranged=0.39-0.48; P<.05). CONCLUSIONS:COVID-19 and symptoms of anxiety were the 2 most commonly discussed health-related topics on social media from June 14 to December 13, 2020. Real-time monitoring of social media posts on symptoms and medical conditions may help assess the population's mental health status and enhance public health surveillance for infectious disease.