Faculty Bibliography

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: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.

Evidence-based and culturally relevant parenting programs strengthen adults' capacity to support children's health and development. Optimizing parent participation in programs implemented at scale is a prevailing challenge. Our collaborative team of program developers, implementers, and researchers applied insights from the field of behavioral economics (BE) to support parent participation in ParentCorps-a family-centered program delivered as an enhancement to pre-kindergarten-as it scaled in a large urban school district. We designed a bundle of BE-infused parent outreach materials and successfully showed their feasibility in site-level randomized pilot implementation. The site-level study did not show a statistically significant impact on family attendance. A sub-study with a family-level randomization design showed that varying the delivery time of BE-infused digital outreach significantly increased the likelihood of families attending the parenting program. Lessons on the potential value of a BE-infused approach to support outreach and engagement in parenting programs are discussed in the context of scaling up efforts.

BACKGROUND:While specific interventions have previously demonstrated benefit in patients with the Acute Respiratory Distress Syndrome (ARDS), use of these interventions is inconsistent, and patient mortality remains high. The impact of variability in center management practices on ARDS mortality rates remains unknown. RESEARCH QUESTION/OBJECTIVE:What is the impact of treatment variability on mortality in patients with moderate-to-severe ARDS in the United States (US)? STUDY DESIGN AND METHODS/METHODS:O, who were admitted to 29 US centers between October 1, 2016 and April 30, 2017. The primary outcome was 28-day in-hospital mortality. Center variation in ventilator management, adjunctive therapy use, and mortality were also assessed. RESULTS:O) was 31.4% and varied between centers (0%-65%), as did rates of adjunctive therapy use (27.1%-96.4%), types of modalities used (neuromuscular blockade, prone positioning, systemic steroids, pulmonary vasodilators, and extracorporeal support), and mortality (16.7-73.3%). Center standardized mortality ratios (SMRs), calculated using baseline patient-level characteristics to derive expected mortality rate, ranged from 0.33 to 1.98. Of the treatment-level factors explored, only center adherence to early lung protective ventilation (LPV) was correlated with SMR. INTERPRETATION/CONCLUSIONS:Substantial center-to-center variability exists in ARDS management, suggesting that further opportunities for improving ARDS outcomes exist. Early adherence to LPV was associated with lower center mortality and may be a surrogate for overall quality of care processes. Future collaboration is needed to identify additional treatment-level factors influencing center-level outcomes. CLINICAL TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov Identifier: NCT03021824.

BACKGROUND:Infusion therapy refers to the intravenous administration of medicines and fluids for the treatment of status migrainosus, severe persistent headaches, or chronic headache. Headache practices and centers offer this treatment for patients as an alternative to the emergency department (ED) setting. However, little information is available in the literature on understanding the operations of an infusion center. OBJECTIVE:We sought to survey the Inpatient Headache & Emergency Medicine specialty section and the Academic Program Directors listserv of the American Headache Society (AHS) to better understand current practices. METHODS:A survey was advertised and distributed to the listservs of both the Inpatient Headache & Emergency Medicine specialty section and the Academic Program Directors, which combined included both academic and private practices. In addition, the survey was available on laptops at related events at an annual AHS meeting in Scottsdale. RESULTS:Of the 127 members of the combined group of both listservs, 50 responded with an overall survey response rate of 39%. Ten out of fifty were from programs with more than one responder completing the survey, leaving 40 unique headache programs. Academic programs made up the majority of programs (85%, 34/40). The total of 40 participating programs is comparable with the 47 academic headache programs listed on the American Migraine Foundation website at the time of the survey. Of the academic programs surveyed, most were hospital based (n = 23) compared with a satellite location (n = 11). Of all programs surveyed, 68% (27/40) offered infusion therapy. Of those that did not have an infusion practice (n = 13), the most common reason cited was insufficient staffing (n = 8). Key highlights of the survey included the following: The majority of programs offering infusions obtain prior authorization before scheduling (70%, 19/27) and offer patient availability 5 days/week (78%, 21/27) typically only during business hours (81%, 22/27). Programs reported that they typically give three to four medications during each infusion session (72%, 18/25). Treatment paradigms varied between programs. Programs surveyed were concentrated in the Northeast and Midwest regions of the United States. CONCLUSION/CONCLUSIONS:The limited number of headache infusion centers overall may contribute to the limited ability of headache infusion centers to prevent ED migraine visits. Headache patients can have unpredictable headache onset, and most of the infusion practices surveyed appeared to adapt to this by offering infusions most days during a work week. However, this need for multiple days per week may also explain the most common reason for not having an infusion practice, which is insufficient staffing. Various treatment paradigms are implemented by different practitioners, and future studies will have to focus on investigation of best practice.

Federally Qualified Health Centers (FQHCs) provide primary care services in underserved areas and are governed by patient-majority boards. A phenomenological approach was used to explore the lived experiences of board members as they addressed the need for fundamental change to meet the demands of a rapidly changing, highly competitive health care market (2010-2020). Findings were that board members rely upon personal experience and monthly board meetings to be alerted to change that affects health care delivery. They may need additional training to adjust governance and organizational performance to address the new patient consumerism, market conditions, and competition from other providers.

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.

We identified family risk profiles at 6 months using socioeconomic status (SES) and maternal mental health indicators with data from the Family Life Project (N = 1,292). We related profiles to executive function (EF) at 36 months (intercept) and growth in EF between 36 and 60 months. Latent profile analysis revealed five distinct profiles, characterized by different combinations of SES and maternal mental health symptoms. Maternal sensitivity predicted faster growth in EF among children in the profile characterized by deep poverty and the absence of maternal mental health symptoms. Maternal sensitivity also predicted higher EF intercept but slower EF growth among children in the profile characterized by deep poverty and maternal mental health symptoms, and children in the near poor (low SES), mentally healthy profile. Maternal sensitivity also predicted higher EF intercept but had no effect on growth in EF in the near poor, mentally distressed profile. In contrast, maternal sensitivity did not predict the intercept or growth of EF in the privileged SES/mentally healthy profile. Our findings using a person-centered approach provide a more nuanced understanding of the role of maternal sensitivity in the growth of EF, such that maternal sensitivity may differentially affect the growth of EF in various contexts.

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.