Faculty Bibliography

Introduction/UNASSIGNED:Few treatments have demonstrated mortality benefits among hospitalized hypoxic COVID-19 patients. We evaluated the use of hyperbaric oxygen (HBO2) therapy as a therapeutic intervention among hospitalized patients with a high oxygen requirement prior to vaccine approval. Methods/UNASSIGNED:We extracted data on patients with COVID-19 hypoxia who required oxygen supplementation ranging from a 6L nasal cannula up to a high-flow nasal cannula at 100% FiO2 at 60L/minute with a 100% non-rebreather mask at 15 L/minute and were eligible for off-label HBO2 therapy from October 2020 to February 2021. We followed the Monitored Emergency use of Unregistered and Investigational Interventions or (MEURI) in conjunction with the consistent re-evaluation of the protocol using the Plan-Do-Study-Act (PDSA) tool [1]. We compared patient characteristics and used Fisher's exact test and a survival analysis to assess the primary endpoint of inpatient death. Results/UNASSIGNED:HBO2 therapy was offered to 36 patients, of which 24 received treatment and 12 did not receive treatment. Patients who did not receive treatment were significantly older (p ≺ 0.01) and had worse baseline hypoxia (p = 0.06). Three of the 24 (13%) patients who received treatment died compared to six of 12 (50%) patients who did not receive treatment (RR ratio: 0.25, p = 0.04, 95% CI: 0.08 to 0.83). In the survival analysis, there was a statistically significant reduction in inpatient mortality in the treatment group (HR: 0.19, p = 0.02, 95% CI: 0.05-0.74). However, after adjusting for age and baseline hypoxia, there was no difference in inpatient mortality (hazard ratio: 0.48, p = 0.42, 95% CI: 0.08-2.86). Conclusion/UNASSIGNED:The survival benefit of HBO2 therapy observed in our unadjusted analysis suggests that there may be therapeutic benefits of HBO2 in treating COVID-19 hypoxia as an adjunct to standard care.

Background: Mobile health cardiac rehabilitation (mHealth-CR) may improve outcomes among older adults with ischemic heart disease, but variable engagement is not yet understood. We therefore analyzed preliminary data from the RESILIENT trial, an ongoing randomized trial of mHealth-CR vs. usual care in patients age >= 65.
Method(s): Data from the first 50 intervention participants were analyzed. Weekly engagement was scored from 0-11 based on exercise completion, therapist communication, video viewing, and BP self-measurement. Participants were classified as high or low engagement based on median engagement scores. Groups were compared by age, sex, social support (living alone), depression (PHQ-8), and Charlson Comorbidity Index (CCI).
Result(s): There was widely varying engagement with mHealth-CR that ranged from nearly zero to nearly perfect engagement (Figure). There were no significant differences between high and low engagement groups based on mean age (72.6 vs. 72.8, P=0.8), sex (76% male vs. 80%, P=0.7), living alone (28% vs. 44%, P=0.2), depression (mean 3.9 vs. 4.6, P=0.5), or comorbidity burden (mean 4.4 vs. 4.8, P=0.3), although the sample size was small.
Conclusion(s): Our early findings show wide variation in mHealth-CR engagement among older adults

OBJECTIVE/UNASSIGNED:Little is known about psychiatric symptoms among patients with migraine and newly diagnosed focal epilepsy. The investigators compared symptoms of depression, anxiety, and suicidality among people with newly diagnosed focal epilepsy with migraine versus without migraine. METHODS/UNASSIGNED:The Human Epilepsy Project is a prospective multicenter study of patients with newly diagnosed focal epilepsy. Depression (measured with the Center for Epidemiologic Studies Depression Scale), anxiety (measured with the 7-item Generalized Anxiety Disorder scale), and suicidality scores (measured with the Columbia-Suicide Severity Rating Scale [C-SSRS]) were compared between participants with versus without migraine. Data analysis was performed with the Kolmogorov-Smirnov test for normality assessment, the Mann-Whitney U test, chi-square test, and linear regression. RESULTS/UNASSIGNED:Of 349 patients with new-onset focal epilepsy, 74 (21.2%) had migraine. There were no differences between the patients without migraine versus those with migraine in terms of age, race, and level of education. There were more women in the group with migraine than in the group without migraine (75.7% vs. 55.6%, p=0.0018). The patients with epilepsy and comorbid migraine had more depressive symptoms than the patients with epilepsy without migraine (35.2% vs. 22.7%, p=0.031). Patients with epilepsy with comorbid migraine had more anxiety symptoms than patients with epilepsy without migraine, but this relation was mediated by age in logistic regression, with younger age being associated with anxiety. Comorbid migraine was not associated with C-SSRS ideation or behavior. CONCLUSIONS/UNASSIGNED:Among a sample of patients with newly diagnosed focal epilepsy, 21.2% had migraine. Migraine comorbidity was associated with higher incidence of depressive symptoms. Future studies should be performed to better assess these relationships and possible treatment implications.

Digital Diabetes Prevention Programs (dDPP) are novel mHealth applications that leverage digital features such as tracking and messaging to support behavior change for diabetes prevention. Despite their clinical effectiveness, long-term engagement to these programs remains a challenge, creating barriers to adherence and meaningful health outcomes. We partnered with a dDPP vendor to develop a personalized automatic message system (PAMS) to promote user engagement to the dDPP platform by sending messages on behalf of their primary care provider. PAMS innovates by integrating into clinical workflows. User-centered design (UCD) methodologies in the form of iterative cycles of focus groups, user interviews, design workshops, and other core UCD activities were utilized to defined PAMS requirements. PAMS uses computational tools to deliver theory-based, automated, tailored messages, and content to support patient use of dDPP. In this article, we discuss the design and development of our system, including key requirements and features, the technical architecture and build, and preliminary user testing.

INTRODUCTION:Globally, approximately half of the estimated 6.3 million under-5 deaths occur in the neonatal period (within the first 28 days of life). Kenya ranks among countries with the highest number of neonatal deaths, at 20 per 1000 live births. Improved identification and management of neonates with potentially life-threatening illness is critical to meet the WHO's target of ≤12 neonatal deaths per 1000 live births by 2035. We developed an interactive (two-way) short messaging service (SMS) communication intervention, Mobile Solutions for Neonatal Health (Mobile women's and children's health (WACh) NEO), focused on the perinatal period. Mobile WACh NEO sends automated tailored SMS messages to mothers during pregnancy and up to 6 weeks post partum. Messages employ the Information-Motivation-Behaviour Skills framework to promote (1) maternal implementation of essential newborn care (ENC, including early, exclusive breast feeding, cord care and thermal care), (2) maternal identification of neonatal danger signs and care-seeking, and (3) maternal social support and self-efficacy. Participants can also send SMS to the study nurse, enabling on-demand remote support. METHODS AND ANALYSIS:We describe a two-arm unblinded randomised controlled trial of the Mobile WACh NEO intervention. We will enrol 5000 pregnant women in the third trimester of pregnancy at 4 facilities in Kenya and randomise them 1:1 to receive interactive SMS or no SMS (control), and conduct follow-up visits at 2 and 6 weeks post partum. Neonatal mortality will be compared between arms as the primary outcome. Secondary outcomes include care-seeking, practice of ENC and psychosocial health. Exploratory analysis will investigate associations between maternal mental health, practice of ENC, care-seeking and SMS engagement. ETHICS AND DISSEMINATION:This study received ethical approval from the University of Washington (STUDY00006395), Women and Infants Hospital (1755292-1) and Kenyatta National Hospital/University of Nairobi (P310/04/2019). All participants will provide written informed consent. Findings will be published in peer-reviewed journals and international conferences. TRIAL REGISTRATION NUMBER:NCT04598165.

BACKGROUND/UNASSIGNED:Androgens facilitate entrance of the severe acute respiratory syndrome coronavirus 2 into respiratory epithelial cells, and male sex is associated with a higher risk of death from corona virus disease (COVID-19). Androgen deprivation therapy (ADT) could possibly improve COVID-19 outcomes. METHODS/UNASSIGNED:In a case-control study nested in the Prostate Cancer data Base Sweden (PCBaSe) RAPID 2019, we evaluated the association between ADT and COVID-19 as registered cause of death in men with prostate cancer. Each case was matched to 50 controls by region. We used conditional logistic regression to adjust for confounders and also evaluated potential impact of residual confounding. RESULTS/UNASSIGNED:We identified 474 men who died from COVID-19 in March-December 2020. In crude analyses, ADT exposure was associated with an increased risk of COVID-19 death (odds ratio [OR] 5.05, 95% CI: 4.18-6.10); however, the OR was substantially attenuated after adjustment for age, comorbidity, prostate cancer characteristics at diagnosis, recent healthcare use, and indicators of advanced cancer (adjusted OR 1.25, 95% CI: 0.95-1.65). If adjustment has accounted for at least 85% of confounding, then the true effect could be no more than a 5% reduction of the odds for COVID-19 death. CONCLUSIONS/UNASSIGNED:The increased mortality from COVID-19 in men with prostate cancer treated with ADT was mainly related to high age, comorbidity, and more advanced prostate cancer. There was no evidence to support the hypothesis that ADT is associated with improved COVID-19 outcomes.

INTRODUCTION:Communities are seeking to learn if and how they can improve the well-being of their residents. We therefore examined the impact of a community-led, collective-impact initiative, deployed through Blue Zones Project by Sharecare, aimed at improving health and well-being in one set of US communities. METHODS:We used data from cross-sectional surveys of the Well-Being Index (2010-2017) to assess how the Life Evaluation Index (LEI) in Hermosa Beach, Manhattan Beach and Redondo Beach in California (Beach Cities) changed over time and how this change compares with change for similar cities (Beach Cities-like) and for the USA as a whole. We examined types of interventions, perceived impacts, and relationships between intervention type and change in LEI. RESULTS:The Beach Cities experienced greater increases in LEI than Beach Cities-like communities and the nation. The entire portfolio of interventions was positively associated with change in LEI in the Beach Cities (+1.12, p=0.012), with process-oriented interventions most closely associated with improvement. CONCLUSIONS:Community-led collective action that leverages community engagement and activation, strategic use of programming and large-scale built-environment and policy change can improve health and well-being at scale.

Prenatal exposure to nonpersistent chemicals such as phthalates, bisphenols, and organophosphate (OP) pesticides is ubiquitous and occurs in mixtures. So far, epidemiological studies investigating neurodevelopmental consequences of these exposures have mainly been restricted to single-pollutant models. Thus, we studied the association between prenatal exposure to nonpersistent chemical mixtures and child IQ and emotional and behavioral problems. Data came from 782 mother-child pairs. Eleven phthalate, one bisphenol, and five OP pesticide urinary exposure biomarkers were measured three times during pregnancy and averaged. Nonverbal IQ, internalizing and attention problems, aggressive behavior, and autistic traits were assessed at child age 6 years. We used quantile g-computation to estimate the change in each outcome per quartile increase in all chemicals within the mixture. Higher exposure to the mixture was associated with lower nonverbal IQ (-4.0 points (95%CI = -7.0, -1.0), -5.5 points (95%CI = -10.2, -0.9), and -4.6 points (95%CI = -10.8, 1.5) for the second, third, and fourth quartile, respectively, compared to the first quartile). These results were mainly driven by the phthalate mixture. No association was observed with emotional and behavioral problems. Prenatal exposure to nonpersistent chemical mixtures was associated with lower nonverbal IQ in children. Exposure to chemical mixtures during gestation is universal and may impact neurodevelopment.

Mean residual life (MRL) function defines the remaining life expectancy of a subject who has survived to a time point and is an important alternative to the hazard function for characterizing the distribution of a time-to-event variable. Existing MRL models primarily focus on studying the association between risk factors and disease risks using linear model specifications in multiplicative or additive scale. When risk factors have complex correlation structures, nonlinear effects, or interactions, the prefixed linearity assumption may be insufficient to capture the relationship. Single-index modeling framework offers flexibility in reducing dimensionality and modeling nonlinear effects. In this article, we propose a class of partially linear single-index generalized MRL models, the regression component of which consists of both a semiparametric single-index part and a linear regression part. Regression spline technique is employed to approximate the nonparametric single-index function, and parameters are estimated using an iterative algorithm. Double-robust estimators are also proposed to protect against the misspecification of censoring distribution or MRL models. A further contribution of this article is a nonparametric test proposed to formally evaluate the linearity of the single-index function. Asymptotic properties of the estimators are established, and the finite-sample performance is evaluated through extensive numerical simulations. The proposed models and inference approaches are demonstrated by a New York University Langone Health (NYULH) COVID-19 dataset.