Correlates of COVID-19 Vaccine Uptake in Black Adults Residing in Allegheny County, PA.

Introduction: Efforts to address vaccine uptake and access among black adults will be relevant for continued coronavirus disease 2019 (COVID-19) eradication efforts and can be transferable to other prevention efforts in future pandemics. This study investigated factors related to COVID-19 vaccine uptake and access among black residents in Allegheny County, PA. Methods: Surveys were administered electronically from October 2021 to January 2022 to black Allegheny County residents aged 18 and older. Questions included thoughts on COVID mitigation strategies (e.g., masking, social distancing), vaccination status, intention to vaccinate children, trust of COVID-19 information sources and vaccines, family needs, access to support services, and social media use to access information. Descriptive statistics and significant correlates of being vaccinated using adjusted logistic regression models are reported. Results: Of the overall sample (N=397), the majority were fully vaccinated (n=306, 77%). Fully vaccinated participants were more likely to be female (62.5%, p=0.010), age 60 years or older (34.3%, p=0.0002), have some college education (23.2%, p<0.0001), and be employed full time (50.0%, p=0.0001) compared with nonvaccinated individuals. Among the unvaccinated participants (n=91), the primary reason was fear of illness (8.9%), long-term effects (6.5%), mistrust in the vaccine (6.3%), and needing more information (4.5%). Vaccine-hesitant participants were more likely to be unvaccinated (adjusted odds ratio=2.3, 95% confidence interval 1.25-4.14) after adjusting for age, education, employment, insurance, health status, and income. Conclusion: Vaccine hesitancy may be improved by directly addressing fear of illness resulting from vaccines and improving clarity in the vaccine development and approval process to improve uptake among black adults.

Understanding Pregnancy and Postpartum Health Using Ecological Momentary Assessment and Mobile Technology: Protocol for the Postpartum Mothers Mobile Study.

BACKGROUND: There are significant racial disparities in pregnancy and postpartum health outcomes, including postpartum weight retention and cardiometabolic risk. These racial disparities are a result of a complex interplay between contextual, environmental, behavioral, and psychosocial factors. OBJECTIVE: This protocol provides a description of the development and infrastructure for the Postpartum Mothers Mobile Study (PMOMS), designed to better capture women's daily experiences and exposures from late pregnancy through 1 year postpartum. The primary aims of PMOMS are to understand the contextual, psychosocial, and behavioral factors contributing to racial disparities in postpartum weight and cardiometabolic health, with a focus on the daily experiences of stress and racism, as well as contextual forms of stress (eg, neighborhood stress and structural racism). METHODS: PMOMS is a longitudinal observation study that is ancillary to an existing randomized control trial, GDM2 (Comparison of Two Screening Strategies for Gestational Diabetes). PMOMS uses an efficient and cost-effective approach for recruitment by leveraging the infrastructure of GDM2, facilitating enrollment of participants while consolidating staff support from both studies. The primary data collection method is ecological momentary assessment (EMA) and through smart technology (ie, smartphones and scales). The development of the study includes: (1) the pilot phase and development of the smartphone app; (2) feedback and further development of the app including selection of key measures; and (3) implementation, recruitment, and retention. RESULTS: PMOMS aims to recruit 350 participants during pregnancy, to be followed through the first year after delivery. Recruitment and data collection started in December 2017 and are expected to continue through September 2020. Initial results are expected in December 2020. As of early May 2019, PMOMS recruited a total of 305 participants. Key strengths and features of PMOMS have included data collection via smartphone technology to reduce the burden of multiple on-site visits, low attrition rate because of participation in an ongoing trial in which women are already motivated and enrolled, high EMA survey completion and the use of EMA as a unique data collection method to understand daily experiences, and shorter than expected timeframe for enrollment because of the infrastructure of the GDM2 trial. CONCLUSIONS: This protocol outlines the development of the PMOMS, one of the first published studies to use an ongoing EMA and mobile technology protocol during pregnancy and throughout 1 year postpartum to understand the health of childbearing populations and enduring racial disparities in postpartum weight and cardiometabolic health. Our findings will contribute to the improvement of data collection methods, particularly the role of EMA in capturing multiple exposures and knowledge in real time. Furthermore, the results of the study will inform future studies investigating weight and cardiometabolic health during pregnancy and the postpartum period, including how social determinants produce population disparities in these outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/13569.

Traumatic Brain Injury and Infectious Encephalopathy in Children From Four Resource-Limited Settings in Africa.

OBJECTIVES: To assess the frequency, interventions, and outcomes of children presenting with traumatic brain injury or infectious encephalopathy in low-resource settings. DESIGN: Prospective study. SETTING: Four hospitals in Sub-Saharan Africa. PATIENTS: Children age 1 day to 17 years old evaluated at the hospital with traumatic brain injury or infectious encephalopathy. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We evaluated the frequency and outcomes of children presenting consecutively over 4 weeks to any hospital department with traumatic brain injury or infectious encephalopathy. Pediatric Cerebral Performance Category score was assessed pre morbidity and at hospital discharge. Overall, 130 children were studied (58 [45%] had traumatic brain injury) from hospitals in Ethiopia (n = 51), Kenya (n = 50), Rwanda (n = 20), and Ghana (n = 7). Forty-six percent had no prehospital care, and 64% required interhospital transport over 18 km (1-521 km). On comparing traumatic brain injury with infectious encephalopathy, there was no difference in presentation with altered mental state (80% vs 82%), but a greater proportion of traumatic brain injury cases had loss of consciousness (80% vs 53%; p = 0.004). Traumatic brain injury patients were older (median [range], 120 mo [6-204 mo] vs 13 mo [0.3-204 mo]), p value of less than 0.001, and more likely male (73% vs 51%), p value of less than 0.01. In 78% of infectious encephalopathy cases, cause was unknown. More infectious encephalopathy cases had a seizure (69% vs 12%; p < 0.001). In regard to outcome, infectious encephalopathy versus traumatic brain injury: hospital lengths of stay were longer for infectious encephalopathy (8 d [2-30 d] vs 4 d [1-36 d]; p = 0.003), discharge rate to home, or for inpatient rehabilitation, or death differed between infectious encephalopathy (85%, 1%, and 13%) and traumatic brain injury (79%, 12%, and 1%), respectively, p value equals to 0.044. There was no difference in the proportion of children surviving with normal or mild disability (73% traumatic brain injury vs 79% infectious encephalopathy; p = 0.526). CONCLUSIONS: The epidemiology and outcomes of pediatric traumatic brain injury and infectious encephalopathy varied by center and disease. To improve outcomes of these conditions in low-resource setting, focus should be on neurocritical care protocols for pre-hospital, hospital, and rehabilitative care.

24 vs. 72 hours of hypothermia for pediatric cardiac arrest: A pilot, randomized controlled trial.

AIM: Children surviving cardiac arrest (CA) lack proven neuroprotective therapies. The role of biomarkers in assessing response to interventions is unknown. We hypothesized that 72 versus 24 h of hypothermia (HT) would produce more favorable biomarker profiles after pediatric CA. METHODS: This single center pilot randomized trial tested HT (33 ±â€¯1 °C) for 24 vs. 72 h in 34 children with CA. Children comatose after return of circulation aged 1 week to 17 years and treated with HT by their physician were eligible. Serum was collected twice daily on days 1-4 and once on day 7. Mortality was assessed at 6 months. RESULTS: Patient characteristics, baseline biomarker concentrations, and adverse events were similar between groups. Eight (47%) and 4 (24%) children died in the 24 h and 72 h groups, p = .3. Serum neuron specific enolase (NSE) concentration was increased in the 24 vs. 72 h group at 84 h-96 h (median [interquartile range] 47.7 [3.9, 79.9] vs. 1.4 [0.0, 11.1] ng/ml, p = .02) and on day 7 (18.2 [3.2, 74.0] vs. 2.6 [0.0, 12.8] ng/ml, p = .047). Serum S100b was increased in the 24 h vs. 72 h group at 12 h-24 h, 36 h-84 h, and on day 7, all p < 0.05. HT duration was associated with S100b (but not NSE or MBP) concentration on day 7 in multivariate analyses. CONCLUSION: Serum biomarkers show promise as theragnostic tools in pediatric CA. Our biomarker and safety data also suggest that 72 h duration after pediatric CA warrants additional exploration.

Group-Based Trajectory Modeling of Suppression Ratio After Cardiac Arrest.

BACKGROUND: Existing studies of quantitative electroencephalography (qEEG) as a prognostic tool after cardiac arrest (CA) use methods that ignore the longitudinal pattern of qEEG data, resulting in significant information loss and precluding analysis of clinically important temporal trends. We tested the utility of group-based trajectory modeling (GBTM) for qEEG classification, focusing on the specific example of suppression ratio (SR). METHODS: We included comatose CA patients hospitalized from April 2010 to October 2014, excluding CA from trauma or neurological catastrophe. We used Persyst®v12 to generate SR trends and used semi-quantitative methods to choose appropriate sampling and averaging strategies. We used GBTM to partition SR data into different trajectories and regression associate trajectories with outcome. We derived a multivariate logistic model using clinical variables without qEEG to predict survival, then added trajectories and/or non-longitudinal SR estimates, and assessed changes in model performance. RESULTS: Overall, 289 CA patients had ≥36 h of EEG yielding 10,404 h of data (mean age 57 years, 81 % arrested out-of-hospital, 33 % shockable rhythms, 31 % overall survival, 17 % discharged to home or acute rehabilitation). We identified 4 distinct SR trajectories associated with survival (62, 26, 12, and 0 %, P < 0.0001 across groups) and CPC (35, 10, 4, and 0 %, P < 0.0001 across groups). Adding trajectories significantly improved model performance compared to adding non-longitudinal data. CONCLUSIONS: Longitudinal analysis of continuous qEEG data using GBTM provides more predictive information than analysis of qEEG at single time-points after CA.