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Ebola virus disease (EVD) is a potentially fatal infectious disease, easily transmitted through direct contact with infected body fluids. Children exhibit a range of nonspecific clinical signs that mirror common [...]

Background: Ebola Virus Disease (EVD) causes high case fatality rates (CFRs) in young children, yet there are limited data focusing on predicting mortality in pediatric patients. Here we present machine learning-derived prognostic models to predict clinical outcomes in children infected with Ebola virus. Methods: Using retrospective data from the Ebola Data Platform, we investigated children with EVD from the West African EVD outbreak in 2014–2016. Elastic net regularization was used to create a prognostic model for EVD mortality. In addition to external validation with data from the 2018–2020 EVD epidemic in the Democratic Republic of the Congo (DRC), we updated the model using selected serum biomarkers. Findings: Pediatric EVD mortality was significantly associated with younger age, lower PCR cycle threshold (Ct) values, unexplained bleeding, respiratory distress, bone/muscle pain, anorexia, dysphagia, and diarrhea. These variables were combined to develop the newly described EVD Prognosis in Children (EPiC) predictive model. The area under the receiver operating characteristic curve (AUC) for EPiC was 0.77 (95% CI: 0.74–0.81) in the West Africa derivation dataset and 0.76 (95% CI: 0.64–0.88) in the DRC validation dataset. Updating the model with peak aspartate aminotransferase (AST) or creatinine kinase (CK) measured within the first 48 hours after admission increased the AUC to 0.90 (0.77–1.00) and 0.87 (0.74–1.00), respectively. Conclusion: The novel EPiC prognostic model that incorporates clinical information and commonly used biochemical tests, such as AST and CK, can be used to predict mortality in children with EVD. Author summary: Although case fatality rates remain high, there are limited data on predicting mortality in children with Ebola Virus Disease (EVD). Furthermore, challenges in predicting EVD outcomes using clinical and laboratory data highlight the need for the development and validation of pediatric predictive models. The novel EVD Prognosis in Children (EPiC) model uses clinical and biochemical information, such as AST and CK, to predict mortality in infected children. While few prognostic models or scoring systems have been developed to predict clinical outcomes of EVD, the majority of them were limited in geographical and temporal scope having been derived using data from one location. As such, the EPiC model is the first externally validated model for the prognosis of pediatric EVD using diverse datasets from geographically and temporally separate outbreaks. This model can be easily applied by bedside clinicians to assess pediatric patients at risk for death and help to allocate resources accordingly. [ABSTRACT FROM AUTHOR]

In response to the coronavirus disease (COVID-19) pandemic, Project HOPE®, an international humanitarian organization, partnered with Brown University to develop and deploy a virtual training-of-trainers (TOT) program to provide practical knowledge to healthcare stakeholders. This study is designed to evaluate this TOT program. The goal of this study is to assess the effectiveness of this educational intervention in enhancing knowledge on COVID-19 concepts and to present relative change in score of each competency domains of the training. The training was created by interdisciplinary faculty from Brown University and delivered virtually. Training included eight COVID-19 specific modules on infection prevention and control, screening and triage, diagnosis and management, stabilization and resuscitation, surge capacity, surveillance, and risk communication and community education. The assessment of knowledge attainment in each of the course competency domain was conducted using 10 question pre-and post-test evaluations. Paired t-test were used to compare interval knowledge scores in the overall cohort and stratified by WHO regions. TOT dissemination data was collected from in-country partners by Project Hope. Over the period of 7 months, 4,291 personnel completed the TOT training in 55 countries, including all WHO regions. Pre-test and post-test were completed by 1,198 and 706 primary training participants, respectively. The mean scores on the pre-test and post-test were 68.45% and 81.4%, respectively. The mean change in score was 11.72%, with P value <0.0005. All WHO regions had a statistically significant improvement in their score in post-test. The training was disseminated to 97,809 health workers through local secondary training. Innovative educational tools resulted in improvement in knowledge related to the COVID-19 pandemic, significantly increasing the average score on knowledge assessment testing. Academic – humanitarian partnerships can serve to implement and disseminate effective education rapidly across the globe. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To compare treatment retention in a Medication for Opioid Use Disorder program between older and younger adults with opioid use disorder. METHODS: This retrospective cohort study was conducted from 2015 to 2018 at an urban academic hospital's opioid and drug treatment center. Participants were adults, 18 and older, diagnosed with Opioid Type Dependence. Older adults were defined as age 50 and older. Poisson and logistic regression analyses examined whether older age was associated with treatment retention. RESULTS: Overall, 288 individual charts were reviewed; 123 were aged 18-49, and 78 were aged 50 and older. Older adults were more likely to stay in treatment for six months or longer (OR=1.73, [1.02, 2.96], P-value = 0.04] and have a higher number of treatment visits overall (RR=1.06, [0.98, 1.16] (P-value=0.16). CONCLUSIONS: Older adults are more likely than younger adults to be retained in long-term treatment in Medication for Opioid Use Disorder program. [ABSTRACT FROM AUTHOR]

Objective: To compare treatment retention in a Medication for Opioid Use Disorder program between older and younger adults with opioid use disorder., Methods: This retrospective cohort study was conducted from 2015 to 2018 at an urban academic hospital's opioid and drug treatment center. Participants were adults, 18 and older, diagnosed with Opioid Type Dependence. Older adults were defined as age 50 and older. Poisson and logistic regression analyses examined whether older age was associated with treatment retention., Results: Overall, 288 individual charts were reviewed; 123 were aged 18-49, and 78 were aged 50 and older. Older adults were more likely to stay in treatment for six months or longer (OR=1.73, [1.02, 2.96], P-value = 0.04] and have a higher number of treatment visits overall (RR=1.06, [0.98, 1.16] (P-value=0.16)., Conclusions: Older adults are more likely than younger adults to be retained in long-term treatment in a Medication for Opioid Use Disorder program.