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1.
Clin Infect Dis ; 2024 Jun 26.
Artigo em Inglês | MEDLINE | ID: mdl-38920297

RESUMO

BACKGROUND: Remdesivir, an RNA-polymerase prodrug inhibitor approved for treatment of COVID-19, shortens recovery time and improves clinical outcomes. This prespecified analysis compared remdesivir plus standard-of-care (SOC) with SOC alone in adults hospitalized with COVID-19 requiring oxygen support in the early stage of the pandemic. METHODS: Data for 10-day remdesivir treatment plus SOC from the extension phase of an open-label study (NCT04292899) were compared with real-world, retrospective data on SOC alone (EUPAS34303). Both studies included patients aged ≥18 years hospitalized with SARS-CoV-2 up to 30 May 2020, with oxygen saturation ≤94%, on room air or supplemental oxygen (all forms), and with pulmonary infiltrates. Propensity score weighting was used to balance patient demographics and clinical characteristics across treatment groups. The primary endpoint was time to all-cause mortality or end of study (day 28). Time-to-discharge, with a 10-day landmark to account for duration of remdesivir treatment, was a secondary endpoint. RESULTS: 1974 patients treated with remdesivir plus SOC, and 1426 with SOC alone, were included after weighting. Remdesivir significantly reduced mortality versus SOC (hazard ratio [HR]: 0.46, 95% confidence interval: 0.39-0.54). This association was observed at each oxygen support level, with the lowest HR for patients on low-flow oxygen. Remdesivir significantly increased the likelihood of discharge at day 28 versus SOC in the 10-day landmark analysis (HR: 1.64; 95% confidence interval: 1.43-1.87). CONCLUSIONS: Remdesivir plus early-2020 SOC was associated with a 54% lower mortality risk and shorter hospital stays compared with SOC alone in patients hospitalized with COVID-19 requiring oxygen support. CLINICAL TRIALS REGISTRATION: ClinicalTrials.gov NCT04292899 and EUPAS34303.

2.
Biometrics ; 80(1)2024 Jan 29.
Artigo em Inglês | MEDLINE | ID: mdl-38281772

RESUMO

Strategic test allocation is important for control of both emerging and existing pandemics (eg, COVID-19, HIV). It supports effective epidemic control by (1) reducing transmission via identifying cases and (2) tracking outbreak dynamics to inform targeted interventions. However, infectious disease surveillance presents unique statistical challenges. For instance, the true outcome of interest (positive infection status) is often a latent variable. In addition, presence of both network and temporal dependence reduces data to a single observation. In this work, we study an adaptive sequential design, which allows for unspecified dependence among individuals and across time. Our causal parameter is the mean latent outcome we would have obtained, if, starting at time t given the observed past, we had carried out a stochastic intervention that maximizes the outcome under a resource constraint. The key strength of the method is that we do not have to model network and time dependence: a short-term performance Online Super Learner is used to select among dependence models and randomization schemes. The proposed strategy learns the optimal choice of testing over time while adapting to the current state of the outbreak and learning across samples, through time, or both. We demonstrate the superior performance of the proposed strategy in an agent-based simulation modeling a residential university environment during the COVID-19 pandemic.


Assuntos
COVID-19 , Doenças Transmissíveis , Humanos , Pandemias/prevenção & controle , COVID-19/epidemiologia , Simulação por Computador , Surtos de Doenças
3.
Behav Res Ther ; 178: 104554, 2024 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-38714104

RESUMO

Digital interventions can enhance access to healthcare in under-resourced settings. However, guided digital interventions may be costly for low- and middle-income countries, despite their effectiveness. In this randomised control trial, we evaluated the effectiveness of two digital interventions designed to address this issue: (1) a Cognitive Behavioral Therapy Skills Training (CST) intervention that increased scalability by using remote online group administration; and (2) the SuperBetter gamified self-guided CBT skills training app, which uses other participants rather than paid staff as guides. The study was implemented among anxious and/or depressed South African undergraduates (n = 371) randomised with equal allocation to Remote Group CST, SuperBetter, or a MoodFlow mood monitoring control. Symptoms were assessed with the Generalized Anxiety Disorder-7 (GAD-7) and the Patient Health Questionnaire-9 (PHQ-9). Intention-to-treat analysis found effect sizes at the high end of prior digital intervention trials, including significantly higher adjusted risk differences (ARD; primary outcome) in joint anxiety/depression remission at 3-months and 6-months for Remote Group CST (ARD = 23.3-18.9%, p = 0.001-0.035) and SuperBetter (ARD = 12.7-22.2%, p = 0.047-0.006) than MoodFlow and mean combined PHQ-9/GAD-7 scores (secondary outcome) significantly lower for Remote Group CST and SuperBetter than MoodFlow. These results illustrate how innovative delivery methods can increase the scalability of standard one-on-one guided digital interventions. PREREGISTRATION INTERNATIONAL STANDARD RANDOMISED CONTROLLED TRIAL NUMBER (ISRTCN) SUBMISSION #: 47,089,643.


Assuntos
Terapia Cognitivo-Comportamental , Estudantes , Humanos , Terapia Cognitivo-Comportamental/métodos , Feminino , Masculino , Adulto Jovem , Estudantes/psicologia , Depressão/terapia , Depressão/psicologia , Adulto , Adolescente , Resultado do Tratamento , Psicoterapia de Grupo/métodos , Transtornos de Ansiedade/terapia , Ansiedade/terapia , Ansiedade/psicologia , Universidades , África do Sul , Aplicativos Móveis , Transtorno Depressivo/terapia , Transtorno Depressivo/psicologia
4.
medRxiv ; 2024 Mar 26.
Artigo em Inglês | MEDLINE | ID: mdl-38585931

RESUMO

Background: Water, sanitation, hygiene (WSH), nutrition (N), and combined (N+WSH) interventions are often implemented by global health organizations, but WSH interventions may insufficiently reduce pathogen exposure, and nutrition interventions may be modified by environmental enteric dysfunction (EED), a condition of increased intestinal permeability and inflammation. This study investigated the heterogeneity of these treatments' effects based on individual pathogen and EED biomarker status with respect to child linear growth. Methods: We applied cross-validated targeted maximum likelihood estimation and super learner ensemble machine learning to assess the conditional treatment effects in subgroups defined by biomarker and pathogen status. We analyzed treatment (N+WSH, WSH, N, or control) randomly assigned in-utero, child pathogen and EED data at 14 months of age, and child LAZ at 28 months of age. We estimated the difference in mean child length for age Z-score (LAZ) under the treatment rule and the difference in stratified treatment effect (treatment effect difference) comparing children with high versus low pathogen/biomarker status while controlling for baseline covariates. Results: We analyzed data from 1,522 children, who had median LAZ of -1.56. We found that myeloperoxidase (N+WSH treatment effect difference 0.0007 LAZ, WSH treatment effect difference 0.1032 LAZ, N treatment effect difference 0.0037 LAZ) and Campylobacter infection (N+WSH treatment effect difference 0.0011 LAZ, WSH difference 0.0119 LAZ, N difference 0.0255 LAZ) were associated with greater effect of all interventions on growth. In other words, children with high myeloperoxidase or Campylobacter infection experienced a greater impact of the interventions on growth. We found that a treatment rule that assigned the N+WSH (LAZ difference 0.23, 95% CI (0.05, 0.41)) and WSH (LAZ difference 0.17, 95% CI (0.04, 0.30)) interventions based on EED biomarkers and pathogens increased predicted child growth compared to the randomly allocated intervention. Conclusions: These findings indicate that EED biomarker and pathogen status, particularly Campylobacter and myeloperoxidase (a measure of gut inflammation), may be related to impact of N+WSH, WSH, and N interventions on child linear growth.

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