ABSTRACT
BACKGROUND: The COVID-19 pandemic underscored the criticality and complexity of decision making for novel treatment approval and further research. Our study aims to assess potential decision-making methodologies, an evaluation vital for refining future public health crisis responses. METHODS: We compared 4 decision-making approaches to drug approval and research: the Food and Drug Administration's policy decisions, cumulative meta-analysis, a prospective value-of-information (VOI) approach (using information available at the time of decision), and a reference standard (retrospective VOI analysis using information available in hindsight). Possible decisions were to reject, accept, provide emergency use authorization, or allow access to new therapies only in research settings. We used monoclonal antibodies provided to hospitalized COVID-19 patients as a case study, examining the evidence from September 2020 to December 2021 and focusing on each method's capacity to optimize health outcomes and resource allocation. RESULTS: Our findings indicate a notable discrepancy between policy decisions and the reference standard retrospective VOI approach with expected losses up to $269 billion USD, suggesting suboptimal resource use during the wait for emergency use authorization. Relying solely on cumulative meta-analysis for decision making results in the largest expected loss, while the policy approach showed a loss up to $16 billion and the prospective VOI approach presented the least loss (up to $2 billion). CONCLUSION: Our research suggests that incorporating VOI analysis may be particularly useful for research prioritization and treatment implementation decisions during pandemics. While the prospective VOI approach was favored in this case study, further studies should validate the ideal decision-making method across various contexts. This study's findings not only enhance our understanding of decision-making strategies during a health crisis but also provide a potential framework for future pandemic responses. HIGHLIGHTS: This study reviews discrepancies between a reference standard (retrospective VOI, using hindsight information) and 3 conceivable real-time approaches to research-treatment decisions during a pandemic, suggesting suboptimal use of resources.Of all prospective decision-making approaches considered, VOI closely mirrored the reference standard, yielding the least expected value loss across our study timeline.This study illustrates the possible benefit of VOI results and the need for evidence accumulation accompanied by modeling in health technology assessment for emerging therapies.
ABSTRACT
Students in health professions often face high levels of stress due to demanding academic schedules, heavy workloads, disrupted work-life balance, and sleep deprivation. Addressing stress during their education can prevent negative consequences for their mental health and the well-being of their future patients. Previous reviews on the effectiveness of mindfulness-based interventions (MBIs) focused on working health professionals or included a wide range of intervention types and durations. This study aims to investigate the effect of 6- to 12-week MBIs with 1- to 2-h weekly sessions on stress in future health professionals. We conducted a systematic review and meta-analysis of randomized controlled trials published in English by searching Embase, Medline, Web of Science, Cochrane Central Register of Controlled Trials, and PsycINFO. We used post-intervention stress levels and standard deviations to assess the ability of MBIs to reduce stress, summarized by the standardized mean difference (SMD). This review is reported according to the PRISMA checklist (2020). We identified 2932 studies, of which 11 were included in the systematic review and 10 had sufficient data for inclusion in the meta-analysis. The overall effect of MBIs on reducing stress was a SMD of 0.60 (95% CI [0.27, 0.94]). Our study provides evidence that MBIs have a moderate reducing effect on stress in students in health professions; however, given the high risk of bias, these findings should be interpreted with caution, and further high-quality studies are needed.
