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Introduction: There were more than 100,000 fatal drug overdoses in the U.S. in 2021 alone. In recent years, there has been a shift in opioid mortality from predominantly White rural communities to Black urban communities. This study aimed to identify the Virginia communities disproportionately affected by the overdose crisis and to better understand the systemic factors contributing to disparities in opioid mortality. Methods: Using the state all-payer claims database, state mortality records, and census data, we created a multivariate model to examine the community-level factors contributing to racial disparities in opioid mortality. We used generalized linear mixed models to examine the associations between socioecologic factors and fatal opioid overdoses, opioid use disorder diagnoses, opioid-related emergency department visits, and mental health diagnoses. Results: Between 2015 and 2020, racial disparities in mortality widened. In 2020, Black males were 1.5 times more likely to die of an opioid overdose than White males (47.3 vs 31.6 per 100,000; p

The balance of primary energy sources for Europe has been changing rapidly over recent decades, shifting towards more renewables and using fossil fuels with lower carbon emissions. However, the latter is being impacted by the Russia-Ukraine conflict. Here, we determine the potential bounds of how this may affect global warming, based on whether the European use of Russian gas and oil is replaced with either less efficient burning of coal (with and without the gas and oil then used in new markets elsewhere) or with renewables. We perform calculations as perturbations from a baseline carbon dioxide (CO _2 ) trajectory associated with ‘middle range’ and ‘low’ Shared Socioeconomic Pathways (SSP), SSP2-45 and SSP1-26. We calculate the CO _2 perturbations as a simulated step change in emissions for the year 2023, which then decays linearly to zero by 2043. The emission profiles drive the FaIR simple climate model. FaIR links greenhouse gas emissions to global warming levels and includes a representation of warming uncertainty based on projections made using more complex Earth system models. We find that the direct impact of the conflict on the global mean temperature is likely to be relatively small, amounting to the worst case of nearly one-hundredth of a degree. This warming is equivalent to approximately an extra half year of current global CO _2 emissions. However, we suggest that it is important to consider the implications of the precedents set by the European response to the reduced availability of Russian gas and oil. Such action may reveal the potential for faster uptake of low-carbon energy sources or the converse of backtracking on current Nationally Determined Contributions (NDCs).

Abstract Objective With adolescent mass casualty incidents (MCI) on the rise, out‐of‐hospital readiness is critical to optimize disaster response. We sought to test the feasibility and acceptability of a 360 Virtual Reality (360 VR) platform for disaster event decisionmaking. Methods This was a cross‐sectional observational assessment of a subject's ability to triage and perform out‐of‐hospital interventions using a 360 VR MCI module. A convenience sample of attendees was recruited over 1.5 days from the American College of Emergency Physicians (ACEP) national conference in San Diego, CA. Results Two hundred and seven (207) subjects were enrolled. Ninety‐six (46%) subjects identified as attendings, 66 (32%) as residents, 13 (6%) as medical students, 4 (2%) as emergency medical technicians and 28 (14%) as other. When comparing mean scores between groups, physicians who were 40 years old (8.7 vs 6.5, P < 0.001). Residents achieved higher scores than attendings (8.6 vs 7.5, P = 0.005). Based on a 5‐point Likert scale, participants felt the 360 VR experience was engaging (median = 5) and enjoyable (median = 5). Most felt that 360 VR was more immersive than mannequin‐based simulation training (median = 5). Conclusion We conclude that 360 VR is a feasible platform for assessing triage and intervention decisionmaking for adolescent MCIs. It is well received by subjects and may have a role as a training and education tool for disaster readiness. In this era of distanced learning, 360 VR is an attractive option for future immersive educational experiences.

Abstract Pediatric head injury is a common presenting complaint in the emergency department (ED), often requiring neuroimaging or ED observation for diagnosis. However, the traditional diagnostic neuroimaging modality, head computed tomography (CT), is associated with radiation exposure while prolonged ED observation impacts patient flow and resource utilization. Recent scientific literature supports abbreviated, or focused and shorter, brain magnetic resonance imaging (MRI) as a feasible and accurate diagnostic alternative to CT for traumatic brain injury. However, this is a relatively new application and its use is not widespread. The aims of this review are to describe the science and applications of abbreviated brain MRI and report a model protocol's development and ED implementation in the evaluation of children with head injury for replication in other institutions.

Observations facilitate model evaluation and provide constraints that are relevant to future predictions and projections. Constraints for uninitialized projections are generally based on model performance in simulating climatology and climate change. For initialized predictions, skill scores over the hindcast period provide insight into the relative performance of models, and the value of initialization as compared to projections. Predictions and projections combined can, in principle, provide seamless decadal to multi-decadal climate information. For that, though, the role of observations in skill estimates and constraints needs to be understood in order to use both consistently across the prediction and projection time horizons. This paper discusses the challenges in doing so, illustrated by examples of state-of-the-art methods for predicting and projecting changes in European climate. It discusses constraints across prediction and projection methods, their interpretation, and the metrics that drive them such as process accuracy, accurate trends or high signal-to-noise ratio. We also discuss the potential to combine constraints to arrive at more reliable climate prediction systems from years to decades. To illustrate constraints on projections, we discuss their use in the UK's climate prediction system UKCP18, the case of model performance weights obtained from the Climate model Weighting by Independence and Performance (ClimWIP) method, and the estimated magnitude of the forced signal in observations from detection and attribution. For initialized predictions, skill scores are used to evaluate which models perform well, what might contribute to this performance, and how skill may vary over time. Skill estimates also vary with different phases of climate variability and climatic conditions, and are influenced by the presence of external forcing. This complicates the systematic use of observational constraints. Furthermore, we illustrate that sub-selecting simulations from large ensembles based on reproduction of the observed evolution of climate variations is a good testbed for combining projections and predictions. Finally, the methods described in this paper potentially add value to projections and predictions for users, but must be used with caution.

Introduction: Cluster randomized crossover trials are often faced with a dilemma when selecting an optimal model of consent, as the traditional model of obtaining informed consent from participant's before initiating any trial related activities may not be suitable. We describe our experience of engaging patient advisors to identify an optimal model of consent for the PREP-IT trials. This paper also examines surrogate measures of success for the selected model of consent. Methods: The PREP-IT program consists of two multi-center cluster randomized crossover trials that engaged patient advisors to determine an optimal model of consent. Patient advisors and stakeholders met regularly and reached consensus on decisions related to the trial design including the model for consent. Patient advisors provided valuable insight on how key decisions on trial design and conduct would be received by participants and the impact these decisions will have. Results: Patient advisors, together with stakeholders, reviewed the pros and cons and the requirements for the traditional model of consent, deferred consent, and waiver of consent. Collectively, they agreed upon a deferred consent model, in which patients may be approached for consent after their fracture surgery and prior to data collection. The consent rate in PREP-IT is 80.7%, and 0.67% of participants have withdrawn consent for participation. Discussion: Involvement of patient advisors in the development of an optimal model of consent has been successful. Engagement of patient advisors is recommended for other large trials where the traditional model of consent may not be optimal.

Introduction: In March 2020, shelter-in-place orders were enacted to attenuate the spread of coronavirus 2019 (COVID-19). Emergency departments (EDs) experienced unexpected and dramatic decreases in patient volume, raising concerns about exacerbating health disparities. Methods: We queried our electronic health record to describe the overall change in visits to a two-ED healthcare system in Northern California from March–June 2020 compared to 2019. We compared weekly absolute numbers and proportional change in visits focusing on race/ethnicity, insurance, household income, and acuity. We calculated the z-score to identify whether there was a statistically significant difference in proportions between 2020 and 2019. Results: Overall ED volume declined 28% during the study period. The nadir of volume was 52% of 2019 levels and occurred five weeks after a shelter-in-place order was enacted. Patient demographics also shifted. By week 4 (April 5), the proportion of Hispanic patients decreased by 3.3 percentage points (pp) (P = 0.0053) compared to a 6.2 pp increase in White patients (P = 0.000005). The proportion of patients with commercial insurance increased by 11.6 pp, while Medicaid visits decreased by 9.5 pp (P < 0.00001) at the initiation of shelter-in-place orders. For patients from neighborhoods 400% FPL the week of the shelter-in-place order. Overall, 2020 evidenced a consistently elevated proportion of high-acuity Emergency Severity Index (ESI) level 1 patients compared to 2019. Increased acuity was also demonstrated by an increase in the admission rate, with a 10.8 pp increase from 2019. Although there was an increased proportion of high-acuity patients, the overall census was decreased. Conclusion: Our results demonstrate changing ED utilization patterns circa the shelter-in-place orders. Those from historically vulnerable populations such as Hispanics, those from lower socioeconomic areas, and Medicaid users presented at disproportionately lower rates and numbers than other groups. As the pandemic continues, hospitals should use operations data to monitor utilization patterns by demographic, in addition to clinical indicators. Messaging about availability of emergency care and other services should include vulnerable populations to avoid exacerbating healthcare disparities.

Introduction: Pragmatic trials in comparative effectiveness research assess the effects of different treatment, therapeutic, or healthcare options in clinical practice. They are characterized by broad eligibility criteria and large sample sizes, which can lead to an unmanageable number of participants, increasing the risk of bias and affecting the integrity of the trial. We describe the development of a sampling strategy tool and its use in the PREPARE trial to circumvent the challenge of unmanageable work flow. Methods: Given the broad eligibility criteria and high fracture volume at participating clinical sites in the PREPARE trial, a pragmatic sampling strategy was needed. Using data from PREPARE, descriptive statistics were used to describe the use of the sampling strategy across clinical sites. A Chi-square test was performed to explore whether use of the sampling strategy was associated with a reduction in the number of missed eligible patients. Results: 7 of 20 clinical sites (35%) elected to adopt a sampling strategy. There were 1539 patients excluded due to the use of the sampling strategy, which represents 30% of all excluded patients and 20% of all patients screened for participation. Use of the sampling strategy was associated with lower odds of missed eligible patients (297/4545 (6.5%) versus 341/3200 (10.7%) p

We undertake a systematic review of peer-reviewed literature to arrive at recommendations for shaping communications about uncertainty in scientific climate-related findings. Climate communications often report on scientific findings that contain different sources of uncertainty. Potential users of these communications are members of the general public, as well as decision makers and climate advisors from government, business and non-governmental institutions worldwide. Many of these users may lack formal training in climate science or related disciplines. We systematically review the English-language peer-reviewed empirical literature from cognitive and behavioral sciences and related fields, which examines how users perceive communications about uncertainty in scientific climate-related findings. We aim to summarize how users’ responses to communications about uncertainty in scientific climate-related findings are associated with characteristics of the decision context, including climate change consequences and types of uncertainty as well as user characteristics, such as climate change beliefs, environmental worldviews, political ideology, numerical skills, and others. We also aimed to identify what general recommendations for communications about uncertainty in scientific climate-related findings can be delineated. We find that studies of communications about uncertainty in scientific climate-related findings substantially varied in how they operationalized uncertainty, as well as how they measured responses. Studies mostly focused on uncertainty stemming from conflicting information, such as diverging model estimates or experts, or from expressions of imprecision such as ranges. Among other things, users’ understanding was improved when climate communications about uncertainty in scientific climate-related findings were presented with explanations about why climate information was uncertain, and when ranges were presented with lower and upper numerical bounds. Users’ understanding also improved if they expressed stronger beliefs about climate change, or had better numerical skills. Based on these findings, we provide emerging recommendations on how to best present communications about uncertainty in scientific climate-related findings; and we identify research gaps.

To understand the importance of methane on the levels of carbon emission reductions required to achieve temperature goals, a processed-based approach is necessary rather than reliance on the transient climate response to emissions. We show that plausible levels of methane (CH _4 ) mitigation can make a substantial difference to the feasibility of achieving the Paris climate targets through increasing the allowable carbon emissions. This benefit is enhanced by the indirect effects of CH _4 on ozone (O _3 ). Here the differing effects of CH _4 and CO _2 on land carbon storage, including the effects of surface O _3 , lead to an additional increase in the allowable carbon emissions with CH _4 mitigation. We find a simple robust relationship between the change in the 2100 CH _4 concentration and the extra allowable cumulative carbon emissions between now and 2100 (0.27 ± 0.05 GtC per ppb CH _4 ). This relationship is independent of modelled climate sensitivity and precise temperature target, although later mitigation of CH _4 reduces its value and thus methane reduction effectiveness. Up to 12% of this increase in allowable emissions is due to the effect of surface ozone. We conclude early mitigation of CH _4 emissions would significantly increase the feasibility of stabilising global warming below 1.5 °C, alongside having co-benefits for human and ecosystem health.

This paper analyses the emissions and cost impacts of mitigation of non-CO2 greenhouse gases (GHGs) at a global level, in scenarios aimed at meeting a range of long-term temperature goals (LTTGs). The study combines an integrated assessment model (TIAM-Grantham) representing CO2 emissions (and their mitigation) from the fossil fuel combustion and industrial sectors, coupled with a model covering non-CO2 emissions (GAINS), using the latest global warming potentials from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report. We illustrate that in general non-CO2 mitigation measures are less costly than CO2 mitigation measures, with the majority of their abatement potential achievable at US2005$100/tCO2e or less throughout the 21st century (compared to a marginal CO2 mitigation cost which is already greater than this by 2030 in the most stringent mitigation scenario). As a result, the total cumulative discounted cost over the period 2010–2100 (at a 5% discount rate) of limiting global average temperature change to 2.5 °C by 2100 is $48 trillion (about 1.6% of cumulative discounted GDP over the period 2010–2100) if only CO2 from the fossil fuel and industrial sectors is targeted, whereas the cost falls to $17 trillion (0.6% of GDP) by including non-CO2 GHG mitigation in the portfolio of options—a cost reduction of about 65%. The criticality of non-CO2 mitigation recommends further research, given its relatively less well-explored nature when compared to CO2 mitigation.

This study explores the critical notion of how feasible it is to achieve long-term mitigation goals to limit global temperature change. It uses a model inter-comparison of three integrated assessment models (TIAM-Grantham, MESSAGE-GLOBIOM and WITCH) harmonized for socio-economic growth drivers using one of the new shared socio-economic pathways (SSP2), to analyse multiple mitigation scenarios aimed at different temperature changes in 2100, in order to assess the model outputs against a range of indicators developed so as to systematically compare the feasibility across scenarios. These indicators include mitigation costs and carbon prices, rates of emissions reductions and energy efficiency improvements, rates of deployment of key low-carbon technologies, reliance on negative emissions, and stranding of power generation assets. The results highlight how much more challenging the 2 °C goal is, when compared to the 2.5–4 °C goals, across virtually all measures of feasibility. Any delay in mitigation or limitation in technology options also renders the 2 °C goal much less feasible across the economic and technical dimensions explored. Finally, a sensitivity analysis indicates that aiming for less than 2 °C is even less plausible, with significantly higher mitigation costs and faster carbon price increases, significantly faster decarbonization and zero-carbon technology deployment rates, earlier occurrence of very significant carbon capture and earlier onset of global net negative emissions. Such a systematic analysis allows a more in-depth consideration of what realistic level of long-term temperature changes can be achieved and what adaptation strategies are therefore required.

The scenarios generated by energy systems models provide a picture of the range of possible pathways to a low-carbon future. However, in order to be truly useful, these scenarios should not only be possible but also plausible. In this paper, we have used lessons from historical energy transitions to create a set of diagnostic tests to assess the feasibility of an example 2 °C scenario (generated using the least cost optimization model, TIAM-Grantham). The key assessment criteria included the rate of deployment of low carbon technologies and the rate of transition between primary energy resources. The rates of deployment of key low-carbon technologies were found to exceed the maximum historically observed rate of deployment of 20% per annum. When constraints were added to limit the scenario to within historically observed rates of change, the model no longer solved for 2 °C. Under these constraints, the lowest median 2100 temperature change for which a solution was found was about 2.1 °C and at more than double the cumulative cost of the unconstrained scenario. The analysis in this paper highlights the considerable challenge of meeting 2 °C, requiring rates of energy supply technology deployment and rates of declines in fossil fuels which are unprecedented.

There exists considerable uncertainty over both shale and conventional gas resource availability and extraction costs, as well as the fugitive methane emissions associated with shale gas extraction and its possible role in mitigating climate change. This study uses a multi-region energy system model, TIAM (TIMES integrated assessment model), to consider the impact of a range of conventional and shale gas cost and availability assessments on mitigation scenarios aimed at achieving a limit to global warming of below 2 °C in 2100, with a 50% likelihood. When adding shale gas to the global energy mix, the reduction to the global energy system cost is relatively small (up to 0.4%), and the mitigation cost increases by 1%–3% under all cost assumptions. The impact of a “dash for shale gas”, of unavailability of carbon capture and storage, of increased barriers to investment in low carbon technologies, and of higher than expected leakage rates, are also considered; and are each found to have the potential to increase the cost and reduce feasibility of meeting global temperature goals. We conclude that the extraction of shale gas is not likely to significantly reduce the effort required to mitigate climate change under globally coordinated action, but could increase required mitigation effort if not handled sufficiently carefully.