Frequency of medically attended adverse events following tetanus and diphtheria toxoid vaccine in adolescents and young adults: a Vaccine Safety Datalink study

<p>Abstract</p> <p>Background</p> <p>Local reactions are the most commonly reported adverse events following tetanus and diphtheria toxoid (Td) vaccine and the risk of local reactions may increase with number of prior Td vaccinations.</p> <p>Methods</p> <p>To estimate the risk of medically attended local reactions following Td vaccination in adolescents and young adults we conducted a six-year retrospective cohort study assessing 436,828 Td vaccinations given to persons 9 through 25 years of age in the Vaccine Safety Datalink population from 1999 through 2004.</p> <p>Results</p> <p>Overall, the estimated risk of a medically attended local reaction was 3.6 events per 10,000 Td vaccinations. The lowest risk (2.8 events per 10,000 vaccinations) was found in the 11 to 15 year old age group. In comparison with that group, the event risks were significantly higher in both the 9 to 10 and 21 to 25 year old age groups. The risk of a local reaction was significantly higher in persons who had received another tetanus and diphtheria toxoid containing vaccine (TDCV) in the previous five years (incidence rate ratio, 2.9; 95% confidence interval, 1.2 to 7.2). Twenty-eight percent of persons with a local reaction to Td vaccine were prescribed antibiotics.</p> <p>Conclusion</p> <p>Medically attended local reactions were uncommon following Td vaccination. The risk of those reactions varied by age and by prior receipt of TDCVs. These findings provide a point of reference for future evaluations of the safety profile of newer vaccines containing tetanus or diphtheria toxoid.</p

Comprehensive evidence implies a higher social cost of CO2

The social cost of carbon dioxide (SC-CO2) measures the monetized value of the damages to society caused by an incremental metric tonne of CO2 emissions and is a key metric informing climate policy. Used by governments and other decision-makers in beneft–cost analysis for over a decade, SC-CO2 estimates draw on climate science, economics, demography and other disciplines. However, a 2017 report by the US National Academies of Sciences, Engineering, and Medicine1 (NASEM) highlighted that current SC-CO2 estimates no longer refect the latest research. The report provided a series of recommendations for improving the scientifc basis, transparency and uncertainty characterization of SC-CO2 estimates. Here we show that improved probabilistic socioeconomic projections, climate models, damage functions, and discounting methods that collectively refect theoretically consistent valuation of risk, substantially increase estimates of the SC-CO2. Our preferred mean SC-CO2 estimate is $185 per tonne of CO2 ($44–$413 per tCO2: 5$51 per tCO2. Our estimates incorporate updated scientifc understanding throughout all components of SC-CO2 estimation in the new open-source Greenhouse Gas Impact Value Estimator (GIVE) model, in a manner fully responsive to the near-term NASEM recommendations. Our higher SC-CO2 values, compared with estimates currently used in policy evaluation, substantially increase the estimated benefts of greenhouse gas mitigation and thereby increase the expected net benefts of more stringent climate policies

Plasticity may change inputs as well as processes, structures, and responses

Significant work has documented neuroplasticity in development, demonstrating that developmental pathways are shaped by experience. Plasticity is often discussed in terms of the results of differences in input; differences in brain structures, processes, or responses reflect differences in experience. In this paper, I discuss how developmental plasticity also effectively changes input into the system. That is, structures and processes change in response to input, and those changed structures and processes influence future inputs. For example, plasticity may change the pattern of eye movements to a stimulus, thereby changing which part of the scene becomes the input. Thus, plasticity is not only seen in the structures and processes that result from differences in experience, but also is seen in the changes in the input as those structures and processes adapt. The systematic study of the nature of experience, and how differences in experience shape learning, can contribute to our understanding of neuroplasticity in general

MimiIWG FAIRv162 Temperature Trajectories

&lt;h3&gt;1. Introduction&lt;/h3&gt;&lt;p&gt;These files represent the source code and input files for the temperature trajectories used for the pairing of the MimiIWG integrated assessment model with the MimiFAIRv162 climate model. They serve as inputs for &lt;strong&gt;sensitivity analysis&lt;/strong&gt; in Tan, Rennels, and Parthum 2023 "The Social Costs of Hydrofluorocarbons and the Benefits from Their Expedited Phasedown" with replication code for that paper &lt;a href="https://github.com/bryanparthum/schfc-paper"&gt;here&lt;/a&gt;.&nbsp;&lt;/p&gt;&lt;p&gt;They are used as automatically downloaded inputs to the &lt;a href="https://github.com/lrennels/MimiIWG_FAIRv162"&gt;MimiIWG_FAIRv162&lt;/a&gt; repository, a dependency of the paper replication code.&lt;/p&gt;&lt;p&gt;Please see the publication and repositories for important details about methodology and assumptions &lt;strong&gt;before considering use &lt;/strong&gt;and feel free to be in touch with the authors with any further questions or clarifications.&lt;/p&gt;&lt;h3&gt;2. temp_trajectory_replication.zip&lt;/h3&gt;&lt;p&gt;This folder holds all code and data to replicate the data outputs. Note that the &lt;i&gt;Manifest.toml &lt;/i&gt;and &lt;i&gt;Project.toml&lt;/i&gt; files are accessed by the source code to define the Julia environment and should not be modified.&lt;/p&gt;&lt;h4&gt;data folder (data)&lt;/h4&gt;&lt;p&gt;Data input files that define the emissions for the IWG EMF-22 scenarios including&lt;/p&gt;&lt;ul&gt;&lt;li&gt;dice_inputs_ch4 - &lt;a href="https://github.com/rffscghg/MimiIWG.jl"&gt;https://github.com/rffscghg/MimiIWG.jl&lt;/a&gt; repository at path MimiIWG/data/IWG_inputs/DICE/CH4N20emissions_annualversion.xls in CH4annual Tab Units of MtCH4&lt;/li&gt;&lt;li&gt;dice_inputs_n2o - &lt;a href="https://github.com/rffscghg/MimiIWG.jl"&gt;https://github.com/rffscghg/MimiIWG.jl&lt;/a&gt; repository at path MimiIWG/data/IWG_inputs/DICE/CH4N20emissions_annualversion.xls in N2Oannual Tab Units of MtN2O&lt;/li&gt;&lt;li&gt;dice_inputs_land_co2 - &lt;a href="https://github.com/rffscghg/MimiIWG.jl"&gt;https://github.com/rffscghg/MimiIWG.jl&lt;/a&gt; repository at path MimiIWG/data/IWG_inputs/DICE/SCC_input_EMFscenarios.xls in LandCO2 tab Units of GtC per decade&lt;/li&gt;&lt;li&gt;dice_inputs_industrial_co2 - &lt;a href="https://github.com/rffscghg/MimiIWG.jl"&gt;https://github.com/rffscghg/MimiIWG.jl&lt;/a&gt; repository at path MimiIWG/data/IWG_inputs/DICE/SCC_input_EMFscenarios.xls in IndustrialCO2 tab Units of GtC per decade&lt;/li&gt;&lt;/ul&gt;&lt;h4&gt;src folder (src)&lt;/h4&gt;&lt;p&gt;Source code files to produce files in &lt;i&gt;mimiiwg_fairv162_temp_trajectories&lt;/i&gt; folder. To produce the output use Julia to run &lt;i&gt;main.jl&lt;/i&gt; to produce the output files (~12GB) in an &lt;i&gt;output&lt;/i&gt; folder created in the parent directory of &lt;i&gt;main.jl&lt;/i&gt;.&lt;/p&gt;&lt;h3&gt;3. mimiiwg_fairv162_temp_trajectories.zip&lt;/h3&gt;&lt;p&gt;This folder holds input data files as produced by the src source code above. These are used as inputs for running the MimiIWG-FAIRv162 model.&lt;/p&gt;&lt;p&gt;There is one file per gas (CO2 and 11 HFCs) - socioeconomic scenario (5 EMF-22 scenarios) making 60 files, plus a key file. All files are in Arrow format for efficiency.&lt;/p&gt;&lt;p&gt;Each gas-scenario input file has the following specifications:&lt;/p&gt;&lt;ul&gt;&lt;li&gt;2237 sets of temperature trajectories, one for each FAIR constrained parameter set to be used in a Monte Carlo Analysis&lt;/li&gt;&lt;li&gt;within each set, one baseline temperature trajectory and one trajectory for a pulse of emissions (1GtC or 1 MtHFC) in a given year from 2005 to 2105 in five-year intervals&lt;/li&gt;&lt;li&gt;units are anomaly from pre-industrial in degrees Celsius&lt;/li&gt;&lt;/ul&gt

Mimi-RICE-2010.jl v1.1.0

No description provided

Impacts of emissions uncertainty on Antarctic instability and sea-level rise

Uncertainty in future carbon dioxide (CO2) emissions, and the geophysical response to emissions, drives variability in future sea-level rise (SLR). However, the relative contribution of emissions and geophysical dynamics (e.g. Antarctic Ice Sheet (AIS) tipping points) to future sea-level projections is poorly understood. Here, we disentangle their relative importance by propagating several ensembles of CO2 emissions trajectories, representing relevant deep uncertainties, through a calibrated carbon cycle-climate-sea-level model chain. The CO2 emissions trajectory, particularly the timing of when emissions are reduced, becomes the primary driver of sea-level variability only after 2075. The most extreme global mean SLR (exceeding 4m by 2200) is projected to occur regardless of optimism about limiting CO2 emissions if accelerated AIS melting occurs. Further, delaying decarbonization reduces the “safe operating space” associated with the geophysical uncertainties. Our results highlight the potential that similar adaptation requirements may be needed regardless of optimism about future levels of CO2 mitigation

MimiBRICK.jl: A Julia package for the BRICK model for sea-level change in the Mimi integrated modeling framework

BRICK is a semi-empirical model for global and local mean sea-level change (Wong et al., 2017). The core model includes component sub-models for the major contributors to global mean sea-level change - glaciers and ice caps, thermal expansion, land water storage, and the Greenland and Antarctic ice sheets. The resulting global mean sea levels can be downscaled via a data set that represents the “fingerprint” of each sea-level component on local mean sea level (Slangen et al., 2014). In this way, BRICK provides information about local sea-level changes, including characterizations of key uncertainties. BRICK is flexible and efficient enough to resolve high-risk upper tails of probability distributions. BRICK has been used in a number of recent assessments, including for examining the impacts of sea-level rise as a constraint on estimates of climate sensitivity (Vega-Westhoff et al., 2018), estimates of deep uncertainty in coastal flood risk (Ruckert et al., 2019), and most recently was noted in comparisons of sea-level projections in the Sixth Assessment Report of the IPCC (Fox-Kemper et al., 2021)

Projecting future costs to U.S. electric utility customers from power interruptions.

This analysis integrates regional models of power system reliability, output from atmosphere-ocean general circulation models, and results from the Interruption Cost Estimate (ICE) Calculator to project long-run costs to electric utility customers from power interruptions under different future severe weather and electricity system scenarios. We discuss the challenges when attempting to model long-run costs to utility customers including the use of imperfect metrics to measure severe weather. Despite these challenges, initial findings show that discounted cumulative customer costs, through the middle of the century, could range from $1.5-$3.4 trillion (2015) without aggressive undergrounding of the power system and increased utility operations and maintenance (O&M) spending and 1.5-$2.5 trillion with aggressive undergrounding and increased spending. By the end of the century, cumulative customer costs could range from$1.9-$5.6 trillion (without aggressive undergrounding and increased spending) and$2.0-$3.6 trillion (with aggressive undergrounding and increased spending). We find that, in some scenarios, aggressive undergrounding of distribution lines and increased O&amp;M spending is not always cost-effective. We conclude by identifying important topics for follow-on research, which have the potential to improve the cost estimates of this model