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IntroductionThe Accreditation Council for Graduate Medical Education (ACGME) offers descriptions of competencies and milestones but does not provide standardized assessments to track trainee competency. Entrustable professional activities (EPAs) and special assessments (SAs) are emerging methods to assess the level of competency obtained by regional anesthesiology and acute pain medicine (RAAPM) fellows.MethodsA panel of RAAPM physicians with experience in education and competency assessment and one medical student were recruited to participate in a modified Delphi method with iterative rounds to reach consensus on: a list of EPAs, SAs, and procedural skills; detailed definitions for each EPA and SA; a mapping of the EPAs and SAs to the ACGME milestones; and a target level of entrustment for graduating US RAAPM fellows for each EPA and procedural skill. A gap analysis was performed and a heat map was created to cross-check the EPAs and SAs to the ACGME milestones.ResultsParticipants in EPA and SA development included 19 physicians and 1 medical student from 18 different programs. The Delphi rounds yielded a final list of 23 EPAs, a defined entrustment scale, mapping of the EPAs to ACGME milestones, and graduation targets. A list of 73 procedural skills and 7 SAs were similarly developed.DiscussionA list of 23 RAAPM EPAs, 73 procedural skills, and 7 SAs were created using a rigorous methodology to reach consensus. This framework can be utilized to help assess RAAPM fellows in the USA for competency and allow for meaningful performance feedback.

Background and objectives: documentation is important for quality improvement, education, and research. There is currently a lack of recommendations regarding key aspects of documentation in regional anesthesia. The aim of this study was to establish recommendations for documentation in regional anesthesia. Methods: following the formation of the executive committee and a directed literature review, a long list of potential documentation components was created. A modified Delphi process was then employed to achieve consensus amongst a group of international experts in regional anesthesia. This consisted of 2 rounds of anonymous electronic voting and a final virtual round table discussion with live polling on items not yet excluded or accepted from previous rounds. Progression or exclusion of potential components through the rounds was based on the achievement of strong consensus. Strong consensus was defined as >= 75% agreement and weak consensus as 50%-74% agreement. Results: seventy-seven collaborators participated in both rounds 1 and 2, while 50 collaborators took part in round 3. In total, experts voted on 83 items and achieved a strong consensus on 51 items, weak consensus on 3 and rejected 29. Conclusion: by means of a modified Delphi process, we have established expert consensus on documentation in regional anesthesia., European Society of Regional Anaesthesia and Pain Therapy (ESRA); ASRA Pain Medicine

ObjectiveA healthcare organization’s medical malpractice data can help identify patient safety risks and drive improvement. In most organizations, obstetric malpractice losses are assumed to be par...

A Reduced Form Model (RFM) is a mathematical relationship between the inputs and outputs of an air quality model, permitting estimation of additional modeling without costly new regional-scale simulations. A 21-year Community Multiscale Air Quality (CMAQ) simulation for the continental United States provided the basis for the RFM developed in this study. Predictors included the principal component scores (PCS) of emissions and meteorological variables, while the predictand was the monthly mean of daily maximum 8-hour CMAQ ozone for the ozone season at each model grid. The PCS form an orthogonal basis for RFM inputs. A few PCS incorporate most of the variability of emissions and meteorology, thereby reducing the dimensionality of the source-receptor problem. Stochastic kriging was used to estimate the model. The RFM was used to separate the effects of emissions and meteorology on ozone concentrations. by running the RFM with emissions constant (ozone dependent on meteorology), or constant meteorology (ozone dependent on emissions). Years with ozone-conducive meteorology were identified, and meteorological variables best explaining meteorology-dependent ozone were identified. Meteorology accounted for 19% to 55% of ozone variability in the eastern US, and 39% to 92% in the western US. Temporal trends estimated for original CMAQ ozone data and emission-dependent ozone were mostly negative, but the confidence intervals for emission-dependent ozone are much narrower. Emission-driven changes in monthly mean ozone levels for the period 2000-2010 ranged from 6.4 to 10.9 ppb for the eastern US and from 1.4 to 2.5 ppb for the western US.

In the United States, regional-scale photochemical models are being used to design emission control strategies needed to meet the relevant National Ambient Air Quality Standards (NAAQS) within the framework of the attainment demonstration process. Previous studies have shown that the current generation of regional photochemical models can have large biases and errors in simulating absolute levels of pollutant concentrations. Studies have also revealed that regional air quality models were not always accurately reproducing even the relative changes in ozone air quality stemming from changes in emissions. This paper introduces four approaches to adjust for model bias and errors in order to provide greater confidence for their use in estimating future concentrations as well as using modeled pollutant concentrations in exposure assessments. The four methods considered here are a mean and variance (MV) adjustment, temporal component decomposition (TC) adjustment of modeled concentrations, and two variants of cumulative distribution function (CDF) mapping. These methods were compared against each other as well as against unadjusted model concentrations and a version of the relative response approach based on unadjusted model predictions. The analysis uses ozone concentrations simulated by the Community Multiscale Air Quality (CMAQ) model for the northeastern United States domain for the years 1996–2005. Ensuring that base case conditions are adequately represented through the combined use of observations and model simulations is shown to result in improved estimates of future air quality under changing emissions and meteorological conditions.

Local anesthetics (LA) work by blocking sodium conductance through voltage-gated sodium channels. Complete local anesthetic resistance is infrequent, and the cause is unknown. Genetic variation in sodium channels is a potential mechanism for local anesthetic resistance. A patient with a history of inadequate loss of sensation following LA administration underwent an ultrasound-guided brachial plexus nerve block with a complete failure of the block. We hypothesized that LA resistance is due to a variant form of voltage-gated sodium channel.Whole-Exome Sequencing. The patient and her immediate family provided consent for exome sequencing, and they were screened with a questionnaire to identify family members with a history of LA resistance. Exome sequencing results for four individuals were referenced to the 1000 Genomes Project and the NHLBI ESP to identify variants associated with local anesthetic resistance present in less than 1% of the general population and located in functional regions of the genome.Exome sequencing of the four family members identified one genetic variant in the voltage-gated sodium channel shared by the three individuals with LA resistance but not present in the unaffected family member. Specifically, we noted the A572D mutation in the SCN5A gene encoding for Nav1.5.We identified a genetic variant that is associated with LA resistance in the gene encoding for Nav1.5. We also demonstrate that Nav1.5 is present in human peripheral nerves to support the plausibility that an abnormal form of the Nav1.5 protein could be responsible for the observed local anesthetic resistance.

Danny is a 28-year-old, white male with a history of depression, anxiety and panic attacks, childhood abuse, and multiple medical problems. As a newborn he was treated with ventriculo-peritoneal sh...

Air quality models are used to predict changes in pollutant concentrations resulting from envisioned emission control policies. Recognizing the need to assess the credibility of air quality models in a policy-relevant context, we perform a dynamic evaluation of the Community Multiscale Air Quality (CMAQ) modeling system for the “weekend ozone effect” to determine if observed changes in ozone due to weekday-to-weekend (WDWE) reductions in precursor emissions can be accurately simulated. The weekend ozone effect offers a unique opportunity for dynamic evaluation, as it is a widely documented phenomenon that has persisted since the 1970s. In many urban areas of the Unites States, higher ozone has been observed on weekends than weekdays, despite dramatically reduced emissions of ozone precursors (nitrogen oxides [NOx] and volatile organic compounds [VOCs]) on weekends. More recent measurements, however, suggest shifts in the spatial extent or reductions in WDWE ozone differences. Using 18 years (1988–2005) of observed and modeled ozone and temperature data across the northeastern United States, we re-examine the long-term trends in the weekend effect and confounding factors that may be complicating the interpretation of this trend and explore whether CMAQ can replicate the temporal features of the observed weekend effect. The amplitudes of the weekly ozone cycle have decreased during the 18-year period in our study domain, but the year-to-year variability in weekend minus weekday (WEWD) ozone amplitudes is quite large. Inter-annual variability in meteorology appears to influence WEWD differences in ozone, as well as WEWD differences in VOC and NOx emissions. Because of the large inter-annual variability, modeling strategies using a single episode lasting a few days or a few episodes in a given year may not capture the WEWD signal that exists over longer time periods. The CMAQ model showed skill in predicting the absolute values of ozone concentrations during the daytime. However, early morning NOx concentrations were underestimated and ozone levels were overestimated. Also, the modeled response of ozone to WEWD differences in emissions was somewhat less than that observed. This study reveals that model performance may be improved by (1) properly estimating mobile source NOx emissions and their temporal distributions, especially for diesel vehicles; (2) reducing the grid cell size in the lowest layer of CMAQ; and, (3) using time-dependent and more realistic boundary conditions for the CMAQ simulations.

A metamodel for ozone is a mathematical relationship between the inputs and outputs of an air quality modeling experiment, permitting calculation of outputs for scenarios of interest without having to run the model again. In this study we compare three metamodel estimation techniques applied to an 18 year long CMAQ simulation covering the Northeastern US (NEUS). The estimation methods considered here include projection onto latent structures, stochastic kriging and a combination of principal components and stochastic kriging.

Photochemical air quality modeling systems are the primary tools used in regulatory applications to assess the impact of different emission reduction strategies aimed at reducing air pollutant concentrations to levels considered safe for public health. Two such modeling systems are the community multiscale air quality (CMAQ) model and the regional modeling system for aerosols and deposition (REMSAD). To facilitate their inter-comparison, the United States Environmental Protection Agency performed simulations of air quality over the contiguous United States during year 2001 (horizontal grid cell size of 36×36 km) with CMAQ and REMSAD driven by identical emission and meteorological fields. Here, we compare the abilities of CMAQ and REMSAD to reproduce measured aerosol nitrate and sulfate concentrations. Model estimates are compared to observations reported by the interagency monitoring of protected visual environment (IMPROVE) and the clean air status and trend network (CASTNet). Root mean squared errors are calculated for simulation/observation pairs from ten geographic regions and 12 seasons (months). Following the application of the Wilcoxon matched-pair signed rank test, we conclude that CMAQ is more skillful than REMSAD for simulation of aerosol sulfate. Simulations of particulate nitrate concentrations by CMAQ and REMSAD can seldom be differentiated, leading to the conclusion that both models perform equally for this pollutant specie.

Airborne fine particulate matter across the United States is monitored by different networks, the three prevalent ones presently being the Clean Air Status and Trend Network (CASTNet), the Interagency Monitoring of PROtected Visual Environment Network (IMPROVE) and the Speciation and Trend Network (STN). If combined, these three networks provide speciated fine particulate data at several hundred locations throughout the United States. Yet, differences in sampling protocols and samples handling may not allow their joint use. With these concerns in mind, the objective of this study is to assess the spatial and temporal comparability of the sulfate, nitrate and ammonium concentrations reported by each of these networks. One of the major differences between networks is the sampling frequency they adopted. While CASTNet measures pollution levels on seven-day integrated samples, STN and IMPROVE data pertain to 24-hour samples collected every three days. STN and IMPROVE data therefore exhibit considerably more short-term variability than their CASTNet counterpart. We show that, despite their apparent incongruity, averaging the data with a window size of four to six weeks is sufficient to remove the effects of differences in sampling frequency and duration and allow meaningful comparison of the signals reported by the three networks of concern. After averaging, all the sulfate and, to a lesser degree, ammonium concentrations reported are fairly similar. Nitrate concentrations, on the other hand, are still divergent. We speculate that this divergence originates from the different types of filters used to collect particulate nitrate. Finally, using a rotated principal component technique (RPCA), we determined the number and the geographical organization of the significant temporal modes of variation (clusters) detected by each network for the three pollutants of interest. For sulfate and ammonium, the clusters' geographical boundaries established for each network and the modes of variations within each cluster seem to correspond. RPCA performed on nitrate concentrations revealed that, for the CASTNet and IMPROVE networks, the modes of variation do not correspond to unified geographical regions but are found more sporadically. For STN, the clustered areas are unified and easily delineable. We conclude that the possibility of jointly using the data collected by CASTNet, IMPROVE and STN has to be weighed pollutant by pollutant. While sulfate and ammonium data show some potential for joint use, at this point, combining the nitrate data from these monitoring networks may not be a judicious choice.

In this paper, four methods for spectrally decomposing time series of atmospheric variables are compared. Two of these methods have been previously applied to the analysis of time series of atmospheric variables, while the others are being applied for the first time. This paper focuses on the practical applications of time scale separation techniques rather than on an in-depth comparison of the mathematical features of the filtering techniques. The performance of the above filtering methods is illustrated and evaluated using both simulated and observed ozone time series data. The adaptive window Fourier transform filter is shown to extract fluctuations of known frequency as cleanly as the Morlet wavelet and, therefore, is a useful new tool for time–frequency analyses of atmospheric variables. Simulation results indicate that all four of these filters provide qualitatively similar results when used to extract the energy in five frequency bands of particular interest in time series of atmospheric variables. However, differences can exist when different filters are used to study the temporal variations of the extracted components. In addition, it is shown that all filters are able to capture the year-to-year fluctuations in the magnitudes of individual components. Such analysis can be used to discern the time scales that cause long-term changes in pollutant concentrations. As no single filter performs best in separating the various time scales, great care has to be taken to match the filter characteristics with the objectives of a given analysis.

Assessment of regulatory programs aimed at improving ambient O3 air quality is of considerable interest to the scientific community and to policymakers. Trend detection, the identification of statistically significant long-term changes, and attribution, linking change to specific climatological and anthropogenic forcings, are instrumental to this assessment. Detection and attribution are difficult because changes in pollutant concentrations of interest to policymakers may be much smaller than natural variations due to weather and climate. In addition, there are considerable differences in reported trends seemingly based on similar statistical methods and databases. Differences arise from the variety of techniques used to reduce nontrend variation in time series, including mitigating the effects of meteorology and the variety of metrics used to track changes. In this paper, we review the trend assessment techniques being used in the air pollution field and discuss their strengths and limitations in discerning and attributing changes in O3 to emission control policies.

To study the underlying forcing mechanisms that distinguish the days with high ozone concentrations from average or nonepisodic days, the observed and model-predicted ozone time series are spectrally decomposed into different temporal components; the modeled values are based on the results of a three-month simulation with the Urban Airshed Model—Variable Grid Version photochemical modeling system. The ozone power spectrum is represented as the sum of four temporal components, ranging from the intraday timescale to the multiweek timescale. The results reveal that only those components that contain fluctuations with periods equal to or greater than one day carry the information that distinguishes ozone episode days from nonepisodic days. Which of the longer-term fluctuations is dominant in a particular episode varies from episode to episode. However, the magnitude of the intraday fluctuations is nearly invariant in time. The promulgation of the 8-h standard for ozone further emphasizes the importance of lon...

This paper illustrates inhomogeneities in pollutant time-series data caused by changes in data reporting practices. These inhomogeneities may have a substantial effect on trend assessments. Therefore, in estimating trends or mean levels in pollutant time series or attributing changes in pollutant levels to an emission control policy, it is important to homogenize time-series data.

To improve the understanding of the problem of long-range transport and source-receptor relationships for trace-level toxic air contaminants, the authors examine the use of several multiple comparison procedures (MCPs) in the analysis and interpretation of multiply-censored data sets. Censoring is a chronic problem for some of the toxic elements of interest (As, Se, Mn, etc.) because their atmospheric concentrations are often too low to be measured precisely. Such concentrations are commonly reported in a nonquantitative way as below the limit of detection, leaving the data analyst with censored data sets. Since the standard statistical MCPs are not readily applicable to such data sets, they employ Monte Carlo simulations to evaluate two nonparametric rank-type MCPs for their applicability to the interpretation of censored data. Two different methods for ranking censored data are evaluated: average rank method and substitution with half the detection limit. The results suggest that the Kruskal-Wallis-Dunn MCP with the half-detection limit replacement for censored data is most appropriate for comparing independent, multiply-censored samples of moderate size (20--100 elements). Application of this method to pollutant clusters at several sites in the northeastern USA enabled them to identify potential pollution source regions and atmospheric patterns associated with the long-range transport more » of air pollutants. « less

We present a robust methodology for examining the relationship between synoptic-scale atmospheric transport patterns and pollutant concentration levels observed at a site. Our approach entails calculating a large number of back-trajectories from the observational site over a long period of time and subjecting them to cluster analysis. The short-term component (weather-related variations) of the pollutant concentration time-series data is segregated according to the back-trajectory clusters. Non-parametric statistics are then used to test for “significant” differences in the chemical composition of pollutant data associated with each cluster. Additional information about the spatial and temporal scales of pollutant transport is obtained from the time-lagged inter-site correlation analysis of ozone for a specific cluster. To illustrate the application of this methodology, we examined 5 yr long time-series data of ozone concentrations measured at Whiteface Mountain, NY, Cliffside Park, NJ, and Quabbin Summit, MA. The results provide evidence of ozone transport to these sites, revealing the spatial and temporal scales involved in the transport.

Confidence interval construction for central tendency is a problem of practical consequence for those who must analyze air contaminant data. Determination of compliance with relevant ambient air quality criteria and assessment of associated health risks depend upon quantifying the uncertainty of estimated mean pollutant concentrations. The bootstrap is a resampling technique that has been steadily gaining popularity and acceptance during the past several years. A potentially powerful application of the bootstrap is the construction of confidence intervals for any parameter of any underlying distribution. Properties of bootstrap confidence intervals were determined for samples generated from lognormal, gamma, and Weibull distributions. Bootstrap t intervals, while having smaller coverage errors than Student's t or other bootstrap methods, under-cover for small samples from skewed distributions. Therefore, we caution against using the bootstrap to construct confidence intervals for the mean without first considering the effects of sample size and skew. When sample sizes are small, one might consider using the median as an estimate of central tendency. Confidence intervals for the median are easy to construct and do not under-cover. Data collected by the Northeast States for Coordinated Air Use Management (NESCAUM) are used to illustrate application of the methods discussed.