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This paper describes a variational data assimilation algorithm based on the Simulating WAves Nearshore (SWAN) wave spectrum model. The approach allows single-point wave spectrum observations to be used to estimate the wave field for a nearshore region under stationary conditions, assuming a spatially uniform incident wave spectrum at the offshore boundary. The assimilated data are in the form of Fourier directional coefficients, the standard output from operational wave buoys, and are used directly by incorporating the relationship between directional spectrum and the Fourier coefficients into the formulation. The algorithm was tested on data from nearshore buoys deployed off the coast of North Carolina in May 2012, and the estimated wave field is compared to both the input data and independent observation data. The results compare favorably to the independent data with overall RMS errors of 10%-20% for significant wave height, about half a second for mean wave period, and as much as three to four SWAN spectral grid cells for mean direction. Overall, the results show that the algorithm can be effectively used to estimate the offshore boundary spectrum and accurately reproduce wave conditions in the domain. [ABSTRACT FROM AUTHOR]

Background: We developed an algorithm to quantitatively estimate endotoxin exposure for farmers in the Biomarkers of Exposure and Effect in Agriculture (BEEA) Study. Methods: The algorithm combined task intensity estimates derived from published data with questionnaire responses on activity duration to estimate task‐specific cumulative endotoxin exposures for 13 tasks during four time windows, ranging from "past 12 months" to "yesterday/today." We applied the algorithm to 1681 participants in Iowa and North Carolina. We examined correlations in endotoxin metrics within‐ and between‐task. We also compared these metrics to prior day full‐shift inhalable endotoxin concentrations from 32 farmers. Results: The highest median task‐specific cumulative exposures were observed for swine confinement, poultry confinement, and grind feed. Inter‐quartile ranges showed substantial between‐subject variability for most tasks. Time window‐specific metrics of the same task were moderately‐highly correlated. Between‐task correlation was variable, with moderately‐high correlations observed for similar tasks (e.g., between animal‐related tasks). Prior day endotoxin concentration increased with the total metric and with task metrics for swine confinement, clean other animal facilities, and clean grain bins. Significance: This study provides insight into the variability and sources of endotoxin exposure among farmers in the BEEA study and summarizes exposure estimates for future investigations in this population. [ABSTRACT FROM AUTHOR]

Objective: To compare differences in healthcare utilization and costs for Medicaid-insured children with medical complexity (CMC) by race/ethnicity and rurality. Methods: Retrospective cohort of North Carolina (NC) Medicaid claims for children 3–20 years old with 3 years continuous Medicaid coverage (10/1/2015–9/30/2018). Exposures were medical complexity, race/ethnicity, and rurality. Three medical complexity levels were: without chronic disease, non-complex chronic disease, and complex chronic disease; the latter were defined as CMC. Race/ethnicity was self-reported in claims; we defined rurality by home residence ZIP codes. Utilization and costs were summarized for 1 year (10/1/2018–9/30/2019) by complexity level classification and categorized as acute care (hospitalization, emergency [ED]), outpatient care (primary, specialty, allied health), and pharmacy. Per-complexity group utilization rates (per 1000 person-years) by race/ethnicity and rurality were compared using adjusted rate ratios (ARR). Results: Among 859,166 Medicaid-insured children, 118,210 (13.8%) were CMC. Among CMC, 36% were categorized as Black non-Hispanic, 42.7% White non-Hispanic, 14.3% Hispanic, and 35% rural. Compared to White non-Hispanic CMC, Black non-Hispanic CMC had higher hospitalization (ARR = 1.12; confidence interval, CI 1.08–1.17) and ED visit (ARR = 1.17; CI 1.16–1.19) rates; Hispanic CMC had lower ED visit (ARR = 0.77; CI 0.75–0.78) and hospitalization rates (ARR = 0.79; CI 0.73–0.84). Black non-Hispanic and Hispanic CMC had lower outpatient visit rates than White non-Hispanic CMC. Rural CMC had higher ED (ARR = 1.13; CI 1.11–1.15) and lower primary care utilization rates (ARR = 0.87; CI 0.86–0.88) than urban CMC. Discussion: Healthcare utilization varied by race/ethnicity and rurality for Medicaid-insured CMC. Further studies should investigate mechanisms for these variations and expand higher value, equitable care delivery for CMC. [ABSTRACT FROM AUTHOR]

Objective  This study compares the number of units of red blood cells (RBCs) transfused in patients with placenta accreta spectrum (PAS) treated with or without a multidisciplinary algorithm that includes placental uterine arterial embolization (P-UAE) and selective use of either immediate or delayed hysterectomy. Study Design  This is a retrospective study of deliveries conducted at a tertiary care hospital from 2001 to 2018 with pathology-confirmed PAS. Those with previable pregnancies or microinvasive histology were excluded. To improve the equity of comparison, analyses were made separately among scheduled and unscheduled cases, therefore patients were assigned to one of four cohorts as follows: (1) scheduled/per-algorithm, (2) scheduled/off-algorithm, (3) unscheduled/per-algorithm, or (4) unscheduled/off-algorithm. Primary outcomes included RBCs transfused and estimated blood loss (EBL). Secondary outcomes included perioperative complications and disposition. Results  Overall, 95 patients were identified, with 87 patients meeting inclusion criteria: 36 treated per-algorithm (30 scheduled and 6 unscheduled) and 51 off-algorithm patients (24 scheduled and 27 unscheduled). Among scheduled deliveries, 9 (30.0%) patients treated per-algorithm received RBCs compared with 20 (83.3%) patients treated off-algorithm (p  < 0.01), with a median (interquartile range [IQR]) of 3.0 (2.0, 4.0) and 6.0 (2.5, 7.5) units transfused (p  = 0.13), respectively. Among unscheduled deliveries, 5 (83.3%) per-algorithm patients were transfused RBCs compared with 25 (92.6%) off-algorithm patients (p  = 0.47) with a median (IQR) of 4.0 (2.0, 6.0) and 8.0 (3.0, 10.0) units transfused (p  = 0.47), respectively. Perioperative complications were similar between cohorts. Conclusion  A multidisciplinary algorithm including P-UAE and selective use of delayed hysterectomy is associated with a lower rate of blood transfusion in scheduled but not unscheduled cases. Key Points An algorithm with delayed hysterectomy had less transfusion in scheduled, but not unscheduled, cases. Over time, more cases were managed per algorithm; among scheduled cases, the transfusion rate and volume transfused decreased. There were similar transfusion outcomes among off-algorithm cases, regardless if delivery was scheduled. [ABSTRACT FROM AUTHOR]

Using NOAA's S-band High-Power Snow-Level Radar (HPSLR), a technique for estimating the rain drop size distribution (DSD) above the radar is presented. This technique assumes the DSD can be described by a four parameter, generalized gamma distribution (GGD). Using the radar's measured average Doppler velocity spectrum and a value (assumed, measured, or estimated) of the vertical air motion w, an estimate of the GGD is obtained. Four different methods can be used to obtain w. One method that estimates a mean mass-weighted raindrop diameter Dm from the measured reflectivity Z produces realistic DSDs compared to prior literature examples. These estimated DSDs provide evidence that the radar can retrieve the smaller drop sizes constituting the "drizzle" mode part of the DSD. This estimation technique was applied to 19 h of observations from Hankins, North Carolina. Results support the concept that DSDs can be modeled using GGDs with a limited range of parameters. Further work is needed to validate the described technique for estimating DSDs in more varied precipitation types and to verify the vertical air motion estimates. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study was to develop a system which used the BERT natural language understanding model to identify pedal misapplication (PM) crashes from their crash narratives and validate the accuracy of the system.Methods: The training dataset used for this study was 11 cases from the NMVCCS study and 952 cases from the North Carolina state crash database. Cases for this study were selected from their respective full datasets using a keyword search algorithm containing terms indicative of a pedal-related mistake. A BERT language model was used to classify each case narrative as either no pedal misapplication, PM by vehicle 1, PM by vehicle 2, or PM by vehicle 3. After training, the language model was used to determine the incidence of pedal misapplication in a test dataset of 8,668 North Carolina and NMVCCS cases and these results were compared to a manual review of the dataset. After manual review, 2,969 cases were pedal misapplications.Results: The model's AUC ROC performance at detecting PM was quantified on the entire testing dataset to evaluate the power of the system to generalize to case narratives unseen at training time. The AUC ROC value was 0.9835, indicating strong generalization to all crash narratives. By choosing the optimal threshold using the ROC curve, the system correctly identified PM in 95.7% of crash narratives. When pedal misapplication was correctly identified, the correct vehicle was identified in 95.9% of cases. A total of 3,062 pedal misapplications were identified. The model labeled cases 353 times faster than a researcher.Conclusions: The strong performance of the model suggests that the automated interpretation of case narratives can be used for future research studies without any manual review. This would save time and enable the use of datasets where manual review would be infeasible. The automated extraction of information from crash narratives using deep learning natural language models has not been demonstrated previously in the literature, to the best of the authors' knowledge. This technique can be applied to large, infrequently used datasets of crash narratives and extended to extract useful vehicle, occupant, or environment information to make these datasets amenable to traditional statistical analyses. [ABSTRACT FROM AUTHOR]

Decision tree (DT) algorithms have been applied for classification and change detection in various geospatial studies and more recently, for urban expansion and land use/land cover (LULC) change modeling. However, these studies have not elaborated on specification of DT algorithms regarding data sampling, predictor variables, model configuration, and model evaluation. The focus of this study is to explore several balanced and unbalanced sampling methods, various predictor variables, different configurations of stopping rules, and reliable evaluation metrics to enhance the performance of classification and regression tree (CART), one of the most efficacious DT algorithms, for urban expansion modeling. The implementation of the model in the Triangle Region, North Carolina (NC) State, over the period of 2001 to 2011 demonstrates a striking performance with the training accuracy of 97%, the testing accuracy of 94%, and the Kappa value of 0.80. This performance was achieved using a training dataset containing all changed land cells and three times of that randomly selected from unchanged land cells and regulating the minimum number of records in a leaf node equal to 1, the minimum number of records in a parent node equal to 2, and the value of 10,000 for the maximum number of splits. The CART DT algorithm indicates that proximity to built areas, proximity to highways, current LULC type, elevation, and distance to water bodies are the most significant predictor variables for the urban expansion prediction in the study area. [ABSTRACT FROM AUTHOR]

Objectives: Tracking nonfatal overdoses in the escalating opioid overdose epidemic is important but challenging. The objective of this study was to create an innovative case definition of opioid overdose in North Carolina emergency medical services (EMS) data, with flexible methodology for application to other states' data. Methods: This study used de-identified North Carolina EMS encounter data from 2010-2015 for patients aged >12 years to develop a case definition of opioid overdose using an expert knowledge, rule-based algorithm reflecting whether key variables identified drug use/poisoning or overdose or whether the patient received naloxone. We text mined EMS narratives and applied a machine-learning classification tree model to the text to predict cases of opioid overdose. We trained models on the basis of whether the chief concern identified opioid overdose. Results: Using a random sample from the data, we found the positive predictive value of this case definition to be 90.0%, as compared with 82.7% using a previously published case definition. Using our case definition, the number of unresponsive opioid overdoses increased from 3412 in 2010 to 7194 in 2015. The corresponding monthly rate increased by a factor of 1.7 from January 2010 (3.0 per 1000 encounters; n = 261 encounters) to December 2015 (5.1 per 1000 encounters; n = 622 encounters). Among EMS responses for unresponsive opioid overdose, the prevalence of naloxone use was 83%. Conclusions: This study demonstrates the potential for using machine learning in combination with a more traditional substantive knowledge algorithm-based approach to create a case definition for opioid overdose in EMS data. [ABSTRACT FROM AUTHOR]