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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]

In a recidivism prediction context, there is no consensus on which modeling strategy should be followed for obtaining an optimal prediction model. In previous papers, a range of statistical and machine learning techniques were benchmarked on recidivism data with a binary outcome. However, two important tree ensemble methods, namely gradient boosting and random forests were not extensively evaluated. In this paper, we further explore the modeling potential of these techniques in the binary outcome criminal prediction context. Additionally, we explore the predictive potential of classical statistical and machine learning methods for censored time-to-event data. A range of statistical manually specified statistical and (semi-)automatic machine learning models is fitted on Dutch recidivism data, both for the binary outcome case and censored outcome case. To enhance generalizability of results, the same models are applied to two historical American data sets, the North Carolina prison data. For all datasets, (semi-) automatic modeling in the binary case seems to provide no improvement over an appropriately manually specified traditional statistical model. There is however evidence of slightly improved performance of gradient boosting in survival data. Results on the reconviction data from two sources suggest that both statistical and machine learning should be tried out for obtaining an optimal model. Even if a flexible black-box model does not improve upon the predictions of a manually specified model, it can serve as a test whether important interactions are missing or other misspecification of the model are present and can thus provide more security in the modeling process. [ABSTRACT FROM AUTHOR]

Joint models with shared Gaussian random effects have been conventionally used in analysis of longitudinal outcome and survival endpoint in biomedical or public health research. However, misspecifying the normality assumption of random effects can lead to serious bias in parameter estimation and future prediction. In this paper, we study joint models of general longitudinal outcomes and survival endpoint but allow the underlying distribution of shared random effect to be completely unknown. For inference, we propose to use a mixture of Gaussian distributions as an approximation to this unknown distribution and adopt an Expectation-Maximization (EM) algorithm for computation. Either AIC and BIC criteria are adopted for selecting the number of mixtures. We demonstrate the proposed method via a number of simulation studies. We illustrate our approach with the data from the Carolina Head and Neck Cancer Study (CHANCE). [ABSTRACT FROM AUTHOR]

The Study to Explore Early Development (SEED) is a multi-site case-control study designed to explore the relationship between autism spectrum disorder (ASD) phenotypes and etiologies. The goals of this paper are to (1) describe the SEED algorithm that uses the Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule (ADOS) to classify children with ASD, (2) examine psychometric properties of different ASD classification methods, including the SEED method that incorporates rules for resolving ADI-R and ADOS discordance, and (3) determine whether restricted interests and repetitive behaviors were noted for children who had instrument discordance resolved using ADI-R social and communication scores. Results support the utility of SEED criteria when well-defined groups of children are an important clinical or research outcome. [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]

To assess whether Google search activity predicts lead-time for pediatric respiratory syncytial virus (RSV) encounters within a major health care system. Internet user search and health system encounter database analysis. Pediatric RSV encounter volumes across all clinics and hospitals in the Duke Health system were tabulated from 2005 to 2016. North Carolina Google user search activity for RSV were obtained over the same time period. Time series analysis was used to compare RSV encounters and search activity. Cross-correlation was used to determine the 'lag' time difference between Google user search interest for RSV and observed Pediatric RSV encounter volumes. Google search activity and Pediatric RSV encounter volumes demonstrated strong seasonality with predilection for winter months. Granger Causality testing revealed that North Carolina RSV Google search activity can predict pediatric RSV encounters at our health system (F = 5.72, p < 0.0001). Using cross-correlation, increases in Google search activity provided lead time of 0.21 weeks (1.47 days) prior to observed increases in Pediatric RSV encounter volumes at our health system. RSV is a common cause of upper airway obstruction in pediatric patients for which pediatric otolaryngologists are consulted. We demonstrate that Google search activity can predict RSV patient interactions with a major health system with a measurable lead-time. The ability to predict when illnesses in a population result in increased health care utilization would be an asset to health system providers, planners and administrators. Prediction of RSV would allow specific care pathways to be developed and resource needs to be anticipated before actual presentation. [ABSTRACT FROM AUTHOR]

North Carolina's constitution requires that state legislative districts should not split counties. However, counties must be split to comply with the "one person, one vote" mandate of the U.S. Supreme Court. Given that counties must be split, the North Carolina legislature and the courts have provided guidelines that seek to reduce counties split across districts while also complying with the "one person, one vote" criterion. Under these guidelines, the counties are separated into clusters; each cluster contains a specified number of districts and that are drawn independent from other clusters. The primary goal of this work is to develop, present, and publicly release an algorithm to optimally cluster counties according to the guidelines set by the court in 2015. We use this tool to investigate the optimality and uniqueness of the enacted clusters under the 2017 redistricting process. We verify that the enacted clusters are optimal, but find other optimal choices. We emphasize that the tool we provide lists all possible optimal county clusterings. We also explore the stability of clustering under changing statewide populations and project what the county clusters may look like in the next redistricting cycle beginning in 2020/2021. for this article are available online. [ABSTRACT FROM AUTHOR]

Objectives To better understand the relationship between perceived risk of falling and awareness and adoption of four specific precautions that older adults have taken to reduce this risk. Design Cross-sectional. Setting Data were collected in in-person interviews conducted in the homes of study participants. Interviews conducted between March 2011 and September 2013 and lasted an average of 60-90 minutes. Participants A stratified sampling strategy designed to enroll an equal number of homebound and nonhomebound participants was used. All participants (N = 164) were recruited from central North Carolina. Measurements Participants were asked about 1-year fall history, perceived risk of falling, restriction of activities because of fear of falling, awareness of four recommended fall prevention behaviors (exercise, annual medication review, bathroom grab bars, safe footwear), and current practice of these behaviors. Results In bivariate analyses, individuals who were aware of two behaviors recommended to reduce the risk of falling (exercise, use of safe footwear) and had adopted these behaviors perceived their risk of falling as lower than individuals who were aware of the recommended behaviors but had not adopted them. Moreover, in multivariate analyses, individuals who did not know that exercise is recommended to reduce the risk of falling perceived their risk of falling as lower than those who were aware of this recommendation and had adopted it. Individuals were least likely to be aware that medication reviews and exercise are recommended to reduce fall risk. Conclusion Awareness of behaviors recommended to reduce fall risk appears necessary for adoption of these behaviors to reduce perceived risk. Fall-prevention campaigns should emphasize behaviors where awareness is low. [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]

Nearshore morphology is a key driver in wave breaking and the resulting nearshore circulation, recreational safety, and nutrient dispersion. Morphology persists within the nearshore in specific shapes that can be classified into equilibrium states. Equilibrium states convey qualitative information about bathymetry and relevant physical processes. While nearshore bathymetry is a challenge to collect, much information about the underlying bathymetry can be gained from remote sensing of the surfzone. This study presents a new method to automatically classify beach state from Argus daytimexposure imagery using a machine learning technique called convolutional neural networks (CNNs). The CNN processed imagery from two locations: Narrabeen, New South Wales, Australia and Duck, North Carolina, USA. Three different CNN models are examined, one trained at Narrabeen, one at Duck, and one trained at both locations. Each model was tested at the location where it was trained in a self-test, and the single-beach models were tested at the location where it was not trained in a transfer-test. For the self-tests, skill (as measured by the F-score) was comparable to expert agreement (CNN F-values at Duck = 0.80 and Narrabeen = 0.59). For the transfer-tests, the CNN model skill was reduced by 24–48%, suggesting the algorithm requires additional local data to improve transferability performance. Transferability tests showed that comparable F-scores (within 10%) to the self-trained cases can be achieved at both locations when at least 25% of the training data is from each site. This suggests that if applied to additional locations, a CNN model trained at one location may be skillful at new sites with limited new imagery data needed. Finally, a CNN visualization technique (Guided-Grad-CAM) confirmed that the CNN determined classifications using image regions (e.g., incised rip channels, terraces) that were consistent with beach state labelling rules. [ABSTRACT FROM AUTHOR]