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This research utilizes data collected in Florida to examine the differentials in injury severities among single-vehicle drivers involved in work zone-related incidents, specifically focusing on the distinctions between rural and urban areas. The study encompasses a four-year period (2016-2019) of crash dataset. A likelihood ratio test was performed to examine model estimate's temporal consistency in datasets from rural and urban areas across several time periods throughout the year. Separate statistical models were estimated for both rural and urban datasets to understand different driver injury severity outcomes (no injury, minor injury, and severe injury) using a mixed logit approach with possible heterogeneity in mean and variance of random parameters. Out-of-sample simulations were conducted to see the effect of different parameter changes on injury severity probabilities in rural and urban work zone crashes. Over multiple years, various years in both rural and urban models have generated statistically significant random factors that effectively capture the presence of heterogeneity in means, accounting for unobservable variations within the data. Clear evidence of factors such as speed limits, work zone type, and traffic volume affecting the work zone injury severities were found to vary significantly between rural and urban work zone areas. However, despite this difference, rural and urban work zones share common safety problems and countermeasures such as driver education, improved signage, and appropriate traffic controls; combining ITS technologies and enhanced law enforcement can help mitigate crash severity in urban and rural work zone areas., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective : Develop corridor-level network screening models to identify high-risk corridors where safety improvements could be implemented to reduce fatal and injury (FI) crashes. Methods : A novel corridor definition focused on context classification and lane count was developed and applied to urban and suburban four-lane divided arterial roadways in Florida. Negative binomial regression models were developed for multi- and single-vehicle crashes using 80% of the corridors (training set). Crash frequency predictions were obtained from the developed corridor models and similar site-level models from the Highway Safety Manual (HSM) models for the remaining 20% of the corridors (testing set). Results from all models were adjusted using the empirical Bayes (EB) method. Results : A total of 130 corridors were identified across seven counties. These corridors contained approximately 349 km (217 miles) of roadway and experienced 11,437 multi-vehicle and 746 single-vehicle crashes that resulted in fatalities or injuries from 2017 to 2021. After applying the HSM site-level models and the developed corridor-level models to the testing set (both with and without EB adjustments), the corridor-level models with EB adjustments were the most accurate for corridor crash prediction. Applying the corridor-level models with EB adjustments to the testing set gave a predicted value of 386.44 crashes/year, which was the closest to the observed crash frequency of 383.20 crashes/year. From the corridor-level models, a 3.48-km (2.16-mile) high-risk corridor in Miami-Dade County was identified and analyzed site-by-site using the HSM methodology to identify specific sites within the corridor where safety improvements could provide the most FI crash reductions. Conclusions : The corridor-level models were more accurate and statistically reliable than similar HSM models while being less data intensive. They also only required corridor-level data rather than data for each intersection and segment. By using readily available data, the methods in this paper can be easily replicated by agencies to develop their own network screening corridor-level models and expedite the identification of corridors in need of safety improvements to reduce FI crashes. Existing site-level network screening methods can be used to supplement the developed corridor-level methodology by identifying high-risk sites within identified high-risk corridors.

Recently, it has been shown how to estimate model-adjusted risks, risk differences, and risk ratios from complex survey data based on risk averaging and SUDAAN (Research Triangle Institute, Research Triangle Park, North Carolina). The authors present an alternative approach based on marginal structural models (MSMs) and SAS (SAS Institute, Inc., Cary, North Carolina). The authors estimate the parameters of the MSM using inverse weights that are the product of 2 terms. The first term is a survey weight that adjusts the sample to represent the unstandardized population. The second term is an inverse-probability-of-exposure weight that standardizes the population in order to adjust for confounding; it must be estimated using the survey weights. The authors show how to use the MSM parameter estimates and contrasts to test and estimate effect-measure modification; SAS code is provided. They also explain how to program the previous risk-averaging approach in SAS. The 2 methods are applied and compared using data from the 2007 Florida Behavioral Risk Factor Surveillance System Survey to assess effect modification by age of the difference in risk of cost barriers to health care between persons with disability and persons without disability. [ABSTRACT FROM PUBLISHER]

In recent years, pedestrian safety has emerged to be of major concern to selected States in the US and at the national level. With the transition to the mobility of people, pedestrian safety has become more prevalent for governmental agencies to address and prioritize for strategic implementation. It was crucial to address the pedestrian-vehicular conflict within Florida as it was reported as having the highest four pedestrian incident locations in the country. Many pedestrian sidewalks in Florida are not continuous and there is a concern among planners and engineers in the Florida Department of Transportation (FDOT) that these gaps constitute discontinuity of flow and are potentially posing threats to pedestrian safety. This paper investigates the correlation between pedestrian safety and sidewalk gaps within the Central Florida region. A sidewalk gap is defined as a length of a roadway segment with missing or absent sidewalk between existing sidewalks (before and after) along the same segment. "Missing" sidewalks are locations where the sidewalk is not only absent, but there is a desire to have one constructed or it would be used if it were constructed. The missing sidewalk can be on one side of the road or both sides. A wide array of Geographic Information Systems (GIS) information associated with the sidewalk gaps and the safety data were secured by FDOT. The data included all the Roadway Characteristics Inventory (RCI) within the district. Crash data combined with other geometric design data, traffic data, and census data were used to develop a statistical model. The results showed that presence of sidewalk along roadway segments is one of the main factors that have significant impact on the expected number of pedestrian crashes at a specific location. Other factors included daily traffic volumes, roadway category, specifically along urban two-way divided and undivided arterials with four to six lanes as well as the population within half-mile radius surrounding the crash location. The model results revealed that the Incident Risk Ratio (IRR) of a pedestrian crash along roadways with no sidewalk is 1.67 times greater than the likelihood of a crash with the presence of a sidewalk at 95% Confidence Interval (C.I.). The analysis also concluded that the likelihood of a pedestrian crash per mile along roadways with no sidewalk is three times greater than the likelihood of a crash per mile with the presence of a sidewalk., (Published by Elsevier Ltd.)

Introduction: Safety performance functions (SPF) are employed to predict crash counts at the different roadway elements. Several SPFs were developed for the various roadway elements based on different classifications such as functional classification and area type. Since a more detailed classification of roadway elements leads to more accurate crash predictions, multiple states have developed new classification systems to classify roads based on a comprehensive classification. In Florida, the new roadway context classification system incorporates geographic, demographic, and road characteristics information., Method: In this study, SPFs were developed in the framework of the FDOT roadway context classification system at three levels of modeling, context classification (CC-SPFs), area type (AT-SPFs), and statewide (SW-SPF) levels. Crash and traffic data from 2015-2019 were obtained. Road characteristics and road environment information have also been gathered along Florida roads for the SPF development., Results: The developed SPFs showed that there are several variables that influence the frequency of crashes, such as annual average daily traffic (AADT), signalized intersections and access point densities, speed limit, and shoulder width. However, there are other variables that did not have an influence in crash occurrence such as concrete surface and the presence of bicycle slots. CC-SPFs had the best performance among others. Moreover, network screening to determine the most problematic road segments has been accomplished. The results of the network screening indicated that the most problematic roads in Florida are the suburban commercial and the urban general roads. Practical Applications: This research provides a solid reference for decision-makers regarding crash prediction and safety improvement along Florida roads., (Copyright © 2021. Published by Elsevier Ltd.)

A central tenant of the Comprehensive Everglades Restoration Plan (CERP) is nutrient reduction to levels supportive of ecosystem health. A particular focus is phosphorus. We examine links between agricultural production and phosphorus concentration in the Everglades headwaters: Kissimmee River basin and Lake Okeechobee, considered an important source of water for restoration efforts. Over a span of 47 years we find strong correspondence between milk production in Florida and total phosphate in the lake, and, over the last decade, evidence that phosphorus concentrations in the lake water column may have initiated a long-anticipated decline., Competing Interests: The authors have declared that no competing interests exist.

Traditionally, in developing non-motorized crash prediction models, safety researchers have employed land use and urban form variables as surrogate for exposure information (such as pedestrian, bicyclist volumes and vehicular traffic). The quality of these crash prediction models is affected by the lack of "true" non-motorized exposure data. High-resolution modeling frameworks such as activity-based or trip-based approach could be pursued for evaluating planning level non-motorist demand. However, running a travel demand model system to generate demand inputs for non-motorized safety is cumbersome and resource intensive. The current study is focused on addressing this drawback by developing an integrated non-motorized demand and crash prediction framework for mobility and safety analysis. Towards this end, we propose a three-step framework to evaluate non-motorists safety: (1) develop aggregate level models for non-motorist generation and attraction at a zonal level, (2) develop non-motorists trip exposure matrices for safety evaluation and (3) develop aggregate level non-motorists crash frequency and severity proportion models. The framework is developed for the Central Florida region using non-motorist demand data from National Household Travel Survey (2009) Florida Add-on and non-motorist crash frequency and severity data from Florida. The applicability of the framework is illustrated through extensive policy scenario analysis., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Objectives. To determine the number of excess deaths (i.e., those exceeding historical trends after accounting for COVID-19 deaths) occurring in Florida during the COVID-19 pandemic. Methods. Using seasonal autoregressive integrated moving average time-series modeling and historical mortality trends in Florida, we forecasted monthly deaths from January to September of 2020 in the absence of the pandemic. We compared estimated deaths with monthly recorded total deaths (i.e., all deaths regardless of cause) during the COVID-19 pandemic and deaths only from COVID-19 to measure excess deaths in Florida. Results. Our results suggest that Florida experienced 19 241 (15.5%) excess deaths above historical trends from March to September 2020, including 14 317 COVID-19 deaths and an additional 4924 all-cause, excluding COVID-19, deaths in that period. Conclusions. Total deaths are significantly higher than historical trends in Florida even when accounting for COVID-19-related deaths. The impact of COVID-19 on mortality is significantly greater than the official COVID-19 data suggest.

Many species distribution maps indicate the ranges of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) overlap in Florida despite the well-documented range reduction of Ae. aegypti. Within the last 30 yr, competitive displacement of Ae. aegypti by Ae. albopictus has resulted in partial spatial segregation of the two species, with Ae. aegypti persisting primarily in urban refugia. We modeled fine-scale distributions of both species, with the goal of capturing the outcome of interspecific competition across space by building habitat suitability maps. We empirically parameterized models by sampling 59 sites in south and central Florida over time and incorporated climatic, landscape, and human population data to identify predictors of habitat suitability for both species. Our results show human density, precipitation, and urban land cover drive Ae. aegypti habitat suitability, compared with exclusively climatic variables driving Ae. albopictus habitat suitability. Remotely sensed variables (macrohabitat) were more predictive than locally collected metrics (microhabitat), although recorded minimum daily temperature showed significant, inverse relationships with both species. We detected minor Aedes habitat segregation; some periurban areas that were highly suitable for Ae. albopictus were unsuitable for Ae. aegypti. Fine-scale empirical models like those presented here have the potential for precise risk assessment and the improvement of operational applications to control container-breeding Aedes mosquitoes., (© The Author(s) 2020. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Safety Performance Functions (SPFs) have been widely used by researchers and practitioners to conduct roadway safety evaluation. Traditional SPFs are usually developed by using annual average daily traffic (AADT) along with geometric characteristics. However, the high level of aggregation may lead to a failure to capture the temporal variation in traffic characteristics (e.g., traffic volume and speed) and crash frequencies. In this study, SPFs at different aggregation levels were developed based on microscopic traffic detector data from California, Florida, and Virginia. More specifically, five aggregation levels were considered: (1) annual average weekday hourly traffic (AAWDHT), (2) annual average weekend hourly traffic (AAWEHT), (3) annual average weekday peak/off-peak traffic (AAWDPT), (4) annual average day of the week traffic (AADOWT), and (5) annual average daily traffic (AADT). Model estimation results showed that the segment length and volume, as exposure variables, are significant across all the aggregation levels. Average speed is significant with a negative coefficient, and the standard deviation of speed was found to be positively associated with the crash frequency. It is noteworthy that the operation of the high occupancy vehicle (HOV) lanes was found to have a positive effect on crash frequency across all the aggregation levels. The model results also showed that the AAWDPT and AADOWT models consistently performed better (the improvements range from 3.14%-16.20%) than the AADT-based SPF, which implies that the differences between the day of the week and peak/off-peak periods should be considered in the development of crash prediction models. The model transferability results indicated that the SPFs between Florida and Virginia are transferrable, while the models between California and the other two states are not transferrable., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background: Geographic information systems (GISs) are often used to analyze trauma systems. Geographic information system-based approaches can model access to a trauma center (TC), including estimates of transport time and population coverage, when accurate trauma registry and emergency medical systems (EMS) data are not available. We hypothesized that estimates of trauma system performance calculated using a standard GIS method with public data would be comparable with trauma registry data., Methods: A standardized GIS-based method was used to estimate metrics of TC access in a regional trauma system in which the number of TCs increased from one to three during a 3-year period. Registry data from the index TC in the system were evaluated for different periods during this evolution. The number of admissions to the TC in different periods was compared with changes predicted by the GIS-based model, and the distribution of observed ground-based transportation times was compared with the predicted distribution., Results: With the addition of two TCs to the system, the volume of patients transported by ground to the index TC decreased by 30%. However, the model predicted a 68% decrease in population having the shortest predicted transport time to the index TC. The model predicted the geographic trend seen in the registry data, but many patients were transported to the index TC even though it was not the closest center. Observed transport times were uniformly shorter than predicted times., Conclusion: The GIS-based model qualitatively predicted changes in distribution of trauma patients, but registry data highlight that field triage decisions are more complex than model assumptions. Similarly, transport times were systematically overestimated. This suggests that model assumptions, such as vehicle speed, based on normal traffic may not fully reflect emergency medical systems (EMS) operations. There remains great need for metrics to guide policy based on widely available data., Level of Evidence: Epidemiological, level III.

Multinomial N -mixture models are commonly used to fit data from a removal sampling protocol. If the mixing distribution is negative binomial, the distribution of the counts does not appear to have been identified, and practitioners approximate the requisite likelihood by placing an upper bound on the embedded infinite sum. In this paper, the distribution which underpins the multinomial N -mixture model with a negative binomial mixing distribution is shown to belong to the broad class of multivariate negative binomial distributions. Specifically, the likelihood can be expressed in closed form as the product of conditional and marginal likelihoods and the information matrix shown to be block diagonal. As a consequence, the nature of the maximum likelihood estimates of the unknown parameters and their attendant standard errors can be examined and tests of the hypothesis of the Poisson against the negative binomial mixing distribution formulated. In addition, appropriate multinomial N -mixture models for data sets which include zero site totals can also be constructed. Two illustrative examples are provided., (© 2019 The International Biometric Society.)

The current study focuses on adolescents with sex offense histories and examines sexual reoffending patterns within 2 years of a prior sex offense. We employed inductive statistical models using archival official records maintained by the Florida Department of Juvenile Justice (FDJJ), which provides social, offense, placement, and risk assessment history data for all youth referred for delinquent behavior. The predictive accuracy of the random forest models is tested using receiver operator characteristic (ROC) curves, the area under the curve (AUC), and precision/recall plots. The strongest predictor of sexual recidivism was the number of prior felony and misdemeanor sex offenses. The AUC values range between 0.71 and 0.65, suggesting modest predictive accuracy of the models presented. These results parallel the existing literature on sexual recidivism and highlight the challenges associated with predicting sex offense recidivism. Furthermore, results inform risk assessment literature by testing various factors recorded by an official institution.

Objective: To develop a physiological data-driven model for early identification of impending cardiac arrest in neonates and infants with cardiac disease hospitalised in the cardiovascular ICU., Methods: We performed a single-institution retrospective cohort study (11 January 2013-16 September 2015) of patients ≤1 year old with cardiac disease who were hospitalised in the cardiovascular ICU at a tertiary care children's hospital. Demographics and diagnostic codes of cardiac arrest were obtained via the electronic health record. Diagnosis of cardiac arrest was validated by expert clinician review. Minute-to-minute physiological monitoring data were recorded via bedside monitors. A generalized linear model was used to compute a minute by minute risk score. Training and test data sets both included data from patients who did and did not develop cardiac arrest. An optimal risk-score threshold was derived based on the model's discriminatory capacity for impending arrest versus non-arrest. Model performance measures included sensitivity, specificity, accuracy, likelihood ratios, and post-test probability of arrest., Results: The final model consisting of multiple clinical parameters was able to identify impending cardiac arrest at least 2 hours prior to the event with an overall accuracy of 75% (sensitivity = 61%, specificity = 80%) and observed an increase in probability of detection of cardiac arrest from a pre-test probability of 9.6% to a post-test probability of 21.2%., Conclusions: Our findings demonstrate that a predictive model using physiologic monitoring data in neonates and infants with cardiac disease hospitalised in the paediatric cardiovascular ICU can identify impending cardiac arrest on average 17 hours prior to arrest.

Public health and governmental organizations have acknowledged the importance of obtaining information of various characteristics for small areas, such as counties. Spatial smoothing models have been developed to gain reliable information on the geographical distribution of the outcome of interest. When the geographical analysis is based on survey data, two issues pose challenges: (1) the complex design of the survey and (2) the presence of missing data due to non-response. We investigate the influence of missing data and the adjustment thereof in the context of the 2013 Florida Behavioral Risk Factor Surveillance System (BRFSS) health survey. We focus on the application and comparison of the Hajek ratio estimator and two model-based approaches for estimation of the spatial trend of the prevalence of having no health insurance coverage. The model-based methods are compared using the Deviance Information Criterion which show the benefits of modeling the weights as flexibly as possible. Methods are extended towards subgroup analyses and the estimation of area-specific standardized rates, where household incomes was identified as an important factor to include in the analysis., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The objective of this study was to develop crash modification factors (CMFs) for bicycle crashes for different roadway segment and intersection facility types in urban areas. The study used four years (2011-2014) of crash data from Florida to quantify the safety impacts of roadway and traffic characteristics, bicycle infrastructure, and bicycle activity data on bicycle crashes. A cross-sectional analysis using Generalized Linear Model (GLM) approach with Zero Inflated Negative Binomial (ZINB) distribution was adopted to develop the relevant regression models in this study. Lane width, speed limit, and grass in the median were observed to have positive impacts on reducing bicycle crashes. On the contrary, presence of sidewalk and sidewalk barrier were found to increase the bicycle crash probabilities. Increased bicycle activity was found to reduce the bicycle crash probabilities on segments, while increased bicycle activity resulted in higher bicycle crash probabilities at intersections. Bus stops were found to increase the bicycle crash probabilities at intersections, whereas, protected signal control had a positive impact on bicycle safety. This research provides a greater insight into how various characteristics affect bicycle safety, a topic that is seldom considered by researchers and practitioners., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The mosquito Aedes ( Stegomyia ) aegypti ( L .) is the primary vector of dengue, chikungunya, and Zika viruses in the United States. Surveillance for adult Ae. aegypti is limited, hindering understanding of the mosquito's seasonal patterns and predictions of areas at elevated risk for autochthonous virus transmission. We developed a simple, intuitive empirical model that uses readily available temperature and humidity variables to predict environmental suitability for low, medium, or high potential abundance of adult Ae. aegypti in a given city 1 month in advance. Potential abundance was correctly predicted in 73% of months in arid Phoenix, AZ (over a 10-year period), and 63% of months in humid Miami, FL (over a 2-year period). The monthly model predictions can be updated daily, weekly, or monthly and thus may be applied to forecast suitable conditions for Ae. aegypti to inform vector-control activities and guide household-level actions to reduce mosquito habitat and human exposure.

Objective: Nighttime crashes are overrepresented on the U.S. highway system. Roadway lighting, which provides additional visibility by supplementing vehicle headlights, has been identified as an effective countermeasure to improve nighttime safety. However, the existing literature does not provide a thorough understanding of the effects of street lighting photometric characteristics on nighttime crash occurrence on roadway segments. This study aimed to investigate the relationship between lighting photometric measures and nighttime crash risk on roadway segments and develop a crash modification function/factor (CMF). Methods: The research team collected horizontal illuminance data on 440 roadway segments between 2 successive signalized intersections in Florida for 2012-2014 and matched 4 years of nighttime and daylight crash data (2011-2014). Random parameter negative binomial models were estimated for both nighttime and daylight crash frequencies. The expected night-to-day crash odds ratio, as an equivalent of CMF, was derived from the fitted models with the correction of estimation variances. The confidence intervals (CIs) of the developed CMF were estimated using the Cox method. Results: The coefficient of the mean of horizontal illuminance is significantly negative in the nighttime model. The coefficients of the standard deviation of horizontal illuminance are significantly positive and normally distributed in both the nighttime and daylight models. The significance of the standard deviation in the daylight model captures the confounding effects-a high standard deviation correlates with high traffic exposures, poor safety design standards, and low maintenance quality. The CMF based on the expected daylight-to-day odds ratio was developed as an exponential function of the increments and the increment squares of the mean and the standard deviation of horizontal illuminance. Its 95% CIs indicate that the CMF is almost significant over the whole range. Other significant variables contributing to nighttime crash risk include annual average daily traffic, truck percentage, segment length, access density, undivided roads, and urban/city limits. Conclusions: Horizontal illuminance characteristics have a significant impact on nighttime crash risk on roadway segments. An increase in the mean of horizontal illuminance, indicating an improvement in average lighting level, tends to decrease nighttime crash risk; an increase in the standard deviation, representing a poor uniformity of lighting pattern on a roadway segment, is more likely to raise nighttime crash risk. Because the 2 measures are strongly correlated in a low mean range (<0.44 fc), the 2 photometric measures need to be considered together to interpret the safety effects of lighting patterns. The standard deviation shows better performance in measuring lighting uniformity on a roadway segment than the traditional ratios (max-to-min and mean-to-min). However, a new photometric measure is needed to capture the true lighting pattern influencing driver vision at night.

The increase in 65 years and older population in the United States compels the investigation of the crashes involving all aging (65+) roadway users (drivers, passengers, bicyclists, and pedestrians) in order to ensure their safety. As such, the objective of this research is to provide a spatiotemporal comparative investigation of the crashes involving these aging roadway users in Florida via concurrently using the same set of predictors in order to obtain comparable findings among them. First, a new metric, namely Crash Rate Difference (CRD) approach is developed, which enables one to capture potential spatial and temporal (e.g., weekend and weekday) variations in crash rates of aging user-involved crashes. Second, a multivariate random parameter Tobit model is utilized to determine the factors that drive both the crash occurrence probability and the crash rate of 65+ roadway users, accounting for the unobserved heterogeneity. Findings show that there are statistically significant heterogeneous effects of predictors on the crash rates of different roadway users, which evidences the unobserved heterogeneity across observations. Results also indicate that the presence of facilities such as hospitals, religious facilities, or supermarkets is very influential on crash rates of 65+ roadway users, advocating that roadways around these facilities should be particularly scrutinized by road safety stakeholders. Interestingly, the effect of these facilities on crashes also differs significantly between weekdays and weekends. Moreover, the roadway segments with high crash rates vary temporally depending on whether it is a weekday or a weekend. These findings regarding the spatiotemporal variations clearly indicate the need to develop and design better traffic safety measures and plans addressing these specific roadway segments, which can be tailored to alleviate traffic safety problems for 65+ roadway users., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The geographic distribution of lung cancer rates tends to vary across a geographic landscape, and covariates (e.g., smoking rates, demographic factors, socio-economic indicators) commonly are employed in spatial analysis to explain the spatial heterogeneity of these cancer rates. However, such cancer risk factors often are not available, and conventional statistical models are unable to fully capture hidden spatial effects in cancer rates. Introducing random effects in the model specifications can furnish an efficient approach to account for variations that are unexplained due to omitted variables. Especially, a random effects model can be effective for a phenomenon that is static over time. The goal of this paper is to investigate geographic variation in Florida lung cancer incidence data for the time period 2000⁻2011 using random effects models. In doing so, a Moran eigenvector spatial filtering technique is utilized, which can allow a decomposition of random effects into spatially structured (SSRE) and spatially unstructured (SURE) components. Analysis results confirm that random effects models capture a substantial amount of variation in the cancer data. Furthermore, the results suggest that spatial pattern in the cancer data displays a mixture of positive and negative spatial autocorrelation, although the global map pattern of the random effects term may appear random.

In the USA, the United States Department of Agriculture (USDA) Natural Resource Conservation Service (NRCS) has restored millions of acres of wetlands through its Wetland Reserve Easement (WRE) programs. However, few quantitative studies have explored whether WREs have enhanced wetland hydrology and wetland plant communities. Additionally, USDA Compatible Use Permits for cattle grazing and other management practices are sometimes issued for WREs, but little is known about potential benefits/detriments of such practice on the success of wetland restoration. In this study, we tested if hydrological restoration of previously drained species poor pastures increased water depth and hydroperiod. Restoration involved plugging key ditches, adding water control structures and a berm. We also tested if hydrological restoration increased plant diversity (alpha and beta), floristic quality (using coefficient of conservatism) and increased the cover of wetland species (using species wetland status). Finally, we tested if cattle grazing had an effect on the success of restoration by comparing grazed plots to fenced plots. We studied two conservation easements (a total of 748 acres) located on semi-native pastures in central Florida, USA. We monitored vegetation using permanent transects stratified by vegetation type before (2004-2005) and after (2012) the restoration (2008). We assessed wetland hydroperiod using groundwater wells set up in 2003 and located within and outside the boundaries of these two easements. We used linear mixed models and multivariate analyses to compare vegetation and hydroperiods pre- and post-restoration. Number of flooded days increased following restoration in one of the easements, but we did not detect significant changes in hydrology in the other easement. Floristic quality, beta diversity and cover of obligate wetland species increased in both conservation easements and in most vegetation types. These vegetation changes were likely due to restoration activities since annual rainfall was not significantly different pre- and post-restoration. Cattle grazing did not have a negative or positive effect on the success of restoration, nor did we detect any positive effect of grazing on the success of restoration. Overall, our study shows that hydrological restoration can enhance wetland hydroperiod, water depth and wetland vegetation, but more resources should be allocated to short- and long-term monitoring of the restoration success., Competing Interests: The authors received funding form The Mosaic Company (www.mosaicco.com/). This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development, or marketed products to declare.

Background: Duffy's Quality-Caring Model guides nursing practice at the H. Lee Moffitt Cancer Center and Research Institute in Tampa, FL. No published studies using this model in oncology were found., Objectives: The purpose of this article is to determine patients' perceptions of nurse caring behaviors at the time of discharge after surgery., Methods: A descriptive correlational design was used. Data were collected using Duffy's Caring Assessment Tool (CAT). Simple regression analyses were performed., Findings: Spearman's correlation coefficients between the scores and age, length of stay, and number of nurses caring for the patients were not significant. No significant association was found between surgical site and CAT scores, nor between gender, race, and disposition posthospitalization. Appreciation of unique meanings and mutual problem solving showed need for improvement.

Objective: To develop an automated, data-driven, and scale-flexible method to delineate hospital service areas (HSAs) and hospital referral regions (HRRs) that are up-to-date, representative of all patients, and have the optimal localization of hospital visits., Data Sources: The 2011 state inpatient database in Florida from the Healthcare Cost and Utilization Project., Study Design: A network optimization method was used to redefine HSAs and HRRs by maximizing patient-to-hospital flows within each HSA/HRR while minimizing flows between them. We first constructed as many HSAs/HRRs as existing Dartmouth units in Florida, and then compared the two by various metrics. Next, we sought to derive the optimal numbers and configurations of HSAs/HRRs that best reflect the modularity of hospitalization patterns in Florida., Principal Findings: The HSAs/HRRs by our method are favored over the Dartmouth units in balance of region size and market structure, shape, and most important, local hospitalization., Conclusions: The new method is automated, scale-flexible, and effective in capturing the natural structure of the health care system. It has great potential for applications in delineating other health care service areas or in larger geographic regions., (© Health Research and Educational Trust.)

Physicians serve as the nexus of treatment decision-making in hospitalized patients; however, little empirical evidence describes the influence of individual physicians on hospital costs. In this study, we examine the extent to which hospital costs vary across physicians and physician characteristics. We used all-payer data from 2 states representing 15 237 physicians and 2.5 million hospital visits. Regression analysis and propensity score matching were used to understand the role of observable provider characteristics on hospital costs controlling for patient demographics, socioeconomic characteristics, clinical risk, and hospital characteristics. We used hierarchical models to estimate the amount of variation attributable to physicians. We found that the average cost of hospital inpatient stays registered to female physicians was consistently lower across all empirical specifications when compared with male physicians. We also found a negative association between physicians' years of experience and the average costs. The average cost of hospital inpatient stays registered to foreign-trained physicians was lower than US-trained physicians. We observed sizable variation in average costs of hospital inpatient stays across medical specialties. In addition, we used hierarchical methods and estimated the amount of remaining variation attributable to physicians and found that it was nonnegligible (intraclass correlation coefficient [ICC]: 0.33 in the full sample). Historically, most physicians have been reimbursed separately from hospitals, and our study shows that physicians play a role in influencing hospital costs. Future policies and practices should acknowledge these important dependencies. This study lends further support for alignment of physician and hospital incentives to control costs and improve outcomes.

Background: Prominent studies continue to measure the hospital volume-outcome relation using simple logistic or random-effects models. These regression models may not appropriately account for unobserved differences across hospitals (such as differences in organizational effectiveness) which could be mistaken for a volume outcome relation., Objective: To explore alternative estimation methods for measuring the volume-outcome relation for six major cancer operations, and to determine which estimation method is most appropriate., Methods: We analyzed patient-level hospital discharge data from three USA states and data from the American Hospital Association Annual Survey of Hospitals from 2000 to 2011. We studied six major cancer operations using three regression frameworks (logistic, fixed-effects, and random-effects) to determine the correlation between patient outcome (mortality) and hospital volume., Results: For our data, logistic and random-effects models suggest a non-zero volume effect, whereas fixed-effects models do not. Model-specification tests support the fixed-effects or random-effects model, depending on the surgical procedure; the basic logistic model is always rejected. Esophagectomy and rectal resection do not exhibit significant volume effects, whereas colectomy, pancreatic resection, pneumonectomy, and pulmonary lobectomy do., Conclusions: The statistical significance of the hospital volume-outcome relation depends critically on the regression model. A simple logistic model cannot control for unobserved differences across hospitals that may be mistaken for a volume effect. Even when one applies panel-data methods, one must carefully choose between fixed- and random-effects models.

Conflict between work and family is a human resource management issue that is particularly relevant for nurses. Nursing is a demanding profession, and a high proportion of nurses are women, who tend to have greater family responsibilities than men. Little is known regarding work-family conflict among nurses, and even less is known about how this affects newly licensed registered nurses (NLRNs), who can be stressed from their new jobs and careers. This study empirically tests a model of antecedents and outcomes of work-family and family-work conflict among a sample of NLRNs. We developed a model of the relationships between personal and work environment characteristics, work-family and family-work conflicts, job satisfaction, and intent to leave the job and profession. We used structural equation modeling (Amos, IBM SPSS) to test the model with data from.a survey of NLRNs. We examined a number of latent variables, as well as direct and mediating relationships. The measurement models for all latent variables were validated. The final model indicated that age, health, and family responsibilities are antecedents of family-work conflict; job demands lead to work-family conflict; family-work conflict contributes to job difficulties, which lowers job satisfaction, which, in turn, increases the intent to leave the job and profession; and work-family conflict increases the intent to leave the job and profession (but does not directly affect job satisfaction). Policies to help NLRNs with family responsibilities could reduce family-work conflict, which might reduce job difficulties and improve satisfaction and retention. In addition, policies to reduce job demands could reduce work-family conflict and improve retention.

Background: Individuals with pre-diabetes and diabetes have increased risks of developing macro-vascular complications including heart disease and stroke; which are the leading causes of death globally. The objective of this study was to estimate the prevalence of pre-diabetes and diabetes, and to investigate their predictors among adults ≥18 years in Florida., Methods: Data covering the time period January-December 2013, were obtained from Florida's Behavioral Risk Factor Surveillance System (BRFSS). Survey design of the study was declared using SVYSET statement of STATA 13.1. Descriptive analyses were performed to estimate the prevalence of pre-diabetes and diabetes. Predictors of pre-diabetes and diabetes were investigated using multinomial logistic regression model. Model goodness-of-fit was evaluated using both the multinomial goodness-of-fit test proposed by Fagerland, Hosmer, and Bofin, as well as, the Hosmer-Lemeshow's goodness of fit test., Results: There were approximately 2,983 (7.3%) and 5,189 (12.1%) adults in Florida diagnosed with pre-diabetes and diabetes, respectively. Over half of the study respondents were white, married and over the age of 45 years while 36.4% reported being physically inactive, overweight (36.4%) or obese (26.4%), hypertensive (34.6%), hypercholesteremic (40.3%), and 26% were arthritic. Based on the final multivariable multinomial model, only being overweight (Relative Risk Ratio [RRR] = 1.85, 95% Confidence Interval [95% CI] = 1.41, 2.42), obese (RRR = 3.41, 95% CI = 2.61, 4.45), hypertensive (RRR = 1.69, 95% CI = 1.33, 2.15), hypercholesterolemic (RRR = 1.94, 95% CI = 1.55, 2.43), and arthritic (RRR = 1.24, 95% CI = 1.00, 1.55) had significant associations with pre-diabetes. However, more predictors had significant associations with diabetes and the strengths of associations tended to be higher than for the association with pre-diabetes. For instance, the relative risk ratios for the association between diabetes and being overweight (RRR = 2.00, 95% CI = 1.55, 2.57), or obese (RRR = 4.04, 95% CI = 3.22, 5.07), hypertensive (RRR = 2.66, 95% CI = 2.08, 3.41), hypercholesterolemic (RRR = 1.98, 95% CI = 1.61, 2.45) and arthritic (RRR = 1.28, 95% CI = 1.04, 1.58) were all further away from the null than their associations with pre-diabetes. Moreover, a number of variables such as age, income level, sex, and level of physical activity had significant association with diabetes but not pre-diabetes. The risk of diabetes increased with increasing age, lower income, in males, and with physical inactivity. Insufficient physical activity had no significant association with the risk of diabetes or pre-diabetes., Conclusions: There is evidence of differences in the strength of association of the predictors across levels of diabetes status (pre-diabetes and diabetes) among adults ≥18 years in Florida. It is important to monitor populations at high risk for pre-diabetes and diabetes, so as to help guide health programming decisions and resource allocations to control the condition.

Background: Using current climate models, regional-scale changes for Florida over the next 100 years are predicted to include warming over terrestrial areas and very likely increases in the number of high temperature extremes. No uniform definition of a heat wave exists. Most past research on heat waves has focused on evaluating the aftermath of known heat waves, with minimal consideration of missing exposure information., Objectives: To identify and discuss methods of handling and imputing missing weather data and how those methods can affect identified periods of extreme heat in Florida., Methods: In addition to ignoring missing data, temporal, spatial, and spatio-temporal models are described and utilized to impute missing historical weather data from 1973 to 2012 from 43 Florida weather monitors. Calculated thresholds are used to define periods of extreme heat across Florida., Results: Modeling of missing data and imputing missing values can affect the identified periods of extreme heat, through the missing data itself or through the computed thresholds. The differences observed are related to the amount of missingness during June, July, and August, the warmest months of the warm season (April through September)., Conclusions: Missing data considerations are important when defining periods of extreme heat. Spatio-temporal methods are recommended for data imputation. A heat wave definition that incorporates information from all monitors is advised.

This study examines the patterns of length of stay (LOS) and inpatient costs for both Medicaid managed care and nonmanaged care patients using data from Medicaid patients aged 18 to 64 years who were discharged from hospitals in Florida between 2006 and 2012. This study used pooled cross-sectional multilevel modeling. The results show that the type of Medicaid program in which patients were enrolled was significantly related to the hospital LOS and inpatient costs. Medicaid managed care patients had 7% shorter LOSs and a 1.9% lower inpatient cost than did Medicaid fee-for-service (FFS) patients. Medicaid managed care patients had shorter LOSs in the Medicaid managed care market with high competition. High managed care penetration generates a cost-decreasing spillover to Medicaid FFS patients., (© The Author(s) 2015.)

The purpose of the article was to assess whether smoking affects survival in male breast cancer patients for the overall population and when stratified by race, ethnicity, and socioeconomic status. Data were obtained by linking the 1996-2007 Florida Cancer Data System, the Florida Agency for Health Care Administration, and the US Census. Inclusion criteria were males ≥18 years, diagnosed with breast cancer and residing in Florida (n = 1573). To analyze the association between smoking and survival, we performed sequential multivariate Cox proportional hazards regression models with progressive adjustment for main confounders. Compared to never smokers, worse survival was found in current (hazard ratio = 1.63; 95 % CI = 1.23-2.16) but not in former smokers (1.26; 0.99-1.59). Those who smoked ≥1 packs/day had worse survival (2.48; 1.59-3.87) than never smokers with a significant dose-response (P for linear trend <0.001). Race-ethnic stratified models comparing current and former smokers with never smokers found significant differences among Whites [(1.88; 1.44-2.44) and (1.31; 1.04-1.65, respectively)] and non-Hispanics, [(1.73; 1.31-2.28) and (1.31; 1.04-1.66, respectively)]. Overall, current smokers were found to have significantly reduced survival, which was worse by intensity of smoking. Also, any smoking history is associated with worse survival in White and non-Hispanic male breast cancer patients compared to never smokers. Thus, male breast cancer patients should be advised to quit smoking.

Calf (Bos taurus) depredation by the federally endangered Florida panther (Puma concolor coryi) on ranches in southwest Florida is an important issue because ranches represent mixed landscapes that provide habitat critical to panther recovery. The objectives of this study were to (1) quantify calf depredation by panthers on two ranches in southwest Florida, and (2) develop a habitat suitability model to evaluate the quality of panther hunting habitat on ranchlands, assess whether the model could predict predation risk to calves, and discuss its potential to be incorporated into an incentive-based compensation program. We ear-tagged 409 calves with VHF transmitters on two ranches during 2011-2013 to document calf mortality. We developed a model to evaluate the quality of panther hunting habitat on private lands in southwest Florida using environmental variables obtained from the Florida Natural Areas Inventory (FNAI) Cooperative Landcover Database and nocturnal GPS locations of panthers provided by the Florida Fish and Wildlife Conservation Commission (FWC). We then tested whether the model could predict the location of calf depredation sites. Tagged calf loss to panthers varied between the two ranches (0.5%/yr to 5.3%/yr) and may have been influenced by the amount of panther hunting habitat on each ranch as the ranch that experienced higher depredation rates contained a significantly higher probability of panther presence. Depredation sites of tagged calves had a significantly greater probability of panther presence than depredation sites of untagged calves that were found by ranchers in open pastures. This suggests that there may be more calves killed in high risk environments than are being found and reported by ranchers and that panthers can hunt effectively in open environments. It also suggests that the model may provide a means for evaluating the quality of panther hunting habitat and the corresponding risk of depredation to livestock across the landscape. We suggest that our approach could be applied to prioritize and categorize private lands for participation in a Payment for Ecosystem Services program that compensates landowners for livestock loss and incentivizes conserving high quality habitat for large carnivores where livestock depredation is a concern.

Although local eradication is routinely attempted following introduction of disease into a new region, failure is commonplace. Epidemiological principles governing the design of successful control are not well-understood. We analyse factors underlying the effectiveness of reactive eradication of localised outbreaks of invading plant disease, using citrus canker in Florida as a case study, although our results are largely generic, and apply to other plant pathogens (as we show via our second case study, citrus greening). We demonstrate how to optimise control via removal of hosts surrounding detected infection (i.e. localised culling) using a spatially-explicit, stochastic epidemiological model. We show how to define optimal culling strategies that take account of stochasticity in disease spread, and how the effectiveness of disease control depends on epidemiological parameters determining pathogen infectivity, symptom emergence and spread, the initial level of infection, and the logistics and implementation of detection and control. We also consider how optimal culling strategies are conditioned on the levels of risk acceptance/aversion of decision makers, and show how to extend the analyses to account for potential larger-scale impacts of a small-scale outbreak. Control of local outbreaks by culling can be very effective, particularly when started quickly, but the optimum strategy and its performance are strongly dependent on epidemiological parameters (particularly those controlling dispersal and the extent of any cryptic infection, i.e. infectious hosts prior to symptoms), the logistics of detection and control, and the level of local and global risk that is deemed to be acceptable. A version of the model we developed to illustrate our methodology and results to an audience of stakeholders, including policy makers, regulators and growers, is available online as an interactive, user-friendly interface at http://www.webidemics.com/. This version of our model allows the complex epidemiological principles that underlie our results to be communicated to a non-specialist audience.

Animals must move to find food and mates, and to avoid predators; movement thus influences survival and reproduction, and ultimately determines fitness. Precise description of movement and understanding of spatial and temporal patterns as well as relationships with intrinsic and extrinsic factors is important both for theoretical and applied reasons. We applied hidden semi-Markov models (HSMM) to hourly geographic positioning system (GPS) location data to understand movement patterns of the endangered Florida panther (Puma concolor coryi) and to discern factors influencing these patterns. Three distinct movement modes were identified: (1) Resting mode, characterized by short step lengths and turning angles around 180(o); (2) Moderately active (or intermediate) mode characterized by intermediate step lengths and variable turning angles, and (3) Traveling mode, characterized by long step lengths and turning angles around 0(o). Males and females, and females with and without kittens, exhibited distinctly different movement patterns. Using the Viterbi algorithm, we show that differences in movement patterns of male and female Florida panthers were a consequence of sex-specific differences in diurnal patterns of state occupancy and sex-specific differences in state-specific movement parameters, whereas the differences between females with and without dependent kittens were caused solely by variation in state occupancy. Our study demonstrates the use of HSMM methodology to precisely describe movement and to dissect differences in movement patterns according to sex, and reproductive status., (© 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society.)

The study proposes a convex combination (CC) algorithm to fast and stably train a neural network (NN) model for crash injury severity prediction, and a modified NN pruning for function approximation (N2PFA) algorithm to optimize the network structure. To demonstrate the proposed approaches and to compare them with the NN trained by traditional back-propagation (BP) algorithm and an ordered logit (OL) model, a two-vehicle crash dataset in 2006 provided by the Florida Department of Highway Safety and Motor Vehicles (DHSMV) was employed. According to the results, the CC algorithm outperforms the BP algorithm both in convergence ability and training speed. Compared with a fully connected NN, the optimized NN contains much less network nodes and achieves comparable classification accuracy. Both of them have better fitting and predicting performance than the OL model, which again demonstrates the NN's superiority over statistical models for predicting crash injury severity. The pruned input nodes also justify the ability of the structure optimization method for identifying the factors irrelevant to crash-injury outcomes. A sensitivity analysis of the optimized NN is further conducted to determine the explanatory variables' impact on each injury severity outcome. While most of the results conform to the coefficient estimation in the OL model and previous studies, some variables are found to have non-linear relationships with injury severity, which further verifies the strength of the proposed method., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Hispanic immigrant youth engage in increased health risk behaviors, such as alcohol misuse, due in part to being confronted with acculturative stress in addition to facing major normative developmental challenges, such as identity consolidation (Berry et al. in Appl Psychol 55:303-332, 2006). Using a developmental psychopathology framework, in the present study we examined the effect of bicultural stress on alcohol misuse among immigrated Hispanic adolescents, indirectly through trajectories of identity formation and alcohol expectancies. Our sample consisted of 302 recently immigrated Hispanic adolescents (53 % male; Mage = 14.5 at baseline) who were interviewed every 6 months for 3 years. Bivariate growth curve modeling was used to examine the influence of initial early bicultural stress on later alcohol misuse via change in identity development (i.e., coherence and confusion) and subsequent growth in cognitive alcohol expectancies. Findings revealed that initial levels and growth of identity coherence were not significantly associated with either bicultural stress or tension reduction (TR) alcohol expectancies. Multiple mediation analyses indicated that the effect of bicultural stress at time 1 on the frequency of being drunk at time 6 was mediated via high initial levels of identity confusion, followed by growth in risky TR expectancies (T4-T6). A developmental approach to the genesis of alcohol use problems in immigrant youth is discussed.

To examine the associations between maternal hepatitis B (HBV) and hepatitis C (HCV) infection status and selected infant neurological outcomes diagnosed at birth, we conducted a population-based, retrospective cohort study on singleton live births in Florida from 1998 to 2009. Primary exposures included maternal HBV and HCV monoinfection. The neurological outcomes included brachial plexus injury, cephalhematoma, foetal distress, feeding difficulties, intraventricular h aemorrhage and neonatal seizures. Multivariable logistic regression models were used to generate odds ratios (OR) and 95% confidence intervals (CI) that were adjusted for socio-demographic characteristics, risky behaviours, pregnancy complications and pre-existing medical conditions, and timing of delivery. The risk of an adverse neurological outcome was higher in infants born to mothers with hepatitis viral infection (7.2% for HCV, 5.0% for HBV), compared with infants of hepatitis virus-free mothers (4.2%). After adjusting for potential confounders, women with HBV were twice as likely to have infants who suffered from brachial plexus injury (OR = 2.04, 95% CI = 1.15-3.60), while those with HCV had an elevated odds of having an infant with feeding difficulties (OR: 1.32, 95% CI = 1.06-1.64) and a borderline increased likelihood for neonatal seizures (OR = 1.74, 95% CI = 0.98-3.10). Additionally, HCV+ mothers had a 22% increased odds of having an infant with some type of adverse neurological outcome (OR: 1.22, 95% CI = 1.03-1.44). Our findings add to current understanding of the association between maternal HBV/HCV infections and infant neurological outcomes. Further research evaluating the role of maternal HBV and HCV infections (including viraemia, treatment) on pregnancy outcomes is warranted., (© 2014 John Wiley & Sons Ltd.)

A wide array of spatial units has been explored in current regional safety analysis. Since traffic crashes exhibit extreme spatiotemporal heterogeneity which has rarely been a consideration in partitioning these zoning systems, research based on these areal units may be subjected to the modifiable areal unit problem (MAUP). This study attempted to conduct a sensitivity analysis to quantitatively investigate the MAUP effect in the context of regional safety modeling. The emerging regionalization method-RECDAP (regionalization with dynamically constrained agglomerative clustering and partitioning) was employed to aggregate 738 traffic analysis zones in the county of Hillsborough to 14 zoning schemes at an incremental step-size of 50 zones based on spatial homogeneity of crash risk. At each level of aggregation, a Bayesian Poisson lognormal model and a Bayesian spatial model were calibrated to explain observed variations in total/severe crash counts given a number of zone-level factors. Results revealed that as the number of zones increases, the spatial autocorrelation of crash data increases. The Bayesian spatial model outperforms the Bayesian Poisson-lognormal model in accurately accounting for spatial autocorrelation effects, unbiased parameter estimates, and model performance, especially in cases with higher disaggregated levels. Zoning schemes with higher number of zones tend to have increasing number of significant variables, more stable coefficient estimation, smaller standard error, whereas worse model performance. The variables of population density and median household income show consistently significant effects on crash risk and are robust to variation in data aggregation. The MAUP effects may be significantly reduced if we just maintain at about 50% of the original number of zones (350 or larger). The present study highlights MAUP that is generally ignored by transportation safety analysts, and provides insights into the nature of parameter sensitivity to data aggregation in the context of regional safety modeling., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Objectives: Although 72-hour emergency department (ED) revisits are increasingly used as a hospital metric, there is no known empirical basis for this 72-hour threshold. The objective of this study was to determine the timing of ED revisits for adult patients within 30 days of ED discharge., Methods: This was a retrospective cohort study of all nonfederal ED discharges in Florida and Nebraska from April 1, 2010, to March 31, 2011, using data from the Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project (HCUP). ED discharges were followed forward to identify ED revisits occurring at any hospital within the same state within 30 days. The cumulative hazard of an ED revisit was plotted. Parametric and nonparametric modeling was performed to characterize the rate of ED revisits., Results: There were 4,782,045 ED discharges, with 7.5% (95% confidence interval [CI] = 7.4% to 7.5%) associated with 3-day revisits, and 22.4% (95% CI = 22.3% to 22.4%) associated with 30-day revisits, inclusive of the 3-day revisits. A double-exponential model fit the data best (p < 0.0001), and a single hinge point at 9 days (multivariate adaptive regression splines [MARS] model) yielded the best linear fit to the data, suggesting 9 days as the most reasonable cutoff for identification of acute ED revisits. Multiple stratified and subgroup analyses produced similar results. Future work should focus on identifying primary reasons for potentially avoidable return ED visits instead of on the revisit occurrence itself, thus more directly measuring potential lapses in delivery of high-quality care., Conclusions: Almost one-quarter of ED discharges are linked to 30-day ED revisits, and the current 72-hour ED metric misses close to 70% of these patients. Our findings support 9 days as a more inclusive cutoff for studies of ED revisits., (© 2014 by the Society for Academic Emergency Medicine.)

This study proposes a Bayesian spatial joint model of crash prediction including both road segments and intersections located in an urban road network, through which the spatial correlations between heterogeneous types of entities could be considered. A road network in Hillsborough, Florida, with crash, road, and traffic characteristics data for a three-year period was selected in order to compare the proposed joint model with three site-level crash prediction models, that is, the Poisson, negative binomial (NB), and conditional autoregressive (CAR) models. According to the results, the CAR and Joint models outperform the Poisson and NB models in terms of model fitting and predictive performance, which indicates the reasonableness of considering cross-entity spatial correlations. Although the goodness-of-fit and predictive performance of the CAR and Joint models are equivalent in this case study, spatial correlations between segments and the connected intersections are found to be more significant than those solely between segments or between intersections, which supports the employment of the Joint model as an alternative in road-network-level safety modeling., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Arsenic exposure from wood treated with chromated copper arsenate (CCA) remains a concern due to its presence around homes. This study evaluated children's exposure to As from CCA-treated staircases through determination of bioaccessible soil As and measurements of dislodgeable As on hand railings, steps and surfaces of household objects impacted by CCA-wood leachate. Total As concentrations in 84 soil samples from 4 apartment complexes were elevated at 1.2-66.6 mg/kg with bioaccessible As at 17-84%. Deterministic risk equations were used to estimate daily doses of As in children with estimates ranging from 0.41-54.9 μg/day from ingestion of dislodgeable As. Lifetime average daily doses from ingestion of dislodgeable As and soil ranged from 8.1×10(-6) to 3.0×10(-5) mg/kg/day, with estimated cancer risks being 1.2-4.5×10(-5). Collectively, these results highlight potential health risks in children who have near-daily exposure to As from CCA-wood and are consistent with estimates generated by USEPA's SHEDS-Wood probabilistic exposure model., (Published by Elsevier B.V.)

Anthracnose crown rot is an important disease of strawberry primarily caused by Colletotrichum gloeosporioides in Florida and North Carolina. Information on the magnitude of additive and nonadditive genetic variation is required to define breeding strategies and to estimate potential genetic gains. However, little is known about the genetic control of resistance and its utility in breeding. Our objectives were to obtain estimates of heritabilities and of components of genetic variances, genotype-environment interactions, and gains for resistance, and to examine the effects of locations and transplant types on the estimates. An incomplete diallel mating design generated 42 full-sib families, which were propagated in plugs from seed (seedling tests) and as bare-root runner plants (clonal tests) of different genotypes of the same families. Both seedlings and clones were inoculated with C. gloeosporioides under field conditions in North Carolina and Florida during the 2010-11 season. Narrow-sense heritability (h(2)) and broad-sense heritability (H(2)) for both clones and seedlings were higher at the North Carolina location (h(2) = 0.34 to 0.62 and H(2) = 0.46 to 0.85) than at the Florida location (h(2) = 0.16 to 0.22 and H(2) = 0.37 to 0.46). Likewise, the seedling tests showed higher genetic control than the clonal tests at both locations. Estimates of dominance variance were approximately one-third of the additive variance at North Carolina and were even larger at Florida. Epistasis was negative at both locations and assumed zero for heritability (H(2)) calculations. Genotype-environment interactions were different by transplant type, suggesting rank changes across locations. 'Pelican' was the most resistant parent at both locations, followed by 'NCH09-68' at the NC location and 'Winter Dawn' at the Florida location. Selection and deployment of the most resistant clone within each of the five best families is estimated to produce average genetic gains of 53.0 and 73.7% at the North Carolina and Florida locations, respectively.

Severe crashes are causing serious social and economic loss, and because of this, reducing crash injury severity has become one of the key objectives of the high speed facilities' (freeway and expressway) management. Traditional crash injury severity analysis utilized data mainly from crash reports concerning the crash occurrence information, drivers' characteristics and roadway geometric related variables. In this study, real-time traffic and weather data were introduced to analyze the crash injury severity. The space mean speeds captured by the Automatic Vehicle Identification (AVI) system on the two roadways were used as explanatory variables in this study; and data from a mountainous freeway (I-70 in Colorado) and an urban expressway (State Road 408 in Orlando) have been used to identify the analysis result's consistence. Binary probit (BP) models were estimated to classify the non-severe (property damage only) crashes and severe (injury and fatality) crashes. Firstly, Bayesian BP models' results were compared to the results from Maximum Likelihood Estimation BP models and it was concluded that Bayesian inference was superior with more significant variables. Then different levels of hierarchical Bayesian BP models were developed with random effects accounting for the unobserved heterogeneity at segment level and crash individual level, respectively. Modeling results from both studied locations demonstrate that large variations of speed prior to the crash occurrence would increase the likelihood of severe crash occurrence. Moreover, with considering unobserved heterogeneity in the Bayesian BP models, the model goodness-of-fit has improved substantially. Finally, possible future applications of the model results and the hierarchical Bayesian probit models were discussed., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Objective: Access design is a critical factor that influences the safety and mobility of urban/suburban multilane highways due to the interactions between access movements and through traffic. An effective way for improving the safety and mobility of multilane highways is to control access maneuvers by implementing appropriate access designs. Understanding the impact of access designs on crash injury severity is beneficial for implementing effective countermeasures to mitigate crash injury at access points. Thus, the objectives of this article are to investigate the impact of access designs on crash injury severity and identify contributing factors of crash injury severity at access points of multilane highways., Methods: A total of 1830 crash records were collected at 149 access points with different access designs for a period of 3 years (2008-2010) in Florida. A heterogeneous logit model, relaxing the constraint of identical variances across observations in the traditional ordered choice models, was developed to evaluate the impact of access designs on crash injury severity and identify contributing factors. The marginal effects of the developed model were used to interpret the impact of access designs and other contributing factors., Results: At 4-leg access points, given that a crash has occurred, replacing full median openings with closed medians will reduce the probability of severe injury (fatality, incapacitating injury, or nonincapacitating injury) by 9.73 percent; substituting full median openings with directional median openings will reduce the probability of severe injury by 11.02 percent. At 3-leg access points, given that a crash has occurred, closed medians significantly experience a lower risk of severe injury than full median openings; however, there is no evidence that directional median openings are similarly effective. Other contributing factors of crash injury severity at access points were identified as number of lanes, shoulder width, median width, driveway density, left-turn bay on major roads, speed limit, average annual daily traffic (AADT), high-density residential area, daylight, driver age, and truck involvement., Conclusions: Installation of directional median openings is a reasonable safety treatment at 4-leg access points because this access design has better safety performance than full median openings in terms of crash injury severity; in addition, it provides fewer restrictions on accessibility than closed medians. Other effective treatments include installing left-turn storage bays at median openings and increasing the width of shoulders and medians. Supplemental materials are available for this article. Go to the publisher's online edition of Traffic Injury Prevention to view the supplemental file.

Squash vein yellowing virus (SqVYV) is the causal agent of viral watermelon vine decline, one of the most serious diseases in watermelon (Citrullus lanatus L.) production in the southeastern United States. At present, there is not a gold standard diagnostic test for determining the true status of SqVYV infection in plants. Current diagnostic methods for identification of SqVYV-infected plants or tissues are based on the reverse-transcription polymerase chain reaction (RT-PCR), tissue blot nucleic acid hybridization assays (TB), and expression of visual symptoms. A quantitative assessment of the performance of these diagnostic tests is lacking, which may lead to an incorrect interpretation of results. In this study, latent class analysis (LCA) was used to estimate the sensitivities and specificities of RT-PCR, TB, and visual assessment of symptoms as diagnostic tests for SqVYV. The LCA model assumes that the observed diagnostic test responses are linked to an underlying latent (nonobserved) disease status of the population, and can be used to estimate sensitivity and specificity of the individual tests, as well as to derive an estimate of the incidence of disease when a gold standard test does not exist. LCA can also be expanded to evaluate the effect of factors and was done here to determine whether diagnostic test performances varied among the type of plant tissue being tested (crown versus vine tissue), where plant samples were taken relative to the position of the crown (i.e., distance from the crown), host (i.e., genus), and habitat (field-grown versus greenhouse-grown plants). Results showed that RT-PCR had the highest sensitivity (0.94) and specificity (0.98) of the three tests. TB had better sensitivity than symptoms for detection of SqVYV infection (0.70 versus 0.32), while the visual assessment of symptoms was more specific than TB and, thus, a better indicator of noninfection (0.98 versus 0.65). With respect to the grouping variables, RT-PCR and TB had better sensitivity but poorer specificity for diagnosing SqVYV infection in crown tissue than it did in vine tissue, whereas symptoms had very poor sensitivity but excellent specificity in both tissues for all cucurbits analyzed in this study. Test performance also varied with habitat and genus but not with distance from the crown. The results given here provide quantitative measurements of test performance for a range of conditions and provide the information needed to interpret test results when tests are used in parallel or serial combination for a diagnosis.

Background: Community-level associations between pain clinics and drug-related outcomes have not been empirically demonstrated., Methods: To explore these associations we correlated overdose death rates, hospital-discharge rates for drug-related hospitalizations including neonatal abstinence syndrome, and emergency department rates for drug-related visits with registered pain clinic density and rate of opioid pills dispensed per person at the county-level Florida in 2009. Negative binomial regression was used to model the crude associations and associations adjusted for exposure measures and county demographic characteristics., Results: An estimated 732 pain clinics operated in Florida in 2009, a rate of 3.9/100,000 people. Among the 67 counties in Florida, 23 (34.3%) had no pain clinics, and three had 90 or more. Adjusted negative binomial regression determined no significant association between pain clinic rate and drug-related outcomes. However, rates of drug-caused, opioid-caused, and oxycodone-caused death correlated significantly with rates of opioid and oxycodone pills dispensed per person in adjusted analyses. For every increase of one pill in the rate of oxycodone pills per person, there was a 6% increase in the rate of oxycodone-related overdose death., Conclusions: Although pain clinics, some of which are "pill mills," are clearly a source of drugs used nonmedically, their impact on health outcomes might be difficult to quantify because the pills they prescribe might be consumed in other counties or states. The impact of "pill mill" laws might be better measured by more proximal measures such as the number of such facilities., (Published by Elsevier Ireland Ltd.)

This study sought to determine the impact of passive smoking on the risk for depressive symptoms during pregnancy. In this prospective study, 236 pregnant women were recruited at less than 20 weeks of gestation from a university-affiliated obstetric clinic from November 2009 through July 2011. Tobacco use/exposure was measured using questionnaire and confirmed by salivary cotinine analysis. The Edinburgh Perinatal Depression Scale (EPDS) was employed to capture perinatal depressive symptomatology. Traditionally, a cutoff of 13 is utilized to indicate depressive symptoms in the perinatal population. However, this approach is vulnerable to measurement errors that are inherent in assessing depression using cutoff points. Therefore, in this analysis, we apply a flexible approach (latent variable modeling) that accounts for measurement errors thereby reducing bias in the estimates of association. Significant differences were observed in the mean EPDS scores across non-smokers (mean ± SD = 4.8 ± 4.8), passive smokers (5.3 ± 5.5) and active smokers (7.4 ± 6.1) [p value = 0.02]. For each itemized response of the EPDS, passive smokers demonstrated an increased risk for depressive symptoms with the greatest risk exhibited by items 8 and 9 of the questionnaire (feeling sad or miserable and feeling unhappy [and]crying, respectively). In addition, for each item of the EPDS, a dose-response pattern was revealed with non-smokers having the least risk of depressive symptoms during pregnancy and active smokers having the greatest risk. Women who are exposed to secondhand smoke are at elevated risk for depressive symptoms during pregnancy.

The emphasis on high-quality care has spawned the development of quality programs, most of which focus on broad outcome measures across a diverse group of providers. Our aim was to investigate the clinical outcomes for a department of surgery with multiple service lines of patient care using a relational database. Mortality, length of stay (LOS), patient safety indicators (PSIs), and hospital-acquired conditions were examined for each service line. Expected values for mortality and LOS were derived from University HealthSystem Consortium regression models, whereas expected values for PSIs were derived from Agency for Healthcare Research and Quality regression models. Overall, 5200 patients were evaluated from the months of January through May of both 2011 (n = 2550) and 2012 (n = 2650). The overall observed-to-expected (O/E) ratio of mortality improved from 1.03 to 0.92. The overall O/E ratio for LOS improved from 0.92 to 0.89. PSIs that predicted mortality included postoperative sepsis (O/E:1.89), postoperative respiratory failure (O/E:1.83), postoperative metabolic derangement (O/E:1.81), and postoperative deep vein thrombosis or pulmonary embolus (O/E:1.8). Mortality and LOS can be improved by using a relational database with outcomes reported to specific service lines. Service line quality can be influenced by distribution of frequent reports, group meetings, and service line-directed interventions.

The analysis of road network designs can provide useful information to transportation planners as they seek to improve the safety of road networks. The objectives of this study were to compare and define the effective road network indices and to analyze the relationship between road network structure and traffic safety at the level of the Traffic Analysis Zone (TAZ). One problem in comparing different road networks is establishing criteria that can be used to scale networks in terms of their structures. Based on data from Orange and Hillsborough Counties in Florida, road network structural properties within TAZs were scaled using 3 indices: Closeness Centrality, Betweenness Centrality, and Meshedness Coefficient. The Meshedness Coefficient performed best in capturing the structural features of the road network. Bayesian Conditional Autoregressive (CAR) models were developed to assess the safety of various network configurations as measured by total crashes, crashes on state roads, and crashes on local roads. The models' results showed that crash frequencies on local roads were closely related to factors within the TAZs (e.g., zonal network structure, TAZ population), while crash frequencies on state roads were closely related to the road and traffic features of state roads. For the safety effects of different networks, the Grid type was associated with the highest frequency of crashes, followed by the Mixed type, the Loops & Lollipops type, and the Sparse type. This study shows that it is possible to develop a quantitative scale for structural properties of a road network, and to use that scale to calculate the relationships between network structural properties and safety., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Crash modification factors (CMFs) are used to measure the safety impacts of changes in specific geometric characteristics. Their development has gained much interest following the adoption of CMFs by the recently released Highway Safety Manual (HSM) and SafetyAnalyst tool in the United States. This paper describes a study to develop CMFs for interchange influence areas on urban freeways in the state of Florida. Despite the very different traffic and geometric conditions that exist in interchange influence areas, most previous studies have not separated them from the rest of the freeway system in their analyses. In this study, a promising data mining method known as multivariate adaptive regression splines (MARS) was applied to develop CMFs for median width and inside and outside shoulder widths for "total" and "fatal and injury" (FI) crashes. In addition, CMFs were also developed for the two most frequent crash types, i.e., rear-end and sideswipe. MARS is characterized by its ability to accommodate the nonlinearity in crash predictors and to allow the impact of more than one geometric variable to be simultaneously considered. The methodology further implements crash predictions from the model to identify changes in geometric design features. Four years of crashes from 2007 to 2010 were used in the analysis and the results showed that MARS's prediction capability and goodness-of-fit statistics outperformed those of the negative binomial model. The influential variables identified included the outside and inside shoulder widths, median width, lane width, traffic volume, and shoulder type. It was deduced that a 2-ft increase in the outside and inside shoulders (from 10ft to 12ft) reduces FI crashes by 10% and 33%, respectively. Further, a 42-ft reduction in the median width (from 64ft to 22ft) increases the rear-end, total, and FI crashes by 473%, 263%, and 223%, respectively., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Objective: In this retrospective cohort (1998 to 2007), 145,445 singleton live births in Hillsborough, Florida, were analyzed to elucidate the relationship between fetal morbidity and prenatal exposure to six criteria air pollutants., Methods: This study was based on three linked databases: Florida Hospital Discharge, vital statistics records, and air pollution meteorological data from the Environmental Protection Agency. The primary outcomes of interest were low birth weight, preterm births, and small for gestational age. This study used structural equation modeling and trimester groupings to evaluate the relationship between air pollution and birth outcomes of pregnant residents., Results: The latent variables of structural equation modeling yielded a significant B value of 0.35, indicating that exposure to the criteria pollutants in pregnancy may have a significant relationship to fetal morbidity., Conclusion: Exposure to criteria air pollutants in pregnancy is associated with fetal morbidity outcomes.