The Priority Risk Index is increasingly used as a methodology for quantifying jurisdictional risk for hazard mitigation planning purposes, and it can evolve to meet specific community needs. The index incorporates probability, impact, spatial extent, warning time, and duration when assessing each hazard, but it does not explicitly integrate a vulnerability and consequence analysis into its final scoring. To address this gap, a new index was developed- the Enhanced Priority Risk Index (EPRI). The new index adds a sixth category, vulnerability, calculated from a vulnerability and consequence analysis of the impacts on seven sectors identified in Standard 4.1.2 of the Emergency Management Accreditation Program (EMAP). To obtain a vulnerability score, impacts are ranked by sector from low (1) to very high (4), then a weighting factor is applied to each sector. The vulnerability score is added to the EPRI and provides risk levels based on the number of exploitable weaknesses and countermeasures identified within a specific jurisdiction. The vulnerability score and resulting EPRI are scalable and can be applied across jurisdictions, providing a transferable methodology that improves the hazard identification and risk assessment process and provides an approach for meeting EMAP accreditation standards.

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O principal objetivo do estudo foi utilizar informações de ocorrência do Traumatismo Crânio Encefálico (TCE) que possam inferir/gerar descobertas associadas ao risco de gravidade do paciente, bem como auxiliar na tomada de decisão médica ao definir o melhor prognóstico, indicando quais as possíveis medidas que podem ser escolhidas para a gravidade na lesão sofrida pela vítima. Inicialmente, foram analisadas as estatísticas descritivas dos dados dos pacientes de TCE de um hospital do interior de São Paulo. Participaram desse estudo 50 pacientes. Os resultados mostraram que a maior frequência do trauma é por acidentes de trânsito (62%), seguidos de acidentes por queda (24%). Traumas em pacientes do sexo masculino (88%) são muito mais frequentes do que em pacientes do sexo feminino. Para modelagem, transformou-se a variável resposta \"Abbreviated Injury Scale (AIS)\" em dicotômica, considerando 0 (zero) aos pacientes fora de risco e 1 (um) aos que apresentaram algum tipo de risco. Em seguida, técnicas de aprendizado estatístico foram utilizadas de modo a comparar o desempenho dos classificadores Regressão Logística sendo um caso do Generalized Linear Model (GLM), Random Forest (RF), Support Vector Machine (SVM) e redes probabilísticas Naïve Bayes (NB). O modelo com melhor desempenho (RF) combinou os índices Accuracy (ACC) , Area Under ROC Curve (AUC) , Sensitivity (SEN), Specificity (SPE) e Matthews Correlation Coefficient (MCC), que apresentaram os resultados mais favoráveis no quesito de apoio no auxílio da tomada de decisão médica, possibilitando escolher o estudo clínico mais adequado das vítimas traumatizadas ao considerar o risco de vida do indivíduo. Conforme o modelo selecionado foi possível gerar um ranking para estimar a probabilidade de risco de vida do paciente. Em seguida foi realizado uma comparação de desempenho entre o modelo RF (novo classificador) e os índices Revisited Trauma Score (RTS), Injury Severity Score (ISS) , Índice de Barthel (IB) referente à classificação de risco dos pacientes. / The main objective of this study was to consider the information related to the occurrence of traumatic brain injury (TBI) that can infer new results associated with the patients risk of severity as well as assisting in the medical decision in order to find the best prognosis; this can lead to indicate possible measures that can be chosen for severity in the injury suffered by the victim. Initially, we have presented descriptive statistics from the patients with TBI from a hospital located in the heartland of São Paulo. Fifty patients were recruited for this study. Descriptive analyzes showed that the highest frequency of trauma is due to traffic accidents (62 %) followed by crashes per accident (24 %). The causes related to trauma occur much more often in male patients (88 %) than in female patients. To order model, the response variable Abbreviated Injury Scale (AIS) was considered as dichotomous, where 0 (zero) was to out-of-risk patients and 1 (one) to those who presented some type of risk. Further, statistical learning techniques were used in order to compare the performance of the Logistic Regression as a Generalized Linear Model (GLM), Random Forest (RF), Support Vector Machine (SVM) and Naive Bayes (NB) model. The best performing (RF) model combined the Accuracy (ACC) , Area Under ROC Curve (AUC) , Sensitivity (SEN), Specificity (SPE) e Matthews Correlation Coefficient (MCC), which presented the most favorable results in terms of support in medical decision, making it possible to choose the most appropriate clinical study of traumatized victims based on the individual life risk. According to the selected model it was possible to generate a rank to estimate the probability of life risk of the patient. Then a performance comparison was performed between the RF model (proposed classifier) and the Revisited Trauma Score (RTS), Injury Severity Score (ISS), Barthel index (IB) referring to the risk classification of patients.

A expansão do tecido urbano e o adensamento das cidades têm um impacto negativo sobre os recursos hídricos, tanto na quantidade quanto na qualidade das águas no ambiente, pois aumenta as cargas de poluentes no meio e altera o ciclo hidrológico natural, criando riscos à população. É possível reduzir tais riscos através do diagnóstico, planejamento e gestão adequada das áreas de risco para a proteção civil e das comunidades. A abordagem ecohidrológica, que considera a relação funcional entre hidrologia, sistemas aquáticos e sua biota na escala de bacia hidrográfica, incorporando aspectos quantitativos e qualitativos da água em uma visão de interdependência entre Ecologia e Hidrologia, permite o diagnóstico, planejamento e manejo adequado dos cursos hídricos em benefício tanto dos humanos quando dos demais seres vivos. Este trabalho busca investigar a dinâmica ecohidrológica no espaço e no tempo da Bacia do Rio Monjolinho, localizada no Município de São Carlos (SP), no contexto de gestão de riscos de desastres hidrológicos. Os processos quantitativos são investigados por meio de simulações com o SWMM (Storm Water Management Model) e os dados de simulação são utilizados, por vez, na determinação do IP (Índice de Perigo) de pontos estratégicos da bacia hidrográfica e também das áreas com maior risco de inundação. Com relação à qualidade, onze variáveis clássicas de qualidade dágua são determinadas experimentalmente em 15 pontos da bacia a fim de caracterizar a dinâmica das cargas de poluentes, permitindo desta maneira a avaliação do risco biológico na bacia. Os resultados quali-quantitativos mostram que os vales dos rios oferecem riscos tanto devido às inundações quanto devido ao alto risco de contaminação. / The urban tissue expansion and the urban area densification have a negative impact on water resources, in terms of quality and quantity of available water, since it increases the polutant load at the same time it changes the natural hydrological cycle, exposing population to risk. It is possible to reduce the risks by means of assessment, planning and correct management of risk areas in order to protect the communities. The ecohydrology, which takes into account the functional relationship between hydrology, aquatic systems and biota in watershed scale, considering quali-quantitative aspects and their interdependecies, enable us to assess, plan and manage risks in an advantageous way for humans and living beings as well. This work investigates the ecohydrology dynamics in space and time of Rio Monjolinho basin, located within the municipality of São Carlos (SP), Brazil, in the context of disaster risk management. The quantitative aspects are investigated using the SWMM (Storm Water Management Model) simulation model, and the simulation data generated are used to calculate the Risk Index (RI) and to map the flooding risk areas in the basin. Eleven classic water quality variables are experimentaly determined to assess the polutant load dynamics and its distribution in the sub-basins, enabling us to assess the biologic risks. The results show that some areas in the catchment are not just flood risk areas but also areas of high biological risk of contamination.

O principal objetivo do estudo foi utilizar informações de ocorrência do Traumatismo Crânio Encefálico (TCE) que possam inferir/gerar descobertas associadas ao risco de gravidade do paciente, bem como auxiliar na tomada de decisão médica ao definir o melhor prognóstico, indicando quais as possíveis medidas que podem ser escolhidas para a gravidade na lesão sofrida pela vítima. Inicialmente, foram analisadas as estatísticas descritivas dos dados dos pacientes de TCE de um hospital do interior de São Paulo. Participaram desse estudo 50 pacientes. Os resultados mostraram que a maior frequência do trauma é por acidentes de trânsito (62%), seguidos de acidentes por queda (24%). Traumas em pacientes do sexo masculino (88%) são muito mais frequentes do que em pacientes do sexo feminino. Para modelagem, transformou-se a variável resposta \"Abbreviated Injury Scale (AIS)\" em dicotômica, considerando 0 (zero) aos pacientes fora de risco e 1 (um) aos que apresentaram algum tipo de risco. Em seguida, técnicas de aprendizado estatístico foram utilizadas de modo a comparar o desempenho dos classificadores Regressão Logística sendo um caso do Generalized Linear Model (GLM), Random Forest (RF), Support Vector Machine (SVM) e redes probabilísticas Naïve Bayes (NB). O modelo com melhor desempenho (RF) combinou os índices Accuracy (ACC) , Area Under ROC Curve (AUC) , Sensitivity (SEN), Specificity (SPE) e Matthews Correlation Coefficient (MCC), que apresentaram os resultados mais favoráveis no quesito de apoio no auxílio da tomada de decisão médica, possibilitando escolher o estudo clínico mais adequado das vítimas traumatizadas ao considerar o risco de vida do indivíduo. Conforme o modelo selecionado foi possível gerar um ranking para estimar a probabilidade de risco de vida do paciente. Em seguida foi realizado uma comparação de desempenho entre o modelo RF (novo classificador) e os índices Revisited Trauma Score (RTS), Injury Severity Score (ISS) , Índice de Barthel (IB) referente à classificação de risco dos pacientes. / The main objective of this study was to consider the information related to the occurrence of traumatic brain injury (TBI) that can infer new results associated with the patients risk of severity as well as assisting in the medical decision in order to find the best prognosis; this can lead to indicate possible measures that can be chosen for severity in the injury suffered by the victim. Initially, we have presented descriptive statistics from the patients with TBI from a hospital located in the heartland of São Paulo. Fifty patients were recruited for this study. Descriptive analyzes showed that the highest frequency of trauma is due to traffic accidents (62 %) followed by crashes per accident (24 %). The causes related to trauma occur much more often in male patients (88 %) than in female patients. To order model, the response variable Abbreviated Injury Scale (AIS) was considered as dichotomous, where 0 (zero) was to out-of-risk patients and 1 (one) to those who presented some type of risk. Further, statistical learning techniques were used in order to compare the performance of the Logistic Regression as a Generalized Linear Model (GLM), Random Forest (RF), Support Vector Machine (SVM) and Naive Bayes (NB) model. The best performing (RF) model combined the Accuracy (ACC) , Area Under ROC Curve (AUC) , Sensitivity (SEN), Specificity (SPE) e Matthews Correlation Coefficient (MCC), which presented the most favorable results in terms of support in medical decision, making it possible to choose the most appropriate clinical study of traumatized victims based on the individual life risk. According to the selected model it was possible to generate a rank to estimate the probability of life risk of the patient. Then a performance comparison was performed between the RF model (proposed classifier) and the Revisited Trauma Score (RTS), Injury Severity Score (ISS), Barthel index (IB) referring to the risk classification of patients.

Elevated phosphorus (P) loading of freshwater lakes and reservoirs often results in poor water quality and negative ecological effects. Critical source areas (CSA) of P in the watershed can be difficult to identify and control. A useful concept for identification of a CSA is the P risk index (P Index) that evaluates the P risk associated with distinct source and transport pathways. The objectives of this study were to create a GIS model that adapts the Minnesota (MN) P Index for use at the watershed scale in a mixed-use, mountain environment, and to evaluate its effectiveness relative to field-based assessment. A GIS-based model of the MN P Index, adapted for montane environments and relying primarily on publicly available geospatial data, was created and applied in the Wallsburg watershed, located in the mountains of Central Utah. One necessary data input, P found in plant residue of common Utah ecosystems, was found lacking after literature review. We experimentally determined a range of observed values from multiple ecosystems to adapt and validate the GIS model. The GIS P Index was evaluated against the results of 58 field scale applications of the MN P Index conducted throughout the watershed. The field-scale analysis resulted in about 14% of the sites sampled being identified as high or very high risk for P transport to surface water. Spatially, these high risk areas were determined to be a geographic cluster of fields near the lower middle agricultural section of the watershed. The GIS model visually and spatially identified the same cluster of fields as high risk areas. Various soil test P scenarios were explored and compared to the known 58 site values. Soil test phosphorus had little effect on the GIS model's ability to accurately predict P risk in this watershed suggesting that high volume soil sampling is not always necessary to identify CSAs of P. Variable hypothetical livestock density scenarios were also simulated. The GIS model proved sensitive to variable P inputs and highlighted the necessity of accurate applied P source data. On average the model under-predicted the known field-site values by a risk score of 1.3, which suggests reasonable success in P assessment based on the categorical risk scores of the MN P Index and some potential for improvement. The GIS model has great potential to give land managers the ability to quickly locate potential CSAs and prioritizing remediation efforts to sites with greatest risk.

Historically, students from ethnically diverse backgrounds in grades K-12 have been over-represented in special education, yet little research on disproportionate representation has been conducted with preschool-aged children. This study examined if 72,525 preschool-aged children with disabilities from ethnically diverse backgrounds were disproportionately represented in special education within and across five southern states. Data were gathered from the 2006 December 1st Child Count reported by each State Department of Education to the U.S. Department of Education. Chosen states offered state-funded pre-kindergarten programs, which should have provided equal opportunities for inclusion across states. Analyses compared children with disabilities for disproportionate representation across state of residence, across special education eligibilities, across educational placements, and amount of inclusion provided. Data were analyzed for child and placement characteristics. Due to data suppression by individual states, analyses were conducted using children from Black and White backgrounds, and children from Hispanic backgrounds were used when reported by individual states. Child characteristics considered included the child’s: (a) type of disability eligibility category, (b) age, and (c) ethnicity. Placement characteristics included: (a) type of educational placement, (b) state in which child resided, and (c) amount of inclusion received. Indices of disproportionate representation were calculated using: (a) composition index, (b) risk index, (c) odds ratio, and (d) relative risk ratio. A 3 x 5 ANOVA was used to calculate placement differences between states. Factorial analysis was used to calculate determinants of placement status for preschool-aged children with disabilities. Results revealed disproportionate representation does occur at the preschool level, although between state variability was great, and patterns differed from the K-12 literature. Children from American Indian backgrounds were over-represented due to high proportions in states of Alabama and North Carolina, while children from Asian and Hispanic backgrounds were under-represented. Children from Black and White backgrounds were represented in special education at expected rates. The most common eligibility categories were speech/language impairments and developmental delay. Placement results revealed over-representation for White preschoolers and males, although type of state-funded pre-k program was a non-significant factor. Inclusion analyses favored Whites and males. Child demographic factors explained the majority of variability in inclusion status.

Source water protection planning (SWPP) is an approach to prevent contamination of ground and surface water in watersheds where these resources may be abstracted for drinking or used for recreation. For SWPP the hazards within a watershed that could contribute to water contamination are identified together with the pathways that link them to the water resource. In rural areas, farms are significant potential sources of pathogens. A risk-based index can be used to support the assessment of the potential for contamination following guidelines on safety and operational efficacy of processes and practices developed as beneficial approaches to agricultural land management. Evaluation of the health risk for a target population requires knowledge of the strength of the hazard with respect to the pathogen load (mass concentration). Manure handling and on-site wastewater treatment systems form the most important hazards, and both can comprise confined and unconfined source elements. There is also a need to understand the modification of pathogen numbers (attenuation) together with characteristics of the established pathways (surface or subsurface), which allow the movement of the contaminant species from a source to a receptor (water source). Many practices for manure management have not been fully evaluated for their impact on pathogen survival and transport in the environment. A key component is the identification of potential pathways of contaminant transport. This requires the development of a suitable digital elevation model of the watershed for surface movement and information on local groundwater aquifer systems for subsurface flows. Both require detailed soils and geological information. The pathways to surface and groundwater resources can then be identified. Details of land management, farm management practices(including animal and manure management) and agronomic practices have to be obtained, possibly from questionnaires completed by each producer within the watershed. To confirm that potential pathways are active requires some microbial source tracking. One possibility is to identify the molecular types of Escherichia coli present in each hazard on a farm. An essential part of any such index is the identification of mitigation strategies and practices that can reduce the magnitude of the hazard or block open pathways.