Risk assessment of post-wildfire hydrological response in semiarid basins: the effects of varying rainfall representations in the KINEROS2/AGWA model

2016 ◽  
Vol 25 (3) ◽  
pp. 268 ◽  
Author(s):  
Gabriel Sidman ◽  
D. Phillip Guertin ◽  
David C. Goodrich ◽  
Carl L. Unkrich ◽  
I. Shea Burns
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
Peak Flow ◽  
Radar Data ◽  
Burned Area ◽  
Rainfall Runoff ◽  
National Monument ◽  
Bandelier National Monument ◽  
The Impact ◽  
Runoff Events

Representation of precipitation is one of the most difficult aspects of modelling post-fire runoff and erosion and also one of the most sensitive input parameters to rainfall-runoff models. The impact of post-fire convective rainstorms, especially in semiarid watersheds, depends on the overlap between locations of high-intensity rainfall and areas of high-severity burns. One of the most useful applications of models in post-fire situations is risk assessment to quantify peak flow and identify areas at high risk of flooding and erosion. This study used the KINEROS2/AGWA model to compare several spatial and temporal rainfall representations of post-fire rainfall-runoff events to determine the effect of differing representations on modelled peak flow and determine at-risk locations within a watershed. Post-fire rainfall-runoff events at Zion National Park in Utah and Bandelier National Monument in New Mexico were modelled. Representations considered included both uniform and Soil Conservation Service Type II hyetographs, applying rain over the entire watershed and applying rain only on the burned area, and varying rainfall both temporally and spatially according to radar data. Results showed that rainfall representation greatly affected modelled peak flow, but did not significantly alter the model’s predictions for high-risk locations. This has important implications for post-fire assessments before a flood-inducing rainfall event, or for post-storm assessments in areas with low-gauge density or lack of radar data due to mountain beam blockage.

scholarly journals Breast cancer risk assessment: How risk models can “overdiagnose” risk.

2013 ◽  
Vol 31 (31_suppl) ◽  
pp. 184-184
Author(s):  
Elissa Ozanne ◽  
Brian Drohan ◽  
Kevin S. Hughes
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
High Risk ◽  
Resource Utilization ◽  
Management Strategies ◽  
Cancer Risk Assessment ◽  
Cancer Society ◽  
Total Study Population ◽  
Breast Cancer Risk Assessment ◽  
The Impact

184 Background: Overdiagnosis is commonly defined as a diagnosis of "disease" which will never cause symptoms or death during a patient's lifetime. Similarly, overdiagnosis can also happen when individuals are given the diagnosis of being at risk for a disease, such as being at high-risk for developing breast cancer. Women can be given such a diagnosis by meeting a set of risk assessment criteria, which are often accompanied by recommended management strategies. We sought to identify the extent and consequences of overdiagnosis for individuals being at high risk for breast cancer using the American Cancer Society (ACS) guidelines for the appropriate use of Magnetic Resonance Imaging (MRI). Methods: We identified women who fit the ACS criteria in a population based sample at a community hospital. The ACS criteria mentions three risk assessment models for determining a woman’s risk, and these criteria were reviewed to determine the extent of possible overdiagnosis in this population. The expected resource utilization resulting from this overdiagnosis, and the impact on patient quality of life are extrapolated. Results: 5,894 women who received mammography screening at the study site were included. 342 (5.8%) of the women were diagnosed as high risk by at least one model. However, only 0.2% of the total study population were diagnosed as high risk by all three models. One model identified 330 (5.6%) to be at high risk, while the other two models identified many fewer eligible women (25, 0.4% and 54, 0.9% respectively). Conclusions: Using different models to evaluation the ACS criteria identifies very different populations, implying a large potential for overdiagnosis. Further, this overdiagnosis is likely to result in the outcome of screening too many women, incurring false positives and unnecessary resource utilization.

scholarly journals Climate and hydrological variability: the catchment filtering role

2015 ◽  
Vol 19 (1) ◽  
pp. 379-387 ◽  
Author(s):  
I. Andrés-Doménech ◽  
R. García-Bartual ◽  
A. Montanari ◽  
J. B. Marco
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Peak Flow ◽  
Flood Frequency ◽  
Annual Maximum ◽  
Return Periods ◽  
Rainfall Runoff ◽  
Maximum Peak ◽  
Hydrological Variability ◽  
The Impact

Abstract. Measuring the impact of climate change on flood frequency is a complex and controversial task. Identifying hydrological changes is difficult given the factors, other than climate variability, which lead to significant variations in runoff series. The catchment filtering role is often overlooked and thus may hinder the correct identification of climate variability signatures on hydrological processes. Does climate variability necessarily imply hydrological variability? This research aims to analytically derive the flood frequency distribution based on realistic hypotheses about the rainfall process and the rainfall–runoff transformation. The annual maximum peak flow probability distribution is analytically derived to quantify the filtering effect of the rainfall–runoff process on climate change. A sensitivity analysis is performed according to typical semi-arid Mediterranean climatic and hydrological conditions, assuming a simple but common scheme for the rainfall–runoff transformation in small-size ungauged catchments, i.e. the CN-SCS model. Variability in annual maximum peak flows and its statistical significance are analysed when changes in the climatic input are introduced. Results show that depending on changes in the annual number of rainfall events, the catchment filtering role is particularly significant, especially when the event rainfall volume distribution is not strongly skewed. Results largely depend on the return period: for large return periods, peak flow variability is significantly affected by the climatic input, while for lower return periods, infiltration processes smooth out the impact of climate change.

A Validated Risk Model for Prediction of Early Readmission in Patients with Hepatic Encephalopathy

2018 ◽  
Vol 17 (5) ◽  
pp. 0-10
Author(s):  
Andrew J. Kruger ◽  
Fasika Aberra ◽  
Sylvester M. Black ◽  
Alice Hinton ◽  
James Hanje ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Hepatic Encephalopathy ◽  
High Risk ◽  
Risk Model ◽  
Cox Regression ◽  
Low Risk ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Early Readmission ◽  
The Impact

Introduction and aim. Hepatic encephalopathy (HE) is a common complication in cirrhotics and is associated with an increased healthcare burden. Our aim was to study independent predictors of 30-day readmission and develop a readmission risk model in patients with HE. Secondary aims included studying readmission rates, cost, and the impact of readmission on mortality. Material and methods. We utilized the 2013 Nationwide Readmission Database (NRD) for hospitalized patients with HE. A risk assessment model based on index hospitalization variables for predicting 30-day readmission was developed using multivariate logistic regression and validated with the 2014 NRD. Patients were stratified into Low Risk and High Risk groups. Cox regression models were fit to identify predictors of calendar-year mortality. Results. Of 24,473 cirrhosis patients hospitalized with HE, 32.4% were readmitted within 30-days. Predictors of readmission included presence of ascites (OR: 1.19; 95% CI: 1.06-1.33), receiving paracentesis (OR: 1.43; 95% CI: 1.26-1.62) and acute kidney injury (OR: 1.11; 95% CI: 1.00-1.22). Our validated model stratified patients into Low Risk and High Risk of 30-day readmissions (29% and 40%, respectively). The cost of the first readmission was higher than index admission in the 30-day readmission cohort ($14,198 vs. $10,386; p-value < 0.001). Thirty-day readmission was the strongest predictor of calendar-year mortality (HR: 4.03; 95% CI: 3.49-4.65). Conclusions. Nearly one-third of patients with HE were readmitted within 30-days, and early readmission adversely impacted healthcare utilization and calendar-year mortality. With our proposed simple risk assessment model, patients at high risk for early readmissions can be identified to potentially avert poor outcomes.

Research into behavioral traits of the susceptibility to the risk of significant financial misstatements

2020 ◽  
Vol 23 (3) ◽  
pp. 262-275
Author(s):  
S.V. Arzhenovskii ◽  
T.G. Sinyavskaya ◽  
A.V. Bakhteev
Keyword(s):  
Risk Assessment ◽  
Statistical Analysis ◽  
High Risk ◽  
Financial Reporting ◽  
Influential Factors ◽  
Assessment Model ◽  
Expert Survey ◽  
Personal Traits ◽  
Behavioral Traits ◽  
The Impact

Subject. The article identifies behavioral signs of the susceptibility to the risk of material misstatements through the expert survey of professional auditors. Objectives. We do empirical research into the impact five behavioral traits have, which we discovered through the two parameter risk assessment model, i.e. tolerance to violation of laws, money pathology, susceptibility to high risk, aspiration of impunity and legislative illiteracy in finance. Method.s We performed the expert survey of professional auditors to discover what determines the susceptibility to fraud among those charged with financial reporting. The expert group was made on the basis of an unbiased approach and documentation. We applied the Rasch model to rank personal traits. The collected data were processed with methods of descriptive statistics and multivariate statistical analysis. Results. Carrying out the statistical analysis of experts’ opinions, we found that their significantly correlated. Personal traits were sorted by their impact on risk assessment. Money pathology, susceptibility to high risk, aspiration of impunity and legislative illiteracy in finance were acknowledged as the most influential factors in terms of the susceptibility to misstatements of financial reporting. Conclusions and Relevance. We empirically proved the importance of factors influencing the propensity to risk of misstating financial reports. We used our own theoretical concept. The findings can be useful to auditing forms to detect the customers’ propensity to the risk of manipulating financial reporting.

Impact of Venous Thromboembolism (VTE) Prophylaxis and Risk Stratification with the Caprini Model in Hospitalized Patients with Cancer

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 364-364
Author(s):  
Joshua M Ruch ◽  
Hsou M Hu ◽  
Vinita Bahl ◽  
Suman L. Sood
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
Conflicts Of Interest ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Medical Patients ◽  
Vte Prophylaxis ◽  
Patients With Cancer ◽  
Pharmacologic Prophylaxis ◽  
The Impact

Abstract Abstract 364 Introduction: VTE is a common complication in hospitalized medical patients and the role of pharmacologic anticoagulation prophylaxis is well-established. Patients with active malignancy are at higher risk for VTE during hospitalization. However, VTE prophylaxis is underutilized in these patients due to many real and perceived contraindications to prophylaxis. To aid clinicians in determining VTE risk and guide choice of prophylaxis, our institution adopted the Caprini risk assessment model (Ann Surg, 2010; 251[2]:344–50), based on clinical factors such as age, comorbidities, and recent surgery. Our primary objective was to assess adherence to recommended VTE prophylaxis in hospitalized medical patients with solid tumors, hematological malignancies, and bone marrow transplant (BMT) patients in comparison to general medical (GM) patients, and the impact of recommended prophylaxis use on VTE outcomes. Secondary objectives were to evaluate the distribution of Caprini risk scores and the utility of the Caprini risk assessment model for guiding prophylaxis in this population. Methods: Patients admitted to the hematology/oncology (HO; oncology, malignant hematology, and BMT) and GM inpatient services at the University of Michigan between July 1, 2009 to December 31, 2011 were included in the study. After IRB approval, patient information was extracted from the electronic medical record (EMR). A point-scoring method based on the Caprini risk assessment model was used to calculate VTE risk at admission. A score of 3–4 was high risk and ≥ 5 highest risk for VTE. Type of VTE prophylaxis and VTE rate were determined. Recommended prophylaxis was 5000 units TID SQ heparin, 30–40 mg SQ enoxaparin, or 2.5 mg SQ fondaparinux, ± sequential compression devices (SCDs). Pharmacological prophylaxis administration was verified in the EMR. VTE is defined as deep venous thrombosis (DVT) or pulmonary embolism (PE) occurring during hospitalization or within 90 days, confirmed by Doppler, CT or V/Q scan. Adherence was defined as the percentage of patients at high or highest risk for VTE with a length of stay ≥ 2 days who received guideline recommended prophylaxis within 2 days of admission. Patients with a contraindication to prophylaxis were excluded. A retrospective cohort study was performed. Chi-squared test was used to test differences in proportions and Cochran-Armitage test for trends. Results: 4300 patients were admitted to HO and 18,347 to GM services. Compared to GM patients (86.8%), the rate of adherence to recommended VTE prophylaxis was similar for oncology (87.6%), hematology (85.4%), and lower (45.6%) for BMT patients (p<0.0001). The overall VTE rate on HO services was 2.77%. Compared with 1.45% in GM, VTE rate was 3.02% in oncology (p=0.070), 2.01% in hematology (p=0.220), and 3.61% for BMT (p=0.001). Over half (51.3%) of VTE in HO patients occurred in patients who did not receive pharmacologic prophylaxis. In HO patients with a VTE, ordered prophylaxis included 16.0% combined pharmacological and SCD, 32.8% pharmacological alone, 32.8% SCD alone, and 18.5% none. Use of combined or pharmacologic prophylaxis alone was non-significantly increased in the non-VTE HO patients. By the Caprini risk assessment model, 33.3% of all patients on HO services were high and 62.2% highest risk, with less oncology (p=0.0001) and more BMT (p=0.0003) patients classified as high or highest risk. VTE rate in HO patients rose as Caprini risk score increased: score (n, % with VTE) 0–1 (23, 4.35%,); 2 (169, 0.59%); 3–4 (1434, 1.67%); 5–6 (1691, 2.90%); 7–8 (745, 3.76%); and 9 (238, 6.72%), p<0.0001 for trend. Conclusions: Adherence to recommended VTE prophylaxis was high in medical patients with cancer, resulting in low overall rates of VTE during and following discharge. The majority of patients with VTE did not receive recommended pharmacologic prophylaxis. Most VTE occurred in patients at highest risk (Caprini risk assessment score ≥ 5), with a trend to higher VTE rate as individual score increased. These data suggest that the individual Caprini score may provide more detailed VTE risk assessment and may help inform the need for prophylaxis despite perceived relative contraindications in this high risk cancer population. Further study is needed to understand the barriers to ordering VTE prophylaxis in this population and encourage increased prophylaxis use. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Adequacy of methodologies for determining SCS / CN in a watershed with characteristics of the Pampa biome

2021 ◽  
Vol 16 (4) ◽  
pp. 1
Author(s):  
Zandra Almeida da Cunha ◽  
Samuel Beskow ◽  
Maíra Martim de Moura ◽  
Tamara Leitzke Caldeira Beskow ◽  
Carlos Rogério de Mello
Keyword(s):  
Curve Number ◽  
Rainfall Runoff ◽  
Antecedent Moisture ◽  
Pampa Biome ◽  
Definition Of ◽  
Service Curve ◽  
The Impact ◽  
Runoff Events

The Soil Conservation Service Curve Number Model is a conceptual model intended for estimating effective rainfall (ER). This model is grounded in a parameter – referred to as Curve Number (CN), which is determined from information on the characteristics of the watershed. The Standard Method (M1) for determining the CN is based on soil and land-use tables; however, some authors have proposed alternative methodologies for defining the CN value from monitored rainfall-runoff events, such as those described by Hawkins (1993) (M2), Soulis and Valiantzas (2012) (M3), and Soulis and Valiantzas (2013) (M4). The objective of this study was to evaluate the impact of using these methods for determination of the CN parameter on the estimation of ER, taking as reference forty rainfall-runoff events monitored between 2015 and 2018 in the Cadeia River Watershed, which has characteristics of the Pampa biome. The different methods assessed for definition of the CN parameter resulted in contrasting performances with respect to the estimation of ER for CRW, as the following findings: i) M1 gave ER values with little reliability, mainly due to the classification of antecedent moisture content classes; ii) M3 provided the best results in determining ER, followed by M2; and iii) the ER values estimated according to M4 differed from those observed, mainly for events with lower rainfall depths.

scholarly journals Evaluating the Impact of a Web-Based Risk Assessment System (CareSage) and Tailored Interventions on Health Care Utilization: Protocol for a Randomized Controlled Trial (Preprint)

2018 ◽  
Author(s):  
Ramya Sita Palacholla ◽  
Nils C Fischer ◽  
Stephen Agboola ◽  
Mariana Nikolova-Simons ◽  
Sharon Odametey ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Health Care ◽  
High Risk ◽  
Controlled Trial ◽  
Study Groups ◽  
Emergency Department Visits ◽  
Assessment System ◽  
Care Utilization ◽  
Value Based Care ◽  
The Impact

BACKGROUND Soaring health care costs and a rapidly aging population, with multiple comorbidities, necessitates the development of innovative strategies to deliver high-quality, value-based care. OBJECTIVE The goal of this study is to evaluate the impact of a risk assessment system (CareSage) and targeted interventions on health care utilization. METHODS This is a two-arm randomized controlled trial recruiting 370 participants from a pool of high-risk patients receiving care at a home health agency. CareSage is a risk assessment system that utilizes both real-time data collected via a Personal Emergency Response Service and historical patient data collected from the electronic medical records. All patients will first be observed for 3 months (observation period) to allow the CareSage algorithm to calibrate based on patient data. During the next 6 months (intervention period), CareSage will use a predictive algorithm to classify patients in the intervention group as “high” or “low” risk for emergency transport every 30 days. All patients flagged as “high risk” by CareSage will receive nurse triage calls to assess their needs and personalized interventions including patient education, home visits, and tele-monitoring. The primary outcome is the number of 180-day emergency department visits. Secondary outcomes include the number of 90-day emergency department visits, total medical expenses, 180-day mortality rates, time to first readmission, total number of readmissions and avoidable readmissions, 30-, 90-, and 180-day readmission rates, as well as cost of intervention per patient. The two study groups will be compared using the Student t test (two-tailed) for normally distributed and Mann Whitney U test for skewed continuous variables, respectively. The chi-square test will be used for categorical variables. Time to event (readmission) and 180-day mortality between the two study groups will be compared by using the Kaplan-Meier survival plots and the log-rank test. Cox proportional hazard regression will be used to compute hazard ratio and compare outcomes between the two groups. RESULTS We are actively enrolling participants and the study is expected to be completed by end of 2018; results are expected to be published in early 2019. CONCLUSIONS Innovative solutions for identifying high-risk patients and personalizing interventions based on individual risk and needs may help facilitate the delivery of value-based care, improve long-term patient health outcomes and decrease health care costs. CLINICALTRIAL ClinicalTrials.gov NCT03126565; https://clinicaltrials.gov/ct2/show/NCT03126565 (Archived by WebCite at http://www.webcitation.org/6ymDuAwQA).

A System for Predicting Unprecedented Injury by Spatiotemporally Superimposing Children’s Normal Behavior

2012 ◽  
Vol 24 (5) ◽  
pp. 838-850
Author(s):  
Yoshinori Koizumi ◽  
◽  
Yoshifumi Nishida ◽  
Koji Kitamura ◽  
Yusuke Miyazaki ◽  
...  
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
Child Behavior ◽  
System Application ◽  
Daily Life ◽  
Prediction System ◽  
Injury Prediction ◽  
Normal Behavior ◽  
Safety Design ◽  
The Impact

Predicting injuries in daily life is important in the field of product safety design and risk assessment. However, in the case of children, it is usually thought that unprecedented injuries are difficult to predict because they are caused by “irregular” child behavior. Despite the prevalence of this belief, this study proposes a new injury prediction system based on the view that unprecedented injuries can, in fact, be predicted by identifying high-risk combinations of “normal” behaviors and environmental states. In this article, we also propose an injury prediction system based on spatiotemporally superimposing normal child behavior. The proposed system enables us to consistently predict injury processes consisting of the situation leading to the injury, the impact occurrence, and the resulting injury. This paper also presents an example of a system application for predicting potential injuries around a swing set in an actual park. To prove the effectiveness of the proposed system, we compare the patterns of accident processes predicted by the system with those of actual incident processes found in our observations of normal behaviors.

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