Shark Nursery Grounds of the Gulf of Mexico and the East Coast Waters of the United States

2007 ◽  
Keyword(s):  
Logistic Regression ◽  
Chesapeake Bay ◽  
Regression Models ◽  
Management Practices ◽  
Fish Habitat ◽  
Virginia Institute ◽  
Predictive Ability ◽  
The United States ◽  
Carcharhinus Plumbeus ◽  
Logistic Regression Models

<em>Abstract.</em>—The lower Chesapeake Bay is the largest summer nursery for sandbar sharks <em>Carcharhinus plumbeus </em>in the western Atlantic. The objective of this study was to define essential fish habitat for juvenile sandbar sharks in this estuary. The longline survey conducted by the Virginia Institute of Marine Science was expanded from 1990 to 1999 to include ancillary stations throughout the Virginia portion of Chesapeake Bay to delineate this nursery spatially. We analyzed catch per unit of effort data from 83 stations as a function of nine physical and environmental variables using tree-based regression models. The highest abundance of juvenile sandbar sharks was predicted where salinity was greater than 20.5 (practical salinity scale) and depth was greater than 5.5 m. The models also suggested that dissolved oxygen concentration may influence shark distribution. To increase applicability of the models to management practices, we introduced distance to the mouth of the estuary as a surrogate variable for salinity. The models estimated that the highest abundance of sharks was in areas less than 34.5 km from the mouth of the estuary and in depths greater than 5.5 m. The areas of the estuary that met the criteria of the models, based on the threshold values of the variables, were mapped spatially in a geographic information system. The resulting response surfaces were interpreted to represent essential nursery habitat for sandbar sharks in Chesapeake Bay. Both models performed very well using several dependent and independent measures to estimate their classification and predictive ability. We used logistic regression with presence/absence data to validate the tree models. The logistic regression models agreed very well with the tree-based regression models, selecting the same variable combinations to predict sandbar shark presence and absence.

Correlates of Perceived HIV Prevalence and Associations With HIV Testing Behavior Among Men Who Have Sex With Men in the United States

2014 ◽  
Vol 10 (2) ◽  
pp. 90-99 ◽  
Author(s):  
Darcy White ◽  
Rob Stephenson
Keyword(s):  
United States ◽  
Logistic Regression ◽  
Hiv Testing ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
The United States ◽  
Hiv Prevalence ◽  
Logistic Regression Models ◽  
Sex With Men ◽  
Hiv Testing Behavior

As the rate of HIV infection continues to rise among men who have sex with men (MSM) in the United States, a focus of current prevention efforts is to encourage frequent HIV testing. Although levels of lifetime testing are high, low levels of routine testing among MSM are concerning. Using data from an online sample of 768 MSM, this article explores how perceptions of HIV prevalence are associated with HIV testing behavior. Ordinal logistic regression models were fitted to examine correlates of perceived prevalence, and binary logistic regression models were fitted to assess associations between perceived prevalence and HIV testing. The results indicate that perceptions of higher prevalence among more proximal reference groups such as friends and sex partners are associated with greater odds of HIV testing. Perceptions of HIV prevalence were nonuniform across the sample; these variations point to groups to target with strategic messaging and interventions to increase HIV testing among MSM.

scholarly journals Blood Transfusions in Total Hip and Knee Arthroplasty: An Analysis of Outcomes

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Thomas Danninger ◽  
Rehana Rasul ◽  
Jashvant Poeran ◽  
Ottokar Stundner ◽  
Madhu Mazumdar ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Blood Transfusion ◽  
Knee Arthroplasty ◽  
Regression Models ◽  
The United States ◽  
Perioperative Outcomes ◽  
Blood Transfusions ◽  
Logistic Regression Models ◽  
Total Hip ◽  
Major Complications

Background.Various studies have raised concern of worse outcomes in patients receiving blood transfusions perioperatively compared to those who do not. In this study we attempted to determine the proportion of perioperative complications in the orthopedic population attributable to the use of a blood transfusion.Methods.Data from 400 hospitals in the United States were used to identify patients undergoing total hip or knee arthroplasty (THA and TKA) from 2006 to 2010. Patient and health care demographics, as well as comorbidities and perioperative outcomes were compared. Multivariable logistic regression models were fitted to determine associations between transfusion, age, and comorbidities and various perioperative outcomes. Population attributable fraction (PAF) was determined to measure the proportion of outcome attributable to transfusion and other risk factors.Results.Of 530,089 patients, 18.93% received a blood transfusion during their hospitalization. Patients requiring blood transfusion were significantly older and showed a higher comorbidity burden. In addition, these patients had significantly higher rates of major complications and a longer length of hospitalization. The logistic regression models showed that transfused patients were more likely to have adverse health outcomes than nontransfused patients. However, patients who were older or had preexisting diseases carried a higher risk than use of a transfusion for these outcomes. The need for a blood transfusion explained 9.51% (95% CI 9.12–9.90) of all major complications.Conclusions.Advanced age and high comorbidity may be responsible for a higher proportion of adverse outcomes in THA and TKA patients than blood transfusions.

Abstract WP92: Risk Factors for Thrombotic Events in the Endovascular Treatment of Ruptured Cerebral Aneurysms

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Ali Sultan-Qurraie ◽  
Adam de Havenon ◽  
John Lynch ◽  
David Tirschwell ◽  
Marc Lazzaro ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Endovascular Treatment ◽  
Endovascular Therapy ◽  
Regression Models ◽  
Platelet Transfusion ◽  
Endovascular Aneurysm Repair ◽  
The United States ◽  
Active Smoking ◽  
Logistic Regression Models

Introduction: Aneurysmal subarachnoid hemorrhage (aSAH) carries a high risk of morbidity and mortality. Endovascular coiling is an effective treatment option for ruptured aneurysms and offers certain advantages over microsurgical clipping. Thromboembolism is a known complication of endovascular therapy, but little has been reported regarding its causes. We hypothesized that platelet transfusion (PT) is a risk factor for thrombotic events (TE) during endovascular therapy. Methods: We retrospectively evaluated 84 patients presenting with aSAH to a Comprehensive Stroke Center in the United States between 2011 and 2015 who underwent endovascular treatment. In addition to TE related to endovascular aneurysm repair, charts were reviewed for variables including length of hospitalization, 3-month modified Rankin Scale (mRS), and smoking. Intergroup differences were evaluated with a Chi-squared and Student’s t-test. Logistic regression models were also fitted to the outcome of TE. Results: Patient demographics and clinical variables are seen in Table 1. 23% of patients incurred TE. Platelet transfusion was more common in patients with TE, but this association was not statistically significant in the logistic regression models (Table 2). In the adjusted model, active smoking and procedure length remained significantly associated with TE. Conclusion: To our knowledge, risk factors for thrombotic events in the setting of endovascular cerebral aneurysm treatment have not been previously reported. Procedure length and active smoking are associated with TE, and PT has a trend towards significance. After adjusting for potential confounders, active smoking remains the best predictor of TE (p=0.004), conferring an 11-fold risk.

scholarly journals Serious Psychological Distress and Emergency Room Use among Adults with Multimorbidity in the United States

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Khalid Alhussain ◽  
Abdulkarim M. Meraya ◽  
Usha Sambamoorthi
Keyword(s):  
United States ◽  
Logistic Regression ◽  
Psychological Distress ◽  
Emergency Room ◽  
Regression Models ◽  
The United States ◽  
Serious Psychological Distress ◽  
Cross Sectional ◽  
Logistic Regression Models ◽  
Adjusted Logistic Regression

Objectives. (1) To examine the association between serious psychological distress (SPD) and emergency room (ER) use in the past 12 months among adults with multimorbidity in the United States (US) and (2) to investigate the association between SPD and the reasons for ER use. Methods. The current study used a cross-sectional design with retrospective data from the 2015 National Health Interview Survey. Logistic regression models were used to assess the association between SPD and ER use among adults with multimorbidity. Among ER users, adjusted logistic regression models were conducted to examine the association between SPD and the reasons for the ER use. Results. After controlling for other variables, adults with multimorbidity and SPD were more likely to use ER than those with multimorbidity and no SPD (AOR = 1.61, 95% CI = 1.26, 2.04). Among ER users, there were no significant associations between SPD and the reasons for ER use after controlling for other variables. Conclusion. Adults with multimorbidity and SPD were more likely to use ER as compared to those with multimorbidity and no SPD. Among adults with multimorbidity, routine screening for SPD may be needed to reduce the ER use.

scholarly journals Youth Access to School Salad Bars in the United States—2011 to 2014

2016 ◽  
Vol 32 (1) ◽  
pp. 84-88 ◽  
Author(s):  
Brenna K. VanFrank ◽  
Stephen Onufrak ◽  
Diane M. Harris
Keyword(s):  
United States ◽  
Logistic Regression ◽  
Regression Models ◽  
The United States ◽  
Logistic Regression Models ◽  
Multivariable Logistic Regression ◽  
Let's Move ◽  
Race Ethnicity ◽  
Nationally Representative ◽  
Over Time

Purpose: To examine differences in students’ access to school salad bars across sociodemographic groups and changes in availability over time. Design: Nonexperimental. Setting: Nationally representative 2011 and 2014 YouthStyles surveys. Participants: A total of 833 (2011) and 994 (2014) US youth aged 12 to 17 years. Measures: Youth-reported availability of school salad bars. Analysis: Multivariable logistic regression models were used to assess differences in school salad bar availability by sociodemographics and changes in availability from 2011 to 2014. Results: Youth-reported salad bar availability differed by age in 2011 and race/ethnicity in 2014, but not by sex, income, metropolitan residence, or region in either year. Salad bars were reported by 62% of youth in 2011 and 67% in 2014; the increase was not statistically significant ( P = .07). Significant increases from 2011 to 2014 were noted among youth aged 12 to 14 years (56%-69%; P < .01), youth of non-Hispanic other races (60%-85%; P < .01), and youth in the Midwest (58%-72%; P = .01). Conclusion: These results suggest that youth-reported access to school salad bars does not differ significantly across most sociodemographic groups. Although overall salad bar availability did not increase significantly from 2011 to 2014, some increases were observed among subgroups. Continued efforts to promote school salad bars through initiatives such as Let’s Move Salad Bars to Schools could help increase access for the nearly one-third of US youth reporting no access.

scholarly journals Predicting Fusarium Head Blight Epidemics With Weather-Driven Pre- and Post-Anthesis Logistic Regression Models

2013 ◽  
Vol 103 (9) ◽  
pp. 906-919 ◽  
Author(s):  
D. A. Shah ◽  
J. E. Molineros ◽  
P. A. Paul ◽  
K. T. Willyerd ◽  
L. V. Madden ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Relative Humidity ◽  
Fusarium Head Blight ◽  
Regression Models ◽  
The United States ◽  
Misclassification Rate ◽  
Predictor Variables ◽  
Head Blight ◽  
Data Set ◽  
Logistic Regression Models

Our objective was to identify weather-based variables in pre- and post-anthesis time windows for predicting major Fusarium head blight (FHB) epidemics (defined as FHB severity ≥ 10%) in the United States. A binary indicator of major epidemics for 527 unique observations (31% of which were major epidemics) was linked to 380 predictor variables summarizing temperature, relative humidity, and rainfall in 5-, 7-, 10-, 14-, or 15-day-long windows either pre- or post-anthesis. Logistic regression models were built with a training data set (70% of the 527 observations) using the leaps-and-bounds algorithm, coupled with bootstrap variable and model selection methods. Misclassification rates were estimated on the training and remaining (test) data. The predictive performance of models with indicator variables for cultivar resistance, wheat type (spring or winter), and corn residue presence was improved by adding up to four weather-based predictors. Because weather variables were intercorrelated, no single model or subset of predictor variables was best based on accuracy, model fit, and complexity. Weather-based predictors in the 15 final empirical models selected were all derivatives of relative humidity or temperature, except for one rainfall-based predictor, suggesting that relative humidity was better at characterizing moisture effects on FHB than other variables. The average test misclassification rate of the final models was 19% lower than that of models currently used in a national FHB prediction system.

scholarly journals Sociodemographic Characteristics and Health Status of Mindfulness Users in the United States

Mindfulness ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 2725-2729 ◽  
Author(s):  
Otto Simonsson ◽  
Maryanne Martin ◽  
Stephen Fisher
Keyword(s):  
United States ◽  
Mental Illness ◽  
Logistic Regression ◽  
Regression Models ◽  
The United States ◽  
Access Barriers ◽  
Physical Pain ◽  
Unequal Distribution ◽  
Logistic Regression Models ◽  
Barriers To Healthcare

Abstract Objectives The aims of the present study are to provide population estimates for the prevalence of mindfulness use in the United States and to identify which groups are more likely to self-report mindfulness use. Methods Using data from the 2017 National Health Interview Survey (NHIS), the current study analyzed 26,742 responses from adults in the United States and estimated patterns in the likelihood of self-reported mindfulness use across groups using logistic regression models. Results The results suggest that 5% of adults in the United States in 2017 had used mindfulness over the prior year, which is significantly more than the finding that 2% of adults in the United States had used mindfulness during the 12 months prior to the 2012 NHIS interview. The logistic regression models show that self-reported mindfulness use was less likely among married adults and more likely among women, sexual minorities, young and middle-aged adults, white adults, employed adults, adults without minor children in the family, adults from the West of the United States, adults with access barriers to healthcare, adults with cost barriers to healthcare, adults with mental illness, and adults with physical pain. Most notably, mindfulness use was reported by substantial numbers of respondents with access barriers to healthcare (10%), cost barriers to healthcare (9%), mental illness (15%), or physical pain (7%). Conclusions The results of the present study suggest an unequal distribution of mindfulness use across groups in the United States.

scholarly journals Rise in Use of Digital Mental Health Tools and Technologies in the United States During the COVID-19 Pandemic: Survey Study (Preprint)

2021 ◽  
Author(s):  
Dara H Sorkin ◽  
Emily A Janio ◽  
Elizabeth V Eikey ◽  
Margaret Schneider ◽  
Katelyn Davis ◽  
...  
Keyword(s):  
Mental Health ◽  
United States ◽  
Logistic Regression ◽  
Regression Models ◽  
The United States ◽  
Clinical Depression ◽  
Depression And Anxiety ◽  
Logistic Regression Models ◽  
Symptoms Of Depression ◽  
Time Point

BACKGROUND Accompanying the rising rates of reported mental distress during the COVID-19 pandemic has been a reported increase in the use of digital technologies to manage health generally, and mental health more specifically. OBJECTIVE The objective of this study was to systematically examine whether there was a COVID-19 pandemic–related increase in the self-reported use of digital mental health tools and other technologies to manage mental health. METHODS We analyzed results from a survey of 5907 individuals in the United States using Amazon Mechanical Turk (MTurk); the survey was administered during 4 week-long periods in 2020 and survey respondents were from all 50 states and Washington DC. The first set of analyses employed two different logistic regression models to estimate the likelihood of having symptoms indicative of clinical depression and anxiety, respectively, as a function of the rate of COVID-19 cases per 10 people and survey time point. The second set employed seven different logistic regression models to estimate the likelihood of using seven different types of digital mental health tools and other technologies to manage one’s mental health, as a function of symptoms indicative of clinical depression and anxiety, rate of COVID-19 cases per 10 people, and survey time point. These models also examined potential interactions between symptoms of clinical depression and anxiety, respectively, and rate of COVID-19 cases. All models controlled for respondent sociodemographic characteristics and state fixed effects. RESULTS Higher COVID-19 case rates were associated with a significantly greater likelihood of reporting symptoms of depression (odds ratio [OR] 2.06, 95% CI 1.27-3.35), but not anxiety (OR 1.21, 95% CI 0.77-1.88). Survey time point, a proxy for time, was associated with a greater likelihood of reporting clinically meaningful symptoms of depression and anxiety (OR 1.19, 95% CI 1.12-1.27 and OR 1.12, 95% CI 1.05-1.19, respectively). Reported symptoms of depression and anxiety were associated with a greater likelihood of using each type of technology. Higher COVID-19 case rates were associated with a significantly greater likelihood of using mental health forums, websites, or apps (OR 2.70, 95% CI 1.49-4.88), and other health forums, websites, or apps (OR 2.60, 95% CI 1.55-4.34). Time was associated with increased odds of reported use of mental health forums, websites, or apps (OR 1.20, 95% CI 1.11-1.30), phone-based or text-based crisis lines (OR 1.20, 95% CI 1.10-1.31), and online, computer, or console gaming/video gaming (OR 1.12, 95% CI 1.05-1.19). Interactions between COVID-19 case rate and mental health symptoms were not significantly associated with any of the technology types. CONCLUSIONS Findings suggested increased use of digital mental health tools and other technologies over time during the early stages of the COVID-19 pandemic. As such, additional effort is urgently needed to consider the quality of these products, either by ensuring users have access to evidence-based and evidence-informed technologies and/or by providing them with the skills to make informed decisions around their potential efficacy.

Sign in / Sign up

Export Citation Format

Share Document