scholarly journals Correlates of Lifetime Physical Abuse Among Schoolchildren Aged 15 Years in Post-communist Albania

2021 ◽  
Vol 9 ◽  
Author(s):  
Gentiana Qirjako ◽  
Qamil Dika ◽  
Iris Mone ◽  
Xheladin Draçini ◽  
Loreta Kuneshka ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Life Satisfaction ◽  
Logistic Regression ◽  
Alcohol Consumption ◽  
Physical Abuse ◽  
Binary Logistic Regression ◽  
Sociodemographic Factors ◽  
Communist Country ◽  
Logistic Regression Models ◽  
Wide Range

Aim: Our aim was to assess the prevalence and correlates of lifetime physical abuse among schoolchildren in Albania, a post-communist country in South Eastern Europe which is currently undergoing a rapid socioeconomic transition.Methods: The third wave of Health Behavior in School-Aged Children (HBSC) in Albania was conducted in 2017–18 including a nationwide representative sample of 1,708 schoolchildren aged 15 years (54% girls; response rate: 95%). Children were asked to report on lifetime physical abuse and a wide range of socio-demographic factors, lifestyle factors and health status characteristics. Binary logistic regression was used to assess the independent association of lifetime physical abuse with covariates.Results: Overall, the prevalence of lifetime physical abuse was about 32% (30% in boys vs. 32% in girls). In multivariable-adjusted logistic regression models, independent positive correlates of lifetime physical abuse among Albanian schoolchildren included lifetime smoking (OR = 1.5, 95% CI = 1.1–2.2), lifetime alcohol consumption (OR = 1.6, 95%CI = 1.2–2.1), irritability (OR[dailyvs.rarely/never] = 2.0, 95%CI = 1.3–3.0), and especially lifetime witnessed domestic violence (OR = 4.2, 95%CI = 2.2–7.9). Conversely, a higher score on life satisfaction was inversely related to lifetime physical abuse (P < 0.01).Conclusion: Our study provides novel evidence about the magnitude and selected independent correlates of lifetime physical abuse among schoolchildren in Albania, a country still embedded in an everlasting transition which is associated with tremendous changes in family structure, community links and societal norms and values. Irrespective of a wide range of sociodemographic factors and health characteristics, lifetime smoking, alcohol consumption, irritability, a lower score on life satisfaction and, particularly, witnessed domestic violence were strong and significant correlates of lifetime physical abuse among Albanian schoolchildren aged 15 years.

scholarly journals EPID-34. THE DETRIMENTAL EFFECT OF BIOPSY PRECEDING RESECTION IN SURGICALLY ACCESSIBLE GLIOBLASTOMAS: RESULTS FROM THE NATIONAL CANCER DATABASE

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii85-ii86
Author(s):  
Ping Zhu ◽  
Xianglin Du ◽  
Angel Blanco ◽  
Leomar Y Ballester ◽  
Nitin Tandon ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Failure Time ◽  
Binary Logistic Regression ◽  
Rank Test ◽  
Imaging Features ◽  
National Cancer Database ◽  
Prolonged Length ◽  
Logistic Regression Models ◽  
Operative Outcomes ◽  
The Impact

Abstract OBJECTIVES To investigate the impact of biopsy preceding resection compared to upfront resection in glioblastoma overall survival (OS) and post-operative outcomes using the National Cancer Database (NCDB). METHODS A total of 17,334 GBM patients diagnosed between 2010 and 2014 were derived from the NCDB. Patients were categorized into two groups: “upfront resection” versus “biopsy followed by resection”. Primary outcome was OS. Post-operative outcomes including 30-day readmission/mortality, 90-day mortality, and prolonged length of inpatient hospital stay (LOS) were secondary endpoints. Kaplan-Meier methods and accelerated failure time (AFT) models with gamma distribution were applied for survival analysis. Multivariable binary logistic regression models were performed to compare differences in the post-operative outcomes between these groups. RESULTS Patients undergoing “upfront resection” experienced superior survival compared to those undergoing “biopsy followed by resection” (median OS: 12.4 versus 11.1 months, log-rank test: P=0.001). In multivariable AFT models, significant survival benefits were observed among patients undergoing “upfront resection” (time ratio [TR]: 0.83, 95% CI: 0.75–0.93, P=0.001). Patients undergoing upfront GTR had the longest survival compared to upfront STR, GTR following STR, or GTR and STR following an initial biopsy (14.4 vs. 10.3, 13.5, 13.3, and 9.1, months), respectively (TR: 1.00 [Ref.], 0.75, 0.82, 0.88, and 0.67). Recent years of diagnosis, higher income and treatment at academic facilities were significantly associated with the likelihood of undergoing upfront resection after adjusting the covariates. Multivariable logistic regression revealed that 30-day mortality and 90-day mortality were decreased by 73% and 44% for patients undergoing “upfront resection” over “biopsy followed by resection”, respectively (both p < 0.001). CONCLUSIONS Pre-operative biopsies for surgically accessible tumors with characteristic imaging features of Glioblastoma lead to worse survival despite subsequent resection compared to patients undergoing upfront resection.

Predictors of Child Abuse Charges Within the Context of Domestic Violence Arrests

2016 ◽  
Vol 63 (13) ◽  
pp. 1777-1803 ◽  
Author(s):  
Jennifer Roark ◽  
Kelly E. Knight ◽  
Heather Olson ◽  
Heidi DeSandre
Keyword(s):  
Gender Differences ◽  
Domestic Violence ◽  
Logistic Regression ◽  
Child Abuse ◽  
Regression Models ◽  
Police Response ◽  
Men And Women ◽  
Logistic Regression Models ◽  
Underdeveloped Area

This article investigates how different factors of a domestic violence incident impact the likelihood of a child abuse charge within the context of domestic violence arrests. Data from 5,148 domestic violence arrests were used to test whether domestic violence-, incident-, and child-based predictors increased the likelihood of a child abuse charge. Logistic regression models of gender-stratified samples were employed to test for gender differences among domestic violence arrestees. The results demonstrated predictors affected men’s odds of a child abuse charge when compared with women. For men and women, children witnessing the domestic violence incident had the largest impact on a child abuse charge. These results contribute to the underdeveloped area of police response to child abuse in domestic violence cases.

scholarly journals Does Physical Fitness Predict Future Karate Success? A Study in Young Female Karatekas

2020 ◽  
Vol 15 (6) ◽  
pp. 868-873
Author(s):  
Óscar Martínez de Quel ◽  
Ignacio Ara ◽  
Mikel Izquierdo ◽  
Carlos Ayán
Keyword(s):  
Logistic Regression ◽  
Physical Fitness ◽  
Muscle Power ◽  
Binary Logistic Regression ◽  
Young Female ◽  
Binary Logistic Regression Analysis ◽  
Discriminative Ability ◽  
Logistic Regression Models ◽  
Elite Group ◽  
Competitive Success

Objective: To assess the discriminative ability of several fitness dimensions and anthropometric attributes for forecasting competitive success in female karate athletes. Methods: Fitness and anthropometric data from 98 female junior karatekas obtained during the training camps of the Spanish National Karate Federation between 1999 and 2012 were used. Binary logistic-regression models were built to ascertain whether the set of fitness and anthropometric variables could predict future sporting-performance levels. For this purpose, participants were classified as elite (medalist in World or European Championships in the senior category) or subelite (at least a medalist in Spanish National Championships in cadet or junior but not included in the elite group), according to the results achieved up to 2019. Results: Participants who were subsequently classified as elite karatekas showed significant differences in agility, upper- and lower-body muscle power, and general fitness in comparison with those who were classified as subelite in the senior category. A total of 57 junior female karatekas who were subsequently classified as elite (7) or subelite (50) were included in the binary logistic-regression analysis. Resultant models showed significant capacity to predict karate performance. Conclusions: Assessing physical fitness in junior categories can be a useful resource to determine future karate success. Coaches in this sport should pay special attention to the levels of muscle power and agility shown by their athletes, as both fitness dimensions could be indicators of future sportive success.

Domestic Violence Reported to the Police: Correlates of Victims’ Reporting Behavior and Support to Legal Proceedings

2016 ◽  
Vol 31 (3) ◽  
pp. 402-415 ◽  
Author(s):  
Rémi Boivin ◽  
Chloé Leclerc
Keyword(s):  
Domestic Violence ◽  
Logistic Regression ◽  
Regression Models ◽  
Multinomial Logistic Regression ◽  
The Other ◽  
Individual Factors ◽  
Initial Report ◽  
Logistic Regression Models ◽  
Legal Proceedings ◽  
History Of

This article analyzes reported incidents of domestic violence according to the source of the complaint and whether the victim initially supported judicial action against the offender. Almost three quarters of incidents studied were reported by the victim (72%), and a little more than half of victims initially wanted to press charges (55%). Using multinomial logistic regression models, situational and individual factors are used to distinguish 4 incident profiles. Incidents in which the victim made the initial report to the police and wished to press charges are the most distinct and involve partners who were already separated at the time of the incident or had a history of domestic violence. The other profiles also show important differences.

scholarly journals Application of Logistic Regression Models for the Marketability of Cucumber Cultivars

Agronomy ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 17 ◽  
Author(s):  
Manuel Díaz-Pérez ◽  
Ángel Carreño-Ortega ◽  
José-Antonio Salinas-Andújar ◽  
Ángel-Jesús Callejón-Ferre
Keyword(s):  
Weight Loss ◽  
Logistic Regression ◽  
Shelf Life ◽  
Regression Models ◽  
Chilling Injury ◽  
Binary Logistic Regression ◽  
Fruit Weight ◽  
Binary Logistic Regression Analysis ◽  
Cucumis Sativus L ◽  
Logistic Regression Models

The aim of this study is to establish a binary logistic regression method to evaluate and select cucumber cultivars (Cucumis sativus L.) with a longer postharvest shelf life. Each sample was evaluated for commercial quality (fruit aging, weight loss, wilting, yellowing, chilling injury, and rotting) every 7 days of storage. Simple and multiple binary logistic regression models were applied in which the dependent variable was the probability of marketability and the independent variables were the days of storage, cultivars, fruit weight loss, and months of evaluation. The results showed that cucumber cultivars with a longer shelf life can be selected by a simple and multiple binary logistic regression analysis. Storage time was the main determinant of fruit marketability. Fruit weight loss strongly influenced the probability of marketability. The logistic model allowed us to determine the cucumber weight loss percentage over which a fruit would be rejected in the market.

scholarly journals The sensitivity of modeled snow accumulation and melt to precipitation phase methods across a climatic gradient

2019 ◽  
Vol 23 (9) ◽  
pp. 3765-3786 ◽  
Author(s):  
Keith S. Jennings ◽  
Noah P. Molotch
Keyword(s):  
Logistic Regression ◽  
Air Temperature ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Snow Accumulation ◽  
Phase Method ◽  
Logistic Regression Models ◽  
Temperature Thresholds ◽  
Snow Cover Duration ◽  
Precipitation Phase

Abstract. A critical component of hydrologic modeling in cold and temperate regions is partitioning precipitation into snow and rain, yet little is known about how uncertainty in precipitation phase propagates into variability in simulated snow accumulation and melt. Given the wide variety of methods for distinguishing between snow and rain, it is imperative to evaluate the sensitivity of snowpack model output to precipitation phase determination methods, especially considering the potential of snow-to-rain shifts associated with climate warming to fundamentally change the hydrology of snow-dominated areas. To address these needs we quantified the sensitivity of simulated snow accumulation and melt to rain–snow partitioning methods at sites in the western United States using the SNOWPACK model without the canopy module activated. The methods in this study included different permutations of air, wet bulb and dew point temperature thresholds, air temperature ranges, and binary logistic regression models. Compared to observations of snow depth and snow water equivalent (SWE), the binary logistic regression models produced the lowest mean biases, while high and low air temperature thresholds tended to overpredict and underpredict snow accumulation, respectively. Relative differences between the minimum and maximum annual snowfall fractions predicted by the different methods sometimes exceeded 100 % at elevations less than 2000 m in the Oregon Cascades and California's Sierra Nevada. This led to ranges in annual peak SWE typically greater than 200 mm, exceeding 400 mm in certain years. At the warmer sites, ranges in snowmelt timing predicted by the different methods were generally larger than 2 weeks, while ranges in snow cover duration approached 1 month and greater. Conversely, the three coldest sites in this work were relatively insensitive to the choice of a precipitation phase method, with average ranges in annual snowfall fraction, peak SWE, snowmelt timing, and snow cover duration of less than 18 %, 62 mm, 10 d, and 15 d, respectively. Average ranges in snowmelt rate were typically less than 4 mm d−1 and exhibited a small relationship to seasonal climate. Overall, sites with a greater proportion of precipitation falling at air temperatures between 0 and 4 ∘C exhibited the greatest sensitivity to method selection, suggesting that the identification and use of an optimal precipitation phase method is most important at the warmer fringes of the seasonal snow zone.

Defining the Growth/No-Growth Interface for Listeria monocytogenes in Mexican-Style Cheese Based on Salt, pH, and Moisture Content

1999 ◽  
Vol 62 (6) ◽  
pp. 601-609 ◽  
Author(s):  
LANCE F. BOLTON ◽  
JOSEPH F. FRANK
Keyword(s):  
Logistic Regression ◽  
Listeria Monocytogenes ◽  
Binary Logistic Regression ◽  
Ordinal Logistic Regression ◽  
Growth Interface ◽  
Data Set ◽  
Logistic Regression Models ◽  
Binary Model ◽  
Fresh Cheese ◽  
Brine Concentration

The objective of this study was to define combinations of pH, salt, and moisture that produce growth, stasis, or inactivation of Listeria monocytogenes in Mexican-style cheese. A soft, directly acidified, rennet-coagulated, fresh cheese similar to Mexican-style cheese was produced. The cheese was subsequently altered in composition as required by the experimental protocol. A factorial design with four moisture contents (42, 50, 55, and 60%), four salt concentrations (2.0, 4.0, 6.0, and 8.0% wt/wt), six pH levels (5.0, 5.25, 5.50, 5.75, 6.0, and 6.5), and three replications was used. Observations of growth, stasis, or death were obtained for each combination after 21 and 42 days of incubation at 10°C. Binary logistic regression was used to develop an equation to determine the probability of growth or no growth for any combination within the range of the data set. In addition, ordinal logistic regression was used to calculate proportional odds ratios for growth, stasis, and death for each treatment combination. Ordinal logistic regression was also used to develop equations to determine the probability of growth, stasis, and death for formulations within the range of the data set. Models were validated with independently produced data. Of 60 samples formulated to have a 5% probability of Listeria growth (pH, 5.0 to 6.0; brine concentration, 8.17 to 16.00%), none supported growth. Of 30 samples formulated to have 50% probability of growth using the binary model (pH, 5.50 to 6.50; brine concentration, 3.23 to 12.50%), 20 supported growth. Of 30 samples formulated to have a 50% probability of growth according to the ordinal model (pH, 5.50 to 6.50; brine concentration, 3.37 to 10.90%), 16 supported growth. These data indicate that the logistic regression models presented accurately predict the behavior of L. monocytogenes in Mexican-style cheese.

Performance simulations for categorical mediation: Analyzing khb estimates of mediation in ordinal regression models

2019 ◽  
Vol 19 (4) ◽  
pp. 913-930 ◽  
Author(s):  
E. Keith Smith ◽  
Michael G. Lacy ◽  
Adam Mayer
Keyword(s):  
Logistic Regression ◽  
Nonlinear Regression ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Experimental Conditions ◽  
Logistic Regression Models ◽  
Nonlinear Regression Models ◽  
Size Number ◽  
Confidence Interval Coverage ◽  
The Difference

Standard mediation techniques for fitting mediation models cannot readily be translated to nonlinear regression models because of scaling issues. Methods to assess mediation in regression models with categorical and limited response variables have expanded in recent years, and these techniques vary in their approach and versatility. The recently developed khb technique purports to solve the scaling problem and produce valid estimates across a range of nonlinear regression models. Prior studies demonstrate that khb performs well in binary logistic regression models, but performance in other models has yet to be investigated. In this article, we evaluate khb‘s performance in fitting ordinal logistic regression models as an exemplar of the wider set of models to which it applies. We examined performance across 38,400 experimental conditions involving sample size, number of response categories, distribution of variables, and amount of mediation. Results indicate that under all experimental conditions, khb estimates the difference (mediation) coefficient and its associated standard error with little bias and that the nominal confidence interval coverage closely matches the actual. Our results suggest that researchers using khb can assume that the routine reasonably approximates population parameters.

scholarly journals The impact of demographic, clinical, genetic, and imaging variables on tau PET status

2020 ◽  
Author(s):  
Rik Ossenkoppele ◽  
◽  
Antoine Leuzy ◽  
Hanna Cho ◽  
Carole H. Sudre ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cortical Thickness ◽  
Regression Models ◽  
Neurodegenerative Disorder ◽  
Amyloid Β ◽  
Binary Logistic Regression ◽  
Clinical Genetic ◽  
Logistic Regression Models ◽  
Tau Pet ◽  
The Impact

Abstract Purpose A substantial proportion of amyloid-β (Aβ)+ patients with clinically diagnosed Alzheimer’s disease (AD) dementia and mild cognitive impairment (MCI) are tau PET–negative, while some clinically diagnosed non-AD neurodegenerative disorder (non-AD) patients or cognitively unimpaired (CU) subjects are tau PET–positive. We investigated which demographic, clinical, genetic, and imaging variables contributed to tau PET status. Methods We included 2338 participants (430 Aβ+ AD dementia, 381 Aβ+ MCI, 370 non-AD, and 1157 CU) who underwent [18F]flortaucipir (n = 1944) or [18F]RO948 (n = 719) PET. Tau PET positivity was determined in the entorhinal cortex, temporal meta-ROI, and Braak V-VI regions using previously established cutoffs. We performed bivariate binary logistic regression models with tau PET status (positive/negative) as dependent variable and age, sex, APOEε4, Aβ status (only in CU and non-AD analyses), MMSE, global white matter hyperintensities (WMH), and AD-signature cortical thickness as predictors. Additionally, we performed multivariable binary logistic regression models to account for all other predictors in the same model. Results Tau PET positivity in the temporal meta-ROI was 88.6% for AD dementia, 46.5% for MCI, 9.5% for non-AD, and 6.1% for CU. Among Aβ+ participants with AD dementia and MCI, lower age, MMSE score, and AD-signature cortical thickness showed the strongest associations with tau PET positivity. In non-AD and CU participants, presence of Aβ was the strongest predictor of a positive tau PET scan. Conclusion We identified several demographic, clinical, and neurobiological factors that are important to explain the variance in tau PET retention observed across the AD pathological continuum, non-AD neurodegenerative disorders, and cognitively unimpaired persons.

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