scholarly journals Study of risk factors affecting both hypertension and obesity outcome by using multivariate multilevel logistic regression models

2016 ◽  
Vol 19 (3) ◽  
pp. 385-397 ◽  
Author(s):  
Sepedeh Gholizadeh ◽  
Abbas Moghimbeigi ◽  
Jalal Poorolajal ◽  
Mohammadali Khjeian ◽  
Fatemeh Bahramian ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Models ◽  
Multilevel Logistic Regression ◽  
Factors Affecting ◽  
Logistic Regression Models

Abstract 17220: Risk Factors for Recurrent Peripartum Cardiomyopathy in 783 Women in California

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_1) ◽  
Author(s):  
Punag Divanji ◽  
Gregory Nah ◽  
Ian Harris ◽  
Anu Agarwal ◽  
Nisha I Parikh
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Logistic Regression ◽  
Regression Models ◽  
Subsequent Pregnancy ◽  
Peripartum Cardiomyopathy ◽  
Lv Function ◽  
Logistic Regression Models ◽  
Grand Multiparity ◽  
Adjusted Logistic Regression

Introduction: Characterized by significant left ventricular (LV) dysfunction and clinical heart failure (HF), peripartum cardiomyopathy (PPCM) has an incidence of approximately 1/2200 live births (0.04%). Prior studies estimate that approximately 25% of those with recovered LV function will have recurrent clinical PPCM during subsequent pregnancies, compared to 50% of those without recovered LV function. Specific predictors of recurrent PPCM have not been studied in cohorts with large numbers. Methods: From 2005-2011, we identified 1,872,227 pregnancies by International Classification of Diseases, 9th Revision (ICD-9) codes in the California Healthcare Cost and Utilization Project (HCUP) database, which captures over 95% of the California hospitalized population. Excluding 15,765 women with prior cardiovascular disease (myocardial infarction, coronary artery disease, stroke, HF, valve disease, or congenital heart disease), yielded n=1,856,462 women. Among women without prior cardiovascular disease, we identified index and subsequent pregnancies with PPCM to determine episodes of recurrent PPCM. We considered the following potential predictors of PPCM recurrence in both univariate and age-adjusted logistic regression models: age, race, hypertension, diabetes, smoking, obesity, chronic kidney disease, family history, pre-eclampsia, ectopic pregnancy, income, and insurance status. Results: In HCUP, n=783 women had pregnancies complicated by PPCM (mean age=30.8 years). Among these women, n=133 had a subsequent pregnancy (17%; mean age=28.1 years), with a mean follow-up of 4.34 years (±1.71 years). In this group of 133 subsequent pregnancies, n=14 (10.5%) were complicated by recurrent PPCM, with a mean time-to-event of 2.2 years (±1.89 years). Among the risk factors studied, the only univariate predictor of recurrent PPCM was grand multiparity, defined as ≥ 5 previous deliveries (odds ratio: 22; 95% confidence interval 4.43-118.22). The other predictors we studied were not significantly associated with recurrent PPCM in either univariate or multivariable models. Conclusion: In a large population database in California with 783 cases of PPCM over a 6-year period, 17% of women had a subsequent pregnancy, of which 10.5% had recurrent PPCM. In age-adjusted logistic regression models, grand multiparity was the only statistically significant predictor of recurrent PPCM.

Forced Sexual Victimization Among Youth in Custody: Do Risk Factors Vary by Gender and Perpetrator?

2019 ◽  
Vol 100 (2) ◽  
pp. 151-172
Author(s):  
Eileen M. Ahlin
Keyword(s):  
Risk Factors ◽  
Gender Differences ◽  
Logistic Regression ◽  
National Survey ◽  
Regression Models ◽  
Sexual Victimization ◽  
Multivariate Logistic Regression ◽  
Logistic Regression Models ◽  
Factors Associated ◽  
Using Data

There is relatively little literature examining risk factors associated with sexual victimization among youth in custody. The current study explored whether risk of forced sexual victimization among youth in custody differs by gender or perpetrator. Using data from a sample of 8,659 youth who participated in the National Survey of Youth in Custody, multivariate logistic regression models were employed to investigate gender differences in risk factors associated with overall forced sexual victimization and staff-on-inmate and inmate-on-inmate forced sexual victimization. Findings suggest that gender differences are more pronounced when perpetrator type is considered.

Comparison between students and residents on determinants of willingness to separate waste and waste separation behaviour in Zhengzhou, China

2017 ◽  
Vol 35 (9) ◽  
pp. 949-957 ◽  
Author(s):  
Xiaoping Dai ◽  
Yuping Han ◽  
Xiaohong Zhang ◽  
Wei Hu ◽  
Liangji Huang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Undergraduate Students ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Education Level ◽  
Factors Affecting ◽  
Logistic Regression Models ◽  
Management Policies ◽  
Waste Separation ◽  
Do So

A better understanding of willingness to separate waste and waste separation behaviour can aid the design and improvement of waste management policies. Based on the intercept questionnaire survey data of undergraduate students and residents in Zhengzhou City of China, this article compared factors affecting the willingness and behaviour of students and residents to participate in waste separation using two binary logistic regression models. Improvement opportunities for waste separation were also discussed. Binary logistic regression results indicate that knowledge of and attitude to waste separation and acceptance of waste education significantly affect the willingness of undergraduate students to separate waste, and demographic factors, such as gender, age, education level, and income, significantly affect the willingness of residents to do so. Presence of waste-specific bins and attitude to waste separation are drivers of waste separation behaviour for both students and residents. Improved education about waste separation and facilities are effective to stimulate waste separation, and charging on unsorted waste may be an effective way to improve it in Zhengzhou.

scholarly journals Risk factors associated with stillbirth of piglets born from oxytocin-assisted parturitions

2020 ◽  
Vol 13 (10) ◽  
pp. 2172-2177
Author(s):  
Nguyen Hoai Nam ◽  
Peerapol Sukon
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Models ◽  
Significant Risk ◽  
Gestation Length ◽  
Stillbirth Rate ◽  
Multivariate Logistic Regression ◽  
Crown Rump Length ◽  
Logistic Regression Models ◽  
Factors Associated

Aim: The present study aimed to investigate the effects of different risk factors on stillbirth of piglets born from oxytocin-assisted parturitions. Materials and Methods: Data were collected from a total of 1121 piglets born from 74 Landrace x Yorkshire crossbred sows from a herd. Logistic regression models were used to determine the associations between stillbirth and different risk factors including parity (1, 2, 3-5, and 6-10), gestation length (GL) (112-113, 114-116, and 117-119 days), litter size, birth order (BO), sex, birth interval (BI), cumulative farrowing duration, birth weight (BW), crown rump length, BW deviation, body mass index, ponderal index (PI), and the use of oxytocin during expulsive stage of farrowing. Results: The incidence of stillbirth at litter level and stillbirth rate was 59.5% (44/74) and 8.1% (89/1094), respectively. The final multivariate logistic regression selected BO, BI, PI, GL, and parity as the five most significant risk factors for stillbirth. Increased BO and BI, GL <114 and >116 days, parity 6-10, and low PI increased the stillbirth rate in piglets. Conclusion: Several factors previously determined as risks for stillbirth in exogenous oxytocin-free parturitions also existed in exogenous oxytocin-assisted parturitions. One dose of oxytocin at fairly high BO did not increase stillbirth, whereas two doses of oxytocin were potentially associated with increased values.

scholarly journals Sample size issues in multilevel logistic regression models

PLoS ONE ◽  
2019 ◽  
Vol 14 (11) ◽  
pp. e0225427 ◽  
Author(s):  
Amjad Ali ◽  
Sabz Ali ◽  
Sajjad Ahmad Khan ◽  
Dost Muhammad Khan ◽  
Kamran Abbas ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Sample Size ◽  
Regression Models ◽  
Multilevel Logistic Regression ◽  
Logistic Regression Models

Use of Logistic Regression for Developing Risk-adjusted Quality Indicators in Nursing

2010 ◽  
Vol 49 (06) ◽  
pp. 608-612 ◽  
Author(s):  
D. Renner ◽  
B. Fischer ◽  
M. Kutschmann
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Quality Indicators ◽  
Regression Models ◽  
Pressure Ulcers ◽  
Logistic Function ◽  
Multiple Logistic Regression ◽  
Logistic Regression Models ◽  
Joint Influence ◽  
Developed Pressure

Summary Objectives: A low rate of newly developed pressure ulcers is considered as an important quality indicator in nursing. However, the result of a hospital depends not only on the quality of care but on the risk profile of its patients as well. Therefore, based on multiple logistic regression models we describe a method for calculating risk-adjusted quality indicators in nursing. Method: Based on data of 1,009,989 patients from 1747 hospitals in 2009, we developed two multiple logistic regression models to identify and to weigh a possible joint influence of several risk factors on newly developed pressure ulcers. In a further step, we calculated risk-adjusted rates. Results: Factors remaining in the regression models were “micro-movements on admission”, “diabetes mellitus”, “age” and “days on intensive care unit”. Based on the corresponding regression coefficients and the logistic function, the expected rate of newly developed pressure ulcers was calculated for every hospital. Fi nally, expected rates and observed rates both were used to calculate risk-adjusted rates. Conclusion: The simultaneous consideration of relevant risk factors by means of risk- adjusted quality indicators ensures a fair comparison of hospitals.

scholarly journals Factors associated with the serological prevalence of Salmonella enterica in Greek finishing swineherds

2003 ◽  
Vol 131 (1) ◽  
pp. 599-606 ◽  
Author(s):  
L. S. LEONTIDES ◽  
E. GRAFANAKIS ◽  
C. GENIGEORGIS
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Salmonella Enterica ◽  
Regression Models ◽  
Finishing Pigs ◽  
Blood Samples ◽  
Lower Odds ◽  
Logistic Regression Models ◽  
Factors Associated ◽  
Procaine Penicillin

Blood samples were taken from 50 finishing pigs at 90–105 kg in each of 59 randomly selected farrow-to-finish herds. The sera were tested for antibodies to Salmonella enterica by the Danish mix-ELISA. Samples with an optical density of >10% were considered to be positive. Associations between the odds of seropositivity of pigs and possible risk factors were evaluated in multivariable logistic regression models. The results of the analysis indicated that pigs fed non-pelleted dry or wet ration had 11 (P=0·0004) or 9 (P=0·02) times, respectively, lower odds of seropositivity than those fed pelleted ration. The risk of seropositivity was 4 (P=0·0006) times higher in pigs fed a combination of chlortetracycline, procaine penicillin and sulphamethazine during fattening than in those fed an approved growth promotor or a probiotic.

Marginal Effects—Quantifying the Effect of Changes in Risk Factors in Logistic Regression Models

JAMA ◽  
2019 ◽  
Vol 321 (13) ◽  
pp. 1304 ◽  
Author(s):  
Edward C. Norton ◽  
Bryan E. Dowd ◽  
Matthew L. Maciejewski
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Models ◽  
Logistic Regression Models ◽  
Marginal Effects ◽  
Changes In Risk
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