scholarly journals Association between Obesity and Asthma in Preschool Mexican Children

2010 ◽  
Vol 10 ◽  
pp. 1339-1346 ◽  
Author(s):  
Francisco Vázquez-Nava ◽  
Jaime Morales Romero ◽  
Arturo Córdova-Fernandez ◽  
Atenógenes H. Saldívar-González ◽  
Carlos F. Vázquez-Rodríguez ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Preschool Children ◽  
Regression Model ◽  
Logistic Regression Model ◽  
International Study ◽  
Normal Weight ◽  
The Body ◽  
Mexican Children ◽  
Prevalence Of Obesity ◽  
And Gender

The elevated prevalence of obesity as well as of asthma in preschool children has prompted investigators to speculate that obesity in childhood might be a causal factor in the development of asthma. The results obtained to date are debatable. We investigated the association between obesity and asthma in 1,160 preschool Mexican children. Diagnosis of asthma was performed using the International Study of Asthma and Allergy in Childhood (ISAAC) questionnaire. The body mass index (BMI) in units of kg/m2was determined, and children were categorized according to age- and gender-specific criteria, such as normal weight (5th-85thpercentile), overweight (ࣙ85thand <95thpercentile), and obesity (ࣙ95thpercentile). Power test for logistic regression model was calculated. We found no association between overweight (adjusted OR = 1.02; 95% CI = 0.66–1.58), obesity (adjusted OR = 0.94; 95% CI = 0.68–1.30), and wheezing during the last year as determined by logistic regression model adjusted. We did not find an association between overweight, obesity, and asthma-associated hospitalizations. Further longitudinal studies are required to provide a better understanding of the relationship between obesity and asthma in preschool children.

scholarly journals Developing a Multi-variate Logistic Regression Model to Analyze Accident Scenarios: Case of Electrical Contractors

2020 ◽  
Vol 17 (13) ◽  
pp. 4852
Author(s):  
Pouya Gholizadeh ◽  
Behzad Esmaeili
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Odds Ratio ◽  
Logistic Regression Model ◽  
The Body ◽  
Occupational Accidents ◽  
Multivariate Logistic Regression Model ◽  
Ratio Analysis ◽  
Accident Scenarios ◽  
Significant Factors

The ability to identify factors that influence serious injuries and fatalities would help construction firms triage hazardous situations and direct their resources towards more effective interventions. Therefore, this study used odds ratio analysis and logistic regression modeling on historical accident data to investigate the contributing factors impacting occupational accidents among small electrical contracting enterprises. After conducting a thorough content analysis to ensure the reliability of reports, the authors adopted a purposeful variable selection approach to determine the most significant factors that can explain the fatality rates in different scenarios. Thereafter, this study performed an odds ratio analysis among significant factors to determine which factors increase the likelihood of fatality. For example, it was found that having a fatal accident is 4.4 times more likely when the source is a “vehicle” than when it is a “tool, instrument, or equipment”. After validating the consistency of the model, 105 accident scenarios were developed and assessed using the model. The findings revealed which severe accident scenarios happen commonly to people in this trade, with nine scenarios having fatality rates of 50% or more. The highest fatality rates occurred in “fencing, installing lights, signs, etc.” tasks in “alteration and rehabilitation” projects where the source of injury was “parts and materials”. The proposed analysis/modeling approach can be applied among all specialty contracting companies to identify and prioritize more hazardous situations within specific trades. The proposed model-development process also contributes to the body of knowledge around accident analysis by providing a framework for analyzing accident reports through a multivariate logistic regression model.

scholarly journals Association between Maternal Pre-pregnancy Body Mass Index and Breastfeeding Duration in Taiwan: A Population-Based Cohort Study

Nutrients ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2361
Author(s):  
Chi-Nien Chen ◽  
Hung-Chen Yu ◽  
An-Kuo Chou
Keyword(s):  
Body Mass Index ◽  
Logistic Regression ◽  
Regression Model ◽  
Body Mass ◽  
Logistic Regression Model ◽  
Maternal Obesity ◽  
Population Based ◽  
Normal Weight ◽  
Breastfeeding Duration ◽  
Breastfeeding Cessation

An association between high pre-pregnancy body mass index (BMI) and early breastfeeding cessation has been previously observed, but studies examining the effect of underweight are still scant and remain inconclusive. This study analyzed data from a nationally representative cohort of 18,312 women (mean age 28.3 years; underweight 20.1%; overweight 8.2%; obesity 1.9%) who delivered singleton live births in 2005 in Taiwan. Comprehensive face-to-face interviews and surveys were completed at 6 and 18 months postpartum. BMI status and breastfeeding duration were calculated from the self-reported data in the questionnaires. In the adjusted ordinal logistic regression model, maternal obesity and underweight had a higher odds of shorter breastfeeding duration compared with normal-weight women. The risk of breastfeeding cessation was significantly higher in underweight women than in normal-weight women after adjustments in the logistic regression model (2 m: aOR = 1.11, 95% CI = 1.03–1.2; 4 m: aOR = 1.32, 95% CI = 1.21–1.43; 6 m: aOR = 1.3, 95% CI = 1.18–1.42). Our findings indicated that maternal underweight and obesity are associated with earlier breastfeeding cessation in Taiwan. Optimizing maternal BMI during the pre-conception period is essential, and future interventions to promote and support breastfeeding in underweight mothers are necessary to improve maternal and child health.

Missing Posterior Teeth and Risk of Temporomandibular Disorders

2009 ◽  
Vol 88 (10) ◽  
pp. 942-945 ◽  
Author(s):  
M.Q. Wang ◽  
F. Xue ◽  
J.J. He ◽  
J.H. Chen ◽  
C.S. Chen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Young Women ◽  
Temporomandibular Disorders ◽  
Logistic Model ◽  
Logistic Regression Model ◽  
Age And Gender ◽  
Posterior Teeth ◽  
Men 1 ◽  
And Gender

There is disagreement about the association between missing posterior teeth and the presence of temporomandibular disorders (TMD). Here, the purpose was to investigate whether the number of missing posterior teeth, their distribution, age, and gender are associated with TMD. Seven hundred and forty-one individuals, aged 21–60 years, with missing posterior teeth, 386 with and 355 without TMD, were included. Four variables—gender, age, the number of missing posterior teeth, and the number of dental quadrants with missing posterior teeth—were analyzed with a logistic regression model. All four variables—gender (OR = 1.59, men = 1, women = 2), age (OR = 0.98), the number of missing posterior teeth (OR = 0.51), and the number of dental quadrants with missing posterior teeth (OR = 7.71)—were entered into the logistic model (P < 0.01). The results indicate that individuals who lose posterior teeth, with fewer missing posterior teeth but in more quadrants, have a higher prevalence of TMD, especially young women.

scholarly journals Retrocollis, anterocollis or head tremor may predict the spreading of dystonic movements in primary cervical dystonia

2009 ◽  
Vol 67 (2b) ◽  
pp. 402-406 ◽  
Author(s):  
Clecio Godeiro-Junior ◽  
Andre Carvalho Felício ◽  
Patrícia Maria de Carvalho Aguiar ◽  
Vanderci Borges ◽  
Sonia Maria Azevedo Silva ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Cervical Dystonia ◽  
Logistic Regression Model ◽  
Botulinum Toxin Type A ◽  
Botulinum Toxin Type ◽  
The Body ◽  
Clinical Predictors ◽  
Binary Logistic Regression Model ◽  
Head Tremor

BACKGROUND AND PURPOSE: Few studies have attempted to develop clinical predictors for cervical dystonia (CD) aiming at progression of the dystonic movement. METHOD: We retrospectively evaluated 73 patients with primary CD who underwent treatment with Botulinum toxin type-A (BTX-A). The patients were assembled in two groups according to the spread of dystonia during follow-up: spreading and non-spreading CD. We performed a binary logistic regression model using spreading of cervical dystonia as dependent variable aiming to find covariates which increase the risk of spreading. RESULTS: Our logistic regression model found the following covariates and their respective risk ratios: time of disease >18.5 months=2.4, retrocollis=1.9, anterocollis=1.8, head tremor=1.6. CONCLUSION: Time of disease >18.5 months, retrocollis, anterocollis and head tremor may predict spreading of dystonic movement to other regions of the body in CD patients.

scholarly journals Evaluation of probability of linkages between enterprises and farmer's potatoes in Lam Dong province

2019 ◽  
Vol 18 (01) ◽  
pp. 1-8
Author(s):  
Nam H. Tran
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Potato Production ◽  
Farm Size ◽  
Multinomial Logistic Regression Model ◽  
Factors Affecting ◽  
And Gender

In this study, the collaboration between buyer and the famers in potato production was evaluated by using a multinomial Logistic regression model with MLE estimating. The data were collected by directly interviewing of 245 farmers at the Xuan Tho commune, Da Lat city and Don Duong district, Lam Dong province. Results of the research showed that a tight collaboration between the companies and farmers would increase in a higher productivity. The estimation showed that the probability of farmer which would and would not collaborate with buyer were 14.6% (Y2/Y1) and 63.0% (Y3/Y1). The factors affecting the probability of linkages between enterprises and farmers were enterprises and farmers were experience, farm, size, profit, policy supports and gender. The results also revealed that when price of potatoes increase, farmers would not comply with agreement.

scholarly journals Rice Marketing as a Means of Poverty Alleviation in Niger State, Nigeria

2014 ◽  
Vol 47 (4) ◽  
pp. 137-141
Author(s):  
Eniola Oluwatoyin Olorunsanya ◽  
Josephine Utsunu Ugbong
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Poverty Alleviation ◽  
Poverty Reduction ◽  
Logistic Regression Model ◽  
Educational Status ◽  
Household Size ◽  
Descriptive Statistics ◽  
Poverty Measures ◽  
And Gender

Abstract This study examined rice marketing as a means of poverty alleviation in Niger State, North Central Nigeria. Ninety-eight representative rice marketers’ households were used for the study. Descriptive statistics, Foster, Greer and Thorbecke poverty measures as well as logistic regression model were used as the analytical tools for the study. The result of the descriptive statistics shows that forty-nine percent of the rice marketers have no western education and majority of the rice marketers’ households used open spaces for defecation. The result of the poverty profile shows that all the representative households were poor using 1.25 dollar a day poverty benchmark and only 32 percent were poor using the estimated relative poverty benchmark of N 1,894.2 per capita. The result of the logistic regression model shows the following factors influenced the poverty status of the rice marketers’ households in the study area. These are age and gender of the rice marketers, household size, other sources of income, marital status of the rice marketers and their educational status. The study recommends manageable household size as well as improved level of education for members of the rice marketers’ households for poverty reduction in the study area.

scholarly journals Body Mass Index Prediction and Classification Based on Facial Morphological Cues Using Multinomial Logistic Regression

2021 ◽  
Vol 35 (2) ◽  
pp. 105-113
Author(s):  
Venkata Rao Maddumala ◽  
Arunkumar R
Keyword(s):  
Body Mass Index ◽  
Logistic Regression ◽  
Regression Model ◽  
Body Mass ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
The Body ◽  
Model Parameters ◽  
Geometrical Features ◽  
Novel Method

This paper presents a novel method for body mass index prediction and classification based on the multinomial logistic regression model. The facial geometrical features are extracted and the logistic regression model parameters estimated based on the features. Based on the model parameters, the logistic model is fit in to predict the body mass index and classifies. Two different facial datasets are taken into account for the experiments. Each dataset is divided into two sets. One set is used to estimate the parameters while the other is used to fit-in the model and predicts the body mass index and classifies itself. The obtained outcome results show that the performance of the proposed method is comparable to the state-of-the-art techniques.

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