scholarly journals Multinomial logistic regression model for assessing factors associated with body mass index of government employee of Gulariya Municipality, Nepal

2021 ◽  
Vol 6 (2) ◽  
pp. 37-42
Author(s):  
Ramesh Prasad Tharu ◽  
Ravi Singh Mahatra
Keyword(s):  
Body Mass Index ◽  
Logistic Regression ◽  
Regression Model ◽  
Body Mass ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Multinomial Logistic Regression Model ◽  
Government Employee ◽  
Factors Associated

scholarly journals Multinomial Logistic Regression Model to Identify Factors Associated with Food Insecurity in Rural Households in Nepal

2020 ◽  
Vol 4 ◽  
pp. 17-32
Author(s):  
Santosh Kumar Shah
Keyword(s):  
Logistic Regression ◽  
Food Insecurity ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Wealth Index ◽  
Multinomial Logistic Regression Model ◽  
Factors Associated ◽  
Independent Variables ◽  
Index Size

Background: Food is basics of our lives and many people experiences food insecurity at some time because of food deprivation and lack of access to food due to different resource constraints. It is a global challenge and threatens the rural people in developing countries like Nepal. Objective: The objective of the study is to identify the factors associated with food insecurity in rural area of Nepal. Materials and Methods: The analysis is based on rural household data extracted from the data of Nepal Demographic and Health Survey 2016. The dependent variable food insecurity status was measured in four levels namely food secure, mildly food insecure, moderately food insecure and severely food insecure household using Household Food Insecurity Access Scale. Independent variables were categorical and quantitative variables. In order to identify the factors associated with food insecurity, ordinal logit model was fitted initially. Due to violation of test of parallel lines by overall as well as some of the independent variables, multinomial logistic regression model was finally adopted by examining the model adequacy test. Results: The fitted multinomial logistic regression satisfied the diagnostic test including tests of goodness of fit, multicolinearity diagnostic criteria and minimum criteria of utilization of the model with about 29% predictive power. The variables ecological region, wealth index, size of agriculture land, any member(s) having saving account in any financial institution, any member(s) had gone to foreign employment in last 5 years other than India, family size, number of members completed secondary education and household member rearing cattle(s) were found to be significant. The poorest households (HHs) had 3.14 (CI: 1.88-5.26) times, poorer HHs 2.51 (CI: 1.55-4.07) times and moderate HHs 1.42 times higher chances of being severely food insecure relative to rich HHs. Conclusion: The study revealed that food insecurity of the rural HHs increases with decrease in the wealth index, size of land and number of members of the HHs with completed secondary education. The food insecurity of the households decreases with increase in the access to bank service.

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.

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.

scholarly journals Burden and predictors ofStaphylococcus aureusandS. pseudintermediusinfections among dogs presented at an academic veterinary hospital in South Africa (2007–2012)

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3198 ◽  
Author(s):  
Daniel N. Qekwana ◽  
James Wabwire Oguttu ◽  
Fortune Sithole ◽  
Agricola Odoi
Keyword(s):  
South Africa ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Risk Of Infection ◽  
Multinomial Logistic Regression Model ◽  
Tract Infections ◽  
Veterinary Hospital ◽  
Isolated Species

BackgroundStaphylococci are commensals of the mucosal surface and skin of humans and animals, but have been implicated in infections such as otitis externa, pyoderma, urinary tract infections and post-surgical complications. Laboratory records provide useful information to help investigate these infections. Therefore, the objective of this study was to investigate the burdens of these infections and use multinomial regression to examine the associations between variousStaphylococcusinfections and demographic and temporal factors among dogs admitted to an academic veterinary hospital in South Africa.MethodsRecords of 1,497 clinical canine samples submitted to the bacteriology laboratory at a veterinary academic hospital between 2007 and 2012 were included in this study. Proportions of staphylococcal positive samples were calculated, and a multinomial logistic regression model was used to identify predictors of staphylococcal infections.ResultsTwenty-seven percent of the samples tested positive forStaphylococcusspp. The species ofStaphylococcusidentified wereS. pseudintermedius(19.0%),S. aureus(3.8%),S. epidermidis(0.7%) andS. felis(0.1%). The remaining 2.87% consisted of unspeciatedStaphylococcus. Distribution of the species by age of dog showed thatS. pseudintermediuswas the most common (25.6%) in dogs aged 2–4 years whileS. aureuswas most frequent (6.3%) in dogs aged 5–6 years.S. pseudintermedius(34.1%) andS. aureus(35.1%) were the most frequently isolated species from skin samples. The results of the multivariable multinomial logistic regression model identified specimen, year and age of the dog as significant predictors of the risk of infection withStaphylococcus. There was a significant temporal increase (RRR = 1.17; 95% CI [1.06–1.29]) in the likelihood of a dog testing positive forS. pseudintermediuscompared to testing negative. Dogs ≤ 8 years of age were significantly more likely to test positive forS. aureusthan those >8 years of age. Similarly, dogs between 2–8 years of age were significantly more likely to test positive forS. pseudintermediusthan those >8 years of age. In addition, dogs 2–4 years of age (RRR = 1.83; 1.09–3.06) were significantly more likely to test positive forS. pseudintermediuscompared to those <2 years of age. The risk of infection withS. pseudintermediusorS. aureuswas significantly higher in ear canal and skin specimens compared to other specimens.ConclusionsThe findings suggest thatS. pseudintermediusandS. aureuswere the most commonly isolated species from dogs presented at the study hospital. Age of the dog and the location of infection were significant predictors of infection with bothStaphylococcusspecies investigated. Significant increasing temporal trend was observed forS. pseudintermediusbut notS. aureus. This information is useful for guiding clinical decisions as well as future research.

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