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 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 Effect of fluid administration on scene to traffic accident patients by EMS personnel: a propensity score-matched study using population-based ambulance records and nationwide trauma registry in Japan

2021 ◽  
Author(s):  
Yusuke Katayama ◽  
Tetsuhisa Kitamura ◽  
Kosuke Kiyohara ◽  
Kenichiro Ishida ◽  
Tomoya Hirose ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Propensity Score ◽  
Hospital Mortality ◽  
Regression Model ◽  
Traffic Accident ◽  
Logistic Regression Model ◽  
Cardiopulmonary Arrest ◽  
Population Based ◽  
Fluid Administration ◽  
Secondary Outcome

Abstract Purpose The aim of this study was to assess the effect of fluid administration by emergency life-saving technicians (ELST) on the prognosis of traffic accident patients by using a propensity score (PS)-matching method. Methods The study included traffic accident patients registered in the JTDB database from January 2016 to December 2017. The main outcome was hospital mortality, and the secondary outcome was cardiopulmonary arrest on hospital arrival (CPAOA). To reduce potential confounding effects in the comparisons between two groups, we estimated a propensity score (PS) by fitting a logistic regression model that was adjusted for 17 variables before the implementation of fluid administration by ELST at the scene. Results During the study period, 10,908 traffic accident patients were registered in the JTDB database, and we included 3502 patients in this study. Of these patients, 142 were administered fluid by ELST and 3360 were not administered fluid by ELST. After PS matching, 141 patients were selected from each group. In the PS-matched model, fluid administration by ELST at the scene was not associated with discharge to death (crude OR: 0.859 [95% CI, 0.500–1.475]; p = 0.582). However, the fluid group showed statistically better outcome for CPAOA than the no fluid group in the multiple logistic regression model (adjusted OR: 0.231 [95% CI, 0.055–0.967]; p = 0.045). Conclusion In this study, fluid administration to traffic accident patients by ELST was associated not with hospital mortality but with a lower proportion of CPAOA.

scholarly journals Machine-Learning vs. Expert-Opinion Driven Logistic Regression Modelling for Predicting 30-Day Unplanned Rehospitalisation in Preterm Babies: A Prospective, Population-Based Study (EPIPAGE 2)

2021 ◽  
Vol 8 ◽  
Author(s):  
Robert A. Reed ◽  
Andrei S. Morgan ◽  
Jennifer Zeitlin ◽  
Pierre-Henri Jarreau ◽  
Héloïse Torchin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Regression Model ◽  
Expert Opinion ◽  
Logistic Regression Model ◽  
Population Based ◽  
Regression Modelling ◽  
Preterm Babies ◽  
Logistic Regression Modelling

Introduction: Preterm babies are a vulnerable population that experience significant short and long-term morbidity. Rehospitalisations constitute an important, potentially modifiable adverse event in this population. Improving the ability of clinicians to identify those patients at the greatest risk of rehospitalisation has the potential to improve outcomes and reduce costs. Machine-learning algorithms can provide potentially advantageous methods of prediction compared to conventional approaches like logistic regression.Objective: To compare two machine-learning methods (least absolute shrinkage and selection operator (LASSO) and random forest) to expert-opinion driven logistic regression modelling for predicting unplanned rehospitalisation within 30 days in a large French cohort of preterm babies.Design, Setting and Participants: This study used data derived exclusively from the population-based prospective cohort study of French preterm babies, EPIPAGE 2. Only those babies discharged home alive and whose parents completed the 1-year survey were eligible for inclusion in our study. All predictive models used a binary outcome, denoting a baby's status for an unplanned rehospitalisation within 30 days of discharge. Predictors included those quantifying clinical, treatment, maternal and socio-demographic factors. The predictive abilities of models constructed using LASSO and random forest algorithms were compared with a traditional logistic regression model. The logistic regression model comprised 10 predictors, selected by expert clinicians, while the LASSO and random forest included 75 predictors. Performance measures were derived using 10-fold cross-validation. Performance was quantified using area under the receiver operator characteristic curve, sensitivity, specificity, Tjur's coefficient of determination and calibration measures.Results: The rate of 30-day unplanned rehospitalisation in the eligible population used to construct the models was 9.1% (95% CI 8.2–10.1) (350/3,841). The random forest model demonstrated both an improved AUROC (0.65; 95% CI 0.59–0.7; p = 0.03) and specificity vs. logistic regression (AUROC 0.57; 95% CI 0.51–0.62, p = 0.04). The LASSO performed similarly (AUROC 0.59; 95% CI 0.53–0.65; p = 0.68) to logistic regression.Conclusions: Compared to an expert-specified logistic regression model, random forest offered improved prediction of 30-day unplanned rehospitalisation in preterm babies. However, all models offered relatively low levels of predictive ability, regardless of modelling method.

Are observed associations between a high maternal prepregnancy body mass index and offspring IQ likely to be causal?

2019 ◽  
Vol 73 (10) ◽  
pp. 920-928 ◽  
Author(s):  
Helen Coo ◽  
Leandre Fabrigar ◽  
Gregory Davies ◽  
Renee Fitzpatrick ◽  
Michael Flavin
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Maternal Obesity ◽  
Normal Weight ◽  
Offspring Performance ◽  
Logistic Regression Models ◽  
Prepregnancy Body Mass Index ◽  
Parents And Children ◽  
Prepregnancy Obesity ◽  
Maternal Prepregnancy Obesity

BackgroundA high maternal prepregnancy body mass index has been associated with lower offspring IQ, but it is unclear if the relationship is causal. To explore this, our objectives were to compare maternal and paternal estimates and to assess whether certain factors mediate the association.MethodsWe analysed data from the Avon Longitudinal Study of Parents and Children, which initially recruited 14 541 women residing in Avon, UK, with an expected date of delivery in 1991–1992. Data were collected during and after pregnancy by questionnaire, medical record abstraction and clinical assessment. At approximately 8 years of age, psychologists administered an abbreviated form of the Wechsler Intelligence Scale for Children-III. We fit multivariable logistic regression models to estimate parental prepregnancy obesity and overweight–offspring IQ associations. Counterfactually defined indirect (mediated) effects of maternal prepregnancy obesity on offspring IQ were estimated through path analysis.ResultsAmong 4324 mother–father–child triads and using normal weight as the referent, we observed consistently stronger associations for maternal prepregnancy obesity and offspring performance IQ (eg, adjusted β (95% CI)=−3.4 (−5.7 to −1.2) vs −0.97 (−2.9 to 0.96) for paternal obesity). The indirect effects of maternal obesity on offspring IQ through pathways involving gestational weight gain and duration of breastfeeding were small but significant.ConclusionOur findings are consistent with a weak biologic effect of maternal adiposity in pregnancy on offspring performance IQ. Given the growing prevalence of obesity worldwide, more evidence is needed to resolve the correlation versus causation debate in this area.

Total gestational weight gain and the risk of preeclampsia by pre-pregnancy body mass index categories: a population-based cohort study from 2013 to 2017

2019 ◽  
Vol 47 (6) ◽  
pp. 585-591 ◽  
Author(s):  
Tanja Premru-Srsen ◽  
Zorana Kocic ◽  
Vesna Fabjan Vodusek ◽  
Ksenija Geršak ◽  
Ivan Verdenik
Keyword(s):  
Body Mass Index ◽  
Weight Gain ◽  
Gestational Weight Gain ◽  
Body Mass ◽  
Odds Ratio ◽  
Protective Factor ◽  
Population Based ◽  
Normal Weight ◽  
Obese Women ◽  
Gestational Weight

Abstract Background Identifying the risk factors for preeclampsia (PE) is essential for the implementation of preventive actions. In the present study, we aimed at exploring the association between total gestational weight gain (GWG) and PE. Methods We performed a population-based cohort survey of 98,820 women with singleton pregnancies who delivered in Slovenia from 2013 to 2017. Aggregated data were obtained from the National Perinatal Information System (NPIS). The main outcome measure was the incidence of PE. The main exposure variable was total GWG standardized for the gestational duration by calculating the z-scores. The associations between total GWG and PE stratified by pre-pregnancy body mass index (BMI) categories adjusted for a variety of covariates were determined using multivariable logistic regression. We calculated the crude odds ratio (OR) and adjusted odds ratio (aOR) with a 95% confidence interval using a two-way test. Results Excessive GWG was associated with increased odds of PE in all pre-pregnancy BMI categories. The increase in the odds of PE by 445% was the highest in underweight women and by 122% was the lowest in obese women. Low GWG was associated with decreased odds of PE in all pre-pregnancy BMI categories except in normal-weight women with a GWG below −2 standard deviation (SD) and underweight women. The decrease in the odds of PE by 67% was the highest in obese women and by 41% was the lowest in normal-weight women. Conclusion Excessive GWG is a significant risk factor for PE, especially in underweight women, while low GWG is an important protective factor against PE, especially in obese women.

Glaucoma may be a non-memory manifestation of Alzheimer's disease in older people

2017 ◽  
Vol 29 (9) ◽  
pp. 1535-1541 ◽  
Author(s):  
Shih-Wei Lai ◽  
Cheng-Li Lin ◽  
Kuan-Fu Liao
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Logistic Regression ◽  
Older People ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Population Based ◽  
Multivariable Logistic Regression Model ◽  
Retrospective Case ◽  
Taiwan National Health Insurance

ABSTRACTBackground:The purpose of this paper was to examine whether glaucoma could be a non-memory manifestation of Alzheimer's disease in older people.Methods:We conducted a population-based, retrospective, case-control study to analyze the database of the Taiwan National Health Insurance Program. There were 1,351 subjects ≥65 years old with newly diagnosed Alzheimer's disease as the cases, and 5,329 subjects without any type of dementias as the controls during 2000–2011. The odds ratio (OR) and 95% confidence interval (CI) for the risk of Alzheimer's disease associated with glaucoma was estimated by the multivariable unconditional logistic regression model.Results:After controlling for confounders, the multivariable logistic regression model demonstrated that the adjusted OR of Alzheimer's disease was 1.50 in subjects with glaucoma (95% CI 1.19, 1.89), compared to subjects without glaucoma.Conclusions:Older people with glaucoma are associated with 1.5-fold increased odds of Alzheimer's disease in Taiwan. Glaucoma may be a non-memory manifestation of Alzheimer's disease in older people. Further research is needed to confirm this issue.

scholarly journals COVID-19 associated hospitalization in 571 patients with fibromyalgia—A population-based study

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261772
Author(s):  
Mor Amital ◽  
Niv Ben-Shabat ◽  
Howard Amital ◽  
Dan Buskila ◽  
Arnon D. Cohen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multivariable Analysis ◽  
Population Based ◽  
Female Dominance ◽  
Categorical Variables ◽  
Continuous Variables ◽  
Multivariable Logistic Regression Model ◽  
Population Based Study

Objective To identify predicators of patients with fibromyalgia (FM) that are associated with a severe COVID-19 disease course. Methods We utilized the data base of the Clalit Health Services (CHS); the largest public organization in Israel, and extracted data concerning patients with FM. We matched two subjects without FM to each subject with FM by sex and age and geographic location. Baseline characteristics were evaluated by t-test for continuous variables and chi-square for categorical variables. Predictors of COVID-19 associated hospitalization were identified using univariable logistic regression model, significant variables were selected and analyzed by a multivariable logistic regression model. Results The initial cohort comprised 18,598 patients with FM and 36,985 matched controls. The mean age was 57.5± 14.5(SD), with a female dominance of 91%. Out of this cohort we extracted the study population, which included all patients contracted with COVID-19, and consisted of 571 patients with FM and 1008 controls. By multivariable analysis, the following variables were found to predict COVID-19 associated hospitalization in patients with FM: older age (OR, 1.25; CI, 1.13–1.39; p<0.001), male sex (OR, 2.63; CI, 1.18–5.88; p<0.05) and hypertension (OR, 1.75; CI, 1.04–2.95; p<0.05). Conclusion The current population-based study revealed that FM per se was not directly associated with COVID-19 hospitalization or related mortality. Yet classical risk factors endangering the general population were also relevant among patients with FM.

scholarly journals Association of maternal obesity with fetal and neonatal death: Evidence from South and South-East Asian countries

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256725
Author(s):  
Rezwanul Haque ◽  
Syed Afroz Keramat ◽  
Syed Mahbubur Rahman ◽  
Maimun Ur Rashid Mustafa ◽  
Khorshed Alam
Keyword(s):  
Logistic Regression ◽  
Risk Factor ◽  
Regression Model ◽  
Neonatal Death ◽  
Logistic Regression Model ◽  
Maternal Obesity ◽  
East Asian ◽  
Multilevel Logistic Regression ◽  
Asian Countries ◽  
Neonatal Deaths

Background Obesity prevalence is increasing in many countries in the world, including Asia. Maternal obesity is highly associated with fetal and neonatal deaths. This study investigated whether maternal obesity is a risk factor of fetal death (measured in terms of miscarriage and stillbirth) and neonatal mortality in South and South-East Asian countries. Methods This cross-sectional study pooled the most recent Demographic and Health Surveys (DHS) from eight South and South-East Asian countries (2014–2018). Multivariate logistic regression was deployed to check the relationships between maternal obesity with fetal and neonatal deaths. Finally, multilevel logistic regression model was employed since the DHS data has a hierarchical structure. Results The pooled logistic regression model illustrated that maternal obesity is associated with higher odds of miscarriage (adjusted odds ratio [aOR]: 1.26, 95% CI: 1.20–1.33) and stillbirths (aOR: 1.46, 95% CI: 1.27–1.67) after adjustment of confounders. Children of obese mothers were at 1.18 (aOR: 1.18, 95% CI: 1.08–1.28) times greater risk of dying during the early neonatal period than mothers with a healthy weight. However, whether maternal obesity is statistically a significant risk factor for the offspring’s late neonatal deaths was not confirmed. The significant association between maternal obesity with miscarriage, stillbirth and early neonatal mortality was further confirmed by multilevel logistic regression results. Conclusion Maternal obesity in South and South-East Asian countries is associated with a greater risk of fetal and early neonatal deaths. This finding has substantial public health implications. Strategies to prevent and reduce obesity should be developed before planning pregnancy to reduce the fetal and neonatal death burden. Obese women need to deliver at the institutional facility centre that can offer obstetrics and early neonatal care.

scholarly journals Associations Between Anxiety Disorder Diagnoses and Body Mass Index Differ by Age, Sex and Race: A Population Based Study

2016 ◽  
Vol 12 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Ramona S. DeJesus ◽  
Carmen R. Breitkopf ◽  
Jon O. Ebbert ◽  
Lila J. Finney Rutten ◽  
Robert M. Jacobson ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Anxiety Disorders ◽  
Body Mass ◽  
Inverse Correlation ◽  
Black Population ◽  
Population Based ◽  
Normal Weight ◽  
Overweight And Obesity ◽  
Racial Groups ◽  
Rochester Epidemiology Project

Background: Few large studies have examined correlations between anxiety and body mass index (BMI) by gender or racial groups using clinical data. Objective: This study aimed to determine associations between diagnosed anxiety disorders and BMI, and evaluate whether observed associations varied by demographic characteristics. Method: Data from the Rochester Epidemiology Project (REP) data linkage system were analyzed to examine associations between anxiety disorders and BMI among adults ages 18-85 residing in Olmsted County, MN in 2009 (n=103,557). Height and weight data were available for 75,958 people (73%). The international classification of underweight, overweight, and obesity by BMI was used. Results: Population consisted of 56% females, 92.8% White individuals, with median age of 46 years. When adjusted for age, sex, and race, we observed a U-shaped association between anxiety and BMI group. Underweight and obese individuals were more likely to have an anxiety diagnosis compared to normal weight individuals. Stratification by sex yielded a U-shaped association between anxiety and BMI only in women. Stratification by race showed a U-shaped association between anxiety and BMI only in the White population. Anxiety was significantly associated only with obesity in the Black population. Anxiety was not associated with a BMI category in Asian or Hispanic groups. Among elderly group, there is inverse correlation between anxiety and obesity. Conclusion: Our results suggest that anxiety may have heterogeneous associations with BMI in the population. Further research on potential mechanisms contributing to these findings will help direct efforts in anxiety and obesity management across diverse population groups.

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