scholarly journals Importance of maternal education on antenatal care visits in Bangladesh

2018 ◽  
Vol 30 (1-2) ◽  
pp. 23-33
Author(s):  
Khandoker Akib Mohammad ◽  
Fatima Tuz Zahura ◽  
Md Morshadur Rahman
Keyword(s):  
Antenatal Care ◽  
Regression Models ◽  
Maternal Education ◽  
Positive Association ◽  
Education Level ◽  
Place Of Residence ◽  
Education Programs ◽  
Data Set ◽  
Logistic Regression Models ◽  
Strong Positive Association

An attempt has been made to examine whether maternal education influences the antenatal care (ANC) visit in Bangladesh using sequential logistic regression models with an interaction between maternal education and place of residence. For the purpose of analysis, Bangladesh Demographic and Health Survey (BDHS), 2014 data set have been used. The findings emerged from the study show a significant increase of adequate ANC visits among pregnant women with the increase in maternal education level. Moreover, interaction between maternal education and place of residence provides a significant effect on complete ANC visits. The finding justifies an influential impact of maternal education on ANC visits over place of residence. Female participation in the education programs needs to be increased because maternal education signifies a strong positive association with ANC visits.Bangladesh J. Sci. Res. 30(1&2): 23-33, December-2017

Challenging the Link Between Early Childhood Television Exposure and Later Attention Problems: A Multiverse Approach

2021 ◽  
pp. 095679762097165
Author(s):  
Matthew T. McBee ◽  
Rebecca J. Brand ◽  
Wallace E. Dixon
Keyword(s):  
Early Childhood ◽  
Regression Models ◽  
Propensity Score Analysis ◽  
Harmful Effect ◽  
Attention Problems ◽  
Linear Regression Models ◽  
Data Set ◽  
Logistic Regression Models ◽  
Score Analysis ◽  
Television Exposure

In 2004, Christakis and colleagues published an article in which they claimed that early childhood television exposure causes later attention problems, a claim that continues to be frequently promoted by the popular media. Using the same National Longitudinal Survey of Youth 1979 data set ( N = 2,108), we conducted two multiverse analyses to examine whether the finding reported by Christakis and colleagues was robust to different analytic choices. We evaluated 848 models, including logistic regression models, linear regression models, and two forms of propensity-score analysis. If the claim were true, we would expect most of the justifiable analyses to produce significant results in the predicted direction. However, only 166 models (19.6%) yielded a statistically significant relationship, and most of these employed questionable analytic choices. We concluded that these data do not provide compelling evidence of a harmful effect of TV exposure on attention.

Association Between Opioid Use and Diabetes in Patients With Ankle Fracture Repair

2021 ◽  
pp. 107110072110581
Author(s):  
Wenye Song ◽  
Naohiro Shibuya ◽  
Daniel C. Jupiter
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Ankle Fracture ◽  
Regression Models ◽  
Fracture Repair ◽  
Opioid Use ◽  
Data Set ◽  
Opioid Prescription ◽  
Logistic Regression Models ◽  
Patients With Diabetes

Background: Ankle fractures in patients with diabetes mellitus have long been recognized as a challenge to practicing clinicians. Ankle fracture patients with diabetes may experience prolonged healing, higher risk of hardware failure, an increased risk of wound dehiscence and infection, and higher pain scores pre- and postoperatively, compared to patients without diabetes. However, the duration of opioid use among this patient cohort has not been previously evaluated. The purpose of this study is to retrospectively compare the time span of opioid utilization between ankle fracture patients with and without diabetes mellitus. Methods: We conducted a retrospective cohort study using our institution’s TriNetX database. A total of 640 ankle fracture patients were included in the analysis, of whom 73 had diabetes. All dates of opioid use for each patient were extracted from the data set, including the first and last date of opioid prescription. Descriptive analysis and logistic regression models were employed to explore the differences in opioid use between patients with and without diabetes after ankle fracture repair. A 2-tailed P value of .05 was set as the threshold for statistical significance. Results: Logistic regression models revealed that patients with diabetes are less likely to stop using opioids within 90 days, or within 180 days, after repair compared to patients without diabetes. Female sex, neuropathy, and prefracture opioid use are also associated with prolonged opioid use after ankle fracture repair. Conclusion: In our study cohort, ankle fracture patients with diabetes were more likely to require prolonged opioid use after fracture repair. Level of Evidence: Level III, prognostic.

scholarly journals Predicting Fusarium Head Blight Epidemics with Boosted Regression Trees

2014 ◽  
Vol 104 (7) ◽  
pp. 702-714 ◽  
Author(s):  
D. A. Shah ◽  
E. D. De Wolf ◽  
P. A. Paul ◽  
L. V. Madden
Keyword(s):  
Logistic Regression ◽  
Relative Humidity ◽  
Fusarium Head Blight ◽  
Regression Models ◽  
Boosted Regression Trees ◽  
Misclassification Rate ◽  
Head Blight ◽  
Data Set ◽  
Logistic Regression Models ◽  
Daily Relative Humidity

Predicting major Fusarium head blight (FHB) epidemics allows for the judicious use of fungicides in suppressing disease development. Our objectives were to investigate the utility of boosted regression trees (BRTs) for predictive modeling of FHB epidemics in the United States, and to compare the predictive performances of the BRT models with those of logistic regression models we had developed previously. The data included 527 FHB observations from 15 states over 26 years. BRTs were fit to a training data set of 369 FHB observations, in which FHB epidemics were classified as either major (severity ≥ 10%) or non-major (severity < 10%), linked to a predictor matrix consisting of 350 weather-based variables and categorical variables for wheat type (spring or winter), presence or absence of corn residue, and cultivar resistance. Predictive performance was estimated on a test (holdout) data set consisting of the remaining 158 observations. BRTs had a misclassification rate of 0.23 on the test data, which was 31% lower than the average misclassification rate over 15 logistic regression models we had presented earlier. The strongest predictors were generally one of mean daily relative humidity, mean daily temperature, and the number of hours in which the temperature was between 9 and 30°C and relative humidity ≥ 90% simultaneously. Moreover, the predicted risk of major epidemics increased substantially when mean daily relative humidity rose above 70%, which is a lower threshold than previously modeled for most plant pathosystems. BRTs led to novel insights into the weather–epidemic relationship.

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 Predicting Violence in Merseyside: a Network-Based Approach Using No Demographic Information

2020 ◽  
Vol 4 (3-4) ◽  
pp. 89-102
Author(s):  
Paolo Campana ◽  
Andrea Giovannetti
Keyword(s):  
Regression Models ◽  
Predictive Power ◽  
Demographic Information ◽  
Data Set ◽  
Logistic Regression Models ◽  
Individual Level ◽  
The Individual ◽  
High Level ◽  
Very High ◽  
Individual Information

Abstract Purpose We explore how we can best predict violent attacks with injury using a limited set of information on (a) previous violence, (b) previous knife and weapon carrying, and (c) violence-related behaviour of known associates, without analysing any demographic characteristics. Data Our initial data set consists of 63,022 individuals involved in 375,599 events that police recorded in Merseyside (UK) from 1 January 2015 to 18 October 2018. Methods We split our data into two periods: T1 (initial 2 years) and T2 (the remaining period). We predict “violence with injury” at time T2 as defined by Merseyside Police using the following individual-level predictors at time T1: violence with injury; involvement in a knife incident and involvement in a weapon incident. Furthermore, we relied on social network analysis to reconstruct the network of associates at time T1 (co-offending network) for those individuals who have committed violence at T2, and built three additional network-based predictors (associates’ violence; associates’ knife incident; associates’ weapon incident). Finally, we tackled the issue of predicting violence (a) through a series of robust logistic regression models using a bootstrapping method and (b) through a specificity/sensitivity analysis. Findings We found that 7720 individuals committed violence with injury at T2. Of those, 2004 were also present at T1 (27.7%) and co-offended with a total of 7202 individuals. Regression models suggest that previous violence at time T1 is the strongest predictor of future violence (with an increase in odds never smaller than 123%), knife incidents and weapon incidents at the individual level have some predictive power (but only when no information on previous violence is considered), and the behaviour of one’s associates matters. Prior association with a violent individual and prior association with a knife-flagged individual were the two strongest network predictors, with a slightly stronger effect for knife flags. The best performing regressors are (a) individual past violence (36% of future violence cases correctly identified); (b) associates’ past violence (25%); and (c) associates’ knife involvement (14%). All regressors are characterised by a very high level of specificity in predicting who will not commit violence (80% or more). Conclusions Network-based indicators add to the explanation of future violence, especially prior association with a knife-flagged individual and association with a violent individual. Information about the knife involvement of associates appears to be more informative than a subject’s own prior knife involvement.

Predictive Factors for Pneumonia Onset After Cardiac Surgery in Rio de Janeiro, Brazil

2007 ◽  
Vol 28 (4) ◽  
pp. 382-388 ◽  
Author(s):  
Marisa Santos ◽  
José Ueleres Braga ◽  
Renato Vieira Gomes ◽  
Guilherme L. Werneck
Keyword(s):  
Logistic Regression ◽  
Cardiac Surgery ◽  
Predictive Models ◽  
Rio De Janeiro ◽  
Regression Models ◽  
Tertiary Care ◽  
Classification And Regression Tree ◽  
Data Set ◽  
Logistic Regression Models ◽  
Cart Analysis

Objective.To develop a predictive system for the occurrence of nosocomial pneumonia in patients who had cardiac surgery performed.Design.Retrospective cohort study.Setting.Two cardiologic tertiary care hospitals in Rio de Janeiro, Brazil.Patients.Between June 2000 and August 2002, there were 1,158 consecutive patients who had complex heart surgery performed. Patients older than 18 years who survived the first 48 postoperative hours were included in the study. The occurrence of pneumonia was diagnosed through active surveillance by an infectious diseases specialist according to the following criteria: the presence of new infiltrate on a radiograph in association with purulent sputum and either fever or leukocytosis until day 10 after cardiac surgery. Predictive models were built on the basis of logistic regression analysis and classification and regression tree (CART) analysis. The original data set was divided randomly into 2 parts, one used to construct the models (ie, “test sample”) and the other used for validation (ie, “validation sample”).Results.The area under the receiver–operating characteristic (ROC) curve was 69% for the logistic regression model and 76% for the CART model. Considering a probability greater than 7% to be predictive of pneumonia for both models, sensitivity was higher for the logistic regression models, compared with the CART models (64% vs 56%). However, the CART models had a higher specificity (92% vs 70%) and global accuracy (90% vs 70%) than the logistic regression models. Both models showed good performance, based on the 2-graph ROC, considering that 84.6% and 84.3% of the predictions obtained by regression and CART analyses were regarded as valid.Conclusion.Although our findings are preliminary, the predictive models we created showed fairly good specificity and fair sensitivity.

scholarly journals Individual and Combined Associations of Glucose Metabolic Components With Cognitive Function Modified by Obesity

2021 ◽  
Vol 12 ◽  
Author(s):  
Ruixin He ◽  
Ruizhi Zheng ◽  
Jie Li ◽  
Qiuyu Cao ◽  
Tianzhichao Hou ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Cognitive Function ◽  
Regression Models ◽  
Positive Association ◽  
Digit Symbol Substitution Test ◽  
Metabolic Risk ◽  
Model Assessment ◽  
Linear Regression Models ◽  
Logistic Regression Models ◽  
The Individual

AimWe aimed to detect the individual and combined effect of glucose metabolic components on cognitive function in particular domains among older adults.MethodsData of 2,925 adults aged over 60 years from the 2011 to 2014 National Health and Nutrition Examination Survey were analyzed. Individuals’ cognitive function was evaluated using the Digit Symbol Substitution Test (DSST), the Animal Fluency Test (AF), the Consortium to Establish a Registry for Alzheimer’s Disease Immediate Recall (CERAD-IR), and CERAD Delayed Recall (CERAD-DR). Participants’ glucose metabolic health status was determined based on fasting plasma glucose, insulin, homeostasis model assessment of insulin resistance (HOMA-IR), glycated hemoglobin (HbA1c), and 2-h postload glucose. Linear regression models were used to delineate the associations of cognitive function with individual glucose metabolic component and with metformin use. Logistic regression models were performed to evaluate the associations of cognition with the number of glucose metabolic risk components.ResultsCERAD-IR was significantly associated with HOMA-IR and insulin. HbA1c was related to all the cognitive tests except AF. Among participants without obesity, HOMA-IR and insulin were both negatively associated with CERAD-IR and CERAD-DR. Odds of scoring low in DSST increased with the number of glucose metabolic risk components (odds ratio 1.94, 95% confidence interval [CI] 1.26 to 2.98). Metformin use was associated with better performance in DSST among diabetes patients (β = 4.184, 95% CI 1.655 to 6.713).ConclusionsOur findings support the associations of insulin resistance and glycemic level with cognitive function in key domains, especially among adults without obesity. There is a positive association between metformin use and cognition.

scholarly journals Utilization of Antenatal Care, Skilled Birth Assistant and Institutional Delivery in Bangladesh

2018 ◽  
Vol 66 (1) ◽  
pp. 21-27
Author(s):  
AHM Musfiqur Rahman Nabeen ◽  
Nur E Jannat ◽  
Md Abdus Salam Akanda
Keyword(s):  
Antenatal Care ◽  
Maternal Mortality ◽  
Media Exposure ◽  
Wealth Index ◽  
Education Level ◽  
Place Of Residence ◽  
Place Of Delivery ◽  
Mother’S Education ◽  
Significant Factors ◽  
Mother's Education

Maternal mortality is an important phenomenon to assess the overall health status of a society. To reduce maternal mortality the worldwide recognized vital three factors are: antenatal care, presence of skilled birth assistance and selection of place of delivery. This study made an initiative to identify potential risk factors which can influence the three factors employing the Bangladesh Demographic and Health Survey (BDHS), 2014 data. The parameters are estimated using Poisson count model and Logistic regression model. The estimation results indicate that mother’s education level, place of residence, mother’s age at birth, wealth index, husband’s education level, media exposure and region are significant factors for antenatal care visits. The significant factors for skilled birth assistance are mother’s education level, place of residence, wealth index, media exposure and husband’s education level where as place of residence, mother’s education level, wealth index, media exposure and husband’s education level are significant factors for selection of place of delivery. These results may help the policy makers to develop policies that may facilitate the reduction of maternal mortality in Bangladesh. Dhaka Univ. J. Sci. 66(1): 21-27, 2018 (January)

scholarly journals Adolescent school injuries and classroom sex compositions in German secondary schools

2021 ◽  
Author(s):  
Andreas Filser ◽  
Sven Stadtmüller ◽  
Robert Lipp ◽  
Richard Preetz
Keyword(s):  
Sex Ratio ◽  
Secondary Schools ◽  
Risk Taking ◽  
Regression Models ◽  
Sex Ratios ◽  
Peer Groups ◽  
Positive Association ◽  
Logistic Regression Models ◽  
Adolescent Students ◽  
Males And Females

School injuries are an important adolescent health problem. Previous research suggests that relevant risk behaviors for school injuries, risk-taking and aggression, are highly susceptible to peer effects. Specifically, evidence suggests that the ratio of males and females in peer groups (sex ratio) affects individuals’ propensity for aggression and risk-taking. However, research so far has ignored potential associations of classroom sex ratios with adolescent school injury risks. In this paper, we investigate the association of classroom sex compositions with adolescent school injuries in a longitudinal survey dataset containing 13,131 observations from 9,204 adolescent students (ages 13-16) from secondary schools in Germany. The data also allow us to identify injuries that were due to aggressive behavior and analyze these injuries in detail. Results from multilevel logistic regression models reveal that adolescent students’ risk for school injuries is significantly and positively associated with male-skewed classroom sex ratios. Moreover, we find an even stronger positive association between male-dominated classrooms and aggression-injury risks. Finally, we find that both boys’ and girls’ injury risks equally increase with a higher proportion of males in their classroom. We discuss the implications of our findings with regard to the sex ratio literature and potential interventions.

scholarly journals Individual and local level factors and antenatal care use in Colombia: a multilevel analysis

2014 ◽  
Vol 30 (5) ◽  
pp. 1079-1092 ◽  
Author(s):  
Ana María Osorio ◽  
Luis Miguel Tovar ◽  
Katharina Rathmann
Keyword(s):  
Antenatal Care ◽  
Maternal Care ◽  
Regression Models ◽  
Local Level ◽  
Local Context ◽  
Individual Factors ◽  
Multilevel Logistic Regression ◽  
Logistic Regression Models ◽  
Regional Inequalities ◽  
Using Data

This paper examined the association between individual and local level factors and the number of antenatal care visits completed by women in Colombia using data from the 2010 Colombian Demographic and Health Survey and multilevel logistic regression models. Our findings suggest that, in addition to maternal socioeconomic status, contextual factors influence whether pregnant women complete the minimum recommended number of antenatal care visits. These factors include: level of women’s autonomy in the community, regional inequalities and access barriers caused by distance (OR = 0.057), costs of services (OR = 0.035), and/or a lack of confidence in doctors (OR = 0.036). Our results highlight the existence of inequalities in access to antenatal care and the importance of considering the local context in the design of effective maternal care policies in Colombia. Furthermore, our findings regarding individual factors corroborate the evidence from other countries and offer new insights into the association between local level factors and number of antenatal care visits.

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