scholarly journals Determinants of Knowledge and Attitude towards Breastfeeding in Rural Pregnant Women Using Validated Instruments in Ethiopia

2021 ◽  
Vol 18 (15) ◽  
pp. 7930
Author(s):  
Misra Abdulahi ◽  
Atle Fretheim ◽  
Alemayehu Argaw ◽  
Jeanette H. Magnus
Keyword(s):  
Linear Regression ◽  
Pregnant Women ◽  
Multiple Linear Regression ◽  
Multiple Linear Regression Model ◽  
Educational Interventions ◽  
Attitude Scale ◽  
Linear Regression Models ◽  
Breastfeeding Knowledge ◽  
Breastfeeding Practices ◽  
Knowledge And Attitude

Understanding the underlying determinants of maternal knowledge and attitude towards breastfeeding guides the development of context-specific interventions to improve breastfeeding practices. This study aimed to assess the level and determinants of breastfeeding knowledge and attitude using validated instruments in pregnant women in rural Ethiopia. In total, 468 pregnant women were interviewed using the Afan Oromo versions of the Breastfeeding Knowledge Questionnaire (BFKQ-AO) and the Iowa Infant Feeding Attitude Scale (IIFAS-AO). We standardized the breastfeeding knowledge and attitude scores and fitted multiple linear regression models to identify the determinants of knowledge and attitude. 52.4% of the women had adequate knowledge, while 60.9% of the women had a neutral attitude towards breastfeeding. In a multiple linear regression model, maternal occupation was the only predictor of the BFKQ-AO score (0.56SD; 95%CI, 1.28, 4.59SD; p = 0.009). Age (0.57SD; 95%CI, 0.24, 0.90SD; p = 0.001), parity (−0.24SD; 95%CI, −0.47, −0.02SD; p = 0.034), antenatal care visits (0.41SD; 95%CI, 0.07, 0.74SD; p = 0.017) and the BFKQ-AO score (0.08SD; 95% CI, 0.06, 0.09SD; p < 0.000) were predictors of the IIFAS-AO score. Nearly half of the respondents had inadequate knowledge and most women had a neutral attitude towards breastfeeding. Policymakers and managers could address these factors when planning educational interventions to improve breastfeeding practices.

scholarly journals Determinants of Knowledge and Attitude Towards Breastfeeding Using Validated Instruments in Pregnant Women From a Rural Setting in Ethiopia

2021 ◽  
Author(s):  
Misra Abdulahi Ahmed ◽  
Atle Fretheim ◽  
Alemayehu Argaw ◽  
Jeanette H. Magnus
Keyword(s):  
Linear Regression ◽  
Antenatal Care ◽  
Pregnant Women ◽  
Multiple Linear Regression ◽  
Multiple Linear Regression Model ◽  
Educational Interventions ◽  
Rural Setting ◽  
Breastfeeding Knowledge ◽  
Breastfeeding Practices ◽  
Knowledge And Attitude

Abstract BackgroundUnderstanding the important underlying determinants of maternal knowledge and attitude towards breastfeeding guides the development of context-specific interventions aimed at increasing the rates of optimal breastfeeding practices. However, studies that used validated instruments to assess breastfeeding knowledge and attitude are nonexistent in Ethiopia.ObjectiveTo assess the level and determinants of breastfeeding knowledge and attitude using validated instruments in pregnant women who participated in breastfeeding education and support intervention in a rural district in Ethiopia.Methods468 pregnant women in their second or third trimester were interviewed at baseline to assess their knowledge and attitude towards breastfeeding practices using locally adapted and validated instruments. We used the Afan-Oromo versions of the Breastfeeding Knowledge Questionnaire (BFKQ-AO) and the Iowa Infant Feeding Attitude Scale (IIFAS-AO). Breastfeeding knowledge and attitude scores were standardized based on the distribution of the population and multiple linear regression models were fitted to identify the independent determinants knowledge and attitude.Results52.4% of the mothers had a high level of knowledge while 60.9% of the women had a neutral attitude towards breastfeeding. In a multiple linear regression model, the maternal occupation was the only predictor of the overall BFKQ-AO score (0.56 SD; 95% CI, 1.28, 4.59 SD; P=0.009). Age (0.57 SD; 95% CI, 0.24, 0.90 SD; P=0.001), parity (-0.24 SD; 95%CI, -0.47, -0.02SD; P=0.034), antenatal care visits (0.41 SD; 95% CI, (0.07, 0.74 SD; P=0.017) and the BFKQ score (0.08 SD; 95% CI, 0.06, 0.09 SD; P<0.000) were predictors of the IIFAS-AO score.ConclusionsAlthough more than half of the respondents had adequate knowledge about breastfeeding, most women had a neutral attitude towards breastfeeding. Occupation of mothers was an independent predictor of breastfeeding knowledge, whereas age, parity, antenatal care visits, and breastfeeding knowledge score were predictors of breastfeeding attitude. Thus, policymakers and managers should address these factors when planning educational interventions on breastfeeding to improve knowledge and attitude thereby improving breastfeeding practices.

scholarly journals Pedestrian crossing model in urban street (Case study on commercial area in Bandung)

2018 ◽  
Vol 181 ◽  
pp. 02004
Author(s):  
Sony Sulaksono Wibowo ◽  
Rian Wicaksana
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Group Size ◽  
Multiple Linear Regression Model ◽  
Delay Model ◽  
Affecting Factors ◽  
Linear Regression Models ◽  
Urban Street ◽  
Commercial Area ◽  
Pedestrian Crossing

Pedestrians who cross without any crossing facilities and under mixed-traffic tend to have varying responses. The responses can be analyzed by using multiple linear regression model, with pedestrian crossing delay and pedestrian crossing speed set as response variables. This research aims to develop two pedestrian crossing models based on the condition at the midblock part of urban street, in particular commercial area and without specific crossing facilities. The two models are pedestrian crossing delay model and pedestrian crossing speed model. The affecting factors are considered in linear relationship and the multiple-linear regression models are used. The principal factor in the pedestrian crossing delay model is group size of more than 3 persons, while in the model of pedestrian crossing speed, the principal factors are number of group size and pedestrian baggage. The mean of pedestrian crossing delay was about 3 seconds while pedestrian crossing speed was about 1 m/s.

scholarly journals Multiple Linear Regression Model of Meningococcal Disease in Ukraine: 1992–2015

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Hennadii Mokhort
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Meningococcal Disease ◽  
Total Population ◽  
Linear Models ◽  
National Level ◽  
Multiple Linear Regression Model ◽  
National Database ◽  
Linear Regression Models ◽  
Susceptible Population

Estimating the rates of invasive meningococcal disease (IMD) from epidemiologic data remains critical for making public health decisions. In Ukraine, such estimations have not been performed. We used epidemiological data to develop a national database. These data were used to estimate the population susceptible to IMD and identify the prevalence of asymptomatic carriers of N. meningitidis using simple epidemiological models of meningococcal disease that may be used by the national policy makers. The goal was to create simple, easily understood analysis of patterns of the infection within Ukraine that would capture the major features of the infection dynamics. Studies used nationally reported data during 1992–2015. A logic model identified the prevalence of carriage and the proportion of the population susceptible to IMD as key drivers of IMD incidence. Multiple linear regression models for all ages (total population) and for children ≤14 years old were fit to national-level data. Linear models with the incidence of IMD as an outcome were highly associated with carriage and estimated susceptible population in both total population and children (R2 = 0.994 and R2 = 0.978, respectively). The susceptibility rate to IMD in the study total population averaged 0.0034 ± 0.0009% annually. At the national level, IMD can be characterized by the simple interaction between the prevalence of asymptomatic carriage and the proportion of the susceptible population. IMD association with prevalence rates of carriage and the proportion of susceptible population is sufficiently strong for national-level planning of intervention strategies for IMD.

scholarly journals Um modelo alternativo de risco para companhias não-financeiras aplicado ao setor brasileiro de papel e celulose

2009 ◽  
Vol 7 (3) ◽  
pp. 347
Author(s):  
Hsia Hua Sheng ◽  
Cristiane Karcher ◽  
Paulo Hubert Jr.
Keyword(s):  
At Risk ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Financial Risk ◽  
Risk Measure ◽  
Multiple Linear Regression Model ◽  
Principal Component ◽  
Linear Regression Models ◽  
Explanatory Factors ◽  
Paper And Pulp

Earnings at Risk (EaR) is a financial risk measure that can be applied to non-financial companies, similarly to Cash Flow at Risk (CFaR). It is based on a relation that can be quantified using a multiple linear regression model, where the dependent variable is the change on the company's results and the independent variables are changes in distinct risk factors. The presence of correlation between explanatory factors (multicollinearity) in this kind of model may cause problems when calculating EaR and CFaR. In this paper, we indicate some possible consequences of these problems when calculating EaR, and propose a method to solve it based on Principal Component Analysis technique. To test the model, we choose the Brazilian agriculture-business industry, more specifically the paper and pulp sectors. We will show that, on the absence of significant correlation between variables, the proposed model has equivalent performance to usual multiple linear regression models. We find evidence that when correlation appears, the model here proposed yields more accurate and reliable forecasts.

scholarly journals Simulation of wheat biomass yield by thermal time, rainfall and nitrogen

2017 ◽  
Vol 21 (11) ◽  
pp. 763-768 ◽  
Author(s):  
Ana P. B. Trautmann ◽  
José A. G. da Silva ◽  
Manuel O. Binelo ◽  
Osmar B. Scremin ◽  
Ângela T. W De Mamann ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Biomass Yield ◽  
Multiple Linear Regression Model ◽  
Thermal Time ◽  
Linear Regression Models ◽  
Split Application ◽  
N Supply

ABSTRACT Wheat biomass yield focused on the production of quality silage is dependent on rainfall, temperature and nitrogen (N). The objective of the study was to validate the use of rainfall, thermal time and N as potential variables for the composition of the multiple linear regression model and simulation of wheat biomass yield for silage production under N supply conditions during the cycle, in the systems of succession. The study was conducted in 2012, 2013 and 2014, in randomized blocks with four replicates in 4 x 3 factorial, for N-fertilizer doses (0, 30, 60, 120 kg ha-1) and forms of N supply [single application (100%) in the stage V3 (third expanded leaf); split application (70%/30%) in the stages V3/V6 (third and sixth expanded leaves); split application (70%/30%) in the stages V3/E (third expanded leaf and beginning of grain filling)], respectively, in the systems soybean/wheat and maize/wheat. Rainfall and N are potential variables in the composition of the multiple linear regression model. Multiple linear regression models are efficient in the simulation of wheat biomass yield for silage under the N supply conditions during the cycle in the succession systems.

Applications of a Multiple Linear Regression Model to the Analysis of Relationships between Eastward- and Westward-Moving Intraseasonal Modes

2004 ◽  
Vol 61 (24) ◽  
pp. 3041-3048 ◽  
Author(s):  
Paul E. Roundy ◽  
William M. Frank
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Spatial Scales ◽  
Statistical Significance ◽  
Multiple Linear Regression Model ◽  
Longwave Radiation ◽  
Linear Regression Models ◽  
Linear Predictor

Abstract Multiple linear regression models with nonlinear power terms may be applied to find relationships between interacting wave modes that may be characterized by different frequencies. Such regression techniques have been explored in other disciplines, but they have not been used in the analysis of atmospheric circulations. In this study, such a model is developed to predict anomalies of westward-moving intraseasonal precipitable water by utilizing the first through fourth powers of a time series of outgoing longwave radiation that is filtered for eastward propagation and for the temporal and spatial scales of the tropical intraseasonal oscillations. An independent and simpler compositing method is applied to show that the results of this multiple linear regression model provide a better description of the actual relationships between eastward- and westward-moving intraseasonal modes than a regression model that includes only the linear predictor. A statistical significance test is applied to the coefficients of the multiple linear regression model, and they are found to be significant over broad regions of the Tropics. Correlations between the predictors are shown to not significantly influence results for this case. Results show that this regression model reveals physical relationships between eastward- and westward-moving intraseasonal modes. The physical interpretation of these regression relationships is given in a companion paper.

scholarly journals Multi-criteria approach to pair-multiple linear regression models constructing

2021 ◽  
Vol 21 (1) ◽  
pp. 88-99
Author(s):  
Mikhail P. Bazilevskiy ◽  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Multiple Regression ◽  
Linear Regression Model ◽  
Regression Models ◽  
Multiple Linear Regression Model ◽  
Linear Regression Models ◽  
Deming Regression ◽  
Multiple Linear Regression Models

A pair-multiple linear regression model which is a synthesis of Deming regression and multiple linear regression model is considered. It is shown that with a change in the type of minimized distance, the pair-multiple regression model transforms smoothly from the pair model into the multiple linear regression model. In this case, pair-multiple regression models retain the ability to interpret the coefficients and predict the values of the explained variable. An aggregated quality criterion of regression models based on four well-known indicators: the coefficient of determination, Darbin – Watson, the consistency of behaviour and the average relative error of approximation is proposed. Using this criterion, the problem of multi-criteria construction of a pair-multiple linear regression model is formalized as a nonlinear programming problem. An algorithm for its approximate solution is developed. The results of this work can be used to improve the overall qualitative characteristics of multiple linear regression models.

Comparison of Artificial Neural Network Models and Multiple Linear Regression Models in Cargo Port Performance Prediction

2011 ◽  
Vol 403-408 ◽  
pp. 3570-3577 ◽  
Author(s):  
P.Oliver Jayaprakash ◽  
K. Gunasekaran ◽  
S. Muralidharan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Service Time ◽  
Multiple Linear Regression Model ◽  
Linear Regression Models ◽  
Neural Network Models ◽  
Operational Characteristics ◽  
Artificial Neural

Cargo ports operational performance was specified typically through revenue earned, quantum of cargo handled and number of ships serviced. It was predisposed by infrastructural facilities and cargo handling rate; it had an effect over pre-berth waiting time of vessels waiting and berthing time of ships at a port. An Indian port’s ship movement and port operational characteristics had been studied for five years (2005-2009). Ship’s service time was the crucial parameter used to quantify the port performance. This paper focused on building an artificial neural network technique based model to illustrate the relationship between service time and port operational characteristics. Validations of ANN model, comparing multiple linear regression model outputs were reported.

Folate Nutritional Status among Psoriasis Patients not Exposed to Antifolate Drug

2020 ◽  
Vol 16 (4) ◽  
pp. 543-553
Author(s):  
Luciana Y. Tomita ◽  
Andréia C. da Costa ◽  
Solange Andreoni ◽  
Luiza K.M. Oyafuso ◽  
Vânia D’Almeida ◽  
...  
Keyword(s):  
Folic Acid ◽  
Linear Regression ◽  
Multiple Linear Regression Model ◽  
Folate Intake ◽  
Serum Folate ◽  
Linear Regression Models ◽  
Cross Sectional ◽  
Folic Acid Fortification ◽  
Plasma Folate ◽  
Psoriasis Severity

Background: Folic acid fortification program has been established to prevent tube defects. However, concern has been raised among patients using anti-folate drug, i.e. psoriatic patients, a common, chronic, autoimmune inflammatory skin disease associated with obesity and smoking. Objective: To investigate dietary and circulating folate, vitamin B12 (B12) and homocysteine (hcy) in psoriatic subjects exposed to the national mandatory folic acid fortification program. Methods: Cross-sectional study using the Food Frequency Questionnaire, plasma folate, B12, hcy and psoriasis severity using the Psoriasis Area and Severity Index score. Median, interquartile ranges (IQRs) and linear regression models were conducted to investigate factors associated with plasma folate, B12 and hcy. Results: 82 (73%) mild psoriasis, 18 (16%) moderate and 12 (11%) severe psoriasis. 58% female, 61% non-white, 31% former smokers, and 20% current smokers. Median (IQRs) were 51 (40, 60) years. Only 32% reached the Estimated Average Requirement of folate intake. Folate and B12 deficiencies were observed in 9% and 6% of the blood sample respectively, but hyperhomocysteinaemia in 21%. Severity of psoriasis was negatively correlated with folate and B12 concentrations. In a multiple linear regression model, folate intake contributed positively to 14% of serum folate, and negative predictors were psoriasis severity, smoking habits and saturated fatty acid explaining 29% of circulating folate. Conclusion: Only one third reached dietary intake of folate, but deficiencies of folate and B12 were low. Psoriasis severity was negatively correlated with circulating folate and B12. Stopping smoking and a folate rich diet may be important targets for managing psoriasis.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

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