scholarly journals Understanding Primary Ciliary Dyskinesia: Experience From a Mediterranean Diagnostic Reference Centre

2020 ◽  
Vol 9 (3) ◽  
pp. 810
Author(s):  
Miguel Armengot-Carceller ◽  
Ana Reula ◽  
Manuel Mata-Roig ◽  
Jordi Pérez-Panadés ◽  
Lara Milian-Medina ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Model ◽  
Logistic Regression Analysis ◽  
Situs Inversus ◽  
Primary Ciliary Dyskinesia ◽  
Logistic Regression Model ◽  
Productive Cough ◽  
Simple Logistic ◽  
Ciliary Dyskinesia

Background: Due to the lack of a gold standard diagnostic test, reference centres with experienced personnel and costly procedures are needed for primary ciliary dyskinesia (PCD) diagnostics. Diagnostic flowcharts always start with clinical symptoms. Therefore, the aim of this work is to define differential clinical criteria so that only patients clinically compatible with PCD are referred to reference centres. Materials and methods: 18 variables from 476 Mediterranean patients with clinically suspicious PCD were collected. After analysing cilia function and ultrastructure, 89 individuals were diagnosed with PCD and 387 had a negative diagnosis. Simple logistic regression analysis, considering PCD as a dependent variable and the others as independent variables, was done. In order to define the variables that best explain PCD, a step-wise logistic regression model was defined. Aiming to classify individuals as PCD or PCD-like patients, based on variables included in the study, a classification and regression tree (CART) was designed. Results and conclusions: Simple logistic regression analysis shows statistically significant association between age at the beginning of their symptomatology, periodicity, fertility, situs inversus, recurrent otitis, atelectasis, bronchiectasis, chronic productive cough, rhinorrea, rhinusinusitis and recurrent pneumonias, and PCD. The step-wise logistic regression model selected situs inversus, atelectasis, rhinorrea, chronic productive cough, bronchiectasis, recurrent pneumonias, and otitis as PCD predictive variables (82% sensitivity, 88% specificity, and 0.92 Area Under the Curve (AUC)). A decision tree was designed in order to classify new individuals based on pansinusitis, situs inversus, periodicity, rhinorrea, bronchiectasis, and chronic wet cough.

scholarly journals LOGISTIC REGRESSION ON THE PRIMIPARAL AGE OF WOMEN IN GOMBE, GOMBE STATE

2020 ◽  
Vol 5 (7) ◽  
pp. 7-16
Author(s):  
A.U. Kinafa ◽  
M.B. Mohammed ◽  
A. Abdulkadir
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Model ◽  
Logistic Regression Analysis ◽  
Logistic Regression Model ◽  
Healthcare Providers ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Analysis ◽  
Challenging Problem ◽  
First Child

Failure of women to undergo a successful first child delivery is becoming one of the most challenging problem and a major concern to most of our healthcare providers. In this paper, we apply the binary logistic regression analysis to investigate whether age of women at first birth have a relationship with the outcome of their delivery (Success or failure). The data was collected from Gombe Town Maternity and was subjected to analysis. From the result of the analysis, we observed that most of the women at tender age (12-17) are classified to fail (69%) during their first child delivery while most of the women at higher age (19 and above) have a better chance of succeeding during their first parturition. Also, the result shows that the average age at which women ought to conceive successfully is 19 years. The Wald statistics result also shows that the logistic regression model fits the data very well.

scholarly journals The Determinant Factors of Muzakki Toward Paying Zakat, Infaq, Sedekah (ZIS) in Zakat Institutions

2020 ◽  
Vol 3 (2) ◽  
pp. 143
Author(s):  
Hening Pratika Nila Hapsari ◽  
Unggul Priyadi
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Model ◽  
Logistic Regression Analysis ◽  
Logistic Regression Model ◽  
Primary Data ◽  
Role Of Government ◽  
Financial Accountability ◽  
Objective Study

Introductions to The Problem: Zakat is one of worship which is often mentioned in the Al Quran. It's just that the potential for Zakat, Infaq, Alms (ZIS) is not comparable to the actual actual figures. Many factors influence muzakki in paying ZIS.Purpose/Objective Study: This study aims to analyze the factors that influence muzakki to pay ZIS in zakat institutions, namely Yatim MandiriDesign/ Methodology/ Approach: The sample in this study amounted to 200 respondents. LAZ Yatim Mandiri was chosen because it is an Amil Zakat Institution that is consistent in collecting ZIS funds from the smallest amount to the large amount. This study uses logistic regression analysis and the data used are primary data. Based on the analysis that has been done, it is found that 61% results can be predicted correctly in the logistic regression model in this study.Findings: The consistency of muzakki in paying ZIS at the Yatim Mandiri Amil Zakat Institution is influenced by the variables of religiosity, income, trust, shariah compliance, knowledge, justice, data publication, financial accountability, motivation, the role of ulama, the role of government. And the consistency of muzakki in paying ZIS at the Yatim Mandiri Amil Zakat Institution is not influenced by the variables of shariah compliance and financial accountability.

scholarly journals Aplicación de modelos de regresión logística en metodología observacional: modalidades de competición en la iniciación al fútbol.

2015 ◽  
Vol 32 (1) ◽  
pp. 288 ◽  
Author(s):  
Daniel Lapresa ◽  
Javier Arana ◽  
M.Teresa Anguera ◽  
J.Ignacio Pérez-Castellanos ◽  
Mario Amatria
Keyword(s):  
Physical Activity ◽  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Multiple Logistic Regression ◽  
Observational Methodology ◽  
Logistic Regression Models ◽  
Game Format ◽  
Simple Logistic

This study shows how simple and multiple logistic regression can be used in observational methodology and more specifically, in the fields of physical activity and sport. We demonstrate this in a study designed to determine whether three-a-side futsal or five-a-side futsal is more suited to the needs and potential of children aged 6-to-8 years. We constructed a multiple logistic regression model to analyze use of space (depth of play) and three simple logistic regression models to determine which game format is more likely to potentiate effective technical and tactical performance.

scholarly journals Determinants of acute respiratory infection among under-five children in rural Ethiopia

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Amanuel Mengistu Merera
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Model ◽  
Respiratory Infection ◽  
Acute Respiratory Infection ◽  
Logistic Regression Model ◽  
P Value ◽  
Under Five ◽  
Under Five Children ◽  
Rural Ethiopia

Abstract Introduction In low- and middle-income nations, acute respiratory infection (ARI) is the primary cause of morbidity and mortality. According to some studies, Ethiopia has a higher prevalence of childhood acute respiratory infection, ranging from 16 to 33.5%. The goal of this study was to determine the risk factors for acute respiratory infection in children under the age of five in rural Ethiopia. Methods A cross-sectional study involving 7911 children under the age of five from rural Ethiopia was carried out from January 18 to June 27, 2016. A two stage cluster sampling technique was used recruit study subjects and SPSS version 20 was used to extract and analyze data. A binary logistic regression model was used to identify factors associated with a childhood acute respiratory infection. The multivariable logistic regression analysis includes variables with a p-value less than 0.2 during the bivariate logistic regression analysis. Adjusted odds ratios were used as measures of effect with a 95% confidence interval (CI) and variables with a p-value less than 0.05 were considered as significantly associated with an acute respiratory infection. Results The total ARI prevalence rate among 7911 under-five children from rural Ethiopia was 7.8%, according to the findings of the study. The highest prevalence of ARI was found in Oromia (12.8%), followed by Tigray (12.7%), with the lowest frequency found in Benishangul Gumuz (2.4%). A multivariable logistic regression model revealed that child from Poor household (AOR = 2.170, 95% CI: 1.631–2.887), mother’s no education (AOR = 2.050,95% CI: 1.017–4.133), mother’s Primary education (AOR = 2.387, 95% CI:1.176–4.845), child had not received vitamin A (AOR = 1.926, 95% CI:1.578–2.351), child had no diarrhea (AOR = 0.257, 95% CI: 0.210–0.314), mothers not working (AOR = 0.773, 95% CI:0.630–0.948), not stunted (AOR = 0.663, 95% CI: 0.552–0.796), and not improved water source (AOR = 1.715, 95% CI: 1.395–2.109). Similarly, among under-five children, the age of the child, the month of data collection, anemia status, and the province were all substantially linked to ARI. Conclusions Childhood ARI morbidity is a serious health challenge in rural Ethiopia, according to this study, with demographic, socioeconomic, nutritional, health, and environmental factors all having a role. As a result, regional governments, healthcare staff, and concerned groups should place a priority on reducing ARI, and attempts to solve the issue should take these variables into account.

scholarly journals Determinants of Acute Respiratory Infection among Under-Five Children in Rural Ethiopia

2021 ◽  
Author(s):  
Amanuel Mengistu Merera
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Model ◽  
Respiratory Infection ◽  
Acute Respiratory Infection ◽  
Logistic Regression Model ◽  
P Value ◽  
Under Five ◽  
Under Five Children ◽  
Rural Ethiopia

Abstract Introduction: In low- and middle-income nations, acute respiratory infection (ARI) is the primary cause of morbidity and mortality. According to some studies, Ethiopia has a higher prevalence of childhood acute respiratory infection, ranging from 16 % to 33.5 %. The goal of this study was to determine the risk factors for acute respiratory infection in children under the age of five in rural Ethiopia. Methods: A cross-sectional study involving 7,911 children under the age of five from rural Ethiopia was carried out from January 18 to June 27, 2016. A two stage cluster sampling technique was used recruit study subjects and SPSS version 20 was used to extract and analyze data. A binary logistic regression model was used to identify factors associated with a childhood acute respiratory infection. The multivariable logistic regression analysis includes variables with a p-value less than 0.2 during the bivariate logistic regression analysis. Adjusted odds ratios were used as measures of effect with a 95% confidence interval (CI) and variables with a p-value less than 0.05 were considered as significantly associated with an acute respiratory infection. Results: The total ARI prevalence rate among 7,911 under-five children from rural Ethiopia was 7.8%, according to the findings of the study. The highest prevalence of ARI was found in Oromia (12.8 %), followed by Tigray (12.7 %), with the lowest frequency found in Benishangul Gumuz (2.4 %). A multivariable logistic regression model revealed that child from Poor household (AOR=2.170, 95% CI: 1.631-2.887), mother’s no education (AOR=2.050,95% CI: 1.017-4.133), mother’s Primary education (AOR=2.387, 95% CI:1.176-4.845), child had not received vitamin A (AOR=1.926, 95% CI:1.578-2.351), child had no diarrhea (AOR=0.257, 95% CI: 0.210-0.314), mothers not working (AOR=0.773, 95% CI:0.630-0.948), not stunted (AOR=0.663, 95% CI: 0.552-0.796), and not improved water source (AOR=1.715, 95% CI: 1.395-2.109). Similarly, among under-five children, the age of the child, the month of data collection, anemia status, and the province were all substantially linked to ARI. Conclusions: Childhood ARI morbidity is a serious health challenge in rural Ethiopia, according to this study, with demographic, socioeconomic, nutritional, health, and environmental factors all having a role. As a result, regional governments, healthcare staff, and concerned groups should place a priority on reducing ARI, and attempts to solve the issue should take these variables into account.

scholarly journals Factors Affecting Postnatal Care in Bangladesh: Clustered Data Analysis

2018 ◽  
Vol 66 (1) ◽  
pp. 59-65
Author(s):  
Mehejabeen Mahbub ◽  
Most Fatima Tuz Zahura
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Data Analysis ◽  
Antenatal Care ◽  
Regression Model ◽  
Birth Order ◽  
Postnatal Care ◽  
Logistic Regression Model ◽  
Wealth Index ◽  
Factors Affecting

The study aims to determine the factors affecting postnatal care in Bangladesh using the data extracted from Bangladesh Demographic and Health Survey (BDHS), 2014. For the purpose of regression analysis, mixed logistic regression model has been utilized to take into account the possible correlation among subjects within clusters. It is found that region, place of residence, mother’s education, wealth index, access to media, birth order and antenatal care visits have significant association with postnatal care. Dhaka Univ. J. Sci. 66(1): 59-65, 2018 (January)

Determinants of the Success of Corporate Recovery in Financial Distressed Company

2018 ◽  
Vol 3 (2) ◽  
pp. 09-14
Author(s):  
Giriati Giriati ◽  
Mustaruddin Mustaruddin ◽  
M. Rustam
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Firm Size ◽  
Financial Distress ◽  
Stock Exchange ◽  
Jel Classification ◽  
Company Size ◽  
Simple Logistic ◽  
Positive Effect

Objective - This study aims to examine and analyze the influence of severity, free assets, company size, asset retrenchment and CEO expertise on the success of recovery companies experiencing financial distress that are listed on the Indonesian Stock Exchange (IDX). Methodology/Technique - The population used in this study are all companies listed on the Indonesian Stock Exchange between 2011 and 2016. This study uses a simple logistic regression analysis to test the hypotheses. Findings - The results indicate that free assets and CEO expertise have a significant and positive effect on the success of a company's recovery. Meanwhile, variable severity, asset retrenchment and firm size do not affect the success of the company's recovery. Type of Paper - Empirical. Keywords: Turnaround/Recovery; Δ Severity; Free Assets; Company Size; Asset Retrenchment; CEO Expertise. JEL Classification: G30, G33, G39.

scholarly journals Quantification of the Ecological Value of Railroad Development Areas Using Logistic Regression Analysis

2021 ◽  
Vol 18 (22) ◽  
pp. 11764
Author(s):  
Min-Kyeong Kim ◽  
Duckshin Park ◽  
Dong Yeob Kim
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Model ◽  
Environmental Quality ◽  
Logistic Regression Analysis ◽  
Environmentally Friendly ◽  
Tree Age ◽  
Receiver Operating Curve ◽  
Ecological Value ◽  
Railway Network

According to the national railway network construction plan, Investment in railways has increased due to the need for environmentally friendly transportation, and the rail network is expanding throughout South Korea. Railway projects should be evaluated using strategic environmental impact assessments. In the “Guidelines for the Construction of Environment-friendly Railways,” seven priority headings that must be considered for railway projects are described. This guide notes that qualitative evaluation must be conducted during the survey process to reasonably predict impacts on the environment. However, quantitative evaluation with specific indicator values may also be necessary. In this study, independence analysis and logistic regression analysis were used to quantitatively evaluate railway environmental and ecological indicators. The results were used to develop a regression model reflecting seven indicators; biodiversity class, ecosystem type, vegetation conservation class, tree age class, ecological naturalness, presence of river ecosystems, and fragmented patch size. The fitness regression model showed 90.3% classification accuracy and the receiver operating curve (ROC) model fit was 88.6%. An environmental quality assessment map was prepared by classifying areas of environmental quality according to five grades. This is the first model for environmental and ecological evaluation of railway projects. Evaluation using the map showed that the railroad passes through areas with lower protection values compared to the results obtained using the national environmental evaluation map. Kappa analysis showed a low level of agreement between the two maps (kappa coefficient = 0.212). The results of this study can be applied to railway development project sites and may help to identify the best sites for the development of an environmentally friendly railway system.

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