Factors determining child mortality in Uganda

2017 ◽  
Vol 44 (5) ◽  
pp. 633-642 ◽  
Author(s):  
Will Kaberuka ◽  
Alex Mugarura ◽  
Javan Tindyebwa ◽  
Debra S. Bishop
Keyword(s):  
Health Care ◽  
Logistic Regression ◽  
Birth Weight ◽  
Regression Model ◽  
Marital Status ◽  
Birth Order ◽  
Child Mortality ◽  
Logistic Regression Model ◽  
Household Wealth ◽  
Content Type

Purpose The purpose of this paper is to establish socio-economic factors and maternal practices that determine child mortality in Uganda. Design/methodology/approach The paper examines the role of sex, birth weight, birth order and duration of breastfeeding of a child; age, marital status and education of the mother; and household wealth in determining child mortality. The study employs a logistic regression model to establish which of the factors significantly impacts child mortality in Uganda. Findings The study established that education level, age and marital status of the mother as well as household wealth significantly impact child mortality. Also important are the sex, birth weight, birth order and breastfeeding duration. Research limitations/implications Policies aimed at promoting breastfeeding and education of female children can make a significant contribution to the reduction of child mortality in Uganda. Practical implications Health care intervention programs should focus on single, poor and uneducated mothers as their children are at great risk due to poor and inadequate health care utilization. Originality/value This paper could be the first effort in examining child mortality status in Uganda using a logistic regression model.

Institutions, entrepreneurial adaptation, and the legal form of the organization

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Indu Khurana ◽  
Dmitriy Krichevskiy ◽  
Gregory Dempster ◽  
Sean Stimpson
Keyword(s):  
Logistic Regression ◽  
Survival Rate ◽  
Regression Model ◽  
Economic Freedom ◽  
Logistic Regression Model ◽  
Legal Form ◽  
Content Type ◽  
Legal Structure ◽  
The Usa ◽  
Over Time

PurposeThis paper aims to examine how economic freedom impacts the initial choice of legal structure for startup firms. The authors do this by first exploring whether economic freedom is an essential determinant of the initial legal form of organization (LFO). The authors then explore the impact of economic freedom on firms' choice of changing their initial legal structure over time and how this change impacts their survival rate.Design/methodology/approachThe authors employ a multinomial logistic regression model to measure the initial determinants of LFO by utilizing an eight-year panel data set of 4,928 startups in the USA through the Kauffman firm survey and merge it with the Economic Freedom in North American index from the Fraser Institute. The authors then employ a logistic regression model to examine the determinants facilitating a change in legal structure over time.FindingsThe results show that economic freedom is a significant determinant in the choice of legal structure. The findings also report that the majority of startups do not change their legal form, but of those that do change the legal structure show a higher survival rate.Research limitations/implicationsMajor limitations are the size of the data and the nature of somewhat limited economic freedom differences with the USA. More nuanced measures of economic freedom would be highly desirable.Practical implicationsPolicymakers should take note that limited red tape, smoothly working labor markets and straightforward processes for changes of legal structures of organizations would improve survival and growth odds for entrepreneurs.Originality/valueDrawing on the theory of institutions, the authors attempt to bridge a gap in the literature by explicitly analyzing the determinants of the legal structure in startups in light of economic freedom. Institutional factors do not work in isolation; therefore, the authors also employ traditional entrepreneur-specific variables that affect the choice of legal structure in addition to the institutional framework.

scholarly journals Determinants of adaptation strategies to climate change among the smallholder farmers in Adama District, Ethiopia

2020 ◽  
Vol 12 (4) ◽  
pp. 463-476
Author(s):  
Hurgesa Hundera Hirpha ◽  
Sylvester Mpandeli ◽  
Amare Bantider
Keyword(s):  
Climate Change ◽  
Logistic Regression ◽  
Regression Model ◽  
Climate Change Adaptation ◽  
Logistic Regression Model ◽  
Smallholder Farmers ◽  
Binary Logistic Regression ◽  
Adaptation Strategies ◽  
Binary Logistic Regression Model ◽  
Content Type

Purpose The Ethiopian economy is mainly based on the rain-fed agriculture practiced by smallholder farmers. The sector is highly vulnerable to climate change impacts. This study aims to examine the determinants of adaptation strategies to climate change among the smallholder farmers in Adama District, Ethiopia. Design/methodology/approach A cross-sectional survey design was used to collect quantitative data using questionnaire with 351 randomly selected smallholder farmers. To collect qualitative data focus group discussions, key informant interviews and field observations were also used. Triangulated with thematic analysis, descriptive statistics and binary logistic regression model were used for the analysis. Findings The result indicated that the majority of the smallholder farmers use at least one climate change adaptation strategy in their local areas though the strategy is generally weak. In this regard, some of the dominant climate change adaptation activities identified in the study area are using improved crop varieties, planting trees, watershed management, adjusting planting date and terracing. The result from binary logistic regression model showed that age and sex of household head, as well as their education, family size, access to agricultural extension services and training on climate change significantly influence the practices of adaptation measures. Originality/value This study would help the practitioners to modify the existing weak adaptation activities by introducing advanced and technological-based adaptation strategies to the rural farming communities.

Procurement planning as a strategic tool for public procurement effectiveness: Experience from selected public procuring entities in Dodoma city, Tanzania

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ismail Abdi Changalima ◽  
Gift Onesmo Mushi ◽  
Shadrack Samwel Mwaiseje
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Public Procurement ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Cross Sectional ◽  
Content Type ◽  
Survival And Development ◽  
Procurement Planning

Purpose The significance of the public procurement function is on the survival and development of procuring entities and that of a nation at large. This study aims to focus on examining the influence of procurement planning on the effectiveness of public procurement, experience from selected public procuring entities in Dodoma city, Tanzania. Design/methodology/approach A cross-sectional design was adopted from which data were collected from 146 respondents who were purposively chosen from several public procuring entities located at Dodoma city in the United Republic of Tanzania by using a survey structured questionnaire and analysed through a binary logistic regression model. Findings Findings revealed that procurement planning as a strategic function significantly influences the effectiveness of public procurement. The binary logistic regression model included the implementation of prepared procurement plans (p = 0.039), the involvement of users as stakeholders and important institutional actors in public procurement (p = 0.033), compliance to procurement laws and regulations when planning (p = 0.016) and adequacy of budget (p = 0.042) as predictors of procurement planning were tested to be significantly related to the effectiveness of public procurement. It was concluded that public procuring entities can achieve public procurement effectiveness through procurement planning. Social implications The implications and policy recommendations of the findings of this study would be useful to procurement practitioners mostly in public procuring entities. Originality/value This study contributes to adding knowledge to the existing body of knowledge on procurement planning as an imperative activity in public procurement in Tanzania.

scholarly journals Comparison between the maximum likelihood and the bayesian estimation methods for logistic regression model (case study: risk of low birth weight in Indonesia)

2021 ◽  
Vol 2106 (1) ◽  
pp. 012001
Author(s):  
P R Sihombing ◽  
S R Rohimah ◽  
A Kurnia
Keyword(s):  
Logistic Regression ◽  
Birth Weight ◽  
Maximum Likelihood ◽  
Low Birth Weight ◽  
Regression Model ◽  
Bayesian Estimation ◽  
Logistic Regression Model ◽  
Estimation Method ◽  
Estimation Methods ◽  
Model Case

Abstract This study aims to compare the efficacy of logistic regression model for identifying the risk factors of low-birth-weight babies in Indonesia using the maximum likelihood estimation (MLE)and the Bayesian estimation methods. The data used in this study is secondary data derived from the 2017 Indonesian Demographic Health Survey with a total sample of 16,344 newborn babies. Selection of the best logistic regression model was based on the smaller Bayesian Schwartz Information Criterion (BIC) value. The logistic regression model with the Bayesian estimation method has a smaller BIC value than the MLE method. Twin births, baby girl, maternal age at risk, birth spacing that is too close, iron deficiency, low education, low economy, inadequate drinking water sources have provided a higher risk of low-birth-weight incidence.

scholarly journals Trends of proximate low birth weight and associations among children under-five years of age: Evidence from the 2016 Ethiopian demographic and health survey data

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246587
Author(s):  
Mesfin Wudu Kassaw ◽  
Ayele Mamo Abebe ◽  
Ayelign Mengesha Kassie ◽  
Biruk Beletew Abate ◽  
Seteamlak Adane Masresha
Keyword(s):  
Logistic Regression ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Regression Model ◽  
Maternal Age ◽  
Logistic Regression Model ◽  
Maternal Education ◽  
Multiple Birth ◽  
Health Survey Data ◽  
Birth Type

Background Low birth weight puts a newborn at increased risk of death and illness, and limits their productivity in the adulthood period later. The incidence of low birth weight has been selected as an important indicator for monitoring major health goals by the World Summit for Children. The 2014 World Health Organization estimation of child death indicated that 4.53% of total deaths in Ethiopia were due to low birth weight. The aim of this study was to assess trends of proximate low birth weight and associations of low birth weight with potential determinants from 2011 to 2016. Methods This study used the 2016 Ethiopian Demographic and Health Survey data (EDHS) as data sources. According to the 2016 EDHS data, all the regions were stratified into urban and rural areas. The variable “size of child” measured according to the report of mothers before two weeks of the EDHS takes placed. The study sample refined from EDHS data and used for this further analysis were 7919 children. A logistic regression model was used to assess the association of proximate low birth weight and potential determinates of proximate low birth weight. But, the data were tested to model fitness and were fitted to Hosmer-Lemeshow-goodness of fit. Results The prevalence of proximate low birth weight in Ethiopia was 26.9% (2132), (95%CI = 25.4, 27.9). Of the prevalence of child size in year from 2011 to 2016, 17.1% was very small, and 9.8% was small. In the final multivariate logistic regression model, region (AOR = xx), (955%CI = xx), Afar (AOR = 2.44), (95%CI = 1.82, 3.27), Somalia (AOR = 0.73), (95%CI = 0.55, 0.97), Benishangul-Gumz (AOR = 0.48), (95%CI = 0.35, 0.67), SNNPR (AOR = 0.67), (95%CI = 0.48, 0.93), religion, Protestant (AOR = 0.76), (95%CI = 0.60, 0.95), residence, rural (AOR = 1.39), (95%CI = 1.07, 1.81), child sex, female (AOR = 1.43), (95%CI = 1.29, 1.59), birth type, multiple birth during first parity (AOR = 2.18), (95%CI = 1.41, 3.37), multiple birth during second parity (AOR = 2.92), (95%CI = 1.86, 4.58), preparedness for birth, wanted latter child (AOR = 1.26), (95%CI = 1.09, 1.47), fast and rapid breathing (AOR = 1.22), (95%CI = 1.02, 1.45), maternal education, unable to read and write (AOR = 1.46), (95%CI = 1.56, 2.17), and maternal age, 15–19 years old (AOR = 1.86), (95%CI = 1.19, 2.92) associated with proximate low birth weight. Conclusions The proximate LBW prevalence as indicated by small child size is high. Region, religion, residence, birth type, preparedness for birth, fast and rapid breathing, maternal education, and maternal age were associated with proximate low birth weight. Health institutions should mitigating measures on low birth weight with a special emphasis on factors identified in this study.

Incidence of workplace bullying among hospitality employees

2017 ◽  
Vol 29 (4) ◽  
pp. 1116-1132 ◽  
Author(s):  
Antonio Ariza-Montes ◽  
Juan M. Arjona-Fuentes ◽  
Rob Law ◽  
Heesup Han
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Working Conditions ◽  
Workplace Bullying ◽  
Logistic Regression Model ◽  
Organizational Factors ◽  
Sample Population ◽  
Binary Logistic Regression Model ◽  
Content Type ◽  
European Working

Purpose The purpose of this study is to the address the key factors of workplace bullying among hospitality employees, as workplace bullying results in damaging consequences on both individuals and organizations. Design/methodology/approach This study first defines the phenomenon of workplace bullying and then reviews the related literature. Data are collected from a sample population of 238 hospitality employees obtained from the latest European Working Conditions Survey. Logistic regression analysis is used to achieve the study objectives. Findings Results from the binary logistic regression model show the main personal and organizational factors related to the probability of workplace bullying. The logistic regression model explains 76.4 per cent of the total variations in the sample. The model correctly classifies 78.1 per cent of hospitality employees who did not feel bullied in their profession and 74.1 per cent of employees who did feel bullied. Practical implications The authors’ findings imply that responsible managers in hospitality enterprises may reduce the organizational levels of workplace bullying by adjusting certain working conditions and establishing a supportive environment. Originality/value Studies on personalities inclined to bullying are inconclusive. To the best of the authors’ knowledge, this research is the first to develop a comprehensive and exploratory conceptual model of workplace bullying that links personal variables, working conditions and contextual factors to the prevalence of workplace bullying within the hospitality sector in the European context.

scholarly journals Two Level Logistic Regression Model of Factors Influencing in Early Childbearing and its Consequences on Nutritional Status of Bangladeshi Mothers: Nationally Representative Data

2021 ◽  
Vol 10 ◽  
pp. 97-106
Author(s):  
Khirujjaman Sumon ◽  
Md. Abu Sayem ◽  
Abu Sayed Md. Al Mamun ◽  
Premananda Bharati ◽  
Suman Chakrabarty ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Nutritional Status ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Household Wealth ◽  
Medical Problem ◽  
Early Marriage ◽  
Early Childbearing ◽  
Modifiable Factors ◽  
Study Results

Background: Early marriage and early pregnancy is a social as well as a medical problem in developing countries, which may have an impact on the health and nutritional status of teenage mothers. Therefore, the objective of this study was to determine the influencing factors of early childbearing (ECB) and its consequences on the nutritional status of Bangladeshi mothers. Methods: Data was extracted from Bangladesh Demographic and Health Survey (BDHS-2014). Women who delivered their first baby before the age of 20 years are considered ECB mothers. Nutritional status was measured by body mass index (BMI). Chi-square test and both univariable and multivariable logistic regressions, and z-proportional test were used in this study. Results: The prevalence of ECB among currently non-pregnant mothers in Bangladesh was 83%. The logistic regression model provided the following six risk factors of ECB: (i) living location (division) (p<0.01), (ii) respondents’ education (p<0.05), (iii) husbands’ education (p<0.05), (iv) household wealth quintiles (p<0.01), (v) respondents’ age at first marriage (p<0.05), and (vi) number of family members (p<0.05). Still, 17.6% of mothers were undernourished in Bangladesh; among them, 18.5% and 13.4% were ECB and non- ECB mothers respectively. ECB mothers had a greater risk to be undernourished than non-ECB mothers [COR=1.26, 95% CI: 1.11-1.43; p<0.01]. Conclusions: In this study, some modifiable factors were found as predictors of ECB in Bangladesh. ECB mothers were more prone to become under-nourished. These findings can be considered to reduce the number of ECB mothers in Bangladesh consequently improve their nutritional status.

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)

Evaluating negative attitudes of the students and shoppers towards halal cosmetics products

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Mominul Islam
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Chi Square ◽  
Negative Attitudes ◽  
Content Type ◽  
Animal Fat ◽  
Chi Square Test

Purpose This study aims to reveal how consumers and shoppers are negative toward alcohol, animal fat, producers and certification issues concerned with halal cosmetics products. Design/methodology/approach In total, 527 students of 4 public universities and a medical college across Bangladesh took part in a survey and 150 shoppers from 2 cities participated in the face to face interview with the structured questionnaires. Frequency distribution was used for categorical and numerical data, and the chi-square test with a binary logistic regression model has tested the association between gender and attitudes toward halal cosmetics. Besides, narratives of Sharīʿah regarding alcohol, meat, fat and halal certification have helped understand the halal issue. Findings In total, 83% of the respondents perceived negative attitudes against haram animal fat followed by alcohol (74%) and animal fat (64%). The chi-square test shows that consumers held a significant association toward haram animal fat, (p-value 0.000) alcohol, (p-value 0.000) non-Muslim producers (p-value 0.000) and non-Muslim countries (p-value 0.026). Imperatively, the binary logistic regression model has found a significant negative association to haram animal fat (ß2 −0.295) and alcohol (ß1 −0.200). Practical implications Marketers ought to avoid haram animal fat in halal cosmetics besides focusing on alcohol freeness. Also, non-Muslim marketers need to be extra cautious in showcasing their identities. However, Islamic marketers will enjoy a competitive advantage in the halal market because of their demographic factors. Social implications Islamic principles on alcohol, meat, fat and certification potentially can help other stakeholders sense the halal norms. Originality/value This study has blended the elements of Sharīʿah with empirical evidence to shed light on the fundamental and trust factors for the marketing of halal cosmetics products.

Discrete choice and survival models in employee turnover analysis

2018 ◽  
Vol 40 (2) ◽  
pp. 381-395 ◽  
Author(s):  
Rafa Madariaga ◽  
Ramon Oller ◽  
Joan Carles Martori
Keyword(s):  
Logistic Regression ◽  
Survival Analysis ◽  
Regression Model ◽  
Discrete Choice ◽  
Survival Data ◽  
Employee Turnover ◽  
Logistic Regression Model ◽  
Data Set ◽  
Content Type ◽  
Set Up

Purpose The purpose of this paper is to assess the capacity of two methodological approaches – discrete choice and survival analysis models – to investigate the relationship between socio-economic characteristics and turnover in a retailing company. A comparison of the estimation results under each model and their interpretation is carried out. The study provides a guide to determine, assess and interpret the effects of different driving factors behind turnover. Design/methodology/approach The authors use a data set containing information about 1,199 workers followed up between January 2007 and December 2009. First, not distinguishing voluntary and involuntary resignation, a binary logistic regression model and a Cox proportional hazards (PH) model for univariate survival data are set up and estimated. Second, distinguishing voluntary and involuntary resignation, a multinomial logistic regression model and a Cox PH model for competing risk data are set up and estimated. Findings When no distinction is made, the results point that wage and age exert a negative effect on turnover. Risk of resignation is higher for male, single, not married and Spanish nationals. When the distinction is made, previous results hold for voluntary turnover: wage, age, gender, marital status and nationality are significant. However, when explaining involuntary turnover, all variables except wage lose explaining power. The survival analysis approach is better suited as it measures risk of resignation in a longitudinal way. Discrete choice models only study the risk at a particular cut-off point (24 months in case of this study). Originality/value This paper is a systematic application, evaluation and comparison of four different statistical models for analysing employee turnover in a single firm. This work is original because no systematic comparison has been done in the context of turnover.

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