Access to safe drinking water and human health: empirical evidence from rural Bhutan

2016 ◽  
Vol 16 (5) ◽  
pp. 1349-1360 ◽  
Author(s):  
Dil Bahadur Rahut ◽  
Akhter Ali ◽  
Nar Bahadur Chhetri ◽  
Bhagirath Behera ◽  
Pradyot Ranjan Jena
Keyword(s):  
Drinking Water ◽  
Logistic Regression ◽  
Human Health ◽  
Health Expenditure ◽  
Logistic Regression Model ◽  
Skin Diseases ◽  
Well Being ◽  
Living Standard ◽  
Safe Drinking Water ◽  
Absolute Deviation

Provision of safe drinking water is essential for the promotion of human well-being. This paper makes an attempt to examine the patterns of access to drinking water, identify and analyze the factors that influence households access to safe drinking water sources, and analyze factors determining the extent of households travel to fetch drinking water, and assess the effects of access to safe drinking water on human health in Bhutan, using the data from the Bhutan Living Standard Survey 2012 (BLSS 2012). For this, various methodological tools have been adopted such as logistic regression model, censored least absolute deviation model, and the propensity score matching (PSM) approach. The logistic regression results show that households with educated, younger, and male members are more likely to have access to safe drinking water. Wealthier households also prefer safe drinking water than their poorer counterparts. The PSM results suggest that households having access to safe drinking water have fewer stomach disorders and skin diseases, and are likely to incur less expenditure on medicine. Keeping these findings in mind, the paper suggests that the Bhutanese government should invest in water infrastructure, which may lead to a significant reduction in water-borne diseases and health expenditure.

Violencia en el hogar contra mujeres adultas durante el confinamiento por la pandemia de Covid-19 en México

2021 ◽  
Vol 63 (6, Nov-Dic) ◽  
pp. 782-788
Author(s):  
Rosario Valdez-Santiago ◽  
Aremis Litai Villalobos-Hernández ◽  
Luz Arenas-Monreal ◽  
Karla Flores ◽  
Luciana Ramos-Lira
Keyword(s):  
Domestic Violence ◽  
Logistic Regression ◽  
Associated Factors ◽  
Logistic Regression Model ◽  
Well Being ◽  
Adult Women ◽  
Nutrition Survey ◽  
Health And Nutrition ◽  
National Representation ◽  
Low Levels

Objective. To analyze the prevalence of domestic violence in adult women during confinement derived from the Co­vid-19 pandemic and individual, familiar and communitarian associated factors. Materials and methods. A second­ary analysis was carried out the 2020 National Health and Nutrition Survey on Covid-19, with national representation. A logistic regression model adjusted for the variables of interest was performed. Results. The prevalence was 5.8%. The most reported acts were shouting, insults or threats (4.3%). Most of the women who reported some type of violence in the home had already experienced it before the confinement. Low levels of well-being (OR= 1.96, 95%CI: 1.28,2.99), and living in a home where job was lost due to contingency (OR= 1.96, 95%CI: 1.41,2.73) were associated factors. Conclusions. In care interventions, it is necessary to take into account factors that deepen the vulnerability of women, such as pre-existing violence and loss of employment.

Determinants of users’ willingness to contribute to safe water provision in rural Uganda

2014 ◽  
Vol 12 (3) ◽  
pp. 695-714 ◽  
Author(s):  
Resty Naiga ◽  
Marianne Penker
Keyword(s):  
Drinking Water ◽  
Logistic Regression ◽  
Logistic Regression Model ◽  
User Fees ◽  
Water User ◽  
Operation And Maintenance ◽  
Factors Influencing ◽  
Water Provision ◽  
Actual Contribution ◽  
Willingness To Contribute

In the context of recent devolution processes in Uganda, operation and maintenance of drinking water infrastructure still pose a major challenge. Given the importance of water user fees and local collective action for operation and maintenance, it is paramount to consider factors influencing the users’ willingness to contribute. Based on 802 structured household interviews, this article looks into the link between willingness to contribute and actual contribution and presents variables influencing users’ willingness to contribute to water provision. The variables demonstrated by the logistic regression model to increase the likelihood of users’ willingness to contribute are categorized as institutional, bio-physical and demographic ones.

scholarly journals An Autonomic, Adaptive and High-Precision Statistical Model to Determine Bee Colonies Well-Being Scenarios

2020 ◽  
Author(s):  
Daniel Da Silva ◽  
Ícaro Rodrigues ◽  
Antonio Braga ◽  
Juvêncio Nobre ◽  
Breno Freitas ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Statistical Model ◽  
Honey Bees ◽  
Logistic Regression Model ◽  
Well Being ◽  
Winter Period ◽  
Extreme Climatic Events ◽  
On Site Inspection ◽  
Two Stages ◽  
Bee Colonies

Honey bees, important pollinators, are threatened by a variety of pests, pathogens and extreme climatic events, such as the winter period. This paper proposes a two-stages model that seeks to define and predict evolutionary scenarios for improving the bee colonies’ well-being. The used dataset has data from both internal and external beehive sensors, and on-site inspection of beekeepers from six apiaries between the years 2016-2018. In the first stage, three evolutionary scenarios were obtained (pessimistic, conservative and optimistic) through the clustering technique. In the second one, aiming to classify these scenarios, an elastic net penalty logistic regression model was obtained with an accuracy of ~99.5%.

scholarly journals Exploring the important determinants of access to safe drinking water and improved sanitation in Punjab, Pakistan

Water Policy ◽  
2021 ◽  
Author(s):  
Shahid Adil ◽  
Muhammad Nadeem ◽  
Irfan Malik
Keyword(s):  
Drinking Water ◽  
Social Norms ◽  
Household Head ◽  
Household Wealth ◽  
Education Level ◽  
Living Standard ◽  
Safe Drinking Water ◽  
Wealth Status ◽  
Improved Sanitation

Abstract Access to safe drinking water and improved sanitation is a fundamental human right and basic ingredient of public health. However, one of the major problems faced by developing countries in the twenty-first century is the lack of access to these facilities. Punjab is the most populous province of Pakistan with more than 50% of the country's population is no exception. Keeping in view its importance, the current study is an effort to investigate important determinants of access to safe drinking water and improved sanitation in Punjab to ensure the provision of these services to the masses. Multiple Indicator Cluster Survey Household data from 2017 to 2018 has been used for analysis. The results of a logistic regression model revealed that household media exposure, education level of household head, household wealth status, and ethnic background of the household head are some of the important determinants of household access to safe drinking water. For household access to improved sanitation, along with these factors, the role of social norms and place of residence are also important. Particularly, the role of social norms is very profound. Findings from the study suggest that efforts should be made to provide readily available media access, household education level needs to be enhanced, policies should be made to raise the living standard of the poorest households, and the social norm for the use of improved sanitation needs to be promoted.

scholarly journals A L1 Regularized Logistic Regression Model for Highdimensional Questionnaire Data Analysis

2021 ◽  
Vol 2078 (1) ◽  
pp. 012052
Author(s):  
Jiasheng Wang
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regularization Method ◽  
Logistic Regression Model ◽  
Likelihood Function ◽  
Screening Method ◽  
Well Being ◽  
Coefficient Function ◽  
Subjective Well Being ◽  
L1 Regularization

Abstract The LI regularization method, or Lasso, is a technique for feature selection in high-dimensional statistical analysis. This method compresses the coefficients of the model by using the absolute value of the coefficient function as a penalty term. By adding L1 regularization to log-likelihood function of Logistic model, variable screening method based on the logistic regression model can be realized. The process of variable selection via Lasso is illustrated in Figure 1. The purpose of the experiment is to figure out the important factors that influence interviewees' subjective well-being using L1 regularized logistic regression. Experiments have been performed on CGSS 2017 data. Important features have been successfully selected by using the L1 regularization method.

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