Determinants of Wasting Among Under-Five Children in Ethiopia: (A Multilevel Logistic Regression Model Approach)

2014 ◽  
Vol 3 (4) ◽  
pp. 368-377 ◽  
Author(s):  
Gutu Dabale ◽  
◽  
M.K Sharma
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multilevel Logistic Regression ◽  
Under Five ◽  
Under Five Children ◽  
Multilevel Logistic Regression Model ◽  
Model Approach

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 Determinants of Contraceptive Practice in Oman by Using Multilevel Modeling

2015 ◽  
Vol 20 (1) ◽  
pp. 83 ◽  
Author(s):  
Moza S. Al-Balushi ◽  
Mohammed S. Ahmed ◽  
M. Mazharul Islam
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regional Variation ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Contraceptive Use ◽  
Multilevel Logistic Regression ◽  
Logistic Regression Models ◽  
Living Children ◽  
Multilevel Logistic Regression Model

In this paper, multilevel logistic regression models are developed for examining the hierarchical effects of contraceptive use and its selected determinants in Oman using the 2008 Oman National Reproductive Health Survey (ONRHS). Comparison between single level and multilevel logistic regression models has been made to examine the plausibility of multilevel effects of contraceptive use. From the multilevel logistic regression model analysis, it was found that there is real multilevel variation among contraceptive users in Oman. The results indicate that a multilevel logistic regression model is the best fit over ordinary multiple logistic regression models. Generally, this study revealed that women’s age, education, number of living children and region of residence are important factors that affect contraceptive use in Oman. The effect of regional variation for age of women, education of women and number of living children further implies that there exists considerable differences in modern contraceptive use among regions, and a model with a random coefficient or slope is more appropriate to explain the regional variation than a model with fixed coefficients or without random effects. The study suggests that researchers should use multilevel models rather than traditional regression methods when their data structure is hierarchal.  

Household means of transportation and place of child delivery in Nigeria

2020 ◽  
Author(s):  
Lukman Bola Solanke ◽  
Omolayo Bukola Oluwatope ◽  
Yinusa Rasheed Adebayo ◽  
Olaoye James Oyeleye ◽  
Benjamin Bukky Ilesanmi ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Pregnant Women ◽  
Logistic Regression Model ◽  
Institutional Delivery ◽  
Multilevel Logistic Regression ◽  
Place Of Delivery ◽  
Pooled Data ◽  
Support Programme ◽  
Mode Of Transport ◽  
Multilevel Logistic Regression Model

Abstract Background The means of transportation available to pregnant women in households may serve either as a driver or deterrent of institutional delivery. However, how household means of transportation associates with place of delivery has been less explored in Nigeria. Methods This study was based on pooled data of 2008-2013 Nigeria Demographic and Health Survey. The study analysed a weighted sample size of 6,540 women. The multilevel logistic regression model was applied using STATA 14. Results The study revealed 37% institutional delivery among women in Nigeria. Women whose household mode of transport were cars were twice more likely to have institutional delivery compared to women who had no viable household means of transportation (AOR=2.044, p<0.01; CI=1.781-2.345). Women who live in communities with high proportions of households with no means of transportation were 12.8% less likely to have institutional delivery (AOR=0.872, p=0.01; CI: 0.788-0.967). Women who live in communities with high proportions of household who owned motorcycle compared to those in communities with low proportion were 31.9% more likely to have institutional delivery (AOR=1.319, p<0.05; CI: 1.071-1.625). Women who live in communities with high proportions of households who owned cars compared to those in communities with low proportion were more than three times more likely to have institutional delivery (AOR=3.146, p<0.01; CI: 2.621-3.777).Conclusion Means of transportation significantly explains choice of place of child delivery in urban Nigeria. A public-private transport support programme to reduce transportation burden among pregnant women is imperative in the country.

scholarly journals Tracking Progress in Anthropometric Failure Among Under-five Children in Ethiopia: A Geospatial and Multilevel Analysis.

2020 ◽  
Author(s):  
Binyam Tariku Seboka ◽  
Samuel Hailegebreal ◽  
Delelegn Emwodew Yehualashet ◽  
Abel Desalegn Demeke
Keyword(s):  
Logistic Regression ◽  
Associated Factors ◽  
Composite Index ◽  
Temporal Distribution ◽  
Health Concern ◽  
Multilevel Logistic Regression ◽  
Statistical Tool ◽  
Under Five ◽  
Under Five Children ◽  
Under Nutrition

Abstract Background Under-nutrition is a major public health concern among under-five children in many developing countries. This work evaluated the overall prevalence of under-nutrition by using a composite index of anthropometric failure (CIAF), which helps in the detection of children with multiple anthropometric failures. Additionally, this study provides a Spatio-temporal distribution and associated factors of childhood anthropometric failures across time.Methods Secondary data was obtained from the Ethiopian Demographic and Health Survey for the survey 2005, 2011, and 2016 years. Data included 23,864 samples of children between the ages of 0-59 months, which is a nationally representative sample in Ethiopia. Analytical methods used in this paper include multivariate multilevel logistic regression to identify associated factors and Getis-Ord spatial statistical tool to identify high and low hotspots areas of anthropometric failures. ResultThe prevalence obtained with CIAF in 2005, 2011, and 2016 was, 53.5%, 51%, and 46.2% of children were suffering from under-nutrition respectively. The spatial analysis revealed areas that are at a higher risk of anthropometric failures consistently were found in northern parts of the country, largely in the Amhara, Tigray, and Afar regions. Multilevel logistic regression analysis showed that the risk of anthropometric failure was higher among older children, had low birth weight, had a mother with low BMI, was in a rural area, had mothers and fathers without formal education. Conclusion In addition to identifying wasted, stunted, and overweight children, CIAF also identified children with multiple conditions, which are generally neglected in most nutritional surveys. As revealed by this composite index, the prevalence of anthropometric failure remains considerably high and its spatial distribution also significantly varied across the regions in the country. The identified socio-demographic characteristics and districts at an increased likelihood of anthropometric failure can inform localized intervention and prevention strategies to improve the nutritional status and healthcare of children in Ethiopia.

scholarly journals Association of maternal obesity with fetal and neonatal death: Evidence from South and South-East Asian countries

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256725
Author(s):  
Rezwanul Haque ◽  
Syed Afroz Keramat ◽  
Syed Mahbubur Rahman ◽  
Maimun Ur Rashid Mustafa ◽  
Khorshed Alam
Keyword(s):  
Logistic Regression ◽  
Risk Factor ◽  
Regression Model ◽  
Neonatal Death ◽  
Logistic Regression Model ◽  
Maternal Obesity ◽  
East Asian ◽  
Multilevel Logistic Regression ◽  
Asian Countries ◽  
Neonatal Deaths

Background Obesity prevalence is increasing in many countries in the world, including Asia. Maternal obesity is highly associated with fetal and neonatal deaths. This study investigated whether maternal obesity is a risk factor of fetal death (measured in terms of miscarriage and stillbirth) and neonatal mortality in South and South-East Asian countries. Methods This cross-sectional study pooled the most recent Demographic and Health Surveys (DHS) from eight South and South-East Asian countries (2014–2018). Multivariate logistic regression was deployed to check the relationships between maternal obesity with fetal and neonatal deaths. Finally, multilevel logistic regression model was employed since the DHS data has a hierarchical structure. Results The pooled logistic regression model illustrated that maternal obesity is associated with higher odds of miscarriage (adjusted odds ratio [aOR]: 1.26, 95% CI: 1.20–1.33) and stillbirths (aOR: 1.46, 95% CI: 1.27–1.67) after adjustment of confounders. Children of obese mothers were at 1.18 (aOR: 1.18, 95% CI: 1.08–1.28) times greater risk of dying during the early neonatal period than mothers with a healthy weight. However, whether maternal obesity is statistically a significant risk factor for the offspring’s late neonatal deaths was not confirmed. The significant association between maternal obesity with miscarriage, stillbirth and early neonatal mortality was further confirmed by multilevel logistic regression results. Conclusion Maternal obesity in South and South-East Asian countries is associated with a greater risk of fetal and early neonatal deaths. This finding has substantial public health implications. Strategies to prevent and reduce obesity should be developed before planning pregnancy to reduce the fetal and neonatal death burden. Obese women need to deliver at the institutional facility centre that can offer obstetrics and early neonatal care.

scholarly journals A One Health perspective to identify environmental factors that affect Rift Valley fever transmission in Gezira state, Central Sudan

2019 ◽  
Vol 47 (1) ◽  
Author(s):  
Rania Salah Eldien Bashir ◽  
Osama Ahmed Hassan
Keyword(s):  
Remote Sensing ◽  
Logistic Regression ◽  
Environmental Factors ◽  
Soil Type ◽  
Rift Valley ◽  
Rift Valley Fever ◽  
Logistic Regression Model ◽  
Multilevel Logistic Regression ◽  
Valley Fever ◽  
Multilevel Logistic Regression Model

Abstract Background Rift Valley fever (RVF) is a zoonotic viral vector-borne disease that affects both animals and humans and leads to severe economic consequences. RVF outbreaks are triggered by a favorable environment and flooding, which enable mosquitoes to proliferate and spread the virus further. RVF is endemic to Africa and has spread to Saudi Arabia and Yemen. There is great concern that RVF may spread to previously unaffected geographic regions due to climate change. We aimed to better understand the spatiotemporal pattern of the 2007 RVF outbreak at the human–animal–environment interface and to determine environmental factors that may have effects on RVF occurrence in Gezira state, Sudan. Materials and methods We compiled epidemiological, environmental, and spatiotemporal data across time and space using remote sensing and a geographical information system (GIS). The epidemiological data included 430 RVF human cases as well as human and animal population demographic data for each locality. The cases were collected from 41 locations in Gezira state. The environmental data represent classified land cover during 2007, the year of the RVF outbreak, and the average of the Normalized Difference Vegetation Index (NDVI) for 6 months of 2007 is compared with those of 2010 and 2014, when there was no RVF outbreak. To determine the effect of the environmental factors such as NDVI, soil type, and RVF case’s location on the Blue Nile riverbank on RVF incidence in Gezira state, a multilevel logistic regression model was carried out. Results We found that the outbreak in Gezira state occurred as a result of interaction among animals, humans, and the environment. The multilevel logistic regression model (F = 43,858, df = 3, p = 0.000) explained 23% of the variance in RVF incidence due to the explanatory variables. Notably, soil type (β = 0.613, t = 11.284, p = 0.000) and NDVI (β = − 0.165, t = − 3.254, p = 0.001) were the explanatory environmental factors that had significant effects on RVF incidence in 2007 in Gezira state, Sudan. Conclusions Precise remote sensing and the GIS technique, which rely on environmental indices such as NDVI and soil type that are satellite-derived, can contribute to establishing an early warning system for RVF in Sudan. Future preparedness and strengthening the capacity of regional laboratories are necessary for early notification of outbreaks in animals and humans.

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