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Bayesian Gaussian regression analysis of malnutrition for children under five years of age in Ethiopia, EMDHS 2014.

ABSTRACT: Background:The term malnutrition generally refers to both under-nutrition and over-nutrition, but this study uses the term to refer solely to a deficiency of nutrition. In Ethiopia, child malnutrition is one of the most serious public health problem and the highest in the world. The purpose of the present study was to identify the high risk factors of malnutrition and test different statistical models for childhood malnutrition and, thereafter weighing the preferable model through model comparison criteria. Methods:Bayesian Gaussian regression model was used to analyze the effect of selected socioeconomic, demographic, health and environmental covariates on malnutrition under five years old child's. Inference was made using Bayesian approach based on Markov Chain Monte Carlo (MCMC) simulation techniques in BayesX. Results:The study found that the variables such as sex of a child, preceding birth interval, age of the child, father's education level, source of water, mother's body mass index, head of household sex, mother's age at birth, wealth index, birth order, diarrhea, child's size at birth and duration of breast feeding showed significant effects on children's malnutrition in Ethiopia. The age of child, mother's age at birth and mother's body mass index could also be important factors with a non linear effect for the child's malnutrition in Ethiopia. Conclusions:Thus, the present study emphasizes a special care on variables such as sex of child, preceding birth interval, father's education level, source of water, sex of head of household, wealth index, birth order, diarrhea, child's size at birth, duration of breast feeding, age of child, mother's age at birth and mother's body mass index to combat childhood malnutrition in developing countries.

SUBMITTER: Mohammed S

PROVIDER: S-EPMC5883335 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Bayesian Gaussian regression analysis of malnutrition for children under five years of age in Ethiopia, EMDHS 2014.

Mohammed Seid S Asfaw Zeytu G ZG

Archives of public health = Archives belges de sante publique 20180326

<h4>Background</h4>The term malnutrition generally refers to both under-nutrition and over-nutrition, but this study uses the term to refer solely to a deficiency of nutrition. In Ethiopia, child malnutrition is one of the most serious public health problem and the highest in the world. The purpose of the present study was to identify the high risk factors of malnutrition and test different statistical models for childhood malnutrition and, thereafter weighing the preferable model through model ...[more]

PMID: 29636912

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Project description:IntroductionUndernutrition is a serious global health issue, and stunting is a key indicator of children's nutritional status which results from long-term deprivation of basic needs. Ethiopia, the largest and most populous country in Sub-Saharan Africa, has the greatest rate of stunting among children under the age of five, yet the problem is unevenly distributed across the country. Thus, we investigate spatial heterogeneity and explore spatial projection of stunting among under-five children. Further, spatial predictors of stunting were assessed using geospatial regression models.MethodsThe Ethiopia Demographic and Health Surveys (EDHS) data from 2011, 2016, and 2019 were examined using a geostatistical technique that took into account spatial autocorrelation. Ordinary kriging was used to interpolate stunting data, and Kulldorff spatial scan statistics were used to identify spatial clusters with high and low stunting prevalence. In spatial regression modeling, the ordinary least square (OLS) model was employed to investigate spatial predictors of stunting and to examine local spatial variations geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) models were employed.ResultsOverall, stunting prevalence was decreased from 44.42% [95%, CI: 0.425-0.444] in 2011 to 36.77% [95%, CI: 0.349-0.375] in 2019. Across three waves of EDHS, clusters with a high prevalence of stunting in children under 5 years were consistently observed in northern Ethiopia stretching in Tigray, Amhara, Afar, and Benishangul-Gumuz. Another area of very high stunting incidence was observed in the Southern parts of Ethiopia and the Somali region of Ethiopia. Our spatial regression analysis revealed that the observed geographical variation of under-five stunting significantly correlated with poor sanitation, poor wealth index, inadequate diet, residency, and mothers' education.ConclusionsIn Ethiopia, substantial progress has been made in decreasing stunting among children under the age of 5 years; although disparities varied in some areas and districts between surveys, the pattern generally remained constant over time. These findings suggest a need for region and district-specific policies where priority should be given to children in areas where most likely to exhibit high-risk stunting.

Project description:BACKGROUND:Under-five malnutrition is a major public health issue contributing to mortality and morbidity, especially in developing countries like Ghana where the rates remain unacceptably high. Identification of critical risk factors of under-five malnutrition using appropriate and advanced statistical methods can help formulate appropriate health programmes and policies aimed at achieving the United Nations SDG Goal 2 target 2. This study attempts to develop a simultaneous quantile regression, an in-depth statistical model to identify critical risk factors of under-five severe chronic malnutrition (severe stunting). METHODS:Based on the nationally representative data from the 2014 Ghana Demographic and Health Survey, height-for-age z-score (HAZ) was estimated. Multivariable simultaneous quantile regression modelling was employed to identify critical risk factors for severe stunting based on HAZ (a measure of chronic malnutrition in populations). Quantiles of HAZ with focus on severe stunting were modelled and the impact of the risk factors determined. Significant test of the difference between slopes at different selected quantiles of severe stunting and other quantiles were performed. A quantile regression plots of slopes were developed to visually examine the impact of the risk factors across these quantiles. RESULTS:Data on a total of 2716 children were analysed out of which 144 (5.3%) were severely stunted. The models identified child level factors such as type of birth, sex, age, place of delivery and size at birth as significant risk factors of under-five severe stunting. Maternal and household level factors identified as significant predictors of under-five severe stunting were maternal age and education, maternal national health insurance status, household wealth status, and number of children under-five in households. Highly significant differences exist in the slopes between 0.1 and 0.9 quantiles. The quantile regression plots for the selected quantiles from 0.1 to 0.9 showed substantial differences in the impact of the covariates across the quantiles of HAZ considered. CONCLUSION:Critical risk factors that can aid formulation of child nutrition and health policies and interventions that will improve child nutritional outcomes and survival were identified. Modelling under-five severe stunting using multivariable simultaneous quantile regression models could be beneficial to addressing the under-five severe stunting.

Project description:BackgroundUnderstanding the spatial distribution of stunting and underlying factors operating at meso-scale is of paramount importance for intervention designing and implementations. Yet, little is known about the spatial distribution of stunting and some discrepancies are documented on the relative importance of reported risk factors. Therefore, the present study aims at exploring the spatial distribution of stunting at meso- (district) scale, and evaluates the effect of spatial dependency on the identification of risk factors and their relative contribution to the occurrence of stunting and severe stunting in a rural area of Ethiopia.MethodsA community based cross sectional study was conducted to measure the occurrence of stunting and severe stunting among children aged 0-59 months. Additionally, we collected relevant information on anthropometric measures, dietary habits, parent and child-related demographic and socio-economic status. Latitude and longitude of surveyed households were also recorded. Local Anselin Moran's I was calculated to investigate the spatial variation of stunting prevalence and identify potential local pockets (hotspots) of high prevalence. Finally, we employed a Bayesian geo-statistical model, which accounted for spatial dependency structure in the data, to identify potential risk factors for stunting in the study area.ResultsOverall, the prevalence of stunting and severe stunting in the district was 43.7% [95%CI: 40.9, 46.4] and 21.3% [95%CI: 19.5, 23.3] respectively. We identified statistically significant clusters of high prevalence of stunting (hotspots) in the eastern part of the district and clusters of low prevalence (cold spots) in the western. We found out that the inclusion of spatial structure of the data into the Bayesian model has shown to improve the fit for stunting model. The Bayesian geo-statistical model indicated that the risk of stunting increased as the child's age increased (OR 4.74; 95% Bayesian credible interval [BCI]:3.35-6.58) and among boys (OR 1.28; 95%BCI; 1.12-1.45). However, maternal education and household food security were found to be protective against stunting and severe stunting.ConclusionStunting prevalence may vary across space at different scale. For this, it's important that nutrition studies and, more importantly, control interventions take into account this spatial heterogeneity in the distribution of nutritional deficits and their underlying associated factors. The findings of this study also indicated that interventions integrating household food insecurity in nutrition programs in the district might help to avert the burden of stunting.

Project description:BackgroundDiarrhoea and malnutrition are the leading cause of morbidity and mortality among children in areas with poor access to clean water, improved sanitation, and with low socioeconomic status. This study was designed to determine the prevalence of diarrhoea, malnutrition and risk factors among children aged 6-59 months in the Tigray Region of Northern Ethiopia.MethodsA community based cross-sectional study design was conducted from June to August 2017 to assess the magnitude and factors associated with diarrhoea and malnutrition among children. A standardized questionnaire was used to collect data on diarrhoea, environmental, demographic and behavioural factors from 610 mother-child pairs. Anthropometric measurements were collected from the children. SPSS ver.21 statistical software was used for analysis. Factors associated with diarrhoea and nutritional status were identified using bivariate and multivariate logistic regression. A p-value ≤ 0.05 was considered statistically significant.ResultsOf the 610 children monitored in this study, the incidence of diarrhoea among 6-59 month-old children in the two weeks preceding the day of the interview day was 27.2% (95% CI: 23.6-31%). Specifically, 35.9%, 9.7%, and 1.8% had 1-2, 3-4 and 5-6 times of diarrhoea episodes in a one year of time, respectively. The prevalence of stunting, underweight, wasting, and acute under-nutrition were 36.1% (95% CI: 31-38.6%), 37% (95% CI: 32-39.6%), 7.9% (95% CI: 5.5-9.7%), and 5.4% (95% CI: 3.8-7.4%), respectively. In a multivariate logistic regression analysis, type of drinking water source [AOR = 3.69; 95% CI: 2.03-6.71], mothers not hand washing at critical times [AOR = 15.42; 95% CI: 2.02-117.78], improper solid waste disposal [AOR = 12.81; 95% CI: 2.50-65.62], and child age (36-47 months) [AOR = 2.57; 95% CI: 1.45-4.55] were found to be predictors of diarrhoea. Being within the age range of 12-23 months was a predictor for wasting [AOR = 4.38; 95% CI: 1.61-11.90] and being underweight [AOR = 4.4; 95% CI: 1.7-11.2]. Similarly, the age range of 36-47 months was associated with wasting [AOR = 2.3; 95% CI: 1.45-3.85] and stunting [AOR = 1.7; 95% CI: 1.03-2.67]. Family size (less than 4) [AOR = 0.56; 95% CI: 0.368-0.959] was inversely associated for wasting.ConclusionsOur study revealed that the problem of diarrhoea and malnutrition amongst 6-59 months children in the study area was significant. Access to clean water was the main problem in the study area. Hence, improving access to clean water and providing health education to mothers on personal and environmental hygiene, and proper waste disposal could improve diarrhoea in the study area. Intervention on children's nutrition should also be implemented to minimize the problem of malnutrition.

Project description:BackgroundRotavirus caused an estimated 151,714 deaths from diarrhea among children under 5 in 2019. To reduce mortality, countries are considering adding rotavirus vaccination to their routine immunization program. Cost-effectiveness analyses (CEAs) to inform these decisions are not available in every setting, and where they are, results are sensitive to modeling assumptions, especially about vaccine efficacy. We used advances in meta-regression methods and estimates of vaccine efficacy by location to estimate incremental cost-effectiveness ratios (ICERs) for rotavirus vaccination in 195 countries.MethodsBeginning with Tufts University CEA and Global Health CEA registries we used 515 ICERs from 68 articles published through 2017, extracted 938 additional one-way sensitivity analyses, and excluded 33 ICERs for a sample of 1,418. We used a five-stage, mixed-effects, Bayesian metaregression framework to predict ICERs, and logistic regression model to predict the probability that the vaccine was cost-saving. For both models, covariates were vaccine characteristics including efficacy, study methods, and country-specific rotavirus disability-adjusted life-years (DALYs) and gross domestic product (GDP) per capita. All results are reported in 2017 United States dollars.ResultsVaccine efficacy, vaccine cost, GDP per capita and rotavirus DALYs were important drivers of variability in ICERs. Globally, the median ICER was $2,289 (95% uncertainty interval (UI): $147-$38,993) and ranged from $85 per DALY averted (95% UI: $13-$302) in Central African Republic to $70,599 per DALY averted (95% UI: $11,030-$263,858) in the United States. Among countries eligible for support from Gavi, The Vaccine Alliance, the mean ICER was $255 per DALY averted (95% UI: $39-$918), and among countries eligible for the PAHO revolving fund, the mean ICER was $2,464 per DALY averted (95% UI: $382-$3,118).ConclusionOur findings incorporate recent evidence that vaccine efficacy differs across locations, and support expansion of rotavirus vaccination programs, particularly in countries eligible for support from Gavi, The Vaccine Alliance.

Dataset Information

Bayesian Gaussian regression analysis of malnutrition for children under five years of age in Ethiopia, EMDHS 2014.

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Bayesian Gaussian regression analysis of malnutrition for children under five years of age in Ethiopia, EMDHS 2014.

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