Non random distribution of child undernutrition in Ethiopia: spatial analysis from the 2011 Ethiopia demographic and health survey

Determinants of aggregate anthropometric failure among children under-five years in Ethiopia: Application of multilevel mixed-effects negative binomial regression modeling

Undernutrition significantly contributes to failure to thrive in children under five, with those experiencing multiple forms of malnutrition facing the highest risks of morbidity and mortality. Conventional markers such as stunting, wasting, and underweight have received much attention but are insufficient to identify multiple types of malnutrition, prompting the development of the Composite Index of Anthropometric Failure (CIAF) and the Composite Index of Severe Anthropometric Failure (CISAF) as an aggregate indicators. This study aimed to identify factors associated with CIAF and CISAF among Ethiopian children aged 0-59 months using data from the 2019 Ethiopia Mini Demographic and Health Survey. The study included a weighted sample of 5,259 children and used multilevel mixed-effects negative binomial regression modeling to identify determinants of CIAF and CISAF. The result showed higher incidence-rate ratio (IRR) of CIAF in male children (adjusted IRR = 1.27; 95% CI = 1.13-1.42), children aged 12-24 months (aIRR = 2.01, 95%CI: 1.63-2.48), and 24-59 months (aIRR = 2.36, 95%CI: 1.91-2.92), those from households with multiple under-five children (aIRR = 1.16, 95%CI: 1.01-1.33), poorer households (aIRR = 1.48; 95%CI: 1.02-2.15), and those who lived in houses with an earthen floor (aIRR = 1.37, 95%CI: 1.03-1.82). Similarly, the factors positively associated with CISAF among children aged 0-59 months were male children (aIRR = 1.47, 95% CI = 1.21-1.79), age group 6-11 months (aIRR = 2.30, 95%CI: 1.40-3.78), age group 12-24 months (aIRR = 3.76, 95%CI: 2.40-5.88), age group 25-59 months (aIRR = 4.23, 95%CI: 2.79-6.39), children from households living with two and more under-five children (aIRR = 1.27, 95%CI:1.01-1.59), and children from poorer households (aIRR = 1.93, 95% CI = 1.02-3.67). Children were more likely to suffer from multiple anthropometric failures if they were: aged 6-23 months, aged 24-59 months, male sex, living in households with multiple under-five children, and living in households with poor environments. These findings underscore the need to employ a wide range of strategies to effectively intervene in multiple anthropometric failures in under-five children.

Childhood stunting is highly clustered in Northern Province of Rwanda: A spatial analysis of a population-based study

Background

In Northern Province, Rwanda, stunting is common among children aged under 5 years. However, previous studies on spatial analysis of childhood stunting in Rwanda did not assess its randomness and clustering, and none were conducted in Northern Province. We conducted a spatial-pattern analysis of childhood undernutrition to identify stunting clusters and hotspots for targeted interventions in Northern Province.

Methods

Using a household population-based questionnaire survey of the characteristics and causes of undernutrition in households with biological mothers of children aged 1-36 months, we collected anthropometric measurements of the children and their mothers and captured the coordinates of the households. Descriptive statistics were computed for the sociodemographic characteristics and anthropometric measurements. Spatial patterns of childhood stunting were determined using global and local Moran's I and Getis-Ord Gi* statistics, and the corresponding maps were produced.

Results

The z-scores of the three anthropometric measurements were normally distributed, but the z-scores of height-for-age were generally lower than those of weight-for-age and weight-for-height, prompting us to focus on height-for-age for the spatial analysis. The estimated incidence of stunting among 601 children aged 1-36 months was 27.1 %. The sample points were interpolated to the administrative level of the sector. The global Moran's I was positive and significant (Moran's I = 0.403, p < 0.001, z-score = 7.813), indicating clustering of childhood stunting across different sectors of Northern Province. The local Moran's I and hotspot analysis based on the Getis-Ord Gi* statistic showed statistically significant hotspots, which were strongest within Musanze district, followed by Gakenke and Gicumbi districts.

Conclusion

Childhood stunting in Northern Province showed statistically significant hotspots in Musanze, Gakenke, and Gicumbi districts. Factors associated with such clusters and hotspots should be assessed to identify possible geographically targeted interventions.

Trends in social determinants of inequality in child undernutrition from the Ethiopian Demographic and Health Surveys, 2005-2016

BACKGROUND

While child undernutrition has been eliminated in some middle-income countries, it remains highly prevalent in sub-Sahara African (SSA) and South Asian regions, and is disproportionately concentrated among the poor. In this study, we estimated trends in child undernutrition by social determinants and related risks from wealth inequality in Ethiopia, from 2005 to 2016.

METHOD

We analyzed data from three consecutive surveys (2005, 2011, and 2016) from the Ethiopian Demographic and Health Survey. First, we estimated trends in the prevalence of childhood undernutrition variables (stunting, underweight, and wasting) and social determinants (household wealth status, education level, place of residence, and administrative regions). Then we assessed evidence of undernutrition by wealth-related inequality with concentration curves (visual) and concentration indeces (quantitative). A multilevel mixed-effect Poisson regression model was used to identify predictors of undernutrition variables expressed as covariate-adjusted rate ratios, with 95% confidence intervals (RRs, 95%CI).

RESULT

A total of 23,934 mother-child pairs were obtained from the three surveys. The average prevalence decreased by 12.4 percentage points for stunting (from 50.8 to 38.4%, P<0.01), 9.5 percentage points for underweight (33.2% to23.7%, P<0.01), and 2.1 percentage points for wasting (12.2% to10.1%, P<0.01). There was persistent and statistically evidence of wealth inequality in stunting, underweight, and wasting (concentration indeces of -0.2 to -0.04, all P values <0.05). Stunting, underweight, and wasting variables were associated with male sex of the child (RR 0.94, 0.95, 0.85, all P-values <0.01) recent diarrhea (RR 1.18, 1.27, 1.37, all P-values <0.01), secondary education status of the mother (RR 0.66, 0.57, 0.61, all P-values < 0.057), increasing wealth index (richest) (RR 0.73, 0.70, 0.50, all P-values < 0.05), and having no toilet facility (RR 1.16, 1.22, 1.18, all P-values < 0.05).

CONCLUSION

Despite the decreased burden of stunting and underweight, the prevalence of wasting remained relatively unchanged in Ethiopia from 2005 to 2016. Moreover, wealth-related inequality in child undernutrition increased for most of the child undernutrition indicators during this period. Social determinants of child undernutrition warrant urgent implementation of strategies to reduce their health impacts in SSA.

Geospatial Overlap of Undernutrition and Tuberculosis in Ethiopia

Undernutrition is a key driver of the global tuberculosis (TB) epidemic, yet there is limited understanding regarding the spatial overlap of both diseases. This study aimed to determine the geographical co-distribution and socio-climatic factors of undernutrition and TB in Ethiopia. Data on undernutrition were found from the Ethiopian Demographic and Health Survey (EDHS). Data on TB were obtained from the Ethiopia national TB prevalence survey. We applied a geostatistical model using a Bayesian framework to predict the prevalence of undernutrition and TB. Spatial overlap of undernutrition and TB prevalence was detected in the Afar and Somali regions. Population density was associated with the spatial distribution of TB [β: 0.008; 95% CrI: 0.001, 0.014], wasting [β: -0.017; 95% CrI: -0.032, -0.004], underweight [β: -0.02; 95% CrI: -0.031, -0.011], stunting [β: -0.012; 95% CrI: -0.017, -0.006], and adult undernutrition [β: -0.007; 95% CrI: -0.01, -0.005]. Distance to a health facility was associated with the spatial distribution of stunting [β: 0.269; 95% CrI: 0.08, 0.46] and adult undernutrition [β: 0.176; 95% CrI: 0.044, 0.308]. Healthcare access and demographic factors were associated with the spatial distribution of TB and undernutrition. Therefore, geographically targeted service integration may be more effective than nationwide service integration.

Gender-specific disaggregated analysis of childhood undernutrition in Ethiopia: evidence from 2000-2016 nationwide survey

INTRODUCTION

Childhood undernutrition has been investigated extensively in previous literature but gender inequality detailing the burden of undernutrition has not been adequately addressed in scientific papers, especially in Ethiopia, where undernutrition is known to be a public health problem of high significance, necessitating increased efforts to address it and reduce this inequality. This study was carried out to: (1) explore gender differences in the prevalence of stunting, wasting, and underweight, and (2) compare the factors associated with childhood undernutrition between boys and girls in Ethiopia.

METHODS

The study used a dataset of more than 33,564 children aged under 5 years (boys: 17,078 and girls: 16,486) who were included in the nationally representative Ethiopia Demographic and Health Survey (EDHS) from 2000 to 2016. The outcome variables were anthropometric indices: stunting (height-for-age < -2 standard deviations), wasting (weight-for-height < -2 standard deviations), and underweight (weight-for-age < -2 standard deviations). Gender-specific multilevel analyses were used to examine and compare the factors associated with child undernutrition.

RESULTS

The overall prevalence of stunting (49.1% for boys vs 45.3% for girls, p < 0.001), wasting (11.9% for boys vs 9.9% for girls, p < 0.001), and underweight (33.1% for boys vs 29.8% for girls, p < 0.001) higher among boys compared to girls. Boys significantly had higher odds of stunting (aOR: 1.31, 95%CI: 1.21-1.42), wasting (aOR: 1.35, 1.23-1.48), and underweight (aOR: 1.38, 95%CI: 1.26-1.50) than girls. The common factors associated with childhood undernutrition for male and female children were the child's age, perceived size of the child at birth, breastfeeding status, maternal stature, maternal education, toilet facility, wealth index, and place of residence. Boys who were perceived by their mothers to be average sized at birth and were born to uneducated mothers had a higher likelihood of experiencing wasting, in contrast to girls. Among boys, birth order (firstborn), household size (1-4), and place of residence (urban) were associated with lower odds of being underweight. Boys living in cities had lower odds of being stunted. While girls born to mothers with no education and worked in agriculture were at a higher odd of being stunted.

CONCLUSION

Our study revealed that boys were more likely to be malnourished than girls, regardless of their age category, and there were variations in the factors determining undernutrition among boys and girls. The differences in the burden of undernutrition were significant and alarming, positioning Ethiopia to be questioned whether it will meet the set Sustainable Development Goals (SDGs), including SDG 2 of zero hunger by 2030. These findings call for more effort to address malnutrition as a significant public health issue in Ethiopia, and to urgently recognise the need for enhanced interventions that address the gender gap in childhood undernutrition.

Mapping wasted children using data from the Ethiopia Demographic and Health Surveys between 2000 and 2019: A bayesian geospatial analysis

OBJECTIVES

Undernutrition, particularly wasting, is continuing to be a major challenge in developing countries like Ethiopia. However, data on the geographic variations over time are limited. We aimed to investigate the spatiotemporal variation of wasting in Ethiopia using two decades of Ethiopia Demographic and Health Surveys data, from 2000 to 2019.

METHODS

Trend and geospatial regression analysis using a bayesian framework were used to predict wasting in Ethiopia among children aged <5 y. The primary outcomes (wasting) were obtained from the Ethiopia Demographic and Health Surveys conducted between 2000 and 2019. Covariates were accessed from different publicly available credible sources at a high resolution. The spatial binomial regression model through the bayesian framework was fitted to identify drivers of wasting among children in Ethiopia.

RESULTS

The overall national prevalence of wasting among children aged <5 y in Ethiopia was 11.9% in 2000, 11.1% in 2005, 10.2% in 2011, 12.3% in 2016, and 9.4% in 2019, with substantial spatial variation across subnational and local levels over time. Spatial clustering of wasting was observed in eastern Ethiopia (Afar and Somali regions). Altitude (mean regression coefficient = -0.38; 95% credible interval, -0.69 to -0.07) and population density (mean regression coefficient = -0.02; 95% credible interval, -0.03 to -0.01) were negatively associated with wasting, whereas distance to health facilities (mean regression coefficient = 0.13; 95% credible interval, 0.03-0.23) was positively associated with wasting.

CONCLUSIONS

The reduction in the national prevalence of wasting among children was not as expected. Spatial clustering of wasting was observed in the northern, northeastern, eastern, and western parts of Ethiopia. Spatial clustering of wasting was associated with altitude, precipitation, population density, distance to health facilities, travel time to the nearest cities, and distance to a water body. Early screening and treatment of wasted children should be strengthened. Furthermore, outreach community awareness, especially in rural parts of the country, should be recommended through community health extension workers.

Spatial trends and projections of chronic malnutrition among children under 5 years of age in Ethiopia from 2011 to 2019: a geographically weighted regression analysis

Introduction

Undernutrition 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.

Methods

The 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.

Results

Overall, 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.

Conclusions

In 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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41043-022-00309-7.

Multilevel analysis of undernutrition and associated factors among adolescent girls and young women in Ethiopia

Background

The consequences of undernutrition have serious implication for the health and future reproductive periods of adolescent girls and young women aged 15–24 years. Inspite of this, they are neglected age groups and there is limited information about the nutritional status of this age group in Ethiopia. Therefore, estimating the extent and associated factors of undernutrition among adolescent girls and young women in a national context using multilevel analysis is essential.

Methods

Secondary data analysis was conducted from the Ethiopian Demographic and Health Survey 2016. A total sample weight of 5362 adolescent girls and young women was included in this study. A multilevel mixed-effect binary logistic regression model with cluster-level random effects was fitted to determine the associated factors of undernutrition among adolescent girls and young women in Ethiopia. Finally, the odds ratios along with the 95% confidence interval was generated to determine the individual and community level factors of undernutrition. A p-value less than 0.05 was declared as the level of statistical significance.

Results

Overall, 25.6% (95%CI: 24.5–26.9) of adolescent girls and young women were undernourished. Statistically significant individual level factors includes adolescent girls and young women aged 15–19 years (AOR: 1.53, 95%CI: 1.32–1.77), individual media exposure (AOR: 0.82, 95%CI: 0.69–0.97), and unprotected drinking water source (AOR: 1.24, 95%CI: 1.04–1.48). Whereas, Southern Nations, Nationalities, and Peoples' Region (AOR: 0.33, 95%CI: 0.13–0.83) and rural residence (AOR: 1.69, 95%CI: 1.24–2.32), were community level factors for adolescent girls and young women undernutrition.

Conclusion

One quarter of the Ethiopian adolescent girls and young women were undernourished. Therefore, the Ethiopian government should better engage this age group in different aspects of the food system. To improve nutritional status, public health interventions such as increased media exposure for rural residents and interventions that improve access to protected water sources will be critical.

The Influence of Street Microenvironment on the Walking Activities of Older Adults: A Longitudinal Study Based on the Structural Equation Model and Manipulated Photos

There is growing evidence of an association between the built environment and walking activity, but knowledge of street microenvironmental factors and older adults’ walking is limited and inconsistent. We used a socio-ecological model as the theoretical framework for this study, aiming to investigate longitudinally the important influences of the street microenvironment on older people’s walking by manipulated street photographs and whether there are different groups of older people with different environmental preferences, and to determine the influence of environmental factors in the socio-ecological model. The clustering analysis method was used to divide the samples into four groups, and those four subgroups were put in a comparative analysis regarding their street environments by methods of semantic differential (SD). The preferences of various subgroups were checked by the variance analysis and post hoc tests, and the structural equation model (SEM) was applied to discuss the relations of correlation and influence among each factor. The results showed that “parking on the sidewalk” was rated the lowest in the pre-intervention photos, and “shaded” and “hedge” were rated the highest in the post-intervention photos, and the environmental requirements were different for the older, poorer and higher income and education subgroups. There was a significant positive relationship between residential status and architectural interventions as well as self-efficacy and physical condition. These findings suggest that when developing environmental interventions to promote walking among older adults, priority should be given to those factors that need to be improved most. In addition, attention should be paid to the differences in walking needs among different groups of older adults, with particular attention on the impact of self-selected living environments on street-level environmental interventions.

Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia

OBJECTIVE

Child undernutrition is a global public health problem with serious implications. In this study, we estimate predictive algorithms for the determinants of childhood stunting by using various machine learning (ML) algorithms.

DESIGN

This study draws on data from the Ethiopian Demographic and Health Survey of 2016. Five ML algorithms including eXtreme gradient boosting, k-nearest neighbours (k-NN), random forest, neural network and the generalised linear models were considered to predict the socio-demographic risk factors for undernutrition in Ethiopia.

SETTING

Households in Ethiopia.

PARTICIPANTS

A total of 9471 children below 5 years of age participated in this study.

RESULTS

The descriptive results show substantial regional variations in child stunting, wasting and underweight in Ethiopia. Also, among the five ML algorithms, xgbTree algorithm shows a better prediction ability than the generalised linear mixed algorithm. The best predicting algorithm (xgbTree) shows diverse important predictors of undernutrition across the three outcomes which include time to water source, anaemia history, child age greater than 30 months, small birth size and maternal underweight, among others.

CONCLUSIONS

The xgbTree algorithm was a reasonably superior ML algorithm for predicting childhood undernutrition in Ethiopia compared to other ML algorithms considered in this study. The findings support improvement in access to water supply, food security and fertility regulation, among others, in the quest to considerably improve childhood nutrition in Ethiopia.

Exploring Spatial Variations and Determinants of Dietary Diversity Among Children in Ethiopia: Spatial and Multilevel Analysis Using EDHS (2011-2016)

Introduction

Dietary diversity has a significant impact on children’s nutritional health. For developing and implementing interventions, it is critical to understand the regional distribution of dietary diversity and underlying factors. However, the application of spatial techniques in dietary studies has not been well documented. The study’s goal was to look into the regional variances and factors that influence children’s dietary diversity. Further, we have discussed the spatial correlation of dietary diversity with nutritional status.

Methods

Data from the National Demographic and Health Survey were used during analyses. This work evaluated the overall dietary diversity of children aged 6–23 months based on the 2017 WHO and UNICEF classification of minimum dietary diversity (MDD). The Local Anselin Moran’s I was estimated to look into the regional variation of dietary diversity and hotspot and cold spot areas. Further, multivariate multilevel logistic regression was used for factor analyses.

Results

Overall, only 13.3% (95% CI: 10.2–14.7%) of children in 2011 and 24% (95% CI: 15.5–26.5%) in 2016 achieved MDD. We identified statistically significant clusters of high inadequate dietary diversity (hotspots) in the districts of northern Ethiopia, notably in the Amhara, Tigray, and Afar regions, and clusters of low inadequate dietary diversity (cold spots) in the country’s central and western regions. In both studies, the frequency of dietary diversity was significantly higher among older children, those who had media exposure, and mothers and fathers who had received formal education.

Conclusion

According to our findings, the MDD of children in Ethiopia, as measured by WHO dietary assessment, slightly increased from 2011 to 2016. The dietary diversity of children was distributed non-randomly in different districts across regions of Ethiopia. Localized intervention and preventative methods to improve dietary patterns and culture can be developed using existing socio-demographic factors and districts with a larger distribution of inadequate dietary diversity.

Spatial Variation of Child Stunting and Maternal Malnutrition after Controlling for Known Risk Factors in a Drought-Prone Rural Community in Southern Ethiopia

Background

Globally, understanding spatial analysis of malnutrition is increasingly recognized. However, our knowledge on spatial clustering of malnutrition after controlling for known risk factors of malnutrition such as wealth status, food insecurity, altitude and maternal characteristics is limited from Ethiopia. Previous studies from southern Ethiopia have shown seasonal patterns of malnutrition, yet they did not evaluate spatial clustering of malnutrition.

Objective

The aim of this study was to assess whether child stunting and maternal malnutrition were spatially clustered in drought-prone areas after controlling for previously known risk factors of malnutrition.

Methods

We used a community-based cohort study design for a one-year study period. We used SaTScan software to identify high rates of child stunting and maternal malnutrition clustering. The outcome based was the presence or absence of stunting and maternal malnutrition ([BMI] <18.5 kg/m2). We controlled for previously known predictors of child stunting and maternal malnutrition to evaluate the presence of clustering. We did a logistic regression model with declaring data to be time-series using Stata version 15 for further evaluation of the predictors of spatial clustering.

Results

The crude analysis of SaTScan showed that there were areas (clusters) with a higher risk of stunting and maternal malnutrition than in the underlying at risk populations. Stunted children within an identified spatial cluster were more likely to be from poor households, had younger and illiterate mothers, and often the mothers were farmers and housewives. Children identified within the most likely clusters were 1.6 times more at risk of stunting in the unadjusted analysis. Similarly, mothers within the clusters were 2.4 times more at risk of malnutrition in the unadjusted analysis. However, after adjusting for known risk factors such as wealth status, household food insecurity, altitude, maternal age, maternal education, and maternal occupation with SaTScan analysis, we show that child stunting and maternal malnutrition were not spatially clustered.

Conclusion

The observed spatial clustering of child stunting and maternal malnutrition before controlling for known risk factors for child stunting and maternal malnutrition could be due to non-random distribution of risk factors such as poverty and maternal characteristics. Moreover, our results indicated the need for geographically targeted nutritional interventions in a drought-prone area.

Dynamics of inequality in child under-nutrition in Ethiopia

INTRODUCTION

Although Ethiopia has already achieved a remarkable progress in reducing under-five mortality in the last decades, undernutrition among children is still a common problem in this country. Socioeconomic inequalities in health outcomes in Ethiopia have been thus of focus in academia and policy spheres for a while now. This study provides new evidence on child undernutrition inequalities in Ethiopia using longitudinal perspective.

METHOD

Using three round of household panel survey (from 2012 to 2016), we use concentration index (associated curve), different mobility index approaches for measuring inequalities and its dynamics, and decomposition method to identify contributing factors.

RESULTS

In all concentration index computing approaches and socioeconomic status ranking variables, the concentration indices are significant with negative value. This implies that in either of short-run or long-run inequality estimates, the burden of unequal distribution of undernutrition remains on the poor with significant difference across regions. While employing different SES ranking variables, the difference in the concentration indices is only found significant in case of Height-for-age Z-score. It signifies that relatively higher inequality is measured using consumption as ranking variable. Significant difference in inequality is also shown across regions. With respect to dynamics of inequalities, results on mobility indices computed based on Allanson et al. (Longitudinal analysis of income-related health inequality. Dundee Discussion Working Paper No. 214, 2010) approach show that inequality remain stable (persistent) in Height-for- age Z-score, and reduction of inequality in Weight-for- age Z-score while in case of Weight-for- height Z-score, there is no clear trend over subsequent waves. Results on decomposition of inequalities show that the major contributors are wealth index, consumption and mother's education.

CONCLUSION

The argument of the choice of welfare indicator can have a large and significant impact on measured socioeconomic inequalities in a health variable which it depends on the variable examined. Employing longitudinal perspective rather than weighted average of cross-sectional data is justifiable to see the dynamic of inequality in child malnutrition. In both socioeconomic status ranking variables, the bulk of inequality in malnutrition is caused by inequality in socioeconomic status in which it disfavours the poor in both cases. This calls for enhancing the policy measures that narrow socioeconomic gaps between groups in the population and targeting on early childhood intervention and nutrition sensitive.

Spatial distribution and geographical heterogeneity factors associated with poor consumption of foods rich in vitamin A among children age 6-23 months in Ethiopia: Geographical weighted regression analysis

INTRODUCTION

Vitamin A deficiency is a major public health problem in poor societies. Dietary consumption of foods rich in vitamin A was low in Ethiopia. This study aimed to assess the spatial distribution and spatial determinants of dietary consumption of foods rich in vitamin A among children aged 6-23 months in Ethiopia.

METHODS

Ethiopian 2016 demographic and health survey dataset using a total of 3055 children were used to conduct this study. The data were cleaned and weighed by STATA version 14.1 software and Microsoft Excel. Children who consumed foods rich in vitamin A (Egg, Meat, Vegetables, Green leafy vegetables, Fruits, Organ meat, and Fish) at least one food item in the last 24 hours were declared as good consumption. The Bernoulli model was fitted using Kuldorff's SaTScan version 9.6 software. ArcGIS version 10.7 software was used to visualize spatial distributions for poor consumption of foods rich in vitamin A. Geographical weighted regression analysis was employed using MGWR version 2.0 software. A P-value of less than 0.05 was used to declare statistically significant predictors spatially.

RESULTS

Overall, 62% (95% CI: 60.56-64.00) of children aged 6-23 months had poor consumption of foods rich in vitamin A in Ethiopia. Poor consumption of foods rich in vitamin A highly clustered in Afar, eastern Tigray, southeast Amhara, and the eastern Somali region of Ethiopia. Spatial scan statistics identified 142 primary spatial clusters located in Afar, the eastern part of Tigray, most of Amhara and some part of the Oromia Regional State of Ethiopia. Children living in the primary cluster were 46% more likely vulnerable to poor consumption of foods rich in vitamin A than those living outside the window (RR = 1.46, LLR = 83.78, P < 0.001). Poor wealth status of the household, rural residence and living tropical area of Ethiopia were spatially significant predictors.

CONCLUSION

Overall, the consumption of foods rich in vitamin A was low and spatially non-random in Ethiopia. Poor wealth status of the household, rural residence and living tropical area were spatially significant predictors for the consumption of foods rich in vitamin A in Ethiopia. Policymakers and health planners should intervene in nutrition intervention at the identified hot spot areas to reduce the poor consumption of foods rich in vitamin A among children aged 6-23 months.

Using geographically weighted regression analysis to cluster under-nutrition and its predictors among under-five children in Ethiopia: Evidence from demographic and health survey

BACKGROUND

Malnutrition among under-five children is a common public health problem and it is one of the main cause for the mortality of under-five children in developing countries, including Ethiopia. Therefore, lack of evidence about geographic heterogeneity and predictors of under-nutrition hinders for evidence-based decision-making process for the prevention and control programs of under-nutrition in Ethiopia. Thus, this study aimed to address this gap.

METHODS

The data were obtained from the Ethiopian Demographic and Health Survey (EDHS) 2016. A total of 9,384 under-five children nested in 645 clusters were included with a stratified two-stage cluster sampling. ArcGIS version 10.5 software was used for global, local and ordinary least square analysis and mapping. The spatial autocorrelation (Global Moran's I) statistic was held in order to assess the pattern of wasting, stunting, and underweight whether it was dispersed, clustered, or randomly distributed. In addition, a Bernoulli model was used to analyze the purely spatial cluster detection of under-nutrition indicators through SaTScan version 9.6 software. Geographically weighted regression (GWR) version 4.0 software was used to model spatial relationships in the GWR analysis. Finally, a statistical decision was made at p-value<0.05 with 95%CI for ordinary least square analysis and geographically weighted regression.

MAIN FINDINGS

Childhood under-nutrition showed geographical variations at zonal levels in Ethiopia. Accordingly, Somali region (Afder, Gode, Korahe, Warder Zones), Afar region (Zone 2), Tigray region (Southern Zone), and Amhara region (Waghmira Zones) for wasting, Amhara region (West Gojam, Awi, South Gondar, and Waghmira Zones) for stunting and Amhara region (South Wollo, North Wollo, Awi, South Gondar, and Waghmira zones), Afar region (Zone 2), Tigray region (Eastern Zone, North Western Zone, Central Zone, Southern Zone, and Mekele Special Zones), and Benshangul region (Metekel and Assosa Zones) for underweight were detected as hot spot (high risk) regions. In GWR analysis, had unimproved toilet facility for stunting, wasting and underweight, father had primary education for stunting and wasting, father had secondary education for stunting and underweight, mothers age 35-49 years for wasting and underweight, having female children for stunting, having children eight and above for wasting, and mother had primary education for underweight were significant predictors at (p<0.001).

CONCLUSIONS

Our study showed that the spatial distribution of under-nutrition was clustered and high-risk areas were identified in all forms of under-nutrition indicators. Predictors of under-nutrition were identified in all forms of under-nutrition indicators. Thus, geographic-based nutritional interventions mainly mobilizing additional resources could be held to reduce the burden of childhood under-nutrition in hot spot areas. In addition, improving sanitation and hygiene practice, improving the life style of the community, and promotion of parent education in the identified hot spot zones for under-nutrition should be more emphasized.

Drivers of stunting reduction in Ethiopia: a country case study

BACKGROUND

Chronic undernutrition in children continues to be a global public health concern. Ethiopia has documented a significant decline in the prevalence of childhood stunting, a measure of chronic undernutrition, over the last 20 y.

OBJECTIVES

The aim of this research was to conduct a systematic assessment of the determinants that have driven child stunting reduction in Ethiopia from 2000 to 2016, focused on the national, community, household, and individual level.

METHODS

This study employed both quantitative and qualitative methods. Specifically, a systematic literature review, retrospective quantitative data analysis using Demographic and Health Surveys from 2000-2016, qualitative data collection and analysis, and analyses of key nutrition-specific and -sensitive policies and programs were undertaken.

RESULTS

National stunting prevalence improved from 51% in 2000 to 32% in 2016. Regional variations exist, as do pro-rich, pro-urban, and pro-educated inequalities. Child height-for-age z score (HAZ) decomposition explained >100% of predicted change in mean HAZ between 2000 and 2016, with key factors including increases in total consumable crop yield (32% of change), increased number of health workers (28%), reduction in open defecation (13%), parental education (10%), maternal nutrition (5%), economic improvement (4%), and reduced diarrhea incidence (4%). Policies and programs that were key to stunting decline focused on promoting rural agriculture to improve food security; decentralization of the health system, incorporating health extension workers to improve rural access to health services and reduce open defecation; multisectoral poverty reduction strategies; and a commitment to improving girls' education. Interviews with national and regional stakeholders and mothers in communities presented improvements in health service access, women and girls' education, improved agricultural production, and improved sanitation and child care practices as drivers of stunting reduction.

CONCLUSIONS

Ethiopia's stunting decline was driven by both nutrition-specific and -sensitive sectors, with particular focus on the agriculture sector, health care access, sanitation, and education.

Does economic growth reduce childhood stunting? A multicountry analysis of 89 Demographic and Health Surveys in sub-Saharan Africa

Background

There is mixed evidence and lack of consensus on the impact of economic development on stunting, and likewise there is a dearth of empirical studies on this relationship in the case of sub-Saharan Africa. Thus, this paper examines whether economic growth is associated with childhood stunting in low-income and middle-income sub-Saharan African countries.

Methods

We analysed data from 89 Demographic and Health Surveys conducted between 1987 and 2016 available as of October 2018 using multivariable multilevel logistic regression models to show the association between gross domestic product (GDP) per capita and stunting. We adjusted the models for child's age, survey year, child's sex, birth order and country random effect, and presented adjusted and unadjusted ORs.

Results

We included data from 490 526 children. We found that the prevalence of stunting decreased with increasing GDP per capita (correlation coefficient=-0.606, p<0.0001). In the unadjusted model for full sample, for every US$1000 increase in GDP per capita, the odds of stunting decreased by 23% (OR=0.77, 95% CI 0.76 to 0.78). The magnitude of the association between GDP per capita and stunting was stronger among children in the richest quintile. After adjustment was made, the association was not significant among children from the poorest quintile. However, the magnitude of the association was more pronounced among children from low-income countries, such that, in the model adjusted for child's age, survey year, child's sex, birth order and country random effect, the association between GDP per capita and stunting remained statistically significant; for every US$1000 increase in GDP per capita, the odds of stunting decreased by 12% (OR=0.88, 95% CI 0.87 to 0.90).

Conclusion

There was no significant association between economic growth and child nutritional status. The prevalence of stunting decreased with increasing GDP per capita. This was more pronounced among children from the richest quintile. The magnitude of the association was higher among children from low-income countries, suggesting that households in the poorest quintile were typically the least likely to benefit from economic gains. The findings could serve as a building block needed to modify current policy as per child nutrition-related programmes in Africa.

Determinants of undernutrition among children under-five years old in southern Ethiopia: does pregnancy intention matter? A community-based unmatched case-control study

Background

Stunting, which describes a small height for one’s age, is an indicator of chronic malnutrition. It develops mainly as a result of prolonged food deprivation or a chronic disease or illness. Unintended pregnancies and unplanned births are among the psychological factors that negatively affect the nutritional status of children. Therefore, this study aimed to determine the effects of unintended pregnancies and other family and child characteristics on the nutritional status of children under 5 years old.

Methods

A community-based unmatched case-control study was conducted among 302 children (151 cases and 151 controls) 6–59 months old in Wonago town, Gedeo Zone, Southern Ethiopia. The cases were stunted children and the controls were non-stunted children in the study area. The cases were randomly selected from among the stunted children, and the controls were randomly selected from among the non-stunted children. The descriptive characteristics of the respondents were compared using the chi-squared test, and a multivariable logistic regression was used to assess the effects of an unintended pregnancy on stunting, after controlling for the other variables, with a p value of 0.05.

Results

The result revealed that unintended pregnancy is found to be among predictors of stunting where children from unintended pregnancy were about three times more likely to be stunted [AOR: 2.62, CI: (1.26, 5.45)]. The other predictors identified in this study were educational status of the father, wealth index of the household and daily meal frequency. From the finding, children from illiterate fathers [AOR: 3.43, CI: (1.04, 11.29)], children from poorer household economic status [AOR: 2.32, CI: (1.20, 4.49)] and children whom their daily meal frequency is below the recommended number of feeding [AOR: 4.50, CI: (1.31, 15.49)] were found to be more stunted.

Conclusions

Based on the results of this study, the children born from unintended pregnancies exhibited a significantly higher risk of stunting. Therefore, preventing unintended pregnancy could play a great role in decreasing the risk of stunting in children.

Poor nutrition for under-five children from poor households in Ethiopia: Evidence from 2016 Demographic and Health Survey

Background

Ethiopia is commonly affected by drought and famine, and this has taken quite a toll on citizens of the country, particularly the under-five children. Undernutrition among under-five children in Ethiopia is a prominent public health concern, and it lacked attention for decades. However, the government of Ethiopia, together with other stakeholders, committed to overcoming the impact of malnutrition through the transformational plan. Here we show the magnitude of undernutrition among under-five children and the factors predicting the achievement of global nutrition targets set for 2025 at the World Health Assembly.

Methods

Ethiopian Demographic and Health Survey (EDHS) 2016 was used for this study. A total of 9494 child-mother pairs were included in this analysis. The nutritional status indicators (Height-for-age, Weight-for-height and Weight-for-age) of children were measured and categorized based on the World Health Organization child growth standards. A multilevel logistic regression model adjusted for clusters and sampling weights were used to identify factors associated with stunting, underweight, and wasting. The independent variables were assessed by calculating the odds ratios with 95% confidence interval (CI).

Result

The prevalence of stunting was 38.3% (95% CI: 36.4% to 40.2%), under-weight 23.3% (95%CI: 21.9% to 24.9%) and wasting 10.1% (95%, CI: 9.1% to 11.2%). Sex of the child (male), children older than 24 months, recent experience of diarrhea, household wealth index (poorest), and administrative regions (Tigray, Amhara and developing regions) had a higher risk of undernutrition. On the other hand, children born from overweight mothers and educated mother (primary, secondary or higher) had a lower risk of undernutrition.

Conclusion

The burden of undernutrition is still considerably high in Ethiopia. Implimentation of strategies and policies that focus on improving the socioeconomic educatiional status of the community need to be sustained. Generally, actions targeted on factors contributing to undernutrition among under-five children demands immediate attention to achieve national and global nutrition target.

Influence of the built environment of Nanjing's Urban Community on the leisure physical activity of the elderly: an empirical study

Background

Urbanization and aging are global phenomena that offer unique challenges in different countries. A supportive environment plays an important role in addressing the issues of health behavioral change and health promotion (e.g., prevent chronic illnesses, promote mental health) among older adults. With the development of the socio-ecological theoretical model, studies on the impact of supportive environments on physical activity have become popular in the public health field in the EU and US. Meanwhile, very few Chinese studies have examined the relationship between built environment features and older adults’ physical activity at the ecological level. The purpose of the study is to investigate how the factors part of the built environment of Nanjing’s communities also influence leisure time physical activity among the elderly.

Methods

Using a socio-ecological model as a theoretical framework, we conducted a cross-sectional study of 399 elderly people from 19 communities in Nanjing, China, using a one-on-one questionnaire to collect data, including participants’ perceived built environment and self-reported physical activity. A multivariate linear regression method was used to analyze the factors influencing their recreational physical activity.

Results

This study found that compared to older people with low average monthly income, the recreational physical activity of the elderly with average monthly incomes between 1001 and 2000 ¥ (β = 23.31, p < 0.001) and 2001 ¥ or more (β = 21.15, p < 0.001) are significantly higher. After controlling for individual covariates, street connectivity (β = 7.34, p = 0.030) and street pavement slope (β = − 7.72, p = 0.020), we found that two out of ten built environment factors indicators influence their physical activity. The importance of each influencing factor ranked from highest to lowest are monthly average income, street pavement slope, and street connectivity. Other factors were not significantly related to recreational physical activity by the elderly.

Conclusions

Older adults with a high income were more likely to participate in recreational physical activity than those with a low income. In order to positively impact physical activity in older adults and ultimately improve health, policymakers and urban planners need to ensure that street connectivity and street pavement slope are factored into the design and development of the urban environment.

Ethiopia's high childhood undernutrition explained: analysis of the prevalence and key correlates based on recent nationally representative data

OBJECTIVE

To examine the contribution of child, maternal and household factors in stunting, wasting and underweight among children under 5 years in Ethiopia.

DESIGN

Quantitative cross-sectional design based on nationally representative data.

SETTING

Urban and rural areas of Ethiopia.ParticipantsYounger (0-24 months; n 4199) and older age groups (25-59 months; n 5497), giving a total of 9696 children.

RESULTS

Among the younger age group, 29 % were stunted, 14 % were wasted and 19 % were underweight; and among the older age group, the prevalence of stunting, wasting and underweight was 47, 8 and 28 %, respectively. Being female, intake of multiple micronutrients, household having a piped source of drinking-water, high maternal BMI, higher household wealth and higher maternal education were associated with decreased odds of at least one form of undernutrition in both groups. On the other hand, children who were anaemic, had low birth weight, drank from a bottle, and children of stunted or wasted or working mothers were more likely to be stunted, wasted or underweight in both groups (P&lt;0·05). While most predictors and/or risk factors followed a similar pattern across the two age groups, child factors had higher leverage in the younger than the older group across the three forms of undernutrition.

CONCLUSIONS

Multiple set of factors predicted childhood undernutrition in Ethiopia. The study underscores the importance of intervening in the first 1000 days through promoting maternal education, maternal-child health services, mother's nutrition and improving intrahousehold food distribution.

Understanding the geographical burden of stunting in India: A regression-decomposition analysis of district-level data from 2015-16

India accounts for approximately one third of the world's total population of stunted preschoolers. Addressing global undernutrition, therefore, requires an understanding of the determinants of stunting across India's diverse states and districts. We created a district-level aggregate data set from the recently released 2015-2016 National and Family Health Survey, which covered 601,509 households in 640 districts. We used mapping and descriptive analyses to understand spatial differences in distribution of stunting. We then used population-weighted regressions to identify stunting determinants and regression-based decompositions to explain differences between high- and low-stunting districts across India. Stunting prevalence is high (38.4%) and varies considerably across districts (range: 12.4% to 65.1%), with 239 of the 640 districts have stunting levels above 40% and 202 have prevalence of 30-40%. High-stunting districts are heavily clustered in the north and centre of the country. Differences in stunting prevalence between low and high burden districts were explained by differences in women's low body mass index (19% of the difference), education (12%), children's adequate diet (9%), assets (7%), open defecation (7%), age at marriage (7%), antenatal care (6%), and household size (5%). The decomposition models explained 71% of the observed difference in stunting prevalence. Our findings emphasize the variability in stunting across India, reinforce the multifactorial determinants of stunting, and highlight that interdistrict differences in stunting are strongly explained by a multitude of economic, health, hygiene, and demographic factors. A nationwide focus for stunting prevention is required, while addressing critical determinants district-by-district to reduce inequalities and prevalence of childhood stunting.