Local spatial clustering of stunting and wasting among children under the age of 5 years: implications for intervention strategies

Spatial regression models to assess variations of composite index for anthropometric failure across the administrative zones in Ethiopia

BACKGROUND

There are a number of previous studies that investigated undernutrition and its determinants in Ethiopia. However, the national average in the level of undernutrition conceals large variation across administrative zones of Ethiopia. Hence, this study aimed to determine the geographic distribution of composite index for anthropometric failure (CIAF) and identify the influencing factors it' might be more appropriate.

METHODS

We used the zonal-level undernutrition data for the under-five children in Ethiopia from the Ethiopian Demographic and Health Survey (EDHS) dataset. Different spatial models were applied to explore the spatial distribution of the CIAF and the covariates.

RESULTS

The Univariate Moran's I statistics for CIAF showed spatial heterogeneity of undernutrition in Ethiopian administrative zones. The spatial autocorrelation model (SAC) was the best fit based on the AIC criteria. Results from the SAC model suggested that the CIAF was positively associated with mothers' illiteracy rate (0.61, pvalue 0.001), lower body mass index (0.92, pvalue = 0.023), and maximum temperature (0.2, pvalue = 0.0231) respectively. However, the CIAF was negatively associated with children without any comorbidity (-0.82, pvalue = 0.023), from families with accessibility of improved drinking water (-0.26, pvalue = 0.012), and minimum temperature (-0.16).

CONCLUSION

The CIAF across the administrative zones of Ethiopia is spatially clustered. Improving women's education, improving drinking water, and improving child breast feeding can reduce the prevalence of undernutrition (CIAF) across Ethiopian administrative zones. Moreover, targeted intervention in the geographical hotspots of CIAF can reduce the burden of CIAF across the administrative zones.

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.

Spatial patterns and predictors of unintended pregnancy among reproductive age women in Ethiopia

INTRODUCTION

Unintended pregnancy is amajor sexual and reproductive health problem that imposes substantial health, economical and psychosocial costs to individuals and society as well as significant emotional distress to women, families, and society. The main aim of this study was to investigate the spatial distribution and predictors of unintended pregnancy in Ethiopian regions and administrative zones.

METHODS

This study was conducted based on data from 2016 Ethiopian Demographic and Health Survey. The prevalence of unintended pregnancy across regions and or zones was assessed using spatial analysis, and the effect of different factors on unintended pregnancy in Ethiopia was investigated using a generalized linear mixed model with a multistage clustered sampling strategy. The crude and best linear unbiased predictor estimations of zones were integrated with the shape file data to demonstrate the performance of each zone on maps.

RESULTS

The prevalence of unintended pregnancy for reproductive women in Ethiopia was29.49%. The highest rates of unintended pregnancy were recorded in the North Gondar zone of the Amhara region and the Jima zone in the Oromiya region. The mixed effects model revealed that age [AOR = 0.78, 95% CI, 0.62-0.97], residence [AOR = 2.62, 95%CI, 1.94, 7.27], marital status [AOR = 0.05, 95%CI, 0.01-0.38], women education [AOR = 1.34, 95%CI, 0.75-2.39], smoking cigarettes [AOR = 3.67, 95CI, 1.17-11.56], and poorer wealth index [AOR = 1.89, 95% CI, 1.51-2.31] were significantly associated with unintended pregnancy.

CONCLUSION

In Ethiopia, unintended pregnancy is a public health issue, and prevention stratagem for unintended pregnancy among reproductive women need to be focused based on the identified predictors. The spatial distribution of unintended pregnancy varied greatly at zonal and regional levels in Ethiopia. Hence, we recommended that, creating awareness of sexual and reproductive health with special priority to the identified hotspot areas (Amhara, Oromiya and SNN regions) to reduce unintended pregnancy. Emphasis on fertility and contraceptive techniques should be given to couples by health professionals.

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.

Socioeconomic inequalities in child growth failure in Ethiopia: findings from the 2000 and 2016 Demographic and Health Surveys

OBJECTIVE

Socioeconomic inequalities in child growth failure (CGF) remain one of the main challenges in Ethiopia. This study examined socioeconomic inequalities in CGF and determinants that contributed to these inequalities in Ethiopia.

METHODS

The Ethiopia Demographic and Health Surveys 2000 and 2016 data were used in this study. A pooled unweighted sample of the two surveys yielded 21514 mother-child pairs (10873 in 2000 and 10641 in 2016). We assessed socioeconomic inequalities in CGF indicators using the concentration curve and concentration index (CI). We then decomposed the CI to identify percentage contribution of each determinant to inequalities.

RESULTS

Socioeconomic inequalities in CGF have increased in Ethiopia between 2000 and 2016. The CI increased from -0.072 and -0.139 for stunting, -0.088 and -0.131 for underweight and -0.015 and -0.050 for wasting between 2000 and 2016, respectively. Factors that mainly contributed to inequalities in stunting included geographical region (49.43%), number of antenatal care visits (31.40%) and child age in months (22.20%) in 2000. While in 2016, inequality in stunting was contributed mainly by wealth quintile (46.16%) and geographical region (-13.70%). The main contributors to inequality in underweight were geographical regions (82.21%) and wealth quintile (27.21%) in 2000, while in 2016, wealth quintile (29.18%), handwashing (18.59%) and access to improved water facilities (-17.55%) were the main contributors. Inequality in wasting was mainly contributed to by maternal body mass index (-66.07%), wealth quintile (-45.68%), geographical region (36.88%) and paternal education (33.55%) in 2000, while in 2016, wealth quintile (52.87%) and urban areas of residence (-17.81%) were the main driving factors.

CONCLUSIONS

This study identified substantial socioeconomic inequalities in CGF, and factors that relatively contributed to the disparities. A plausible approach to tackling rising disparities may involve developing interventions on the identified predictors and prioritising actions for the most socioeconomically disadvantaged groups.

Spatial Variations and Determinants of Acute Malnutrition Among Under-Five Children in Ethiopia: Evidence from 2019 Ethiopian Demographic Health Survey

Background and aims:

Childhood acute malnutrition, in the form of wasting defined by a severe weight loss as a result of acute food shortage and/or illness. It is a critical public health problem that needs urgent attention in developing countries, like Ethiopia. Despite its variation between localities, the risk factors and its geospatial variation were not addressed enough across the various corner of the country. Therefore, the current study was undertaken to assess spatial variation and factors associated with acute malnutrition among under-five children in Ethiopia.

Methods:

A total weighted sample of 4 955 under-five children were included from the 2019 Demographic and Health Survey. Getis-Ord spatial statistical tool used to identify the hot and cold spot areas of severe and acute malnutrition. A multilevel multivariable logistic regression model using was used to examine predictors of acute malnutrition. In the multivariable multilevel analysis, Adjusted Odds Ratio with 95% CI was used to declare significant determinants of acute malnutrition among children.

Result:

Among 4 955 under-five children, 7% of them were wasted and 1% of them were severely wasted in Ethiopia during the 2019 national demographic survey. The distribution was followed some spatial geo-locations where most parts of Somali were severely affected (RR = 1.46, P37 value <0.001), and the distribution affected few areas of Afar, Gambella, and Benishangul Gumz regions. Factors that significantly associated with childhood wasting were: gender(male)1.9 (1.3–2.7), age (above 36 months) 0.5 (0.2–0.9), wealth index(richest) 0.5 (0.2–0.8), and water source (unimproved source) 1.5 (1.0–2.3).

Conclusions:

Our finding implies, the distribution of childhood wasting was not random. Regions like Afar, Somali, and pocket areas in Gambella and SNNP should be considered as priority areas nutritional interventions for reducing acute malnutrition. The established socio-demographic and economic characteristics can be also used to develop strategies.

Spatial data analysis of malnutrition among children under-five years in Ethiopia

Background

Childhood malnutrition is a major cause of child mortality under the age of 5 in the sub-Saharan Africa region. This study sought to identify the risk factors and spatial distribution of the composite index of anthropometric failure (CIAF).

Methods

Secondary data from 2000, 2005, 2011, and 2016 Ethiopian Health and Demographic Survey (EDHS) were used. The generalized geo-additive mixed model was adopted via the Integrated Nested Laplace Approximation (INLA) with a binomial family and logit link function.

Results

The CIAF status of children was found to be positively associated with the male gender, the potency of contracting a disease, and multiple births. However, it was negatively associated with family wealth quartiles, parental level of education, place of residence, unemployment status of mothers, improved sanitation, media exposure, and survey years. Moreover, the study revealed significant spatial variations on the level of CIAF among administrative zones.

Conclusions

The generalized geo-additive mixed-effects model results identified gender of the child, presence of comorbidity, size of child at birth, dietary diversity, birth type, place of residence, age of the child, parental level of education, wealth index, sanitation facilities, and media exposure as main drivers of CIAF. The results would help decision-makers to develop and carry out target-oriented programs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01391-x.

A machine learning classifier approach for identifying the determinants of under-five child undernutrition in Ethiopian administrative zones

Background

Undernutrition is the main cause of child death in developing countries. This paper aimed to explore the efficacy of machine learning (ML) approaches in predicting under-five undernutrition in Ethiopian administrative zones and to identify the most important predictors.

Method

The study employed ML techniques using retrospective cross-sectional survey data from Ethiopia, a national-representative data collected in the year (2000, 2005, 2011, and 2016). We explored six commonly used ML algorithms; Logistic regression, Least Absolute Shrinkage and Selection Operator (L-1 regularization logistic regression), L-2 regularization (Ridge), Elastic net, neural network, and random forest (RF). Sensitivity, specificity, accuracy, and area under the curve were used to evaluate the performance of those models.

Results

Based on different performance evaluations, the RF algorithm was selected as the best ML model. In the order of importance; urban–rural settlement, literacy rate of parents, and place of residence were the major determinants of disparities of nutritional status for under-five children among Ethiopian administrative zones.

Conclusion

Our results showed that the considered machine learning classification algorithms can effectively predict the under-five undernutrition status in Ethiopian administrative zones. Persistent under-five undernutrition status was found in the northern part of Ethiopia. The identification of such high-risk zones could provide useful information to decision-makers trying to reduce child undernutrition.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01652-1.

Disparities in childhood composite index of anthropometric failure prevalence and determinants across Ethiopian administrative zones

BACKGROUND

The prevalence of under-five children's undernutrition in Ethiopia is among the highest in the world. This study aimed at exploring the prevalence and risk factors of the composite index for anthropometric failure (CIAF) of under-five children in Ethiopia by incorporating the zonal (district) effects.

METHODS

The data was drawn from Ethiopian Demographic and Health Surveys (EDHSs), a population-based cross-sectional study of 29,599 under-five year children from 72 Zones in the years 2000, 2005, 2011, and 2016. Fixed effect variables related to child and maternal-household were included in the model. We adopted a generalized mixed model with CIAF as outcome variable and Zones as random effects.

RESULTS

The prevalence of CIAF in Ethiopia was 53.78% with the highest prevalence of 61.30% in 2000 and the lowest prevalence of 46.58% in 2016. The model result revealed that being a female child, absence of comorbidity, singleton births, and the first order of birth showed significantly lower CIAF prevalence than their counterparts. Among the household characteristics, children from mothers of underweight body mass index, uneducated parents, poor household sanitation, and rural residents were more likely to be undernourished than their counterparts. Based on the best linear unbiased prediction for the zonal-level random effect, significant variations of CIAF among zones were observed.

CONCLUSION

The generalized linear mixed-effects model results identified gender of the child, size of child at birth, dietary diversity, birth type, place of residence, age of the child, parental level of education, wealth index, sanitation facilities, and media exposure as main drivers of CIAF. Disparities of CIAF were observed between and within the Ethiopian administrative Zones over time.

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.

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.

Spatial distribution of under immunization among children 12-23 months old in Butajira HDSS, southern Ethiopia

Background

Immunization is essential to prevent between 2 and 3 million deaths globally each year and it is widely accepted that it is one of the most cost-effective health interventions. Despite all its advantages, immunization in Ethiopia is still far from the target set by the United Nations Sustainable Development Goals to achieve universal immunization by all countries in 2030. The 2016 Ethiopian Demographic and Health Survey (EDHS) reported an overall full immunization rate of only 38.3%. The objective of this study was to evaluate the spatial distribution of under immunization in 12 to 23 months old children and further identify the determinants of under immunization clustering in the Butajira Health and Demographic Surveillance Site (HDSS).

Methods

We conducted a community based sectional survey from March to April, 2016 in Butajira HDSS. We collected data on immunization status from a total of 482 children between the age of 12 to 23 months. We randomly selected household and interviewed mothers and /or observed vaccination cards when available to collect data on child’s immunization status. We also collected the geographic location of all villages within the ten Kebeles using a Handheld Global Positioning System (GPS) (Garmin GPSMAP®). We analyzed the spatial distribution of under immunization and clustering using the SatScan® software which employs a purely spatial Bernoulli’s model. We also ran a logistic regression model to help evaluate the causes of clustering.

Results

We found that only 22.4% [95% CI: 18.9, 26.4%] of children were fully immunized. This study identified one significant cluster of under immunization among children 12–23 months of age within the Butajira HDSS (relative risk (RR) = 1.24,P < 0·01). We found that children residing in this cluster had more than 1.24 times risk of under immunization compared with children residing outside of the identified cluster. We found significant differences with regard to Maternal Tetanus Toxoid immunization status and place of delivery between cases found within a spatial cluster and cases found outside the cluster. For example, the odds of home delivery is more than two times [AOR 2.21: 95%CI; 1.06, 4.63] among children within an identified spatial cluster than the odds among children found outside the identified cluster.

Conclusions

Under immunization of 12–23 months old children and under immunization with specific vaccines such as Polio, BCG, DPT (1–3) and Measles clustered geographically. Spatial studies could be effective in identifying geographic areas of under immunization for targeted intervention like in this study to gear health education to the specific locality.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12887-021-02690-4.

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.

Determinants of child anthropometric indicators in Ethiopia

Background

Malnutrition is one of the major contributors to child mortality in Ethiopia. Currently established, child nutrition status is assessed by four anthropometric indicators. However, there are other factors affecting children’s anthropometric statuses. Thus, the main objective of this paper is to explore some of the determinants of child anthropometric indicators in Ethiopia.

Methods

Data from GROW (the Growing Nutrition for Mothers and Children), a survey including 1261 mothers and 1261 children was carried out in Ethiopia in 2016. Based on the data gathered, the goal of GROW is to improve the nutritional status of women of reproductive age (15–49), as well as boys and girls under 5 years of age in Ethiopia. In order to investigate the association between different factors and child anthropometric indicators, this study employs various statistical methods, such as ANOVA, T-test, and linear regressions.

Results

Child’s sex (confidence intervals for (wasting = − 0.782, − 0.151; stunting = − 0.936,-0.243) (underweight = − 0.530, − 0.008), child’s age (confidence intervals for (wasting = − 0.020, 0.007; stunting = − 0.042,-0.011) (underweight = − 0.025, − 0.002), maternal MUAC (confidence intervals for (wasting = 0.189, 0.985; BMI-for-age = 0.077, 0.895), maternal education (stunting = 0.095, 0.897; underweight = 0.120, 0.729), and open defecation (stunting = 0.055, 0.332; underweight = 0.042, 0.257) were found to be significantly associated with anthropometric indicators. Contrary to some findings, maternal dietary diversity does not present significance in aforementioned child anthropometric indicators.

Conclusion

Depending on the choice of children anthropometric indicator, different conclusions were drawn demonstrating the association between each factor to child nutritional status. Results showed child’s sex, age, region, open defecation, and maternal MUAC significantly increases the risk of child anthropometric indicators. Highlighting the factors influencing child undernutrition will help inform future policies and programs designed to approach this major problem in Ethiopia.

Spatiotemporal heterogeneity of malnutrition indicators in children under 5 years of age in Bangladesh, 1999–2011

Objective

To examine changes in the spatial clustering of malnutrition in children under 5 years of age (under-5s) for the period 1999 to 2011 in Bangladesh.

Design

We used data from four nationally representative Demographic and Health Surveys (DHS) conducted in 1999–2000, 2004, 2007 and 2011 in Bangladesh involving a total of 24 211 under-5s located in 1661 primary sampling units (PSU; geographical unit of analysis) throughout Bangladesh. The prevalence of stunting (height/length-for-age Z-score <−2), underweight (weight-for-age Z-score <−2) and wasting (weight-for-height/length Z-score <−2) at each PSU site and for each survey year were estimated based on the WHO child growth standard. The extent of spatial clustering was quantified using semivariograms.

Setting

Whole of Bangladesh.

Subjects

Children under 5 years of age.

Results

Our results demonstrate that in 1999–2000 most PSU throughout Bangladesh experienced stunting, underweight and wasting prevalence which exceeded the WHO thresholds. By 2011, this situation improved, although in two of the six divisions (Barisal and Sylhet) PSU still exhibited higher levels of malnutrition compared with other divisions of the country. The pattern of spatial clustering for stunting, underweight and wasting also changed between 1999 and 2011 both at national and sub-national (division) levels.

Conclusions

We identified divisions where malnutrition indicators (stunting, underweight and wasting) remain highly clustered and other divisions where they are more widely spread in Bangladesh. This has important implications on how interventions for malnutrition need to be delivered (geographically targeted interventions v. random interventions) within each division of the country.

Stunting among children under 3 years of age in Côte d'Ivoire: spatial and temporal variations between 1994 and 2011

OBJECTIVE

To investigate spatial heterogeneity of stunting prevalence among children in Côte d'Ivoire and examine changes in stunting between 1994 and 2011, to assess the impact of the 2002-2011 civil war that led to temporary partitioning of the country.

DESIGN

Data from 1994, 1998 and 2011 Côte d'Ivoire Demographic and Health Surveys (DHS) were analysed using a geostatistical approach taking into account spatial autocorrelation. Stunting data were interpolated using ordinary kriging; spatial clusters with high and low stunting prevalence were identified using Kulldorff spatial scan statistics. Multilevel multivariable logistic regression was then carried out, with year of survey as the main independent variable and an interaction term for time by geographic zone (Abidjan, South, North).

SETTING

Côte d'Ivoire, West Africa.

SUBJECTS

Children aged 0-35 months included in three DHS (n 6709).

RESULTS

Overall stunting prevalence was 30·7, 28·7 and 27·8 % in 1994, 1998 and 2011, respectively (P=0·32). Clusters with high prevalence were found in 1994 (in the West region, P<0·001) and 1998 (in the West and North-West regions, P<0·01 and P=0·01, respectively), but not in 2011. Abidjan was included in a cluster with low prevalence in all surveys (P<0·05). Risk of stunting did not change between 1994 and 2011 at national level (adjusted OR; 95 % CI: 1·39; 0·72, 2·64), but decreased in the South (0·74; 0·58, 0·94) and increased from 1998 to 2011 in Abidjan (1·96; 1·06, 3·64).

CONCLUSIONS

In Côte d'Ivoire, significant changes in stunting prevalence were observed at the sub-national level between 1994 and 2011.

Spatial heterogeneity and risk factors for stunting among children under age five in Ethiopia: A Bayesian geo-statistical model

Background

Understanding 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.

Methods

A 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.

Results

Overall, 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.

Conclusion

Stunting 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.