Mapping child growth failure in Africa between 2000 and 2015

Subnational variations in the quality of household survey data in sub-Saharan Africa

Nationally representative household surveys collect geocoded data that are vital to tackling health and other development challenges in sub-Saharan Africa. Scholars and practitioners generally assume uniform data quality but subnational variation of errors in household data has never been investigated at high spatial resolution. Here, we explore within-country variation in the quality of most recent household surveys for 35 African countries at 5 × 5 km resolution and district levels. Findings show a striking heterogeneity in the subnational distribution of sampling and measurement errors. Data quality degrades with greater distance from settlements, and missing data as well as imprecision of estimates add to quality problems that can result in vulnerable remote populations receiving less than optimal services and needed resources. Our easy-to-access geospatial estimates of survey data quality highlight the need to invest in better targeting of household surveys in remote areas.

Unveiling predictive factors for household-level stunting in India: A machine learning approach using NFHS-5 and satellite-driven data

OBJECTIVES

Childhood stunting remains a significant public health issue in India, affecting approximately 35% of children under 5. Despite extensive research, existing prediction models often fail to incorporate diverse data sources and address the complex interplay of socioeconomic, demographic, and environmental factors. This study bridges this gap by employing machine learning methods to predict stunting at the household level, using data from the National Family Health Survey combined with satellite-driven datasets.

METHODS

We used four machine learning models-random forest regression, support vector machine regression, K-nearest neighbors regression, and regularized linear regression-to examine the impact of various factors on stunting. The random forest regression model demonstrated the highest predictive accuracy and robustness.

RESULTS

The proportion of households below the poverty line and the dependency ratio consistently predicted stunting across all models, underscoring the importance of economic status and household structure. Moreover, the educational level of the household head and environmental variables such as average temperature and leaf area index were significant contributors. Spatial analysis revealed significant geographic clustering of high-stunting districts, notably in central and eastern India, further emphasizing the role of regional socioeconomic and environmental factors. Notably, environmental variables like average temperature and leaf area index emerged as strong predictors of stunting, highlighting how regional climate and vegetation conditions shape nutritional outcomes.

CONCLUSIONS

These findings underline the importance of comprehensive interventions that not only address socioeconomic inequities but also consider environmental factors, such as climate and vegetation, to effectively combat childhood stunting in India.

Precipitation, temperature, and child undernutrition: evidence from the Mali demographic and health surveys 2012-2013 and 2018

BACKGROUND

Undernutrition among children remains a severe burden in Sub-Saharan Africa. Climate change is widely recognized as a major obstacle to improving children's nutritional outcomes. Mali, a landlocked country in West Africa, has one of the highest prevalence of child undernutrition in the region and is also considered one of the most vulnerable nations to climate change globally. This study, therefore, aimed to assess the effects of precipitation and temperature on child undernutrition in Mali, with a focus on climatic differences between the southern and northern regions.

METHODS

We pooled the two most recent cross-sectional datasets from the Mali Demographic and Health Surveys (DHS) 2012-2013 and DHS 2018, integrating them with climatic variables at the DHS cluster level. The study included data from 12,281 children under five years of age. Precipitation and temperature data were extracted from the Advancing Research on Nutrition and Agriculture's DHS-Geographical Information System database, which provides a comprehensive range of climatic and geographic variables at the DHS cluster level. We assessed the effects of precipitation and temperature over periods of three months, six months, one year, and two years before the survey on child undernutrition using multivariable multilevel logistic regression models.

RESULTS

In southern Mali, 25.0% of children under five were stunted (95% CI 23.7-26.3%), 24.9% were underweight (95% CI 23.7-26.1%), and 9.3% were wasted (95% CI 8.5-10.1%). In northern Mali, the prevalence rates were higher: 29.6% for stunting (95% CI 27.0-32.1%), 28.7% for underweight (95% CI 26.0-31.3%), and 10.5% for wasting (95% CI 8.8-12.3%). From the pooled data analysis, we found that higher average monthly rainfall over the last three months (AOR = 0.977, p = 0.012) and six months (AOR = 0.974, p = 0.003) preceding the survey was significantly associated with lower odds of wasting in northern Mali, predominantly comprising desert areas. Moreover, in addition to reducing wasting, rainfall over the one year (AOR = 0.985, p = 0.010) and two years (AOR = 0.984, p = 0.009) prior to the survey showed a significant effect in reducing the odds of underweight among children in the north.

CONCLUSIONS

Increased precipitation had a beneficial effect on children's nutritional status, particularly in the northern part of Mali, where water scarcity is a persistent challenge. Amid growing concerns about declining rainfall due to climate change, the risk of child undernutrition is expected to rise in the northern part. To address this escalating threat, it is crucial to implement effective and timely measures to mitigate the impacts of climate change and improve children's nutrition.

High-resolution spatial prediction of anemia risk among children aged 6 to 59 months in low- and middle-income countries

BACKGROUND

Anemia, a severe condition among children associated with adverse health effects such as impaired growth, limited physical and cognitive development, and increased mortality risk, remains widespread, particularly in low- and middle-income countries. This study combines Demographic and Health Surveys data with remotely sensed climate, demographic, environmental, and geo-spatial information, creating a data set comprising about 750,000 observations on childhood anemia from 37 countries. It is used to provide high-resolution spatio-temporal estimates of all forms of childhood anemia between 2005 and 2020.

METHODS

Employing full probabilistic Bayesian distributional regression models, the research accurately predicts age-specific and spatially varying anemia risks. These models enable the assessment of the complete distribution of hemoglobin levels. Additionally, this analysis also provides predictions at a high resolution, allowing precise monitoring of this indicator, aligned with Sustainable Development Goal (SDG) 2.

RESULTS

This analysis provides high-resolution estimates for all forms of anemia and reveals and identifies striking disparities within and between countries. Based on these estimates, the prevalence of anemia decreased from 65.0% [62.6%-67.4%] in sub-Saharan Africa and 63.1% [60.6%-65.5%] in South Asia in 2010 to 63.4% [60.7%-66.0%] in sub-Saharan Africa and 58.8% [56.4%-61.3%] in South Asia in 2020. This translates into approximately 98.7 [94.5-102.8] million and 95.1 [91.1-99.0] million affected children aged 6 to 59 months in 2020, respectively, making it a major public health concern.

CONCLUSIONS

Our approach facilitates the monitoring of age-specific spatio-temporal dynamics and the identification of hotspots related to this important global public health issue. To our knowledge, this represents the first high-resolution mapping of anemia risk in children. In addition, these results reveal striking disparities between and within countries and highlight the influence of socio-economic and environmental factors on this condition. The findings can guide efforts to improve health systems, promote education, and implement interventions that break the cycle of poverty and anemia.

Residence-based disparities of composite index of anthropometric failures in East African under five children; multivariate decomposition analysis

BACKGROUND

Undernutrition remains a global challenge and public health concern, despite the presence of policies, programs and interventions. There is substantial evidence that the majority of the rural children under-5 years old have composite index of anthropometric failure than the urban counter parts. Hence, identifying the main contributors of these disparities will help health policy makers, program designers and implementers for the reduction of composite index of anthropometric failures in children under-5 years old in the study areas.

METHODS

The most recent and nationally representative samples of demographic and health surveys of five East African countries data were used for the current study. To appreciate the residence-based differences of composite index of anthropometric failure in under-5 children, the Blinder-Oaxaca decomposition analysis and its extensions were employed to determine the effects of covariates and coefficients. The country specific survey data analysis was performed.

RESULTS

The current study revealed that the burden of composite index of anthropometric failure (CIAF) in under-5 children were 40.69%, 22.04%, 34.06%, 31.99%, and 33.27% in Ethiopia, Kenya, Rwanda, Uganda, and Tanzania respectively. The residence-based differences in CIAF were 25.49%, 11.38%, 27%, 22.15%, and 20.55% in Ethiopia, Kenya, Rwanda, Uganda, and Tanzania respectively. Results of the Blinder-Oaxaca decomposition analysis and its extensions revealed that 100% of the rural-urban children under-5 composite index of anthropometric failure disparity was explained by endowment characteristics (covariate effect). Wealth index, mother's education, age of child, type of birth, sex of child and birth interval inequality between rural and urban households explains most of the composite index of anthropometric failure disparity in children under-5 years old.

CONCLUSIONS

The residence-based CIAF differences were high in all study countries. The rural-urban CIAF gap is ascribed by household, maternal and child characteristics. This result implies that rural children under-5 is disproportionally disadvantaged with respect to characteristics than their consequences. Through identification of the underlying factors behind the rural-urban CIAF disparities, the result of this study is important in planning effective intervention measures aiming at reducing residence-based inequalities and the population health outcomes. Therefore, should be given for rural children to reduce CIAF by improving house hold wealth index, women education and attentions to older children, and female children.

Using ESPEN data for evidence-based control of neglected tropical diseases in sub-Saharan Africa: A comprehensive model-based geostatistical analysis of soil-transmitted helminths

BACKGROUND

The Expanded Special Project for the Elimination of Neglected Tropical Diseases (ESPEN) was launched in 2019 by the World Health Organization and African nations to combat Neglected Tropical Diseases (NTDs), including Soil-transmitted helminths (STH), which still affect over 1.5 billion people globally. In this study, we present a comprehensive geostatistical analysis of publicly available STH survey data from ESPEN to delineate inter-country disparities in STH prevalence and its environmental drivers while highlighting the strengths and limitations that arise from the use of the ESPEN data. To achieve this, we also propose the use of calibration validation methods to assess the suitability of geostatistical models for disease mapping at the national scale.

METHODS

We analysed the most recent survey data with at least 50 geo-referenced observations, and modelled each STH species data (hookworm, roundworm, whipworm) separately. Binomial geostatistical models were developed for each country, exploring associations between STH and environmental covariates, and were validated using the non-randomized probability integral transform. We produced pixel-, subnational-, and country-level prevalence maps for successfully calibrated countries. All the results were made publicly available through an R Shiny application.

RESULTS

Among 35 countries with STH data that met our inclusion criteria, the reported data years ranged from 2004 to 2018. Models from 25 countries were found to be well-calibrated. Spatial patterns exhibited significant variation in STH species distribution and heterogeneity in spatial correlation scale (1.14 km to 3,027.44 km) and residual spatial variation variance across countries.

CONCLUSION

This study highlights the utility of ESPEN data in assessing spatial variations in STH prevalence across countries using model-based geostatistics. Despite the challenges posed by data sparsity which limit the application of geostatistical models, the insights gained remain crucial for directing focused interventions and shaping future STH assessment strategies within national control programs.

Ethnicity and anthropometric deficits in children: A cross-sectional analysis of national survey data from 18 countries in sub-Saharan Africa

Child anthropometric deficits remain a major public health problem in Sub-Saharan Africa (SSA) and are a key target of the UN Sustainable Development Goals (SDGs). The SDGs recommend disaggregation of health indicators by ethnic group. However, few studies have assessed how ethnicity is associated with anthropometric deficits across SSA. Data were extracted from 37 georeferenced Demographic and Health Surveys carried out during 2006-2019 across SSA that recorded anthropometric data for children aged <5 years. In a cross-sectional analysis, the odds of stunting (low height-for-age), wasting (low weight-for-height) and underweight (low weight-for-age) were modelled in relation to ethnic group using a generalised linear hierarchical mixed-effects model, controlling for survey design and environmental, socioeconomic and clinical variables. The study population comprised 138,312 children spanning 45 ethnic groups across 18 countries. In pairwise comparisons (accounting for multiple comparisons) between ethnic groups, height-for-age z-scores differed by at least 0.5 standard deviations in 29% of comparisons, weight-for-height z-scores in 36% of comparisons and weight-for-age z-scores in 20% of comparisons. Compared to a reference group of Fula children (the largest ethnic group), ethnic group membership was associated with both increases and decreases in growth faltering, ranging from a 69% reduction to a 32% increase in odds of stunting (Igbo: adjusted odds ratio (aOR) 0.31, 95% confidence intervals (CI) 0.27-0.35, p<0.0001; Hausa: aOR 1.32, 95% CI 1.21-1.44, p<0.0001); a 13% to 87% reduction in odds of wasting (Mandinka: aOR 0.87, 95% CI 0.76-0.99, p = 0.034; Bamileke: aOR 0.13, 95% CI 0.05-0.32, p<0.0001) and an 85% reduction to 13% increase in odds of underweight (Bamileke: aOR 0.15, 95% CI 0.08-0.29, p<0.0001; Hausa: aOR 1.13, 95% CI 1.03-1.24, p = 0.010). Major ethnic disparities in stunting, wasting and underweight were observed across 18 countries in SSA. Understanding and accounting for these differences is essential to support progress monitoring and targeting of nutrition interventions in children.

Analysing the contribution of trees and green spaces to household nutrition security in eThekwini, KwaZulu-Natal, South Africa

Food from trees and green spaces can diversify diets and enhance food and nutrition security for households. However, sourcing food from these areas often receives little attention in addressing nutrition issues. This study assessed the contribution of trees and green spaces to household food consumption and nutrition in the eThekwini Municipal Area (EMA) located in KwaZulu-Natal South Africa, focusing on the Osindisweni and Maphephetheni communities, which are biologically diverse and face high poverty, unemployment, and food insecurity. Using stratified random sampling, 280 households were selected to complete questionnaires. Additionally, two Focus Group Discussions (FDG's) and key informant interviews were conducted with community members and municipal representatives. Data were analyzed using descriptive statistics, the Household Food Consumption Score (FCS), Ordered Logistic Regression and a thematic analysis was done to analyse responses from Focus Group Discussions. The results showed that 93.6% of households consumed acceptable diets, with only 5.0% in the borderline and 1.4% in the poor categories. Specifically, Osindisweni and Maphephetheni households reported 93.3% and 93.7% acceptable diets, respectively. Ordered logistic regression indicated that both cultivated and uncultivated green spaces, household size, number of dependants, as well as access to training, agricultural assistance, extension, and advisory services negatively correlated with nutrition security. While communities recognized the contributions of trees and green spaces, they believed that these sources alone were insufficient. It is concluded that consumption of products from trees and green spaces likely did not improve the nutrition security of the households. To improve household nutrition security in eThekwini, it is vital to foster collaboration among stakeholders, including nutritionists and extension agents. Strengthening the knowledge of extension officers regarding the harvesting and consumption of food from trees and green spaces is crucial for disseminating effective guidance to households, thereby enhancing nutrition outcomes.

Temporal Trends in the Prevalence of Child Undernutrition in China From 2000 to 2019, With Projections of Prevalence in 2030: Cross-Sectional Analysis

BACKGROUND

Although the problem of malnutrition among children in China has greatly improved in recent years, there is a gap compared to developed countries, and there are differences between provinces. Research on long-term comprehensive trends in child growth failure (CGF) in China is needed for further improvement.

OBJECTIVE

The purpose of this study was to examine trends in stunting, wasting, and underweight among children younger than 5 years in China from 2000 to 2019, and predict CGF till 2030.

METHODS

We conducted a cross-sectional analysis using data from the local burden of disease (LBD) database. Using Joinpoint Regression Software, we examined trends in CGF among children younger than 5 years in China from 2000 to 2019, and predicted the trends of prevalence in 2030, using the Holt-Winters model with trends but without seasonal components. The assessment was performed with Stata 17 (StataCorp). Data were analyzed from October 17, 2023, to November 22, 2023.

RESULTS

In 2019, the prevalences of stunting, wasting, and underweight decreased to 12%, 3%, and 4%, respectively (decreases of 36.9%, 25.0%, and 42.9%, respectively, compared with the values in 2000). The prevalence of CGF decreased rapidly from 2000 to 2010, and the downward trend slowed down after 2010. Most provinces had stagnated processes of trends after 2017. The age group with the highest stunting prevalence was children aged 1 to 4 years, and the highest prevalence of wasting and underweight was noted in early neonatal infants. From 2000 to 2019, the prevalence of CGF declined in all age groups of children. The largest relative decrease in stunting and underweight was noted in children aged 1 to 4 years, and the largest decrease in wasting was noted in early neonatal infants. The prevalences of stunting, wasting, and underweight in China are estimated to decrease to 11.4%, 3.2%, and 4.1%, respectively, by 2030. China has nationally met the World Health Organization's Global Nutrition Targets for 2030 for stunting but not for wasting.

CONCLUSIONS

This study provides data on the prevalence and trends of CGF among children younger than 5 years and reports declines in CGF. There remain areas with slow progress in China. Most units have achieved the goal for stunting prevalence but not wasting prevalence.

Subnational mapping for targeting anaemia prevention in women of reproductive age in Ethiopia: A coverage-equity paradox

Anaemia in women of reproductive age (WRA) can be effectively addressed if supported by a better understanding of the spatial variations, magnitude, severity and distribution of anaemia. This study aimed to map the subnational spatial distribution of anaemia (any, moderate and severe forms) among WRA in Ethiopia. We identified and mapped (any, moderate and severe) anaemia hotspots in WRA (n = 14,923) at the subnational level and identified risk factors using multilevel logistic regression. Kulldorff scan statistics were used to identify hotspot regions. Ordinary kringing was used to predict the anaemia prevalence in unmeasured areas. The overall anaemia prevalence increased from 16.6% in 2011 to 23.6% in 2016, a rise that was mostly related to the widening of existing hotspot areas. The primary clusters of (any) anaemia were in Somali and Afar regions. The horn of the Somali region represented a cluster of 330 km where 10% of WRA were severely anaemic. The Oromia-Somali border represented a significant cluster covering 247 km, with 9% severe anaemia. Population-dense areas with low anaemia prevalence had high absolute number of cases. Women education, taking iron-folic-acid tablets during pregnancy and birth-delivery in health facilities reduced the risk of any anaemia (P < 0.05). The local-level mapping of anaemia helped identify clusters that require attention but also highlighted the urgent need to study the aetiology of anaemia to improve the effectiveness and safety of interventions. Both relative and absolute anaemia estimates are critical to determine where additional attention is needed.

Classification of Healthy Family Indicators in Indonesia Based on a K-means Cluster Analysis

OBJECTIVES

Health development is a key element of national development. The goal of improving health development at the societal level will be readily achieved if it is directed from the smallest social unit, namely the family. This was the goal of the Healthy Indonesia Program with a Family Approach. The objective of the study was to analyze variables of family health indicators across all provinces in Indonesia to identify provincial disparities based on the status of healthy families.

METHODS

This study examined secondary data for 2021 from the Indonesia Health Profile, provided by the Ministry of Health of the Republic of Indonesia, and from the 2021 welfare statistics by Statistics Indonesia (BPS). From these sources, we identified 10 variables for analysis using the k-means method, a non-hierarchical method of cluster analysis.

RESULTS

The results of the cluster analysis of healthy family indicators yielded 5 clusters. In general, cluster 1 (Papua and West Papua Provinces) had the lowest average achievements for healthy family indicators, while cluster 5 (Jakarta Province) had the highest indicator scores.

CONCLUSIONS

In Indonesia, disparities in healthy family indicators persist. Nutrition, maternal health, and child health are among the indicators that require government attention.

Anthropometric failures and its predictors among under five children in Ethiopia: multilevel logistic regression model using 2019 Ethiopian demographic and health survey data

BACKGROUND

Composite Index of Anthropometric Failure (CIAF) combines all three forms of anthropometric failures to assess undernutrition status of children. There is no study on CIAF to identify the real and severe form of under nutrition among Ethiopian children that addressed community level factors. So, this study determined CIAF and identified important factors which helps to design appropriate intervention strategies by using multi-level advanced statistical model.

METHODS

The study included 5,530 under five children and utilized a secondary data (EMDHS 2019) which was collected through community-based and cross-sectionally from March 21 to June 28, 2019. Composite index of anthropometric failure among under five children was assessed and a two-stage sampling technique was used to select the study participants. Descriptive summary statistics was computed. A multi-level binary logistic regression model was employed to identify important predictors of CIAF in under five children. Adjusted odds ratio with its 95% CI was estimated and level of significance 0.05 was used to determine significant predictors of CIAF.

RESULTS

The prevalence of composite index of anthropometric failure (CIAF) was 40.69% (95% CI: 39.41, 42.00) in Ethiopia. Both individual and community level predictors were identified for CIAF in under five children. Among individual level predictors being male sex, older age, short birth interval, from mothers who have not formal education, and from poor household wealth quintile were associated with higher odds of CIAF among under five children. Low community women literacy and being from agriculturally based regions were the community level predictors that were associated with higher odds of CIAF in under five children in Ethiopia.

CONCLUSIONS

The burden of composite index of anthropometric failure in under five children was high in Ethiopia. Age of child, sex of child, preceding birth interval, mother's education, household wealth index, community women literacy and administrative regions of Ethiopia were identified as significant predictors of CIAF. Therefore, emphasis should be given for those factors to decrease the prevalence of CIAF in under five children in Ethiopia.

Interviewer biases in medical survey data: The example of blood pressure measurements

Health agencies rely upon survey-based physical measures to estimate the prevalence of key global health indicators such as hypertension. Such measures are usually collected by nonhealthcare worker personnel and are potentially subject to measurement error due to variations in interviewer technique and setting, termed "interviewer effects." In the context of physical measurements, particularly in low- and middle-income countries, interviewer-induced biases have not yet been examined. Using blood pressure as a case study, we aimed to determine the relative contribution of interviewer effects on the total variance of blood pressure measurements in three large nationally representative health surveys from the Global South. We utilized 169,681 observations between 2008 and 2019 from three health surveys (Indonesia Family Life Survey, National Income Dynamics Study of South Africa, and Longitudinal Aging Study in India). In a linear mixed model, we modeled systolic blood pressure as a continuous dependent variable and interviewer effects as random effects alongside individual factors as covariates. To quantify the interviewer effect-induced uncertainty in hypertension prevalence, we utilized a bootstrap approach comparing subsamples of observed blood pressure measurements to their adjusted counterparts. Our analysis revealed that the proportion of variation contributed by interviewers to blood pressure measurements was statistically significant but small: ∼ 0.24 - - 2.2 % depending on the cohort. Thus, hypertension prevalence estimates were not substantially impacted at national scales. However, individual extreme interviewers could account for measurement divergences as high as 12%. Thus, highly biased interviewers could have important impacts on hypertension estimates at the subdistrict level.

Trends in chronic childhood undernutrition in Bangladesh for small domains

Chronic childhood undernutrition, known as stunting, is an important population health problem with short- and long-term adverse outcomes. Bangladesh has made strides to reduce chronic childhood undernutrition, yet progress is falling short of the 2030 Sustainable Development Goals targets. This study estimates trends in age-specific chronic childhood undernutrition in Bangladesh's 64 districts during 1997-2018, using underlying direct estimates extracted from seven Demographic and Health Surveys in the development of small area time-series models. These models combine cross-sectional, temporal, and spatial data to predict in all districts in both survey and non-survey years. Nationally, there has been a steep decline in stunting from about three in five to one in three children. However, our results highlight significant inequalities in chronic undernutrition, with several districts experiencing less pronounced declines. These differences are more nuanced at the district-by-age level, with only districts in more socio-economically advantaged areas of Bangladesh consistently reporting declines in stunting across all age groups.

Estimating the subnational prevalence of antimicrobial resistant Salmonella enterica serovars Typhi and Paratyphi A infections in 75 endemic countries, 1990-2019: a modelling study

BACKGROUND

Enteric fever, a systemic infection caused by Salmonella enterica serovars Typhi and Paratyphi A, remains a major cause of morbidity and mortality in low-income and middle-income countries. Enteric fever is preventable through the provision of clean water and adequate sanitation and can be successfully treated with antibiotics. However, high levels of antimicrobial resistance (AMR) compromise the effectiveness of treatment. We provide estimates of the prevalence of AMR S Typhi and S Paratyphi A in 75 endemic countries, including 30 locations without data.

METHODS

We used a Bayesian spatiotemporal modelling framework to estimate the percentage of multidrug resistance (MDR), fluoroquinolone non-susceptibility (FQNS), and third-generation cephalosporin resistance in S Typhi and S Paratyphi A infections for 1403 administrative level one districts in 75 endemic countries from 1990 to 2019. We incorporated data from a comprehensive systematic review, public health surveillance networks, and large multicountry studies on enteric fever. Estimates of the prevalence of AMR and the number of AMR infections (based on enteric fever incidence estimates by the Global Burden of Diseases study) were produced at the country, super-region, and total endemic area level for each year of the study.

FINDINGS

We collated data from 601 sources, comprising 184 225 isolates of S Typhi and S Paratyphi A, covering 45 countries over 30 years. We identified a decline of MDR S Typhi in south Asia and southeast Asia, whereas in sub-Saharan Africa, the overall prevalence increased from 6·0% (95% uncertainty interval 4·3-8·0) in 1990 to 72·7% (67·7-77·3) in 2019. Starting from low levels in 1990, the prevalence of FQNS S Typhi increased rapidly, reaching 95·2% (91·4-97·7) in south Asia in 2019. This corresponded to 2·5 million (1·5-3·8) MDR S Typhi infections and 7·4 million (4·7-11·3) FQNS S Typhi infections in endemic countries in 2019. The prevalence of third-generation cephalosporin-resistant S Typhi remained low across the whole endemic area over the study period, except for Pakistan where prevalence of third-generation cephalosporin resistance in S Typhi reached 61·0% (58·0-63·8) in 2019. For S Paratyphi A, we estimated low prevalence of MDR and third-generation cephalosporin resistance in all endemic countries, but a drastic increase of FQNS, which reached 95·0% (93·7-96·1; 3·5 million [2·2-5·6] infections) in 2019.

INTERPRETATION

This study provides a comprehensive and detailed analysis of the prevalence of MDR, FQNS, and third-generation cephalosporin resistance in S Typhi and S Paratyphi A infections in endemic countries, spanning the last 30 years. Our analysis highlights the increasing levels of AMR in this preventable infection and serves as a resource to guide urgently needed public health interventions, such as improvements in water, sanitation, and hygiene and typhoid fever vaccination campaigns.

FUNDING

Fleming Fund, UK Department of Health and Social Care; Wellcome Trust; and Bill and Melinda Gates Foundation.

Socioeconomic inequalities in underweight children: a cross-sectional analysis of trends in West Africa over two decades

OBJECTIVE

To study trends in socioeconomic inequalities in underweight children in West Africa, and specifically to analyse the concentration index of underweight inequalities and measure inequalities in the risk of being malnourished by household wealth index.

DESIGN

Cross-sectional study.

SETTING

The study used 50 Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys conducted between 1999 and 2020 across 14 countries by the DHS and UNICEF.

PARTICIPANTS

The study included 481 349 children under the age of 5 years.

PRIMARY AND SECONDARY OUTCOME MEASURES

The analysis used three variables: weight-for-age index, household wealth index and household residence. The inequality concentration index for underweight children and the relative risk of being underweight between 2000 and 2020 were calculated.

RESULTS

The prevalence of underweight in West Africa showed a downward trend from 2000 to 2020. Nonetheless, the prevalence of underweight children under 5 years of age is still very high in West Africa compared with other sub-Saharan African countries, and the sustainable development objective is yet to be achieved. There was a wide disparity among countries and significant socioeconomic inequalities in underweight children within countries. The proportions of underweight children were concentrated in poor households in all countries in West Africa and over all periods. Socioeconomic inequalities in underweight children were more significant in countries where the prevalence of underweight was low. These inequalities were more pronounced in urban areas in West Africa from 2000 to 2020.

CONCLUSIONS AND RELEVANCE

There is a high concentration of socioeconomic inequalities in underweight children in disadvantaged households in West Africa.

Mapping heterogeneity in family planning indicators in Burkina Faso, Kenya, and Nigeria, 2000-2020

BACKGROUND

Family planning is fundamental to women's reproductive health and is a basic human right. Global targets such as Sustainable Development Goal 3 (specifically, Target 3.7) have been established to promote universal access to sexual and reproductive healthcare services. Country-level estimates of contraceptive use and other family planning indicators are already available and are used for tracking progress towards these goals. However, there is likely heterogeneity in these indicators within countries, and more local estimates can provide crucial additional information about progress towards these goals in specific populations. In this analysis, we develop estimates of six family indicators at a local scale, and use these estimates to describe heterogeneity and spatial-temporal patterns in these indicators in Burkina Faso, Kenya, and Nigeria.

METHODS

We used a Bayesian geostatistical modelling framework to analyse geo-located data on contraceptive use and family planning from 61 household surveys in Burkina Faso, Kenya, and Nigeria in order to generate subnational estimates of prevalence and associated uncertainty for six indicators from 2000 to 2020: contraceptive prevalence rate (CPR), modern contraceptive prevalence rate (mCPR), traditional contraceptive prevalence rate (tCPR), unmet need for modern methods of contraception, met need for family planning with modern methods, and intention to use contraception. For each country and indicator, we generated estimates at an approximately 5 × 5-km resolution and at the first and second administrative levels (regions and provinces in Burkina Faso; counties and sub-counties in Kenya; and states and local government areas in Nigeria).

RESULTS

We found substantial variation among locations in Burkina Faso, Kenya, and Nigeria for each of the family planning indicators estimated. For example, estimated CPR in 2020 ranged from 13.2% (95% Uncertainty Interval, 8.0-20.0%) in Oudalan to 38.9% (30.1-48.6%) in Kadiogo among provinces in Burkina Faso; from 0.4% (0.0-1.9%) in Banissa to 76.3% (58.1-89.6%) in Makueni among sub-counties in Kenya; and from 0.9% (0.3-2.0%) in Yunusari to 31.8% (19.9-46.9%) in Somolu among local government areas in Nigeria. There were also considerable differences among locations in each country in the magnitude of change over time for any given indicator; however, in most cases, there was more consistency in the direction of that change: for example, CPR, mCPR, and met need for family planning with modern methods increased nationally in all three countries between 2000 and 2020, and similarly increased in all provinces of Burkina Faso, and in large majorities of sub-counties in Kenya and local government areas in Nigeria.

CONCLUSIONS

Despite substantial increases in contraceptive use, too many women still have an unmet need for modern methods of contraception. Moreover, country-level estimates of family planning indicators obscure important differences among locations within the same country. The modelling approach described here enables estimating family planning indicators at a subnational level and could be readily adapted to estimate subnational trends in family planning indicators in other countries. These estimates provide a tool for better understanding local needs and informing continued efforts to ensure universal access to sexual and reproductive healthcare services.

Development of prediction models to identify hotspots of schistosomiasis in endemic regions to guide mass drug administration

Schistosomiasis is a neglected tropical disease affecting over 150 million people. Hotspots of Schistosoma transmission-communities where infection prevalence does not decline adequately with mass drug administration-present a key challenge in eliminating schistosomiasis. Current approaches to identify hotspots require evaluation 2-5 y after a baseline survey and subsequent mass drug administration. Here, we develop statistical models to predict hotspots at baseline prior to treatment comparing three common hotspot definitions, using epidemiologic, survey-based, and remote sensing data. In a reanalysis of randomized trials in 589 communities in five endemic countries, a regression model predicts whether Schistosoma mansoni infection prevalence will exceed the WHO threshold of 10% in year 5 ("prevalence hotspot") with 86% sensitivity, 74% specificity, and 93% negative predictive value (NPV; assuming 30% hotspot prevalence), and a regression model for Schistosoma haematobium achieves 90% sensitivity, 90% specificity, and 96% NPV. A random forest model predicts whether S. mansoni moderate and heavy infection prevalence will exceed a public health goal of 1% in year 5 ("intensity hotspot") with 92% sensitivity, 79% specificity, and 96% NPV, and a boosted trees model for S. haematobium achieves 77% sensitivity, 95% specificity, and 91% NPV. Baseline prevalence is a top predictor in all models. Prediction is less accurate in countries not represented in training data and for a third hotspot definition based on relative prevalence reduction over time ("persistent hotspot"). These models may be a tool to prioritize high-risk communities for more frequent surveillance or intervention against schistosomiasis, but prediction of hotspots remains a challenge.

Urbanicity and child health in 26 sub-Saharan African countries: Settlement type and its association with mortality and morbidity

Urbanization and changing settlement patterns have affected health environments in African countries. A profound understanding of the intricate association between urbanicity and health is imperative for formulating effective interventions. This study aims to classify settlement types based on urbanicity and assess their effects on child health in 26 African countries, utilizing data from the Demographic and Health Survey and the Global Human Settlements Layer. The advanced settlement classification incorporates a multidimensional urbanicity scale and globally standardized urban extents, along with identifying urban slums. This approach derives six distinct settlement types: urban center, urban cluster, deprived urban settlement, rural town, rural cluster, and rural village. A multilevel logistic regression model examines the relationship between settlement types and health outcomes, encompassing mortality, fever, anemia, diarrhea, and cough in children under five. The analysis reveals that children living in rural villages and deprived urban settlements face a high burden of adverse health conditions. However, the size and direction of urbanicity's effects vary depending on the specific outcome. These findings highlight the significance of tailored interventions acknowledging health environments within each settlement to promote health equity.

Terrorism and child mortality

How does terrorism affect child mortality? We use geo-coded data on terrorism and spatially disaggregated data on child mortality to study the relationship between both variables for 52 African countries between 2000 and 2017 at the 0.5 × 0.5° grid level. Our estimates suggest that moderate increases in terrorism are linked to several thousand additional annual deaths of children under the age of five. A panel event-study points to economic effects that are larger and compound over time. Interrogating our data, we show that the direct impact of terrorism tends to be very small. Instead, we theorize that terrorism causes child mortality primarily by triggering adverse behavioral responses by parents, medical workers, and policymakers. We provide tentative evidence in support of this argument.

Geostatistical modelling of child undernutrition in developing countries using remote-sensed data: evidence from Bangladesh and Ghana demographic and health surveys

Childhood chronic undernutrition, known as stunting, remains a critical public health problem globally. Unfortunately while the global stunting prevalence has been declining over time, as a result of concerted public health efforts, there are areas (notably in sub-Saharan Africa and South Asia) where progress has stagnated. These regions are also resource-poor, and monitoring progress in the fight against chronic undernutrition can be problematic. We propose geostatistical modelling using data from existing demographic surveys supplemented by remote-sensed information to provide improved estimates of childhood stunting, accounting for spatial and non-spatial differences across regions. We use two study areas-Bangladesh and Ghana-and our results, in the form of prevalence maps, identify communities for targeted intervention. For Bangladesh, the maps show that all districts in the south-eastern region are identified to have greater risk of stunting, while in Ghana the greater northern region had the highest prevalence of stunting. In countries like Bangladesh and Ghana with limited resources, these maps can be useful diagnostic tools for health planning, decision making and implementation.

Child wasting and concurrent stunting in low- and middle-income countries

Sustainable Development Goal 2.2-to end malnutrition by 2030-includes the elimination of child wasting, defined as a weight-for-length z-score that is more than two standard deviations below the median of the World Health Organization standards for child growth1. Prevailing methods to measure wasting rely on cross-sectional surveys that cannot measure onset, recovery and persistence-key features that inform preventive interventions and estimates of disease burden. Here we analyse 21 longitudinal cohorts and show that wasting is a highly dynamic process of onset and recovery, with incidence peaking between birth and 3 months. Many more children experience an episode of wasting at some point during their first 24 months than prevalent cases at a single point in time suggest. For example, at the age of 24 months, 5.6% of children were wasted, but by the same age (24 months), 29.2% of children had experienced at least one wasting episode and 10.0% had experienced two or more episodes. Children who were wasted before the age of 6 months had a faster recovery and shorter episodes than did children who were wasted at older ages; however, early wasting increased the risk of later growth faltering, including concurrent wasting and stunting (low length-for-age z-score), and thus increased the risk of mortality. In diverse populations with high seasonal rainfall, the population average weight-for-length z-score varied substantially (more than 0.5 z in some cohorts), with the lowest mean z-scores occurring during the rainiest months; this indicates that seasonally targeted interventions could be considered. Our results show the importance of establishing interventions to prevent wasting from birth to the age of 6 months, probably through improved maternal nutrition, to complement current programmes that focus on children aged 6-59 months.

The determinants of growth failure in children under five in 25 low- and middle-income countries

BACKGROUND

Past studies have identified determinants of growth failure (GF) such as socio-economic, nutritional, parenting, and inequality factors. However, few studies investigate the numerous causes of GF across multiple countries. By analysing the data of children under five in 25 low and middle-income countries, this study aims to examine the correlations of determinants with GF to identify the strongest modifiable risk factors.

METHODS

Cross-sectional study design was used, and data were collected across 25 LMICs by the United Nations Children's Fund in 2019. Regions and households were randomly selected in participating LMICs. The four outcome measures were stunting, wasting, underweight and low body mass index (BMI).

RESULTS

Multilevel analysis was performed to identify the impact of country, suburb, and household levels on the variance of outcome variables. GF measures were significantly correlated with low gross domestic product (GDP) per capita (odds ratio (OR) = 2.482), rural areas (OR = 1.223), lack of health insurance (OR = 1.474), low maternal education (OR = 2.260), lack of plain water (OR = 1.402), poor maternal physical caregiving ability (OR = 1.112), low carbohydrate consumption (OR = 1.470), and continued breastfeeding in children >12 months old (OR = 0.802).

CONCLUSIONS

By identifying key GF risk factors, this study may provide valuable insights for policymaking and interventions. This may allow the prioritisation of resources within countries for preventative measures to be developed.

Treatment outcomes and associated factors for hospitalization of children treated for acute malnutrition under the OptiMA simplified protocol: a prospective observational cohort in rural Niger

Introduction

Globally, access to treatment for severe and moderate acute malnutrition is very low, in part because different protocols and products are used in separate programs. New approaches, defining acute malnutrition (AM) as mid-upper arm circumference (MUAC) < 125 mm or oedema, are being investigated to compare effectiveness to current programs. Optimizing Malnutrition treatment (OptiMA) is one such strategy that treats AM with one product - ready-to-use therapeutic food, or RUTF - at reduced dosage as the child improves.

Methods

This study aimed to determine whether OptiMA achieved effectiveness benchmarks established in the Nigerien National Nutrition protocol. A prospective cohort study of children in the rural Mirriah district evaluated outcomes among children 6-59 months with uncomplicated AM treated under OptiMA. In a parallel, unconnected program in one of the two trial sites, all non-malnourished children 6-23 months of age were provided small quantity lipid-based nutritional supplements (SQ-LNS). A multivariate logistic regression identified factors associated with hospitalization.

Results

From July-December 2019, 1,105 children were included for analysis. Prior to treatment, 39.3% of children received SQ-LNS. Recovery, non-response, and mortality rates were 82.3%, 12.6%, and 0.7%, respectively, and the hospitalization rate was 15.1%. Children who received SQ-LNS before an episode of AM were 43% less likely to be hospitalized (ORa=0.57; 0.39-0.85, p = 0.004).

Discussion

OptiMA had acceptable recovery compared to the Nigerien reference but non-response was high. Children who received SQ-LNS before treatment under OptiMA were less likely to be hospitalized, showing potential health benefits of combining simplified treatment protocols with food-based prevention in an area with a high burden of malnutrition such as rural Niger.

Spatial parasitology and the unmapped human helminthiases

Helminthiases are a class of neglected tropical diseases that affect at least 1 billion people worldwide, with a disproportionate impact on resource-poor areas with limited disease surveillance. Geospatial methods can offer valuable insights into the burden of these infections, particularly given that many are subject to strong ecological influences on the environmental, vector-borne or zoonotic stages of their life cycle. In this study, we screened 6829 abstracts and analysed 485 studies that use maps to document, infer or predict transmission patterns for over 200 species of parasitic worms. We found that quantitative mapping methods are increasingly used in medical parasitology, drawing on One Health surveillance data from the community scale to model geographic distributions and burdens up to the regional or global scale. However, we found that the vast majority of the human helminthiases may be entirely unmapped, with research effort focused disproportionately on a half-dozen infections that are targeted by mass drug administration programmes. Entire regions were also surprisingly under-represented in the literature, particularly southern Asia and the Neotropics. We conclude by proposing a shortlist of possible priorities for future research, including several neglected helminthiases with a burden that may be underestimated.

Effect of women's literacy on maternal and child health: Evidence from demographic Health Survey data in Senegal

BACKGROUND

Senegal has certainly made significant efforts in adult literacy and in the fight against maternal and infant mortality. However, a large proportion of the female population is illiterate, and the country's maternal and infant mortality rates are still higher than WHO recommendations. This article examined the effect of women's literacy on maternal and child health in Senegal.

METHODS

Data were extracted from the last Senegal Demographic and Health Surveys (DHS) collected in 2019. A binary logistic model was performed to assess the effect of women's literacy on ten outcomes of maternal and child health indicators.

RESULTS

Results indicate that women's literacy has a positive and significant effect on nine of key indicators outcomes. For instance, women's literacy increases the odds of contraceptive use by 1.29 (95% Confidence Interval [CI], 1.13-1.48; p < 0.01), compliance with the number of prenatal visits by 1.57 (95% CI, 1.35-1.83; p < 0.01) and consultation in the first trimester of pregnancy by 1.52 (95% CI, 1.31-1.78; p < 0.01). Literacy is associated with increased odds of breastfeeding up to six months (Odds Ratio [OR], 1.17; 95% CI, 0.97-1.42; p < 0.1) and healthy birth interval (OR, 1.18; 95% CI, 0.97-1.44; p < 0.1) only in rural areas. Women literacy reduces the risk of stunting by 0.81 (95% CI, 0.68-0.96; p < 0.05) and the risk of underweight by 0.72 (95% CI, 0.59-0.87; p < 0.01) among children under five years. The mother's ability to read and write favors compliance with the increase of odds of DPT vaccination record of her children, especially in rural areas (OR, 1.69; 95% CI, 1.05-2.74; p < 0.05). Moreover, there are serval other factors influencing positively the maternal and child health, like health insurance access, media exposure, and clean water and improved sanitation facilities access.

CONCLUSIONS

This study emphasizes on the need to strengthen adult literacy programs, especially for women in rural areas. Indeed, this could help generalize health insurance by income-sensitive premiums or exemptions for the poor as well as increased awareness campaigns to promote reproductive and maternal health benefits, via the radio or television. Furthermore, improving access to clean water supply and improved sanitation facilities would greatly ameliorate maternal and child health.

Essential childhood immunization in 43 low- and middle-income countries: Analysis of spatial trends and socioeconomic inequalities in vaccine coverage

BACKGROUND

Globally, access to life-saving vaccines has improved considerably in the past 5 decades. However, progress has started to slow down and even reverse in recent years. Understanding subnational heterogeneities in essential child immunization will be critical for closing the global vaccination gap.

METHODS AND FINDINGS

We use vaccination information for over 220,000 children across 1,366 administrative regions in 43 low- and middle-income countries (LMICs) from the most recent Demographic and Health Surveys. We estimate essential immunization coverage at the national and subnational levels and quantify socioeconomic inequalities in such coverage using adjusted concentration indices. Within- and between-country variations are summarized via the Theil index. We use local indicator of spatial association (LISA) statistics to identify clusters of administrative regions with high or low values. Finally, we estimate the number of missed vaccinations among children aged 15 to 35 months across all 43 countries and the types of vaccines most often missed. We show that national-level vaccination rates can conceal wide subnational heterogeneities. Large gaps in child immunization are found across West and Central Africa and in South Asia, particularly in regions of Angola, Chad, Nigeria, Guinea, and Afghanistan, where less than 10% of children are fully immunized. Furthermore, children living in these countries consistently lack all 4 basic vaccines included in the WHO's recommended schedule for young children. Across most countries, children from poorer households are less likely to be fully immunized. The main limitations include subnational estimates based on large administrative divisions for some countries and different periods of survey data collection.

CONCLUSIONS

The identified heterogeneities in essential childhood immunization, especially given that some regions consistently are underserved for all basic vaccines, can be used to inform the design and implementation of localized intervention programs aimed at eliminating child suffering and deaths from existing and novel vaccine-preventable diseases.

Predicting socioeconomic indicators using transfer learning on imagery data: an application in Brazil

Censuses and other surveys responsible for gathering socioeconomic data are expensive and time consuming. For this reason, in poor and developing countries there often is a long gap between these surveys, which hinders the appropriate formulation of public policies as well as the development of researches. One possible approach to overcome this challenge for some socioeconomic indicators is to use satellite imagery to estimate these variables, although it is not possible to replace demographic census surveys completely due to its territorial coverage, level of disaggregation of information and large set of information. Even though using orbital images properly requires, at least, a basic remote sensing knowledge level, these images have the advantage of being commonly free and easy to access. In this paper, we use daytime and nighttime satellite imagery and apply a transfer learning technique to estimate average income, GDP per capita and a constructed water index at the city level in two Brazilian states, Bahia and Rio Grande do Sul. The transfer learning approach could explain up to 64% of the variation in city-level variables depending on the state and variable. Although data from different countries may be considerably different, results are consistent with the literature and encouraging as it is a first analysis of its kind for Brazil.

Occupations on the map: Using a super learner algorithm to downscale labor statistics

Detailed and accurate labor statistics are fundamental to support social policies that aim to improve the match between labor supply and demand, and support the creation of jobs. Despite overwhelming evidence that labor activities are distributed unevenly across space, detailed statistics on the geographical distribution of labor and work are not readily available. To fill this gap, we demonstrated an approach to create fine-scale gridded occupation maps by means of downscaling district-level labor statistics, informed by remote sensing and other spatial information. We applied a super-learner algorithm that combined the results of different machine learning models to predict the shares of six major occupation categories and the labor force participation rate at a resolution of 30 arc seconds (~1x1 km) in Vietnam. The results were subsequently combined with gridded information on the working-age population to produce maps of the number of workers per occupation. The super learners outperformed (n = 6) or had similar (n = 1) accuracy in comparison to best-performing single machine learning algorithms. A comparison with an independent high-resolution wealth index showed that the shares of the four low-skilled occupation categories (91% of the labor force), were able to explain between 28% and 43% of the spatial variation in wealth in Vietnam, pointing at a strong spatial relationship between work, income and wealth. The proposed approach can also be applied to produce maps of other (labor) statistics, which are only available at aggregated levels.

The overlapping burden of the three leading causes of disability and death in sub-Saharan African children

Despite substantial declines since 2000, lower respiratory infections (LRIs), diarrhoeal diseases, and malaria remain among the leading causes of nonfatal and fatal disease burden for children under 5 years of age (under 5), primarily in sub-Saharan Africa (SSA). The spatial burden of each of these diseases has been estimated subnationally across SSA, yet no prior analyses have examined the pattern of their combined burden. Here we synthesise subnational estimates of the burden of LRIs, diarrhoea, and malaria in children under-5 from 2000 to 2017 for 43 sub-Saharan countries. Some units faced a relatively equal burden from each of the three diseases, while others had one or two dominant sources of unit-level burden, with no consistent pattern geographically across the entire subcontinent. Using a subnational counterfactual analysis, we show that nearly 300 million DALYs could have been averted since 2000 by raising all units to their national average. Our findings are directly relevant for decision-makers in determining which and targeting where the most appropriate interventions are for increasing child survival.

Spatial aggregation with respect to a population distribution: Impact on inference

Spatial aggregation with respect to a population distribution involves estimating aggregate population quantities based on observations from individuals. In this context, a geostatistical workflow must account for three major sources of aggregation error: aggregation weights, fine scale variation, and finite population variation. However, these sources of aggregation error are commonly ignored, and the population instead treated as a fixed population density surface. We improve common practice by introducing a sampling frame model allowing aggregation models to account for aggregation error simply and transparently. This preserves aggregate point estimates while increasing their uncertainties. We compare the proposed and the traditional approach using two simulation studies mimicking neonatal mortality rate (NMR) data from the 2014 Kenya Demographic and Health Survey. In the traditional approach, undercoverage/overcoverage of interval estimates depends arbitrarily on the aggregation grid resolution, while the new approach is resolution robust. Differences between the aggregation approaches increase as an area's population decreases, and are particularly large at the second administrative level and finer, but also at the first administrative level for some population quantities. These findings are consistent with those of an application to the true NMR data. We demonstrate in a sensitivity analysis that burden estimates and their uncertainties are not robust to changes in population density and census information, while prevalence estimates and uncertainties seem stable.

The geo-spatial perspective of biological, social and environmental determinants of early pregnancy anaemia in rural Sri Lanka: Need for context-specific approaches on prevention

We provide a novel approach to understanding the multiple causations of maternal anaemia in a geospatial context, highlighting how genetics, environment and socioeconomic disparities at the micro-geographical level lead to the inequitable distribution of anaemia. All first-trimester pregnant women registered for the antenatal care programme in Anuradhapura District, Sri Lanka from July to September 2019 were invited to the Rajarata Pregnancy Cohort (RaPCo), which assessed the prevalence of anaemia in early pregnancy. The combination of the prevalence of anaemia and minor haemoglobinopathy-related anaemia (MHA) with the poverty headcount index of the 22 health divisions in the district was investigated using GeoDa spatial K-means clustering. Sociodemographic and economic data at the divisional level were compared between identified clusters. Combining the analysis with the geographical and environmental characteristics of the region, further hypotheses regarding anaemia in this community were formulated. The study included data from 3,137 pregnant women in early pregnancy. The anaemia and MHA prevalence varied from 13.6 to 21.7% and from 2.6% to 5%, respectively. We identified four distinct spatial clusters. The cluster with the highest anaemia prevalence also included high poverty and the highest prevalence of MHA. The clusters had significant differences with regard to ethnic distribution, access to water, sanitation and dietary patterns. Areas supplied by major irrigation projects had significantly low levels of anaemia, probably attributable to internal migration and improved livelihood. It was evident that genetic, socioeconomic and environmental risk factors were grouped at the divisional level, and that their complex interactions make controlling anaemia with blanket interventions unsuccessful. Analysis of the distribution of heterogeneous risk factors at the micro-geospatial level helped identify context-specific approaches to tackle anaemia in pregnancy.

Dietary patterns and childhood stunting in Zimbabwe

Background

Diet is one important predictor of children’s growth, and often dietary interventions can assist with reversing adverse nutrition outcomes. Traditionally research has focused on individual food items or food classes to generate an understanding of disease risk. Dietary patterns provide a holistic approach to understanding the relationship between exposure and outcome.

Method

A matched case-control study was conducted. Caregivers of 450 children (225 cases, 225 controls) aged 6–59 months were asked to describe the diet their children had consumed in the previous 7 days using a Food Frequency Questionnaire. Dietary patterns were developed using factor analysis and regression analysis was conducted to assess which dietary pattern was associated with childhood stunting.

Results

Three dietary patterns were identified: modern (n = 181), low animal-source (n = 158), and traditional (n = 111). Children with the low animal source dietary pattern had increased odds of being stunted (AOR 1.03, p < 0.05). Three demographic factors (Child’s age, father’s age and having a sibling < 24 months apart) were identified as significant predictors of consumption of any of the traditional and low animal source diet (P < 0.001).

Conclusion

Nutrition intervention such as health education, counselling and supplementary feeding should include a holistic approach to dietary education not only focusing on promoting a balanced diet but improvement strengthening the upgrading of child’s dietary pattern taking into cognisant both quantity, and quality of nutrients provided to the child.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40795-022-00607-7.

The state of health in Indonesia's provinces, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019

BACKGROUND

Analysing trends and levels of the burden of disease at the national level can mask inequalities in health-related progress in lower administrative units such as provinces and districts. We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to analyse health patterns in Indonesia at the provincial level between 1990 and 2019. Long-term analyses of disease burden provide insights on Indonesia's advance to universal health coverage and its ability to meet the United Nations Sustainable Development Goals by 2030.

METHODS

We analysed GBD 2019 estimated cause-specific mortality, years of life lost (YLLs), years lived with disability (YLDs), disability-adjusted life-years (DALYs), life expectancy at birth, healthy life expectancy, and risk factors for 286 causes of death, 369 causes of non-fatal health loss, and 87 risk factors by year, age, and sex for Indonesia and its 34 provinces from 1990 to 2019. To generate estimates for Indonesia at the national level, we used 138 location-years of data to estimate Indonesia-specific demographic indicators, 317 location-years of data for Indonesia-specific causes of death, 689 location-years of data for Indonesia-specific non-fatal outcomes, 250 location-years of data for Indonesia-specific risk factors, and 1641 location-years of data for Indonesia-specific covariates. For subnational estimates, we used the following source counts: 138 location-years of data to estimate Indonesia-specific demographic indicators; 5848 location-years of data for Indonesia-specific causes of death; 1534 location-years of data for Indonesia-specific non-fatal outcomes; 650 location-years of data for Indonesia-specific risk factors; and 16 016 location-years of data for Indonesia-specific covariates. We generated our GBD 2019 estimates for Indonesia by including 1 915 207 total source metadata rows, and we used 821 total citations.

FINDINGS

Life expectancy for males across Indonesia increased from 62·5 years (95% uncertainty interval 61·3-63·7) to 69·4 years (67·2-71·6) between 1990 and 2019, a positive change of 6·9 years. For females during the same period, life expectancy increased from 65·7 years (64·5-66·8) to 73·5 years (71·6-75·6), an increase of 7·8 years. There were large disparities in health outcomes among provinces. In 2019, Bali had the highest life expectancy at birth for males (74·4 years, 70·90-77·9) and North Kalimantan had the highest life expectancy at birth for females (77·7 years, 74·7-81·2), whereas Papua had the lowest life expectancy at birth for males (64·5 years, 60·9-68·2) and North Maluku had the lowest life expectancy at birth for females (64·0 years, 60·7-67·3). The difference in life expectancy for males between the highest-ranked and lowest-ranked provinces was 9·9 years and the difference in life expectacy for females between the highest-ranked and lowest-ranked provinces was 13·7 years. Age-standardised death, YLL, and YLD rates also varied widely among the provinces in 2019. High systolic blood pressure, tobacco, dietary risks, high fasting plasma glucose, and high BMI were the five leading risks contributing to health loss measured as DALYs in 2019.

INTERPRETATION

Our findings highlight that Indonesia faces a double burden of communicable and non-communicable diseases that varies across provinces. From 1990 to 2019, Indonesia witnessed a decline in the infectious disease burden, although communicable diseases such as tuberculosis, diarrhoeal diseases, and lower respiratory infections have remained a main source of DALYs in Indonesia. During that same period, however, all-ages death and disability rates from non-communicable diseases and exposure to their risk factors accounted for larger shares of health loss. The differences in health outcomes between the highest-performing and lowest-performing provinces have also widened since 1990. Our findings support a comprehensive process to revisit current health policies, examine the root causes of variation in the burden of disease among provinces, and strengthen programmes and policies aimed at reducing disparities across the country.

FUNDING

The Bill & Melinda Gates Foundation and the Government of Indonesia.

TRANSLATION

For the Bahasa Indonesia translation of the abstract see Supplementary Materials section.

Mapping development and health effects of cooking with solid fuels in low-income and middle-income countries, 2000-18: a geospatial modelling study

BACKGROUND

More than 3 billion people do not have access to clean energy and primarily use solid fuels to cook. Use of solid fuels generates household air pollution, which was associated with more than 2 million deaths in 2019. Although local patterns in cooking vary systematically, subnational trends in use of solid fuels have yet to be comprehensively analysed. We estimated the prevalence of solid-fuel use with high spatial resolution to explore subnational inequalities, assess local progress, and assess the effects on health in low-income and middle-income countries (LMICs) without universal access to clean fuels.

METHODS

We did a geospatial modelling study to map the prevalence of solid-fuel use for cooking at a 5 km × 5 km resolution in 98 LMICs based on 2·1 million household observations of the primary cooking fuel used from 663 population-based household surveys over the years 2000 to 2018. We use observed temporal patterns to forecast household air pollution in 2030 and to assess the probability of attaining the Sustainable Development Goal (SDG) target indicator for clean cooking. We aligned our estimates of household air pollution to geospatial estimates of ambient air pollution to establish the risk transition occurring in LMICs. Finally, we quantified the effect of residual primary solid-fuel use for cooking on child health by doing a counterfactual risk assessment to estimate the proportion of deaths from lower respiratory tract infections in children younger than 5 years that could be associated with household air pollution.

FINDINGS

Although primary reliance on solid-fuel use for cooking has declined globally, it remains widespread. 593 million people live in districts where the prevalence of solid-fuel use for cooking exceeds 95%. 66% of people in LMICs live in districts that are not on track to meet the SDG target for universal access to clean energy by 2030. Household air pollution continues to be a major contributor to particulate exposure in LMICs, and rising ambient air pollution is undermining potential gains from reductions in the prevalence of solid-fuel use for cooking in many countries. We estimated that, in 2018, 205 000 (95% uncertainty interval 147 000-257 000) children younger than 5 years died from lower respiratory tract infections that could be attributed to household air pollution.

INTERPRETATION

Efforts to accelerate the adoption of clean cooking fuels need to be substantially increased and recalibrated to account for subnational inequalities, because there are substantial opportunities to improve air quality and avert child mortality associated with household air pollution.

FUNDING

Bill & Melinda Gates Foundation.

The prevalence of onchocerciasis in Africa and Yemen, 2000-2018: a geospatial analysis

BACKGROUND

Onchocerciasis is a disease caused by infection with Onchocerca volvulus, which is transmitted to humans via the bite of several species of black fly, and is responsible for permanent blindness or vision loss, as well as severe skin disease. Predominantly endemic in parts of Africa and Yemen, preventive chemotherapy with mass drug administration of ivermectin is the primary intervention recommended for the elimination of its transmission.

METHODS

A dataset of 18,116 geo-referenced prevalence survey datapoints was used to model annual 2000-2018 infection prevalence in Africa and Yemen. Using Bayesian model-based geostatistics, we generated spatially continuous estimates of all-age 2000-2018 onchocerciasis infection prevalence at the 5 × 5-km resolution as well as aggregations to the national level, along with corresponding estimates of the uncertainty in these predictions.

RESULTS

As of 2018, the prevalence of onchocerciasis infection continues to be concentrated across central and western Africa, with the highest mean estimates at the national level in Ghana (12.2%, 95% uncertainty interval [UI] 5.0-22.7). Mean estimates exceed 5% infection prevalence at the national level for Cameroon, Central African Republic, Democratic Republic of the Congo (DRC), Guinea-Bissau, Sierra Leone, and South Sudan.

CONCLUSIONS

Our analysis suggests that onchocerciasis infection has declined over the last two decades throughout western and central Africa. Focal areas of Angola, Cameroon, the Democratic Republic of the Congo, Ethiopia, Ghana, Guinea, Mali, Nigeria, South Sudan, and Uganda continue to have mean microfiladermia prevalence estimates exceeding 25%. At and above this level, the continuation or initiation of mass drug administration with ivermectin is supported. If national programs aim to eliminate onchocerciasis infection, additional surveillance or supervision of areas of predicted high prevalence would be warranted to ensure sufficiently high coverage of program interventions.

Modelling the ecological niche of naturally occurring anthrax at global and circumpolar extents using an ensemble modelling framework

Bacillus anthracis, the causative agent of anthrax, is a spore-forming bacterium that primarily affects herbivorous livestock, wildlife and humans exposed to direct contact with infected animal carcasses or products. To date, there are a limited number of studies that have delineated the potential global distribution of anthrax, despite the importance of the disease from both an economic and public health standpoint. This study compiled occurrence data (n = 874) of confirmed human and animal cases from 1954 to 2021 in 94 countries. Using an ensemble ecological niche model framework, we developed updated maps of the global predicted ecological suitability of anthrax to measure relative risk at multiple scales of analysis, including a model for circumpolar regions. Additionally, we produced maps quantifying the disease transmission risk associated with anthrax to cattle, sheep and goat populations. Environmental suitability for B. anthracis globally is concentred throughout Eurasia, sub-Saharan Africa, the Americas, Southeast Asia, Australia and Oceania. Suitable environments for B. anthracis at the circumpolar scale extend above the Arctic Circle into portions of Russia, Canada, Alaska and northern Scandinavia. Environmental factors driving B. anthracis suitability globally include vegetation, land surface temperature, soil characteristics, primary climate conditions and topography. At the circumpolar scale, suitability is influenced by soil factors, topography and the derived climate characteristics. The greatest risk to livestock is concentrated within the Indian subcontinent, Australia, Anatolia, the Caucasus region, Central Asia, the European Union, Argentina, Uruguay, China, the United States, Canada and East Africa. This study expands on previous work by providing enhanced knowledge of the potential spatial distribution of anthrax in the Southern Hemisphere, sub-Saharan Africa, Asia and circumpolar regions of the Northern Hemisphere. We conclude that these updated maps will provide pertinent information to guide disease control programs, inform policymakers and raise awareness at the global level to lessen morbidity and mortality among animals and humans located in environmentally suitable areas.

Can we predict the burden of acute malnutrition in crisis-affected countries? Findings from Somalia and South Sudan

BACKGROUND

Sample surveys are the mainstay of surveillance for acute malnutrition in settings affected by crises but are burdensome and have limited geographical coverage due to insecurity and other access issues. As a possible complement to surveys, we explored a statistical approach to predict the prevalent burden of acute malnutrition for small population strata in two crisis-affected countries, Somalia (2014-2018) and South Sudan (2015-2018).

METHODS

For each country, we sourced datasets generated by humanitarian actors or other entities on insecurity, displacement, food insecurity, access to services, epidemic occurrence and other factors on the causal pathway to malnutrition. We merged these with datasets of sample household anthropometric surveys done at administrative level 3 (district, county) as part of nutritional surveillance, and, for each of several outcomes including binary and continuous indices based on either weight-for-height or middle-upper-arm circumference, fitted and evaluated the predictive performance of generalised linear models and, as an alternative, machine learning random forests.

RESULTS

We developed models based on 85 ground surveys in Somalia and 175 in South Sudan. Livelihood type, armed conflict intensity, measles incidence, vegetation index and water price were important predictors in Somalia, and livelihood, measles incidence, rainfall and terms of trade (purchasing power) in South Sudan. However, both generalised linear models and random forests had low performance for both binary and continuous anthropometric outcomes.

CONCLUSIONS

Predictive models had disappointing performance and are not usable for action. The range of data used and their quality probably limited our analysis. The predictive approach remains theoretically attractive and deserves further evaluation with larger datasets across multiple settings.

Mapping local variation in household overcrowding across Africa from 2000 to 2018: a modelling study

BACKGROUND

Household overcrowding is a serious public health threat associated with high morbidity and mortality. Rapid population growth and urbanisation contribute to overcrowding and poor sanitation in low-income and middle- income countries, and are risk factors for the spread of infectious diseases, including COVID-19, and antimicrobial resistance. Many countries do not have adequate surveillance capacity to monitor household overcrowding. Geostatistical models are therefore useful tools for estimating household overcrowding. In this study, we aimed to estimate household overcrowding in Africa between 2000 and 2018 by combining available household survey data, population censuses, and other country-specific household surveys within a geostatistical framework.

METHODS

We used data from household surveys and population censuses to generate a Bayesian geostatistical model of household overcrowding in Africa for the 19-year period between 2000 and 2018. Additional sociodemographic and health-related covariates informed the model, which covered 54 African countries.

FINDINGS

We analysed 287 surveys and population censuses, covering 78 695 991 households. Spatial and temporal variability arose in household overcrowding estimates over time. In 2018, the highest overcrowding estimates were observed in the Horn of Africa region (median proportion 62% [IQR 57-63]); the lowest regional median proportion was estimated for the north of Africa region (16% [14-19]). Overall, 474·4 million (95% uncertainty interval [UI] 250·1 million-740·7 million) people were estimated to be living in overcrowded conditions in Africa in 2018, a 62·7% increase from the estimated 291·5 million (180·8 million-417·3 million) people who lived in overcrowded conditions in the year 2000. 48·5% (229·9 million) of people living in overcrowded conditions came from six African countries (Nigeria, Ethiopia, Democratic Republic of the Congo, Sudan, Uganda, and Kenya), with a combined population of 538·3 million people.

INTERPRETATION

This study incorporated survey and population censuses data and used geostatistical modelling to estimate continent-wide overcrowding over a 19-year period. Our analysis identified countries and areas with high numbers of people living in overcrowded conditions, thereby providing a benchmark for policy planning and the implementation of interventions such as in infectious disease control.

FUNDING

UK Department of Health and Social Care, Wellcome Trust, Bill & Melinda Gates Foundation.

Geospatial modeling of pre-intervention nodule prevalence of Onchocerca volvulus in Ethiopia as an aid to onchocerciasis elimination

Background

Onchocerciasis is a neglected tropical filarial disease transmitted by the bites of blackflies, causing blindness and severe skin lesions. The change in focus for onchocerciasis management from control to elimination requires thorough mapping of pre-control endemicity to identify areas requiring interventions and to monitor progress. Onchocerca volvulus nodule prevalence in sub-Saharan Africa is spatially continuous and heterogeneous, and highly endemic areas may contribute to transmission in areas of low endemicity or vice-versa. Ethiopia is one such onchocerciasis-endemic country with heterogeneous O. volvulus nodule prevalence, and many districts are still unmapped despite their potential for onchocerciasis transmission.

Methodology/Principle findings

A Bayesian geostatistical model was fitted for retrospective pre-intervention nodule prevalence data collected from 916 unique sites and 35,077 people across Ethiopia. We used multiple environmental, socio-demographic, and climate variables to estimate the pre-intervention prevalence of O. volvulus nodules across Ethiopia and to explore their relationship with prevalence. Prevalence was high in southern and northwestern Ethiopia and low in Ethiopia’s central and eastern parts. Distance to the nearest river (RR: 0.9850, 95% BCI: 0.9751–0.995), precipitation seasonality (RR: 0.9837, 95% BCI: 0.9681–0.9995), and flow accumulation (RR: 0.9586, 95% BCI: 0.9321–0.9816) were negatively associated with O. volvulus nodule prevalence, while soil moisture (RR: 1.0218, 95% BCI: 1.0135–1.0302) was positively associated. The model estimated the number of pre-intervention cases of O. volvulus nodules in Ethiopia to be around 6.48 million (95% BCI: 3.53–13.04 million).

Conclusions/Significance

Nodule prevalence distribution was correlated with habitat suitability for vector breeding and associated biting behavior. The modeled pre-intervention prevalence can be used as a guide for determining priorities for elimination mapping in regions of Ethiopia that are currently unmapped, most of which have comparatively low infection prevalence.

Areas with unknown onchocerciasis endemicity may pose a threat to eliminating transmission because they may re-introduce onchocerciasis to areas where interventions have been successful. Additionally, because vectors (and thus Onchocerca volvulus transmission) have specific ecological requirements for growth and development, changes in these ecological factors due to human activities (deforestation, modification of river flows by dam construction, climate change) might change patterns of parasite transmission and endemicity. To estimate the impact of environmental changes, we must first identify ecological factors determining prevalence. We have employed Bayesian geostatistical modeling to create a nationwide O. volvulus nodule prevalence map for Ethiopia based on pre-intervention nodule prevalence data and have explored the effect of environmental variables on nodule prevalence. We estimated the number of pre-intervention cases of nodules and associated uncertainty in previously unmapped areas of Ethiopia to identify areas that need additional data to increase the prediction accuracy. Hydrological variables such as distance to the nearest river, precipitation seasonality, soil moisture, and flow accumulation are associated significantly with O. volvulus nodule prevalence. We show that the spatial distribution of nodule prevalence can be estimated based on ecological data and that predicted prevalence can be used as a guide to prioritize pre-intervention mapping.

rspatialdata: a collection of data sources and tutorials on downloading and visualising spatial data using R

Spatial and spatio-temporal data are used in a wide range of fields including environmental, health and social disciplines. Several packages in the statistical software R have been recently developed as clients for various databases to meet the growing demands for easily accessible and reliable spatial data. While documentation on how to use many of these packages exist, there is an increasing need for a one stop repository for tutorials on this information. In this paper, we present rspatialdata a website that provides a collection of data sources and tutorials on downloading and visualising spatial data using R. The website includes a wide range of datasets including administrative boundaries of countries, Open Street Map data, population, temperature, vegetation, air pollution, and malaria data. The goal of the website is to equip researchers and communities with the tools to engage in spatial data analysis and visualisation so that they can address important local issues, such as estimating air pollution, quantifying disease burdens, and evaluating and monitoring the United Nation’s sustainable development goals.

Growth delay: an alternative measure of population health based on child height distributions

BACKGROUND

Indicators of child height, such as mean height-for-age Z-scores (HAZ), height-for-age difference (HAD) and stunting prevalence, do not account for differences in population-average bone developmental stage.

AIM

Propose a measure of child height that conveys the dependency of linear growth on stage rather than chronological age.

SUBJECTS AND METHODS

Using Demographic and Health Surveys (2000-2018; 64 countries), we generated: 1) predicted HAZ at specific ages (HAZ regressed on age); 2) height-age (age at which mean height matches the WHO Growth Standards median); 3) Growth delay (GD), the difference between chronological age and height-age; 4) HAD; and 5) stunting prevalence. Metrics were compared based on secular trends within countries and age-related trajectories within surveys.

RESULTS

In the most recent surveys (N = 64), GDs ranged from 1.9 to 19.1 months at 60 months chronological age. Cross-sectionally, HAZ, HAD and GD were perfectly correlated, and showed similar secular trends. However, age-related trajectories differed across metrics. Accumulating GD with age demonstrated growth faltering as slower than expected growth for children of the same height-age. Resumption of growth at the median for height-age was rarely observed.

CONCLUSION

GD is a population-level measure of child health that reflects the role of delayed skeletal development in linear growth faltering.

Geospatial approach to investigate spatial clustering and hotspots of blood lead levels in children within Kabwe, Zambia

BACKGROUND

Communities around Kabwe, Zambia are exposed to lead due to deposits from an old lead (Pb) and zinc (Zn) mining site. Children are particularly more vulnerable than adults, presenting with greatest risk of health complications. They have increased oral uptake due to their hand to mouth activities. Spatial analysis of childhood lead exposure is useful in identifying specific areas with highest risk of pollution. The objective of the current study was to use a geospatial approach to investigate spatial clustering and hotspots of blood lead levels in children within Kabwe.

METHODS

We analysed existing data on blood lead levels (BLL) for 362 children below the age of 15 from Kabwe town. We used spatial autocorrelation methods involving the global Moran's I and local Getis-Ord Gi*statistic in ArcMap 10.5.1, to test for spatial dependency among the blood lead levels in children using the household geolocations.

RESULTS

BLL in children from Kabwe are spatially autocorrelated with a Moran's Index of 0.62 (p < 0.001). We found distinct hotspots (mean 51.9 μg/dL) in communities close to the old lead and zinc-mining site, lying on its western side. Whereas coldspots (mean 7 μg/dL) where observed in areas distant to the mine and traced on the eastern side. This pattern suggests a possible association between observed BLL and distance from the abandoned lead and zinc mine, and prevailing winds.

CONCLUSION

Using geocoded data for households, we found clustering of childhood blood lead and identified distinct hotspot areas with high lead levels for Kabwe town. The geospatial approach used is especially valuable in resource-constrained settings like Zambia, where the precise identification of high risk locations allows for the initiation of targeted remedial and treatment programs.

Modeling the potential impacts of improved monthly income on child stunting in India: a subnational geospatial perspective

OBJECTIVES

Approximately one-third of the world's stunted (low height-for-age) preschool-aged children live in India. The success of interventions designed to tackle stunting appears to vary by location and depth of poverty. We developed small-area estimation models to assess the potential impact of increments in household income on stunting across the country.

DESIGN

Two nationally representative cross-sectional datasets were used: India's National Family Health Survey 4 (2015-2016) and the 68th round of the National Sample Survey on consumer expenditure. The two datasets were combined with statistical matching. Gaussian process regressions were used to perform geospatial modelling of 'stunting' controlling for household wealth and other covariates.

SETTING AND PARTICIPANTS

The number of children in this sample totalled 259 627. Children with implausible height-for-age z-scores (HAZs) >5 or <-5, or missing data on drinking water, sanitation facility, mother's education, or geolocation and children not residing in mainland India were excluded, resulting in 207 695 observations for analysis.

RESULTS

A monthly transfer of ~$7 (500 Indian rupees) per capita to every household (not targeted or conditional) was estimated to reduce stunting nationally by 3.8 percentage points on average (95% credible interval: 0.14%-10%), but with substantial variation by state. Estimated reduction in stunting varied by wealth of households, with the poorest quintile being likely to benefit the most.

CONCLUSION

Improving household income, which can be supported through cash transfers, has the potential to significantly reduce stunting in parts of India where the burdens of both stunting and poverty are high. Modelling shows that for other regions, income transfers may raise incomes and contribute to improved nutrition, but there would be a need for complementary activities for alleviating stunting. While having value for the country as a whole, impact of income gained could be variable, and underlying drivers of stunting need to be tackled through supplementary interventions.

Gender differences in nutritional status and determinants among infants (6–11 m): a cross-sectional study in two regions in Ethiopia

Background

A limited number of studies suggest that boys may have a higher risk of stunting than girls in low-income countries. Little is known about the causes of these gender differences. The objective of the study was to assess gender differences in nutritional status and its determinants among infants in Ethiopia.

Methods

We analyzed data for 2036 children (6–11 months old) collected as the baseline for a multiple micronutrient powders effectiveness study in two regions of Ethiopia in March–April 2015. Child, mother, and household characteristics were investigated as determinants of stunting and wasting. Multiple logistic regression models were used separately for boys and girls to check for gender differences while adjusting for confounders. The study is registered at http://www.clinicaltrials.gov/ with the clinical trials identifier of NCT02479815.

Results

Stunting and wasting prevalence is significantly higher among boys compared to girls, 18.7 vs 10.7% and 7.9 vs 5.4%, respectively. Untimely initiation of breastfeeding, not-exclusive breastfeeding at the age of 6 months, region of residence, and low maternal education are significant predictors of stunting in boys. Untimely introduction to complementary food and low consumption of legumes/nuts are significant predictors of stunting in both boys and girls, and low egg consumption only in girls. Region of residence and age of the mother are significant determinants of wasting in both sexes. Analysis of interaction terms for stunting, however, shows no differences in predictors between boys and girls; only for untimely initiation of breastfeeding do the results for boys (OR 1.46; 95%CI 1.02,2.08) and girls (OR 0.88; 95%CI 0.55,1.41) tend to be different (p = 0.12).

Conclusion

In Ethiopia, boys are more malnourished than girls. Exclusive breastfeeding and adequate dietary diversity of complementary feeding are important determinants of stunting in boys and girls. There are no clear gender interactions for the main determinants of stunting and wasting. These findings suggest that appropriate gender-sensitive guidance on optimum infant and young child feeding practices is needed.

Bayesian geostatistical modelling of stunting in Rwanda: risk factors and spatially explicit residual stunting burden

Background

Stunting remains a significant public health issue in Rwanda and its prevalence exhibits considerable geographical variation. We apply Bayesian geostatistical modelling to study the spatial pattern of stunting in children less than five years considering anthropometric, socioeconomic and demographic risk factors in Rwanda. In addition, we predict the spatial residuals effects to quantify the burden of stunting not accounted for by our geostatistical model.

Methods

We used the data from the 2015 Rwanda Demographic and Health Survey. We fitted two spatial logistic models with similar structures, only differentiated by the inclusion or exclusion of spatially structured random effects.

Results

The risk factors of stunting identified in the geostatistical model were being male (OR = 1.32, 95% CI: 1.16, 1.47), lower birthweight (kg) (OR = 0.96, 95% CI: 0.95, 0.97), non-exclusive breastfeeding (OR = 1.24, 95% CI: 1.04, 1.45), occurrence of diarrhoea in the last two weeks (OR = 1.18, 95% CI: 1.02, 1.37), a lower proportion of mothers with overweight (BMI ≥ 25) (OR = 0.82, 95% CI: 0.71, 0.95), a higher proportion of mothers with no or only primary education (OR = 1.14, 95% CI: 0.99, 1.36). Also, a higher probability of living in a house with poor flooring material (OR = 1.22, 95% CI: 1.06, 1.41), reliance on a non-improved water source (OR = 1.13, 95% CI: 1.00, 1.27), and a low wealth index were identified as risk factors of stunting. Mapping of the spatial residuals effects showed that, in particular, the Northern and Western regions, followed by the Southern region of Rwanda, still exhibit a higher risk of stunting even after accounting for all the covariates in the spatial model.

Conclusions

Further studies are needed to identify the still unknown spatially explicit factors associated with higher risk of stunting. Finally, given the spatial heterogeneity of stunting, interventions to reduce stunting should be geographically targeted.

Predictive Risk Mapping of Schistosomiasis in Madagascar Using Ecological Niche Modeling and Precision Mapping

Schistosomiasis is a neglected tropical disease (NTD) found throughout tropical and subtropical Africa. In Madagascar, the condition is widespread and endemic in 74% of all administrative districts in the country. Despite the significant burden of the disease, high-resolution risk maps have yet to be produced to guide national control programs. This study used an ecological niche modeling (ENM) and precision mapping approach to estimate environmental suitability and disease transmission risk. The results show that suitability for schistosomiasis is widespread and covers 264,781 km2 (102,232 sq miles). Covariates of significance to the model were the accessibility to cities, distance to water, enhanced vegetation index (EVI), annual mean temperature, land surface temperature (LST), clay content, and annual precipitation. Disease transmission risk is greatest in the central highlands, tropical east coast, arid-southwest, and northwest. An estimated 14.9 million people could be at risk of schistosomiasis; 11.4 million reside in rural areas, while 3.5 million are in urban areas. This study provides valuable insight into the geography of schistosomiasis in Madagascar and its potential risk to human populations. Because of the focal nature of the disease, these maps can inform national surveillance programs while improving understanding of areas in need of medical interventions.

Stunting, double orphanhood and unequal access to public services in democratic South Africa

Orphans who lack household or community support face significant socio-economic disadvantages. In particular, they are at greater risk of malnutrition and stunting in developing countries. Children who have no living parents, also called double orphans, are most likely to require support from extended families or public institutions. This paper explores how WASH infrastructure, and public health and social services relate to stunting. It is one of the first studies to analyse these factors with a specific focus on double orphans, who tend to live in under-serviced areas with high stunting rates and poor access to public resources. We collate a cross sectional spatial dataset with local child stunting rates from 2013, rates of double orphanhood, private household resources, and public services from 2011 for South Africa, a country where the HIV/AIDS pandemic has led to high rates of double orphanhood. We estimate spatial econometric models that account for unobserved regional shocks and measurement bias, but which do not address other biases. Our results show that high stunting rates, particularly in areas with high proportions of double orphans, overlap strongly with poor provision of WASH and the availability of household resources. By contrast, other softer services accessed outside the home, such as access to health, social welfare and early childhood development facilities are not correlated with stunting in the same way. WASH is more strongly related to reduced stunting when infrastructure covers larger geographic areas and with the combined use of services from adjacent areas. This occurs because of economies of scale in provision and preventing transmission of disease across regions. Policy makers can explore the option to reduce stunting by expanding geographic networks of WASH service delivery into under-serviced areas where double orphans tend to locate.

Factors associated with stunting among children 0 to 59 months of age in Angola: A cross-sectional study using the 2015-2016 Demographic and Health Survey

Stunting among children under five years of age is a serious public health problem globally, with life-long consequences to health, well-being, and productivity. Stunted growth has complex and multifactorial causes, reflecting the interaction of a broad range of conditions that determine child health. The Angola 2015-2016 Demographic and Health Survey (DHS) collected nationally representative anthropometry for 6,359 children 0 to 59 months of age in Angola, and ascertained exposure to a wide range of child, parental, socio-economic, and geographic variables. This study used a cross-sectional design to identify exposures associated with stunting among children 0 to 59 months of age in Angola, while considering the multifactorial and multi-level causes of stunting. Main outcome was prevalence of stunting, defined as proportion of children with height-for-age Z-score (HAZ) two or more standard deviations below the median. Prevalence of stunting was associated with individual, household, and area-level exposure variables, including child age and sex, birth order, birthweight, diarrhea, maternal and paternal age and education, source of water, sanitary system, and province. In conclusion, prevalence of stunting in Angola is associated with several factors previously described in the literature. Stunting is associated with exposures at the distal, intermediate, and proximal levels, in line with the framework on the causes of childhood malnutrition. This study identifies opportunities for interventions at multiple levels to decrease prevalence of stunting among children in Angola. Main limitations of this study are the potential for survival bias and residual confounding.

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.