Spatial heterogeneity and correlates of child malnutrition in districts of India

Longitudinal analysis of growth and nutritional disparities across socio-demographics from early childhood to adolescence: Findings from the Indian cohort of the Young Lives Survey

OBJECTIVES

Previous studies generally used cross-sectional data and focused on under-five children to assess the risk factors for malnutrition among Indian children. Some recent studies have reported that recovery from or faltering in malnutrition is possible after five years of age, but socio-demographic subgroup disparities have not been explored. This study aims to find the longitudinal disparity in height-for-age Z-scores (HAZ) and body-mass-index-for-age Z-scores (BMIAZ scores) across various sub-groups of a cohort from childhood to adolescence.

METHODS

This study used a cohort from the Young Lives Survey, which followed children aged of 1-15 years between 2002 and 2016-17 in the states of Andhra Pradesh and Telangana, India. Mixed-effect models were applied to find the main, time, and interaction effects of HAZ scores and BMIAZ scores. In addition, an extended Kitagawa-Oaxaca-Blinder decomposition approach to assess group-based differences over time was used.

RESULTS

The cross-sectional prevalence of stunting reduced across all subgroups, while thinness rose during the same period. The interactions of child sex, mother's education, place of residence, wealth index, and antenatal care with time were statistically significant at p <0.05. The gender disparity in adjusted HAZ score  decreased from 0.214 units at 1 year to 0.011 units at 15 years, whereas BMIAZ score differential increased from 0.106 to 0.538 units over same timeframe. Disparities in scores were also observed across rural-urban, maternal education, social group, religion, socioeconomic status, maternal age at birth, antenatal care, and premature birth status.

CONCLUSION

The study sheds light on the nuanced dynamics of paediatric growth, emphasising the importance of longitudinal approaches in understanding and addressing the health disparities across different stages of childhood and adolescence.

Spatial variation and risk factors of the dual burden of childhood stunting and underweight in India: a copula geoadditive modelling approach

India has one of the highest burdens of childhood undernutrition in the world. The two principal dimensions of childhood undernutrition, namely stunting and underweight can be significantly associated in a particular population, a fact that is rarely explored in the extant literature. In this study, we apply a copula geoadditive modelling framework on nationally representative data of 104,021 children obtained from the National Family Health Survey 5 to assess the spatial distribution and critical drivers of the dual burden of childhood stunting and underweight in India while accounting for this correlation. Prevalence of stunting, underweight and their co-occurrence among under 5 children were 35.37%, 28.63% and 19.45% respectively with significant positive association between the two (Pearsonian Chi square = 19346, P-value = 0). Some of the factors which were significantly associated with stunting and underweight were child gender (Adjusted Odds Ratio (AOR) = 1.13 (1.12) for stunting (underweight)), birthweight (AOR = 1.46 (1.64) for stunting (underweight)), type of delivery (AOR = 1.12 (1.19) for stunting (underweight)), prenatal checkup (AOR = 0.94 (0.96) for stunting (underweight)) and maternal short-stature (AOR = 2.19 (1.85) for stunting (underweight)). There was significant spatial heterogeneity in the dual burden of stunting and underweight with highest prevalence being observed in eastern and western states while northern and southern states having relatively lower prevalence. Overall, the results are indicative of the inadequacy of a "one-size-fits-all" strategy and underscore the necessity of an interventional framework that addresses the nutritional deficiency of the most susceptible regions and population subgroups of the country.

Understanding period product use among young women in rural and urban India from a geospatial perspective

Ensuring proper menstrual hygiene management remains a significant challenge for young women in India. The term "exclusive use of hygienic period products during menstruation" refers to relying solely on period products like sanitary pads, tampons, or menstrual cups. Poor menstrual hygiene practices not only increase the risk of reproductive tract infections but also lead to various negative health outcomes, including discomfort and potential complications. This study explores factors associated with the exclusive use of period products during menstruation aged 15-24, investigates geographic disparities, examines rural-urban gaps, and assesses inequality in India. Utilizing data from the fifth National Family Health Survey (NFHS-5), responses from 2,41,180 women aged 15 to 24 were analysed using logistic regression and multivariate decomposition analyses to explore socioeconomic predictors. Moran's I statistics also assessed spatial dependency, while Lorenz curves and Gini coefficients measured inequality. Quintile and LISA maps visualized regional disparities. The study found that 76.15% of women in India reported exclusive use of hygienic period products during menstruation. Rural areas reported a lower percentage of exclusive use of hygienic period products (72.32%) during menstruation compared to urban areas (89.37%). Key factors associated with the exclusive use of hygienic period products among 15-24-year-old women in India include age, education, place of residence, wealth, access to media, and healthcare discussions. Geographically, central districts exhibited the lowest coverage (< 65%), while the Southern region reported the highest (> 85). The GINI coefficient of 0.39 highlighted moderate inequality in distribution. Decomposition analysis revealed that household wealth contributed 49.25% to rural-urban differences, followed by education (13.41%), media access (7.97%), and region (4.97%). This study highlights significant regional disparities and low utilization of hygienic period products among young women in India, particularly in central districts. Policymakers should prioritize interventions targeting these regions, addressing socio-economic disparities. Strategies to promote education, improve media access, and enhance household wealth can facilitate menstrual hygiene adoption. Initiatives to reduce sanitary napkin costs and increase accessibility, particularly in rural areas, are crucial to mitigating geographical disparities nationwide.

Segmentation of stunting, wasting, and underweight in Southeast Sulawesi using geographically weighted multivariate Poisson regression

The health profile of Southeast Sulawesi Province in 2021 shows that the prevalence of stunting is 11.69 %, wasting 5.89 % and underweight 7.67 %. This relatively high figure should be immediately reduced to zero because it greatly affects the quality of human resources. Cases of stunting, wasting and underweight are an iceberg phenomenon, especially in Southeast Sulawesi. Therefore, it is necessary to research the number of cases of stunting, wasting and underweight in Southeast Sulawesi using GWMPR. The research results show that there is a trivariate correlation between the number of cases of stunting, wasting and underweight. The GWMPR model provides better results in modeling the number of stunting, wasting and underweight cases than the MPR model. The models produced for each sub-district are different from each other based on the predictor variables that have a significant effect and the estimated parameter values ​​for each sub-district. The segmentation of the number of stunting cases consists of 21 regional groups with 10 significant predictor variables, while the number of wasting cases consists of 10 regional groups with 9 significant predictor variables, while the number of underweight cases consists of 37 regional groups with 11 significant predictor variables. Therefore, policies on stunting, wasting, and underweight should be based on local conditions. 3 important components of this study: 1. GWMPR is the development of GWPR model when there are 2 or more response variables that are correlated. 2. GWMPR is a spatial model that considers geography. 3. Application of GWMPR to the analysis of the number of stunting, wasting, and underweight in Southeast Sulawesi province.

Association of Child Growth Failure Indicators With Household Sanitation Practices in India (1998-2021): Spatiotemporal Observational Study

BACKGROUND

Undernutrition among children younger than 5 years is a subtle indicator of a country's health and economic status. Despite substantial macroeconomic progress in India, undernutrition remains a significant burden with geographical variations, compounded by poor access to water, sanitation, and hygiene services.

OBJECTIVE

This study aimed to explore the spatial trends of child growth failure (CGF) indicators and their association with household sanitation practices in India.

METHODS

We used data from the Indian Demographic and Health Surveys spanning 1998-2021. District-level CGF indicators (stunting, wasting, and underweight) were cross-referenced with sanitation and sociodemographic characteristics. Global Moran I and Local Indicator of Spatial Association were used to detect spatial clustering of the indicators. Spatial regression models were used to evaluate the significant determinants of CGF indicators.

RESULTS

Our study showed a decreasing trend in stunting (44.9%-38.4%) and underweight (46.7%-35.7%) but an increasing prevalence of wasting (15.7%-21.0%) over 15 years. The positive values of Moran I between 1998 and 2021 indicate the presence of spatial autocorrelation. Geographic clustering was consistently observed in the states of Madhya Pradesh, Jharkhand, Odisha, Uttar Pradesh, Chhattisgarh, West Bengal, Rajasthan, Bihar, and Gujarat. Improved sanitation facilities, a higher wealth index, and advanced maternal education status showed a significant association in reducing stunting. Relative risk maps identified hotspots of CGF health outcomes, which could be targeted for future interventions.

CONCLUSIONS

Despite numerous policies and programs, malnutrition remains a concern. Its multifaceted causes demand coordinated and sustained interventions that go above and beyond the usual. Identifying hotspot locations will aid in developing control methods for achieving objectives in target areas.

Undernutrition among children and its determinants across the parliamentary constituencies of India: a geospatial analysis

In India, undernutrition among children has been extremely critical for the last few decades. Most analyses of undernutrition among Indian children have used the administrative boundaries of a state or a district level as a unit of analysis. This paper departs from such a practice and focuses instead on the political boundaries of a parliamentary constituency (PC) as the unit of analysis. The PC is a critical geopolitical unit where political parties and party candidates make election promises and implement programmes to improve the socio-economic condition of their electorate. A focus on child undernutrition at this level has the potential for greater policy and political traction and could lead to a paradigm shift in the strategy to tackle the problem by creating a demand for political accountability. Different dimensions and new approaches are also required to evaluate the socio-economic status and generate concrete evidence to find solutions to the problem. Given the significance of advanced analytical methods and models embedded into geographic information system (GIS), the current study, for the first time, uses GIS tools and techniques at the PC level, conducting in-depth analysis of undernutrition and its predictors. Hence, this paper examines the spatial heterogeneity in undernutrition across PCs by using geospatial techniques such as univariate and bivariate local indicator of spatial association and spatial regression models. The analysis highlights the high-low burden areas in terms of local hotspots and identifies the potential spatial risk factors of undernutrition across the constituencies. Striking variations in the prevalence of undernutrition across the constituencies were observed. Most of these constituencies that performed poorly both in terms of child nutrition and socio-economic indicators were located in the northern, western, and eastern parts of India. A statistically significant association of biological, socio-economic, and environmental factors such as women's body mass index, anaemia in children, poverty, household sanitation facilities, and institutional births was established. The results highlight the need to bring in a mechanism of political accountability that directly connects elected representatives to maternal and child health outcomes. The spatial variability and pattern of undernutrition indicators and their correlates indicate that priority setting in research may also be greatly influenced by the neighbourhood association.

Investigating the association between floods and low birth weight in India: Using the geospatial approach

BACKGROUND

Frequent natural disasters like floods pose a major threat to India, with significant implications for public health. Low birth weight (LBW) is a critical global health concern, contributing to neonatal mortality. However, the association between floods and LBW remains underexplored. This study aims to address this gap by investigating the association between flood hazards and LBW in India using a geospatial approach. By analyzing data from the National Family Health Survey (NFHS-5) and flood zonation maps, the study aims to uncover the spatial dynamics of this association, offering insights into the implications of floods on birth weight across diverse geographical regions.

METHODS

The study used the fifth round of NFHS data, 2019-21, which involved 202,194 children selected through a multi-stage stratified sampling technique. The Vulnerability Atlas of India 2019 maps were also utilized to classify areas as flood or non-flood zones. Birth weight data from the NFHS-5 were categorized into three groups: very low, low, and normal birth weight (VLBW, LBW and NBW). Control variables including flood exposure, socio-demographic attributes, and geographic region were considered. Bivariate analysis and multinomial logistic regression were employed for statistical analysis. The spatial analysis involved Moran's I statistics and Geographically Weighted Regression to explore spatial dynamics of the association between floods and birth weight in India.

RESULTS

Floods predominantly affect India's lower Himalayan belts and western coastal regions. Flood-affected areas show higher proportions of VLBW and LBW infants. Groundwater usage and unimproved sanitation are associated with higher risk of VLBW and LBW. Sex, wealth, maternal education, residence type, and geographic region significantly influence birth weights. Multinomial logistic regression reveals 8 % and 27 % higher risks for LBW and VLBW in flood-affected regions. LISA cluster maps identify high-risk areas for both LBW and floods. Geographically Weighted Regression highlights 52 % of the variability in LBW occurrences can be attributed to the influence of flood hazards. Families hailing from the poorest wealth background and exposed to flood hazards bear a 5 % heightened likelihood of delivering LBW infants, in stark contrast to their counterparts from the same economic background yet unaffected by floods.

CONCLUSIONS

The significant association between floods and LBW underscores the importance of robust disaster preparedness and public health strategies. By unraveling the spatial intricacies of flood-induced LBW disparities, this research provides valuable insights for promoting healthier birth outcomes and reducing child mortality rates, particularly in flood-prone regions. These findings emphasize the importance of holistic policies that address both environmental challenges and socioeconomic inequalities to safeguard maternal and infant health across the nation.

Cultural and Contextual Drivers of Triple Burden of Malnutrition among Children in India

This study examines malnutrition's triple burden, including anaemia, overweight, and stunting, among children aged 6-59 months. Using data from the National Family Health Survey-5 (2019-2021), the study identifies risk factors and assesses their contribution at different levels to existing malnutrition burden. A random intercept multilevel logistic regression model and spatial analysis are employed to identify child, maternal, and household level risk factors for stunting, overweight, and anaemia. The study finds that 34% of children were stunted, 4% were overweight, and 66% were anaemic. Stunting and anaemia prevalence were higher in central and eastern regions, while overweight was more prevalent in the north-eastern and northern regions. At the macro-level, the coexistence of stunting, overweight, and anaemia circumstantiates the triple burden of childhood malnutrition with substantial spatial variation (Moran's I: stunting-0.53, overweight-0.41, and anaemia-0.53). Multilevel analysis reveals that child, maternal, and household variables play a substantial role in determining malnutrition burden in India. The nutritional health is significantly influenced by a wide range of determinants, necessitating multilevel treatments targeting households to address this diverse group of coexisting factors. Given the intra-country spatial heterogeneity, the treatment also needs to be tailor-made for various disaggregated levels.

Spatial dependency in child malnutrition across 640 districts in India: need for context-specific planning and interventions

BACKGROUND

Child malnutrition remains a matter of concern in India as the current levels are high and the decline is slow. National Family Health Survey (NFHS-4, 2015-16) data, for the first time, provides credible, good quality data at district level on social, household and health characteristics.

METHODS

Techniques of spatial analysis on data in respect of 640 districts were used to identify spatial characteristics of the nutrition levels for children in the 0-60-month age group. Further, the principal component analysis (PCA) was used to identify 7 important correlates of the malnutrition out of 21 relevant components provided in the NFHS-4. The paper further uses three techniques, ordinary least squares (OLS), spatial lag model (SLM) and spatial error model (SEM) to assess the strength of correlation between the malnutrition levels and the shortlisted correlates.

RESULTS

The use of SLM and SEM shows improvement in the strength of the association (high R-square) compared to OLS. Women's height and Iodized salt in stunting, child anaemia in wasting, women's height and child anaemia in underweight were found to be significant factors (P < 0.01) along with spatial autoregressive constant.

CONCLUSIONS

Such analysis, in combination with PCA, has shown to be more effective in prioritizing the programme interventions for tackling child malnutrition.

Undernutrition among the children below five years of age in Uganda: a spatial analysis approach

BACKGROUND

Undernutrition is a health condition caused by a lack of enough food intake, not having enough of the right combination of food nutrients, or the body's failure to utilize the food eaten resulting in either, stunting, being underweight, or wasting. Globally, undernutrition affects more than 149 million under-five children, while in Uganda about 3 in every 10 children suffer from undernutrition. Undernutrition and its risk factors among under-five children in Uganda were unevenly distributed across the country and a study that focused on spatial distribution was prudent to examine the nature of the problem and salient factors associated with it. The current study addressed the issues of spatial heterogeneity of undernutrition and its determinants with the goal to identify hot spots and advise policymakers on the best actions to be taken to address the problem.

METHODS

Data were obtained from the 2016 Uganda Demographic and Health Survey. Prevalence rates and percentages of risk factors were combined with the Uganda district shape file to allow spatial analysis. Moran's I, Getis-Ord (GI*), and Geographically Weighted Regressions were respectively used to establish the local, global, and geographically weighted regressions across the country. Stata 15 and ArcGIS 10. 7 soft wares were used.

RESULTS

The results indicate that undernutrition in Uganda shows varies spatially across regions. Evidence of hot spots exists in the Karamoja and Arua regions, cold spot areas exist around the central part of the country while the greatest part of Western Uganda, Northern, and Eastern were not significant.

CONCLUSION

The study reveals that a variation in the distribution of undernutrition throughout the country. Significant spatial patterns associated with undernutrition as identified through the hotspot and cold spot analysis do exist in Uganda. Programs targeting to reduce the undernutrition of under-five children in Uganda should consider the spatial distribution of undernutrition and its determinants whereby priority should be given to hotspot areas. The spatial intensity of undernutrition and its determinants indicate that focus should be tailored to meet the local needs as opposed to a holistic national approach.

Local economic growth and infant mortality

We estimate the effect of local economic growth on infant mortality. We use geo-referenced data for non-migrating mothers from 46 developing countries and a total of 128 DHS survey rounds and combine it with nighttime luminosity data at a granular level. Using mother fixed effects we show that an increase in local economic activity significantly reduces the probability that the same mother loses a child before its first birthday.

A Methodology for Integrating Population Health Surveys Using Spatial Statistics and Visualizations for Cross-Sectional Analysis

Large-scale population surveys are beneficial in gathering information on the performance indicators of public well-being, including health and socio-economic standing. However, conducting national population surveys for low and middle-income countries (LMIC) with high population density comes at a high economic cost. To conduct surveys at low-cost and efficiently, multiple surveys with different, but focused, goals are implemented through various organizations in a decentralized manner. Some of the surveys tend to overlap in outcomes with spatial, temporal or both scopes. Mining data jointly from surveys with significant overlap gives new insights while preserving their autonomy. We propose a three-step workflow for integrating surveys using spatial analytic workflow supported by visualizations. We implement the workflow on a case study using two recent population health surveys in India to study malnutrition in children under-five. Our case study focuses on finding hotspots and coldspots for malnutrition, specifically undernutrition, by integrating the outcomes of both surveys. Malnutrition in children under-five is a pertinent global public health problem that is widely prevalent in India. Our work shows that such an integrated analysis is beneficial alongside independent analyses of such existing national surveys to find new insights into national health indicators.

Spatial analysis of provincial and district trends in stunting among children under five years in Nepal from 2001 to 2016

Background

The average prevalence of stunting reported by the Nepal Demographic Health Survey from 2001 to 2016 only reports the prevalence of stunting at the national level and provincial and district level information is missing. Also, no previous study has reported a provincial trend in stunting from 2001 to 2016 in Nepal. This study for the first time presents the spatial trend of stunting among children under five years for 7 provinces and 77 districts of Nepal over 15 years using Demographic Health Survey Global Positioning System coordinates, Demographic Health Survey indicators, and geospatial covariates.

Methods

This is a secondary analysis of data from Nepal Demographic Health Survey from 2001 to 2016. The study population was children under five years. The outcome variable was stunting, which was analyzed as per districts and provinces. Sample weight was applied to calculate the percentage of stunting and 95% confidence interval for all survey years. The geographic dataset was used to provide information about the latitude and longitude of the survey cluster. Poisson-based model was used during the purely spatial analysis in SatScan for identification of clusters with stunting caseload.

Results

The reduction in the prevalence of stunting among children under five years has not been equal when disaggregated for district and provincial level data. In 2001, 57 districts had a prevalence of stunting among children above or equal to 50%, which has reduced over time except for districts in Karnali province. In 2016, 16 districts had a prevalence of stunting above or equal to 50%. Jumla (91.7%) and Kalikot (77.8%) still had the highest prevalence of stunting as of 2001. Among 7 provinces, the prevalence of stunting is found highest in Karnali for all subsequent survey years. Sudurpaschim and Madhesh provinces also had a high proportion of stunted children. The highest reduction in the prevalence of stunting was noted for Province Bagmati (by 30%) and Gandaki (by 28%).

Conclusion

The inequalities in childhood stunting persisted at the district and provincial levels although a good decline was noted at the national level. This calls for rigorous attention to be provided to districts and provinces with a high prevalence of stunting, and being prioritized for a targeted intervention.

Quality of anthropometric data in India's National Family Health Survey: Disentangling interviewer and area effect using a cross-classified multilevel model

India has adopted a target-based approach to reduce the scourge of child malnourishment. Because the monitoring and evaluation required by this approach relies primarily on large-scale data, a data quality assessment is essential. As field teams are the primary mode of data collection in large-scale surveys, this study attempts to understand their contribution to variations in child anthropometric measures. This research can help disentangle the confounding effects of regions/districts and field teams on the quality of child anthropometric data. The anthropometric z-scores of 2,25,002 children below five years were obtained from the fourth round of India's National Family and Health Survey (NFHS-4), 2015–16. Unadjusted and adjusted standard deviations (SD) of the anthropometric measures were estimated to assess the variations in measurements. In addition, a cross-classified multilevel model (CCMM) approach was adopted to estimate the contribution of geographical regions/districts and teams to variations in anthropometric measures. The unadjusted SDs of the measures of stunting, wasting, and underweight were 1.7, 1.4, and 1.2, respectively. The SD of stunting was above the World Health Organisation threshold (0.8–1.2), as well as the Demographic and Health Survey mark. After adjusting for team-level characteristics, the SDs of all three measures reduced marginally, indicating that team-level workload had a marginal but significant role in explaining the variations in anthropometric z-scores. The CCMM showed that the maximum contribution to variations in anthropometric z-scores came from community-level (Primary Sampling Unit (PSU)) characteristics. Team-level characteristics had a higher contribution to variations in anthropometric z-scores than district-level attributes. Variations in measurement were higher for child height than weight. The present study decomposes the effects of district- and team-level factors and highlights the nuances of introducing teams as a level of analysis in multilevel modelling. Population size, density, and terrain variations between PSUs should be considered when allocating field teams in large-scale surveys.

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Unadjusted standard deviation for child malnourishment indicators are above the recommended level of DHS data quality standards.

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Variation in stunting is directly proportional to workload measured by number of eligible children in the PSUs.

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Cross-classified multilevel models show significant team-level contribution in explaining variations in anthropometric scores.

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Team-level contribution to explaining variations in child anthropometric measures is larger than district-level factors.

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The number of days assigned to gather anthropometric measurements should be dependent on the number of eligible respondents in a PSU, which may be identified at the time of mapping & listing, rather than being a fixed number of days across all the states of India.

Food Insecurity in the Informal Settlements of Inanda Households Living with Children under 60 Months in Ethekwini Municipality

Food insecurity is a continuing challenge for many households in South Africa. This challenge poses serious immediate and long-term health and development risks for children. Despite the intensive literature on household food insecurity, there is limited literature on the household food security status in South African informal settlements. Thus, the household food security status and dynamics in informal settlements are not clearly defined. Hence, this study assessed the food security status of households living with children under 60 months in the informal settlements of the Inanda area, eThekwini Municipality. This study employed a cross-sectional quantitative research approach. A non-probability sampling method was used, which used convenience sampling supplemented by a non-discriminative snowball sampling to obtain a sample size of 160 households with children under the age of five. Data was collected through face-to-face interviews, where questionnaires were administered to household child caregivers. Ethical considerations such as informed consent, anonymity, confidentiality, permission from authorities, and cultural considerations were obeyed in this study. The HFIAS and HDDS tools were used to estimate the household food security status. Data were coded and analysed in SPSS version 25 software. This study revealed that higher proportions of the surveyed informal households living with children under 60 months were food insecure. The HFIAS analysis showed that approximately 34, 31, and 28% were severely, mildly, and moderately food insecure, respectively. In contrast, a small (approximately 8%) proportion of the surveyed informal households was estimated to be food secure. The HDDS analysis revealed that most (approximately 77%) of the surveyed informal households had low dietary diversity (deemed food insecure). Cereal, roots, and fatty foods were the main dietary components in the informal settlements of Inanda. It is paramount to improve the food security status of informal households living with children under 60 months through an integrated approach. This study suggests government and private stakeholders' engagement in developing policies and programs directed at informal households living with children under 60 months to alleviate food insecurity.

Space-time dynamics regression models to assess variations of composite index for anthropometric failure across the administrative zones in Ethiopia

BACKGROUND

A single anthropometric index such as stunting, wasting, or underweight does not show the holistic picture of under-five children's undernutrition status. To alleviate this problem, we adopted a multifaceted single index known as the composite index for anthropometric failure (CIAF). Using this undernutrition index, we investigated the disparities of Ethiopian under-five children's undernutrition status in space and time.

METHODS

Data for analysis were extracted from the Ethiopian Demographic and Health Surveys (EDHSs). The space-time dynamics models were formulated to explore the effects of different covariates on undernutrition among children under five in 72 administrative zones in Ethiopia.

RESULTS

The general nested spatial-temporal dynamic model with spatial and temporal lags autoregressive components was found to be the most adequate (AIC = -409.33, R2 = 96.01) model. According to the model results, the increase in the percentage of breastfeeding mothers in the zone decreases the CIAF rates of children in the zone. Similarly, the increase in the percentages of parental education, and mothers' nutritional status in the zones decreases the CIAF rate in the zone. On the hand, increased percentages of households with unimproved water access, unimproved sanitation facilities, deprivation of women's autonomy, unemployment of women, and lower wealth index contributed to the increased CIAF rate in the zone.

CONCLUSION

The CIAF risk factors are spatially and temporally correlated across 72 administrative zones in Ethiopia. There exist geographical differences in CIAF among the zones, which are influenced by spatial neighborhoods of the zone and temporal lags within the zone. Hence these findings emphasize the need to take the spatial neighborhood and historical/temporal contexts into account when planning CIAF prevention.

Small area variations in low birth weight and small size of births in India

The states and districts are the primary focal points for policy formulation and programme intervention in India. The within‐districts variation of key health indicators is not well understood and consequently underemphasised. This study aims to partition geographic variation in low birthweight (LBW) and small birth size (SBS) in India and geovisualize the distribution of small area estimates. Applying a four‐level logistic regression model to the latest round of the National Family Health Survey (2015–2016) covering 640 districts within 36 states and union territories of India, the variance partitioning coefficient and precision‐weighted prevalence of LBW (<2.5 kg) and SBS (mother's self‐report) were estimated. For each outcome, the spatial distribution by districts of mean prevalence and small area variation (as measured by standard deviation) and the correlation between them were computed. Of the total valid sample, 17.6% (out of 193,345 children) had LBW and 12.4% (out of 253,213 children) had SBS. The small areas contributed the highest share of total geographic variance in LBW (52%) and SBS (78%). The variance of LBW attributed to small areas was unevenly distributed across the regions of India. While a strong correlation between district‐wide percent and within‐district standard deviation was identified in both LBW (r = 0.88) and SBS (r = 0.87), they were not necessarily concentrated in the aspirational districts. We find the necessity of precise policy attention specifically to the small areas in the districts of India with a high prevalence of LBW and SBS in programme formulation and intervention that may be beneficial to improve childbirth outcomes.

The small areas contribute the highest share of the total geographic variance of low birth weight (LBW) and small birth size (SBS) in India.

A high burden of LBW is found mostly in the central‐western part of India and Odisha. The prevalence of SBS is high across the district of northern‐western regions and the north‐eastern regions of India.

The mean prevalence and standard deviation are strongly correlated in the case of both LBW (r = 0.88) and SBS (r = 0.87) in India. It indicates that the districts which have a higher prevalence of LBW and SBS also have a higher between small area disparity within the districts.

We find a similar pattern of distribution in LBW and SBS between the policy‐focused aspirational districts and other districts of India.

Findings indicate reprioritizing the policy intervention, focusing on the small areas of India for better childbirth outcomes.

Nursing perspectives about the critical gaps in public health emergency response during the COVID-19 pandemic

INTRODUCTION

The purpose of this qualitative study was to synthesize frontline U.S. nursing perspectives about the current state of U.S. public health emergency preparedness and response. The study findings may inform public health policy change and improve future national pandemic planning and responses.

DESIGN

We conducted a secondary thematic qualitative analysis using grounded theory methodology.

METHODS

Data collection occurred through semi-structured, in-depth focus groups between July and December 2020, from 43 frontline nurses working in hospitals in four states (Ohio, California, Pennsylvania, and New York). Data were analyzed deductively, aligned with Khan et al.'s Public Health Emergency Preparedness Framework and inductively for emergent themes.

RESULTS

Three themes emerged: (1) Validation of the presence of health disparities and inequities across populations; (2) Perceived lack of consistency and coordination of messaging about pandemic policies and plans across all levels; and (3) challenges securing and allocating nursing workforce resources to areas of need.

CONCLUSION

From a frontline nursing perspective, this study demonstrates the critical need to address health inequities and inequalities across populations, a consistent national vehicle for communication, and national plan for securing and allocating nursing workforce resources.

An Evolutionary Model of “Sexual Conflict” Over Women's Age at Marriage: Implications for Child Mortality and Undernutrition

Background

Early women's marriage is associated with adverse outcomes for mothers and their offspring, including reduced human capital and increased child undernutrition and mortality. Despite preventive efforts, it remains common in many populations and is often favored by cultural norms. A key question is why it remains common, given such penalties. Using an evolutionary perspective, a simple mathematical model was developed to explore women's optimal marriage age under different circumstances, if the sole aim were to maximize maternal or paternal lifetime reproductive fitness (surviving offspring).

Methods

The model was based on several assumptions, supported by empirical evidence, regarding relationships between women's marital age and parental and offspring outcomes. It assumes that later marriage promotes women's autonomy, enhancing control over fertility and childcare, but increases paternity uncertainty. Given these assumptions, optimal marriage ages for maximizing maternal and paternal fitness were calculated. The basic model was then used to simulate environmental changes or public health interventions, including shifts in child mortality, suppression of women's autonomy, or promoting women's contraception or education.

Results

In the basic model, paternal fitness is maximized at lower women's marriage age than is maternal fitness, with the paternal optimum worsening child undernutrition and mortality. A family planning intervention delays marriage age and reduces child mortality and undernutrition, at a cost to paternal but not maternal fitness. Reductions in child mortality favor earlier marriage but increase child undernutrition, whereas ecological shocks that increase child mortality favor later marriage but reduce fitness of both parents. An education intervention favors later marriage and reduces child mortality and undernutrition, but at a cost to paternal fitness. Efforts to suppress maternal autonomy substantially increase fitness of both parents, but only if other members of the household provide compensatory childcare.

Conclusion

Early women's marriage maximizes paternal fitness despite relatively high child mortality and undernutrition, by increasing fertility and reducing paternity uncertainty. This tension between the sexes over the optimal marriage age is sensitive to ecological stresses or interventions. Education interventions seem most likely to improve maternal and child outcomes, but may be resisted by males and their kin as they may reduce paternal fitness.

Region matters: Mapping the contours of undernourishment among children in Odisha, India

Background

Levels of child undernutrition and its correlates exhibit considerable spatial variation at different levels of granularity. In India, such variations and their interrelation have not been studied at the sub-district level primarily due to the non-availability of good quality granular data. Given the sheer regional diversity in India, it is essential to develop a region-specific evidence base at the micro-level.

Data and objectives

The current study utilised, for the first time, a sub-district level survey data (Concurrent Child Monitoring Survey-II, 2014–15) to investigate the statistically significant clusters and spatial patterns of burden of undernutrition among children. The emergence of distinct patterns at the level of natural geographical regions of the state–coastal, southern and northern regions, lead to a region-specific analysis to measure the impact of various demographic, socio-economic and maternal factors on the prevalence of undernutrition specific to the three regions, using the National Family Health Survey-IV unit-level data.

Methods

The spatial dependence and clustering of child undernourishment across sub-districts in Odisha were studied using various spatial statistical techniques, including spatial econometric models. Binary logistic regression was applied in the region-specific analysis.

Results

Findings indicated statistically significant spatial clustering of undernutrition among children in specific geographic pockets with poor sanitation, low institutional and skilled deliveries, poor maternal health reinforcing the need for inter-sectoral coordination. Disparities across the three natural-regions, suggest that the parameters requiring priority for intervention may differ across levels of overall development.

Conclusion

The spatial clustering of different socio-demographic indicators in specific geographic pockets highlights the differential impact of these determinants on child undernutrition thereby reinforcing a strong need for targeted intervention in these areas. Present analysis and the evidence-based micro-level analysis can be utilised as a model for other Indian states and low-resource countries, making interventions more effective through multiple, synergistic and a multi-sectoral approach.

Understanding the Socio-Economic Vulnerability in Child Malnutrition Between Migrants and Non-Migrants Children (12-59 Months) in India: Evidence from a Cross-Sectional Study

India has witnessed increasing trends in internal migration over the last three decades. In India, migrant children are not a homogeneous group and their reasons for movement and vulnerabilities vary across socio-economic stratum. For some children, migration may open possibilities and is associated with expanding social and economic spheres, but for many others, it may bring serious risks. Therefore, the study has been carried out to understand socio-economic vulnerability in child nutrition with migration status and other contributing factors in India. This study used data from the National Family Health Survey, the fourth in the NFHS series which was conducted in 2015-2016 (NFHS-4). We were interested in looking at the children age 12-59 months for their nutritional indicators such as stunting and underweight across migrants and non-migrants children. This resulted in a sample of 199,448 children in selected age group and among them 33.1% children belongs to the migrant family as compared to 67% of non-migrant children. Overall, 44.2% of children were stunted and 39.5% were underweight among non-migrant children as compared to 37.4% & 32.8% of migrant children were stunted and underweight respectively. Further, the results showed that among the social groups, scheduled caste children were found a high variation in underweight (34% vs. 41.6%) and stunting (36% vs. 46%) between migrants and non-migrants children. Similar trend of malnourishment is found in the poor wealth quintile, for rural residents and low educated women with non-migrant status. Those children who were poor but non-migrant were more likely to be malnourished as underweight [aOR; 1.15, CI: 1.11-1.18] and stunted [aOR; 1.17, CI:1.13-1.20] as compared to migrant status children in the same category of the household. Similarly in reference to scheduled caste migrant group, the scheduled caste non-migrant were more likely to be underweight [aOR; 1.15, CI: 1.09-1.20] and stunted [aOR; 1.18, CI: 1.12-1.23] than the children with migrant status. There were huge differences between migrant and non-migrant children in nutritional statuses. Education, caste and wealth index are found to be an important variables to explain the differential between migrants and non-migrants in child's nutritional aspects. Children associated with poor socio-economic vulnerability and non-migrant category need to be taken care of more and a community targeted approach is required to understand the gaps. The programs such as ICDS, and Poshan Abhiyan need to be revamped adding the migration aspect of the families and children in terms of their health and nutritional aspects.

Predictive algorithm to stratify newborns at-risk for child undernutrition in India: Secondary analysis of the National Family Health Survey-4

Background

India is at the epicentre of global child undernutrition. Strategies to identify at-risk populations are needed in the context of limited resources

Methods

Data from children under the age of five surveyed in the 2015-2016 National Family Health Survey were used. Child undernutrition was assessed using anthropometric measurements. Predictor variables were identified from the extant literature and included if they could be measured at the time of delivery. Survey-weighted logistic regression was applied to model the outcome. Internal validation of the model was performed using 200 bootstrapped samples representing half of the total data sets.

Results

In 2016, 54.4% (95% CI = 54.0%-54.8%) of Indian children were undernourished, according to a composite index of anthropometric failure. The predictive model for overall undernutrition included maternal (height, education, reproductive history, number of antenatal visits), child (sex, birthweight), and household characteristics (district of residence, caste, rural residence, toilet availability, presence of a separate kitchen). The model demonstrated reasonable discrimination ability (optimism-adjusted c = 0.67). The group of children classified in the lowest decile for risk of undernutrition had a prevalence of 25.9%, while the group classified in the highest decile had a prevalence of 77.4%.

Conclusions

It is possible to stratify newborns at the time of delivery based on their risk for undernutrition in the first five years of life. The model developed by this study represents a first step in adopting a risk-score based approach for the most vulnerable population to receive services in a timely manner.

Is maternal autonomy associated with child nutritional status? Evidence from a cross-sectional study in India

Despite India’s steady economic growth over recent the period, the burden of childhood malnutrition persists, contributing to higher neonatal and infant mortality. There is limited evidence available to contextualise mothers’ crucial role in childcare practices and health status in the Indian context. This study attempts to assess the association between maternal autonomy and the nutritional status of children under five. We used samples of 38,685 mother-child pairs from the fourth round of the National Family Health Survey (NFHS-4), conducted in 2015–16. We considered three widely used indicators of child nutrition as outcome variables: stunting, wasting, and underweight. Maternal autonomy (measured from three dimensions: household decision-making, freedom of physical movement, and access to economic resources/control over assets) was the key predictor variable, and various child demographics, maternal, and household characteristics were considered control variables. Stepwise binary logistic regression models were performed to examine the association. Of study participants, 38%, 21%, and 35% of children were stunted, wasted, and underweight, respectively. Our results (models 1 to 4) indicate that mothers with greater autonomy were significantly associated with lower odds of malnourished children. After controlling for all potential confounding variables (in model 5), maternal autonomy had a statistically insignificant association with children’s stunting (Odds ratio [OR]: 0.93; 95% confidence interval [CI]: 0.87, 1.00) and wasting (OR: 0.92; 95% CI: 0.85, 1.00). However, a significant relationship (though marginally) was retained with underweight (OR: 0.94; 95% CI: 0.88, 0.99). In addition, socio-demographic characteristics such as child age, birth order, maternal education, maternal BMI, place of residence and household wealth quintile were found to be strong predictors of child nutritional status. Future policies should not only inform women’s empowerment programmes but also emphasise effective interventions toward improving female educational attainment and nutritional status of women, as well as addressing socioeconomic inequalities in order to combat the persistent burden of childhood malnutrition in India.

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.

Eco-geographic patterns of child malnutrition in India and its association with cereal cultivation: An analysis using demographic health survey and agriculture datasets

Background: High prevalence of maternal malnutrition, low birth-weight and child malnutrition in India contribute substantially to the global malnutrition burden. Rural India has disproportionately higher levels of child malnutrition. Stunting and wasting are the primary determinants of child malnutrition and their district-level distribution shows clustering in different geographies and regions. Cereals, particularly millets, constitute the bulk of protein intake among the poor, especially in rural areas in India where high prevalence of wasting persists. Methods: The previous round of National Family Health Survey (NFHS4) has disaggregated data by district, enabling a more fine-scale characterisation of the prevalence of markers of malnutrition. We used data from NFHS4 and agricultural statistics datasets to analyse relationship of prevalence of malnutrition at the district level and area under cereal cultivation. We analysed malnutrition through data on under-5 stunting and wasting by district.  Results: Stunting and wasting patterns across districts show a distinct geographical and age distribution; districts with higher wasting showed relatively higher prevalence at six months of age. Wasting prevalence at district level was associated with higher cultivation of millets, with a stronger association seen for jowar and other millets (Kodo millet, little millet, proso millet, barnyard millet and foxtail millet). District level stunting was associated with higher district level cultivation of wheat. In multivariable analysis, wasting was positively associated with women's body mass index and stunting with women's short stature. Conclusions: Well-designed intervention studies will be required to confirm causal pathways contributing to ecogeographic patterns of child malnutrition. The cultivation of other millets has a strong association with prevalence of wasting. State-of-the-art studies that improve our understanding of bio-availability of amino acids and other nutrients from the prevalent dietary matrices of rural poor communities will be needed to confirm causal pathways contributing to potential eco-geographic patterns.

Understanding the Change in the Prevalence and Factors Influencing the Childhood Stunting Using District-Level Data from NFHS-4 and NFHS-5 in India

To compare the district level prevalence of childhood stunting between NFHS-4 and NFHS-5 and to explore the correlates of it at the district level. Although malnutrition rates in India have decreased over a period, country is still a home for the highest number of stunted and wasted children in the world. Among the South Asian countries, India has the second highest number of stunted children. An ecological study conducted by using the data from fourth and fifth round of National Family Health Survey. Study concentrated on percentage of children who were stunted across 692 Indian districts during 2 survey periods and its correlates from NFHS-5. District level change in childhood stunting was calculated by differencing the NFHS-5 estimates from NFHS-4. Descriptive statistics were used to understand the nature of the variables and Moran’s I statistic was calculated to check for the spatial autocorrelation in the childhood stunting. Spatial error regression model was used to identify the correlates of childhood stunting. Among the Indian districts considered, 243 districts showed the increase in childhood stunting between the time periods considered. Currently, about 33.56% of children in India are stunted and there is high spatial disparity in the prevalence of childhood stunting among the districts of it. Major hotspots of childhood stunting were found in the parts of UP, Bihar, Jharkhand, and West Bengal. Households access to improved sanitation facility, iodized salt, clean fuel, women 10 plus years of schooling, post-natal care of mother were found to be the significant protective factors. Closed spacing of births, teenage pregnancy, low BMI of women, childhood diarrhea, and anemia were found to be the significant risk factors of childhood stunting. Stunting depends on several other factors apart from poverty, working on these factors will help in reducing childhood stunting in India.

Evaluating Changes in Determinants of Stunting among Children Under 2 Years and Assessing Their Contribution to Socioeconomic Disparity in Child Nutritional Status across India

Background

Despite concentrated global efforts to bring about reduction in malnutrition among children, it continues to remain a public health concern, especially in developing countries such as India. While substantial reduction in the levels of stunting has taken place over the years, high levels of variation exist in distribution of stunting across the country.

Objective

The study aimed to identify the determinants of stunting in early childhood and their contribution to change in levels of stunting across India. It also compared the socioeconomic disparity in the levels of stunting and changes therein over the last decade.

Methods

The study utilizes data from the National Family Health Survey (NFHS-3 and NFHS-4) on children aged under-2 years. Bivariate and multivariate logistic regression identified determinants of early childhood stunting followed by Oaxaca decomposition model to assess the contribution of each of the factors to reduction in levels of stunting over the years. Concentration index was used to study the socioeconomic disparity in early childhood stunting.

Results

Nearly 19% decrease in early childhood stunting can be attributed to increase in institutional deliveries, 14% to increase in maternal schooling, and 10% to improvement in maternal body mass index. In spite of an overall decrease, very little change is seen in socioeconomic disparity of childhood stunting.

Conclusions

The study identifies institutional deliveries, maternal schooling, and maternal health as major contributors of decrease in early childhood stunting. It identifies persisting socioeconomic disparity in childhood stunting over the last decade.

Spatial Variation in Contraceptive Practice Across the Districts of India, 1998-2016

India is currently one of the most demographically diverse regions of the world. Fertility and mortality rates are known to show considerable variation at the level of regions, states and districts. Little is known however, about the spatial variations of the contraceptive usage—a critical variable that is relevant to fertility as well as health policy. This paper uses data from four national population-based household surveys conducted between 1998 and 2016 to explore district-level variations in the contraceptive prevalence rate. We find no clear evidence of convergence. The gap between the best and worst performing districts is more than 70 percent across the four rounds and does not diminish over time. We also find considerable evidence of spatial clustering across districts. Districts with high prevalence concentrate in Southern states and more recently, in the Northeast of the country. Our analysis suggests that female literacy and health care infrastructure are important correlates of spatial clusters. This suggests that investments in women’s human capital and health-care infrastructure play a role in expanding women’s opportunities to time their births.

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

Background

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

Method

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

Results

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

Conclusion

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

Supplementary Information

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

Eco-geographic patterns of child malnutrition in India and its association with cereal cultivation: An analysis using demographic health survey and agriculture datasets

Background: High prevalence of maternal malnutrition, low birth-weight and child malnutrition in India contribute substantially to the global malnutrition burden. Rural India has disproportionately higher levels of child malnutrition. Stunting and wasting are the primary determinants of child malnutrition and their district-level distribution shows clustering in different geographies and regions. Cereals, particularly millets, constitute the bulk of protein intake among the poor, especially in rural areas in India where high prevalence of wasting persists. Methods: The last round of National Family Health Survey (NFHS4) has disaggregated data by district, enabling a more fine-scale characterisation of the prevalence of markers of malnutrition. We used data from NFHS4 and agricultural statistics datasets to analyse relationship of prevalence of malnutrition at the district level and area under cereal cultivation. We analysed malnutrition through data on under-5 stunting and wasting by district.  Results: Stunting and wasting patterns across districts show a distinct geographical and age distribution; districts with higher wasting showed relatively higher prevalence before six months of age. Wasting prevalence at district level was associated with higher cultivation of millets, with a stronger association seen for jowar and other millets (Kodo millet, little millet, proso millet, barnyard millet and foxtail millet). District level stunting was associated with higher district level cultivation of all crops (except other millets). The analysis was limited by lack of fine-scale data on prevalence of low birth-weight and type of cereal consumed. Conclusions: Better cereal cultivation and consumption data will be needed to confirm causal pathways contributing to potential ecogeographic patterns. The cultivation of other millets has a strong association with prevalence of wasting. State-of-the-art studies that improve our understanding of bio-availability of amino acids and other nutrients from the prevalent dietary matrices of rural poor communities will be needed to confirm causal pathways contributing to potential eco-geographic patterns.

An intersectional analysis of the composite index of anthropometric failures in India

BACKGROUND

Nutritional inequality in India has been estimated typically using stunting, wasting and underweight separately which hide the overall magnitude and severity of undernutrition. We used the Composite Index of Anthropometric Failure (CIAF) that combines all three forms of anthropometric failures to assess the severity of undernutrition and identify the most vulnerable social groups and geographical hotspots.

METHOD

CIAF was constructed using child anthropometric data from the fourth round of the National Family Health Survey (NFHS-4, 2015-16). We considered 24 intersecting sub-groups based on intersections across four main axes of inequality i.e., caste [Scheduled Tribe (ST), Scheduled Caste (SC) and Other], economic position (poor and non-poor), place of residence (rural and urban) and gender (male and female) (eg. ST-Poor-Rural-Female). Cross-tabulation and logistic regression were done to assess the odds of CIAF among intersecting groups and to identify the most vulnerable sub-groups. Concentration curve was plotted to visualise economic position inequality in child undernutrition across caste categories. Choropleth maps were constructed and descriptive analysis of the district-level prevalence of CIAF was performed to identify the geographic clustering of undernutrition.

RESULTS

Overall 55.32% of children were undernourished by CIAF and 6.62% of children have simultaneous three anthropometric failure. In sub-group analysis, children from ST and SC caste have a higher risk of undernutrition irrespective of other axis of inequality. Compared with CIAF, economic position inequality was amplified for simultaneous-three-failures among all caste categories. Economic position inequalities within caste are more for other caste and SC categories than with ST. Economic position, caste and gender based inequality in all three failures is more consistent in rural areas than with urban areas. Based on the analysis of the high prevalence in the co-occurrence of two or three failures, 111 districts from 12 of 29 states in India were identified across four geographic clusters.

CONCLUSIONS

The study shows social and eco-geographical clustering of multi-dimensional anthropometric failures and indicates the need for focused nutritional interventions among SC and ST community in general and ST children from the poor households. Furthermore, governance interventions that target entire regions across districts and states combined with decentralised planning are needed.

Vulnerability of agriculture to climate change increases the risk of child malnutrition: Evidence from a large-scale observational study in India

INTRODUCTION

The impact of climate change on agriculture and food security has been examined quite thoroughly by researchers globally as well as in India. While existing studies provide evidence on how climate variability affects the food security and nutrition, research examining the extent of effect vulnerability of agriculture to climate change can have on nutrition in India are scarce. This study examined a) the association between the degree of vulnerability in agriculture to climate change and child nutrition at the micro-level b) spatial effect of climate vulnerability on child nutrition, and c) the geographical hotspots of both vulnerability in agriculture to climate change and child malnutrition.

METHODS

The study used an index on vulnerability of agriculture to climate change and linked it to child malnutrition indicators (stunting, wasting, underweight and anaemia) from the National Family Health Survey 4 (2015-16). Mixed-effect and spatial autoregressive models were fitted to assess the direction and strength of the relationship between vulnerability and child malnutrition at macro and micro level. Spatial analyses examined the within-district and across-district spill-over effects of climate change vulnerability on child malnutrition.

RESULTS

Both mixed-effect and spatial autoregressive models found that the degree of vulnerability was positively associated with malnutrition among children. Children residing in districts with a very high degree of vulnerability were more like to have malnutrition than those residing in districts with very low vulnerability. The analyses found that the odds of a child suffering from stunting increased by 32%, wasting by 42%, underweight by 45%, and anaemia by 63% if the child belonged to a district categorised as very highly vulnerable when compared to those categorised as very low. The spatial analysis also suggested a high level of clustering in the spatial distribution of vulnerability and malnutrition. Hotspots of child malnutrition and degree of vulnerability were mostly found to be clustered around western-central part of India.

CONCLUSION

Study highlights the consequences that vulnerability of agriculture to climate change can have on child nutrition. Strategies should be developed to mitigate the effect of climate change on areas where there is a clustering of vulnerability and child malnutrition.

Mapping changes in district level prevalence of childhood stunting in India 1998-2016: An application of small area estimation techniques

The four rounds of National Family Health Survey (NFHS) conducted during 1992-93, 1998-99, 2005-06 and 2015-16 is main source to track the health and development related indicators including nutritional status of children at national and state level in India. Except NFHS-4, first three rounds of NFHS were unable to provides district-level estimates of childhood stunting due to the insufficient sample sizes. The small area estimation (SAE) techniques offer a viable solution to overcome the problem of small sample size. Therefore, this study uses SAE techniques to derive district level prevalence of childhood stunting corresponding to NFHS-2 (1998-99). Study further estimated GIS maps, univariate Local indicator of spatial autocorrelation (LISA) and Moran's I to understand the trend in district level childhood stunting between NFHS-2 and NFHS-4. Estimates obtained by SAE techniques suggest that prevalence of childhood stunting ranges from 20.7% (95% CI: 18.8-22.7) in South Goa district of Goa to 64.4% (95%CI: 63.1-65.7) in Dhaulpur district of Rajasthan during 1998-99. The diagnostic measures used to validate the reliability of estimates obtained by SAE techniques indicate that the model-based estimates are reliable and representative at district level. Results of geospatial analysis indicates substantial reduction in childhood stunting between 1998 and 2016. Out of 640 district,about 81 district experience reduction of more than 50%. At the same time 60 district experience less than 10% of reduction between 1998 and 2016. Spatial clustering of childhood stunting remains same over the study period except few additional cluster in Maharashtra, Andhra and Meghalaya in 2016. The district level estimates obtained from this study might be helpful in framing decentralized policies and implementation of vertical programs to enhance the efficacy of various nutrition interventions in priority districts of the country.

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

BACKGROUND

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

METHODS

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

MAIN FINDINGS

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

CONCLUSIONS

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

A success story of reduction in childhood stunting and underweight in India: analysis of pooled data from three rounds of Indian Demographic and Health Surveys (1998-2016)

This study used a series of individual-level datasets from National Family Health Surveys conducted in 1998-99, 2005-06 and 2015-16 to assess the factors behind the reduction in childhood stunting and underweight in India between the years 1998-99 and 2015-16. A multivariable decomposition regression analysis was performed. Results showed that the prevalence of childhood stunting declined from 49.4% in 1998-99 to 34.9% in 2015-16. Over the same period, the prevalence of childhood underweight declined from 41.9% in 1998-99 to 33.1% in 2015-16. The reduction in the prevalence of stunting was found to be contributed largely by a reduction in the combined prevalence of stunting and underweight (60%), followed by stunted only (21%) and the combined prevalence of stunting, underweight and wasting (19%). Likewise, the reduction in the prevalence of underweight was contributed by a reduction in the combined prevalence of stunting and underweight and the combined prevalence of stunting, underweight and wasting. Results of the decomposition analysis showed that over the period 1998-99 to 2015-16, improvement in wealth status and maternal education led to 13% and 12% declines, respectively, in childhood stunting and to 31% and 19% declines, respectively, in childhood underweight. Furthermore, reductions in childhood stunting and underweight were due to an increased average number of antenatal care visits, lower average birth order, decreased share of children with below-average birth size, increased use of clean fuel for cooking and a reduction in the practice of open defecation. These findings suggest that further reduction in the prevalence of childhood stunting and underweight could be attained through more equitable household economic growth, investment in girl's education, greater access to improved toilet facilities, more widespread use of clean fuel for cooking, reduction in average birth order, increased antenatal care visits and greater consumption of IFA tablets by pregnant women. Policymakers need to prioritize these measures to further reduce malnutrition among Indian children.

Inequality in child undernutrition among urban population in India: a decomposition analysis

Background

With increasing urbanization in India, child growth among urban poor has emerged as a paramount public health concern amidst the continuously growing slum population and deteriorating quality of life. This study analyses child undernutrition among urban poor and non-poor and decomposes the contribution of various factors influencing socio-economic inequality. This paper uses data from two recent rounds of National Family Health Survey (NFHS-3&4) conducted during 2005–06 and 2015–16.

Methods

The concentration index (CI) and the concentration curve (CC) measure socio-economic inequality in child growth in terms of stunting, wasting, and underweight. Wagstaff decomposition further analyses key contributors in CI by segregating significant covariates into five groups-mother’s factor, health-seeking factors, environmental factors, child factors, and socio-economic factors.

Results

The prevalence of child undernutrition was more pronounced among children from poor socio-economic strata. The concentration index decreased for stunting (− 0.186 to − 0.156), underweight (− 0.213 to − 0.162) and wasting (− 0.116 to − 0.045) from 2005 to 06 to 2015–16 respectively. The steepness in growth was more among urban poor than among urban non-poor in every age interval. Maternal education contributed about 19%, 29%, and 33% to the inequality in stunting, underweight and wasting, respectively during 2005–06. During 2005–06 as well as 2015–16, maternal factors (specifically mother’s education) were the highest contributory factors in explaining rich-poor inequality in stunting as well as underweight. More than 85% of the economic inequality in stunting, underweight, and wasting among urban children were explained by maternal factors, environmental factors, and health-seeking factors.

Conclusion

All the nutrition-specific and nutrition-sensitive interventions in urban areas should be prioritized, focusing on urban poor, who are often clustered in low-income slums. Rich-poor inequality in child growth calls out for integration and convergence of nutrition interventions with policy interventions aimed at poverty reduction. There is also a need to expand the scope of the Integrated Child Development Services (ICDS) program to provide mass education regarding nutrition and health by making provisions of home visits of workers primarily focusing on pregnant and lactating mothers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-020-09864-2.

The burden of anthropometric failure and child mortality in India

The public health burden of nutritional deficiency and child mortality is the major challenge India is facing upfront. In this context, using National Family Health Survey, 2015–16 data, this study estimated rate of composite index of anthropometric failure (CIAF) among Indian children by their population characteristics, across states and examined the multilevel contextual determinants. We further investigated district level burden of infant and child mortality in terms of multiple anthropometric failure prevalence across India. The multilevel analysis confirms a significant state, district and PSU level variation in the prevalence of anthropometric failures. Factors like- place of residence, household’s economic wellbeing, mother’s educational attainment, age, immunization status and drinking water significantly determine the different forms of multiple anthropometric failures. Wealth status of the household and mother’s educational status show a clear gradient in terms of the estimated odds ratios. The district level estimation of infant and child mortality demonstrates that districts with higher burden of multiple anthropometric failures show elevated risk of infant and child mortality. Unlike previous studies, this study does not use the conventional indices, instead considered the CIAF to identify the exact and severe form of undernutrition among Indian children and the associated nexus with infant and child mortality at the district level.

Correlates and spatial distribution of the co-occurrence of childhood anaemia and stunting in Ghana

Childhood anaemia and stunting are major public health concerns in Ghana. Using the 2014 Ghana Demographic and Health Survey, we evaluated whether childhood anaemia (Haemoglobin concentration < 110 g/L) and stunting (height-for-age z score < -2) co-occur beyond what is expected in Ghana, and employed spatial analysis techniques to determine if their co-occurrence is spatially correlated. There was no statistically significant difference between the observed and expected frequency of co-occurrence. Among 24-35 month and 36-59-month-old children, belonging to a high wealth household compared to low wealth household was associated with lower odds of the co-occurrence of childhood anaemia and stunting (OR, 95% CI: 0.3[0.1, 0.8] and 0.2[0.1, 0.5], respectively). Children aged 6-23 months with caregivers who had formerly been in union compared to their counterparts with caregivers who have never been in union had higher odds of co-occurrence of anaemia and stunting (5.1, [1.1, 24.3]). Overall, households with high wealth and having a mother with secondary or more education were associated with lower odds of the co-occurrence of childhood anaemia and stunting (OR, 95% CI: 0.4[0.2, 0.8] and 0.5[0.3, 0.9], respectively). There was substantial spatial clustering of co-occurrence, particularly in the northern region of the country. Interventions purposed to improve linear growth and anaemia must identify the specific factors or context which contribute to childhood anaemia and stunting.

Does cereal, protein and micronutrient availability hold the key to the malnutrition conundrum? An exploratory analysis of cereal cultivation and wasting patterns of India

Background: High prevalence of maternal malnutrition, low birth-weight and child malnutrition in India contribute substantially to the global malnutrition burden. Rural India has disproportionately higher levels of child malnutrition. Stunting and wasting are the primary determinants of child malnutrition and their district-level distribution shows clustering in different geographies and regions. Methods: The last round of National Family Health Survey (NFHS4) has disaggregated data by district, enabling a more nuanced understanding of the prevalence of markers of malnutrition. We used data from NFHS4 and agricultural statistics datasets to analyse relationship of area under cereal cultivation with the prevalence of malnutrition at the district level. We analysed malnutrition through data on under-5 stunting and wasting; maternal malnutrition was assessed through prevalence of women's low BMI and short stature by district.  Results: Stunting and wasting patterns across districts show a distinct geographical and age distribution; districts with higher wasting showed relatively high prevalence of 40% before six months of age. Wasting was associated with higher cultivation of millets, with a stronger association seen for jowar and other millets (Kodo millet, little millet, proso millet, barnyard millet and foxtail millet). Stunting was associated with cultivation of all crops except other millets. Low women's BMI was seen associated with cultivation of rice and millets. The analysis was limited by lack of fine-scale data on prevalence of low birth-weight and type of cereal consumed. Conclusions: Multi-site observational studies of long-term effects of type of cereals consumed could help explain the ecogeographic distribution of malnutrition in India. Cereals, particularly millets constitute the bulk of protein intake among the poor, especially in rural areas in India where high prevalence of  wasting persists.

Deciphering disparities in childhood stunting in an underdeveloped state of India: an investigation applying the unconditional quantile regression method

Background

Unacceptably high rate of childhood stunting for decades remained a puzzle in the eastern Indian state of Bihar. Despite various programmatic interventions, nearly half of the under-five children (numerically about 10 million) are still stunted in this resource-constrained state.

Data and methods

Using four successive rounds of National Family Health Survey (NFHS) data spread over more than two decades and by employing unconditional quantile regressions and counterfactual decomposition (QR-CD), the present study aims to assess effects of various endowments as well as returns to those endowments in disparities in childhood stunting over the period.

Results

The results show that although the child’s height-for-age Z-scores (HAZ) disparity largely accounted for differing levels of endowments during the earlier decades, in the later periods, inadequate access to the benefits from various development programmes was also found responsible for HAZ disparities. Moreover, effects of endowments and their returns varied across quantiles. We argue that apart from equalizing endowments, ensuring adequate access to different nutrition-centric programmes is essential to lessen the burden of childhood stunting.

Conclusion

The state must focus on intersectoral convergence of different schemes in the form of state nutrition mission, and, strengthen nutrition-centric policy processes and their political underpinnings to harness better dividend.

Malnutrition and poverty in India: does the use of public distribution system matter?

Background

Large scale public investment in Public Distribution System (PDS) have aimed to reduce poverty and malnutrition in India. The PDS is the largest ever welfare programme which provides subsidised food grain to the poor households. This study attempt to examine the extent of stunting and underweight among the children from poor and non-poor households by use of public distribution system (PDS) in India.

Methods

Data from the National Family and Health Survey-4 (NFHS-4), was used for the analysis. A composite variable based on asset deprivation and possession of welfare card provided under PDS (BPL card), was computed for all households and categorised into four mutually exclusive groups, namely real poor, excluded poor, privileged non-poor and non-poor. Real poor are those economically poor and have a welfare card, excluded poor are those economically poor and do not have welfare card, privileged poor are those economically non-poor but have welfare card, and non-poor are those who are not economically poor and do not have welfare card. Estimates of stunting and underweight were provided by these four categories. Descriptive statistics and logistic regression were used for the analysis.

Results

About half of the children from each real poor and excluded poor, two-fifths among privileged non-poor and less than one-third among non-poor households were stunted in India. Controlling for socio-economic and demographic covariates, the adjusted odds ratio of being stunted among real poor was 1.42 [95% CI: 1.38, 1.46], 1.43 [95% CI: 1.39, 1.47], among excluded poor and 1.15 [95% CI: 1.12, 1.18], among privileged non-poor. The pattern was similar for underweight and held true in most of the states of India.

Conclusions

Undernutrition among children from poor households those excluded from PDS is highest, and it warrants inclusion in PDS. Improving the quality of food grains and widening food basket in PDS is recommended for reduction in level of malnutrition in India.

Factors explaining regional variation in under-five mortality in India: An evidence from NFHS-4

Although child mortality has declined in India, pronounced regional and socioeconomic inequality exists. The study examines the effects of individual- and community-level characteristics on under-five mortality and investigates the extent to which they affect regional variation. The study is based on Indian National Family and Health Survey 4, 2015-16 data. A two-level logistic regression model was performed to examine the effects of the socio-economic characteristics, and multivariate decomposition analysis was done to assess the contribution of factors in the inter-regional under-five mortality differentials. Regional variation in the selected variables is observed. For instance, the percentage of children with small childbirth size varied from 9.7% in the southern to 21.6% in the northeastern region. The percentage of poor households, low educated mothers, and childbirths delivered at home facility were higher in the central and eastern region compared to the southern region. The multilevel analysis shows that the region of residence explained 15.8% variance, and community-level characteristics alone could explain 25.3% variation in the risk of under-five deaths. The decomposition analysis indicates that the average number of excess deaths in the central and eastern regions is higher compared to the other regions. The compositional differences account for 50.9% of the under-five mortality gaps between the south and north region, 80.9% of the gap between the south and east, and 42.9% of the gap between the south and central region of India. Special attention and targeted action are needed to address the underlying causes of low birth weight of children in all the regions of India. Region-specific interventions might be priorities; for example, north, and central regions, need an economic and educational uplift, while infrastructural and economic policies should be prioritized for the northeastern region. Also, region-specific community-level interventions are needed to improve child survival in India.