Identifying geospatial patterns in wealth disparity in child malnutrition across 640 districts in India.
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ABSTRACT:
SUBMITTER: Liou L
PROVIDER: S-EPMC6909160 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
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Identifying geospatial patterns in wealth disparity in child malnutrition across 640 districts in India.
Liou Lathan L Kim Rockli R Subramanian S V SV
SSM - population health 20191128
We assessed district-level geospatial trends in precision weighted prevalence and absolute wealth disparity in stunting, underweight, wasting, low birthweight, and anemia among children under five in India. The largest wealth disparities were found for anthropometric failures and substantial variation existed across states. We identified statistically significant (p < 0.001) geospatial patterns in district-wide wealth disparities for all outcomes, which differed from geospatial patterns for the ...[more]
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Project description:Despite sustained economic growth and reduction in money metric poverty in last two decades, prevalence of malnutrition remained high in India. During 1992-2016, the prevalence of underweight among children had declined from 53% to 36%, stunting had declined from 52% to 38% while that of wasting had increased from 17% to 21% in India. The national average in the level of malnutrition conceals large variation across districts of India. Using data from the recent round of National Family Health Survey (NFHS), 2015-16 this paper examined the spatial heterogeneity and meso-scale correlates of child malnutrition across 640 districts of India.Moran's I statistics and bivariate LISA maps were used to understand spatial dependence and clustering of child malnutrition. Multiple regression, spatial lag and error models were used to examine the correlates of malnutrition. Poverty, body mass index (BMI) of mother, breastfeeding practices, full immunization, institutional births, improved sanitation and electrification in the household were used as meso scale correlates of malnutrition.The univariate Moran's I statistics was 0.65, 0.51 and 0.74 for stunting, wasting and underweight respectively suggesting spatial heterogeneity of malnutrition in India. Bivariate Moran's I statistics of stunting with BMI of mother was 0.52, 0.46 with poverty and?-?0.52 with sanitation. The pattern was similar with respect to wasting and underweight suggesting spatial clustering of malnutrition against the meso scale correlates in the geographical hotspots of India. Results of spatial error model suggested that the coefficient of BMI of mother and poverty of household were strong and significant predictors of stunting, wasting and underweight. The coefficient of BMI in spatial error model was largest found for underweight (??=?0.38, 95% CI: 0.29-0.48) followed by stunting (??=?0.23, 95% CI: 0.14-0.33) and wasting (??=?0.11, 95% CI: 0.01-0.22). Women's educational attainment and breastfeeding practices were also found significant for stunting and underweight.Malnutrition across the districts of India is spatially clustered. Reduction of poverty, improving women's education and health, sanitation and child feeding knowledge can reduce the prevalence of malnutrition across India. Multisectoral and targeted intervention in the geographical hotspots of malnutrition can reduce malnutrition in India.
| S-EPMC6098604 | biostudies-literature
Project description:In India, districts serve as central policy unit for program development, administration and implementation. The one-size-fits-all approach based on average prevalence estimates at the district level fails to capture the substantial small area variation. In addition to district average, heterogeneity within districts should be considered in policy design. The objective of this study was to quantify the extent of small area variation in child stunting, underweight and wasting across 36 states/Union Territories (UTs), 640 districts (and 543 PCs), and villages/blocks in India. We utilized the 4th round of Indian National Family Health Survey (NFHS-4) conducted in 2015-2016. The study population included 225,002 children aged 0-59 months whose height and weight information were available. Stunting was defined as height-for-age z-score below 2 SD from the World Health Organization child growth reference standards. Similarly, underweight and wasting were each defined as weight-for-age < -2 SD and weight-for-height < -2 SD from the age- and sex-specific medians. We adopted a four-level logistic regression model to partition the total variation in stunting, underweight and wasting. We computed precision-weighted prevalence of child anthropometric failures across districts and PCs as well as within-district/PC variation using standard deviation (SD) measures. For stunting, 56.4% (var: 0.237; SE: 0.008) of the total variation was attributed to villages/blocks, followed by 25.8% (var: 0.109; SE: 0.030) to states/UTs, and 17.7% (Var: 0.074; SE: 0.006) to districts. For underweight and wasting, villages/blocks accounted for 38.4% (var: 0.224; SE: 0.007) and 50% (var: 0.285; SE: 0.009), respectively, of the total contextual variance in India. Similar findings were shown in multilevel models incorporating PC as a geographical unit instead of districts. We found high positive correlations between mean prevalence and SD for stunting (r = 0.780, p < 0.001), underweight (r = 0.860, p < 0.001), and wasting (r = 0.857, p < 0.001) across all districts in India. A similar pattern of correlation was found for PCs. Within-district and within-PC variation are the primary source of variation for child malnutrition in India. Our results suggest the importance of considering heterogeneity within districts and PCs when planning and administering child nutrition policies.
| S-EPMC7907088 | biostudies-literature
Project description:PurposeDiabetes is one of the leading causes of mortality and morbidity among women in India. The burden of diabetes among women was found to increase with age and exposure to the post-partum period. The present study examines the spatial variation in the prevalence of diabetes among women in the late reproductive age-group of 35-49 years across 640 districts in India.MethodsThe study utilized data from the recent round of the National Family Health Survey, 2015-16. Age-standardized prevalence rates were calculated, followed by an examination of economic inequality using the poor-rich-ratio (PRR) and Wagstaff's concentration index. Spatial variation in the prevalence of diabetes was explored with a series of quantile maps, univariate, and bivariate LISA cluster maps. Further, to explore the district-level diabetes prevalence among women in the country, Ordinary Least Square and Spatial Autoregressive (SAR) models were used.ResultsThe study findings affirm the presence of spatial clustering in the burden of diabetes among women. The burden is relatively higher among women from the Southern and Eastern parts of the country. Findings establish obesity, hypertension, and living in urban areas as major correlates of diabetes.ConclusionProgram with an aim to lower the intensity of community-based prevalence of diabetes, especially among women in their late reproductive ages, should adopt differential approaches across different states/districts in the context of their lifestyle, dietary pattern, working pattern, and other socio-cultural practices.
| S-EPMC7270370 | biostudies-literature
Project description:BackgroundThe conventional indicators of infant and under-five mortality are aggregate deaths occurring in the first year and the first five years, respectively. Monitoring deaths by <1 month (neonatal), 1-11 months (post-neonatal), and 12-59 months (child) can be more informative given various etiological causes that may require different interventions across these three mutually exclusive periods. For optimal resource allocation, it is also necessary to track progress in robust estimates of child survival at a smaller geographic and administrative level.MethodsData on 259 627 children came from the 2015-2016 Indian National Family Health Survey. We used a random effects model to account for the complex survey design and sampling variability, and predicted district-specific probabilities of neonatal, post-neonatal, and child mortality. The resulting precision-weighted estimates are more reliable as they pool information and borrow strength from other districts that share the same state membership. The Pearson correlation and Spearman's rank correlation were assessed for the three mortality estimates, and the Moran's I measure was used to detect spatial clustering of high burden districts for each outcome.ResultsThe majority of under-five deaths was disproportionately concentrated in the neonatal period. Across all districts, the predicted probability of neonatal, post-neonatal, and child mortality varied from 6.0 to 63.9 deaths, 3.8 to 47.6 deaths, and 1.7 to 11.8 deaths per 1000 live births, respectively. The overall correlation between district-wide probabilities of mortality for the three mutually exclusive periods was moderate (Pearson correlation = 0.47-0.58, Spearman's rank correlation = 0.58-0.64). For each outcome, a relatively strong spatial clustering was detected across districts that transcended state boundaries (Moran's I = 0.61-0.76).ConclusionsSufficiently breaking down the under-five mortality to distinct age groups and using the precision-weighted estimations to monitor performances at smaller geographic and administrative units can inform more targeted interventions and foster accountability to improve child survival.
| S-EPMC7568918 | biostudies-literature
Project description:India is the third-largest contributor to global energy-use and anthropogenic carbon emissions. India's urban energy transitions are critical to meet its climate goals due to the country's rapid urbanization. However, no baseline urban energy-use dataset covers all Indian urban districts in ways that align with national totals and integrate social-economic-infrastructural attributes to inform such transitions. This paper develops a novel bottom-up plus top-down approach, comprehensively integrating multiple field surveys and utilizing machine learning, to model All Urban areas' Energy-use (AllUrE) across all 640 districts in India, merged with social-economic-infrastructural data. Energy use estimates in this AllUrE-India dataset are evaluated by comparing with reported energy-use at three scales: nation-wide, state-wide, and city-level. Spatially granular AllUrE data aggregated nationally show good agreement with national totals (<2% difference). The goodness-of-fit ranged from 0.78-0.95 for comparison with state-level totals, and 0.90-0.99 with city-level data for different sectors. The relatively strong alignment at all three spatial scales demonstrates the value of AllUrE-India data for modelling urban energy transitions consistent with national energy and climate goals.
| S-EPMC8041814 | biostudies-literature
Project description:Objectives This study aims to improve the understanding of the relationship between exposure to floods and malnutrition in children aged 6-59 months in rural India. Research has focused exclusively on Bangladeshi children, and few controlled epidemiological studies are available. Method A community-based cross-sectional study of child nutritional status was carried out in 14 flooded and 18 non-flooded villages of Jagatsinghpur district (Orissa) within one month of the September 2008 floods, and similarly affected by flooding in August 2006. Face-to-face interviews were conducted in 757 households in the flooded villages and 816 in the non-flooded communities. Data used in this study were from those households with children aged 6-59 months. In total, 191 and 161 children were measured, respectively. The association between various malnutrition indicators and the exposure to floods was assessed by univariate and multivariate logistic regression. Results Adjusted analyses revealed that children in flooded households were more likely stunted compared with those in non-flooded ones (adjusted prevalence ratio 1.60; 95% CI 1.05 to 2.44). The prevalence of underweight was also higher in children living in the flooded communities (adjusted prevalence ratio 1.86; 95% CI 1.04 to 3.30). Further analyses found that the 26-36-month flooded cohort, thus those children younger than 1 year during the precedent flood in August 2006, attained the largest difference in levels of stunting compared with the unexposed group of the same age. Conclusion Exposure to floods is associated with long-term malnutrition in these rural communities of Orissa, India. Children exposed to floods during their first year of life presented higher levels of chronic malnutrition. Long-term malnutrition prevention programmes after floods should be implemented in flood-prone areas.
| S-EPMC3208901 | biostudies-other
Project description:BACKGROUND:Studies examining the intricate interplay between poverty, female literacy, child malnutrition, and child mortality are rare in demographic literature. Given the recent focus on Millennium Development Goals 4 (child survival) and 5 (maternal health), we explored whether the geographic regions that were underprivileged in terms of wealth, female literacy, child nutrition, or safe delivery were also grappling with the elevated risk of child mortality; whether there were any spatial outliers; whether these relationships have undergone any significant change over historical time periods. METHODOLOGY:The present paper attempted to investigate these critical questions using data from household surveys like NFHS 1992-1993, NFHS 1998-1999 and DLHS 2002-2004. For the first time, we employed geo-spatial techniques like Moran's-I, univariate LISA, bivariate LISA, spatial error regression, and spatiotemporal regression to address the research problem. For carrying out the geospatial analysis, we classified India into 76 natural regions based on the agro-climatic scheme proposed by Bhat and Zavier (1999) following the Census of India Study and all estimates were generated for each of the geographic regions. RESULT/CONCLUSIONS:This study brings out the stark intra-state and inter-regional disparities in infant and under-five mortality in India over the past two decades. It further reveals, for the first time, that geographic regions that were underprivileged in child nutrition or wealth or female literacy were also likely to be disadvantaged in terms of infant and child survival irrespective of the state to which they belong. While the role of economic status in explaining child malnutrition and child survival has weakened, the effect of mother's education has actually become stronger over time.
| S-EPMC3206872 | biostudies-literature
Project description:In India, data on key developmental indicators used to formulate policies and interventions are routinely available for the administrative unit of districts but not for the political unit of parliamentary constituencies (PC). Recently, Swaminathan et al. proposed two methodologies to generate PC estimates using randomly displaced GPS locations of the sampling clusters ('direct') and by building a crosswalk between districts and PCs using boundary shapefiles ('indirect'). We advance these methodologies by using precision-weighted estimations based on hierarchical logistic regression modeling to account for the complex survey design and sampling variability. We exemplify this application using the latest National Family Health Survey (NFHS, 2016) to generate PC-level estimates for two important indicators of child malnutrition - stunting and low birth weight - that are being monitored by the Government of India for the National Nutrition Mission targets. Overall, we found a substantial variation in child malnutrition across 543 PCs. The different methodologies yielded highly consistent estimates with correlation ranging r = 0.92-0.99 for stunting and r = 0.81-0.98 for low birth weight. For analyses involving data with comparable nature to the NFHS (i.e., complex data structure and possibility to identify a potential PC membership), modeling for precision-weighted estimates and direct methodology are preferable. Further field work and data collection at the PC level are necessary to accurately validate our estimates. An ideal solution to overcome this gap in data for PCs would be to make PC identifiers available in routinely collected surveys and the Census.
| S-EPMC6384327 | biostudies-literature
Project description:BackgroundThe impact of unconditional cash transfers on child malnutrition and its determinants remains poorly understood. The aim of this study was evaluate the impact of an unconditional child cash grant on children's nutritional status and its immediate (infant and young child feeding, dietary diversity, food consumption, and child infection and care) and underlying (household food security; Water, Hygiene and Sanitation (WASH) determinants among children younger than five years in the Karnali Zone, Nepal.MethodsThe five districts of the Karnali Zone received standard social welfare services in the form of targeted resource transfers for eligible families, plus an unconditional child cash payment, augmented by a capacity building and behavioural change education. Repeated cross-sectional surveys, with measures taken at baseline (2009, N=3750), midline (2013, N=3750) and endline (2015, N=3647), were carried out using a two-stage cluster sampling method. Multi-level Generalized Linear Mixed Models (GLMMs) with normal, binomial, Poisson, or multinomial link were performed to detect the unadjusted and adjusted trends.ResultsThere was a linear growth among children, with a corresponding increase of 0.41 height-for-age Z-scores (p < 0.001), 0.50 weight-for-age Z-scores (p<0.001), and 0.34 weight-for-height Z-scores (p<0.001) between the study period, equating to a decline in child undernutrition of 9.4, 16.5, and 5.1 percentage points (p<0.001) for stunting, underweight, and wasting respectively. Improvements were also observed in WASH outcomes, care and health seeking behaviours, and food availability.ConclusionUnconditional child cash grant embedded within a government sponsored cash transfer program for families and complemented by capacity building and behavioural change strategies improves child nutritional status and its determinants.
| S-EPMC6540561 | biostudies-literature
Project description:BackgroundThis study examines the geographic variation and the magnitude of wealth inequities in birth registration in India between 2005 and 2015.MethodsData came from India's 2005 (n = 51,940) and 2015 (n = 250,194) Demographic Health Surveys. We estimated absolute wealth inequities at the national and state-level and specified three-level logistic regression models (children, communities, and states) to calculate the variance partitioning coefficient attributable to each level to examine the variation in birth registration at each time point.ResultsNational birth registration coverage was 41.2% in 2005 and improved to 79.6% in 2015. Between 2005 and 2015, coverage among children in the poorest quintile (Q1) improved from 23.9% to 63.8% while coverage among the wealthiest children (Q5) improved from 72.4% to 92.8%. Although the absolute wealth inequity decreased from 48.6%-points to 29.1%-points, children in Q1 still had levels of coverage in 2015 that were lower than children in Q5 in 2005. Between 2005 and 2015, birth registration improved in every state and coverage was higher than 90% in 13 states. Wealth inequities decreased in 21 states and increased in 8 states. In adjusted multi-level models the proportion of total variation in birth registration attributable to states (35.7% 2005 and 29% in 2015) was larger than the variation attributable to communities (15% in 2005 and 13.7% in 2015).ConclusionBirth registration is essential for ensuring inclusive population counts of birth and mortality rates. Efforts to reach universal birth registration in India will require a commitment to reducing wealth inequities within states.
| S-EPMC7823051 | biostudies-literature