Subnational mapping of under-5 and neonatal mortality trends in India: the Global Burden of Disease Study 2000–17

Geospatial approaches are increasingly used to produce fine spatial scale estimates of reproductive, maternal, newborn and child health (RMNCH) indicators in low- and middle-income countries (LMICs). This study aims to describe important methodological aspects and specificities of geospatial approaches applied to RMNCH coverage and impact outcomes and enable non-specialist readers to critically evaluate and interpret these studies. Two independent searches were carried out using Medline, Web of Science, Scopus, SCIELO and LILACS electronic databases. Studies based on survey data using geospatial approaches on RMNCH in LMICs were considered eligible. Studies whose outcomes were not measures of occurrence were excluded. We identified 82 studies focused on over 30 different RMNCH outcomes. Bayesian hierarchical models were the predominant modeling approach found in 62 studies. 5 × 5 km estimates were the most common resolution and the main source of information was Demographic and Health Surveys. Model validation was under reported, with the out-of-sample method being reported in only 56% of the studies and 13% of the studies did not present a single validation metric. Uncertainty assessment and reporting lacked standardization, and more than a quarter of the studies failed to report any uncertainty measure. The field of geospatial estimation focused on RMNCH outcomes is clearly expanding. However, despite the adoption of a standardized conceptual modeling framework for generating finer spatial scale estimates, methodological aspects such as model validation and uncertainty demand further attention as they are both essential in assisting the reader to evaluate the estimates that are being presented.

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Geospatial estimation of reproductive, maternal, newborn and child health indicators: a systematic review of methodological aspects of studies based on household surveys.

Variation in human growth and the genetic and environmental factors that are influencing it have been described worldwide. The objective of this study is to assess the geographical variance of under-five children nutritional status and its related covariates across Indian districts. We use the most recent fourth round of the Indian National Family Health Survey conducted in 2015-2016, which for the first time offers district level information. We employ principal component analysis (PCA) on the demographic and socio-economic determinants of childhood morbidity and conduct hierarchical clustering analysis to identify geographical patterns in nutritional status among children of age under five at the district level. Our results reveal strong geographical clustering among the districts of India. Throughout most of Southern India, children are provided with relatively better conditions for growth and improved nutritional status, as compared to districts in the central, particularly rural parts of India. Looking at average weight, as well as the proportion of children that suffer from underweight and wasting, northeastern Indian districts seem to be offering living conditions more conducive to healthy child development. The geographical clustering of malnutrition, as well as below-average child height and weight coincides with high poverty, low female education, lower BMI among mothers, higher prevalence of both parity 4+ and teenage pregnancies. The present study highlights the importance of combining PCA and cluster analysis methods in studying variation in under-five child growth and nutrition at the district level. We identify the geographical areas, where children are under severe risk of undernutrition, stunting and wasting and contribute to formulating policies to improve child nutrition in India.

Under-Five Child Growth and Nutrition Status:Spatial Clustering of Indian Districts. VID Working Paper 03/2019

Dramatic improvements in data availability and quality are needed to meet the challenge of monitoring and analyzing food systems, so that appropriate policies and actions to improve human and planetary health can be identified and data-informed accountability mechanisms put in place to strengthen food systems governance. Studying food systems is complex due to diverse actors and interlinking processes that operate on multiple spatial and temporal scales, and their multiple outcomes, which may be subject to hidden feedback mechanisms and tradeoffs. However, descriptive research to characterize food system components and make comparisons across geography, income groups, and population groups is an important foundation. The first part of this article details a series of critical data gaps and limitations that are currently hindering food systems learning and accountability, also comparing these gaps across regions and income groups. The second part of the article introduces the Food Systems Dashboard, a new data visualization tool that aims to improve access to and usage of food systems-related data, thus strengthening the data value chain and better informing policies and actions intended to improve diets, nutrition, livelihoods, and environmental sustainability.

You Say You Want a Data Revolution? Taking on Food Systems Accountability

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 207% (95% CI: 188-227) in South Goa district of Goa to 644% (95%CI: 631-657) 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

V. Sreenivas

Papers

Mapping of variations in child stunting, wasting and underweight within the states of India: the Global Burden of Disease Study 2000–2017