Geo-visualizing Diet, Anthropometric and Clinical Indicators for Children in India

Anthropometry is the most commonly used approach for assessing
nutritional need among children. Anthropometry alone, however, cannot
differentiate between the two immediate causes of undernutrition:
inadequate diet vs disease. We present a typology of nutritional need by
simultaneously considering dietary and anthropometric measures, dietary
and anthropometric failures (DAF), and assess its distribution among children
in India. We used the 2015-16 National Family Health Survey, a nationally
representative sample of children aged 6-23 months (n = 67 247), from
India. Dietary failure was operationalized using World Health Organization
(WHO) standards for minimum dietary diversity. Anthropometric failure was
operationalized using WHO child growth reference standard z-score of <-2
for height-for-age (stunting), weight-for-age (underweight) and weight-forheight
(wasting). We also created a combined anthropometric measure for
children who had any one of these three anthropometric failures. We crosstabulated
dietary and anthropometric failures to produce four combinations:
Dietary Failure Only (DFO), Anthropometric Failure Only (AFO), Both Failures
(BF), and Neither Failure (NF). We estimated the prevalence and distribution
of the four types, nationally, and across 640 administrative districts and 543
Parliamentary Constituencies (PCs) in India. Nationally, 80.3% of children
had dietary failure and 53.7% had at least one anthropometric failure. The
prevalence for the four DAF types was: 44.0% (BF), 36.3% (DFO), 9.8% (AFO),
and 9.9% (NF). Dietary and anthropometric measures were discordant for
46.1% of children; these children had nutritional needs identified by only one
of the two measures. Nationally, this translates to 12 181 627 children with
DFO and 3 281 913 children with AFO; the nutritional needs of these children
would not be captured if using only dietary or anthropometric assessment.
Substantial variation was observed across districts and PCs for all DAF types.
The interquartile ranges for districts were largest for BF (29.8%-53.0%) and
lowest for AFO (5.5%-13.4%). The current emphasis on anthropometry for
measuring nutritional need should be complemented with diet- and foodbased
measures. By differentiating inadequate food intake from other causes
of undernutrition, the DAF typology brings precision in identifying nutritional
needs among children. These insights may improve the development and
targeting of nutrition interventions.

Economic growth is widely perceived as a major policy instrument in reducing
childhood undernutrition in India. We assessed the association between
changes in state per capita income and the risk of undernutrition among
children in India. Data for this analysis came from three cross-sectional waves
of the National Family Health Survey (NFHS) conducted in 1992–93, 1998–
99, and 2005–06 in India. The sample sizes in the three waves were 33,816,
30,383, and 28,876 children, respectively. After excluding observations
missing on the child anthropometric measures and the independent variables
included in the study, the analytic sample size was 28,066, 26,121, and 23,139,
respectively, with a pooled sample size of 77,326 children. The proportion
of missing data was 12%–20%. The outcomes were underweight, stunting,
and wasting, defined as more than two standard deviations below the World
Health Organization–determined median scores by age and gender. We also
examined severe underweight, severe stunting, and severe wasting. The main
exposure of interest was per capita income at the state level at each survey
period measured as per capita net state domestic product measured in 2008
prices. We estimated fixed and random effects logistic models that accounted
for the clustering of the data. In models that did not account for survey-period
effects, there appeared to be an inverse association between state economic
growth and risk of undernutrition among children. However, in models
accounting for data structure related to repeated cross-sectional design
through survey period effects, state economic growth was not associated
with the risk of underweight (OR 1.01, 95% CI 0.98, 1.04), stunting (OR 1.02,
95% CI 0.99, 1.05), and wasting (OR 0.99, 95% CI 0.96, 1.02). Adjustment for
demographic and socioeconomic covariates did not alter these estimates.
Similar patterns were observed for severe undernutrition outcomes. We
failed to find consistent evidence that economic growth leads to reduction
in childhood undernutrition in India. Direct investments in appropriate health
interventions may be necessary to reduce childhood undernutrition in India.

The complex etiology of child growth failure and anemia—commonly used
indicators of child undernutrition—involving proximate and distal risk factors
at multiple levels is generally recognized. However, their independent and
joint effects are often assessed with no clear conceptualization of inferential
targets.We utilized hierarchical linear modeling and a nationally representative
sample of 139,116 children aged 6–59 months from India (2015–2016) to
estimate the extent to which a comprehensive set of 27 covariates explained
the within- and between-population variation in height-for-age, weightfor-
age, weight-for-height, and hemoglobin level.Most of the variation in
child anthropometry and hemoglobin measures was attributable to withinpopulation
differences (80–85%), whereas between-population differences
(including communities, districts, and states) accounted for only 15–20%.
The proximate and distal covariates explained 0.2–7.5% of within-population
variation and 2.1–34.0% of between-population variation, depending on
the indicator of interest. Substantial heterogeneity was observed in the
magnitude of within-population variation, and the fraction explained, in
child anthropometry and hemoglobin measures across the 36 states/union
territories of India.Policies and interventions aimed at reducing betweenpopulation
inequalities in child undernutrition may require a different set of
components than those concerned with within-population inequalities. Both
are needed to promote the health of the general population, as well as that
of high-risk children.