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

The nutritional status of children in India fares much worse in global comparisons. Using the disaggregated data from the fourth National Family Health Survey (NFHS 2015-16), we present a dashboard and atlas for 31 nutritional indicators that include diet, anthropometry, clinical and service utilization measures of child nutrition for the districts of India. The geo-visualizations are presented with a motivation to help various stakeholders prioritise indicators and districts for interventions.

Notwithstanding the value of utilising the summary data for select indicators made available by the fifth NFHS (2019-20) for 17 states and 5 Union Territories, it is important to note that a truly all-India picture covering all districts of India will not be available at least until the later part of 2021. Even as we await the availability of disaggregated data for a full range of indicators, there remains much to be investigated and learned from a more detailed examination of the fourth round of the NFHS.

Visualization
Atlas
Methodology & Indicators
Publications

Visualization

Authors: Akhil Kumar, Weixing Zhang, S V Subramanian. Dashboard on Geo-visualising Diet, Anthropometric and Clinical Indicators for Children in India. December 2020, Cambridge MA, Harvard Center for Population and Development Studies.

NOTE: The legend and rank show the Prevalence-Headcount Metric (PHM) which was calculated by adding the normalized values of prevalence and headcount together. Blank areas with unavailable data.

Atlas

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Subramanian S V, Sarwal Rakesh, Joe William, Kim Rockli, 2022, "Geo-visualising Diet, Anthroprometric and Clinical Indicators for Children in India", https://doi.org/10.7910/DVN/ZSH8HR (Corrigendum)

Methodology & Indicators

PHM Methodology

We estimated the burden for each of the nutritional deprivation indicators along two dimensions of Prevalence (P) and Headcount (H), and combined them to derive a Prevalence-Headcount Metric (PHM).

Prevalence

The metric P was calculated as children with nutritional deprivation (q) divided by the eligible sample of children (n) in the district (j) and expressed in percentage as:

P_j = (q_j / n_j) × 100

The P metric quantifies the risk of a child experiencing nutritional deprivation in a district. For example, in the Kupwara district of Jammu and Kashmir, 137 (q) out of 435 (n) sample of eligible children were stunted, translating into a district prevalence of 31.5%; in other words, one out of every three children is at risk of being stunted. Thus, the P metric helps identify districts where the future risk of a specific nutritional deprivation should be reduced.

However, the P metric does not contain any information on the absolute number of children who are at risk because it does not take into account the total population of children in a district. For example, consider the districts of Hyderabad (16.7%) and South Garo Hills (16.6%); both have the same P metric but differ with regards to the under-five population of 398,513 and 23,953, respectively, translating to different levels of absolute burden.

Headcount

The metric H is given as the product of P and the total eligible population N for each district.

H_j = P_j × N_j

Returning to the above example, the number (H) of stunted children is substantially large in Hyderabad (66,553) than South Garo Hills (3,970) despite both districts having the same prevalence because the total population burden by nutrition deprivation in Hyderabad is substantially larger.

Prevalence-Headcount Metric

We developed a combined Prevalence-Headcount metric (PHM) that takes into account the features of both the risk (P) as well as the headcount (H) to provide a comprehensive picture of the burden of nutritional deprivation in a district. We computed the PHM using the following steps. We exemplify these steps using the district of Kupwara, Jammu and Kashmir for the nutritional deprivation indicator of stunting.

P = Prevalence; H = Headcount;

PHM = Prevalence-headcount metric;

j = District;

q = Number of children with nutritional deprivation within the eligible sample;

n = Eligible sample; N = Eligible population;

norm = Normalized; max = district with the Maximum value;

min = district with the Minimum value;

STEP 1: Calculating Prevalence

Formula: P_j = (q_j / n_j) x 100

Examples: q_j = 137

n_j = 435

P_j= (137 / 435) x 100 = 32%

STEP 2: Calculating Headcount

Formula: H_j = P_j x N_j

Examples: P_j = 32%

N_j= 166791

H_j = (32 / 100) x 166791 = 53373

STEP 3: Normalizing the Prevalence

Formula: P_j^norm = (P_j - P_(min)) / (P_(max) - P_(min))

Examples: P_j = 32%

P_(max) = 65%

P_(min) = 13%

P_j^norm = (32 - 13) / (65 - 13) = 0.365

STEP 4: Normalizing the Headcount

Formula: H_j^norm = (H_j - H_(min)) / (H_(max) - H_(min))

Examples: H_j = 53373

H_(max) = 460209

H_(min) = 346

H_j^norm = (53373 - 346) / (460209 - 346) = 0.115

STEP 5: Calculating the PHM

Formula: PHM_j = (P_j^norm + H_j^norm)/2

Examples: P_j^norm = 0.365

H_j^norm = 0.115

PHM_j = (0.365 + 0.115) / 2 = 0.24

Types of Nutritional Deficit

Indicator	Age Group	Definition
Diet and Anthroprometric Failure	6-23 months	Children with both dietary as well as anthropometric failures
Diet Failure Only	6-23 months	Children with at least one or more dietary failures but no anthropometric failure
Anthropometric Failure Only	6-23 months	Children with at least one or more anthropometric failures but no dietary failure

Dietary Measures

Indicator	Age Group	Definition
Inadequate Diet	6-23 months	Children who did not receive minimum acceptable diet
Inadequate Diet Diversity	6-23 months	Children who did not receive minimum dietary diversity
No Solid/Semi-Solid Food	6-23 months	Children who did not consume solid or semi solid food in the day or night preceding the interview
No Dairy	6-23 months	Children who did not consume milk and milk products in the day or night preceding the interview
No Nuts/Legumes	6-23 months	Children who did not consume nuts and legumes in the day or night preceding the interview
No Grains/Roots/Tubers	6-23 months	Children who did not consume grains in the day or night preceding the interview
No Eggs	6-23 months	Children who did not consume eggs in the day or night preceding the interview
No Flesh Foods	6-23 months	Children who did not consume fish, chicken, meat in the day or night preceding the interview
No Vit-A Rich Fruits/Vegetables	6-23 months	Children who did not consume Vit-A rich fruits and vegetables in the day or night preceding the interview
No Other Fruits/Vegetables	6-23 months	Children who did not consume other fruits and Vegetables in the day or night preceding the interview

Anthropometric/Clinical Measures

Indicator	Age Group	Definition
Stunting or Underweight or Wasting	0-59 months	Children who are either stunted, wasted or underweight
Stunting & Underweight & Wasting	0-59 months	Children who are stunted, underweight and wasted
Stunting & Underweight	0-59 months	Children who are stunted and underweight but not wasted
Underweight & Wasting	0-59 months	Children who are underweight and wasted but not stunted
Stunting	0-59 months	Children who are stunted (short height-to-age)
Severe Stunting	0-59 months	Children who are severly stunted (short height-to-age)
Underweight	0-59 months	Children who are underweight (low weight-to-age)
Severe Underweight	0-59 months	Children who are severely underweight (low weight-to-age)
Wasting	0-59 months	Children who are wasted (low weight-to-height)
Severe Wasting	0-59 months	Children who are severely wasted (low weight-to-height)
Anemia	6-59 months	Children with hemoglobin level less than 11.0 g/dL
Severe Anemia	6-59 months	Children with hemoglobin level less than 7.0 g/dL
Low Birth Weight	0-59 months	Children with written record of birthweight less than 2.5 kg

Breastfeeding Practices

Indicator	Age Group	Definition
No Early Breastfeeding	0-12 months	Children who were not breastfed within 1 hour of birth
No Exclusive Breastfeeding	0-12 months	Children who were not exclusively breastfed

Service Utilization

Indicator	Age Group	Definition
No Hot Cooked Meal	> 36 months	Children who did not receive supplemnetary nutrition under ICDS
No Take Home Ration	6-36 months	Children who did not receive supplemnetary nutrition under ICDS
No Vit-A Supplementation	6-59 months	Children who did not receive Vit-A dose in the six months preceding the survey

Publications

Swaminathan A, Kim R, Xu Y, et al. Burden of Child Malnutrition in India: A View from Parliamentary Constituencies. Economic & Political Weekly. 2019;54 (2).Abstract

In India, monitoring and surveillance of health and well-being indicators have been focused primarily on the state and district levels. Analysing population data at the level of parliamentary constituencies has the potential to bring political accountability to the data-driven policy discourse that is currently based on district-level estimates. Using data from the fourth National Family Health Survey 2016, two geographic information systems methodologies have been developed and applied to provide estimates of four child malnutrition indicators (stunting, underweight, wasting, and anemia) for the 543 parliamentary constituencies in India. The results indicate that several constituencies experience a multiple burden of child malnutrition that must be addressed concurrently and as a priority.

Rajpal S, Kim J, Joe W, Kim R, Subramanian SV. Small area variation in child undernutrition across 640 districts and 543 parliamentary constituencies in India. Scientific Reports. 2021;11 (1) :4558.Abstract

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-ageþinspace}< -2 SD and weight-for-heightþinspace}< -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 (rþinspace}=þinspace}0.780, pþinspace}<þinspace}0.001), underweight (rþinspace}=þinspace}0.860, pþinspace}<þinspace}0.001), and wasting (rþinspace}=þinspace}0.857, pþinspace}<þinspace}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.

Green MA, Corsi DJ, Mejía‐Guevara I, Subramanian SV. Distinct clusters of stunted children in India: An observational study. Maternal & child nutrition. 2018;14 (3) :e12592.Abstract

Childhood stunting is often conceptualised as a singular concept (i.e., stunted
or not), and such an approach implies similarity in the experiences of children
who are stunted. Furthermore, risk factors for stunting are often treated in
isolation, and limited research has examined how multiple risk factors interact
together. Our aim was to examine whether there are subgroups among
stunted children, and if parental characteristics influence the likelihood of
these subgroups among children. Children who were stunted were identified
from the 2005-2006 Indian National Family Health Survey (n = 12,417).
Latent class analysis was used to explore the existence of subgroups among
stunted children by their social, demographic, and health characteristics.
We examined whether parental characteristics predicted the likelihood of
a child belonging to each latent class using a multinomial logit regression
model. We found there to be 5 distinct groups of stunted children; “poor,
older, and poor health-related outcomes,” “poor, young, and poorest healthrelated
outcomes,” “poor with mixed health-related outcomes,” “wealthy
and good health-related outcomes,” and “typical traits.” Both mother and
father’s educational attainment, body mass index, and height were important
predictors of class membership. Our findings demonstrate evidence that
there is heterogeneity of the risk factors and behaviours among children who
are stunted. It suggests that stunting is not a singular concept; rather, there
are multiple experiences represented by our “types” of stunting. Adopting a
multidimensional approach to conceptualising stunting may be important for
improving the design and targeting of interventions for managing stunting.

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GA4 Tracking Code

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

Visualization

Visualization

Atlas

Methodology & Indicators

PHM Methodology

Types of Nutritional Deficit

Dietary Measures

Anthropometric/Clinical Measures

Breastfeeding Practices

Service Utilization

Publications

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