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

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 Image

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:

Pj = (qj / nj) × 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.

Hj = Pj × Nj

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: Pj = (qj / nj) x 100

Examples: qj = 137

                    nj = 435

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

STEP 2: Calculating Headcount

Formula: Hj = Pj x Nj

Examples: Pj = 32%

                     Nj = 166791

                     Hj = (32 / 100) x 166791 = 53373

STEP 3: Normalizing the Prevalence

Formula: Pjnorm = (Pj - P(min)) / (P(max) - P(min))

Examples: Pj = 32%

                     P(max) = 65%

                     P(min) = 13%

                     Pjnorm = (32 - 13) / (65 - 13) = 0.365

STEP 4: Normalizing the Headcount

Formula: Hjnorm = (Hj - H(min)) / (H(max) - H(min))

Examples: Hj = 53373

                     H(max) = 460209

                     H(min) = 346

                     Hjnorm = (53373 - 346) / (460209 - 346) = 0.115

STEP 5: Calculating the PHM

Formula: PHMj = (Pjnorm + Hjnorm)/2

Examples: Pjnorm = 0.365

                     Hjnorm = 0.115

                     PHMj = (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

Rajpal S, Joe W, Kim R, Kumar A, Subramanian SV. Child Undernutrition and Convergence of Multisectoral Interventions in India: An Econometric Analysis of National Family Health Survey 2015–16. Frontiers in Public Health. 2020;8 :129.Abstract

In India and worldwide, there has been increased strategic focus on multisectoral convergence of nutrition-specific and nutrition-sensitive interventions to attain rapid reductions in child undernutrition. For instance, a Convergence Action Plan in India has been formed to synchronize and converge various nutrition-related interventions across ministries of union and state governments under a single umbrella. Given the large variation in number, nature and impact of these interventions, this paper aims to quantify the contribution of each intervention (proxied by relevant covariates) toward reducing child stunting and underweight in India. The interventions are classified under six sectors: (a) health, (b) women and child development, (c)education, (d) water, sanitation, and hygiene, (e) clean energy, and (f) growth sector. We estimate the potential reduction in child stunting and underweight in a counterfactual scenario of “convergence” where all the interventions across all the sectors are simultaneously and successfully implemented. The findings from our econometric analysis suggests that under this counterfactual scenario, a reduction of 18.37% points (95% CI: 16.77; 19.95) in stunting and 20.26% points (95% CI: 19.13; 21.39) in underweight can be potentially achieved. Across all the sectors, women and child development and clean energy were identified as the biggest contributors to the potential reductions in stunting and underweight, underscoring the importance of improving sanitation-related practices and clean cooking fuel. The overall impact of this convergent action was relatively stronger for less developed districts. These findings reiterate a clear role and scope of convergent action in achieving India’s national nutritional goals. This warrants a complete outreach of all the interventions from different sectors.

Swaminathan S, Subramanian SV. Improve nutritional content of school meals to tackle stunting. Hindustan Times. 2017.Abstract

As per the latest National Nutrition Monitoring Bureau, which has been collecting data on diet and nutritional status of rural, tribal and urban populations for almost four decades, the calorie intake of children (1-3 years) in rural areas was only about 70% of their requirement due to shortage.

Karlsson O, Kim R, Joe W, Subramanian SV. The relationship of household assets and amenities with child health outcomes: An exploratory cross-sectional study in India 2015–2016. SSM-population health. 2020;10 :100513.Abstract

Healthy development of children in India is far from ensured. Proximate
determinants of poor child health outcomes are infectious diseases and
undernutrition, which are linked to socioeconomic status. In low- and
middle-income countries, researchers rely on wealth indices, constructed
from information on households’ asset ownership and amenities, to study
socioeconomic disparities in child health. Some of these wealth index items
can, however, directly affect the proximate determinants of child health. This
paper explores the independent association of each item used to construct
the Demographic and Health Surveys’ wealth index with diverse child health
outcomes. This cross-sectional study used nationally representative sample
of 245,866 children, age 0-59 months, from the Indian National Family Health
Surveys conducted in 2015-16. The study used conditional Poisson regression
models as well as a range of sensitivity specifications. After controlling for
socioeconomic status, health care use, maternal factors, community-level
factors, and all wealth index items, the following wealth index items were the
most consistently associated with child health; type of toilet facilities, water
source, refrigerator, pressure cooker, type of cooking fuel, land usable for
agriculture, household building material, mobile phone, and motorcycle/
scooter. The association with type of toilet facilities and water source was
particularly strong for mortality, showing a 16-35% and 14-28% lower
mortality, respectively. Most items used to construct the Demographic and
Health Surveys’ wealth index only indicate household socioeconomic status,
while a few items may affect child health directly, and can be useful targets
for policy intervention.

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