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

Onyeneho NG, Ozumba BC, Subramanian SV. Determinants of childhood anemia in india. Scientific reports. 2019;9 (1) :1-7.Abstract

We analyzed a sample of 112714 children from the 2015-2016 Indian National
Fertility and Health Survey with available data on hemoglobin. Multinomial
logistic regression models were used to establish associations between
parent anemia, household characteristics and nutritional intake of children.
Linear regression analysis was also conducted to see the link between the
household characteristic and childhood nutritional intake on one hand and
hemoglobin levels on the other hand. A number of socio-demographic
factors, namely maternal age, type of residence and maternal education, as
well as wealth index, among others correlate with incidence of childhood
anemia. For instance, whereas 52.9% of children in the richest households
were anemic, 63.2% of children in the poorest household were anemic (p
< 0.001). Mean Vitamin A intake in the last six months was 0.63 (0.626-
0.634) which was 0.18% of the recommended intake. Mean iron intake, from
sources other than breast milk, in the last 24 hours was 0.29 (0.286-0.294)
and 2.42% of the recommended daily intake. Fifty-nine percent (58.5%) of
the children surveyed were anemic (Hb level: 9.75 g/dL [9.59-9.91]). Children
with anemia were more prone to being iron deficient (odds ratio [OR]: 0.981
(0.961-1.001), Vitamin A deficient (OR: 0.813 (0.794-0.833)), and have lower
maternal hemoglobin level (OR: 1.992 (1.957-2.027)). Combining nutritional
supplementation and food-fortification programmes with reduction in
maternal anemia and family poverty may yield optimal improvement of
childhood anemia in India.

Subramanian SV, Sarwal R, William J, Kim R. Geo-visualising Diet, Anthroprometric and Clinical Indicators for Children in India. Harvard Dataverse. 2020.Abstract

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Researchers from the Geographic Insights Lab at the Harvard Center for Population and Development Studies and the Institute of Economic Growth geo-visualised diet, anthropometric and clinical indicators for children across districts in India and provide a clear snapshot of high priority districts for targeting nutritional interventions among children in India.

Gausman J, Perkins JM, Lee H-Y, et al. Ecological and social patterns of child dietary diversity in India: a population-based study. Nutrition. 2018;53 :77-84.Abstract

Dietary diversity (DD) measures dietary variation in children. Factors
at the child, community, and state levels may be associated with poor
child nutritional outcomes. However, few studies have examined the role
of macro-level factors on child DD. This study seeks to 1) describe the
distribution of child DD in India, 2) examine the variation in DD attributable
to the child, community and state levels, and 3) explore the relationship
between community socioeconomic context and child DD. Using nationally
representative data from children aged 6-23 months in India, multilevel
models were used to determine the associations between child DD and
individual- and community-level factors. There was substantial variation in
child DD score across demographic and socioeconomic characteristics. In an
age and sex-only adjusted regression model, the largest portion of variation
in child DD was attributable to the child level (75%) while the portions of
variance attributable to the community-level and state level were similar to
each other (15% and 11%). Including individual-level socioeconomic factors
explained 35.6 percent of the total variation attributed to child DD at the
community level and 24.8 percent of the total variation attributed to child
DD at the state level. Finally, measures of community disadvantage were
associated with child DD in when added to the fully adjusted model. This
study suggests that both individual and contextual factors are associated with
child DD. These results suggest that a population-based approach combined
with a targeted intervention for at-risk children may be needed to improve
child DD in India.

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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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