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

Kim R, Swaminathan A, Swaminathan G, et al. Parliamentary Constituency Factsheet for Indicators of Nutrition, Health and Development in India. Harvard Center for Population and Development Studies. 2019;18 (4).Abstract

In India, data on key developmental indicators that formulate policies and interventions are routinely available for the administrative units of districts but not for the political units of Parliamentary Constituencies (PC). Members of Parliament (MPs) in the Lok Sabha, each representing 543 PCs as per the 2014 India map, are the representatives with the most direct interaction with their constituents. The MPs are responsible for articulating the vision and the implementation of public policies at the national level and for their respective constituencies. In order for MPs to efficiently and effectively serve their people, and also for the constituents to understand the performance of their MPs, it is critical to produce the most accurate and up-to-date evidence on the state of health and well-being at the PC-level. However, absence of PC identifiers in nationally representative surveys or the Census has eluded an assessment of how a PC is doing with regards to key indicators of nutrition, health and development.

Beckerman-Hsu JP, Kim R, Sharma S, Subramanian SV. Dietary Variation among Children Meeting and Not Meeting Minimum Dietary Diversity: An Empirical Investigation of Food Group Consumption Patterns among 73,036 Children in India. The Journal of Nutrition. 2020;150 (10) :2818-2824.Abstract

Minimum Dietary Diversity (MDD) is a widely used indicator of adequate
dietary micronutrient density for children 6–23 mo old. MDD food-group data
remain underutilized, despite their potential for further informing nutrition
programs and policies. We aimed to describe the diets of children meeting
MDD and not meeting MDD in India using food group data, nationally and
subnationally. Food group data for children 6–23 mo old (n = 73,036) from
the 2015–16 National Family Health Survey in India were analyzed. Per WHO
standards, children consuming ≥5 of the following food groups in the past
day or night met MDD: breast milk; grains, roots, or tubers; legumes or nuts;
dairy; flesh foods; eggs; vitamin A–rich fruits and vegetables; and other fruits
and vegetables. Children not meeting MDD consumed <5 food groups. We
analyzed the number and types of foods consumed by children meeting
MDD and not meeting MDD at the national and subnational geographic
levels. Nationally, children not meeting MDD most often consumed breast
milk (84.5%), grains, roots, and tubers (62.0%), and/or dairy (42.9%). Children
meeting MDD most often consumed grains, roots, and tubers (97.6%), vitamin
A–rich fruits and vegetables (93.8%), breast milk (84.1%), dairy (82.1%), other
fruits and vegetables (79.5%), and/or eggs (56.5%). For children not meeting
MDD, district-level dairy consumption varied the most (6.4%–79.9%), whereas
flesh foods consumption varied the least (0.0%–43.8%). For children meeting
MDD, district-level egg consumption varied the most (0.0%–100.0%), whereas
grains, roots, and tubers consumption varied the least (66.8%–100.0%).
Children not meeting MDD had low fruit, vegetable, and protein-rich food
consumption. Many children meeting MDD also had low protein-rich food
consumption. Examining the number and types of foods consumed highlights
priorities for children experiencing the greatest dietary deprivation, providing
valuable complementary information to MDD.

Corsi DJ, Mejía-Guevara I, Subramanian SV. Improving household-level nutrition-specific and nutrition-sensitive conditions key to reducing child undernutrition in India. Soc Sci Med. 2016;157 :189-92.Abstract

Since the publication of our study, the Government of India has released
preliminary estimates from the on-going latest round of National Family
Health Surveys (2015–2016) on the prevalence of stunting, underweight
and wasting for a limited number of states in India (International Institute
for Population Sciences, 2016). We compared the prevalence from the most
recent data for 13 states to the corresponding states from 2005–06 (Fig. 3).
Declines in undernutrition between 2005–06 and 2015–16 averaged 1.3%/
year for stunting, 1.6%/year for underweight and 0.4%/year for wasting since
2015 (International Institute for Population Sciences, 2016). The sluggish
pace of decline suggests that undernutrition continues to be a major disease
burden among Indian children. It also suggests that macroeconomic growth
experienced by India in recent years has not contributed to any meaningful
reductions in child undernutrition in India (Subramanian et al., 2016,
Subramanian and Subramanyam, 2015, Subramanyam et al., 2011). As we
observed in our original study (Corsi et al., 2015), and elsewhere (Subramanian
et al., 2016), there is an urgent need to consider direct investments in
conditions that more generally are reflective of the upstream and structural
determinants of undernutrition. Specifically, policies and interventions that
aim to provide and sustain nutritional security at the household level are
critical to eliminating child undernutrition in India. Pursuing interventions in
proximal risk factors without any improvements to broader socioeconomic
and structural conditions, to put it bluntly, is likely to be waste of time and
resources with little impact on reducing the burden of undernutrition among
children in India.

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