The Importance of Preventing Deforestation to Limit Future Emerging Infectious Disease Outbreaks

## **Analyzing the Spatial Contours of Child Health in India: Evidence from NFHS4 to NFHS5**

**Apoorva Nambiar1, Thirumal Reddy2, Ashish Singh2, Dharmalingam Arunachalam3, and Satish B. Agnihotri2**

<sup>1</sup> IITB-Monash Research Academy, Mumbai, India

<sup>2</sup> Indian Institute of Technology Bombay, Mumbai, India

<sup>3</sup> Monash University, Australia

Malnutrition has always been and continues to be one of the critical development issues globally. India has been identified as one of the countries among the LMICs where the prevalence of malnutrition is alarmingly high. Eliminating malnutrition is one of the key goals set by the 2015 UN Sustainable Development Goals. Even though recent evidence shows a nominal reduction in the rates in India, the goals are far from being reached. The geographic information system (GIS) tools help provide better answers to research questions surrounding these areas. The current study explores various GIS techniques to identify pockets of the low and high burden of malnutrition to plan calibrated steps for its elimination, whether through convergence or standalone interventions. For the first time, the most recent 2019–2020 district-level data (National Family Health Survey, wave 5) have been utilized to study the spatial contours and heterogeneity of malnourished children. Various spatial econometrics models were applied to study the spatial pattern and clustering of malnutrition and its risk factors. Geospatial techniques like Moran's I statistics and Univariate and Bivariate LISA were applied to understand the spatial dependence across the districts. Spatial regression models, namely spatial lag and error models and geographically weighted regression, were used to examine the correlates of malnutrition at the micro-level. More than 20% of the districts showed high-high spatial association of children underweight, also showing the strongest geographical clustering with a Moran's I value of 0.68 (p<0.001), followed by children stunted (0.52, p<0.001) and children wasted (0.47, p<0.001). The regression results confirmed that the immediate and underlying determinants of malnutrition, namely, feeding practices, mother's education, age at marriage and pregnancy, and sanitation facilities, were the critical and statistically significant determinants of child nutrition. These results from the analysis facilitate the identification of hotspots of low and high prevalence, and hence it can be used to allocate resources effectively to reduce health inequities between and within districts. The evidence gathered from this study can be used by decision-makers for developing better strategies at the micro-level and long-term planning to find solutions to mitigate the problem of undernutrition.

Apoorva Nambiar1, Thirumal Reddy2, Ashish Singh2, Dharmalingam Arunachalam3, and Satish B. Agnihotri2

1 IITB-Monash Research Academy, IIT Bombay, India

2 Indian Institute of Technology Bombay, India

3 Monash University, Australia

#### Background


### Objectives


#### Data and Methods


models were applied to study the spatial pattern and clustering of undernutrition and its risk factors.

• Geospatial techniques like Moran's I statistics and Univariate and Bivariate LISA were applied to understand the spatial dependence across the districts. Spatial regression models, namely spatial lag and error models, and geographically weighted regression, were used to examine the correlates of malnutrition at the micro level.

#### (2) Bi-variate Local Moran's I: children undernourished with correlates

*Figure 2. Bivariate LlSA cluster of (a) mother's BMl status vs children underweight and (b) early pregnancy vs children underweight. Figure 3. Estimated results of* 

#### (5) Spatial Regression: children underweight and correlates

*Table 2. Results. Spatial regression models to assess the association of child underweight and its correlates across districts, 2019-2020.*


#### (4) LISA Moran's I statistics

*Table 1. Bivariate LlSA Moran's I Statistics showing the spatial dependence for the district level prevalence of child malnutrition against its correlates.*


#### Conclusions


#### Findings


#### (1) Univariate Local Moran's I

#### (3) GWR Model Results
