Multilevel analysis of geographic variation among correlates of child undernutrition in India

Abstract

Prior research has identified a number of risk factors ranging from inadequate household sanitation to maternal characteristics as important determinants of child malnutrition and health in India. What is less known is the extent to which these individual-level risk factors are geographically distributed. Assessing the geographic distribution, especially at multiple levels, matters as it can inform where, and at what level, interventions should be targeted. The three levels of significance in the Indian context are villages, districts, and states. Thus, the purpose of this paper was to (a) examine what proportion of the variation in 21 risk factors is attributable to villages, districts, and states in India and (b) elucidate the specific states where these risk factors are clustered within India. Using the fourth National Family Health Surveydataset, from 2015 to 2016, we found that the proportion of variation attributable to villages ranged from 14% to 63%, 10% to 29% for districts and 17% to 62% for states. Furthermore, we found that Bihar, Jharkhand, Madhya Pradesh, and Uttar Pradesh were in the highest risk quintile for more than 10 of the risk factors included in our study. This is an indication of geographic clustering of risk factors. The risk factors that are clustered in states such as Bihar, Jharkhand, Madhya Pradesh and Uttar Pradesh underscore the need for policies and interventions that address a broader set of child malnutrition determinants beyond those that are nutrition specific.

Small Area Variations in Dietary Diversity Among Children in India: A Multilevel Analysis of 6–23-Month-Old Children

“…First, the relative importance of lower levels has been shown in previous studies in the context of child malnutrition. For example, much of the geographic variation in household poverty and the risk factors for child malnutrition is attributable to cluster and between-cluster variation (19)(20)(21). Another study found that most of the geographic variation in child anthropometry and hemoglobin measures was attributable to within-cluster, or household, differences, further underscoring the importance of lower geographic levels (39).…”

“…Therefore, future research should examine the district and cluster level factors that might explain this small area variation in dietary diversity. Doing so could help lower within district inequality and improve overall nutrition outcomes, especially considering that India's Integrated Child Development Services (ICDS) already implements interventions at the village level ( 19 ). Our results can help state-level policy makers identify the specific determinants of dietary diversity within district and between villages.…”

“…One issue with the NNS, however, is its explicit focus on districts. In the context of child malnutrition, some of the geographic variation is attributable to between-district differences (13), while other studies have shown that a larger share of the variation is attributable to states (14,15) and villages (16)(17)(18), a consequence of the geographic clustering of risk factors (19). Recent studies have also elucidated the small area variation of anthropometric failure within districts.…”

Geographic and Age Variations in Low Body Mass Index Among Community-Dwelling Older People in Xinjiang: A Cross-Sectional Study

“…The incidence and prevalence of disease in certain populations, climates, environments, are all related to the geographical location. Geographic variations in undernutrition and BMI have been documented in children, adolescents, adults, and older residents in nursing homes (24)(25)(26)(27).…”

Quality of anthropometric data in India's National Family Health Survey: Disentangling interviewer and area effect using a cross-classified multilevel model

“…However, regional variations in malnutrition indicators remain a matter of concern, with some states performing significantly poorer than others ( India State-Level Disease Burden Initiative CGF Collaborators, 2019 ). Several studies have used multilevel approaches to assess the contribution of various geographical factors to variations in child malnutrition indicators in India and other developing countries ( Amegbor et al, 2020 ; Jain, Rodgers, Li, Kim, & Subramanian, 2021 ; Marini & Gragnolati, 2006 ; Smith & Shively, 2019 ). However, most of these studies did not consider the confounding effects of team-level performance on regional data quality.…”