Literature DB >> 33633215

Small area variation in child undernutrition across 640 districts and 543 parliamentary constituencies in India.

Sunil Rajpal1, Julie Kim2, William Joe3, Rockli Kim4,5, S V Subramanian6,7.   

Abstract

In India, districts serve as central policy unit for program development, administration and implementation. The one-size-fits-all approach based on average prevalence estimates at the district level fails to capture the substantial small area variation. In addition to district average, heterogeneity within districts should be considered in policy design. The objective of this study was to quantify the extent of small area variation in child stunting, underweight and wasting across 36 states/Union Territories (UTs), 640 districts (and 543 PCs), and villages/blocks in India. We utilized the 4th round of Indian National Family Health Survey (NFHS-4) conducted in 2015-2016. The study population included 225,002 children aged 0-59 months whose height and weight information were available. Stunting was defined as height-for-age z-score below 2 SD from the World Health Organization child growth reference standards. Similarly, underweight and wasting were each defined as weight-for-age < -2 SD and weight-for-height < -2 SD from the age- and sex-specific medians. We adopted a four-level logistic regression model to partition the total variation in stunting, underweight and wasting. We computed precision-weighted prevalence of child anthropometric failures across districts and PCs as well as within-district/PC variation using standard deviation (SD) measures. For stunting, 56.4% (var: 0.237; SE: 0.008) of the total variation was attributed to villages/blocks, followed by 25.8% (var: 0.109; SE: 0.030) to states/UTs, and 17.7% (Var: 0.074; SE: 0.006) to districts. For underweight and wasting, villages/blocks accounted for 38.4% (var: 0.224; SE: 0.007) and 50% (var: 0.285; SE: 0.009), respectively, of the total contextual variance in India. Similar findings were shown in multilevel models incorporating PC as a geographical unit instead of districts. We found high positive correlations between mean prevalence and SD for stunting (r = 0.780, p < 0.001), underweight (r = 0.860, p < 0.001), and wasting (r = 0.857, p < 0.001) across all districts in India. A similar pattern of correlation was found for PCs. Within-district and within-PC variation are the primary source of variation for child malnutrition in India. Our results suggest the importance of considering heterogeneity within districts and PCs when planning and administering child nutrition policies.

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Year:  2021        PMID: 33633215      PMCID: PMC7907088          DOI: 10.1038/s41598-021-83992-6

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  10 in total

1.  Micro-geographic targeting for precision public policy: Analysis of child sex ratio across 587,043 census villages in India, 2011.

Authors:  Rockli Kim; Praveen Kumar Pathak; Yun Xu; William Joe; Alok Kumar; R Venkataramanan; S V Subramanian
Journal:  Health Place       Date:  2019-04-22       Impact factor: 4.078

2.  Geographic Variation in Household and Catastrophic Health Spending in India: Assessing the Relative Importance of Villages, Districts, and States, 2011-2012.

Authors:  Sanjay K Mohanty; Rockli Kim; Pijush Kanti Khan; S V Subramanian
Journal:  Milbank Q       Date:  2018-03       Impact factor: 4.911

3.  Contribution of socioeconomic factors to the variation in body-mass index in 58 low-income and middle-income countries: an econometric analysis of multilevel data.

Authors:  Rockli Kim; Ichiro Kawachi; Brent A Coull; S V Subramanian
Journal:  Lancet Glob Health       Date:  2018-07       Impact factor: 26.763

4.  Household wealth and child health in India.

Authors:  Satvika Chalasani; Shea Rutstein
Journal:  Popul Stud (Camb)       Date:  2013-06-14

5.  Understanding the geographical burden of stunting in India: A regression-decomposition analysis of district-level data from 2015-16.

Authors:  Purnima Menon; Derek Headey; Rasmi Avula; Phuong Hong Nguyen
Journal:  Matern Child Nutr       Date:  2018-05-23       Impact factor: 3.092

6.  Explaining Within- vs Between-Population Variation in Child Anthropometry and Hemoglobin Measures in India: A Multilevel Analysis of the National Family Health Survey 2015-2016.

Authors:  Justin Rodgers; Rockli Kim; S V Subramanian
Journal:  J Epidemiol       Date:  2019-10-12       Impact factor: 3.211

7.  Open defecation and childhood stunting in India: an ecological analysis of new data from 112 districts.

Authors:  Dean Spears; Arabinda Ghosh; Oliver Cumming
Journal:  PLoS One       Date:  2013-09-16       Impact factor: 3.240

8.  Subnational mapping of under-5 and neonatal mortality trends in India: the Global Burden of Disease Study 2000-17.

Authors: 
Journal:  Lancet       Date:  2020-05-12       Impact factor: 79.321

9.  Estimating the burden of child malnutrition across parliamentary constituencies in India: A methodological comparison.

Authors:  Rockli Kim; Akshay Swaminathan; Rakesh Kumar; Yun Xu; Jeffrey C Blossom; R Venkataramanan; Alok Kumar; William Joe; S V Subramanian
Journal:  SSM Popul Health       Date:  2019-02-10

10.  Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019.

Authors: 
Journal:  Lancet       Date:  2020-10-17       Impact factor: 202.731

  10 in total
  6 in total

1.  Eco-geographic patterns of child malnutrition in India and its association with cereal cultivation: An analysis using demographic health survey and agriculture datasets.

Authors:  Rama Krishna Sanjeev; Prashanth Nuggehalli Srinivas; Bindu Krishnan; Yogish Channa Basappa; Akshay S Dinesh; Sabu K Ulahannan
Journal:  Wellcome Open Res       Date:  2022-02-22

2.  Estimating the Burden of Child Undernutrition for Smaller Electoral Units in India.

Authors:  Julie Kim; Yuning Liu; Weiyu Wang; Jeffrey C Blossom; Laxmi Kant Dwivedi; K S James; Rakesh Sarwal; Rockli Kim; S V Subramanian
Journal:  JAMA Netw Open       Date:  2021-10-01

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

Authors:  Anoop Jain; Weiyu Wang; K S James; Rakesh Sarwal; Rockli Kim; S V Subramanian
Journal:  Front Nutr       Date:  2022-02-16

4.  Can administrative health data be used to estimate population level birth and child mortality estimates? A comparison of India's Health Information Management System data with nationally representative survey data.

Authors:  Pritha Chatterjee; Aashish Gupta; S V Subramanian
Journal:  SSM Popul Health       Date:  2022-06-23

5.  Small area variations in low birth weight and small size of births in India.

Authors:  Md Juel Rana; Rockli Kim; Soohyeon Ko; Laxmi K Dwivedi; K S James; Rakesh Sarwal; S V Subramanian
Journal:  Matern Child Nutr       Date:  2022-04-29       Impact factor: 3.660

6.  Small area variation in severe, moderate, and mild anemia among women and children: A multilevel analysis of 707 districts in India.

Authors:  Sunil Rajpal; Akhil Kumar; Md Juel Rana; Rockli Kim; S V Subramanian
Journal:  Front Public Health       Date:  2022-09-20
  6 in total

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