Evidence map›Paper›PMID 33997239›Full record

ArticleSSM - population health2021

Mapping changes in district level prevalence of childhood stunting in India 1998-2016: An application of small area estimation techniques.

Swati Srivastava, Hukum Chandra, Shri Kant Singh, Ashish Kumar Upadhyay

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In one paragraph

Article in SSM - population health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Low Birth Weight, the Differentiating Risk Factor for Stunting among Preschool Children in India.International journal of environmental research and public health · 2022
    Article
  10. Article
  11. Article
4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Swati SrivastavaInternational Institute for Population Sciences, Mumbai, India.
Hukum ChandraICAR-Indian Agricultural Statistics Research Institute (IASRI), India.
Shri Kant SinghInternational Institute for Population Sciences, Mumbai, India.
Ashish Kumar UpadhyayInternational Institute for Population Sciences, Mumbai, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The four rounds of National Family Health Survey (NFHS) conducted during 1992-93, 1998-99, 2005-06 and 2015-16 is main source to track the health and development related indicators including nutritional status of children at national and state level in India. Except NFHS-4, first three rounds of NFHS were unable to provides district-level estimates of childhood stunting due to the insufficient sample sizes. The small area estimation (SAE) techniques offer a viable solution to overcome the problem of small sample size. Therefore, this study uses SAE techniques to derive district level prevalence of childhood stunting corresponding to NFHS-2 (1998-99). Study further estimated GIS maps, univariate Local indicator of spatial autocorrelation (LISA) and Moran's I to understand the trend in district level childhood stunting between NFHS-2 and NFHS-4. Estimates obtained by SAE techniques suggest that prevalence of childhood stunting ranges from 20.7% (95% CI: 18.8-22.7) in South Goa district of Goa to 64.4% (95%CI: 63.1-65.7) in Dhaulpur district of Rajasthan during 1998-99. The diagnostic measures used to validate the reliability of estimates obtained by SAE techniques indicate that the model-based estimates are reliable and representative at district level. Results of geospatial analysis indicates substantial reduction in childhood stunting between 1998 and 2016. Out of 640 district,about 81 district experience reduction of more than 50%. At the same time 60 district experience less than 10% of reduction between 1998 and 2016. Spatial clustering of childhood stunting remains same over the study period except few additional cluster in Maharashtra, Andhra and Meghalaya in 2016. The district level estimates obtained from this study might be helpful in framing decentralized policies and implementation of vertical programs to enhance the efficacy of various nutrition interventions in priority districts of the country.

Indexed as

Generalized liner mixed modelNational family health surveySmall area estimationSpatial analysisStunting

Identifiers

PMID33997239
PMCPMC8093462

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.