Evidence map›Paper›PMID 33903246›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2021

Precision mapping child undernutrition for nearly 600,000 inhabited census villages in India.

Rockli Kim, Avleen S Bijral, Yun Xu, Xiuyuan Zhang, Jeffrey C Blossom, Akshay Swaminathan, Gary King, Alok Kumar, Rakesh Sarwal, Juan M Lavista Ferres and 1 more

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Article
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  3. Review
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  5. Association of affirmative action with health and education outcomes and services in India.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  6. Article
  7. Article
  8. Observational
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  20. The Efficiency of Anganwadi Centers Located in Rural Field Practice Area of a Medical College in North India: Data Envelopment Analysis.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Rockli KimDivision of Health Policy and Management, College of Health Science, Korea University, 02841 Seoul, South Korea.
Avleen S BijralMicrosoft AI For Good Research Lab, Redmond, WA 98052.
Yun XuSuperMap Software Co. Ltd, Beijing 100015, China.
Xiuyuan ZhangInstitute of Remote Sensing and Geographic Information System, Peking University, Beijing 100871, China.
Jeffrey C BlossomCenter for Geographic Analysis, Harvard University, Cambridge, MA 02138.
Akshay SwaminathanFlatiron Health, New York, NY 10012.
Gary KingInstitute for Quantitative Social Science, Harvard University, Cambridge, MA 02138.ORCID 0000-0002-5327-7631
Alok KumarDepartment of Medical Health and Family Welfare, Lucknow 226018, India.
Rakesh SarwalNational Institution for Transforming India Aayog, New Delhi 110001, India.
Juan M Lavista FerresMicrosoft AI For Good Research Lab, Redmond, WA 98052.ORCID 0000-0002-9654-3178
S V SubramanianHarvard Center for Population and Development Studies, Cambridge, MA 02138; svsubram@hsph.harvard.edu.ORCID 0000-0003-2365-4165

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There are emerging opportunities to assess health indicators at truly small areas with increasing availability of data geocoded to micro geographic units and advanced modeling techniques. The utility of such fine-grained data can be fully leveraged if linked to local governance units that are accountable for implementation of programs and interventions. We used data from the 2011 Indian Census for village-level demographic and amenities features and the 2016 Indian Demographic and Health Survey in a bias-corrected semisupervised regression framework to predict child anthropometric failures for all villages in India. Of the total geographic variation in predicted child anthropometric failure estimates, 54.2 to 72.3% were attributed to the village level followed by 20.6 to 39.5% to the state level. The mean predicted stunting was 37.9% (SD: 10.1%; IQR: 31.2 to 44.7%), and substantial variation was found across villages ranging from less than 5% for 691 villages to over 70% in 453 villages. Estimates at the village level can potentially shift the paradigm of policy discussion in India by enabling more informed prioritization and precise targeting. The proposed methodology can be adapted and applied to diverse population health indicators, and in other contexts, to reveal spatial heterogeneity at a finer geographic scale and identify local areas with the greatest needs and with direct implications for actions to take place.

Indexed as

AnthropometryCensusesChildChild Nutrition DisordersChild, PreschoolFemaleGrowth DisordersHumansIndiaMaleMalnutritionRural Populationchild undernutritionIndialocal governancemappingprecision public policy

Identifiers

PMID33903246
PMCPMC8106321

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.