Identifying geospatial patterns in wealth disparity in child malnutrition across 640 districts in India

Identifying geospatial patterns in wealth disparity in child malnutrition across 640 districts in India

We assessed district-level geospatial trends in precision weighted prevalence and absolute wealth disparity in stunting, underweight, wasting, low birthweight, and anemia among children under five in India. The largest wealth disparities were found for anthropometric failures and substantial variati...

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Main Authors: Lathan Liou, Rockli Kim, S.V. Subramanian
Format: Article
Language:English
Published: Elsevier 2020-04-01
Series:SSM: Population Health
Subjects:
India
Districts
Geospatial
Child undernutrition
Wealth disparity
Online Access:http://www.sciencedirect.com/science/article/pii/S2352827319303209
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spelling doaj-5618e3940691455c939d66b0cc0994352020-11-25T02:05:21ZengElsevierSSM: Population Health2352-82732020-04-0110Identifying geospatial patterns in wealth disparity in child malnutrition across 640 districts in IndiaLathan Liou0Rockli Kim1S.V. Subramanian2Department of Public Health and Primary Care, Cambridge University, Cambridge, UKHarvard Center for Population & Development Studies, Cambridge, MA, USA; Corresponding author. Harvard Center for Population and Development Studies, 9 Bow Street, Cambridge, MA, 02138, USA.Harvard Center for Population & Development Studies, Cambridge, MA, USA; Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USAWe assessed district-level geospatial trends in precision weighted prevalence and absolute wealth disparity in stunting, underweight, wasting, low birthweight, and anemia among children under five in India. The largest wealth disparities were found for anthropometric failures and substantial variation existed across states. We identified statistically significant (p < 0.001) geospatial patterns in district-wide wealth disparities for all outcomes, which differed from geospatial patterns for the overall prevalence. We characterized each district as either a “Disparity”, “Pitfall”, “Intensity”, or “Prosperity” area based on its overall burden and wealth disparity, as well as discuss the importance of considering both measures for geographically-targeted public health interventions to improve health equity.http://www.sciencedirect.com/science/article/pii/S2352827319303209IndiaDistrictsGeospatialChild undernutritionWealth disparity
collection DOAJ
language English
format Article
sources DOAJ
author Lathan Liou
Rockli Kim
S.V. Subramanian
spellingShingle Lathan Liou
Rockli Kim
S.V. Subramanian
Identifying geospatial patterns in wealth disparity in child malnutrition across 640 districts in India
SSM: Population Health
India
Districts
Geospatial
Child undernutrition
Wealth disparity
author_facet Lathan Liou
Rockli Kim
S.V. Subramanian
author_sort Lathan Liou
title Identifying geospatial patterns in wealth disparity in child malnutrition across 640 districts in India
title_short Identifying geospatial patterns in wealth disparity in child malnutrition across 640 districts in India
title_full Identifying geospatial patterns in wealth disparity in child malnutrition across 640 districts in India
title_fullStr Identifying geospatial patterns in wealth disparity in child malnutrition across 640 districts in India
title_full_unstemmed Identifying geospatial patterns in wealth disparity in child malnutrition across 640 districts in India
title_sort identifying geospatial patterns in wealth disparity in child malnutrition across 640 districts in india
publisher Elsevier
series SSM: Population Health
issn 2352-8273
publishDate 2020-04-01
description We assessed district-level geospatial trends in precision weighted prevalence and absolute wealth disparity in stunting, underweight, wasting, low birthweight, and anemia among children under five in India. The largest wealth disparities were found for anthropometric failures and substantial variation existed across states. We identified statistically significant (p < 0.001) geospatial patterns in district-wide wealth disparities for all outcomes, which differed from geospatial patterns for the overall prevalence. We characterized each district as either a “Disparity”, “Pitfall”, “Intensity”, or “Prosperity” area based on its overall burden and wealth disparity, as well as discuss the importance of considering both measures for geographically-targeted public health interventions to improve health equity.
topic India
Districts
Geospatial
Child undernutrition
Wealth disparity
url http://www.sciencedirect.com/science/article/pii/S2352827319303209
work_keys_str_mv AT lathanliou identifyinggeospatialpatternsinwealthdisparityinchildmalnutritionacross640districtsinindia
AT rocklikim identifyinggeospatialpatternsinwealthdisparityinchildmalnutritionacross640districtsinindia
AT svsubramanian identifyinggeospatialpatternsinwealthdisparityinchildmalnutritionacross640districtsinindia
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