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Spatial heterogeneity and correlates of child malnutrition in districts of India

Overview of attention for article published in BMC Public Health, August 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

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2 blogs
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Title
Spatial heterogeneity and correlates of child malnutrition in districts of India
Published in
BMC Public Health, August 2018
DOI 10.1186/s12889-018-5873-z
Pubmed ID
Authors

Junaid Khan, Sanjay K Mohanty

Abstract

Despite sustained economic growth and reduction in money metric poverty in last two decades, prevalence of malnutrition remained high in India. During 1992-2016, the prevalence of underweight among children had declined from 53% to 36%, stunting had declined from 52% to 38% while that of wasting had increased from 17% to 21% in India. The national average in the level of malnutrition conceals large variation across districts of India. Using data from the recent round of National Family Health Survey (NFHS), 2015-16 this paper examined the spatial heterogeneity and meso-scale correlates of child malnutrition across 640 districts of India. Moran's I statistics and bivariate LISA maps were used to understand spatial dependence and clustering of child malnutrition. Multiple regression, spatial lag and error models were used to examine the correlates of malnutrition. Poverty, body mass index (BMI) of mother, breastfeeding practices, full immunization, institutional births, improved sanitation and electrification in the household were used as meso scale correlates of malnutrition. The univariate Moran's I statistics was 0.65, 0.51 and 0.74 for stunting, wasting and underweight respectively suggesting spatial heterogeneity of malnutrition in India. Bivariate Moran's I statistics of stunting with BMI of mother was 0.52, 0.46 with poverty and - 0.52 with sanitation. The pattern was similar with respect to wasting and underweight suggesting spatial clustering of malnutrition against the meso scale correlates in the geographical hotspots of India. Results of spatial error model suggested that the coefficient of BMI of mother and poverty of household were strong and significant predictors of stunting, wasting and underweight. The coefficient of BMI in spatial error model was largest found for underweight (β = 0.38, 95% CI: 0.29-0.48) followed by stunting (β = 0.23, 95% CI: 0.14-0.33) and wasting (β = 0.11, 95% CI: 0.01-0.22). Women's educational attainment and breastfeeding practices were also found significant for stunting and underweight. Malnutrition across the districts of India is spatially clustered. Reduction of poverty, improving women's education and health, sanitation and child feeding knowledge can reduce the prevalence of malnutrition across India. Multisectoral and targeted intervention in the geographical hotspots of malnutrition can reduce malnutrition in India.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 289 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 289 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 13%
Student > Master 37 13%
Researcher 27 9%
Student > Bachelor 16 6%
Lecturer 15 5%
Other 47 16%
Unknown 110 38%
Readers by discipline Count As %
Nursing and Health Professions 44 15%
Social Sciences 31 11%
Medicine and Dentistry 28 10%
Economics, Econometrics and Finance 11 4%
Agricultural and Biological Sciences 9 3%
Other 39 13%
Unknown 127 44%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 January 2020.
All research outputs
#2,005,029
of 23,100,534 outputs
Outputs from BMC Public Health
#2,225
of 15,064 outputs
Outputs of similar age
#43,629
of 333,251 outputs
Outputs of similar age from BMC Public Health
#54
of 283 outputs
Altmetric has tracked 23,100,534 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 15,064 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.0. This one has done well, scoring higher than 85% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 333,251 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 283 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.