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Spatial Clustering of Malnutrition and Anemia Among Reproductive Women and Its Associated Risk Factors in India: Evidence From National Family Health Survey-5
Koustav Ghosh
Spatial Clustering of Malnutrition and Anemia Among Reproductive Women and Its Associated Risk Factors in India: Evidence From National Family Health Survey-5 - P. 3-11
Background: Malnutrition and anemia are 2 severe public health concerns, predominantly in low-and middle-income nations.
Malnutrition is defined as poor nutritional condition that encompasses both under nutrition and over nutrition. The prevalence of
overweight or obesity and anemia has increased in India from 2016 to 2021. The study aims to investigate the spatial clustering and
factors responsible for underweight, overweight/obesity, and anemia among reproductive women (15-49 years) in India using the
data from National Family and Health Survey-5 (2019-2021).
Methods: We conducted hot-spot analysis using Moran’s Index (MI) with the help of spatial analysis software (i.e., GeoDa 1.18
and ArcGIS 10.8). It also demonstrates the autocorrelation. Multivariable logistic regression analysis has been performed to
examine different determinants and risk associated with underweight, overweight/obesity, and anemia with various dependent
variable by using Stata-14 software.
Results: Moran’s Index for underweight (MI ¼ 0.68), overweight/obesity (MI ¼ 0.72), and anemia (MI ¼ 0.62) indicates a high
level of spatial-autocorrelation (P < .001) exists across the districts in India. As a result, a total of 156, 143, and 126 hot-spot
districts are detected for underweight, overweight/obesity, and anemia, respectively. The burden of undernutrition and anemia is
higher in rural areas. Risk of under nutrition and anemia are both reduced by media exposure and eating habits. Moreover, low
income and low education level raises the risk of anemia and undernutrition, while obesity shows an inverse trend with income
and education level.
Conclusion: The study recommends targeting hot-spot districts for malnutrition and anemia, and policy level initiatives by
addressing the responsible risk factors.
Plain language title
Spatial Clustering of Malnutrition and Anemia Among Women Across Districts in India
Plain language summary
Most of the low-income and middle-income countries are affected by the double burden of malnutrition. Malnutrition and anemia
are 2 severe public health concerns, predominantly in low-and middle-income nations. However, the prevalence of undernutrition
among women of reproductive age in India has decreased, while the prevalence of overweight or obesity and anemia has also
increased in India from 2016 to 2021. The study aims to investigate the spatial clustering and factors responsible for nutritional
malnutrition
anemia
prevalence
spatial-autocorrelation
ArcGIS
Spatial Clustering of Malnutrition and Anemia Among Reproductive Women and Its Associated Risk Factors in India: Evidence From National Family Health Survey-5 - P. 3-11
Background: Malnutrition and anemia are 2 severe public health concerns, predominantly in low-and middle-income nations.
Malnutrition is defined as poor nutritional condition that encompasses both under nutrition and over nutrition. The prevalence of
overweight or obesity and anemia has increased in India from 2016 to 2021. The study aims to investigate the spatial clustering and
factors responsible for underweight, overweight/obesity, and anemia among reproductive women (15-49 years) in India using the
data from National Family and Health Survey-5 (2019-2021).
Methods: We conducted hot-spot analysis using Moran’s Index (MI) with the help of spatial analysis software (i.e., GeoDa 1.18
and ArcGIS 10.8). It also demonstrates the autocorrelation. Multivariable logistic regression analysis has been performed to
examine different determinants and risk associated with underweight, overweight/obesity, and anemia with various dependent
variable by using Stata-14 software.
Results: Moran’s Index for underweight (MI ¼ 0.68), overweight/obesity (MI ¼ 0.72), and anemia (MI ¼ 0.62) indicates a high
level of spatial-autocorrelation (P < .001) exists across the districts in India. As a result, a total of 156, 143, and 126 hot-spot
districts are detected for underweight, overweight/obesity, and anemia, respectively. The burden of undernutrition and anemia is
higher in rural areas. Risk of under nutrition and anemia are both reduced by media exposure and eating habits. Moreover, low
income and low education level raises the risk of anemia and undernutrition, while obesity shows an inverse trend with income
and education level.
Conclusion: The study recommends targeting hot-spot districts for malnutrition and anemia, and policy level initiatives by
addressing the responsible risk factors.
Plain language title
Spatial Clustering of Malnutrition and Anemia Among Women Across Districts in India
Plain language summary
Most of the low-income and middle-income countries are affected by the double burden of malnutrition. Malnutrition and anemia
are 2 severe public health concerns, predominantly in low-and middle-income nations. However, the prevalence of undernutrition
among women of reproductive age in India has decreased, while the prevalence of overweight or obesity and anemia has also
increased in India from 2016 to 2021. The study aims to investigate the spatial clustering and factors responsible for nutritional
malnutrition
anemia
prevalence
spatial-autocorrelation
ArcGIS