A multilevel multinomial logistic regression model for identifying risk factors of anemia in children aged 6-59 months in northeastern states of India
Sanku Dey, Enayetur Raheem

TL;DR
This study employs a multilevel multinomial logistic regression model to identify key risk factors for anemia severity among children aged 6-59 months in northeastern India, accounting for regional variation.
Contribution
It introduces a multilevel modeling approach to analyze anemia risk factors, considering regional differences and providing detailed probabilistic assessments.
Findings
Age at marriage and number of children significantly affect anemia severity.
Child's age inversely related to severe anemia risk.
Approximately 6% of variation attributed to state-level differences.
Abstract
In this article, we use multilevel multinomial logistic regression model to identify the risk factors of anemia in children of northeastern States of India. The data consisted of 10,136 children of age group 6-59 months. We considered the level of anemia as the outcome variable with four ordinal categories (severe, moderate, mild, and non-anemic) based on hemoglobin concentration in blood as per WHO guidelines. A two-level random intercept model was considered with state of residence as the level-2 variable. The intra-class correlation (ICC) between states is 0.0577 indicating approximately 6% of the total variation in the response variable accounted for by the state of residence. Several multilevel models have been compared, and a final model was decided based on deviance test. We observed that predicted probability of being at or below severely anemic level to be 0.1247, at moderately…
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Taxonomy
TopicsChild Nutrition and Water Access · Iron Metabolism and Disorders · Demographic Trends and Gender Preferences
