Decoding India’s Child Malnutrition Puzzle: A Multivariable Analysis Using a Composite Index

• Published in: Children (Basel) Vol. 11; no. 8; p. 902
• Main Authors: Shah, Gulzar, Siddiqa, Maryam, Shankar, Padmini, Karibayeva, Indira, Zubair, Amber, Shah, Bushra
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 26.07.2024; MDPI
• Subjects: Age; Anemia; Birth weight; Breastfeeding & lactation; Child mortality; children; Children & youth; Comparative analysis; composite index of anthropometric failure; Families & family life; Food security; GDP; Gender; Gross Domestic Product; Hunger; Malnutrition; Malnutrition in children; Mothers; Nutrition; Nutritional status; Pediatrics; Public health; Sanitation; Sex discrimination; Socioeconomic factors; Socioeconomic status; Standard scores; stunting; Surveys; underweight; wasting; India
• ISSN: 2227-9067; 2227-9067
• DOI: 10.3390/children11080902

Background: This study examines the levels and predictors of malnutrition in Indian children under 5 years of age. Methods: Composite Index of Anthropometric Failure was applied to data from the India National Family Health Survey 2019–2021. A multivariable logistic regression model was used to assess the predictors. Results: 52.59% of children experienced anthropometric failure. Child predictors of lower malnutrition risk included female gender (adjusted odds ratio (AOR) = 0.881) and average or large size at birth (AOR = 0.729 and 0.715, respectively, compared to small size). Higher birth order increased malnutrition odds (2nd-4th: AOR = 1.211; 5th or higher: AOR = 1.449) compared to firstborn. Maternal predictors of lower malnutrition risk included age 20–34 years (AOR = 0.806), age 35–49 years (AOR = 0.714) compared to 15–19 years, normal BMI (AOR = 0.752), overweight and obese BMI (AOR = 0.504) compared to underweight, and secondary or higher education vs. no education (AOR = 0.865). Maternal predictors of higher malnutrition risk included severe anemia vs. no anemia (AOR = 1.232). Protective socioeconomic factors included middle (AOR = 0.903) and rich wealth index (AOR = 0.717) compared to poor, and toilet access (AOR = 0.803). Children’s malnutrition risk also declined with paternal education (primary: AOR = 0.901; secondary or higher: AOR = 0.822) vs. no education. Conversely, malnutrition risk increased with Hindu (AOR = 1.258) or Islam religion (AOR = 1.369) vs. other religions. Conclusions: Child malnutrition remains a critical issue in India, necessitating concerted efforts from both private and public sectors. A ‘Health in All Policies’ approach should guide public health leadership in influencing policies that impact children’s nutritional status.