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The Prevalence of Anemia in Children Aged 6–23 Months and its Correlates Differ by District in Kapilvastu and Achham Districts in Nepal

BACKGROUND: Analyses of predictors of anemia or malnutrition often pool national or regional data, which may hide variability at subnational levels. OBJECTIVES: We sought to identify the risk factors for anemia in young Nepali children aged 6–23 mo in 2 districts: Kapilvastu and Achham. METHODS: Thi...

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Detalles Bibliográficos
Autores principales: ocks, Lindsey M., Paudyal, Naveen, Lundsgaard, Sabrina, Thapa, Lila Bikram, Joshi, Nira, Mei, LZuguo, Whitehead, Ralph D., Jefferds, Maria Elena D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Nutrition 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10164769/
https://www.ncbi.nlm.nih.gov/pubmed/37180849
http://dx.doi.org/10.1016/j.cdnut.2023.100063
Descripción
Sumario:BACKGROUND: Analyses of predictors of anemia or malnutrition often pool national or regional data, which may hide variability at subnational levels. OBJECTIVES: We sought to identify the risk factors for anemia in young Nepali children aged 6–23 mo in 2 districts: Kapilvastu and Achham. METHODS: This is an analysis of two cross-sectional surveys that were conducted as part of a program evaluation of an infant and young child feeding and micronutrient powder intervention that included anemia as a primary outcome. Baseline and endline surveys in each district (in 2013 and 2016) included hemoglobin assessments in n = 4709 children who were representative of children 6–23 mo in each district. Log-binomial regression models accounting for the survey design were used to estimate univariable and multivariable prevalence ratios for risk factors at multiple levels—underlying, direct, and biological causes. Average attributable fractions (AFs) for the population were calculated for significant predictor biomarkers of anemia in multivariable models. RESULTS: In Accham, the prevalence of anemia was 31.4%; significant predictors included child’s age, household asset ownership, length-for-age z-score, inflammation (CRP concentration > 0.5 mg/L; α-1 acid glycoprotein concentration > 1 mg/mL), and iron deficiency (serum ferritin concentration < 12 μg/L with BRINDA-inflammation adjustment). In Kapilvastu, the prevalence of anemia was 48.1%; significant predictors included child’s sex and ethnicity, wasting and weight-for-length z-score, any morbidity in the previous 2 wk, consumption of fortified foods, receipt of multiple micronutrient powder distributions, iron deficiency, zinc deficiency (nonfasting serum zinc concentration of <65 μg/dL in the morning and that of <57 μg/dL in the afternoon), and inflammation. In Achham, average AFs were 28.2% and 19.8% for iron deficiency and inflammation, respectively. Average AFs for anemia in Kapilvastu were 32.1%, 4.2%, and 4.9% for iron deficiency, zinc deficiency, and inflammation, respectively. CONCLUSIONS: The prevalence of anemia and its risk factors varied between districts, with inflammation contributing to a greater share of anemia in Achham than in Kapilvastu. The estimated AF for iron deficiency was around 30% in both districts; iron-delivering interventions and multisectoral approaches to anemia are warranted.