Identifying Targets for the Prevention of Childhood Undernutrition in a Resource-Limited Peri-Urban Ecuadorian Community.

Autor:	Attia SL; 4530University of Kentucky, Lexington, KY, USA., Schmidt WP; London School of Hygiene and Tropical Medicine, London, UK., Osorio JC; 4530University of Kentucky, Lexington, KY, USA., Young T; 4530University of Kentucky, Lexington, KY, USA., Schadler A; 4530University of Kentucky, Lexington, KY, USA., Plasencia J; 4530University of Kentucky, Lexington, KY, USA.
Jazyk:	angličtina
Zdroj:	Food and nutrition bulletin [Food Nutr Bull] 2021 Jun; Vol. 42 (2), pp. 210-224. Date of Electronic Publication: 2021 May 19.
DOI:	10.1177/0379572120982500
Abstrakt:	Background: In middle-income countries, malnutrition concentrates in marginalized populations with a lack of effective preventive strategies. Objective: Identify risk factors for undernutrition in a peri-urban Ecuadorian community of children aged 12 to 59 months. Methods: Data from a cross-sectional survey in 2011 of children 1 to 5 years were analyzed including demographic data, medical history and examination, food frequency questionnaire (FFQ), anthropometric measurements, and blood for complete blood count, C-reactive protein, vitamin A, iron, and zinc levels. Dietary Diversity Score (DDS) was calculated from FFQ. Bivariate and multivariate analysis assessed effects on primary outcome of undernutrition by DDS, vitamin deficiencies, and demographic and nutritional data. Results: N = 67, 52.2% undernourished: 49.3% stunted, 25.4% underweight, and 3% wasted; 74.6% (n = 50) were anemic and 95.1% (n = 39) had low serum zinc. Dietary Diversity Score was universally low (mean 4.91 ± 1.36, max 12). Undernutrition was associated with lower vitamin A levels (20 306, IQR: 16605.25-23973.75 vs 23665, IQR: 19292-26474 ng/mL, P = .04); underweight was associated with less parental report of illness (43.8%, n = 7 vs 80% n = 40, P = .005) and higher white blood count (13.7, IQR: 11.95-15.8 vs 10.9, IQR: 7.8-14.23 × 10 9 /L, P = .02). In multiple regression, risk of undernutrition decreased by 4% for every $10 monthly income increase (95 CI%: 0.5%-7.4%, P = .02, n = 23); risk of underweight decreased by 0.06 for every increased DDS point (adjusted odds ratio: 0.06; 95 CI%: 0.004-0.91, P = .04, n = 23). Conclusions: In this peri-urban limited-resource, mostly Indigenous Ecuadorian community, stunting exceeds national prevalence, lower monthly income is the strongest predictor of undernutrition, lower DDS can predict some forms of undernutrition, and vitamin deficiencies are associated with but not predictive of undernutrition.
Databáze:	MEDLINE
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