RESUMO
Iron deficiency anemia is prevalent among infants in Ghana. This study evaluated the effect of micronutrient-fortified infant cereal on the nutritional status of infants in the La Nkwantanang Municipality of the Greater Accra Region of Ghana, located in western Africa. In this double-blind, controlled trial, infants aged 6-18 months were cluster-randomized to receive either micronutrient-fortified infant cereal containing 3.75 mg iron as ferrous fumarate/50 g cereal (INT; n = 107) or the same cereal without iron (CTL; n = 101) to complement other foods and breast milk. The intervention phase lasted six months followed by a two-month post-intervention phase (with no further study product feeding). Hemoglobin and anthropometry were assessed every 2 months for the 8-month study period. After the 6-month intervention phase, adjusted mean ± standard error change in hemoglobin from baseline in INT and CTL was 1.97 ± 0.19 and 1.16 ± 0.21 g/dl, respectively (p < .01 for each); the increase in hemoglobin was significantly larger in INT versus CTL (increase 0.68 ± 0.30 g/dl; p = .02). Prevalence of anemia declined to a significantly greater extent in INT (84.1% to 42.8%) compared to CTL (89.1% to 62.8%; p = .006). There was no significant difference between groups in weight gain (p = .41) or height gain (p = .21) over the study period. In infants aged 6-18 months, micronutrient-fortified infant cereal consumed for 6 months promoted greater reductions in iron-deficiency anemia, which is a significant public health concern not only in Ghana but also in many developing countries globally.
RESUMO
Malnutrition, including both undernutrition and obesity, is a significant problem in low- and middle-income countries (LMICs). In order to study malnutrition and develop effective intervention strategies, it is crucial to evaluate nutritional status in LMICs at the individual, household, and community levels. In a multinational research project supported by the Bill & Melinda Gates Foundation, we have been using a wearable technology to conduct objective dietary assessment in sub-Saharan Africa. Our assessment includes multiple diet-related activities in urban and rural families, including food sources (e.g., shopping, harvesting, and gathering), preservation/storage, preparation, cooking, and consumption (e.g., portion size and nutrition analysis). Our wearable device ("eButton" worn on the chest) acquires real-life images automatically during wake hours at preset time intervals. The recorded images, in amounts of tens of thousands per day, are post-processed to obtain the information of interest. Although we expect future Artificial Intelligence (AI) technology to extract the information automatically, at present we utilize AI to separate the acquired images into two binary classes: images with (Class 1) and without (Class 0) edible items. As a result, researchers need only to study Class-1 images, reducing their workload significantly. In this paper, we present a composite machine learning method to perform this classification, meeting the specific challenges of high complexity and diversity in the real-world LMIC data. Our method consists of a deep neural network (DNN) and a shallow learning network (SLN) connected by a novel probabilistic network interface layer. After presenting the details of our method, an image dataset acquired from Ghana is utilized to train and evaluate the machine learning system. Our comparative experiment indicates that the new composite method performs better than the conventional deep learning method assessed by integrated measures of sensitivity, specificity, and burden index, as indicated by the Receiver Operating Characteristic (ROC) curve.
