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Articles

Dietary Intakes and Socioeconomic Factors Are Associated with the Hemoglobin Concentration of Bangladeshi Women¹

Alok Bhargava^*2, Howarth E. Bouis and Nevin S. Scrimshaw^**

^* From the Department of Economics, University of Houston, Houston, Texas 77204-5882; International Food Policy Research Institute, 2033 K Street NW, Washington, D.C. 20006 and ^** International Nutrition Foundation, Box 500, Charles Street Station, Boston, Massachusetts 02114

²To whom correspondence should be addressed.

Iron deficiency anemia affects a large number of women in developing countries, especially during child-bearing years. The hemoglobin concentration is useful for identifying iron deficiency anemia. The main objectives of this study were, first, to extend algorithms for calculating bioavailable iron from mixed diets, taking into account the enhancers and inhibitors of iron absorption under alternative assumptions on body iron stores. Second, a comprehensive longitudinal model was developed for the proximate determinants of hemoglobin concentration that included the subjects’ dietary intakes, nutritional status, morbidity and socioeconomic factors and the unobserved between-subject differences. The model for hemoglobin concentration was estimated using three repeated observations on 514 free living women in Bangladesh. Socioeconomic factors affecting the iron intake from meat, fish and poultry and from all animal sources were also modeled. The main results were that bioavailable iron, women’s height and mid upper arm circumference and intake of iron tablets were significant predictors of hemoglobin concentration. Increases in household incomes were associated with higher intake of iron from meat, fish and poultry and from all animal sources. The algorithms for estimating bioavailable iron showed the importance of assumptions regarding body iron stores and underscored the need to develop suitable algorithms for subjects in developing countries.

KEY WORDS: • bioavailable iron • iron deficiency anemia • socioeconomic factors • longitudinal data • random effects models

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