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Abstract Details
Activity Number:
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302
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Health Policy Statistics
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Abstract - #301927 |
Title:
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A Genetic Algorithm Approach to Optimize Planning of Food Fortification Programs
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Author(s):
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Dave Osthus*+ and Alicia Carriquiry and Todd Campbell
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Companies:
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Iowa State University and Iowa State University and Iowa State University
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Address:
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, , ,
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Keywords:
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Genetic algorithm ;
optimization ;
nutrition ;
food fortification ;
measurement error
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Abstract:
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Methods for reliably estimating the distributions of usual (long-run average) daily nutrient intakes have been proposed (National Research Council, 1986; Nusser et. al. 1996). These estimates are then utilized for evaluating the adequacy of nutrient intake in sub-populations and for the subsequent development of programs to combat those inadequacies. One potential population-level intervention to reduce the prevalence of inadequacy is food fortification - where specific amounts of a nutrient is added to specific food vehicles. The goal of food fortification is to reduce the proportion of the population with inadequate nutrient consumption, at a reasonable cost. But how are food vehicles and nutrient amounts selected? We propose a method to optimize the process to plan food fortification. The approach we propose relies on the methodology proposed by Nusser et al., and uses a genetic algorithm to minimize the analytically untractable optimization function. The goal of the methodology is not to deliver a single, universal "best" food fortification plan, but rather a "best" plan under a variety of constraints. We illustrate the method by planning vitamin A intakes of Ugandan children.
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