An Economic Optimization Model for Improving the Efficiency of Vitamin A Interventions : An Application to Young Children in Cameroon
© The Author(s) 2015..
BACKGROUND: Vitamin A (VA) intervention programs in developing countries do not generally consider spatial differences in needs or in intervention costs. New data from Cameroon reveal nonuniform spatial distributions of VA deficiency among young children and of costs of some of the programs designed to address them.
METHODS: We develop a spatially explicit, intertemporal economic optimization tool that makes use of subnational dietary intake data and VA intervention program costs to identify more efficient sets of interventions to improve VA nutrition among young children aged 6 to 59 months in Cameroon.
RESULTS: The model suggests substantial changes in the composition and geographic foci of VA intervention programs vis-à-vis a business-as-usual scenario. National VA-fortified edible oil and bouillon cube programs are cost-effective, even when start-up costs are considered. High-dosage VA supplementation delivered via Child Health Days is most cost-effective in the North macro-region, where needs are greatest and the cost per child effectively covered is lowest. Overall, the VA intervention programs suggested by the optimization model are approximately 44% less expensive, with no change in the total number of children effectively covered nationwide.
CONCLUSIONS: The VA intervention programs should consider spatial and temporal differences in needs and in the expected benefits and costs of alternative VA interventions. Doing so will require spatially disaggregated strategies and the data and political will to support them, longer planning time horizons than are currently used in most developing countries, and long-term funding commitments.
Medienart: |
E-Artikel |
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Erscheinungsjahr: |
2015 |
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Erschienen: |
2015 |
Enthalten in: |
Zur Gesamtaufnahme - volume:36 |
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Enthalten in: |
Food and nutrition bulletin - 36(2015), 3 Suppl vom: 29. Sept., Seite S193-207 |
Sprache: |
Englisch |
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Beteiligte Personen: |
Vosti, Stephen A [VerfasserIn] |
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Links: |
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Themen: |
11103-57-4 |
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Anmerkungen: |
Date Completed 30.10.2015 Date Revised 04.01.2016 published: Print Citation Status MEDLINE |
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doi: |
10.1177/0379572115595889 |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
NLM252906233 |
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520 | |a © The Author(s) 2015. | ||
520 | |a BACKGROUND: Vitamin A (VA) intervention programs in developing countries do not generally consider spatial differences in needs or in intervention costs. New data from Cameroon reveal nonuniform spatial distributions of VA deficiency among young children and of costs of some of the programs designed to address them | ||
520 | |a METHODS: We develop a spatially explicit, intertemporal economic optimization tool that makes use of subnational dietary intake data and VA intervention program costs to identify more efficient sets of interventions to improve VA nutrition among young children aged 6 to 59 months in Cameroon | ||
520 | |a RESULTS: The model suggests substantial changes in the composition and geographic foci of VA intervention programs vis-à-vis a business-as-usual scenario. National VA-fortified edible oil and bouillon cube programs are cost-effective, even when start-up costs are considered. High-dosage VA supplementation delivered via Child Health Days is most cost-effective in the North macro-region, where needs are greatest and the cost per child effectively covered is lowest. Overall, the VA intervention programs suggested by the optimization model are approximately 44% less expensive, with no change in the total number of children effectively covered nationwide | ||
520 | |a CONCLUSIONS: The VA intervention programs should consider spatial and temporal differences in needs and in the expected benefits and costs of alternative VA interventions. Doing so will require spatially disaggregated strategies and the data and political will to support them, longer planning time horizons than are currently used in most developing countries, and long-term funding commitments | ||
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