An Economic Optimization Model for Improving the Efficiency of Vitamin A Interventions

Abstract

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.

Overlapping vitamin A interventions in the United States, Guatemala, Zambia, and South Africa: case studies

“…Models can be used to evaluate where VAS may be more effective. In Cameroon, industrially fortified oil and bouillon cubes reach both the North and the South . In evaluating the coverage of overlapping VA programs, the economic model predicted that VAS would be more cost‐effective in the North than the South …”

“…The case studies described in our review relied on biological indicators to determine the risk of hypervitaminosis A in areas with overlapping programs. Other methods to evaluate overlapping coverage of programs include economic models that use cost‐effectiveness and household consumption data . Policy makers and ministries at the country level should weigh both the biological and economic data available when monitoring public health intervention programs.…”

“…In Cameroon, industrially fortified oil and bouillon cubes reach both the North and the South. 65 In evaluating the coverage of overlapping VA programs, the economic model predicted that VAS would be more cost-effective in the North than the South. 65 Population-based surveys in children are needed with appropriate biomarkers on randomly selected subgroups 9,22,66 to support the recommendation on when to scale back VAS in any country.…”

“…65 In evaluating the coverage of overlapping VA programs, the economic model predicted that VAS would be more cost-effective in the North than the South. 65 Population-based surveys in children are needed with appropriate biomarkers on randomly selected subgroups 9,22,66 to support the recommendation on when to scale back VAS in any country. It is premature to recommend disbanding VAS entirely in the same areas because humans are born with low liver reserves of VA and in many countries infection rates are high, expanding the need for therapeutic VA.…”

Risk of excessive intake of vitamins and minerals delivered through public health interventions: objectives, results, conclusions of the meeting, and the way forward

“…Previous research supported using circulating serum retinyl esters as a percentage of total serum VA as a measure of VA intoxication . Other methods to evaluate overlapping coverage of programs include economic models that use cost‐effectiveness and household consumption data …”