Addressing the challenges to immunization practice with an economic algorithm for vaccine selection

Area of Science:

• Biotechnology and Public Health
• Health Economics and Policy

Background:

• The growing number of available vaccines presents challenges for purchasers, who often base selection solely on purchase price.
• Current vaccine purchasing decisions lack systematic consideration of various fiscal factors beyond initial cost.

Purpose of the Study:

• To develop an economic algorithm for vaccine selection that minimizes overall disease control costs through immunization.
• To incorporate multiple factors beyond purchase price into vaccine selection decisions.

Main Methods:

• Developed a pilot binary-integer linear programming model considering purchase price, doses, preparation time, and route of administration.
• Modeled recommended immunization schedules for diphtheria, tetanus, pertussis, Haemophilus influenzae b, and hepatitis B.
• Utilized actual vaccine prices and estimated costs for new vaccines to test the algorithm.

Main Results:

• The algorithm identified lowest-cost vaccine stocking lists, with costs ranging from $450.32 to $529.41 per child depending on assumptions.
• The most economical vaccine selections were not intuitively predictable due to numerous permutations and sensitivity to cost changes.
• Ensuring manufacturer diversity increased costs by approximately $13.00 and required an additional injection.

Conclusions:

• An objective selection method considering economic value of vaccine features can provide 'best value' to purchasers.
• Such a method can incentivize innovation and competition within the vaccine industry.
• Systematic economic evaluation is crucial for optimal vaccine procurement and public health outcomes.