Bruce Y Lee1, Shawn T Brown2, Leila A Haidari3, Samantha Clark4, Taiwo Abimbola5, Sarah E Pallas5, Aaron S Wallace5, Elizabeth A Mitgang6, Jim Leonard3, Sarah M Bartsch6, Tatenda T Yemeke7, Eli Zenkov3, Sachiko Ozawa8. 1. Public Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Global Obesity Prevention Center (GOPC), Johns Hopkins University, Baltimore, MD, United States. Electronic address: brucelee@jhu.edu. 2. Pittsburgh Supercomputing Center (PSC), Carnegie Mellon University, Pittsburgh, PA, United States; McGill Centre for Integrative Neuroscience, McGill Neurological Institute, McGill University, Montreal, Canada. 3. Pittsburgh Supercomputing Center (PSC), Carnegie Mellon University, Pittsburgh, PA, United States. 4. The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, University of Washington, Seattle, WA, United States. 5. Centers for Disease Control and Prevention (CDC), Atlanta, GA, United States. 6. Public Health Computational and Operations Research (PHICOR), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Global Obesity Prevention Center (GOPC), Johns Hopkins University, Baltimore, MD, United States. 7. Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina - Chapel Hill, Chapel Hill, NC, United States. 8. Division of Practice Advancement and Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina - Chapel Hill, Chapel Hill, NC, United States; Department of Maternal and Child Health, UNC Gillings School of Global Public Health, University of North Carolina - Chapel Hill, Chapel Hill, NC, United States.
Abstract
BACKGROUND: Since special efforts are necessary to vaccinate people living far from fixed vaccination posts, decision makers are interested in knowing the economic value of such efforts. METHODS: Using our immunization geospatial information system platform and a measles compartment model, we quantified the health and economic value of a 2-dose measles immunization outreach strategy for children <24 months of age in Kenya who are geographically hard-to-reach (i.e., those living outside a specified catchment radius from fixed vaccination posts, which served as a proxy for access to services). FINDINGS: When geographically hard-to-reach children were not vaccinated, there were 1427 total measles cases from 2016 to 2020, resulting in $9.5 million ($3.1-$18.1 million) in direct medical costs and productivity losses and 7504 (3338-12,903) disability-adjusted life years (DALYs). The outreach strategy cost $76 ($23-$142)/DALY averted (compared to no outreach) when 25% of geographically hard-to-reach children received MCV1, $122 ($40-$226)/DALY averted when 50% received MCV1, and $274 ($123-$478)/DALY averted when 100% received MCV1. CONCLUSION: Outreach vaccination among geographically hard-to-reach populations was highly cost-effective in a wide variety of scenarios, offering support for investment in an effective outreach vaccination strategy.
BACKGROUND: Since special efforts are necessary to vaccinate people living far from fixed vaccination posts, decision makers are interested in knowing the economic value of such efforts. METHODS: Using our immunization geospatial information system platform and a measles compartment model, we quantified the health and economic value of a 2-dose measles immunization outreach strategy for children <24 months of age in Kenya who are geographically hard-to-reach (i.e., those living outside a specified catchment radius from fixed vaccination posts, which served as a proxy for access to services). FINDINGS: When geographically hard-to-reach children were not vaccinated, there were 1427 total measles cases from 2016 to 2020, resulting in $9.5 million ($3.1-$18.1 million) in direct medical costs and productivity losses and 7504 (3338-12,903) disability-adjusted life years (DALYs). The outreach strategy cost $76 ($23-$142)/DALY averted (compared to no outreach) when 25% of geographically hard-to-reach children received MCV1, $122 ($40-$226)/DALY averted when 50% received MCV1, and $274 ($123-$478)/DALY averted when 100% received MCV1. CONCLUSION: Outreach vaccination among geographically hard-to-reach populations was highly cost-effective in a wide variety of scenarios, offering support for investment in an effective outreach vaccination strategy.
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