BACKGROUND: The pandemic potential of influenza A (H5N1) virus is a prominent public health concern of the 21st century. OBJECTIVE: To estimate the effectiveness and cost-effectiveness of alternative pandemic (H5N1) mitigation and response strategies. DESIGN: Compartmental epidemic model in conjunction with a Markov model of disease progression. DATA SOURCES: Literature and expert opinion. TARGET POPULATION: Residents of a U.S. metropolitan city with a population of 8.3 million. TIME HORIZON: Lifetime. PERSPECTIVE: Societal. INTERVENTIONS: 3 scenarios: 1) vaccination and antiviral pharmacotherapy in quantities similar to those currently available in the U.S. stockpile (stockpiled strategy), 2) stockpiled strategy but with expanded distribution of antiviral agents (expanded prophylaxis strategy), and 3) stockpiled strategy but with adjuvanted vaccine (expanded vaccination strategy). All scenarios assumed standard nonpharmaceutical interventions. OUTCOME MEASURES: Infections and deaths averted, costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness. RESULTS OF BASE-CASE ANALYSIS: Expanded vaccination was the most effective and cost-effective of the 3 strategies, averting 68% of infections and deaths and gaining 404 030 QALYs at $10 844 per QALY gained relative to the stockpiled strategy. RESULTS OF SENSITIVITY ANALYSIS: Expanded vaccination remained incrementally cost-effective over a wide range of assumptions. LIMITATIONS: The model assumed homogenous mixing of cases and contacts; heterogeneous mixing would result in faster initial spread, followed by slower spread. We did not model interventions for children or older adults; the model is not designed to target interventions to specific groups. CONCLUSION: Expanded adjuvanted vaccination is an effective and cost-effective mitigation strategy for an influenza A (H5N1) pandemic. Expanded antiviral prophylaxis can help delay the pandemic while additional strategies are implemented. PRIMARY FUNDING SOURCE: National Institutes of Health and Agency for Healthcare Research and Quality.
BACKGROUND: The pandemic potential of influenza A (H5N1) virus is a prominent public health concern of the 21st century. OBJECTIVE: To estimate the effectiveness and cost-effectiveness of alternative pandemic (H5N1) mitigation and response strategies. DESIGN: Compartmental epidemic model in conjunction with a Markov model of disease progression. DATA SOURCES: Literature and expert opinion. TARGET POPULATION: Residents of a U.S. metropolitan city with a population of 8.3 million. TIME HORIZON: Lifetime. PERSPECTIVE: Societal. INTERVENTIONS: 3 scenarios: 1) vaccination and antiviral pharmacotherapy in quantities similar to those currently available in the U.S. stockpile (stockpiled strategy), 2) stockpiled strategy but with expanded distribution of antiviral agents (expanded prophylaxis strategy), and 3) stockpiled strategy but with adjuvanted vaccine (expanded vaccination strategy). All scenarios assumed standard nonpharmaceutical interventions. OUTCOME MEASURES: Infections and deaths averted, costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness. RESULTS OF BASE-CASE ANALYSIS: Expanded vaccination was the most effective and cost-effective of the 3 strategies, averting 68% of infections and deaths and gaining 404 030 QALYs at $10 844 per QALY gained relative to the stockpiled strategy. RESULTS OF SENSITIVITY ANALYSIS: Expanded vaccination remained incrementally cost-effective over a wide range of assumptions. LIMITATIONS: The model assumed homogenous mixing of cases and contacts; heterogeneous mixing would result in faster initial spread, followed by slower spread. We did not model interventions for children or older adults; the model is not designed to target interventions to specific groups. CONCLUSION: Expanded adjuvanted vaccination is an effective and cost-effective mitigation strategy for an influenza A (H5N1) pandemic. Expanded antiviral prophylaxis can help delay the pandemic while additional strategies are implemented. PRIMARY FUNDING SOURCE: National Institutes of Health and Agency for Healthcare Research and Quality.
Authors: J M Katz; W Lim; C B Bridges; T Rowe; J Hu-Primmer; X Lu; R A Abernathy; M Clarke; L Conn; H Kwong; M Lee; G Au; Y Y Ho; K H Mak; N J Cox; K Fukuda Journal: J Infect Dis Date: 1999-12 Impact factor: 5.226
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