BACKGROUND: Decisions on the timing and extent of vaccination against pandemic (H1N1) 2009 virus are complex. OBJECTIVE: To estimate the effectiveness and cost-effectiveness of pandemic influenza (H1N1) vaccination under different scenarios in October or November 2009. DESIGN: Compartmental epidemic model in conjunction with a Markov model of disease progression. DATA SOURCES: Literature and expert opinion. TARGET POPULATION: Residents of a major U.S. metropolitan city with a population of 8.3 million. TIME HORIZON: Lifetime. PERSPECTIVE: Societal. INTERVENTIONS: Vaccination in mid-October or mid-November 2009. OUTCOME MEASURES: Infections and deaths averted, costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness. RESULTS OF BASE-CASE ANALYSIS: Assuming each primary infection causes 1.5 secondary infections, vaccinating 40% of the population in October or November would be cost-saving. Vaccination in October would avert 2051 deaths, gain 69 679 QALYs, and save $469 million compared with no vaccination; vaccination in November would avert 1468 deaths, gain 49 422 QALYs, and save $302 million. RESULTS OF SENSITIVITY ANALYSIS: Vaccination is even more cost-saving if longer incubation periods, lower rates of infectiousness, or increased implementation of nonpharmaceutical interventions delay time to the peak of the pandemic. Vaccination saves fewer lives and is less cost-effective if the epidemic peaks earlier than mid-October. LIMITATIONS: The model assumed homogenous mixing of case-patients and contacts; heterogeneous mixing would result in faster initial spread, followed by slower spread. Additional costs and savings not included in the model would make vaccination more cost-saving. CONCLUSION: Earlier vaccination against pandemic (H1N1) 2009 prevents more deaths and is more cost-saving. Complete population coverage is not necessary to reduce the viral reproductive rate sufficiently to help shorten the pandemic. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality and National Institute on Drug Abuse.
BACKGROUND: Decisions on the timing and extent of vaccination against pandemic (H1N1) 2009 virus are complex. OBJECTIVE: To estimate the effectiveness and cost-effectiveness of pandemic influenza (H1N1) vaccination under different scenarios in October or November 2009. DESIGN: Compartmental epidemic model in conjunction with a Markov model of disease progression. DATA SOURCES: Literature and expert opinion. TARGET POPULATION: Residents of a major U.S. metropolitan city with a population of 8.3 million. TIME HORIZON: Lifetime. PERSPECTIVE: Societal. INTERVENTIONS: Vaccination in mid-October or mid-November 2009. OUTCOME MEASURES: Infections and deaths averted, costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness. RESULTS OF BASE-CASE ANALYSIS: Assuming each primary infection causes 1.5 secondary infections, vaccinating 40% of the population in October or November would be cost-saving. Vaccination in October would avert 2051 deaths, gain 69 679 QALYs, and save $469 million compared with no vaccination; vaccination in November would avert 1468 deaths, gain 49 422 QALYs, and save $302 million. RESULTS OF SENSITIVITY ANALYSIS: Vaccination is even more cost-saving if longer incubation periods, lower rates of infectiousness, or increased implementation of nonpharmaceutical interventions delay time to the peak of the pandemic. Vaccination saves fewer lives and is less cost-effective if the epidemic peaks earlier than mid-October. LIMITATIONS: The model assumed homogenous mixing of case-patients and contacts; heterogeneous mixing would result in faster initial spread, followed by slower spread. Additional costs and savings not included in the model would make vaccination more cost-saving. CONCLUSION: Earlier vaccination against pandemic (H1N1) 2009 prevents more deaths and is more cost-saving. Complete population coverage is not necessary to reduce the viral reproductive rate sufficiently to help shorten the pandemic. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality and National Institute on Drug Abuse.
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
Authors: C Buxton Bridges; J M Katz; W H Seto; P K Chan; D Tsang; W Ho; K H Mak; W Lim; J S Tam; M Clarke; S G Williams; A W Mounts; J S Bresee; L A Conn; T Rowe; J Hu-Primmer; R A Abernathy; X Lu; N J Cox; K Fukuda Journal: J Infect Dis Date: 2000-01 Impact factor: 5.226
Authors: Fatimah S Dawood; Seema Jain; Lyn Finelli; Michael W Shaw; Stephen Lindstrom; Rebecca J Garten; Larisa V Gubareva; Xiyan Xu; Carolyn B Bridges; Timothy M Uyeki Journal: N Engl J Med Date: 2009-05-07 Impact factor: 91.245
Authors: Benjamin J Cowling; Kwok-Hung Chan; Vicky J Fang; Calvin K Y Cheng; Rita O P Fung; Winnie Wai; Joey Sin; Wing Hong Seto; Raymond Yung; Daniel W S Chu; Billy C F Chiu; Paco W Y Lee; Ming Chi Chiu; Hoi Che Lee; Timothy M Uyeki; Peter M Houck; J S Malik Peiris; Gabriel M Leung Journal: Ann Intern Med Date: 2009-08-03 Impact factor: 25.391
Authors: Christophe Fraser; Christl A Donnelly; Simon Cauchemez; William P Hanage; Maria D Van Kerkhove; T Déirdre Hollingsworth; Jamie Griffin; Rebecca F Baggaley; Helen E Jenkins; Emily J Lyons; Thibaut Jombart; Wes R Hinsley; Nicholas C Grassly; Francois Balloux; Azra C Ghani; Neil M Ferguson; Andrew Rambaut; Oliver G Pybus; Hugo Lopez-Gatell; Celia M Alpuche-Aranda; Ietza Bojorquez Chapela; Ethel Palacios Zavala; Dulce Ma Espejo Guevara; Francesco Checchi; Erika Garcia; Stephane Hugonnet; Cathy Roth Journal: Science Date: 2009-05-11 Impact factor: 47.728
Authors: Yen T Nguyen; Samuel B Graitcer; Tuan H Nguyen; Duong N Tran; Tho D Pham; Mai T Q Le; Huu N Tran; Chien T Bui; Dat T Dang; Long T Nguyen; Timothy M Uyeki; David Dennis; James C Kile; Bryan K Kapella; A D Iuliano; Marc-Alain Widdowson; Hien T Nguyen Journal: Vaccine Date: 2013-07-30 Impact factor: 3.641