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Jit M, Cromer D, Baguelin M, Stowe J, Andrews N, Miller E. The cost-effectiveness of vaccinating pregnant women against seasonal influenza in England and Wales. Vaccine 2010; 29:115-22. [PMID: 21055501 DOI: 10.1016/j.vaccine.2010.08.078] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2010] [Revised: 08/16/2010] [Accepted: 08/17/2010] [Indexed: 11/28/2022]
Abstract
We assessed the cost-effectiveness of vaccinating pregnant women against seasonal influenza in England and Wales, taking into account the timing of vaccination relative to both the influenza season and trimester of pregnancy. Women were assumed to be vaccinated in their second or third trimester. Vaccination between September and December was found to have an incremental cost-effectiveness ratio of £23,000 per quality adjusted life year (QALY) (95% CI £10,000-£140,000) if it is assumed that infants are partially protected through their mothers, and of £28,000 per QALY gained (95% CI £13,000-£200,000) if infants are not protected. If some vaccine protection lasts for a second season, then the ratio is only £15,000 per QALY gained (95% CI £6,000-£93,000). Most of the benefit of vaccination is in preventing symptomatic episodes, regardless of health care resource use. Extending vaccination beyond December is unlikely to be cost-effective unless there is good protection into a second influenza season. Key sources of uncertainty are the cost of vaccine delivery and the quality of life detriment due to a clinically apparent episode of confirmed influenza. The cost of vaccine purchase itself is relatively low.
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Affiliation(s)
- Mark Jit
- Centre for Infections, Health Protection Agency, 61 Colindale Avenue, London NW9 6BT, United Kingdom.
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52
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Moghadas SM, Pizzi NJ, Wu J, Tamblyn SE, Fisman DN. Canada in the face of the 2009 H1N1 pandemic. Influenza Other Respir Viruses 2010; 5:83-8. [PMID: 21306571 PMCID: PMC4942003 DOI: 10.1111/j.1750-2659.2010.00184.x] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Initial public health responses to the 2009 influenza H1N1 pandemic were based on difficult decisions in the face of substantial uncertainty. Policy effectiveness depends critically on such decisions, and future planning for maximum protection of community health requires understanding of the impact of public health responses in observed scenarios. OBJECTIVES In alignment with the objectives of the Pandemic Influenza Outbreak Research Modelling Team (Pan-InfORM) and the Centre for Disease Modelling (CDM), a focused workshop was organized to: (i) evaluate Canada's response to the spring and autumn waves of the novel H1N1 pandemic; (ii) learn lessons from public health responses, and identify challenges that await public health planners and decision-makers; and (iii) understand how best to integrate resources to overcome these challenges. MAIN OUTCOME MEASURES We report on key presentations and discussions that took place to achieve the objectives of the workshop. CONCLUSIONS Future emerging infectious diseases are likely to bring far greater challenges than those imposed by the 2009 H1N1 pandemic. Canada must address these challenges and enhance its capacity for emergency responses by integrating modelling, surveillance, planning, and decision-making.
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Affiliation(s)
- Seyed M Moghadas
- Institute for Biodiagnostics, National Research Council Canada, Winnipeg, MB, Canada.
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53
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Morimoto T, Ishikawa H. Evaluation of vaccination strategies to suppress a novel influenza pandemic using an individual-based model. Nihon Eiseigaku Zasshi 2010; 65:459-66. [PMID: 20508388 DOI: 10.1265/jjh.65.459] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVES In this study, we aimed to evaluate vaccination strategies with regard to the impact of prioritization, coverage, and a delay in the vaccination program against a novel influenza pandemic with high-level fatality equivalent to Spanish flu using an individual-based model (IBM). Vaccination is one of the main measures to prevent infection, a serious condition, or death. METHODS We constructed an IBM for the transmission of a novel influenza virus utilizing personal information on the basis of demographic data from Sapporo City, thereby resulting in a more realistic model. We designed simulations for scenarios along the guidelines of the Ministry of Health, Labour and Welfare of Japan, which requests the prioritization of vaccination of pregnant women and persons with underlying diseases, among whom influenza would cause higher fatality than among healthy persons, infants, or their parents, as well as school-aged and old-aged persons. RESULTS A vaccination program fully taking into account the ordered priority groups would more effectively reduce the number of deaths in the priority groups and also the total number of deaths in comparison with a program shortening the transition time to the next priority or a non-priority group. A delay in the vaccination program would lower the effectiveness of reducing the numbers of patients and deaths. CONCLUSIONS According to the simulation-based results, when vaccination programs scheduled on the basis of priority groups start 90 and 150 days after outbreak, the total numbers of patients would be reduced to one-third-one-half, and two-thirds, respectively, in comparison with baseline of no vaccination, which leads to the necessity to conduct a vaccination program as soon as possible.
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Affiliation(s)
- Tomoko Morimoto
- Department of Human Ecology, Graduate School of Environmental Science, Okayama University, Okayama, Japan
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54
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Lee BY, Brown ST, Korch GW, Cooley PC, Zimmerman RK, Wheaton WD, Zimmer SM, Grefenstette JJ, Bailey RR, Assi TM, Burke DS. A computer simulation of vaccine prioritization, allocation, and rationing during the 2009 H1N1 influenza pandemic. Vaccine 2010; 28:4875-9. [PMID: 20483192 PMCID: PMC2906666 DOI: 10.1016/j.vaccine.2010.05.002] [Citation(s) in RCA: 77] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2009] [Revised: 04/28/2010] [Accepted: 05/03/2010] [Indexed: 01/01/2023]
Abstract
In the fall 2009, the University of Pittsburgh Models of Infectious Disease Agent Study (MIDAS) team employed an agent-based computer simulation model (ABM) of the greater Washington, DC, metropolitan region to assist the Office of the Assistant Secretary of Public Preparedness and Response, Department of Health and Human Services, to address several key questions regarding vaccine allocation during the 2009 H1N1 influenza pandemic, including comparing a vaccinating children (i.e., highest transmitters)-first policy versus the Advisory Committee on Immunization Practices (ACIP)-recommended vaccinating at-risk individuals-first policy. Our study supported adherence to the ACIP (instead of a children-first policy) prioritization recommendations for the H1N1 influenza vaccine when vaccine is in limited supply and that within the ACIP groups, children should receive highest priority.
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Affiliation(s)
- Bruce Y Lee
- University of Pittsburgh, Pittsburgh, PA 15213, USA.
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55
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Cooley P, Lee BY, Brown S, Cajka J, Chasteen B, Ganapathi L, Stark JH, Wheaton WD, Wagener DK, Burke DS. Protecting health care workers: a pandemic simulation based on Allegheny County. Influenza Other Respir Viruses 2010; 4:61-72. [PMID: 20167046 PMCID: PMC2894576 DOI: 10.1111/j.1750-2659.2009.00122.x] [Citation(s) in RCA: 55] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Please cite this paper as: Cooley et al. (2010) Protecting health care workers: a pandemic simulation based on Allegheny County. Influenza and Other Respiratory Viruses 4(2), 61–72. Background and Objectives The Advisory Committee on Immunization Practices has identified health care workers (HCWs) as a priority group to receive influenza vaccine. Although the importance of HCW to the health care system is well understood, the potential role of HCW in transmission during an epidemic has not been clearly established. Methods Using a standard SIR (Susceptible–Infected–Recovered) framework similar to previously developed pandemic models, we developed an agent‐based model (ABM) of Allegheny County, PA, that incorporates the key health care system features to simulate the spread of an influenza epidemic and its effect on hospital‐based HCWs. Findings Our simulation runs found the secondary attack rate among unprotected HCWs to be approximately 60% higher (54·3%) as that of all adults (34·1%), which would result in substantial absenteeism and additional risk to HCW families. Understanding how a pandemic may affect HCWs, who must be available to treat infected patients as well as patients with other medical conditions, is crucial to policy makers’ and hospital administrators’ preparedness planning.
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56
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van Boven M, Kretzschmar M, Wallinga J, O'Neill PD, Wichmann O, Hahné S. Estimation of measles vaccine efficacy and critical vaccination coverage in a highly vaccinated population. J R Soc Interface 2010; 7:1537-44. [PMID: 20392713 DOI: 10.1098/rsif.2010.0086] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Measles is a highly infectious disease that has been targeted for elimination from four WHO regions. Whether and under which conditions this goal is feasible is, however, uncertain since outbreaks have been documented in populations with high vaccination coverage (more than 90%). Here, we use the example of a large outbreak in a German public school to show how estimates of key epidemiological parameters such as the basic reproduction number (R(0)), vaccine efficacy (VE(S)) and critical vaccination coverage (p(c)) can be obtained from partially observed outbreaks in highly vaccinated populations. Our analyses rely on Bayesian methods of inference based on the final size distribution of outbreak size, and use data which are easily collected. For the German public school the analyses indicate that the basic reproduction number of measles is higher than previously thought (R(0) = 30.8, 95% credible interval: 23.6-40.4), that the vaccine is highly effective in preventing infection (VE(S) = 0.997, 95% credible interval: 0.993-0.999), and that a vaccination coverage in excess of 95 per cent may be necessary to achieve herd immunity (p(c) = 0.971, 95% credible interval: 0.961-0.978). We discuss the implications for measles elimination from highly vaccinated populations.
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Affiliation(s)
- Michiel van Boven
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
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57
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Lee BY, Brown ST, Cooley PC, Zimmerman RK, Wheaton WD, Zimmer SM, Grefenstette JJ, Assi TM, Furphy TJ, Wagener DK, Burke DS. A computer simulation of employee vaccination to mitigate an influenza epidemic. Am J Prev Med 2010; 38:247-57. [PMID: 20042311 PMCID: PMC2833347 DOI: 10.1016/j.amepre.2009.11.009] [Citation(s) in RCA: 76] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2009] [Revised: 11/09/2009] [Accepted: 11/12/2009] [Indexed: 11/28/2022]
Abstract
BACKGROUND Better understanding the possible effects of vaccinating employees is important and can help policymakers and businesses plan vaccine distribution and administration logistics, especially with the current H1N1 influenza vaccine in short supply. PURPOSE This article aims to determine the effects of varying vaccine coverage, compliance, administration rates, prioritization, and timing among employees during an influenza pandemic. METHODS As part of the H1N1 influenza planning efforts of the Models of Infectious Disease Agent Study network, an agent-based computer simulation model was developed for the Washington DC metropolitan region, encompassing five metropolitan statistical areas. Each simulation run involved introducing 100 infectious individuals to initiate a 1.3 reproductive-rate (R(0)) epidemic, consistent with H1N1 parameters to date. Another set of scenarios represented a R(0)=1.6 epidemic. RESULTS An unmitigated epidemic resulted in substantial productivity losses (a mean of $112.6 million for a serologic 15% attack rate and $193.8 million for a serologic 25% attack rate), even with the relatively low estimated mortality impact of H1N1. Although vaccinating Advisory Committee on Immunization Practices-defined priority groups resulted in the largest savings, vaccinating all remaining workers captured additional savings and, in fact, reduced healthcare workers' and critical infrastructure workers' chances of infection. Moreover, although employee vaccination compliance affected the epidemic, once 20% compliance was achieved, additional increases in compliance provided less incremental benefit. Even though a vast majority of the workplaces in the DC metropolitan region had fewer than 100 employees, focusing on vaccinating only those in larger firms (> or =100 employees) was just as effective in mitigating the epidemic as trying to vaccinate employees in all workplaces. CONCLUSIONS Timely vaccination of at least 20% of the large-company workforce can play an important role in epidemic mitigation.
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Affiliation(s)
- Bruce Y Lee
- University of Pittsburgh, 200 Meyran Avenue, Pittsburgh, PA 15213, USA.
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58
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Chao DL, Halloran ME, Obenchain VJ, Longini IM. FluTE, a publicly available stochastic influenza epidemic simulation model. PLoS Comput Biol 2010; 6:e1000656. [PMID: 20126529 PMCID: PMC2813259 DOI: 10.1371/journal.pcbi.1000656] [Citation(s) in RCA: 236] [Impact Index Per Article: 16.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2009] [Accepted: 12/22/2009] [Indexed: 11/22/2022] Open
Abstract
Mathematical and computer models of epidemics have contributed to our understanding of the spread of infectious disease and the measures needed to contain or mitigate them. To help prepare for future influenza seasonal epidemics or pandemics, we developed a new stochastic model of the spread of influenza across a large population. Individuals in this model have realistic social contact networks, and transmission and infections are based on the current state of knowledge of the natural history of influenza. The model has been calibrated so that outcomes are consistent with the 1957/1958 Asian A(H2N2) and 2009 pandemic A(H1N1) influenza viruses. We present examples of how this model can be used to study the dynamics of influenza epidemics in the United States and simulate how to mitigate or delay them using pharmaceutical interventions and social distancing measures. Computer simulation models play an essential role in informing public policy and evaluating pandemic preparedness plans. We have made the source code of this model publicly available to encourage its use and further development. Computer simulations can provide valuable information to communities preparing for epidemics. These simulations can be used to investigate the effectiveness of various intervention strategies in reducing or delaying the peak of an epidemic. We have made a detailed influenza epidemic simulator for the United States publicly available so that others may use the software to inform public policy or adapt it to suit their needs.
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Affiliation(s)
- Dennis L Chao
- Center for Statistics and Quantitative Infectious Diseases/Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.
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59
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Bajardi P, Poletto C, Balcan D, Hu H, Goncalves B, Ramasco J, Paolotti D, Perra N, Tizzoni M, Van den Broeck W, Colizza V, Vespignani A. Modeling vaccination campaigns and the Fall/Winter 2009 activity of the new A(H1N1) influenza in the Northern Hemisphere. EMERGING HEALTH THREATS JOURNAL 2009; 2:e11. [PMID: 22460281 PMCID: PMC3167647 DOI: 10.3134/ehtj.09.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2009] [Revised: 10/21/2009] [Accepted: 11/02/2009] [Indexed: 11/18/2022]
Abstract
The unfolding of pandemic influenza A(H1N1) for Fall 2009 in the Northern Hemisphere is still uncertain. Plans for vaccination campaigns and vaccine trials are underway, with the first batches expected to be available early October. Several studies point to the possibility of an anticipated pandemic peak that could undermine the effectiveness of vaccination strategies. Here, we use a structured global epidemic and mobility metapopulation model to assess the effectiveness of massive vaccination campaigns for the Fall/Winter 2009. Mitigation effects are explored depending on the interplay between the predicted pandemic evolution and the expected delivery of vaccines. The model is calibrated using recent estimates on the transmissibility of the new A(H1N1) influenza. Results show that if additional intervention strategies were not used to delay the time of pandemic peak, vaccination may not be able to considerably reduce the cumulative number of cases, even when the mass vaccination campaign is started as early as mid-October. Prioritized vaccination would be crucial in slowing down the pandemic evolution and reducing its burden.
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Affiliation(s)
- P Bajardi
- Computational Epidemiology Laboratory, Institute for Scientific Interchange, Turin, Italy
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60
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Yang Y, Sugimoto JD, Halloran ME, Basta NE, Chao DL, Matrajt L, Potter G, Kenah E, Longini IM. The transmissibility and control of pandemic influenza A (H1N1) virus. Science 2009; 326:729-33. [PMID: 19745114 PMCID: PMC2880578 DOI: 10.1126/science.1177373] [Citation(s) in RCA: 364] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Pandemic influenza A (H1N1) 2009 (pandemic H1N1) is spreading throughout the planet. It has become the dominant strain in the Southern Hemisphere, where the influenza season has now ended. Here, on the basis of reported case clusters in the United States, we estimated the household secondary attack rate for pandemic H1N1 to be 27.3% [95% confidence interval (CI) from 12.2% to 50.5%]. From a school outbreak, we estimated that a typical schoolchild infects 2.4 (95% CI from 1.8 to 3.2) other children within the school. We estimated the basic reproductive number, R0, to range from 1.3 to 1.7 and the generation interval to range from 2.6 to 3.2 days. We used a simulation model to evaluate the effectiveness of vaccination strategies in the United States for fall 2009. If a vaccine were available soon enough, vaccination of children, followed by adults, reaching 70% overall coverage, in addition to high-risk and essential workforce groups, could mitigate a severe epidemic.
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Affiliation(s)
- Yang Yang
- Center for Statistics and Quantitative Infectious Diseases, Fred Hutchinson Cancer Research Center and the University of Washington, Seattle, WA, USA
| | - Jonathan D. Sugimoto
- Center for Statistics and Quantitative Infectious Diseases, Fred Hutchinson Cancer Research Center and the University of Washington, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - M. Elizabeth Halloran
- Center for Statistics and Quantitative Infectious Diseases, Fred Hutchinson Cancer Research Center and the University of Washington, Seattle, WA, USA
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Nicole E. Basta
- Center for Statistics and Quantitative Infectious Diseases, Fred Hutchinson Cancer Research Center and the University of Washington, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Dennis L. Chao
- Center for Statistics and Quantitative Infectious Diseases, Fred Hutchinson Cancer Research Center and the University of Washington, Seattle, WA, USA
| | - Laura Matrajt
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| | - Gail Potter
- Department of Statistics, University of Washington, Seattle, WA, USA
| | - Eben Kenah
- Center for Statistics and Quantitative Infectious Diseases, Fred Hutchinson Cancer Research Center and the University of Washington, Seattle, WA, USA
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
- Department of Global Health, University of Washington, Seattle, WA, USA
| | - Ira M. Longini
- Center for Statistics and Quantitative Infectious Diseases, Fred Hutchinson Cancer Research Center and the University of Washington, Seattle, WA, USA
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
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61
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Basta NE, Chao DL, Halloran ME, Matrajt L, Longini IM. Strategies for pandemic and seasonal influenza vaccination of schoolchildren in the United States. Am J Epidemiol 2009; 170:679-86. [PMID: 19679750 PMCID: PMC2737588 DOI: 10.1093/aje/kwp237] [Citation(s) in RCA: 102] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Vaccinating school-aged children against influenza can reduce age-specific and population-level illness attack rates. Using a stochastic simulation model of influenza transmission, the authors assessed strategies for vaccinating children in the United States, varying the vaccine type, coverage level, and reproductive number R (average number of secondary cases produced by a typical primary case). Results indicated that vaccinating children can substantially reduce population-level illness attack rates over a wide range of scenarios. The greatest absolute reduction in influenza illness cases per season occurred at R values ranging from 1.2 to 1.6 for a given vaccine coverage level. The indirect, total, and overall effects of vaccinating children were strong when transmission intensity was low to intermediate. The indirect effects declined rapidly as transmission intensity increased. In a mild influenza season (R = 1.1), approximately 19 million influenza cases could be prevented by vaccinating 70% of children. At most, nearly 100 million cases of influenza illness could be prevented, depending on the proportion of children vaccinated and the transmission intensity. Given the current worldwide threat of novel influenza A (H1N1), with an estimated R of 1.4-1.6, health officials should consider strategies for vaccinating children against novel influenza A (H1N1) as well as seasonal influenza.
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Affiliation(s)
- Nicole E Basta
- Department of Epidemiology,School of Public Health, University of Washington, Seattle,Washington, USA
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Prospects for an influenza vaccine that induces cross-protective cytotoxic T lymphocytes. Immunol Cell Biol 2009; 87:300-8. [PMID: 19308073 DOI: 10.1038/icb.2009.16] [Citation(s) in RCA: 85] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Our approach to vaccination against influenza is unique. For no other pathogen do we construct and produce a new vaccine every year in the face of uncertainty about the strains that will be circulating when it is used. The huge global cooperative effort that underpins this process reflects our awareness of the need to control this major pathogen. Moreover, the threat of devastation by a pandemic due to a newly emerging viral subtype has triggered an intense effort to improve and accelerate the production of vaccines for use if a pandemic arises. However, type A influenza viruses responsible for seasonal epidemics and those with the potential to cause a pandemic share amino acid sequences that form the targets of cytotoxic T lymphocytes (CTL). CTL activated by currently circulating viruses, therefore, offer a possible means to limit the impact of infection with future variant seasonal strains and even new subtypes. This review examines how cross-protective CTL can be exploited to improve influenza vaccination and issues that need to be considered when attempting to induce this type of immunity. We discuss the role of CTL responses in viral control and review the current knowledge relating to specificity and longevity of memory CD8(+) T cells, how vaccine antigen can be loaded into antigen-presenting cells to prime these responses and factors influencing the class of response induced. Application of these principles to the next generation of influenza vaccines should lead to much greater control of infection.
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