Nayer Khazeni, MD, MS; David W. Hutton, MS; Alan M. Garber, MD, PhD; Nathaniel Hupert, MD, MPH; Douglas K. Owens, MD, MS
Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality.
Acknowledgment: The authors thank the reviewers for their extremely thorough and helpful reviews and suggestions.
Grant Support: By the Agency for Healthcare Research and Quality (1 F32 HS018003-01A1; Dr. Khazeni), the National Institute on Drug Abuse (2 R01 DA15612-016; Dr. Owens), a Stanford Graduate Fellowship (Mr. Hutton), and the Department of Veterans Affairs (Drs. Owens and Garber).
Potential Conflicts of Interest: None disclosed.
Reproducible Research Statement: An annotated version of the model is available in Appendix 1 so that others can test the authors' findings and conclusions.
Requests for Single Reprints: Nayer Khazeni, MD, MS, Division of Pulmonary and Critical Care Medicine, Stanford University Medical Center, 300 Pasteur Drive, H3143, Stanford, CA 94305.
Current Author Addresses: Dr. Khazeni: Division of Pulmonary and Critical Care Medicine, Stanford University Medical Center, 300 Pasteur Drive, H3143, Stanford, CA 94305.
Mr. Hutton: 496 Terman Engineering Center, Stanford University, Stanford, CA 94305.
Drs. Garber and Owens: Center for Health Policy and Center for Primary Care and Outcomes Research, Stanford University, 117 Encina Commons, Stanford, CA 94305-6019.
Dr. Hupert: Departments of Public Health and Medicine, Weill Medical College, Cornell University, 402 East 67th Street, LA-219, New York, NY 10065.
Author Contributions: Conception and design: N. Khazeni, D.W. Hutton, A.M. Garber, D.K. Owens.
Analysis and interpretation of the data: N. Khazeni, D.W. Hutton, A.M. Garber, N. Hupert, D.K. Owens.
Drafting of the article: N. Khazeni, D.W. Hutton, A.M. Garber, N. Hupert, D.K. Owens.
Critical revision of the article for important intellectual content: N. Khazeni, D.W. Hutton, A.M. Garber, N. Hupert.
Final approval of the article: N. Khazeni, D.W. Hutton, A.M. Garber, N. Hupert, D.K. Owens.
Provision of study materials or patients: N. Khazeni.
Statistical expertise: N. Khazeni, D.W. Hutton, A.M. Garber, D.K. Owens.
Obtaining of funding: N. Khazeni.
Administrative, technical, or logistic support: N. Khazeni, A.M. Garber.
Collection and assembly of data: N. Khazeni, D.W. Hutton, N. Hupert, D.K. Owens.
Khazeni N, Hutton DW, Garber AM, Hupert N, Owens DK. Effectiveness and Cost-Effectiveness of Vaccination Against Pandemic Influenza (H1N1) 2009. Ann Intern Med. 2009;151:829-839. doi: 10.7326/0000605-200912150-00157
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Published: Ann Intern Med. 2009;151(12):829-839.
Decisions on the timing and extent of vaccination against pandemic (H1N1) 2009 virus are complex.
To estimate the effectiveness and cost-effectiveness of pandemic influenza (H1N1) vaccination under different scenarios in October or November 2009.
Compartmental epidemic model in conjunction with a Markov model of disease progression.
Literature and expert opinion.
Residents of a major U.S. metropolitan city with a population of 8.3 million.
Vaccination in mid-October or mid-November 2009.
Infections and deaths averted, costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness.
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 69Â 679 QALYs, and save $469 million compared with no vaccination; vaccination in November would avert 1468 deaths, gain 49Â 422 QALYs, and save $302 million.
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.
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.
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.
Agency for Healthcare Research and Quality and National Institute on Drug Abuse.
Influenza A (H1N1) vaccine is now being distributed for use in vaccination programs.
This decision model for vaccination suggests that vaccinating 40% of the population in October would save more lives and money than similar vaccination coverage in November.
The model makes several assumptions that may not bear out given the unpredictability of H1N1 infection this fall. However, users can test their own assumptions with a model provided by the authors.
Earlier vaccination is estimated to prevent more deaths and cost less than would later vaccination for influenza A (H1N1).
Pandemic (H1N1) 2009 has caused 182 166 confirmed infections and 1799 deaths in more than 150 countries to date (1). Both the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) have declared public health emergencies in response to global circulation of this virus, and the WHO has raised the influenza pandemic alert level from 3 to 6 (2).
As a result of the strain's novelty, most people lack innate immunity to pandemic (H1N1) (3); currently available vaccines do not protect against the virus; and the time needed to manufacture, test, and distribute a matched vaccine is several months (4, 5).
In the absence of a matched vaccine, infections and deaths from pandemic (H1N1) will continue globally until a sufficient proportion of the population has developed immunity through infection and recovery, inducing “herd immunity” (population immunity that decreases the effective reproductive rate of the virus below 1, ending the pandemic by epidemiologic definitions ). Public health officials were planning to begin vaccination campaigns in mid-October 2009 (7); however, the National Biodefense Science Board, a group of advisors to the U.S. Department of Health and Human Services, recommended moving large-scale vaccine administration to mid-September 2009 (8). Decisions on vaccination timing and distribution are complicated: It is unclear how many individuals would require vaccination to substantially reduce transmission once vaccine is available (some scientists note that the first epidemic wave may in fact already be complete by this time ), and it could be expensive to manufacture and administer the vaccine and to treat its side effects.
To help guide policymakers in advising vaccine manufacturers, we developed a model of progression of the 2009 (H1N1) pandemic to determine how vaccination in October or November 2009 would affect the course of the pandemic. We compared the effectiveness and cost-effectiveness of no vaccination, vaccination in mid-October, and vaccination in mid-November.
We developed a compartmental epidemic model in conjunction with a Markov model of disease progression of the human spread of pandemic (H1N1) to elucidate the dynamics of disease transmission and progression of the first pandemic wave (Appendix Figure A1 [all appendix figures and tables are in Appendix 1]). Following the recommendations of the Panel on Cost-Effectiveness in Health and Medicine (10), we adopted a societal perspective for costs and benefits, discounted at 3% annually. We analyzed outcomes for the remaining lifetime of each individual. We expressed these outcomes in infections and deaths, costs, quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios. We developed the simulation model and performed analyses by using Microsoft Excel, version 2003 (Microsoft, Redmond, Washington). We provide an annotated version of the model (Appendix 1) so that readers can test model output for different assumptions and circumstances.
We followed a hypothetical cohort of 8.3 million persons living in a large U.S. city with a sex distribution (53% women), age range (0 to 100 years), and average remaining life expectancy similar to those of the population of New York City (11). The WHO officially declared the start of the pandemic on 11 June 2009 (2). We assumed that 10 000 individuals were infected at that time on the basis of a New York City telephone survey of influenza-like illness (12) and CDC data on cases of influenza-like illness testing positive for pandemic (H1N1) (13–15) (calculations in Appendix 2). We varied this number from 1000 to 50 000 in sensitivity analysis. According to data showing some preexisting population immunity to pandemic (H1N1) (3), we assumed that 10% of the population entered the model immune to the virus and that 90% of individuals entered susceptible to the virus. In sensitivity analysis, we examined scenarios in which up to 20% of individuals entered immune to the virus.
On the basis of available evidence (16–18), we assumed that in the base-case scenario, the R0 (number of secondary infections caused by each primary infection in a susceptible population) of pandemic (H1N1) is approximately 1.5. As new data on infectious spread emerge, this number may change. In sensitivity analysis, we varied R0 from 1.2 to 1.8.
New York City death rates and other epidemiologic data suggest that viral transmission may be decreased over the warm summer months (12, 19–23). Because the timing of the peak of the epidemic is an important determinant of the effectiveness of vaccination, we modeled seasonal variation (described in Appendix 1) by calibrating to decreased deaths confirmed in New York City over the summer (Appendix Figure B4). We also evaluated the effectiveness of vaccination in scenarios in which the epidemic grew more slowly or more rapidly.
According to influenza A virus infections (24–33), we assumed that 67% of infected individuals developed symptoms. Fifty percent of these individuals entered a state of isolation, either voluntarily or because of physical limitation secondary to illness or admission to a hospital. We assumed that those who were not in isolation (34) continued to infect contacts. On the basis of information to date on pandemic (H1N1) 2009 and other influenza A viruses (35, 36), we assumed that infected individuals had a mean incubation time of 3 days, had symptoms (if they were symptomatic) for 10 days, and could transmit the virus for 4 days. We evaluated infectivity of 7 days in sensitivity analyses. On the basis of studies of influenza A infection and nasal viral shedding (37, 38), we assumed that incubating individuals transmitted influenza at half the rate of symptomatic individuals and that asymptomatic infected individuals transmitted at one quarter the rate of symptomatic individuals. Consistent with published CDC assumptions (39), we estimated that 3.3% of symptomatic individuals required 5 days of hospital care and 10% of hospitalized patients required 10 days of care in the intensive care unit.
Estimates of reinfection with antigenically drifted influenza A viruses range from 2% to 25% (40–43) throughout the course of epidemics. Because most reinfected individuals are asymptomatic or have mild symptoms with a shorter duration of illness and less viral shedding, we assumed that 5% of the recovered population was once again susceptible to infection at an average of 5 months after recovery. We examined a range of reinfection from 2% to 25% in sensitivity analysis.
On the basis of the percentage of cases of influenza-like illness in the United States that have tested positive for pandemic (H1N1) (13–15, 44–54), we calculated a case-fatality proportion of 0.1% after adjusting the documented case-fatality proportions (17, 55) for underreporting nonfatal cases owing to less frequent testing of individuals with influenza-like illness as the pandemic progresses (56, 57). In sensitivity analysis, we modeled a more severe pandemic, with a 1.0% clinical case-fatality proportion (consistent with pandemic [H1N1] global case-fatality proportions ) and a less severe pandemic, with a 0.01% clinical case-fatality proportion. We modeled age-specific mortality with increases in deaths in newborns, young adults, and individuals older than 65 years (54, 55).
Nonpharmaceutical interventions for pandemic (H1N1) recommended by the CDC have included closing school and child care facilities, home isolation, cough etiquette, hand washing, use of alcohol-based hand gels, and use of personal protective equipment (such as facemasks) (59). Incorporating the results of a complex network model of pandemic spread through communities (60), we assumed that these nonpharmaceutical interventions are reducing contacts by 15%. A recent randomized trial of facemasks and hand washing found that under optimal circumstances, these measures reduced transmission among households by 66% (61); therefore, we evaluated reduction in contacts from 10% to 70% in sensitivity analysis (61).
Nonadjuvanted vaccines routinely used for seasonal influenza have had limited success in eliciting human antibodies to novel influenza A viruses. Adjuvanted vaccines have been much more successful—not only do they more frequently elicit antibody responses, they protect against different influenza clades, an important advantage in light of the virus's ability to mutate (62–65). On the basis of these properties, vaccine manufacturers have been testing adjuvanted vaccines for pandemic (H1N1) (66). Consistent with results of trials of adjuvanted pandemic (H1N1) vaccination (67, 68), we assumed a 15-µg adjuvant-to-antigen concentration and examined concentrations from 3.8 µg to 30 µg (63) in sensitivity analyses. We assumed that this vaccination sequence was 75% effective, within the range of effectiveness shown in pandemic (H1N1) trials (67, 68). In sensitivity analyses, we examined ranges of vaccine effectiveness from 60% to 90%.
The U.S. government expects to have 120 million doses of pandemic (H1N1) vaccine available in the autumn, a quantity sufficient to vaccinate 40% of the U.S. population (7, 69). On the basis of historical precedent (New York City, 1976 ) and modern mass vaccination exercises (71), we estimated that a rapid influenza vaccination campaign, using published emergency response logistic plans, could inoculate approximately 250 people per vaccination center per hour, providing coverage for all 8.3 million individuals over a 10-day period (72). On the basis of the results of studies of adjuvanted pandemic (H1N1) vaccination (67, 68), we assumed that the vaccine would provide complete immunity to 75% of recipients 14 days after vaccination.
We assumed that 85% of pandemic (H1N1)–vaccinated individuals experienced mild to moderate adverse reactions, such as pain, redness, swelling, induration, ecchymosis, low-grade fevers, arthralgias, fatigues, headaches, myalgias, shivering, or sweating for up to 7 days, on the basis of adjuvanted pandemic (H1N1) vaccination data (68). We assumed that 0.001% of the population experienced severe adverse reactions, such as angioedema, anaphylaxis, or the Guillain–Barré syndrome, consistent with 1976 vaccination data (73).
We expressed all costs in 2009 U.S. dollars using the Gross Domestic Product deflator. Intervention costs included the cost of a vaccine, administration, the value of an individual's time receiving it, and the costs of treating individuals with severe side effects (Table 1[74–89]). We estimated treatment costs at a hospital from the average cost of general medical hospitalization for influenza (87) or hospitalization in a medical intensive care unit (86). We based utility estimates on EuroQol and time-tradeoff ratings and included the remaining lifetime of individuals alive at the end of the year. We calculated remaining life-years from the New York census, then adjusted life expectancy for quality of life by using age- and sex-specific utilities from the Beaver Dam Health Outcomes Study (81).
We used sensitivity analysis to identify important model uncertainties. When available, we based variable ranges on reported 95% CIs from the data sources. Otherwise, we determined ranges by adding or subtracting 25% from the baseline estimate.
The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, and approval of the manuscript.
For a later peaking epidemic at R0 of 1.5 (in mid-October), 285 566 of the city's 8.3 million individuals would have been symptomatically infected and 286 would have died (Table 2 and Figure 1). In November, 541 865 would have been infected and 542 would have died. Because of the development of immunity in individuals in the population who had been infected and recovered, 85% of individuals would still be susceptible to infection in October, and 80% would be susceptible to infection in November.
The effective viral reproductive rate decreases over time, secondary to the development of immunity in individuals in the population who recover from infection. The R0 is the number of secondary infections caused by each primary infection in the susceptible population.
Varying R0 from 1.2 to 1.8 (Table 2 and Figure 2), symptomatic infections would range from 38 304 to 1.74 million in October and 40 416 to 2.25 million in November; deaths would range from 38 to 1743 in October and 40 to 2247 in November. At R0 of 1.2, fewer individuals would become infected and less immunity would develop; 89% of individuals would still be susceptible to infection in October and November. At R0 of 1.8, a significant number of infections would increase population immunity, with 59% of individuals susceptible to infection in October and 50% susceptible to infection in November.
At R0 (number of secondary infections caused by each primary infection in a susceptible population) of 1.2, fewer individuals would become infected, so less immunity would develop and more individuals would require vaccination to decrease widespread transmission. However, at R0 of 1.8, a significant number of infections would occur, increasing population immunity and decreasing the number of individuals who would require vaccination to decrease widespread transmission.
Death averted by vaccination and the number of individuals requiring vaccination to reduce R0 below 1 (thus helping to end widespread transmission) are related to the initial R0 for the pandemic (Table 2 and Figure 2). At a low and a high R0, fewer individuals require vaccination to slow the epidemic than if R0 is 1.5. At a high R0, the epidemic spreads so quickly that many individuals become ill and develop immunity without vaccination. Vaccination has the largest effect on deaths averted for an intermediate R0.
Vaccinating 40% of the population in October would slow widespread transmission and be cost-saving, adding 69 679 QALYs and saving $469 million. Vaccinating 40% of the population in November would add 49 422 QALYs and save $302 million relative to no vaccination (Table 3). Vaccinating 35% of the population in November would slow widespread transmission and add 45 383 QALYs, saving $282 million relative to no vaccination. Vaccinating 40% of the population in October would be more effective and cost less than vaccinating 40% in November.
When the reproductive rate of the virus is low, the pandemic spreads more slowly, and fewer treatment costs and deaths are averted with vaccination (Figure 1 and Table 3). At R0 of 1.2, vaccination in October would add 25 387 QALYs and save $103 million, and vaccination in November would add 25 236 QALYs and save $102 million relative to no vaccination. When the reproductive rate of the virus is higher, widespread transmission of the virus is already decreasing by November, leading to fewer lives saved with vaccination. At R0 of 1.8, vaccination in October would add 20 967 QALYs, saving $69 million, and vaccination in November would add 10 851 QALYs at $14 million, for a cost of $1303 per QALY relative to no vaccination.
In considering short-term local budgetary implications, at R0 of 1.5, federal costs for vaccination in November for a city of 8.3 million individuals would be $46 million for vaccine antigen and adjuvant; city costs would be $29 million to administer the vaccines; and city and individual costs would be $35 million in vaccine recipient time and $6.4 million for treating severe short-term side effects. Savings to the city and individuals would be $686 million in influenza treatment costs (Table 3).
In light of significant local and regional variations in the growth of the pandemic in the United States (14–16, 45–54), we examined a slower growth scenario than in our initial analysis, based on an average of confirmed U.S. deaths to date (13–15, 44–53). In this scenario, with slower spread of the virus (Appendix Figure B1), more individuals would be susceptible to infection in the autumn: Vaccinating 40% of the population would be cost-saving, adding 89 592 QALYs and saving $629 million relative to no vaccination in October, and adding 72 167 QALYs and saving $486 million relative to no vaccination in November. Vaccinating 44% of the population in October would slow widespread transmission, add 94 363 QALYs, and save $657 million relative to no vaccination. Vaccinating 43% of the population in November would slow widespread transmission, add 76 396 QALYs, and save $513 million relative to no vaccination.
We also examined a scenario with no reduction in viral transmission over the summer months. In this case, with faster spread of the pandemic (Appendix Figure B2), the peak of the epidemic would have passed, and widespread transmission would be decreasing without vaccination by October. Assuming that nonpharmaceutical interventions reducing infectious contacts by 15% remained in effect, vaccinating 40% of the population in October would gain 2854 QALYs at a cost of $27 841 per QALY; vaccinating in November would increase costs by $105 million, with a loss of 318 QALYs secondary to vaccine side effects.
Recent announcements by public health officials suggest that the originally anticipated 120 million vaccine doses may not be ready for distribution in October but that at least 45 million doses, a quantity sufficient to vaccinate 15% of the population, will be available (69, 90). Vaccinating 15% of the population in mid-October would avert 719 474 infections and 720 deaths, saving $162 million versus no vaccination.
The costs and feasibility of expediting vaccination of 15% to 40% of the population in mid-October are unknown. At a willingness-to-pay threshold of $50 000 per QALY, over an R0 range of 1.5 to 1.8, the additional acceptable costs would range from $2.6 billion to $447 million for a city of 8.3 million individuals.
As results of vaccine trials comparing 1- and 2-dose vaccination emerge, policymakers may consider 2-dose vaccination (68, 91). With a 2-dose adjuvanted vaccine, 20% of the population could be vaccinated in mid-November, averting 967 deaths and saving $159 million, as compared with 1468 deaths averted and $302 million saved with 1-dose vaccination.
Policymakers may also consider nonadjuvanted vaccines in light of emerging data showing equal or nearly equal efficacy to adjuvanted vaccines (91). One-dose nonadjuvanted vaccination in mid-November would save $23 million as compared with equally efficacious adjuvanted vaccination for a city of 8.3 million individuals.
We conducted univariate sensitivity analysis on all variables (Appendix Table B1). If more effective nonpharmaceutical interventions were implemented, fewer individuals would become infected, and the peak of the pandemic would be delayed while the interventions were in effect. If 70% reduction in contacts were achieved through nonpharmaceutical interventions, the pandemic would be contained (R0 < 1). Decreases in vaccine efficacy would increase the percentage of the population requiring vaccination to decrease widespread transmission in November (Figure 3).
The percentage of the population requiring vaccination to reduce widespread transmission increases with decreases in vaccine efficacy. The R0 is the number of secondary infections caused by each primary infection in the susceptible population.
Under our base-case assumption of severe side effects from vaccination occurring in 1 in 100 000 vaccinated individuals, vaccinating 40% of the city's population would cause approximately 2 deaths from severe vaccine side effects but would prevent 1468 influenza deaths from vaccination in November. If severe side effects from adjuvanted vaccination occur in 1 in 250 individuals, 664 deaths would result from severe side effects, approximately half the number of lives saved from vaccination in November.
In 45% of Monte Carlo probabilistic sensitivity analysis simulations (Appendix Figure B3), vaccinating 40% of the population in November is cost-saving compared with no vaccination; in 69% of simulations, vaccinating 40% of the population in November has an estimated incremental cost less than $50 000 per QALY saved and in 71% of simulations, an estimated incremental cost less than $100 000 per QALY saved. In 26% of simulations, no vaccination is more cost-effective than vaccinating 40% of the population in November.
We examined the costs and benefits of vaccination in the autumn for the ongoing 2009 (H1N1) pandemic. Our analysis suggests that absent additional harms, earlier vaccination, as advised by the National Biodefense Science Board (8), would be more cost-saving and avert more deaths than vaccination later in the autumn. Because accelerating large-scale vaccination efforts in this time frame may be costly, we have provided a range of acceptable costs of vaccination, given different reproductive rates, to guide policymakers in situations in which they might consider speeding up vaccine production and administration. We defined the number of individuals requiring vaccination to reduce widespread transmission in a metropolitan city under a broad range of possible reproductive rates, and we note that regardless of the timing of vaccination, complete population coverage is not necessary to reduce the viral reproductive rate sufficiently to help shorten the pandemic. These results have important ramifications both for vaccine production goals and preparations for a potentially unprecedented fall vaccination campaign.
We found that the effectiveness and cost-effectiveness of vaccination are most dependent on the speed at which the pandemic grows. Our finding that earlier vaccination saves more costs and averts more deaths may be most important for areas with faster growth of the pandemic. Of note, the virus is not spreading at the same rate throughout the United States but appears to be evolving as different regional and local epidemics (13–15, 44–53). Several factors may delay the peak of the pandemic, leaving a greater proportion of the population susceptible to infection and increasing the effectiveness and cost-effectiveness of vaccination later in the autumn. Viral characteristics that would delay the peak include a lower reproductive rate, a longer incubation period, and a shorter duration of infectiousness. Nonpharmaceutical interventions could also have a marked effect on the speed at which the pandemic grows: Our analysis shows that increased implementation of highly effective nonpharmaceutical interventions, such as early use of hand hygiene and surgical masks (61), can significantly delay the peak of the pandemic, increasing the effectiveness and cost-effectiveness of delayed vaccination.
In contrast, if the epidemic grows rapidly and peaks in October, as some authorities have suggested (92), vaccination becomes substantially less effective and less cost-effective. Our analysis evaluated an epidemic with a peak later than October, but we also evaluated the alternative scenario of an early peaking epidemic. Both are plausible, and the possibility of an epidemic that peaks as early as October underscores the urgency of rapid vaccine distribution.
The U.S. Food and Drug Administration is awaiting results of ongoing trials of adjuvanted and nonadjuvanted pandemic (H1N1) to determine which type of vaccine to administer (91). We chose to examine an adjuvanted vaccine as a conservative estimate in light of potentially increased vaccine side effects and costs with an adjuvant. However, we examined vaccine efficacy consistent with nonadjuvanted pandemic (H1N1) trial results (67), so the decision to use nonadjuvanted vaccine would not affect our conclusions other than decreasing costs, as we describe in our sensitivity analyses.
Although complete population coverage with an effective vaccine for pandemic (H1N1) may be desirable (93), this goal does not seem to be logistically feasible for the autumn vaccination campaign. Our analysis suggests that over a wide range of viral reproductive rates and pandemic growth scenarios, vaccinating up to 44% of the population can be sufficient to slow widespread viral transmission by inducing herd immunity within the population, thereby shortening the pandemic.
We assumed that severe pandemic (H1N1) vaccine side effects could occur in 1 in 100 000 vaccinated individuals (73). Under these assumptions, vaccinating 40% of the city's population in November would cause approximately 1 death secondary to severe vaccine side effects for every 884 lives saved from vaccination. We emphasize that our analysis assumes that vaccination would not cause additional harms, and we encourage thorough testing and evaluation of vaccines before large-scale vaccination campaigns (94).
Key limitations of the analysis include an assumption that disease transmission occurs with homogenous mixing; all individuals, regardless of age and occupation, have the same frequency of contacts, and our model is not designed to make recommendations about the effects of prioritizing vaccination for different groups. In the 1918 and 1957 pandemics, influenza was transmitted more readily in children in proximity, such as in schools (95). If this pattern occurs in the 2009 (H1N1) pandemic, heterogeneous mixing would result in a faster initial spread of the pandemic, followed by slowing as it spreads to groups with decreased contact rates (96). Although individuals older than 60 years are most likely to have preexisting immunity to the virus (3), we assume uniform immunity across age groups. Our analysis provides insights into the magnitude of the pandemic and the response to vaccination (97); however, policymakers may wish to prioritize vaccination on the basis of differing patterns of transmission in specific age groups, as well as groups noted to have higher morbidity and mortality from pandemic (H1N1) infection.
We did not account for all costs to uninfected individuals in the setting of the 2009 (H1N1) pandemic; costs incurred by uninfected individuals from school and workplace closures, decreased tourism and group recreation, and loss of institution-specific knowledge may be greater than costs to sick individuals (98). We did not include potential savings of effective vaccination, such as limiting displacement of hospitalized patients, or decreasing school and workplace closures. However, including these costs and savings would make vaccination even more cost-effective or cost-saving. In addition, we account for normal health care expenditures, which significantly increase total costs for each life saved through vaccination; not including costs of long-term normal health care in our analysis would also make pandemic (H1N1) vaccination more cost-saving.
Covering most of the population with an effective vaccine for pandemic (H1N1) would prevent the most illness and death from influenza but will not be achievable within the short time frame for vaccine development and with projected supplies (69, 90). Our analysis suggests that vaccination can be a valuable and effective intervention even if it reaches less than half the population. Many uncertainties remain about the transmissibility and mortality of pandemic (H1N1) 2009; however, absent serious vaccine side effects, vaccination earlier in the autumn is likely to be cost-saving and avert more deaths than later vaccination. This highlights the urgency of vaccine development, with attention to safety. On 24 June 2009, President Obama signed into law an emergency spending bill devoting $2 billion in additional funding to 2009 (H1N1) pandemic mitigation efforts (99); our analyses suggest that vaccination strategies could be a valuable component of such efforts.
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January 14, 2010
H1N1 Deaths and Lightning Strikes
TO THE EDITOR: Last month's article by Khazeni et al. (1) concludes that H1N1 vaccination of 40% of the American population would "...avert 2051 deaths...and save $469 million dollars." They recommend that earlier vaccination would save more lives and lead to additional savings during an H1N1 pandemic. The specificity of the number of lives saved and dollars saved made me curious about the specificity of the H1N1 data used for these calculations. In this study, the H1N1 positive cases were "based on a New York City telephone survey of influenza-like illness...and ...CDC data on cases of influenza-like illness testing positive for pandemic H1N1(1)." I visited the CDC website to review their definition of positive H1N1 cases, and was surprised to find fewer laboratory confirmed cases of H1N1 deaths than I expected. While the laboratory confirmed cases of H1N1 related death were relatively low, the CDC FluView website reported an increased number of doctor visits for influenza-like illness (ILI) in 2009 as compared to 2008 (2). Are we comfortable using ICD-9 codes for "influenza-like illness" to define an H1N1 infection after a massive TV campaign depicting a deadly viral pandemic sweeping the country? I checked the CDC data from April-October 2009 which described only 95 laboratory confirmed H1N1 deaths in American children (3). They mentioned that fewer than half of these cases were tested for bacterial infection, and reported that Staphylococcus was identified in many of the cases that were tested (3). Did these children die from secondary Staphylococcal septocemia or H1N1 viral pneumonia? Lets assume that bacterial sepsis had nothing to do with these deaths, and 95 American children died from laboratory confirmed H1N1 between April and October. This data from the CDC confirms that approximately 15 of the 74 million American children (0.00002%) died from laboratory confirmed H1N1 infection per month nationwide and that 99.99998% did not. Is this a legitimate cause for level 6 pandemic precautions with universal vaccination costing over $4 billion in emergency H1N1 flu vaccine funding (4)? Keep in mind that over 3000 Americans die each month from car accidents (5) and over 3000 die each month from accidental poisonings (6). The National Weather Service reports approximately 400 Americans experience lightning strikes per year (7), so one could argue that Americans are twice as likely to be struck by lightening than their children are likely to die from laboratory confirmed H1N1 viral infection.
Last month, Harvard's School of Public Health in conjunction with the UK Medical Research Council published data confirming that "...the severity of the H1N1 flu may be less than initially feared" with far fewer deaths than the 36,000 that are typical of an average flu season in America (8). In closing, it would be helpful for those of us who sometimes read only the abstract of an important article such as this one to have the source of the data more clearly defined in the abstract. In this case, the majority of the H1N1 data was not derrived from laboratory testing. A better conclusion might be that it is impossible for even the most accomplished medically-trained professors, clinicians, and statisticians to accurately determine the efficacy of a vaccine in terms of lives saved or potential cost savings if we do not insist on more thorough testing of hospitalized patients with ILI during purported pandemics.
1.) Khazeni N, Hutton DW, Garber AM, Hupert N, Owens DK. Effectiveness and cost-effectiveness of vaccination against pandemic influenza (H1N1) 2009. Ann Intern Med. 2009 Dec 15;151(12):829-39. PubMed PMID: 20008759.
5.) http://www.forbes.com/2009/01/21/car-accident-times-forbeslife- cx_he_0121driving.html
7.) http://geology.com/news/2009/numerous-deaths-by-lightning- strikes.shtml
8.) http://www.newsrx.com/print.php?articleID=1715210 Cauchemez S, Donnelly CA, Reed C, Ghani AC, Fraser C, Kent CK, Finelli L, Ferguson NM. Household transmission of 2009 pandemic influenza A (H1N1) virus in the United States. N Engl J Med. 2009 Dec 31;361(27):2619-27.
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