«The Economic Impact of Pandemic Influenza in the United States: Priorities for Intervention Martin I. Meltzer, Nancy J. Cox, and Keiji Fukuda Centers ...»
Publisher: CDC; Journal: Emerging Infectious Diseases
Article Type: Research; Volume: 5; Issue: 5; Year: 1999; Article ID: 99-0507
DOI: 10.321/eid0505.990507; TOC Head: Research
The Economic Impact of Pandemic
Influenza in the United States: Priorities for
Martin I. Meltzer, Nancy J. Cox, and Keiji Fukuda
Centers for Disease Control and Prevention, Atlanta, Georgia,
Suggested citation: Meltzer MI, Cox NJ, Fukuda K. The Economic Impact of Pandemic
Influenza in the United States: Priorities for Intervention. Emerg Infect Dis [serial on the Internet]. 1999, Oct [date cited]. Available from http://www.cdc.gov/ncidod/eid/vol5no5/meltzer.htm Appendix II This paper provides additional information on methods, data, and results and is intended to supplement the above referenced published paper.
Table of Contents Introduction Methods Objectives Modeling approach Gross attack rates Age group distribution of number of cases Percentages of high risk cases Total numbers of cases Health outcomes: Four categories Health outcomes: Deaths Non-high risk groups
High risk groups:
Publisher: CDC; Journal: Emerging Infectious Diseases Article Type: Research; Volume: 5; Issue: 5; Year: 1999; Article ID: 99-0507 DOI: 10.321/eid0505.990507; TOC Head: Research Comparisons with other data sets Health outcomes: Hospitalizations
Comparisons with other data sets:
Health outcomes: Outpatient (ambulatory care)
Non-high risk groups:
High risk groups:
Comparisons with other data sets: Hospitalizations and outpatient visits:
Health outcomes: Ill (no formal medical care) Converting from general population rates to rates per clinically ill population Resource use: Direct medical costs: Database Resource use: Standardizing year of costs to 1995 Resource use: Valuing death due to influenza Resource use: Inpatient care associated with death Resource use: Hospitalized patients Resource use: Outpatients Resource use: Drug claims related to outpatient visits Resource use: Ill, no medical care sought Resource use: Correlations between Calculating the economic impact of an influenza pandemic Returns to vaccination: Cost of vaccination Returns to vaccination: Vaccine effectiveness Returns to vaccination: Net returns of vaccinating against influenza Returns to vaccination: Sensitivity analyses Implications for policy:"Insurance premiums" and the three P's,/li Implications for policy: Using different criteria Implications for policy: Four scenarios Results Deaths Hospitalizations Outpatients Ill (no formal medical care sought) Burden of impact among the high risk groups Publisher: CDC; Journal: Emerging Infectious Diseases Article Type: Research; Volume: 5; Issue: 5; Year: 1999; Article ID: 99-0507 DOI: 10.321/eid0505.990507; TOC Head: Research Burden of death by age group Estimated economic costs due to Distribution of economic impact: Direct and indirect costs Net value of vaccinating Sensitivity analyses Implications for policy: "Insurance premiums" Implications for policy: Using different criteria Implications for policy: Four scenarios Discussion and conclusions Impact of an influenza pandemic Returns to vaccination Relative importance of input variables Implications for policy: "Insurance premiums" for the 3P's Implications for policy: Setting priorities Implications for policy: Four scenarios Summary of some main points References Tables Table 1: U.S. population estimates Table 2: Two scenarios of age distributions Table 3: Variables used Table 4: International Classification of Diseases Table 5: Average age of patients Table 6: Average inpatient and outpatient charges Table 7: Number and frequency of outpatient visits Table 8: Average age of patients and costs Table 9: Average payments (standard deviations) for drug claims Table 10: Pearson correlation coefficients1 Table 11: Inputs used to calculate the economic impact (direct and indirect costs) of an influenza pandemic in the U.S. (1995 U.S.$) Publisher: CDC; Journal: Emerging Infectious Diseases Article Type: Research; Volume: 5; Issue: 5; Year: 1999; Article ID: 99-0507 DOI: 10.321/eid0505.990507; TOC Head: Research Table 12: Two scenarios for the cost of vaccination Table 13: Two scenarios for vaccine effectiveness Table 14: Four scenarios Table R1: Burden of impact Table R2: Distribution, by age group, of deaths Table R3: Economic impact (direct and indirect costs) of influenza pandemic Table R4: Proportion of costs Table R5: Proportion of total costs attributable to deaths by risk category Table R6: Total net value of vaccinating against an influenza pandemic (Age distribution scenario A, "high" level of vaccine effectiveness) Table R7: Total net value of vaccinating against an influenza pandemic (Age distribution scenario B, "high" level of vaccine effectiveness) Table R8: Total net value of vaccinating against an influenza pandemic (Age distribution scenario A, "low" level of vaccine effectiveness) Table R9: Total net value of vaccinating against an influenza pandemic (Age distribution scenario B, "low" level of vaccine effectiveness) Table R10: The annual "insurance premium" for planning, preparing and practicing to respond
to the next influenza pandemic:
Table R11: Setting priorities:
Figure legends Figure 1: Frequency of outpatient visits per patient for influenza, pneumonia and acute bronchitis: 1993-95 Figure 2: Impact of influenza pandemic in the United States: Mean, minimum, maximum, 5th and 95th percentiles of total deaths and hospitalizations for different gross attack rates Figure 3: Impact of influenza pandemic in the United States: Mean, minimum, and maximum of total outpatients and those ill (but not seeking formal medical care) for different gross attack rates Publisher: CDC; Journal: Emerging Infectious Diseases Article Type: Research; Volume: 5; Issue: 5; Year: 1999; Article ID: 99-0507 DOI: 10.321/eid0505.990507; TOC Head: Research Figure 4: Sensitivity analysis: Mean net returns to vaccination, by age group, for different death rates, vaccine effectiveness, and percentage compliance: Non-high risk patients Figure 5: Four options to respond to an influenza pandemic: Mean net economic returns "A thousand years in the making, the religion of technology has become the common enchantment, not only of the designers of technology but also of those caught up in, and undone by, their godly designs.... This popular faith, subliminally indulged and intensified by corporate, government, and media pitchmen, inspires an awed deference to the practitioners and their promises of deliverance while diverting attention from more urgent concerns.... Pleas for some rationality, for reflection about pace and purpose, for sober assessment of costs and benefits - for evidence even of economic value, much less larger social gains - are dismissed as irrational. From within the faith, any and all criticism appears irrelevant, and irreverent."
Noble, D. The religion of technology. New York, NY: Alfred Knopf, 1997, p.207.
Introduction Although there has been a number of authors have examined or reviewed the economics of influenza vaccination (6,20,22-26,35) only one previous study (5) appears in the literature that examines the economics of a planned intervention aimed at reducing the impact of an influenza epidemic in the United States. The overall objective of this study is to examine the possible economic impact of the next influenza pandemic in the United States, and then use those results to analyze the costs and benefits of interventions designed to reduce the impact. These estimates can then be used in the development of national and state-level plans to respond to an influenza pandemic.1 The model can also be used to examine the economics of various strategies and options that might be available for the use of influenza vaccines.
The specific objectives of the modeling effort are to:
1) provide a range of estimates regarding the number of deaths, hospitalizations, outpatients, and those ill, but not seeking medical care;
2) provide a dollar estimate of the impacts;
3) estimate the potential net present value of some possible vaccination strategies;(2) Publisher: CDC; Journal: Emerging Infectious Diseases Article Type: Research; Volume: 5; Issue: 5; Year: 1999; Article ID: 99-0507 DOI: 10.321/eid0505.990507; TOC Head: Research
4) evaluate the impact of using different criteria (e.g., death rates, economic returns to vaccination) to set vaccination priorities;
5) assess the economic impact of various scenarios regarding the total number of doses of vaccine administered, and the distribution of vaccine among different age and risk groups; and,
6) calculate an "insurance premium" that could reasonably be spent each year on planning, preparedness and practice (the 3 P's).
The intent is not to provide "the" estimate of impact, but rather to examine the effect of altering a number of variables, and evaluating how the results may effect key decisions. For example, if influenza-related deaths rates among 20-64 years old are assumed to range between 0.0675 - 0.15 per 1,000 persons, would it make economic "sense" (i.e., generate a positive net present value) to vaccinate everybody in this age group if a pandemic had an overall (gross) attack rate of, say, 15 percent? If not, would the results change if, say, the maximum death rate were doubled to 0.30 per 1,000? If doubling still does not generate a positive net present value for a "vaccinate all" strategy, then decision makers would be aware that the decision to vaccinate all 20-64 years of age would rest on a valuation of the intangibles, such as the reduction in fear of death due to influenza, and the value of human life above and beyond the economic value of lost productivity.
Mathematically modeling the spread of, and numbers affected by, influenza is a difficult task.
Differences in virology, lack of understanding of how influenza is actually spread in a community, and lack of adequate population-based data are some of the factors that have hampered efforts to produce realistic estimates of the numbers of cases that may be caused by the next influenza pandemic.8 Therefore, in the face of a great deal of uncertainty regarding the possible impact of an influenza pandemic, we used a Monte Carlo simulation approach. In Monte Carlo simulations, uncertainty is explicitly allowed for by using pre-specified probability distributions to describe the range and frequency of probable values of key variables (9-11). The model is run for several iterations, often 1,000 or more, and during each iteration values for each variable are drawn from their probability distributions. The results from all the iterations are then pooled and descriptive statistics (e.g., average, median, mode, 5th and 95th percentiles) can be calculated. For this paper, the impact of some variables, such as attack rates, cost of vaccine, and numbers effectively vaccinated, were examined at pre-determined intervals over fixed ranges, with values for other variables chosen from pre-determined probability distributions.
Gross attack rates
We defined gross attack rate as the number of clinical cases of illness (i.e., not infections) caused by influenza per unit population. Persons who become infected but show no symptoms or only very mild symptoms, such as a headache or mild nausea, are deemed not to have an economically important case of influenza (although such infections may have important epidemiological consequences). Because nobody can predict with any great certainty the attack rate of a pandemic, we modeled a range of attack rates, from 15 to 35 percent, in steps of 5 Publisher: CDC; Journal: Emerging Infectious Diseases Article Type: Research; Volume: 5; Issue: 5; Year: 1999; Article ID: 99-0507 DOI: 10.321/eid0505.990507; TOC Head: Research percent. The number of cases generated by a given attack rate was distributed among the U.S.
population first by age and then by "high risk" status (see later).
Age group distribution of number of cases
The U.S. population (1) for 1997 was categorized into 3 age groups, 0-19 years of age, 20-64 years of age, and 65 years of age and older (Table 1). Using only three age groups simplifies modeling, and the oldest age group matches a defined "target" group for vaccination during interpandemic years (2). Since the actual age distribution of cases during an influenza pandemic is unknown, we calculated two age-related distributions of cases, or scenarios (Table 2).
Percentages of high risk cases
There are a proportion of persons who, because they have a pre-existing medical condition, are deemed as being at a higher risk of contracting an influenza-related illness with a serious health outcome (defined later). For the total U.S. population, we used lower and upper age-weighted averages of 15.4 and 24.8 percent (Table 2). These estimates are similar to the 22.5 percent figure quoted by Schoenbaum et al. (5), and the 19.6 percent for 1970-1978 used by the Office of Technology Assessment (OTA) study (6).