Skip directly to site content Skip directly to page options Skip directly to A-Z link Skip directly to A-Z link Skip directly to A-Z link
Volume 5, Number 5—October 1999
Research

The Economic Impact of Pandemic Influenza in the United States: Priorities for Intervention

Martin I. MeltzerComments to Author , Nancy J. Cox, and Keiji Fukuda
Author affiliations: Centers for Disease Control and Prevention, Atlanta, Georgia, USA

Main Article

Table 3

Input variables used to calculate the economic impact (direct and indirect costs) of health outcomes due to an influenza pandemic in the United States (in 1995 US$)

Outcome category item Type of cost Age group (yrs)
 Sources
0-19 20-64 65+
Deaths
Average age (years) 9 35 74 Assumed
PV earnings lost ($)a Indirect 1,016,101 1,037,673 65,837 16, 30
Most likely + min or maxhospital costs ($)b Direct 3,435 +2,632 7,605 +3,888 8,309 +3,692 Marketscan Database; 31.
Subtotal ($)c 1,019,536 1,045,278 74,146
Hospitalizations
Most likely + min or maxhospital costs ($)b Direct 2,936 +2,099 6,016 +2,086 6,856 +3,200 Marketscan Database; 31.
Most likely + min or max net pay for outpatient visits ($)d Direct 74 + 40 94 + 70 102 + 60 Marketscan Database; 31.
Avg. copayment for outpatient visit ($) Direct 5 4 4 Marketscan Database
Most likely + min or max net payment for drug claims($)e Direct 26 + 9 42 + 30 41 + 10 Marketscan Database
Most likely + min or max days lostf Indirect 5 + 2.7 8 + 4.8 10 +5.4 Marketscan Database; 31.
Value 1 day lost ($)g Indirect 65 100 or 65 65 30
Subtotal ($)c 3,366 6,842 7,653
Outpatient visits
Avg. no. visitsh Direct 1.52 1.52 1.52 Marketscan Database
Most likely + min or max net payment per visit($)i Direct 49 +13 38 + 12 50 + 16 Marketscan Database
Avg. copayment for outpatient visit ($) Direct 5 4 4 Marketscan Database
Most likely + min or max net payment per prescription($)j Direct 25 + 18 36 + 27 36 + 22 Marketscan Database
Avg. prescriptions per visit Direct 0.9 1.8 1.4 Marketscan Database
Avg. copayment per prescription ($) Direct 3 3 3 Marketscan Database
Days lost Indirect 3 2 5 4,5
Value 1 day lost ($)g Indirect 65 100 65 30
Subtotal ($)c 300 330 458
Ill, no medical care sought
Days lost Indirect 3 2 5 4,5
Value 1 day lost ($)g Indirect 65 100 65 30
Over-the-counter drugs ($) Direct 2 2 2 Assumed
Subtotal ($)c 197 202 327

aAverage present value (PV), using a 3% discount rate, of expected future lifetime earnings and housekeeping services, weighted by age and gender (30) and adjusted to 1995 dollars (by multiplying by a factor of 1.07) (16).
bMost likely, with + defining the minimum and maximum costs for a triangular distribution (18) for Monte Carlo analysis (13-15). The values were calculated by using cost data from Marketscan Database (The MEDSTAT Group, Ann Arbor, MI) and multiplying it by a hospital cost-to-charge ratio of 0.53. The latter ratio is a weighted average of the urban and rural (urban = 0.80, rural = 0.20) cost-to-charge ratios calculated by the Health Care Finance Administration for August 1996 (31).
cSubtotals are the totals for each category of outcome, using the most likely estimates.
dMost likely, with minimum and maximum values of net payments for outpatient visits up to 14 days before admission date and up to 30 days after discharge date.
eNet payment for drug claims associated with outpatient visits up to 14 days before admission and up to 30 days after discharge.
fMost likely, with + defining the minimum and maximum days lost due to hospitalization for a triangular distribution (18) for Monte Carlo analysis (13-15). Calculated using length of stay in hospital data from Marketscan Database (The MEDSTAT Group, Ann Arbor, MI) and adding a total of one additional day for convalescence and pre- and posthospitalization outpatient visits for 0-19 and 20-64 years of age. For 65 + years, two additional days were added to length of stay in hospital for convalescence and pre- and posthospitalization outpatient visits.
gFor 0-19 and 65+ years age groups, a day lost to influenza was valued as equivalent to an unspecified day (30), denoting a value for time lost by care givers and family members related to taking care of a patient in these age groups. For 20-64 years of age, 60% of days lost due to hospitalizations and related convalescence and pre- and posthospitalization outpatient visits were valued as day off work ($100/day). The remaining 40% of days lost were valued as unspecified days ($65/day). For 20-64 years of age, when patients were not hospitalized at any point during their illness (i.e., outpatient status), all days lost were assumed days off work ($100/day).
hThe number of visits per episode of influenza is an average across all age groups. From the database, it was found that 85% of all patients had less than three outpatient visits, with an average of 1.52 visits (Appendix 2).
iMost likely, with minimum and maximum values of net payments for outpatient visits without any specified association to hospitalizations.
jMost likely, with + defining the minimum and maximum cost per prescription, with the number of prescriptions per visit.

Main Article

References
  1. Patriarca  PA, Cox  NJ. Influenza pandemic preparedness plan for the United States. J Infect Dis. 1997;176(Suppl 1):S4–7. DOIPubMedGoogle Scholar
  2. Simonsen  L, Clarke  MJ, Schonberger  LB, Arden  NH, Cox  NJ, Fukuda  K. Pandemic versus epidemic influenza mortality: a pattern of changing age distribution. J Infect Dis. 1998;178:53–60.PubMedGoogle Scholar
  3. Office of Technology Assessment. U.S. Congress. Cost effectiveness of influenza vaccination. Washington: Government Printing Office; 1981.
  4. Kavet  J. A perspective on the significance of pandemic influenza. Am J Public Health. 1977;67:1063–70. DOIPubMedGoogle Scholar
  5. Campbell  DS, Rumley  MA. Cost-effectiveness of the influenza vaccine in a healthy, working-age population. J Occup Environ Med. 1997;39:408–14. DOIPubMedGoogle Scholar
  6. Carrat  F, Valleron  A-J. Influenza mortality among the elderly in France, 1980-90: how many deaths may have been avoided through vaccination? J Epidemiol Community Health. 1995;49:419–25. DOIPubMedGoogle Scholar
  7. Riddiough  MA, Sisk  JE, Bell  JC. Influenza vaccination: cost-effectiveness and public policy. JAMA. 1983;249:3189–95. DOIPubMedGoogle Scholar
  8. Patriarca  PA, Arden  NH, Koplan  JP, Goodman  RA. Prevention and control of type A influenza infections in nursing homes: benefits and costs of four approaches using vaccination and amantadine. Ann Intern Med. 1987;107:732–40.PubMedGoogle Scholar
  9. Schoenbaum  SC. Economic impact of influenza: the individuals perspective. Am J Med. 1987;82(Supp 6A):26–30. DOIPubMedGoogle Scholar
  10. Jefferson  T, Demicheli  V. Socioeconomics of influenza. In: Nicholson KG, Webster RG, Hay AJ, editors. Textbook of influenza. London (UK): Blackwell Science; 1998. p. 541-7.
  11. Schoenbaum  SC, McNeil  BJ, Kavet  J. The swine-influenza decision. N Engl J Med. 1976;295:759–65.PubMedGoogle Scholar
  12. Cliff  AD, Haggett  P. Statistical modelling of measles and influenza outbreaks. Stat Methods Med Res. 1993;2:43–73. DOIPubMedGoogle Scholar
  13. Critchfield  GC, Willard  KE. Probabilistic analysis of decision trees using Monte Carlo simulation. Med Decis Making. 1986;6:85–92. DOIPubMedGoogle Scholar
  14. Dobilet  P, Begg  CB, Weinstein  MC, Braun  P, McNeil  BJ. Probabilistic sensitivity analysis using Monte Carlo simulation: a practical approach. Med Decis Making. 1985;5:157–77. DOIPubMedGoogle Scholar
  15. Dittus  RS, Roberts  SD, Wilson  JR. Quantifying uncertainty in medical decisions. J Am Coll Cardiol. 1989;14:23A–8.PubMedGoogle Scholar
  16. U.S. Bureau of the Census. Statistical abstract of the United States: 1997. 117th ed. Washington: The Bureau; 1997.
  17. Centers for Disease Control and Prevention. Prevention and control of influenza: recommendations of the Advisory Committee on Immunization Practices (ACIP). MMWR Morb Mortal Wkly Rep. 1998;47(RR-6):1–26.PubMedGoogle Scholar
  18. Evans  M, Hastings  N, Peacock  B. Statistical distributions. 2nd ed. New York: John Wiley; 1993.
  19. Glezen  PW. Emerging infections: pandemic influenza. Epidemiol Rev. 1996;18:64–76.PubMedGoogle Scholar
  20. Mullooly  JP, Barker  WH. Impact of type A influenza on children: a retrospective study. Am J Public Health. 1982;72:1008–16. DOIPubMedGoogle Scholar
  21. Barker  WH, Mullooly  JP. Impact of epidemic type A influenza in a defined adult population. Am J Epidemiol. 1980;112:798–813.PubMedGoogle Scholar
  22. Simonsen  L, Clarke  MJ, Williamson  GD, Stroup  DF, Arden  NH, Schonberger  LB. The impact of influenza epidemics on mortality: introducing a severity index. Am J Public Health. 1997;87:1944–50. DOIPubMedGoogle Scholar
  23. Fox  JP, Hall  CE, Cooney  MK, Foy  HM. Influenzavirus infections in Seattle families, 1975-1979. I. Study design, methods and the occurrence of infections by time and age. Am J Epidemiol. 1982;116:212–27.PubMedGoogle Scholar
  24. Glezen  WP, Decker  M, Joseph  SW, Mercready  RG. Acute respiratory disease associated with influenza epidemics in Houston, 1981-1983. J Infect Dis. 1987;155:1119–26.PubMedGoogle Scholar
  25. Serfling  RE. Sherman Il, Houseworth WJ. Excess pneumonia-influenza mortality by age and sex in three major influenza A2 epidemics, United States, 1957-58, 1960 and 1963. Am J Epidemiol. 1967;86:433–41.PubMedGoogle Scholar
  26. Barker  WH, Mullooly  JP. Pneumonia and influenza deaths during epidemics: implications for prevention. Arch Intern Med. 1982;142:85–9. DOIPubMedGoogle Scholar
  27. Glezen  WP, Payne  AA, Snyder  DN, Downs  TD. Mortality and influenza. J Infect Dis. 1982;146:313–21.PubMedGoogle Scholar
  28. McBean  AM, Babish  JD, Warren  JL. The impact and cost of influenza in the elderly. Arch Intern Med. 1993;153:2105–11. DOIPubMedGoogle Scholar
  29. Barker  WH. Excess pneumonia and influenza associated hospitalization during influenza epidemics in the United States, 1970-78. Am J Public Health. 1986;76:761–5. DOIPubMedGoogle Scholar
  30. Haddix  AC, Teutsch  SM, Shaffer  PA, Dunet  DO. Prevention effectiveness. New York: Oxford University Press; 1996.
  31. Fed Regist. 1996;61:46301–2.
  32. Kaufmann  AF, Meltzer  MI, Schmid  GP. The economic impact of a bioterrorist attack: are prevention and postattack intervention programs justifiable? Emerg Infect Dis. 1997;3:83–94. DOIPubMedGoogle Scholar
  33. Robinson  LJ, Barry  PJ. The competitive firm's response to risk. New York: Macmillian; 1987.
  34. Neustadt  RE, Fineberg  HV. The swine flu affair: decision making on a slippery disease. Washington: U.S. Department of Health Education, and Welfare; 1978.

Main Article

Page created: December 15, 2010
Page updated: December 15, 2010
Page reviewed: December 15, 2010
The conclusions, findings, and opinions expressed by authors contributing to this journal do not necessarily reflect the official position of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
file_external