. "A7 The Severity of Pandemic H1N1 Influenza in the United States, from April to July 2009: A Bayesian Analysis." The Domestic and International Impacts of the 2009-H1N1 Influenza A Pandemic: Global Challenges, Global Solutions: Workshop Summary. Washington, DC: The National Academies Press, 2010.
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The Domestic and International Impacts of the 2009-H1N1 Influenza a Pandemic: Global Challenges, Global Solutions - Workshop Summary
tember 2009), but national and international authorities have acknowledged that these counts are substantial underestimates, reflecting an inability to identify, test, confirm, and report many cases, especially mild cases. Severity of infection may be measured in many ways, the simplest of which is the case-fatality ratio (CFR), the probability that an infection causes death. Other measures of severity, which are most relevant to the burden a pandemic exerts on a health care system, are the case-hospitalization and case-intensive care ratios (CHR and CIR, respectively), the probabilities that an infection leads to hospitalization or intensive care unit (ICU) admission. In the absence of a widely available and validated serologic test for infection, it is impossible to estimate these quantities directly, and in this report we instead focus on the probabilities of fatality, hospitalization, and ICU admission per symptomatic case; we denote these ratios sCFR, sCHR, and sCIR respectively.
Although it is difficult to assess these quantities, estimates of their values and associated uncertainty are important for decision making, planning, and response during the progression of this pandemic. Initially, some national and international pandemic response plans were tied partly to estimates of the CFR, but such plans had to be modified in the early weeks of this pandemic, as it became clear that the CFR could not at that time be reliably estimated (Lipsitch et al., 2009a). Costly measures to mitigate the pandemic, such as the purchase of medical countermeasures and the use of disruptive social distancing strategies may be acceptable to combat a more severe pandemic but not to slow a milder one. While past experience (Jordan et al., 1958) and mathematical models (Ferguson et al., 2006; Halloran et al., 2008; Mills et al., 2004) suggest that between 40% and 60% of the population will be infected in a pandemic with a reproduction number similar to those seen in previous pandemics, the number of deaths and the burden on the health care system also depend on the age-specific severity of infection, which varies by orders of magnitude between pandemics (Miller et al., 2008) and even between different waves in the same pandemic (Andreasen et al., 2008). Reports from the Southern Hemisphere suggest that a relatively small fraction of the population experienced symptomatic pH1N1 infection (7.5% in New Zealand, for example; Baker et al., 2009), although these numbers are considered highly uncertain (Baker et al., 2009). On the other hand, primary care utilization for influenza-like illness (ILI) has been considerably higher than in recent years (Baker et al., 2009), and anecdotal reports in the Southern Hemisphere have indicated that some intensive care units (ICUs) have been overwhelmed and surgery postponed due to a heavy burden of pH1N1 cases (Bita, 2009; Newton, 2009).
The problem of estimating severity of pH1N1 infection includes the problem of estimating how many of the infected individuals in a given population and time period subsequently develop symptoms, are medically attended, hospitalized, admitted to ICU, and die due to infection with the virus. No large jurisdiction in the world has been able to maintain an accurate count of total pH1N1 cases once