Multiplier to estimate ICU-level flu-related hospitalizations

Flu-related hospitalizations represent a broad range of disease severity, with associated requirements for clinical care, including hospital personnel, equipment, supplies, and other resources. The most severe cases may be admitted to an intensive care unit (ICU) or its equivalent, where they can receive the highest standards of care. Not all countries or setting utilize ICUs, so “ICU-level” refers to the disease severity that would warrant admission to an ICU or similar high-level treatment unit, regardless of whether that patient is actually admitted to an ICU.

The Flu Tool applies a multiplier to extrapolate from hospitalized cases in order to generate an estimate for the number of ICU-level flu hospitalizations. Default values are pre-filled below. You may use the default values or edit, as needed, to reflect your hospitalization estimates. If you choose to enter a custom multiplier, you must also enter confidence intervals.

 
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More about the default multipliers

The default multipliers have been calculated based on a systematic review of the peer-reviewed literature. Multipliers are applied to the number of flu-related hospitalizations to generate estimates of flu cases at other levels of disease severity (e.g., deaths, ICU-level hospitalizations, mild/moderate cases) and accompanying confidence intervals. Additional detail about the methodology is available here.

How would I calculate my own multipliers?

Multipliers are estimates based on analysis of data for flu-related hospitalizations and at least one other level of disease severity (e.g., deaths, ICU-level hospitalizations, mild/moderate cases). If you have reliable data, it is possible to generate your own multiplier by determining the ratio between the number of flu-related hospitalizations and number of cases for the other disease severity level. Additional information is available here (Methods section). The strength of the default multipliers is that they are calculated based on multiple studies from around the world; however, generating custom multipliers using data specific to your country or region could provide more accurate estimates.