Expansion factor for under-detected flu-related hospitalizations

Under-detection of hospitalized cases might occur due to insufficient testing or surveillance, or other factors that result in missed cases. If you think this applies, you may choose to include an expansion factor to help the Flu Tool correct for under-detection and/or under-reporting in the data you have entered. You may choose to not include an expansion factor, use the default expansion factor derived from peer-reviewed literature, or apply your own custom expansion factor.

Do you think your data underestimate your population’s flu-related hospitalizations?

No, I do not need to correct.

Yes, I want to correct with an expansion factor.

More about the default expansion factors

These expansion factors are intended to correct for under-detection of hospitalized cases. The default values are derived from peer-reviewed literature, specifically from studies that examined under-detection of flu-related hospitalizations.

Hospitalization expansion factor data sources:

Hughes MM, Carmack AE, McCaffrey K, et al. Estimating the incidence of influenza at the state level – Utah, 2016-17 and 2017-18 influenza seasons. MMWR Morb Mortal Wkly Rep. 2019;68(50):1158-1161. doi:10.15585/mmwr.mm6850a2
Reed C, Chaves SS, Kirley PD, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. doi:10.1371/journal.pone.0118369
Schanzer DL, Saboui M, Lee L, Nwosu, Bancej C. Burden of influenza, respiratory syncytial virus, and other respiratory viruses and the completeness of respiratory viral identification among respiratory inpatients, Canada, 2003‐2014. Influenza Other Respir Viruses. 2018;12(1):113-121. doi:10.1111/irv.12497

What is the difference between a ‘multiplier’ and an ‘expansion factor’?

Multipliers and expansion factors are similar and are calculated in the same way. However, we use the term ‘multiplier’ in this tool when extrapolating from one flu severity level to another (e.g., estimating deaths from hospitalization data). We use the term ‘expansion factor’ in the tool when the user wants to account for missed cases or deaths in their own data entered at the start of the tool (e.g., to account for hospitalized cases that may not be represented in surveillance data).

How would I calculate my own expansion factors?

Burden of disease models often multiply the reported number of flu cases/deaths by an expansion factor to correct for under-detection and under-reporting in order to improve the accuracy of estimates. For example, if you determine that there is likely 1 flu-related hospitalization that is undetected for every 1 hospitalization recognized by surveillance in each age group, then you would enter a “1.0” for each age group.