The infection fatality risk of pandemic influenza A (H1N1) in Hong Kong in 2009

Abstract

BACKGROUND: One measure of the severity of a pandemic influenza at an individual level is the risk of death among people infected by the new virus. However, there are complications in estimating both the numerator and denominator. Regarding the numerator, statistical estimates of the excess deaths associated with influenza virus infections tend to exceed the number of deaths associated with laboratory-confirmed infection. Regarding the denominator, few infections are laboratory confirmed, while differences in case definitions and approaches to case ascertainment can lead to wide variation in case fatality risk estimates. Serologic surveillance can be used to estimate the cumulative incidence of infection as a denominator that is more comparable across studies. MATERIALS AND METHODS: We compared excess death estimates based on different measures of influenza activity, including pH1N1 incidence rates, influenza-like illness (ILI) data, laboratory (LAB) data, and ILI×LAB data. pH1N1 incidence rates were estimated by deconvoluting the pH1N1 hospitalizations allowing for the delay from infection to hospitalization and scaling to serial cross-sectional serologic data. We applied time series regression models to all-cause mortality rates from 2001-2009 and included one measure of influenza activity as a covariate, and also adjusted for covariates including seasonal influenza activity, respiratory syncytial virus activity, mean temperature, and absolute humidity. Trigonometric components were included to allow for cyclic annual seasonality. We defined the infection fatality risk (IFR) as the number of influenza-associated deaths divided by the number of infections in a population, and the IFRc and IFRe as the IFR where numerators are based on deaths of confirmed cases and estimated excess deaths respectively. RESULTS: We estimated that the first wave of 2009 H1N1 was associated with approximately 232 excess deaths (95% CI, 136-328 excess deaths) in all ages in Hong Kong, mainly in the elderly. The point estimates of the risk of death on a per-infection basis increased substantially with age, from below 1 per 100,000 infections in children to 1100 per 100,000 infections in those 60-69 y of age. We compared the correlation between the ILI data, the LAB data, and ILI×LAB data versus age-standardized incidence rates. ILI data tended to overestimate lower levels of pH1N1 incidence, as did the LAB data to a lesser extent, while the product of ILI×LAB had the strongest correlation with pH1N1 incidence. CONCLUSIONS: The ILI×LAB proxy was highly correlated with the estimated pH1N1 incidence rates suggesting that it may be a better proxy of influenza activity than either the ILI or LAB data alone. Substantial variation in the age-specific infection fatality risk complicates comparison of the severity of different influenza strains.