Dataset Information

Excess mortality impact of two epidemics of pandemic influenza A(H1N1pdm09) virus in Hong Kong.

ABSTRACT: Hong Kong experienced two large epidemics of pandemic influenza A(H1N1pdm09). We used regression methods to estimate the excess mortality associated with each epidemic. The first epidemic of H1N1pdm09 peaked in September 2009 and was associated with 2·13 [95% confidence interval (CI): -8·08, 11·82] excess all-cause deaths per 100 000 population. The second epidemic of H1N1pdm09 in early 2011 was associated with 4·72 [95% CI: -0·70, 10·50] excess deaths per 100 000 population. More than half of the estimated excess all-cause deaths were attributable to respiratory causes in each epidemic. The reasons for substantial impact in the second wave remain to be clarified.

SUBMITTER: Wu P

PROVIDER: S-EPMC3877676 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Excess mortality impact of two epidemics of pandemic influenza A(H1N1pdm09) virus in Hong Kong.

Wu Peng P Goldstein Edward E Ho Lai-Ming LM Wu Joseph T JT Tsang Thomas T Leung Gabriel M GM Cowling Benjamin J BJ

Influenza and other respiratory viruses 20131107 1

Hong Kong experienced two large epidemics of pandemic influenza A(H1N1pdm09). We used regression methods to estimate the excess mortality associated with each epidemic. The first epidemic of H1N1pdm09 peaked in September 2009 and was associated with 2·13 [95% confidence interval (CI): -8·08, 11·82] excess all-cause deaths per 100 000 population. The second epidemic of H1N1pdm09 in early 2011 was associated with 4·72 [95% CI: -0·70, 10·50] excess deaths per 100 000 population. More than half of t ...[more]

PMID: 24373289

Similar Datasets

Project description:BackgroundInfluenza epidemics have a substantial impact on human health, by increasing the mortality from pneumonia and influenza, respiratory and circulatory diseases, and all causes. This paper provides estimates of excess mortality rates associated with influenza virus circulation for 7 causes of death and 8 age groups in Portugal during the period of 1980-2004.Methodology/principal findingsWe compiled monthly mortality time series data by age for all-cause mortality, cerebrovascular diseases, ischemic heart diseases, diseases of the respiratory system, chronic respiratory diseases, pneumonia and influenza. We also used a control outcome, deaths from injuries. Age- and cause-specific baseline mortality was modelled by the ARIMA approach; excess deaths attributable to influenza were calculated by subtracting expected deaths from observed deaths during influenza epidemic periods. Influenza was associated with a seasonal average of 24.7 all-cause excess deaths per 100,000 inhabitants, approximately 90% of which were among seniors over 65 yrs. Excess mortality was 3-6 fold higher during seasons dominated by the A(H3N2) subtype than seasons dominated by A(H1N1)/B. High excess mortality impact was also seen in children under the age of four years. Seasonal excess mortality rates from all the studied causes of death were highly correlated with each other (Pearson correlation range, 0.65 to 0.95, P<0.001) and with seasonal rates of influenza-like-illness (ILI) among seniors over 65 years (Pearson correlation rho>0.64, P<0.05). By contrast, there was no correlation with excess mortality from injuries.Conclusions/significanceOur excess mortality approach is specific to influenza virus activity and produces influenza-related mortality rates for Portugal that are similar to those published for other countries. Our results indicate that all-cause excess mortality is a robust indicator of influenza burden in Portugal, and could be used to monitor the impact of influenza epidemics in this country. Additional studies are warranted to confirm these findings in other settings.

Project description:BACKGROUND:Serial cross-sectional data on antibody levels to the 2009 pandemic H1N1 influenza A virus from a population can be used to estimate the infection attack rates and immunity against future infection in the community. METHODS:From April through December 2009, we obtained 12,217 serum specimens from blood donors (aged 16-59 years), 2520 specimens from hospital outpatients (aged 5-59 years), and 917 specimens from subjects involved in a community pediatric cohort study (aged 5-14 years). We estimated infection attack rates by comparing the proportions of specimens with antibody titers ? 1:40 by viral microneutralization before and after the first wave of the pandemic. Estimates were validated using paired serum samples from 324 individuals that spanned the first wave. Combining these estimates with epidemiologic surveillance data, we calculated the proportion of infections that led to hospitalization, admission to the intensive care unit (ICU), and death. RESULTS:We found that 3.3% and 14% of persons aged 5-59 years had antibody titers ? 1:40 before and after the first wave, respectively. The overall attack rate was 10.7%, with age stratification as follows: 43.4% in persons aged 5-14 years, 15.8% in persons aged 15-19 years, 11.8% in persons aged 20-29 years, and 4%-4.6% in persons aged 30-59 years. Case-hospitalization rates were 0.47%-0.87% among persons aged 5-59 years. Case-ICU rates were 7.9 cases per 100,000 infections in persons aged 5-14 years and 75 cases per 100,000 infections in persons aged 50-59 years, respectively. Case-fatality rates were 0.4 cases per 100,000 infections in persons aged 5-14 years and 26.5 cases per 100,000 infections in persons aged 50-59 years, respectively. CONCLUSIONS:Almost half of all school-aged children in Hong Kong were infected during the first wave. Compared with school children aged 5-14 years, older adults aged 50-59 years had 9.5 and 66 times higher risks of ICU admission and death if infected, respectively.

Dataset Information

Excess mortality impact of two epidemics of pandemic influenza A(H1N1pdm09) virus in Hong Kong.

Publications

Excess mortality impact of two epidemics of pandemic influenza A(H1N1pdm09) virus in Hong Kong.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets