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Mortality attributable to seasonal and pandemic influenza, Australia, 2003 to 2009, using a novel time series smoothing approach.

ABSTRACT: BACKGROUND: Official statistics under-estimate influenza deaths. Time series methods allow the estimation of influenza-attributable mortality. The methods often model background, non-influenza mortality using a cyclic, harmonic regression model based on the Serfling approach. This approach assumes that the seasonal pattern of non-influenza mortality is the same each year, which may not always be accurate. AIM: To estimate Australian seasonal and pandemic influenza-attributable mortality from 2003 to 2009, and to assess a more flexible influenza mortality estimation approach. METHODS: We used a semi-parametric generalized additive model (GAM) to replace the conventional seasonal harmonic terms with a smoothing spline of time ('spline model') to estimate influenza-attributable respiratory, respiratory and circulatory, and all-cause mortality in persons aged <65 and ? 65 years. Influenza A(H1N1)pdm09, seasonal influenza A and B virus laboratory detection time series were used as independent variables. Model fit and estimates were compared with those of a harmonic model. RESULTS: Compared with the harmonic model, the spline model improved model fit by up to 20%. In <65 year-olds, the estimated respiratory mortality attributable to pandemic influenza A(H1N1)pdm09 was 0.5 (95% confidence interval (CI), 0.3, 0.7) per 100,000; similar to that of the years with the highest seasonal influenza A mortality, 2003 and 2007 (A/H3N2 years). In ? 65 year-olds, the highest annual seasonal influenza A mortality estimate was 25.8 (95% CI 22.2, 29.5) per 100,000 in 2003, five-fold higher than the non-statistically significant 2009 pandemic influenza estimate in that age group. Seasonal influenza B mortality estimates were negligible. CONCLUSIONS: The spline model achieved a better model fit. The study provides additional evidence that seasonal influenza, particularly A/H3N2, remains an important cause of mortality in Australia and that the epidemic of pandemic influenza A (H1N1)pdm09 virus in 2009 did not result in mortality greater than seasonal A/H3N2 influenza mortality, even in younger age groups.

SUBMITTER: Muscatello DJ

PROVIDER: S-EPMC3670851 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Mortality attributable to seasonal and pandemic influenza, Australia, 2003 to 2009, using a novel time series smoothing approach.

Muscatello David J DJ Newall Anthony T AT Dwyer Dominic E DE Macintyre C Raina CR

PloS one 20130603 6

<h4>Background</h4>Official statistics under-estimate influenza deaths. Time series methods allow the estimation of influenza-attributable mortality. The methods often model background, non-influenza mortality using a cyclic, harmonic regression model based on the Serfling approach. This approach assumes that the seasonal pattern of non-influenza mortality is the same each year, which may not always be accurate.<h4>Aim</h4>To estimate Australian seasonal and pandemic influenza-attributable morta ...[more]

PMID: 23755139

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Project description:BACKGROUND:Influenza continues to cause seasonal epidemics and pandemics in humans. The burden of influenza is underestimated by traditional laboratory-based surveillance, and modelled estimates are required for influenza-attributable morbidity and mortality. We aimed to estimate the influenza-attributable hospitalisation in Australia, by influenza type. METHODS:A generalised-additive regression model was used to estimate type- and age-specific influenza-attributable hospitalisation rates per 100,000 population by principal diagnosis in Australia, from 2001 through 2013. Weekly counts of laboratory-confirmed influenza notifications and by type, influenza A and B were used as covariates in the model. Main principal diagnosis categories of interest were influenza and pneumonia and respiratory admissions. A smoothing spline was used to control for unmeasured time varying factors. Results for 2009, in which the pandemic influenza A(H1N1)pdm09 virus circulated, were not included in annual averages and are reported separately. RESULTS:During the study period, the estimated annual average, all-age, annual respiratory hospitalisation rates attributable to seasonal influenza type A, B and total influenza were 45.4 (95% CI: 34.9, 55.9), 32.6 (95% CI: 22.8, 42.4), and 76.9 (95% CI: 73.6, 80.2) per 100,000 population, respectively. During 2009, the estimated total pandemic influenza-attributable, all-age, respiratory hospitalisation rate was 56.1 (95% CI: 47.4, 64.9) per 100,000. Older adults (?85 years of age) experienced the highest influenza-attributable hospitalisation rates for both seasonal and 2009 pandemic influenza. Collinearity between influenza A and B time series in some years limited the ability of the model to resolve differences in influenza attribution between the two virus types. CONCLUSION:Both seasonal and pandemic influenza caused considerable morbidity in Australia during the years studied, particularly among older adults. The pandemic hospitalisation rate in 2009 was lower than the average overall annual rate for seasonal influenza, but young to middle aged adults experience a hospitalisation rate similar to that of severe seasonal influenza.

Project description:Very different influenza seasons have been observed from 2008/09-2011/12 in England and Wales, with the reported burden varying overall and by age group. The objective of this study was to estimate the impact of influenza on all-cause and cause-specific mortality during this period. Age-specific generalised linear regression models fitted with an identity link were developed, modelling weekly influenza activity through multiplying clinical influenza-like illness consultation rates with proportion of samples positive for influenza A or B. To adjust for confounding factors, a similar activity indicator was calculated for Respiratory Syncytial Virus. Extreme temperature and seasonal trend were controlled for. Following a severe influenza season in 2008/09 in 65+yr olds (estimated excess of 13,058 influenza A all-cause deaths), attributed all-cause mortality was not significant during the 2009 pandemic in this age group and comparatively low levels of influenza A mortality were seen in post-pandemic seasons. The age shift of the burden of seasonal influenza from the elderly to young adults during the pandemic continued into 2010/11; a comparatively larger impact was seen with the same circulating A(H1N1)pdm09 strain, with the burden of influenza A all-cause excess mortality in 15-64 yr olds the largest reported during 2008/09-2011/12 (436 deaths in 15-44 yr olds and 1,274 in 45-64 yr olds). On average, 76% of seasonal influenza A all-age attributable deaths had a cardiovascular or respiratory cause recorded (average of 5,849 influenza A deaths per season), with nearly a quarter reported for other causes (average of 1,770 influenza A deaths per season), highlighting the importance of all-cause as well as cause-specific estimates. No significant influenza B attributable mortality was detected by season, cause or age group. This analysis forms part of the preparatory work to establish a routine mortality monitoring system ahead of introduction of the UK universal childhood seasonal influenza vaccination programme in 2013/14.

Project description:BACKGROUND:The mortality burden of the 2009 A/H1N1 pandemic remains unclear in many countries due to delays in reporting of death statistics. We estimate the age- and cause-specific excess mortality impact of the pandemic in France, relative to that of other countries and past epidemic and pandemic seasons. METHODS:We applied Serfling and Poisson excess mortality approaches to model weekly age- and cause-specific mortality rates from June 1969 through May 2010 in France. Indicators of influenza activity, time trends, and seasonal terms were included in the models. We also reviewed the literature for country-specific estimates of 2009 pandemic excess mortality rates to characterize geographical differences in the burden of this pandemic. RESULTS:The 2009 A/H1N1 pandemic was associated with 1.0 (95% Confidence Intervals (CI) 0.2-1.9) excess respiratory deaths per 100,000 population in France, compared to rates per 100,000 of 44 (95% CI 43-45) for the A/H3N2 pandemic and 2.9 (95% CI 2.3-3.7) for average inter-pandemic seasons. The 2009 A/H1N1 pandemic had a 10.6-fold higher impact than inter-pandemic seasons in people aged 5-24 years and 3.8-fold lower impact among people over 65 years. CONCLUSIONS:The 2009 pandemic in France had low mortality impact in most age groups, relative to past influenza seasons, except in school-age children and young adults. The historical A/H3N2 pandemic was associated with much larger mortality impact than the 2009 pandemic, across all age groups and outcomes. Our 2009 pandemic excess mortality estimates for France fall within the range of previous estimates for high-income regions. Based on the analysis of several mortality outcomes and comparison with laboratory-confirmed 2009/H1N1 deaths, we conclude that cardio-respiratory and all-cause mortality lack precision to accurately measure the impact of this pandemic in high-income settings and that use of more specific mortality outcomes is important to obtain reliable age-specific estimates.

Dataset Information

Mortality attributable to seasonal and pandemic influenza, Australia, 2003 to 2009, using a novel time series smoothing approach.

Publications

Mortality attributable to seasonal and pandemic influenza, Australia, 2003 to 2009, using a novel time series smoothing approach.

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