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标题：Prospective surveillance of excess mortality due to influenza in New South Wales: feasibility and statistical approach
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作者：David J Muscatello ; Patricia M Morton ; Ingrid Evans 等
期刊名称：Communicable Diseases Intelligence
印刷版ISSN：1447-4514
电子版ISSN：1445-4866
出版年度：2008
卷号：32
期号：4
页码：435-442
出版社：Government Department of Health and Ageing
摘要：Influenza is a serious disease that seasonally causes varying but substantial morbidity and mortality. Therefore, strong, rapid influenza surveillance systems are a priority. Surveillance of the population mortality burden of influenza is difficult because few deaths have laboratory confirmation of infection. Serfling developed a statistical time series model to estimate excess deaths due to influenza. Based on this approach we trialled weekly monitoring of excess influenza mortality. Weekly, certified death information was loaded into a database and aggregated to provide a time series of the proportion of all deaths that mention pneumonia or influenza on the death certificate. A robust regression model was fitted to the time series up to the end of the previous calendar year and used to forecast the current year’s mortality. True and false alarm rates were used to assess the sensitivity and specificity of alternative thresholds signifying excess mortality. Between 1 January 2002 and 9 November 2007, there were 279,968 deaths registered in New South Wales, of which 77% were among people aged 65 years or more. Over this period 33,213 (12%) deaths were classified as pneumonia and influenza. A threshold of 1.2 standard deviations highlighted excess mortality when influenza was circulating while providing an acceptable false alarm rate at other times of the year. Prospective and reasonably rapid monitoring of excess mortality due to influenza in an Australian setting is feasible. The modelling approach allows health departments to make a more objective assessment of the severity of seasonal influenza and the effectiveness of mitigation strategies.
关键词：influenza; surveillance; Serfling;excess mortality; robust regression; false alarm;rate; sensitivity; specificity; seasonal