Automated mortality monitoring in Scotland from 2009

Wagner, Adam P ORCID:, McKenzie, Eddie, Robertson, Chris, McMenamin, J, Reynolds, A and Murdoch, H (2013) Automated mortality monitoring in Scotland from 2009. Eurosurveillance, 18 (15). ISSN 1560-7917

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Mortality monitoring systems are important for gauging the effect of influenza and other wide ranging health threats. We present the daily all-cause mortality monitoring system routinely used in Scotland, which differs from others by using two different statistical models for calculating expected mortality. The first model is an extended Serfling model, which captures annual seasonality in mortality using sine and cosine terms, and is frequently seen in other systems. Serfling models fit to summer seasonality well, but not to the winter peak. Thus, during the winter, there are frequent `excesses’, higher than expected mortality, making it harder to directly judge if winter mortality is higher than in previous years. The second model, a Generalised Additive Model, resolves this by allowing a more flexible seasonal pattern that includes the winter peak. Thus, excesses under the second model directly indicate if winter mortality is higher than in previous years, useful, for example, in judging if a new strain of seasonal influenza is more likely to produce death than previous ones. As common in all-cause mortality monitoring systems, the Scottish system uses a reporting delay correction: we discuss the difficulties of interpretation when such a correction is used and possible avenues for future work that may address these difficulties.

Item Type: Article
Additional Information: Citation style for this article: Wagner AP, McKenzie E, Robertson C, McMenamin J, Reynolds A, Murdoch H. Automated mortality monitoring in Scotland from 2009. Euro Surveill. 2013;18(15):pii=20451. Available online:
Faculty \ School: Faculty of Medicine and Health Sciences > Norwich Medical School
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Depositing User: Pure Connector
Date Deposited: 29 Apr 2016 23:05
Last Modified: 22 Oct 2022 01:05

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