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. 2019 Apr;24(14):1800118.
doi: 10.2807/1560-7917.ES.2019.24.14.1800118.

Mortality attributable to seasonal influenza in Greece, 2013 to 2017: variation by type/subtype and age, and a possible harvesting effect

Theodore Lytras  1 , Katerina Pantavou  2 , Elisavet Mouratidou  1 , Sotirios Tsiodras  1   3
Affiliations

Mortality attributable to seasonal influenza in Greece, 2013 to 2017: variation by type/subtype and age, and a possible harvesting effect

Theodore Lytras et al. Euro Surveill. 2019 Apr.

Abstract

IntroductionEstimating the contribution of influenza to excess mortality in the population presents substantial methodological challenges.AimIn a modelling study we combined environmental, epidemiological and laboratory surveillance data to estimate influenza-attributable mortality in Greece, over four seasons (2013/14 to 2016/17), specifically addressing the lag dimension and the confounding effect of temperature.MethodsAssociations of influenza type/subtype-specific incidence proxies and of daily mean temperature with mortality were estimated with a distributed-lag nonlinear model with 30 days of maximum lag, separately by age group (all ages, 15-64 and ≥ 65 years old). Total and weekly deaths attributable to influenza and cold temperatures were calculated.ResultsOverall influenza-attributable mortality was 23.6 deaths per 100,000 population per year (95% confidence interval (CI): 17.8 to 29.2), and varied greatly between seasons, by influenza type/subtype and by age group, with the vast majority occurring in persons aged ≥ 65 years. Most deaths were attributable to A(H3N2), followed by influenza B. During periods of A(H1N1)pdm09 circulation, weekly attributable mortality to this subtype among people ≥ 65 years old increased rapidly at first, but then fell to zero and even negative, suggesting a mortality displacement (harvesting) effect. Mortality attributable to cold temperatures was much higher than that attributable to influenza.ConclusionsStudies of influenza-attributable mortality need to consider distributed-lag effects, stratify by age group and adjust both for circulating influenza virus types/subtypes and daily mean temperatures, in order to produce reliable estimates. Our approach addresses these issues, is readily applicable in the context of influenza surveillance, and can be useful for other countries.

Keywords: epidemiology; influenza; modelling; surveillance.

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Conflict of interest statement

Conflict of interest: None declared.

Figures

Figure 1
Figure 1
(A) Influenza activity, (B) average daily temperature and (C–D) weekly all-cause mortality, by age group, Greece, May 2013–October 2017
Figure 2
Figure 2
Overall association (relative risk) of temperature with all-cause mortality, across all lags and by age group, Greece, May 2013–October 2017 (n = 518,688 deaths)
Figure 3
Figure 3
Weekly observed all-cause deaths, deaths attributable to influenza and to cold temperatures, Greece, May 2013–October 2017 (n = 518,688 deaths)
Figure 4
Figure 4
Weekly numbers of estimated influenza-attributable deaths for all ages, by influenza type/subtype (panels A, B, C) during four winter seasons in Greece, weeks 40/2013–20/2017
Figure 5
Figure 5
Weekly numbers of estimated deaths attributable to influenza A(H1N1)pdm09, by age group during four winter seasons in Greece, weeks 40/2013–20/2017
See this image and copyright information in PMC

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