The first wave of pandemic influenza (H1N1) 2009 in Germany: from initiation to acceleration.

Background: The first imported case of pandemic influenza (H1N1) 2009 in Germany was confirmed in April 2009. However, the first wave with measurable burden of disease started only in October 2009. The basic epidemiological and clinical characteristics of the pandemic were analysed in order to understand the course of the pandemic in Germany.

Methods: The analysis was based on data from the case-based, mandatory German surveillance system for infectious diseases. Cases notified between 27 April and 11 November 2009 and fulfilling the case definition were included in the study.

Results: Two time periods with distinct epidemiologic characteristics could be determined: 23,789 cases (44.1%) occurred during the initiation period (IP, week 18 to 41), and 30,179 (55.9%) during the acceleration period (AP, week 42 to 45). During IP, coinciding with school summer holidays, 61.1% of cases were travel-related and one death occurred. Strict containment efforts were performed until week 32. During AP the majority of cases (94.3%) was autochthonous, 12 deaths were reported. The main affected age group shifted from 15 to 19 years in IP to 10 to 14 years in AP (median age 19 versus 15 years; p < 0.001). The proportion of cases with underlying medical conditions increased from 4.7% to 6.9% (p < 0.001). Irrespective of the period, these cases were more likely to be hospitalised (OR = 3.6 [95% CI: 3.1; 4.3]) and to develop pneumonia (OR = 8.1 [95% CI: 6.1; 10.7]). Furthermore, young children (0 to 2 years) (OR = 2.8 [95% CI: 1.5; 5.2]) and persons with influenza-like illness (ILI, OR = 1.4 [95% CI: 1.0; 2.1]) had a higher risk to develop pneumonia compared to other age groups and individuals without ILI.

Conclusion: The epidemiological differences we could show between summer and autumn 2009 might have been influenced by the school summer holidays and containment efforts. The spread of disease did not result in change of risk groups or severity. Our results show that analyses of case-based information can advise future public health measures.