Occurrence of invasive pneumococcal disease and number of excess cases due to influenza

Katarzyna Grabowska, Liselotte Högberg, Pasi Penttinen, Ake Svensson, Karl Ekdahl, Katarzyna Grabowska, Liselotte Högberg, Pasi Penttinen, Ake Svensson, Karl Ekdahl

Abstract

Background: Influenza is characterized by seasonal outbreaks, often with a high rate of morbidity and mortality. It is also known to be a cause of significant amount secondary bacterial infections. Streptococcus pneumoniae is the main pathogen causing secondary bacterial pneumonia after influenza and subsequently, influenza could participate in acquiring Invasive Pneumococcal Disease (IPD).

Methods: In this study, we aim to investigate the relation between influenza and IPD by estimating the yearly excess of IPD cases due to influenza. For this purpose, we use influenza periods as an indicator for influenza activity as a risk factor in subsequent analysis. The statistical modeling has been made in two modes. First, we constructed two negative binomial regression models. For each model, we estimated the contribution of influenza in the models, and calculated number of excess number of IPD cases. Also, for each model, we investigated several lag time periods between influenza and IPD. Secondly, we constructed an "influenza free" baseline, and calculated differences in IPD data (observed cases) and baseline (expected cases), in order to estimate a yearly additional number of IPD cases due to influenza. Both modes were calculated using zero to four weeks lag time.

Results: The analysis shows a yearly increase of 72-118 IPD cases due to influenza, which corresponds to 6-10% per year or 12-20% per influenza season. Also, a lag time of one to three weeks appears to be of significant importance in the relation between IPD and influenza.

Conclusion: This epidemiological study confirms the association between influenza and IPD. Furthermore, negative binomial regression models can be used to calculate number of excess cases of IPD, related to influenza.

Figures

Figure 1
Figure 1
Total number of IPD diagnosis (solid line) and number of laboratory confirmed cases of influenza (dashed line).
Figure 2
Figure 2
Total number of IPD diagnosis. The presence of influenza during different years is shown as horizontal solid lines.
Figure 3
Figure 3
A. Each marker (star) represent number of IPD cases during influenza free weeks, in the time frame when influenza was monitored. Hence it shows the data points used in baseline estimation. The solid line represents baseline. B. For each week, the distributions of all IPD data during the period 1993–2004 are presented as box plots. The solid line represents baseline.
Figure 4
Figure 4
Bars represent number of weekly excess cases due to influenza parameter in Model 1 and 2, with three-week lag. Furthermore, the 95% Confidence Intervals for influenza parameter, for Model 1 and 2, are shown.

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Source: PubMed

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