Estimation of influenza-attributable medically attended acute respiratory illness by influenza type/subtype and age, Germany, 2001/02-2014/15

Abstract

Background: The total burden of influenza in primary care is difficult to assess. The case definition of medically attended "acute respiratory infection" (MAARI) in the German physician sentinel is sensitive; however, it requires modelling techniques to derive estimates of disease attributable to influenza. We aimed to examine the impact of type/subtype and age.

Methods: Data on MAARI and virological results of respiratory samples (virological sentinel) were available from 2001/02 until 2014/15. We constructed a generalized additive regression model for the periodic baseline and the secular trend. The weekly number of influenza-positive samples represented influenza activity. In a second step, we distributed the estimated influenza-attributable MAARI (iMAARI) according to the distribution of types/subtypes in the virological sentinel.

Results: Season-specific iMAARI ranged from 0.7% to 8.9% of the population. Seasons with the strongest impact were dominated by A(H3), and iMAARI attack rate of the pandemic 2009 (A(H1)pdm09) was 4.9%. Regularly the two child age groups (0-4 and 5-14 years old) had the highest iMAARI attack rates reaching frequently levels up to 15%-20%. Influenza B affected the age group of 5- to 14-year-old children substantially more than any other age group. Sensitivity analyses demonstrated both comparability and stability of the model.

Conclusion: We constructed a model that is well suited to estimate the substantial impact of influenza on the primary care sector. A(H3) causes overall the greatest number of iMAARI, and influenza B has the greatest impact on school-age children. The model may incorporate time series of other pathogens as they become available.

Keywords: Germany; burden of disease; generalised additive model; influenza; influenza type/subtype; medically attended acute respiratory illness.

Figure 1

Raw data (medically attended acute respiratory illness (MAARI) in per cent of the population (hollow dots), data modelled by the GAM sample‐based model (baseline; green line), secular trend (blue line), MAARI attributed to influenza (iMAARI; red‐shaded area). Vertical lines represent the change of the year, Germany, 2001/2002–2014/2015

Figure 2

Top panel: number of influenza confirmations by type and subtype among sentinel respiratory samples, by season. Bottom panel: type‐ and subtype‐specific influenza‐attributable medically attended acute respiratory illnesses (iMAARI), in % of the population, by season. The extra vertical line indicates the beginning of the pandemic A(H1)pdm09. A(H1)prepan=pre‐pandemic A(H1)

Figure 3

Top panel: estimated number of influenza‐attributable medically attended acute respiratory infections (iMAARI) by influenza type/subtype and season; green=A(H1) (pre‐pandemic A(H1) (A(H1)prepan) and A(H1)pdm09 not separated), red=A(H3), blue=B. Bottom panel: distribution of all iMAARI accumulated for all seasons from 2001/02 until 2014/15, by type/subtype. Colours denote influenza types and subtypes; in contrast to the top panel, A(H1)prepan (orange) and A(H1)pdm09 (green) are coloured separately

Figure 4

Age‐ and season‐specific attack rate of influenza‐attributable medically attended acute respiratory illnesses (iMAARI), in % of age group

Figure 5

Top panel: attack rates of influenza‐attributable medically attended acute respiratory infections (iMAARI), median of 14 seasons (2001/02‐2014/15), by age (shown are mid‐points of five age groups). Bottom panel: age‐specific median and interquartile range of iMAARI attack rates across all seasons with typical pattern stratified by type/subtype. Points for the medians were connected by lines to guide the eye

Figure 6

Observed and predicted iMAARI attack rate based on iMAARI attack rate in the preceding season. Dots show iMAARI attack rates as observed, and lines show modelled iMAARI attack rates as predicted using Poisson regression