Age as a Determinant for Dissemination of Seasonal and Pandemic Influenza: An Open Cohort Study of Influenza Outbreaks in O ¨ stergo ¨ tland County, Sweden Toomas Timpka 1,2,3 *, Olle Eriksson 3 , Armin Spreco 2,3 , Elin A. Gursky 4 , Magnus Stro ¨ mgren 5 , Einar Holm 5 , Joakim Ekberg 1,2,3 ,O ¨ rjan Dahlstro ¨m 1,2,6 , Lars Valter 1,2,3 , Henrik Eriksson 3 1 Department of Public Health, O ¨ stergo ¨ tland County Council, Linko ¨ ping, Sweden, 2 Department of Medical and Health Sciences, Linko ¨ pings Universitet, Linko ¨ ping, Sweden, 3 Department of Computer and Information Science, Linko ¨ pings Universitet, Linko ¨ ping, Sweden, 4 National Strategies Support Directorate, ANSER/Analytic Services Inc, Arlington, Virginia, United States of America, 5 Department of Social and Economic Geography, Umea ˚ University, Umea ˚, Sweden, 6 Linnaeus Centre HEAD, Swedish Institute for Disability Research, Department of Behavioural Sciences, Linko ¨ ping University, Linko ¨ ping, Sweden Abstract An understanding of the occurrence and comparative timing of influenza infections in different age groups is important for developing community response and disease control measures. This study uses data from a Scandinavian county (population 427.000) to investigate whether age was a determinant for being diagnosed with influenza 2005–2010 and to examine if age was associated with case timing during outbreaks. Aggregated demographic data were collected from Statistics Sweden, while influenza case data were collected from a county-wide electronic health record system. A logistic regression analysis was used to explore whether case risk was associated with age and outbreak. An analysis of variance was used to explore whether day for diagnosis was also associated to age and outbreak. The clinical case data were validated against case data from microbiological laboratories during one control year. The proportion of cases from the age groups 10–19 (p,0.001) and 20–29 years old (p,0.01) were found to be larger during the A pH1N1 outbreak in 2009 than during the seasonal outbreaks. An interaction between age and outbreak was observed (p,0.001) indicating a difference in age effects between circulating virus types; this interaction persisted for seasonal outbreaks only (p,0.001). The outbreaks also differed regarding when the age groups received their diagnosis (p,0.001). A post-hoc analysis showed a tendency for the young age groups, in particular the group 10–19 year olds, led outbreaks with influenza type A H1 circulating, while A H3N2 outbreaks displayed little variations in timing. The validation analysis showed a strong correlation (r = 0.625;p,0.001) between the recorded numbers of clinically and microbiologically defined influenza cases. Our findings demonstrate the complexity of age effects underlying the emergence of local influenza outbreaks. Disentangling these effects on the causal pathways will require an integrated information infrastructure for data collection and repeated studies of well-defined communities. Citation: Timpka T, Eriksson O, Spreco A, Gursky EA, Stro ¨ mgren M, et al. (2012) Age as a Determinant for Dissemination of Seasonal and Pandemic Influenza: An Open Cohort Study of Influenza Outbreaks in O ¨ stergo ¨ tland County, Sweden. PLoS ONE 7(2): e31746. doi:10.1371/journal.pone.0031746 Editor: Vittoria Colizza, INSERM & Universite Pierre et Marie Curie, France Received November 4, 2011; Accepted January 12, 2012; Published February 23, 2012 Copyright: ß 2012 Timpka et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This study was supported by grants from the Swedish Civil Contingencies Agency (2010-2788) and the Swedish Science Council (2008-5252). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: At the time of the study, EAG was employed by ANSER (the non-profit section of the organization ‘Analytical Services Inc.’). This does not alter the authors’ adherence to all the PLoS ONE policies on sharing data and materials. * E-mail: toomas.timpka@liu.se Introduction A thorough understanding of the occurrence and comparative timing of influenza infections in different age groups is important for developing community response and disease control measures, e.g. early social distancing measures, risk communication, and vaccinations (WHO 2009). However, the relationship between age and disease transmission patterns within populations is difficult to measure. Viboud et al (2006) reported that working-age adults are responsible for the between-community transfer of influenza infection during outbreaks [1]. Some studies have attributed the local spread of influenza outbreaks to high attack rates among children and adolescents, suggesting the need to target disease mitigation interventions within this age group [2,3,4]. The Houston Family Study reported different age distributions for seasonal H1N1 and H3N2 infections, noting that more than 50% of H1N1 infections were detected among 10–34 year olds [5]. Some studies have identified young children as leading the spread of infection [6], while other studies have identified adolescents and young adults as the age groups most likely to drive local spreads [7]. Other studies have even observed little age-specific difference in the timing of infection onset [8]. Local surveillance is needed to assess community-level influenza activity, as mixing between regions appears to be too weak a variable to infer causality in the direction and timing of spread [9]. The challenge for such surveillance is not to find the causal agent of the disease, but to detect outbreaks and address their proximal and distal causes. Proximal causes of influenza infection include those that influence the probability of exposure to the virus, while distal determinants arise when exposure does not necessarily progress to disease [10]. The age-related impact associated with proximal causes, such as close human-to-human contact patterns PLoS ONE | www.plosone.org 1 February 2012 | Volume 7 | Issue 2 | e31746