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  • 1.
    Alonso, Juan-Manuel
    et al.
    Int Assoc Athlet Federat, Med & Antidoping Commiss, Monaco, Monaco; Qatar Orthoped & Sports Med Hosp, Sports Med Dept, Aspetar, Doha, Qatar.
    Jacobsson, Jenny
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Timpka, Toomas
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences. Region Östergötland, Center for Health and Developmental Care, Center for Public Health.
    Ronsen, Ola
    Int Assoc Athlet Federat, Med & Antidoping Commiss, Monaco, Monaco; Aker Solut, Lysaker, Norway.
    Kajenienne, Alma
    Int Assoc Athlet Federat, Med & Antidoping Commiss, Monaco, Monaco; Lithuanian Univ Hlth Sci, Inst Sport, Kaunas, Lithuania.
    Dahlström, Örjan
    Linköping University, Department of Behavioural Sciences and Learning, Disability Research. Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Behavioural Sciences, The Swedish Institute for Disability Research.
    Spreco, Armin
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences.
    Edouard, Pascal
    Univ Hosp St Etienne, Fac Med, Sports Med Unity, Dept Clin & Exercise Physiol, St Etienne, France; Univ Lyon, Exercise Physiol Lab, LPE EA 4338, St Etienne, France; French Athlet Federat, Med Commiss, Paris, France.
    Preparticipation injury complaint is a risk factor for injury: a prospective study of the Moscow 2013 IAAF Championships.2015In: British Journal of Sports Medicine, ISSN 0306-3674, E-ISSN 1473-0480, Vol. 49, no 17, p. 1118-U45Article in journal (Refereed)
    Abstract [en]

    OBJECTIVES: To determine the health status of athletes before the start of an international athletics championship and to determine whether preparticipation risk factors predicted in-championship injuries.

    METHODS: At the beginning of the 2013 International Association of Athletics Federations (IAAF) World Championships, all registered athletes (n=1784) were invited to complete a preparticipation health questionnaire (PHQ) on health status during the month preceding the championships. New injuries that occurred at the championships were prospectively recorded.

    RESULTS: The PHQ was completed by 698 (39%) athletes; 204 (29.2%) reported an injury complaint during the month before the championships. The most common mode of onset of preparticipation injury complaints was gradual (43.6%). Forty-nine athletes in the study reported at least one injury during the championships. Athletes who reported a preparticipation injury complaint were at twofold increased risk for an in-championship injury (OR=2.09; 95% CI 1.16 to 3.77); p=0.014). Those who reported a preparticipation gradual-onset injury complaint were at an almost fourfold increased risk for an in-championship time-loss injury (OR=3.92; 95% CI 1.69 to 9.08); p=0.001). Importantly, the preparticipation injury complaint severity score was associated with the risk of sustaining an in-championship injury (OR=1.14; 95% CI 1.06 to 1.22); p=0.001).

    SUMMARY AND CONCLUSIONS: About one-third of the athletes participating in the study reported an injury complaint during the month before the championships, which represented a risk factor for sustaining an injury during the championship. This study emphasises the importance of the PHQ as a screening tool to identify athletes at risk of injuries before international championships.

  • 2.
    Eriksson, Henrik
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    Timpka, Toomas
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Health and Developmental Care, Center for Public Health. Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    Spreco, Armin
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Dahlström, Örjan
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Medicine and Health Sciences.
    Strömgren, Magnus
    Dept. of Social and Economic Geography, Umeå University, Umeå, Sweden.
    Holm, Einar
    Dept. of Social and Economic Geography, Umeå University, Umeå, Sweden.
    Dynamic Multicore Processing for Pandemic Influenza Simulation.2016In: AMIA Annual Symposium Proceedings, American Medical Informatics Association , 2016, Vol. 2016, p. 534-540Conference paper (Refereed)
    Abstract [en]

    Pandemic simulation is a useful tool for analyzing outbreaks and exploring the impact of variations in disease, population, and intervention models. Unfortunately, this type of simulation can be quite time-consuming especially for large models and significant outbreaks, which makes it difficult to run the simulations interactively and to use simulation for decision support during ongoing outbreaks. Improved run-time performance enables new applications of pandemic simulations, and can potentially allow decision makers to explore different scenarios and intervention effects. Parallelization of infection-probability calculations and multicore architectures can take advantage of modern processors to achieve significant run-time performance improvements. However, because of the varying computational load during each simulation run, which originates from the changing number of infectious persons during the outbreak, it is not useful to us the same multicore setup during the simulation run. The best performance can be achieved by dynamically changing the use of the available processor cores to balance the overhead of multithreading with the performance gains of parallelization.

  • 3.
    Karlsson, David
    et al.
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Ekberg, Joakim
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences.
    Spreco, Armin
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences.
    Eriksson, Henrik
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Timpka, Toomas
    Linköping University, Department of Medical and Health Sciences, Social Medicine and Public Health Science. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Health and Developmental Care, Center for Public Health.
    Visualization of infectious disease outbreaks in routine practice2013In: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 192, p. 697-701Article in journal (Refereed)
    Abstract [en]

    Throughout the history of epidemiology, visualizations have been used as the interface between public-health professionals and epidemiological data. The aim of this study was to examine the impact of the level of abstraction when using visualizations on routine infectious disease control. We developed three interactive visualization prototypes at increasing levels of abstraction to communicate subsets of influenza outbreak surveillance information. The visualizations were assessed through workshops in an exploratory evaluation with infectious disease epidemiologists. The results show that despite the potential of processed, abstract, and information-dense representations, increased levels of abstraction decreased epidemiologists understanding and confidence in visualizations. Highly abstract representations were deemed not applicable in routine practice without training. Infectious disease epidemiologists work routines and decision-making need to be further studied in order to develop visualizations that meet both the quality requirements imposed by policy-makers and the contextual nature of work practice.

  • 4.
    Spreco, Armin
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Epidemiological and statistical basis for detection and prediction of influenza epidemics2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    A large number of emerging infectious diseases (including influenza epidemics) has been identified during the last century. The emergence and re-emergence of infectious diseases have a negative impact on global health. Influenza epidemics alone cause between 3 and 5 million cases of severe illness annually, and between 250,000 and 500,000 deaths. In addition to the human suffering, influenza epidemics also impose heavy demands on the health care system. For example, hospitals and intensive care units have limited excess capacity during infectious diseases epidemics. Therefore, it is important that increased influenza activity is noticed early at local levels to allow time to adjust primary care and hospital resources that are already under pressure. Algorithms for the detection and prediction of influenza epidemics are essential components to achieve this.

    Although a large number of studies have reported algorithms for detection or prediction of influenza epidemics, outputs that fulfil standard criteria for operational readiness are seldom produced. Furthermore, in the light of the rapidly growing availability of “Big Data” from both diagnostic and prediagnostic (syndromic) data sources in health care and public health settings, a new generation of epidemiologic and statistical methods, using several data sources, is desired for reliable analyses and modeling.

    The rationale for this thesis was to inform the planning of local response measures and adjustments to health care capacity during influenza epidemics. The overall aim was to develop a method for detection and prediction of influenza epidemics. Before developing the method, three preparatory studies were performed. In the first of these studies, the associations (in terms of correlation) between diagnostic and pre-diagnostic data sources were examined, with the aim of investigating the potential of these sources for use in influenza surveillance systems. In the second study, a literature study of detection and prediction algorithms used in the field of influenza surveillance was performed. In the third study, the algorithms found in the previous study were compared in a prospective evaluation study. In the fourth study, a method for nowcasting of influenza activity was developed using electronically available data for real-time surveillance in local settings followed by retrospective application on the same data. This method includes three functions: detection of the start of the epidemic at the local level and predictions of the peak timing and the peak intensity. In the fifth and final study, the nowcasting method was evaluated by prospective application on authentic data from Östergötland County, Sweden.

    In the first study, correlations with large effect sizes between diagnostic and pre-diagnostic data were found, indicating that pre-diagnostic data sources have potential for use in influenza surveillance systems. However, it was concluded that further longitudinal research incorporating prospective evaluations is required before these sources can be used for this purpose. In the second study, a meta-narrative review approach was used in which two narratives for reporting prospective evaluation of influenza detection and prediction algorithms were identified: the biodefence informatics narrative and the health policy research narrative. As a result of the promising performances of one detection algorithm and one prediction algorithm in the third study, it was concluded that both further evaluation research and research on methods for nowcasting of influenza activity were warranted. In the fourth study, the performance of the nowcasting method was promising when applied on retrospective data but it was concluded that thorough prospective evaluations are necessary before recommending the method for broader use. In the fifth study, the performance of the nowcasting method was promising when prospectively applied on authentic data, implying that the method has potential for routine use. In future studies, the validity of the nowcasting method must be investigated by application and further evaluation in multiple local settings, including large urbanizations.

    List of papers
    1. Performance of eHealth data sources in local influenza surveillance: a 5-year open cohort study
    Open this publication in new window or tab >>Performance of eHealth data sources in local influenza surveillance: a 5-year open cohort study
    Show others...
    2014 (English)In: Journal of Medical Internet Research, ISSN 1438-8871, E-ISSN 1438-8871, Vol. 16, no 4, p. e116-Article in journal (Refereed) Published
    Abstract [en]

    BACKGROUND: There is abundant global interest in using syndromic data from population-wide health information systems--referred to as eHealth resources--to improve infectious disease surveillance. Recently, the necessity for these systems to achieve two potentially conflicting requirements has been emphasized. First, they must be evidence-based; second, they must be adjusted for the diversity of populations, lifestyles, and environments.

    OBJECTIVE: The primary objective was to examine correlations between data from Google Flu Trends (GFT), computer-supported telenursing centers, health service websites, and influenza case rates during seasonal and pandemic influenza outbreaks. The secondary objective was to investigate associations between eHealth data, media coverage, and the interaction between circulating influenza strain(s) and the age-related population immunity.

    METHODS: An open cohort design was used for a five-year study in a Swedish county (population 427,000). Syndromic eHealth data were collected from GFT, telenursing call centers, and local health service website visits at page level. Data on mass media coverage of influenza was collected from the major regional newspaper. The performance of eHealth data in surveillance was measured by correlation effect size and time lag to clinically diagnosed influenza cases.

    RESULTS: Local media coverage data and influenza case rates showed correlations with large effect sizes only for the influenza A (A) pH1N1 outbreak in 2009 (r=.74, 95% CI .42-.90; P<.001) and the severe seasonal A H3N2 outbreak in 2011-2012 (r=.79, 95% CI .42-.93; P=.001), with media coverage preceding case rates with one week. Correlations between GFT and influenza case data showed large effect sizes for all outbreaks, the largest being the seasonal A H3N2 outbreak in 2008-2009 (r=.96, 95% CI .88-.99; P<.001). The preceding time lag decreased from two weeks during the first outbreaks to one week from the 2009 A pH1N1 pandemic. Telenursing data and influenza case data showed correlations with large effect sizes for all outbreaks after the seasonal B and A H1 outbreak in 2007-2008, with a time lag decreasing from two weeks for the seasonal A H3N2 outbreak in 2008-2009 (r=.95, 95% CI .82-.98; P<.001) to none for the A p H1N1 outbreak in 2009 (r=.84, 95% CI .62-.94; P<.001). Large effect sizes were also observed between website visits and influenza case data.

    CONCLUSIONS: Correlations between the eHealth data and influenza case rates in a Swedish county showed large effect sizes throughout a five-year period, while the time lag between signals in eHealth data and influenza rates changed. Further research is needed on analytic methods for adjusting eHealth surveillance systems to shifts in media coverage and to variations in age-group related immunity between virus strains. The results can be used to inform the development of alert-generating eHealth surveillance systems that can be subject for prospective evaluations in routine public health practice.

    Place, publisher, year, edition, pages
    Journal of Medical Internet Research, 2014
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-106758 (URN)10.2196/jmir.3099 (DOI)000336501600017 ()24776527 (PubMedID)
    Available from: 2014-05-21 Created: 2014-05-21 Last updated: 2018-04-07Bibliographically approved
    2. Algorithms for detecting and predicting influenza outbreaks: metanarrative review of prospective evaluations
    Open this publication in new window or tab >>Algorithms for detecting and predicting influenza outbreaks: metanarrative review of prospective evaluations
    2016 (English)In: BMJ Open, ISSN 2044-6055, E-ISSN 2044-6055, Vol. 6, no 5, p. e010683-Article, review/survey (Refereed) Published
    Abstract [en]

    Objectives Reliable monitoring of influenza seasons and pandemic outbreaks is essential for response planning, but compilations of reports on detection and prediction algorithm performance in influenza control practice are largely missing. The aim of this study is to perform a metanarrative review of prospective evaluations of influenza outbreak detection and prediction algorithms restricted settings where authentic surveillance data have been used. Design The study was performed as a metanarrative review. An electronic literature search was performed, papers selected and qualitative and semiquantitative content analyses were conducted. For data extraction and interpretations, researcher triangulation was used for quality assurance. Results Eight prospective evaluations were found that used authentic surveillance data: three studies evaluating detection and five studies evaluating prediction. The methodological perspectives and experiences from the evaluations were found to have been reported in narrative formats representing biodefence informatics and health policy research, respectively. The biodefence informatics narrative having an emphasis on verification of technically and mathematically sound algorithms constituted a large part of the reporting. Four evaluations were reported as health policy research narratives, thus formulated in a manner that allows the results to qualify as policy evidence. Conclusions Awareness of the narrative format in which results are reported is essential when interpreting algorithm evaluations from an infectious disease control practice perspective.

    Place, publisher, year, edition, pages
    BMJ PUBLISHING GROUP, 2016
    Keywords
    influenza; detection algorithms; prediction algorithms; evaluation; meta-narrative review
    National Category
    Health Care Service and Management, Health Policy and Services and Health Economy
    Identifiers
    urn:nbn:se:liu:diva-130311 (URN)10.1136/bmjopen-2015-010683 (DOI)000378414700068 ()27154479 (PubMedID)
    Note

    Funding Agencies|Swedish Civil Contingencies Agency [2010-2788]; Swedish Science Council [2008-5252]

    Available from: 2016-07-31 Created: 2016-07-28 Last updated: 2017-11-28
  • 5.
    Spreco, Armin
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Timpka, Toomas
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Health and Developmental Care, Center for Public Health.
    Algorithms for detecting and predicting influenza outbreaks: metanarrative review of prospective evaluations2016In: BMJ Open, ISSN 2044-6055, E-ISSN 2044-6055, Vol. 6, no 5, p. e010683-Article, review/survey (Refereed)
    Abstract [en]

    Objectives Reliable monitoring of influenza seasons and pandemic outbreaks is essential for response planning, but compilations of reports on detection and prediction algorithm performance in influenza control practice are largely missing. The aim of this study is to perform a metanarrative review of prospective evaluations of influenza outbreak detection and prediction algorithms restricted settings where authentic surveillance data have been used. Design The study was performed as a metanarrative review. An electronic literature search was performed, papers selected and qualitative and semiquantitative content analyses were conducted. For data extraction and interpretations, researcher triangulation was used for quality assurance. Results Eight prospective evaluations were found that used authentic surveillance data: three studies evaluating detection and five studies evaluating prediction. The methodological perspectives and experiences from the evaluations were found to have been reported in narrative formats representing biodefence informatics and health policy research, respectively. The biodefence informatics narrative having an emphasis on verification of technically and mathematically sound algorithms constituted a large part of the reporting. Four evaluations were reported as health policy research narratives, thus formulated in a manner that allows the results to qualify as policy evidence. Conclusions Awareness of the narrative format in which results are reported is essential when interpreting algorithm evaluations from an infectious disease control practice perspective.

  • 6.
    Timpka, Jonathan
    et al.
    Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden; 2Department of Neurology, Skåne University Hospital, Lund, Sweden.
    Dahlström, Örjan
    Linköping University, Department of Behavioural Sciences and Learning, Psychology. Linköping University, Faculty of Arts and Sciences.
    Spreco, Armin
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Business support and Development, Department of Health and Care Development.
    Nilsson, Maria H
    Department of Health Sciences, Lund University, Lund, Sweden; 8Memory Clinic, Skåne University Hospital, Malmö, Sweden.
    Iwarsson, Susanne
    Department of Health Sciences, Lund University, Lund, Sweden.
    Timpka, Toomas
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Business support and Development, Department of Health and Care Development.
    Odin, Per
    Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden; 2Department of Neurology, Skåne University Hospital, Lund, Sweden; 9Department of Neurology, Central Hospital, Bremerhaven, Germany.
    Reduced workforce participation 5 years prior to first Parkinsons disease sick-leave2018In: NPJ Parkinsons disease, ISSN 2373-8057, Vol. 4, article id 36Article in journal (Refereed)
    Abstract [en]

    The importance of understanding the prodromal phase of Parkinsons disease (PD) by systematic recording of prediagnostic symptoms and reductions in body functions has been highlighted. The aim of this study was to investigate whether persons later diagnosed with PD exhibit increased physician-certified sickness absence 1, 2, and 5 years prior to a first sick-leave episode attributed to PD. A case-control study was performed to analyze data from all nontrivial (exceeding 14 days) sick-leave episodes in Sweden between 2008 and 2014. The 537 incident PD sick-leave episodes were identified as PD sick-leave cases and compared to 537 sick-leave controls identified by matching age, sex, and date of the first day of the sick-leave episode. The total sickness absence and sickness absence due to musculoskeletal diagnoses were found to be increased among the PD sick-leave cases from 5 years prior to the first sick-leave episode ascribed to PD when compared to the controls. No differences between PD sick-leave cases and sick-leave controls were found with regard to mental and behavioral diagnoses. We conclude that the capacity to participate in working life is reduced already at the early prediagnostic stages of PD. This finding can be used as a basis for further research into the process of identifying individuals at risk for developing PD, particularly in combination with further investigation into biochemical, genetic, and imaging biomarkers.

  • 7.
    Timpka, Toomas
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Health and Developmental Care, Center for Public Health.
    Eriksson, Henrik
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Science & Engineering.
    Holm, E.
    Umeå University, Sweden.
    Strömgren, M.
    Umeå University, Sweden.
    Ekberg, Joakim
    Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Region Östergötland, Center for Health and Developmental Care, Center for Public Health.
    Spreco, Armin
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Medicine and Health Sciences.
    Dahlström, Örjan
    Linköping University, Department of Behavioural Sciences and Learning, Disability Research. Linköping University, Faculty of Arts and Sciences. Linköping University, The Swedish Institute for Disability Research.
    Relevance of workplace social mixing during influenza pandemics: an experimental modelling study of workplace cultures2016In: Epidemiology and Infection, ISSN 0950-2688, E-ISSN 1469-4409, Vol. 144, no 10, p. 2031-2042Article in journal (Refereed)
    Abstract [en]

    Workplaces are one of the most important regular meeting places in society. The aim of this study was to use simulation experiments to examine the impact of different workplace cultures on influenza dissemination during pandemics. The impact is investigated by experiments with defined social-mixing patterns at workplaces using semi-virtual models based on authentic sociodemographic and geographical data from a North European community (population 136 000). A simulated pandemic outbreak was found to affect 33% of the total population in the community with the reference academic-creative workplace culture; virus transmission at the workplace accounted for 10.6% of the cases. A model with a prevailing industrial-administrative workplace culture generated 11% lower incidence than the reference model, while the model with a self-employed workplace culture (also corresponding to a hypothetical scenario with all workplaces closed) produced 20% fewer cases. The model representing an academic-creative workplace culture with restricted workplace interaction generated 12% lower cumulative incidence compared to the reference model. The results display important theoretical associations between workplace social-mixing cultures and community-level incidence rates during influenza pandemics. Social interaction patterns at workplaces should be taken into consideration when analysing virus transmission patterns during influenza pandemics.

  • 8.
    Timpka, Toomas
    et al.
    Linköping University, Department of Medical and Health Sciences, Social Medicine and Public Health Science. Linköping University, Faculty of Health Sciences.
    Eriksson, Olle
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Spreco, Armin
    Linköping University, Department of Medical and Health Sciences, Social Medicine and Public Health Science. Linköping University, Faculty of Health Sciences.
    Gursky, Elin A
    National Strategies Support Directorate, ANSER/Analytic Services Inc, Arlington, Virginia, USA.
    Strömgren, Magnus
    Umeå University.
    Holm, Einar
    Umeå University.
    Ekberg, Joakim
    Linköping University, Department of Medical and Health Sciences, Social Medicine and Public Health Science. Linköping University, Faculty of Health Sciences.
    Dahlström, Örjan
    Linköping University, The Swedish Institute for Disability Research. Linköping University, Department of Behavioural Sciences and Learning, Disability Research. Linköping University, Faculty of Arts and Sciences.
    Valter, Lars
    Östergötlands Läns Landsting, Center for Health and Developmental Care, Center for Public Health.
    Eriksson, Henrik
    Linköping University, Department of Computer and Information Science, MDALAB - Human Computer Interfaces. Linköping University, The Institute of Technology.
    Age as a determinant for dissemination of seasonal and pandemic influenza: an open cohort study of influenza outbreaks in Östergötland County, Sweden2012In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, no 2, p. e31746-Article in journal (Refereed)
    Abstract [en]

    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.

  • 9.
    Timpka, Toomas
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Preventive and Social Medicine and Public Health Science. Linköping University, Faculty of Health Sciences.
    Gursky, Elin A
    ANSER/Analytic Services Inc, Arlington, Virginia, USA.
    Spreco, Armin
    Linköping University, Department of Medical and Health Sciences. Linköping University, Faculty of Health Sciences.
    Eriksson, Olle
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Dahlström, Örjan
    Linköping University, Department of Behavioural Sciences and Learning, Disability Research. Linköping University, Faculty of Arts and Sciences.
    Strömgren, Magnus
    Umeå University, Sweden.
    Holm, Einar
    Umeå University, Sweden.
    Ekberg, Joakim
    Linköping University, Department of Medical and Health Sciences, Division of Preventive and Social Medicine and Public Health Science. Linköping University, Faculty of Health Sciences.
    Hinkula, Jorma
    Linköping University, Department of Clinical and Experimental Medicine, Molecular Virology. Linköping University, Faculty of Health Sciences.
    Nyce, Jim M
    Ball State University, Muncie, IN, USA..
    Eriksson, Henrik
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Predictive value of telenursing complaints in influenza surveillance: a prospective cohort study in Sweden2013Conference paper (Other academic)
  • 10.
    Timpka, Toomas
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Health and Developmental Care, Center for Public Health.
    Spreco, Armin
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences.
    Dahlström, Örjan
    Linköping University, The Swedish Institute for Disability Research. Linköping University, Department of Behavioural Sciences and Learning, Disability Research. Linköping University, Faculty of Arts and Sciences.
    Eriksson, Henrik
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Gursky, Elin
    ANSER/Analytic Services Inc, Arlington, Virginia, USA.
    Ekberg, Joakim
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences.
    Blomqvist, Eva
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Arts and Sciences.
    Strömgren, Magnus
    Umeå University, Sweden.
    Karlsson, David
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Eriksson, Henrik
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Nyce, Jim
    Ball State University, Muncie, IN, USA.
    Hinkula, Jorma
    Linköping University, Department of Clinical and Experimental Medicine, Division of Microbiology and Molecular Medicine. Linköping University, Faculty of Health Sciences.
    Holm, Einar
    Umeå University, Sweden.
    Performance of eHealth data sources in local influenza surveillance: a 5-year open cohort study using data from Google Flu Trends, telenursing call centres, health service provider web-pages, and mass media coverage2013Conference paper (Other academic)
  • 11.
    Timpka, Toomas
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Health and Developmental Care, Center for Public Health.
    Spreco, Armin
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences.
    Dahlström, Örjan
    Linköping University, Department of Behavioural Sciences and Learning, Disability Research. Linköping University, Faculty of Arts and Sciences. Linköping University, The Swedish Institute for Disability Research.
    Eriksson, Olle
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Gursky, Elin
    National Strategies Support Directorate, ANSER/Analytic Services Inc., Arlington, VA, USA.
    Ekberg, Joakim
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences.
    Blomqvist, Eva
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Strömgren, Magnus
    Department of Geography and Economic History, Umeå University, Sweden.
    Karlsson, David
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Eriksson, Henrik
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Nyce, James
    Department of Anthropology, Ball State University, Muncie, IN, USA.
    Hinkula, Jorma
    Linköping University, Department of Clinical and Experimental Medicine, Division of Microbiology and Molecular Medicine. Linköping University, Faculty of Health Sciences.
    Holm, Einar
    Department of Geography and Economic History, Umeå University, Sweden.
    Performance of eHealth data sources in local influenza surveillance: a 5-year open cohort study2014In: Journal of Medical Internet Research, ISSN 1438-8871, E-ISSN 1438-8871, Vol. 16, no 4, p. e116-Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: There is abundant global interest in using syndromic data from population-wide health information systems--referred to as eHealth resources--to improve infectious disease surveillance. Recently, the necessity for these systems to achieve two potentially conflicting requirements has been emphasized. First, they must be evidence-based; second, they must be adjusted for the diversity of populations, lifestyles, and environments.

    OBJECTIVE: The primary objective was to examine correlations between data from Google Flu Trends (GFT), computer-supported telenursing centers, health service websites, and influenza case rates during seasonal and pandemic influenza outbreaks. The secondary objective was to investigate associations between eHealth data, media coverage, and the interaction between circulating influenza strain(s) and the age-related population immunity.

    METHODS: An open cohort design was used for a five-year study in a Swedish county (population 427,000). Syndromic eHealth data were collected from GFT, telenursing call centers, and local health service website visits at page level. Data on mass media coverage of influenza was collected from the major regional newspaper. The performance of eHealth data in surveillance was measured by correlation effect size and time lag to clinically diagnosed influenza cases.

    RESULTS: Local media coverage data and influenza case rates showed correlations with large effect sizes only for the influenza A (A) pH1N1 outbreak in 2009 (r=.74, 95% CI .42-.90; P<.001) and the severe seasonal A H3N2 outbreak in 2011-2012 (r=.79, 95% CI .42-.93; P=.001), with media coverage preceding case rates with one week. Correlations between GFT and influenza case data showed large effect sizes for all outbreaks, the largest being the seasonal A H3N2 outbreak in 2008-2009 (r=.96, 95% CI .88-.99; P<.001). The preceding time lag decreased from two weeks during the first outbreaks to one week from the 2009 A pH1N1 pandemic. Telenursing data and influenza case data showed correlations with large effect sizes for all outbreaks after the seasonal B and A H1 outbreak in 2007-2008, with a time lag decreasing from two weeks for the seasonal A H3N2 outbreak in 2008-2009 (r=.95, 95% CI .82-.98; P<.001) to none for the A p H1N1 outbreak in 2009 (r=.84, 95% CI .62-.94; P<.001). Large effect sizes were also observed between website visits and influenza case data.

    CONCLUSIONS: Correlations between the eHealth data and influenza case rates in a Swedish county showed large effect sizes throughout a five-year period, while the time lag between signals in eHealth data and influenza rates changed. Further research is needed on analytic methods for adjusting eHealth surveillance systems to shifts in media coverage and to variations in age-group related immunity between virus strains. The results can be used to inform the development of alert-generating eHealth surveillance systems that can be subject for prospective evaluations in routine public health practice.

  • 12.
    Timpka, Toomas
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Health and Developmental Care, Center for Public Health.
    Spreco, Armin
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Health and Developmental Care, Center for Public Health.
    Dahlström, Örjan
    Linköping University, The Swedish Institute for Disability Research. Linköping University, Department of Behavioural Sciences and Learning, Disability Research. Linköping University, Faculty of Arts and Sciences.
    Eriksson, Olle
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Gursky, Elin
    ANSER/Analytic Services Inc, Arlington, Virginia, USA.
    Ekberg, Joakim
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences.
    Blomqvist, Eva
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, Faculty of Arts and Sciences.
    Strömgren, Magnus
    Umeå University, Sweden.
    Karlsson, David
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Eriksson, Henrik
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Nyce, Jim
    Ball State University, Muncie, IN, USA.
    Hinkula, Jorma
    Linköping University, Department of Clinical and Experimental Medicine, Division of Microbiology and Molecular Medicine. Linköping University, Faculty of Health Sciences.
    Holm, Einar
    Umeå University, Sweden.
    Predictive value of telenursing complaints in influenza surveillance: a prospective cohort study in Sweden2013Conference paper (Other academic)
  • 13.
    Timpka, Toomas
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Health and Developmental Care, Center for Public Health.
    Spreco, Armin
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences.
    Dahlström, Örjan
    Linköping University, The Swedish Institute for Disability Research. Linköping University, Department of Behavioural Sciences and Learning, Disability Research. Linköping University, Faculty of Arts and Sciences.
    Eriksson, Olle
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Gursky, Elin
    ANSER/Analytic Services Inc, Arlington, Virginia, USA.
    Ekberg, Joakim
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences.
    Strömgren, Magnus
    Umeå University, Sweden.
    Karlsson, David
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Hinkula, Jorma
    Linköping University, Department of Clinical and Experimental Medicine, Division of Microbiology and Molecular Medicine. Linköping University, Faculty of Health Sciences.
    Holm, Einar
    Umeå University, Sweden.
    Intentions to perform non-pharmaceutical protective behaviors during influenza outbreaks: a cross-sectional study of a representative sample of the Swedish adult population2013Conference paper (Other academic)
  • 14.
    Timpka, Toomas
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Health and Developmental Care, Center for Public Health.
    Spreco, Armin
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences.
    Eriksson, Olle
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Dahlström, Örjan
    Linköping University, Department of Behavioural Sciences and Learning, Disability Research. Linköping University, Faculty of Arts and Sciences. Linköping University, The Swedish Institute for Disability Research.
    Gursky, E. A.
    Analytic Serv Inc, VA USA.
    Stromgren, M.
    Umeå University, Sweden.
    Holm, E.
    Umeå University, Sweden.
    Ekberg, Joakim
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences.
    Hinkula, Jorma
    Linköping University, Department of Clinical and Experimental Medicine, Division of Microbiology and Molecular Medicine. Linköping University, Faculty of Health Sciences.
    Nyce, J. M.
    Ball State University, IN 47306 USA.
    Eriksson, Henrik
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Predictive performance of telenursing complaints in influenza surveillance: a prospective cohort study in Sweden2014In: Eurosurveillance, ISSN 1025-496X, E-ISSN 1560-7917, Vol. 19, no 46, p. 24-32Article in journal (Refereed)
    Abstract [en]

    Syndromic data sources have been sought to improve the timely detection of increased influenza transmission. This study set out to examine the prospective performance of telenursing chief complaints in predicting influenza activity. Data from two influenza seasons (2007/08 and 2008/09) were collected in a Swedish county (population 427,000) to retrospectively determine which grouping of telenursing chief complaints had the largest correlation with influenza case rates. This grouping was prospectively evaluated in the three subsequent seasons. The best performing telenursing complaint grouping in the retrospective algorithm calibration was fever (child, adult) and syncope (r=0.66; pless than0.001). In the prospective evaluation, the performance of 14-day predictions was acceptable for the part of the evaluation period including the 2009 influenza pandemic (area under the curve (AUC)=0.84; positive predictive value (PPV)=0.58), while it was strong (AUC=0.89; PPV=0.93) for the remaining evaluation period including only influenza winter seasons. We recommend the use of telenursing complaints for predicting winter influenza seasons. The method requires adjustments when used during pandemics.

  • 15.
    Timpka, Toomas
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Health and Developmental Care, Center for Public Health.
    Spreco, Armin
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Health and Developmental Care, Center for Public Health.
    Gursky, Elin
    National Strategies Support Directorate, ANSER/Analytic Services Inc, Arlington, Virginia, USA.
    Eriksson, Olle
    Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
    Dahlström, Örjan
    Linköping University, The Swedish Institute for Disability Research. Linköping University, Department of Behavioural Sciences and Learning, Disability Research. Linköping University, Faculty of Arts and Sciences.
    Strömgren, Magnus
    Umeå University, Sweden.
    Ekberg, Joakim
    Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Health and Developmental Care, Center for Public Health.
    Pilemalm, Sofie
    Linköping University, Department of Management and Engineering, Information Systems. Linköping University, Faculty of Arts and Sciences.
    Karlsson, David
    Linköping University, Department of Computer and Information Science, Human-Centered systems. Linköping University, The Institute of Technology.
    Hinkula, Jorma
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Clinical and Experimental Medicine, Division of Microbiology and Molecular Medicine.
    Holm, Einar
    Umeå University, Sweden.
    Intentions to perform non-pharmaceutical protective behaviors during influenza outbreaks in Sweden: a cross-sectional study following a mass vaccination campaign2014In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 9, no 3, p. e91060-Article in journal (Refereed)
    Abstract [en]

    Failure to incorporate the beliefs and attitudes of the public into theoretical models of preparedness has been identified as a weakness in strategies to mitigate infectious disease outbreaks. We administered a cross-sectional telephone survey to a representative sample (n = 443) of the Swedish adult population to examine whether self-reported intentions to improve personal hygiene and increase social distancing during influenza outbreaks could be explained by trust in official information, self-reported health (SF-8), sociodemographic factors, and determinants postulated in protection motivation theory, namely threat appraisal and coping appraisal. The interviewees were asked to make their appraisals for two scenarios: a) an influenza with low case fatality and mild lifestyle impact; b) severe influenza with high case fatality and serious disturbances of societal functions. Every second respondent (50.0%) reported high trust in official information about influenza. The proportion that reported intentions to take deliberate actions to improve personal hygiene during outbreaks ranged between 45–85%, while less than 25% said that they intended to increase social distancing. Multiple logistic regression models with coping appraisal as the explanatory factor most frequently contributing to the explanation of the variance in intentions showed strong discriminatory performance for staying home while not ill (mild outbreaks: Area under the curve [AUC] 0.85 (95% confidence interval 0.82;0.89), severe outbreaks AUC 0.82 (95% CI 0.77;0.85)) and acceptable performance with regard to avoiding public transportation (AUC 0.78 (0.74;0.82), AUC 0.77 (0.72;0.82)), using handwash products (AUC 0.70 (0.65;0.75), AUC 0.76 (0.71;0.80)), and frequently washing hands (AUC 0.71 (0.66;0.76), AUC 0.75 (0.71;0.80)). We conclude that coping appraisal was the explanatory factor most frequently included in statistical models explaining self-reported intentions to carry out non-pharmaceutical health actions in the Swedish outlined context, and that variations in threat appraisal played a smaller role in these models despite scientific uncertainties surrounding a recent mass vaccination campaign.

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