liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Performance of eHealth data sources in local influenza surveillance: a 5-year open cohort study
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.ORCID iD: 0000-0001-6049-5402
Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Health Sciences.
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.
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
Show others and affiliations
2014 (English)In: Journal of Medical Internet Research, ISSN 1438-8871, E-ISSN 1438-8871, Vol. 16, no 4, e116- p.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. Vol. 16, no 4, e116- p.
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:liu:diva-106758DOI: 10.2196/jmir.3099ISI: 000336501600017PubMedID: 24776527OAI: oai:DiVA.org:liu-106758DiVA: diva2:718559
Available from: 2014-05-21 Created: 2014-05-21 Last updated: 2017-12-05Bibliographically approved
In thesis
1. Epidemiological and statistical basis for detection and prediction of influenza epidemics
Open this publication in new window or tab >>Epidemiological and statistical basis for detection and prediction of influenza epidemics
2017 (English)Doctoral 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.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2017. 102 p.
Series
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1566
National Category
Biomedical Laboratory Science/Technology Bioinformatics and Systems Biology Computer Science Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
urn:nbn:se:liu:diva-136553 (URN)10.3384/diss.diva-136553 (DOI)9789176855690 (ISBN)
Public defence
2017-05-19, Belladonnan, Campus US, Linköping, 09:00 (English)
Opponent
Supervisors
Available from: 2017-04-19 Created: 2017-04-19 Last updated: 2017-04-20Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textPubMed

Authority records BETA

Timpka, ToomasSpreco, ArminDahlström, ÖrjanEriksson, OlleEkberg, JoakimBlomqvist, EvaKarlsson, DavidEriksson, HenrikHinkula, Jorma

Search in DiVA

By author/editor
Timpka, ToomasSpreco, ArminDahlström, ÖrjanEriksson, OlleEkberg, JoakimBlomqvist, EvaKarlsson, DavidEriksson, HenrikHinkula, Jorma
By organisation
Division of Community MedicineFaculty of Health SciencesCenter for Public HealthDisability ResearchFaculty of Arts and SciencesThe Swedish Institute for Disability ResearchStatisticsHuman-Centered systemsThe Institute of TechnologyDivision of Microbiology and Molecular Medicine
In the same journal
Journal of Medical Internet Research
Medical and Health Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 306 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf