Gert van der Hoek
In Memoriam - Editor, Senior Moderator
Dutch researchers developed a tool for early detection of local outbreaks of Lower-Respiratory Infections (LRI), including Q-fever.
To use this tool for detection of future outbreaks, you need sufficient data, ideally a nationwide electronic health-care information exchange.
Could be combined with virological self-sampling and internetbased surveillance and last but not least: reports from local GP's.
You need good data, real-time, which are not available yet. The researchers used the data and than looked back.
Interesting development.
Epidemiol Infect. 2010 May 18:1-8. [Epub ahead of print]
In search of hidden Q-fever outbreaks: linking syndromic hospital clusters to infected goat farms.
VAN DEN Wijngaard CC, Dijkstra F, VAN Pelt W, VAN Asten L, Kretzschmar M, Schimmer B, Nagelkerke NJ, Vellema P, Donker GA, Koopmans MP.
National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
Abstract
SUMMARY
Large Q-fever outbreaks were reported in The Netherlands from May 2007 to 2009, with dairy-goat farms as the putative source. Since Q-fever outbreaks at such farms were first reported in 2005, we explored whether there was evidence of human outbreaks before May 2007.
Space-time scan statistics were used to look for clusters of lower-respiratory infections (LRIs), hepatitis, and/or endocarditis in hospitalizations, 2005-2007.
We assessed whether these were plausibly caused by Q fever, using patients' age, discharge diagnoses, indications for other causes, and overlap with reported Q fever in goats/humans.
For seven detected LRI clusters and one hepatitis cluster, we considered Q fever a plausible cause. One of these clusters reflected the recognized May 2007 outbreak.
Real-time syndromic surveillance would have detected four of the other clusters in 2007, one in 2006 and two in 2005, which might have resulted in detection of Q-fever outbreaks up to 2 years earlier.
http://www.ncbi.nlm.nih.gov/pubmed/20478085
This article explains more in detail the statistical surveillance.
PLoS One. 2010 Apr 29;5(4):e10406.
Syndromic surveillance for local outbreaks of lower-respiratory infections: would it work?
van den Wijngaard CC, van Asten L, van Pelt W, Doornbos G, Nagelkerke NJ, Donker GA, van der Hoek W, Koopmans MP.
Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands. kees.van.den.wijngaard@rivm.nl
Abstract
BACKGROUND: Although syndromic surveillance is increasingly used to detect unusual illness, there is a debate whether it is useful for detecting local outbreaks. We evaluated whether syndromic surveillance detects local outbreaks of lower-respiratory infections (LRIs) without swamping true signals by false alarms.
METHODS AND FINDINGS: Using retrospective hospitalization data, we simulated prospective surveillance for LRI-elevations. Between 1999-2006, a total of 290762 LRIs were included by date of hospitalization and patients place of residence (>80% coverage, 16 million population).
Two large outbreaks of Legionnaires disease in the Netherlands were used as positive controls to test whether these outbreaks could have been detected as local LRI elevations. We used a space-time permutation scan statistic to detect LRI clusters.
We evaluated how many LRI-clusters were detected in 1999-2006 and assessed likely causes for the cluster-signals by looking for significantly higher proportions of specific hospital discharge diagnoses (e.g. Legionnaires disease) and overlap with regional influenza elevations.
We also evaluated whether the number of space-time signals can be reduced by restricting the scan statistic in space or time. In 1999-2006 the scan-statistic detected 35 local LRI clusters, representing on average 5 clusters per year. The known Legionnaires' disease outbreaks in 1999 and 2006 were detected as LRI-clusters, since cluster-signals were generated with an increased proportion of Legionnaires disease patients (p:<0.0001). 21 other clusters coincided with local influenza and/or respiratory syncytial virus activity, and 1 cluster appeared to be a data artifact.
For 11 clusters no likely cause was defined, some possibly representing as yet undetected LRI-outbreaks. With restrictions on time and spatial windows the scan statistic still detected the Legionnaires' disease outbreaks, without loss of timeliness and with less signals generated in time (up to 42% decline).
CONCLUSIONS: To our knowledge this is the first study that systematically evaluates the performance of space-time syndromic surveillance with nationwide high coverage data over a longer period.
The results show that syndromic surveillance can detect local LRI-outbreaks in a timely manner, independent of laboratory-based outbreak detection. Furthermore, since comparatively few new clusters per year were observed that would prompt investigation, syndromic hospital-surveillance could be a valuable tool for detection of local LRI-outbreaks.
http://www.ncbi.nlm.nih.gov/pubmed/20454449
Full article:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2861591/
This tool could be combined with virological self-sampling:
Epidemiol Infect. 2008 Feb;136(2):222-4. Epub 2007 Mar 30.
Linking syndromic surveillance with virological self-sampling.
Cooper DL, Smith GE, Chinemana F, Joseph C, Loveridge P, Sebastionpillai P, Gerard E, Zambon M.
Regional Surveillance Unit, Health Protection Agency West Midlands, Birmingham, UK. Duncan.cooper@hpa.org.uk
Abstract
Calls to a UK national telephone health helpline (NHS Direct) have been used for syndromic surveillance, aiming to provide early warning of rises in community morbidity. We investigated whether self-sampling by NHS Direct callers could provide viable samples for influenza culture. We recruited 294 NHS Direct callers and sent them self-sampling kits.
Callers were asked to take a swab from each nostril and post them to the laboratory. Forty-two per cent of the samples were returned, 16.2% were positive on PCR for influenza (16 influenza A(H3N2), three influenza A (H1N1), four influenza B) and eight for RSV (5.6%). The mean time between the NHS Direct call and laboratory analysis was 7.4 days.
These samples provided amongst the earliest influenza reports of the season, detected multiple influenza strains, and augmented a national syndromic surveillance system. Self-sampling is a feasible method of enhancing community-based surveillance programmes for detection of influenza.
http://www.ncbi.nlm.nih.gov/pubmed/17394678
Also may be combined with internetbased surveillance:
Internet-based monitoring of influenza-like illness (ILI) in the general population of the Netherlands during the 2003-2004 influenza season.
Marquet RL, Bartelds AI, van Noort SP, Koppeschaar CE, Paget J, Schellevis FG, van der Zee J.
NIVEL (Netherlands Institute for Health Services Research), The Netherlands, Utrecht, The Netherlands. r.marquet@nivel.nl
Abstract
BACKGROUND: An internet-based survey of influenza-like illness (ILI)--the Great Influenza Survey or GIS--was launched in the Netherlands in the 2003-2004 influenza season. The aim of the present study was to validate the representativeness of the GIS population and to compare the GIS data with the official ILI data obtained by Dutch GPs participating in the Dutch Sentinel Practice Network.
METHOD: Direct mailings to schools and universities, and repeated interviews on television and radio, and in newspapers were used to kindle the enthusiasm of a broad section of the public for GIS.
Strict symptomatic criteria for ILI were formulated with the assistance of expert institutes and only participants who responded at least five times to weekly e-mails asking them about possible ILI symptoms were included in the survey. Validation of GIS was done at different levels:
1) some key demographic (age distribution) and public health statistics (prevalence of asthma and diabetes, and influenza vaccination rates) for the Dutch population were compared with corresponding figures calculated from GIS;
2) the ILI rates in GIS were compared with the ILI consultation rates reported by GPs participating in the Dutch Sentinel Practice Network.
RESULTS: 13,300 persons (53% of total responders), replied at least five times to weekly e-mails and were included in the survey.
As expected, there was a marked under-representation of the age groups 0-10 years and 81->90 years in the GIS population, although the similarities were remarkable for most other age groups, albeit that the age groups between 21 and 70 years were slightly overrepresented.
There were striking similarities between GIS and the Dutch population with regard to the prevalence of asthma (6.4% vs. 6.9%) and the influenza vaccination rates, and to a lesser degree for diabetes (2.4% vs. 3.5%). The vaccination rates in patients with asthma or diabetes, and persons older than 65 years were 68%, 85%, and 85% respectively in GIS, while the corresponding percentages in the Dutch population were 73%, 85% and 87%.
There was also a marked similarity between the seasonal course of ILI measured by GIS and the GPs. Although the ILI rate in GIS was about 10 times higher, the curves followed an almost similar pattern, with peak incidences occurring in the same week.
CONCLUSION: The current study demonstrates that recruitment of a high number of persons willing to participate in on-line health surveillance is feasible. The information gathered proved to be reliable, as it paralleled the information obtained via an undisputed route. We believe that the interactive nature of GIS and the appealing subject were keys to its success.
http://www.ncbi.nlm.nih.gov/pubmed/17018161
To use this tool for detection of future outbreaks, you need sufficient data, ideally a nationwide electronic health-care information exchange.
Could be combined with virological self-sampling and internetbased surveillance and last but not least: reports from local GP's.
You need good data, real-time, which are not available yet. The researchers used the data and than looked back.
Interesting development.
Epidemiol Infect. 2010 May 18:1-8. [Epub ahead of print]
In search of hidden Q-fever outbreaks: linking syndromic hospital clusters to infected goat farms.
VAN DEN Wijngaard CC, Dijkstra F, VAN Pelt W, VAN Asten L, Kretzschmar M, Schimmer B, Nagelkerke NJ, Vellema P, Donker GA, Koopmans MP.
National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
Abstract
SUMMARY
Large Q-fever outbreaks were reported in The Netherlands from May 2007 to 2009, with dairy-goat farms as the putative source. Since Q-fever outbreaks at such farms were first reported in 2005, we explored whether there was evidence of human outbreaks before May 2007.
Space-time scan statistics were used to look for clusters of lower-respiratory infections (LRIs), hepatitis, and/or endocarditis in hospitalizations, 2005-2007.
We assessed whether these were plausibly caused by Q fever, using patients' age, discharge diagnoses, indications for other causes, and overlap with reported Q fever in goats/humans.
For seven detected LRI clusters and one hepatitis cluster, we considered Q fever a plausible cause. One of these clusters reflected the recognized May 2007 outbreak.
Real-time syndromic surveillance would have detected four of the other clusters in 2007, one in 2006 and two in 2005, which might have resulted in detection of Q-fever outbreaks up to 2 years earlier.
http://www.ncbi.nlm.nih.gov/pubmed/20478085
This article explains more in detail the statistical surveillance.
PLoS One. 2010 Apr 29;5(4):e10406.
Syndromic surveillance for local outbreaks of lower-respiratory infections: would it work?
van den Wijngaard CC, van Asten L, van Pelt W, Doornbos G, Nagelkerke NJ, Donker GA, van der Hoek W, Koopmans MP.
Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands. kees.van.den.wijngaard@rivm.nl
Abstract
BACKGROUND: Although syndromic surveillance is increasingly used to detect unusual illness, there is a debate whether it is useful for detecting local outbreaks. We evaluated whether syndromic surveillance detects local outbreaks of lower-respiratory infections (LRIs) without swamping true signals by false alarms.
METHODS AND FINDINGS: Using retrospective hospitalization data, we simulated prospective surveillance for LRI-elevations. Between 1999-2006, a total of 290762 LRIs were included by date of hospitalization and patients place of residence (>80% coverage, 16 million population).
Two large outbreaks of Legionnaires disease in the Netherlands were used as positive controls to test whether these outbreaks could have been detected as local LRI elevations. We used a space-time permutation scan statistic to detect LRI clusters.
We evaluated how many LRI-clusters were detected in 1999-2006 and assessed likely causes for the cluster-signals by looking for significantly higher proportions of specific hospital discharge diagnoses (e.g. Legionnaires disease) and overlap with regional influenza elevations.
We also evaluated whether the number of space-time signals can be reduced by restricting the scan statistic in space or time. In 1999-2006 the scan-statistic detected 35 local LRI clusters, representing on average 5 clusters per year. The known Legionnaires' disease outbreaks in 1999 and 2006 were detected as LRI-clusters, since cluster-signals were generated with an increased proportion of Legionnaires disease patients (p:<0.0001). 21 other clusters coincided with local influenza and/or respiratory syncytial virus activity, and 1 cluster appeared to be a data artifact.
For 11 clusters no likely cause was defined, some possibly representing as yet undetected LRI-outbreaks. With restrictions on time and spatial windows the scan statistic still detected the Legionnaires' disease outbreaks, without loss of timeliness and with less signals generated in time (up to 42% decline).
CONCLUSIONS: To our knowledge this is the first study that systematically evaluates the performance of space-time syndromic surveillance with nationwide high coverage data over a longer period.
The results show that syndromic surveillance can detect local LRI-outbreaks in a timely manner, independent of laboratory-based outbreak detection. Furthermore, since comparatively few new clusters per year were observed that would prompt investigation, syndromic hospital-surveillance could be a valuable tool for detection of local LRI-outbreaks.
http://www.ncbi.nlm.nih.gov/pubmed/20454449
Full article:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2861591/
This tool could be combined with virological self-sampling:
Epidemiol Infect. 2008 Feb;136(2):222-4. Epub 2007 Mar 30.
Linking syndromic surveillance with virological self-sampling.
Cooper DL, Smith GE, Chinemana F, Joseph C, Loveridge P, Sebastionpillai P, Gerard E, Zambon M.
Regional Surveillance Unit, Health Protection Agency West Midlands, Birmingham, UK. Duncan.cooper@hpa.org.uk
Abstract
Calls to a UK national telephone health helpline (NHS Direct) have been used for syndromic surveillance, aiming to provide early warning of rises in community morbidity. We investigated whether self-sampling by NHS Direct callers could provide viable samples for influenza culture. We recruited 294 NHS Direct callers and sent them self-sampling kits.
Callers were asked to take a swab from each nostril and post them to the laboratory. Forty-two per cent of the samples were returned, 16.2% were positive on PCR for influenza (16 influenza A(H3N2), three influenza A (H1N1), four influenza B) and eight for RSV (5.6%). The mean time between the NHS Direct call and laboratory analysis was 7.4 days.
These samples provided amongst the earliest influenza reports of the season, detected multiple influenza strains, and augmented a national syndromic surveillance system. Self-sampling is a feasible method of enhancing community-based surveillance programmes for detection of influenza.
http://www.ncbi.nlm.nih.gov/pubmed/17394678
Also may be combined with internetbased surveillance:
Internet-based monitoring of influenza-like illness (ILI) in the general population of the Netherlands during the 2003-2004 influenza season.
Marquet RL, Bartelds AI, van Noort SP, Koppeschaar CE, Paget J, Schellevis FG, van der Zee J.
NIVEL (Netherlands Institute for Health Services Research), The Netherlands, Utrecht, The Netherlands. r.marquet@nivel.nl
Abstract
BACKGROUND: An internet-based survey of influenza-like illness (ILI)--the Great Influenza Survey or GIS--was launched in the Netherlands in the 2003-2004 influenza season. The aim of the present study was to validate the representativeness of the GIS population and to compare the GIS data with the official ILI data obtained by Dutch GPs participating in the Dutch Sentinel Practice Network.
METHOD: Direct mailings to schools and universities, and repeated interviews on television and radio, and in newspapers were used to kindle the enthusiasm of a broad section of the public for GIS.
Strict symptomatic criteria for ILI were formulated with the assistance of expert institutes and only participants who responded at least five times to weekly e-mails asking them about possible ILI symptoms were included in the survey. Validation of GIS was done at different levels:
1) some key demographic (age distribution) and public health statistics (prevalence of asthma and diabetes, and influenza vaccination rates) for the Dutch population were compared with corresponding figures calculated from GIS;
2) the ILI rates in GIS were compared with the ILI consultation rates reported by GPs participating in the Dutch Sentinel Practice Network.
RESULTS: 13,300 persons (53% of total responders), replied at least five times to weekly e-mails and were included in the survey.
As expected, there was a marked under-representation of the age groups 0-10 years and 81->90 years in the GIS population, although the similarities were remarkable for most other age groups, albeit that the age groups between 21 and 70 years were slightly overrepresented.
There were striking similarities between GIS and the Dutch population with regard to the prevalence of asthma (6.4% vs. 6.9%) and the influenza vaccination rates, and to a lesser degree for diabetes (2.4% vs. 3.5%). The vaccination rates in patients with asthma or diabetes, and persons older than 65 years were 68%, 85%, and 85% respectively in GIS, while the corresponding percentages in the Dutch population were 73%, 85% and 87%.
There was also a marked similarity between the seasonal course of ILI measured by GIS and the GPs. Although the ILI rate in GIS was about 10 times higher, the curves followed an almost similar pattern, with peak incidences occurring in the same week.
CONCLUSION: The current study demonstrates that recruitment of a high number of persons willing to participate in on-line health surveillance is feasible. The information gathered proved to be reliable, as it paralleled the information obtained via an undisputed route. We believe that the interactive nature of GIS and the appealing subject were keys to its success.
http://www.ncbi.nlm.nih.gov/pubmed/17018161