Evaluation of Syndromic Surveillance in the Netherlands: Its Added Value and Recommendations for Implementation

In the last decade, syndromic surveillance has increasingly been used worldwide for detecting increases or outbreaks of infectious diseases that might be missed by surveillance based on laboratory diagnoses and notifications by clinicians alone. There is, however, an ongoing debate about the feasibility of syndromic surveillance and its potential added value. Here we present our perspective on syndromic surveillance, based on the results of a retrospective analysis of syndromic data from six Dutch healthcare registries, covering 1999–2009 or part of this period. These registries had been designed for other purposes, but were evaluated for their potential use in signalling infectious disease dynamics and outbreaks. Our results show that syndromic surveillance clearly has added value in revealing the blind spots of traditional surveillance, in particular by detecting unusual, local outbreaks independently of diagnoses of specific pathogens, and by monitoring disease burden and virulence shifts of common pathogens. Therefore we recommend the use of syndromic surveillance for these applications

Syndromic Surveillance for Local Outbreaks of Lower-Respiratory Infections: Would It Work?

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. (aut. ref.

Geographic Distribution of Staphylococcus aureus Causing Invasive Infections in Europe: A Molecular-Epidemiological Analysis

Hajo Grundmann and colleagues describe the development of a new interactive mapping tool for analyzing the spatial distribution of invasive Staphylococcus aureus clones

Self-reported symptoms and health complaints associated with exposure to Ixodes ricinus-borne pathogens

Background: The impact of infections with tick-borne pathogens (TBPs) other than Borrelia burgdorferi (s.l.) and tick-borne encephalitis virus (TBEV) on public health in Europe remains unclear. Our goal is to evaluate whether the presence of these TBPs in ticks can be associated with self-reported health complaints. Methods: We enrolled individuals who were bitten by I. ricinus between 2012 and 2015 and collected their relevant demographic and clinical information using a self-administered online questionnaire. A total of 4163 I. ricinus ticks sent by the participants were subject to molecular analyses for detection of specific TBPs. Associations between the presence of TBPs in ticks and self-reported complaints and symptoms were evaluated by means of a stepwise approach using a generalized linear model (GLM). Results: Of 17 self-reported complaints and symptoms significant in the univariate analyses, 3 had a highly significant association (P < 0.01) with at least one TBP in the multivariate analysis. Self-reported Lyme borreliosis was significantly associated (P < 0.001) with B. burgdorferi (s.l.) infection. Facial paralysis was associated (P < 0.01) with infection with B. miyamotoi, N. mikurensis and R. helvetica. Finally, a significant association (P < 0.001) was found between nocturnal sweating and A. phagocytophilum. Conclusions: We found associations between the presence of TBPs in ticks feeding on humans and self-reported symptoms. Due to the subjective nature of such reports and the fact that infection was determined in the ticks and not in the patient samples, further prospective studies utilizing diagnostic modalities should be performed before any clinical outcome can be causally linked to infection with TBPs. Graphical Abstract: [Figure not available: see fulltext.]

Seroprevalence and Risk Factors of Lyme Borreliosis in The Netherlands: A Population-Based Cross-Sectional Study

Lyme borreliosis (LB) is not notifiable in many European countries, and accurate data on the incidence are often lacking. This study aimed to determine the seroprevalence of Borrelia burgdorferi sensu lato (s.l.)-specific antibodies in the general population of The Netherlands, and to determine risk factors associated with seropositivity. Sera and questionnaires were obtained from participants (n = 5592, aged 0-88 years) enrolled in a nationwide serosurveillance study. The sera were tested for B. burgdorferi s.l.-specific IgM and IgG antibodies using ELISA and immunoblot. Seroprevalence was estimated controlling for the survey design. Risk factors for seropositivity were analyzed using a generalized linear mixed-effect model. In 2016/2017, the seroprevalence in The Netherlands was 4.4% (95% CI 3.5-5.2). Estimates were higher in men (5.7% [95% CI 4.4-7.2]) than in women (3.1% [95% CI 2.0-4.0]), and increased with age from 2.6% (95% CI 1.4-4.4) in children to 7.7% (95% CI 5.9-7.9) in 60- to 88-year-olds. The seroprevalence for B. burgdorferi s.l. in the general population in The Netherlands was comparable to rates reported in European countries. The main risk factors for seropositivity were increasing age, being male and the tick bite frequency. The dynamics of LB infection are complex and involve variables from various disciplines. This could be further elucidated using infectious disease modelling

Validation of Syndromic Surveillance for Respiratory Pathogen Activity

The studied respiratory syndromes are suitable for syndromic surveillance because they reflect respiratory pathogen activity pattern

Comparing Pandemic to Seasonal Influenza Mortality: Moderate Impact Overall but High Mortality in Young Children

Background: We assessed the severity of the 2009 influenza pandemic by comparing pandemic mortality to seasonal influenza mortality. However, reported pandemic deaths were laboratory-confirmed - and thus an underestimation - whereas seasonal influenza mortality is often more inclusively estimated. For a valid comparison, our study used the same statistical methodology and data types to estimate pandemic and seasonal influenza mortality. Methods and Findings: We used data on all-cause mortality (1999-2010, 100% coverage, 16.5 million Dutch population) and influenza-like-illness (ILI) incidence (0.8% coverage). Data was aggregated by week and age category. Using generalized estimating equation regression models, we attributed mortality to influenza by associating mortality with ILI-incidence, while adjusting for annual shifts in association. We also adjusted for respiratory syncytial virus, hot/cold weather, other seasonal factors and autocorrelation. For the 2009 pandemic season, we estimated 612 (range 266-958) influenza-attributed deaths; for seasonal influen