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Background: We aimed to estimate vaccine effectiveness (VE) against COVID-19 mortality, and to explore whether an increased risk of non-COVID-19 mortality exists in the weeks following a COVID-19 vaccine dose. Methods: National registries of causes of death, COVID-19 vaccination, specialized health care and long-term care reimbursements were linked by a unique person identifier using data from 1 January 2021 to 31 January 2022. We used Cox regression with calendar time as underlying time scale to, firstly, estimate VE against COVID-19 mortality after primary and first booster vaccination, per month since vaccination and, secondly, estimate risk of non-COVID-19 mortality in the 5 or 8 weeks following a first, second or first booster dose, adjusting for birth year, sex, medical risk group and country of origin. Results: VE against COVID-19 mortality was >90% for all age groups two months after completion of the primary series. VE gradually decreased thereafter, to around 80% at 7-8 months post-primary series for most groups, and around 60% for elderly receiving a high level of long-term care and for people aged 90+ years. Following a first booster dose, the VE increased to >85% in all groups. The risk of non-COVID-19 mortality was lower or similar in the 5 or 8 weeks following a first dose compared to no vaccination, as well as following a second dose compared to one dose and a booster compared to two doses, for all age and long-term care groups. Conclusion: At the population level, COVID-19 vaccination greatly reduced the risk of COVID-19 mortality and no increased risk of death from other causes was observed.

Background: The burden of severe influenza virus infections is poorly known, for which surveillance of severe acute respiratory infection (SARI) is encouraged. Hospitalized SARI patients are however not always tested for influenza virus infection. Thus, to estimate the impact of influenza circulation we studied how influenza in primary care relates to intensive care unit (ICU) admissions using a modelling approach. Methods: We used time-series regression modelling to estimate a) the number of SARI admissions to ICU associated with medically attended influenza infections in primary care; b) how this varies by season; and c) the time lag between SARI and influenza time series. We analysed weekly adult ICU admissions (registry data) and adult influenza incidence (primary care surveillance data) from July 2007 through June 2016. Results: Depending on the year, 0% to 12% of annual SARI admissions were associated with influenza (0-554 in absolute numbers; population rate: 0/10 000-0.39/10 000 inhabitants), up to 27% during influenza epidemics. The average optimal fitting lag was +1 week (SARI trend preceding influenza by 1 week), varying between seasons (-1 to +4) with most seasons showing positive lags. Conclusion: Up to 12% of yearly SARI admissions to adult ICU are associated with influenza, but with large year-to-year variation and higher during influenza epidemics. In most years, SARI increases earlier than medically attended influenza infections in the general population. SARI surveillance could thus complement influenza-like illness surveillance by providing an indication of the season-specific burden of severe influenza infections and potential early warning of influenza activity and severity. (aut. ref.)

Background: Climate change, environmental change, and globalization affect the geographical distribution of vector-borne diseases. Temperate regions should be prepared for emerging diseases and learn from each other's experiences.Objectives: The vector-borne disease preparedness in two regions, Beijing and the Netherlands, were compared in order understand their similarities and differences leading to learning points on this complex topic.Methods: A comparative study was performed using interviews with vector-borne disease experts from Beijing and the Netherlands and supplemented by literature.Findings: In Beijing, syndromic surveillance is a priority for the identification of suspected vector-borne disease cases. In the Netherlands, the main surveillance emphasis is on laboratory confirmed vector-borne disease cases. Vector-surveillance at potential points of entry and other high-risk locations is performed according to the International Health Regulation (2005) in both settings. Beijing controls invasive and native mosquitos, which is not the case in the Netherlands. In Beijing, vector surveillance is performed to measure mosquito density around hospitals, this is not observed in the Dutch setting. Health risks posed by ticks are a priority in urban areas in the Netherlands, and the public is educated in self-protection. In contrast, ticks seem to occur less often in Beijing's urban areas.Conclusions: The vector-borne disease context framework allowed us to compare the vector-borne disease preparedness between Beijing and the Netherlands, despite differences in vector-borne disease challenges. We can learn valuable lessons concerning surveillance and early detection of emerging vector-borne diseases when comparing the preparedness between different regions.

Introduction Influenza vaccination, besides protecting traditional risk groups, can protect employees and reduce illness-related absence, which is especially relevant in sectors with staff shortages. This study describes current knowledge of influenza vaccination in teachers and estimates its potential impact. Methods We conducted a scoping review of the considerations for and impact of influenza vaccination of schoolteachers (grey and scientific literature up to 2020 March, complemented with interviews). We then estimated the potential impact of teacher vaccination in the Netherlands, with different scenarios of vaccine uptake for 3 influenza seasons (2016–2019). Using published data on multiple input parameters, we calculated potentially averted absenteeism notifications, averted absenteeism duration and averted doctor visits for influenza. Results Only one scientific paper reported on impact; it showed lower absenteeism in vaccinated teachers, whereas more knowledge of vaccination impact was deemed crucial by 50% of interviewed experts. The impact for the Netherlands of a hypothetical 50% vaccine uptake was subsequently estimated: 74–293 potentially averted physician visits and 11,178–28,896 potentially averted days of influenza absenteeism (on ≈200,000 total teacher population). An estimated 12–32 vaccinations were required to prevent one teacher sick-leave notification, or 3.5–9.1 vaccinations to prevent one day of teacher absenteeism (2016–2019). Conclusion Scientific publications on influenza vaccination in teachers are few, while public interest has increased to reduce teacher shortages. However, school boards and public health experts indicate requiring knowledge of impact when considering this vaccination. Estimations of 3.5–9.1 vaccinated teachers preventing one day of influenza-related sick leave suggest a possible substantial vaccination impact on absenteeism. Financial incentives, more accessible on-site vaccinations at workplaces, or both, are expected to increase uptake, but more research is needed on teachers’ views and vaccine uptake potential and its cost-effectiveness. Piloting free on-site influenza vaccination in several schools could provide further information on teacher participation.

The coronavirus disease (COVID-19) global pandemic is ongoing, with Europe and the Americas the current epicentres of transmission. As at 1 January, 2021, over 82 million cases and 1.8 million deaths have been reported globally [1,2]. In Europe alone, the number of cases reported has now passed 26 million, with over 580,000 laboratory-confirmed COVID-19 deaths [3,4]. As many COVID-19 cases and deaths may not be confirmed by virological testing, and as the pandemic causes a range of indirect and collateral harms, the officially reported figures of laboratory-confirmed COVID-19 cases and deaths represent only a part of the total disease, mortality and overall public health burden associated with the pandemic. Here we report some noteworthy estimates of a marked increase in excess all-cause mortality in Europe coinciding with a steep second wave of COVID-19 in many countries since September 2020.

Objective Intensive Care Unit (ICU) data are registered for quality monitoring in the Netherlands with near 100% coverage. They are a ‘big data’ type source that may be useful for infectious disease surveillance. We explored their potential to enhance the surveillance of influenza which is currently based on the milder end of the disease spectrum. We ultimately aim to set up a real-surveillance system of severe acute respiratory infections. Introduction While influenza-like-illness (ILI) surveillance is well-organized at primary care level in Europe, little data is available on more severe cases. With retrospective data from ICU’s we aim to fill this current knowledge gap and to explore its worth for prospective surveillance. Using multiple parameters proposed by the World Health Organization we estimated the burden of severe acute respiratory infections (SARI) to ICU and how this varies between influenza epidemics. Methods We analyzed weekly ICU admissions of adults in the Netherlands (2007-2016) from the national intensive care evaluation (NICE) quality registry (100% coverage of adult ICU in 2016; population size 14 million adults. A SARI syndrome was defined as admission diagnosis being any of 6 pneumonia or pulmonary sepsis codes in the Acute Physiology and Chronic Health Evaluation IV (APACHE IV) prognostic model. Influenza epidemic periods were retrieved from primary care sentinel influenza surveillance data. In recent years NICE has explored and promoted increased timeliness and automation of data transfer. Results Annually, 11-14% of medical admissions to adult ICUs were for a SARI (5-25% weekly). Admissions for bacterial pneumonia (59%) and pulmonary sepsis (25%) contributed most to ICU-SARI. Between influenza epidemics, severity indicators varied: ICU-SARI incidence (between 558-2,400 cumulated admissions nation-wide, rate: 0.40-1.71/10,000 inhabitants), average APACHE score (between 71-78), ICU-SARI mortality (between 13-20%), ICU-SARI/ILI ratio (between 8-17 SARI ICU cases per 1,000 expected medically attended influenza-like-illness in primary care), peak incidence (between 101-188 ICU-SARI admissions nationally in the highest week, rate: between 0.07-0.13/10,000 population). ICUs use different types of electronic health records (EHRs). Data submitted to the NICE registry is mainly based on routinely collected data extracted from these EHRs. The timeliness of data submission varies between a few weeks and three months. Together with ICUs, the NICE registry has recently undertaken actions to increase timeliness of ICU data submission. Conclusions In ICU data, great variation can be seen between the yearly influenza epidemic periods in terms of different influenza severity parameters. The parameters also complement each other by reflecting different aspects of severity. Prospective syndromic ICU-SARI surveillance, as proposed by the World Health Organization would provide insight into severity of ongoing influenza epidemics which differ from season to season. Currently a subset of hospitals provide data with a 6-week delay. This can be a worthwhile addition to current influenza surveillance, which, while timelier, is based on milder cases seen by general practitioners (primary care). Future increases in data timeliness will remain an aim.

Objective Weekly numbers of deaths are monitored to increase the capacity to deal with both expected and unusual (disease) events such as pandemic influenza, other infections and non-infectious incidents. The monitoring information can potentially be used to detect, track and estimate the impact of an outbreak or incident on all-cause mortality. Introduction The mortality monitoring system (initiated in 2009 during the influenza A(H1N1) pandemic) is a collaboration between the Centre for Infectious Disease Control (CIb) of National Institute for Public Health and the Environment (RIVM) and Statistics Netherlands. The system monitors nation-wide reported number of deaths (population size 2017: 17 million) from all causes, as cause of death information is not available real-time. Data is received from Statistics Netherlands by weekly emails. Methods Once a week the number of reported deaths is checked for excess above expected levels at 2 different time-lags: within 1 and 2 weeks after date of death (covering a median 43% and 96% of all deaths respectively). A weekly email bulletin reporting the findings is sent to the Infectious Disease Early Warning Unit (at CIb) and a summary of results is posted on the RIVM website. Any known concurrent and possibly related events are also reported. When excess deaths coincide with hot temperatures, the bulletin is sent to the Heat Plan Team (also at RIVM). Data are also sent to EuroMOMO which monitors excess mortality at a European level. For the Dutch system baselines and prediction limits are calculated using a 5 year historical period (updated each July). A serfling-like algorithm based on regression analysis is used to produce baselines which includes cyclical seasonal trends (models based on historical data in which weeks with extreme underreporting have been removed. Also periods with high excess mortality in winter and summer were removed so as not to influence the baseline with previous outbreaks). Results Increased mortality started two weeks after the influenza epidemic started and remained increased during the remainder of the influenza epidemic except for a drop in week 52 (coinciding with Christmas holidays) (Figure 1). Excess mortality was primarily observed in persons 75 years or older. Cumulative excess mortality was estimated at 7,503 deaths occurring during the 15 weeks of the 2016/2017 influenza epidemic (week 48 of 2016 through week 10 of 2017) and at 8,890 during the total winter season (44 weeks running from week 40 up to week 20 of the next year). Conclusions In terms of number of deaths during the winter season (weeks 40-20 the next year) and during the influenza epidemic (weeks 48-10), the 2016/2017 season in the Netherlands was of high severity compared with the previous five seasons. Mortality was only higher in the 2014/2015 season when the longest influenza epidemic was recorded of 21 weeks.

Background Viral interaction in which outbreaks of influenza and other common respiratory viruses might affect each other has been postulated by several short studies. Regarding longer time periods, influenza epidemics occasionally occur very early in the season, as during the 2009 pandemic. Whether early occurrence of influenza epidemics impacts outbreaks of other common seasonal viruses is not clear. Objectives We investigated whether early occurrence of influenza outbreaks coincides with shifts in the occurrence of other common viruses, including both respiratory and non-respiratory viruses. Methods We investigated time trends of and the correlation between positive laboratory diagnoses of eight common viruses in the Netherlands over a 10-year time period (2003–2012): influenza viruses types A and B, respiratory syncytial virus (RSV), rhinovirus, coronavirus, norovirus, enterovirus, and rotavirus. We compared trends in viruses between early and late influenza seasons. Results Between 2003 and 2012, influenza B, RSV, and coronavirus showed shifts in their occurrence when influenza A epidemics occurred earlier than usual (before week 1). Although shifts were not always consistently of the same type, when influenza type A hit early, RSV outbreaks tended to be delayed, coronavirus outbreaks tended to be intensified, and influenza virus type B tended not to occur at all. Occurrence of rhinovirus, norovirus, rotavirus, and enterovirus did not change. Conclusion When influenza A epidemics occured early, timing of the epidemics of several respiratory winter viruses usually occurring close in time to influenza A was affected, while trends in rhinoviruses (occurring in autumn) and trends in enteral viruses were not.

We estimated the influenza mortality burden in adults aged 60 years or older in the Netherlands in terms of years of life lost, taking into account competing mortality risks. Weekly laboratory surveillance data for influenza and other respiratory pathogens and weekly extreme temperature served as covariates in Poisson regression models fitted to weekly mortality data, specific to age group, for the period 1999-2000 through 2012-2013. Burden for age groups 60-64 years through 85-89 years was computed as years of life lost before age 90 (YLL90), using restricted mean lifetime survival analysis and accounting for competing risks. Influenza-attributable mortality burden was greatest for persons aged 80-84 years, at 914 YLL90 per 100,000 persons (95% uncertainty interval: 867, 963), followed by persons aged 85-89 years (787 YLL90/100,000; 95% uncertainty interval: 741, 834). Ignoring competing mortality risks in the computation of influenza-attributable YLL90 would lead to substantial overestimation of burden, from 3.5% for persons aged 60-64 years to 82% for those aged 80-89 years at death. Failure to account for competing mortality risks has implications for the accuracy of disease-burden estimates, especially among persons aged 80 years or older. Because the mortality burden borne by the elderly is notably high, prevention initiatives may benefit from being redesigned to more effectively prevent infection in the oldest age groups.

Background: Risk assessment and early warning (RAEW) are essential components of any infectious disease surveillance system. In light of the International Health Regulations (IHR)(2005), this study compares the organisation of RAEW in China and the Netherlands. The respective approaches towards surveillance of arboviral disease and unexplained pneumonia were analysed to gain a better understanding of the RAEW mode of operation. This study may be used to explore options for further strengthening of global collaboration and timely detection and surveillance of infectious disease outbreaks.Methods: A qualitative study design was used, combining data retrieved from the literature and from semi-structured interviews with Chinese (5 national-level and 6 provincial-level) and Dutch (5 national-level) experts.Results: The results show that some differences exist such as in the use of automated electronic components of the early warning system in China ('CIDARS'), compared to a more limited automated component in the Netherlands ('barometer'). Moreover, RAEW units in the Netherlands focus exclusively on infectious diseases, while China has a broader 'all hazard' approach (including for example chemical incidents). In the Netherlands, veterinary specialists take part at the RAEW meetings, to enable a structured exchange/assessment of zoonotic signals.Conclusion: Despite these differences, the main conclusion is that for the two infections studied, the early warning system in China and the Netherlands are remarkably similar considering their large differences in infectious disease history, population size and geographical setting. Our main recommendations are continued emphasis on international corporation that requires insight into national infectious disease surveillance systems, the usage of a One Health approach in infectious disease surveillance, and further exploration/strengthening of a combined syndromic and laboratory surveillance system.

Background. Because there may be substantial hidden mortality caused by common seasonal pathogens, we estimated the number of deaths in elderly persons attributable to viruses and bacteria for which robust weekly laboratory surveillance data were available. Methods. On weekly time series (1999-2007) we used regression models to associate total death counts in individuals aged 65-74, 75-84, and >= 85 years (a population of 2.5 million) with pathogen circulation-influenza A (season-specific), influenza B, respiratory syncytial virus (RSV), parainfluenza, enterovirus, rotavirus, norovirus, Campylobacter, and Salmonella-adjusted for extreme outdoor temperatures. Results. Influenza A and RSV were significantly (P= 75 years, and norovirus was additionally associated in those aged >= 85 years. The proportions of deaths attributable to seasonal viruses were 6.8% (>= 85 years), 4.4% (75-84 years), and 1.4% (65-74 years), but with great variations between years. Influenza occasionally showed lower impact than some of the other vi Conclusions. The number of different pathogens associated with mortality in the older population increases with increasing age. Besides influenza A and RSV, influenza B, parainfluenza and norovirus may also contribute substantially to elderly mortality.

Contains fulltext : 81491.pdf (Publisher’s version ) (Closed access) BACKGROUND: Experience with a highly pathogenic avian influenza outbreak in the Netherlands (2003) illustrated that the diagnostic demand for respiratory viruses at different biosafety levels (including BSL3), can increase unexpectedly and dramatically. OBJECTIVES: We describe the measures taken since, aimed at strengthening national laboratory surge capacity and improving preparedness for dealing with diagnostic demand during outbreaks of (emerging) respiratory virus infections, including pandemic influenza virus. STUDY DESIGN: Academic and peripheral medical-microbiological laboratories collaborated to determine minimal laboratory requirements for the identification of viruses in the early stages of a pandemic or a large outbreak of avian influenza virus. Next, an enhanced collaborative national network of outbreak assistance laboratories (OAL) was set up. An inventory was made of the maximum diagnostic throughput that this network can deliver in a period of intensified demand. For an estimate of the potential magnitude of this surge demand, historical counts were calculated from hospital- and physician-based registries of patients presenting with respiratory symptoms. RESULTS: Number of respiratory physician-visits ranged from 140,000 to 615,000 per month and hospitalizations ranged from 3000 to 11,500 per month. The established OAL-network provides rapid diagnostic response with agreed quality requirements and a maximum throughput capacity of 1275 samples/day (38,000 per month), assuming other routine diagnostic work needs to be maintained. CONCLUSIONS: Thus surge demand for diagnostics for hospitalized cases (if not distinguishable from other respiratory illness) could be handled by the OAL network. Assessing etiology of community acquired acute respiratory infection however, may rapidly exceed the capacity of the network. Therefore algorithms are needed for triaging for laboratory diagnostics; currently this is not addressed in pandemic preparedness plans.

Background We assessed the impact of two major modifications of the Dutch National Influenza Prevention Programme – the introduction in 1997 of free‐of‐charge vaccination to persons aged ≥65 years and to high‐risk groups (previously only advised, and not free of charge), and the lowering of the eligible age to 60 years in 2008 – on the estimated incidence of influenza infection leading to influenza‐like illness (ILI). Methods Additive negative‐binomial segmented regression models were fitted to ILI data from GP sentinel surveillance in two‐eight‐season intervals (1993/4 to 2000/1, 2004/5 to 2011/12, comparing pre‐ and post‐policy‐change periods within each interval), with laboratory virological reporting of samples positive for influenza or other ILI‐causing pathogens as covariates. Results For the 2008 policy change, there was a significant step decrease in influenza contribution considering all ages (=−111 per 100 positives; 95% CI: −162, −65·0)

West Nile virus (WNV) is an arthropod-borne flavivirus that is endemic in Africa, Europe, and Eastern Asia. The recent introduction and rapid dissemination of the virus in the United States as well as an increase in WNV outbreaks in Europe, has raised concerns for its spread in Europe. A surveillance system was developed to allow timely detection of an introduction of WNV infections in The Netherlands. This program focuses on cases presenting with neurological disease and includes the monitoring of hospital discharge diagnoses, trends in cerebrospinal fluid (CSF) diagnostic requests, laboratory testing of CSF, and monitoring of neurological disease in horses. Retrospective data from the hospital discharge records showed yearly peaks of unexplained meningitis and (meningo)encephalitis in the summer. A total of 781 CSF samples from humans and 71 serum and/or CSF samples from horses presenting with neurological disease of suspected viral etiology tested negative for the presence of specific antibodies to WNV. With a coverage rate of 59% in 2003, the probability that a cluster of five WNV cases presenting with neurological symptoms would have been detected was 99%. We conclude that, from 1999 to 2004, no evidence of WNV infection could be found in either humans or horses in The Netherlands.

OBJECTIVE: To elucidate the importance of hepatitis C Virus (HCV) genotype in HIV disease progression. DESIGN: This study was conducted among 126 HIV/HCV co-infected drug users with a known interval of HIV seroconversion whose HCV genotype was known early in HIV infection. Both clinical progression (to AIDS) and immunological progression (to a CD4+ T-cell count of 200 x 10(6) cells/l) by HCV genotype were studied using Cox proportional hazards analysis. RESULTS: The median duration of follow-up was 7.3 years [interquartile range (IQR), 4.6-10.1 years]. The majority of the HCV infections concerned genotype 1 and genotype 3; The distribution was: HCV type 1: 48%, HCV type 3: 34%, HCV type 4: 13%, multiple HCV types: 5%. Concurrent multiple infections consisted of HCV genotypes 1b+3a, 1b+4 and 3a+4. HCV genotype 1 and multiple HCV genotype infections were associated with faster immunological progression [hazard ratio (HR), 2.02; 95% confidence interval (CI), 1.04-3.92 and HR, 2.74; 95% CI, 0.95-7.90, respectively]. Multiple HCV genotype infection was also associated with faster clinical progression (HR, 3.36; 95% CI, 0.82-13.79). These hazard ratios increased further and were all significant when analyses were limited to data in the pre-HAART era (HR, 3.92; 95% CI, 1.51-10.20; HR, 4.38; 95% CI, 1.04-18.40 and HR, 6.54; 95% CI, 1.39-30.76, respectively). CONCLUSION: HIV disease progression differs by HCV genotype and is especially faster in individuals whose HCV infection involves more than one HCV genotype. The effect of HCV genotype on HIV progression was greater in the pre-highly active antiretroviral therapy (HAART) era, suggesting that the effectiveness of HAART may diminish the effect of HCV genotype on HIV disease progression

Objective: To study whether immune status prior to HIV seroconversion predicts CD4 T cell decline during HIV infection. Design: Prospective cohort study including 51 injecting drug users (IDU) who were HIV negative at study entry and seroconverted for HIV during follow-up. Methods: Cryopreserved peripheral blood mononuclear cells obtained before HIV seroconversion were used to measure naive (CD45RO-CD27+), memory (CD45RO+CD27+), and total CD4 T cell numbers, the fraction of dividing Ki67+CD4+ T cells, and CD4 T cell receptor excision circles (TREC). The effect of pre-seroconversion immune status, as defined by these markers, on the rate of CD4 T cell decline during HIV infection was assessed using linear regression for repeated measurements. Results: IDU with low pre-seroconversion CD4 T cell TREC contents lost CD4 T cells at a significantly faster rate during HIV infection than those with a high CD4 T cell TREC content. IDU with higher pre-seroconversion CD4 T cell numbers had a 14 significantly steeper CD4 T cell decline in the first 3 months of HIV infection, but their 14 CD4 T cell counts remained higher throughout HIV infection. Intermediate levels of pre-seroconversion dividing Ki67+CD4+ T cells were associated with a significantly steeper CD4 cell decline than high levels. IDU with the highest pre-seroconversion drug-injecting frequencies showed slower CD4 T cell decline than those who injected less. No correlation was present between pre-seroconversion immune markers and the pre-seroconversion duration or intensity of drug use. Conclusion: Among IDU, immune status prior to HIV infection as measured by TREC content affects the disease course after HIV seroconversion. (C) 2004 Lippincott Williams Wilkins

To describe the spread of hepatitis C virus (HCV) among HCV/human immunodeficiency virus (HIV)-coinfected injection drug users (IDUs), the molecular epidemiology of HCV was studied among 108 IDUs from 7 European countries. Phylogenetic analysis based on the NS5B region showed great sequence variation of HCV within each country and no clear phylogenetic clustering by geographic region. The most prevalent subtypes were 1a and 3a, but the percentage of genotype 4 was also relatively high, ranging from 7% in northern Europe to 24% in southern Europe. Genotype 4 consisted mainly of subtype 4d and has entered the majority of the IDU populations studied. The significantly lower evolutionary distances within subtype 4d suggest that this subtype may have entered the European IDU population relatively recently. In conclusion, HCV exchange between European IDU populations has occurred on a large scale, and, overall, country-specific clustering for HCV was less than that shown for HIV.

BackgroundAlthough 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 findingsUsing 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:ConclusionsTo 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.