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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.

Since the 2009 influenza pandemic, the Netherlands has used a weekly death monitoring system to estimate deaths in excess of expectations. We present estimates of excess deaths during the ongoing coronavirus disease (COVID-19) epidemic and 10 previous influenza epidemics. Excess deaths per influenza epidemic averaged 4,000. The estimated 9,554 excess deaths (41% in excess) during the COVID-19 epidemic weeks 12-19 of 2020 appeared comparable to the 9,373 excess deaths (18%) during the severe influenza epidemic of 2017-18. However, these deaths occurred in a shorter time, had a higher peak, and were mitigated by nonpharmaceutical control measures. Excess deaths were 1.8-fold higher than reported laboratory-confirmed COVID-19 deaths (5,449). Based on excess deaths and preliminary results from seroepidemiologic studies, we estimated the infection-fatality rate to be 1%. Monitoring of excess deaths is crucial for timely estimates of disease burden for influenza and COVID-19. Our data complement laboratory-confirmed COVID-19 death reports and enable comparisons between epidemics.

BackgroundSyndromic surveillance can supplement conventional health surveillance by analyzing less-specific, near-real-time data for an indication of disease occurrence. Emergency medical call centre dispatch and ambulance data are examples of routinely and efficiently collected syndromic data that might assist in infectious disease surveillance. Scientific literature on the subject is scarce and an overview of results is lacking.MethodsA scoping review including (i) review of the peer-reviewed literature, (ii) review of grey literature and (iii) interviews with key informants.ResultsForty-four records were selected: 20 peer reviewed and 24 grey publications describing 44 studies and systems. Most publications focused on detecting respiratory illnesses or on outbreak detection at mass gatherings. Most used retrospective data; some described outcomes of temporary systems; only two described continuously active dispatch- and ambulance-based syndromic surveillance. Key informants interviewed valued dispatch- and ambulance-based syndromic surveillance as a potentially useful addition to infectious disease surveillance. Perceived benefits were its potential timeliness, standardization of data and clinical value of the data.ConclusionsVarious dispatch- and ambulance-based syndromic surveillance systems for infectious diseases have been reported, although only roughly half are documented in peer-reviewed literature and most concerned retrospective research instead of continuously active surveillance systems. Dispatch- and ambulance-based syndromic data were mostly assessed in relation to respiratory illnesses; reported use for other infectious disease syndromes is limited. They are perceived by experts in the field of emergency surveillance to achieve time gains in detection of infectious disease outbreaks and to provide a useful addition to traditional surveillance efforts.

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 Ambulance dispatches could be useful for syndromic surveillance of severe respiratory infections. We evaluated whether ambulance dispatch calls of highest urgency reflect the circulation of influenza A virus, influenza B virus, respiratory syncytial virus (RSV), rhinovirus, adenovirus, coronavirus, parainfluenzavirus and human metapneumovirus (hMPV). Methods We analysed calls from four ambulance call centres serving 25% of the population in the Netherlands (2014‐2016). The chief symptom and urgency level is recorded during triage; we restricted our analysis to calls with the highest urgency and identified those compatible with a respiratory syndrome. We modelled the relation between respiratory syndrome calls (RSC) and respiratory virus trends using binomial regression with identity link function. Results We included 211 739 calls, of which 15 385 (7.3%) were RSC. Proportion of RSC showed periodicity with winter peaks and smaller interseasonal increases. Overall, 15% of RSC were attributable to respiratory viruses (20% in out‐of‐office hour calls). There was large variation by age group: in

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.

Background Stroke is the second leading cause of death in the world. It has multiple risk factors of which some, such as ambient temperature, are less well documented. Objective We aimed to examine the association between diurnal temperature range (DTR) and stroke mortality, and to test the possible effect modification of this association according to gender, age and educational level. Methods Daily data on weather and stroke mortality from 16 provincial capital cities in China for the years 2007–2013 were obtained, with a total of 788,783 deaths from stroke. A quasi-Poisson generalized linear regression combined with a distributed lag non-linear model was used to examine the city-specific DTR effect on stroke mortality. The pooled effects of DTR on stroke mortality were then obtained using a meta-analysis, which was based on restricted maximum likelihood estimation. Results The DTR impacts were generally limited to a period of eight days, while significant effects during lag 0–8 days were only found in the cities of Beijing, Zhengzhou, Nanjing, Hefei, Chongqing and Changsha. The DTR effects were significantly and negatively associated with latitudes at lag 0–10 days (rs = − 0.640, P = 0.008). An increase of 1 °C in DTR was associated with pooled estimate of 0.66% (95%CI: 0.28–1.05%), 0.12% (− 0.26% to 0.51%) and 0.67% (0.26–1.07%) increases in stroke mortality at lag 0–10 days during the total, hot and cold days, respectively. The impact of DTR was much higher in southern China than in northern China [1.02% (0.62% to 1.43%) versus 0.10% (-0.27% to 0.47%) ]. For the individual characteristics, only females, the elderly aged ≥ 65 years, and those with lower educational attainment were vulnerable to DTR. Conclusions DTR has considerable effects on risk of mortality from stroke in various cities in China, especially among the elderly, females, those with low educational level, and people living in southern China. The results can inform decisions on developing programs to protect vulnerable subpopulations from adverse impacts of DTR.

ObjectiveWe aim to assess whether influenza circulation, as measured through influenza-like-illness (ILI) in primary care, is reflected in ambulance dispatch (AD) calls.IntroductionSurveillance of severe influenza infections is lacking in the Netherlands. Ambulance dispatch (AD) data may provide information about severity of the influenza epidemic and its burden on emergency services.The current gold standard, primary care-based surveillance of influenza-like-illness (ILI), mainly captures mild to moderate influenza cases, and does not provide adequate information on severe disease.Monitoring the severity of the annual epidemic, particularly among groups most at risk of complications, is of importance for the planning of health services and the public health response.MethodsWe analysed all calls from four ambulance dispatch centers serving 4.3 million people in the Netherlands, between January 2014 and December 2016. The main complaint and urgency level is recorded during triage; those possibly caused by respiratory infections were grouped as respiratory syndrome calls (RSC). We modelled the proportion of all RSC calls against the weekly ILI incidence (we allowed up to 4-week lags and leads), from sentinel primary-care surveillance. We used binomial regression with identity link to obtain differences in proportions. We built separate models by age group, urgency level and time of day. We tested heterogeneity of effects by season.ResultsWe included 289,307 calls; 6.7% were RSC. Overall, proportion of RSC increased by 0.114 percentage points for each increase of 1/10,000 population in ILI incidence. In our study population, this translated into 550 ambulance calls attributable to influenza (as measured by ILI) per year. Association was stronger in the models including only out-of-office hours, children (

Background: Since 2007, trivalent inactivated influenza vaccine (TIV) has been provided free-of-charge to primary, middle school and high school students in Beijing. However, there have been few school-based studies on influenza vaccine effectiveness (VE). In this report, we estimated influenza VE against laboratory-confirmed influenza illness among school children in Beijing, China during the 2016–2017 influenza season. Methods: The VE of 2016–2017 TIV against laboratory-confirmed influenza virus infection among school-age children was assessed through a case–control design. Conditional logistic regression was conducted on matched case–control sets to estimate VE. The effect of prior vaccination on current VE was also examined. Results: All 176 samples tested positive for influenza A virus with the positive rate of 55.5%. The average coverage rate of 2016–2017 TIV among students across the 37 schools was 30.6%. The fully adjusted VE of 2016–2017 TIV against laboratory-confirmed influenza was 69% (95% CI: 51 to 81): 60% (95% CI: −15 to 86) for influenza A(H1N1)pdm09 and 73% (95% CI: 52 to 84) for influenza A(H3N2). The overall VE for receipt of 2015–2016 vaccination only, 2016–2017 vaccination only, and vaccinations in both seasons was 46% (95% CI: −5 to 72), 77% (95% CI: 58 to 87), and 57% (95%CI: 17 to 78), respectively. Conclusions: Our study during school outbreaks found that VE of 2016–2017 TIV was moderate against influenza A(H3N2) as well as A(H1N1)pdm09 viruses.

Background Invasive infections by group A Streptococcus (iGAS, Streptococcus pyogenes) have a winter seasonality which largely coincides with the season for influenza and other respiratory viruses. Influenza superinfections with GAS have been described to occur regularly and to show a severe clinical picture with high mortality. We aimed to study the extent to which influenza A and B viruses (IAV and IBV), respiratory syncytial virus (RSV) and rhinovirus circulation contribute to iGAS incidence and severity. Methods Time‐series regression models were built to explore the temporal associations between weekly laboratory counts of IAV, IBV, RSV and rhinovirus as independent variables and weekly counts of GAS disease notifications or laboratory GAS cultures as dependent variables. Results The weekly number of IAV detections showed a significant temporal association with the number of notifications of streptococcal toxic shock syndrome (STSS), a severe complication of iGAS. Depending on the season, up to 40% of all notified STSS cases was attributable to IAV circulation. Besides STSS, none of the other iGAS manifestations were associated with a respiratory virus. Conclusions Our study found an ecological temporal association between IAV and STSS, the most severe complication of iGAS. Future studies are needed to confirm this association and assess the possible preventability of STSS by influenza vaccination, especially in the age group 60 years and older.

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.

Background While influenza-like-illness (ILI) surveillance is well-organized at primary care level in Europe, few data are available on more severe cases. With retrospective data from intensive care units (ICU) we aim to fill this current knowledge gap. Using multiple parameters proposed by the World Health Organization we estimate the burden of severe acute respiratory infections (SARI) in the ICU and how this varies between influenza epidemics. Methods We analyzed weekly ICU admissions in the Netherlands (2007–2016) from the National Intensive Care Evaluation (NICE) quality registry (100% coverage of adult ICUs in 2016; population size 14 million) to calculate SARI incidence, SARI peak levels, ICU SARI mortality, SARI mean Acute Physiology and Chronic Health Evaluation (APACHE) IV score, and the ICU SARI/ILI ratio. These parameters were calculated both yearly and per separate influenza epidemic (defined epidemic weeks). A SARI syndrome was defined as admission diagnosis being any of six pneumonia or pulmonary sepsis codes in the APACHE IV prognostic model. Influenza epidemic periods were retrieved from primary care sentinel influenza surveillance data. Results Annually, an average of 13% of medical admissions to adult ICUs were for a SARI but varied widely between weeks (minimum 5% to maximum 25% per week). Admissions for bacterial pneumonia (59%) and pulmonary sepsis (25%) contributed most to ICU SARI. Between the eight different influenza epidemics under study, the value of each of the severity parameters varied. Per parameter the minimum and maximum of those eight values were as follows: ICU SARI incidence 558–2400 cumulated admissions nationwide, rate 0.40–1.71/10,000 inhabitants; average APACHE score 71–78; ICU SARI mortality 13–20%; ICU SARI/ILI ratio 8–17 cases per 1000 expected medically attended ILI in primary care); peak-incidence 101–188 ICU SARI admissions in highest-incidence week, rate 0.07–0.13/10,000 population). Conclusions In the ICU there is great variation 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, thereby would provide insight into the severity of ongoing influenza epidemics, which differ from season to season. Electronic supplementary material The online version of this article (10.1186/s13054-018-2274-8) contains supplementary material, which is available to authorized users.

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.

Objective To estimate mortality attributable to influenza adjusted for other common respiratory pathogens, baseline seasonal trends and extreme temperatures. Introduction Assigning causes of deaths to seasonal infectious diseases is difficult in part due to laboratory testing prior to death being uncommon. Since influenza (and other common respiratory pathogens) are therefore notoriously underreported as a (contributing) cause of death in death- cause statistics modeling studies are commonly used to estimate the impact of influenza on mortality. Methods Using primary cause of death (Statistics Netherlands) we modeled weekly timeseries of 1) respiratory deaths (ICD10 codes J00-J99) and 2) circulatory deaths (ICD10 codes I00-I99). We used regression models with an identity link and Poisson error to relate mortality to counts of influenza A & B diagnoses. We adjusted for other common respiratory pathogens (all pathogen data was at population level from the national laboratory surveillance), temperature (from the Dutch Royal Meteorological Institute), and baseline linear and cyclical (i.e. seasonal) trends. To account for the yearly variation in the severity of the main circulating influenza A strain we used time dependent variables for influenza A (fixed at lag 0 – assuming a direct effect of influenza. For influenza B and the confoundig pathogens we considered a 0 tot -4 time lag (thus allowing infection to precede death for up to 4 weeks). We performed the analyses separately per death cause group and by 3 different age groups (0-64, 65-74,75+ years) over a 14-year time- period (mid 1999-mid 2013, thus 14 complete winter seasons). Results In the Netherlands on average 2,636 all cause deaths occur per week varying by season (lower in summer min: 2,219 and higher in winter max: 3,564) with yearly incidence ranging from 20/10,000 in 0-64 year olds to 885/10,000 in 75-plus year olds. Circulatory mortality (31% of total deaths) was higher than respiratory mortality (10% of total deaths) and both showed clear seasonality in all age-groups. Overall, 0.14% of all deaths were actually coded as influenza deaths. Preliminary model estimates showed that the proportion of respiratory deaths attributable to influenza A were quite similar for 0-64 and 65-74 year olds but higher in 75+ (5.1%, 5.7%, 7.0% respectively) while this proportion was stable across age-groups for circulatory deaths (approximately 1.5% in all agegroups for influenza A). Influenza B was significantly associated with respiratory deaths and circulatory deaths in the oldest age group of 75+ years (with proportions of 0.7% and 0.2% respectively) while in the 65-74 year olds it was associated only with circulatory deaths (0.2%). Influenza B was not significantly associated with either respiratory or circulatory mortality in the 0-64 year age group. On average, yearly in the 75+ age group 70/10,000 respiratory deaths and 39/10,000 circulatory deaths were attributable to influenza A. For influenza B the incidences were 7 to 10 fold lower (7/10,000 and 6/10,000 respectively). Conclusions Influenza A was significantly associated with respiratory and circulatory mortality in all age groups while influenza B was significantly associated with respiratory and circulatory mortality in the elderly only.

Since December 2016, excess all-cause mortality was observed in many European countries, especially among people aged ≥ 65 years. We estimated all-cause and influenza-attributable mortality in 19 European countries/regions. Excess mortality was primarily explained by circulation of influenza virus A(H3N2). Cold weather snaps contributed in some countries. The pattern was similar to the last major influenza A(H3N2) season in 2014/15 in Europe, although starting earlier in line with the early influenza season start. info:eu-repo/semantics/publishedVersion

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.

Objectives. We explored whether excesses in influenza severity can be detected by combining respiratory syndromic hospital and mortality data with data on influenza-like illness (ILI) cases obtained from general practitioners. Methods. To identify excesses in the severity of influenza infections in the population of the Netherlands between 1999 and 2005, we looked for increases in influenza-associated hospitalizations and mortality that were disproportionate to the number of ILI cases reported by general practitioners. We used generalized estimating equation regression models to associate syndromic hospital and mortality data with ILI surveillance data obtained from general practitioners. Virus isolation and antigenic characterization data were used to interpret the results. Results. Disproportionate increases in hospitalizations and mortality (relative to ILI cases reported by general practitioners) were identified in 2003/04 during the A/Fujian/411/02(H3N2) drift variant epidemic. Conclusions. Combined surveillance of respiratory hospitalizations and mortality and ILI data obtained from general practitioners can capture increases in severe influenza-associated illness that are disproportionate to influenza incidence rates. Therefore, this novel approach should complement traditional seasonal and pandemic influenza surveillance in efforts to detect increases in influenza case fatality rates and percentages of patients hospitalized.

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.

The studied respiratory syndromes are suitable for syndromic surveillance because they reflect respiratory pathogen activity patterns, Syndromic surveillance is increasingly used to signal unusual illness events. To validate data-source selection, we retrospectively investigated the extent to which 6 respiratory syndromes (based on different medical registries) reflected respiratory pathogen activity. These syndromes showed higher levels in winter, which corresponded with higher laboratory counts of Streptococcus pneumoniae, respiratory syncytial virus, and influenza virus. Multiple linear regression models indicated that most syndrome variations (up to 86%) can be explained by counts of respiratory pathogens. Absenteeism and pharmacy syndromes might reflect nonrespiratory conditions as well. We also observed systematic syndrome elevations in the fall, which were unexplained by pathogen counts but likely reflected rhinovirus activity. Earliest syndrome elevations were observed in absenteeism data, followed by hospital data (+1 week), pharmacy/general practitioner consultations (+2 weeks), and deaths/laboratory submissions (test requests) (+3 weeks). We conclude that these syndromes can be used for respiratory syndromic surveillance, since they reflect patterns in respiratory pathogen activity.

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.

Background: Children attending day care experience substantial gastrointestinal morbidity due to circulating seasonal enteropathogens in the day-care environment. The lack of a distinct clinical presentation of gastroenteritis (GE) in these children, in combination with the high diversity of enteropathogenic agents, complicates the assessment of the individual contributions of enteropathogens that may cause GE. We aimed to estimate the proportion of day-care attendees experiencing GE that could be attributed to a range of enteropathogens circulating in day care in the Netherlands in 2010-2013.Methods: Using time-series data from a national laboratory-based and syndrome-based surveillance system in Dutch day-care centers and generalized estimating equation analysis, we modelled the variation in prevalence of 16 enteropathogens of bacterial (8), viral (5) and parasitic origin (3) circulating in day care to the variation of GE incidence among children attending day care.Results: Rotavirus, norovirus, astrovirus, Giardia and Cryptosporidium were significantly associated with GE morbidity among day-care attendees in our time-series analysis. Together, these enteropathogens accounted for 39% of the GE morbidity: 11% by rotavirus, 10% by norovirus, 8% by Giardia, 7% by astrovirus and 3% by Cryptosporidium.Conclusions: We demonstrate that circulating viruses and parasites, rather than bacteria, contribute to seasonal GE experienced by children in day care.

Syndromic surveillance is a monitoring methodology in which data from disease indicators are collected and interpreted as quickly as possible (in real time) to get an impression of the current morbidity in the population. In this case 'syndrome' means: behavior, signs and symptoms that may be indicative of an infection, without a proven pathogen, or incidents such as an environmental disaster. Syndromic surveillance has gained momentum in response to various international infectious disease threats, including the outbreak of severe acute respiratory syndrome (SARS) in 2003. In several European countries, syndromic surveillance has been used to monitor the impact of outbreaks or natural disasters and the health of visitors of mass events. Following the outbreak of an infectious disease, syndromic surveillance can be reassuring in the absence of an epidemic. Insight into burden of disease can provide guidance in adjusting care capacity to need. Several syndromic surveillance systems and pilot studies have been set up recently in the Netherlands. The costs, benefits and their best application should be evaluated in the near future.

Weekly monitoring of nation-wide number of deaths in the Netherlands (population 16 million) was set up to track events or outbreaks and estimate their impact. The system monitors reported number of deaths from all causes at 3 different time-lags: deaths reported within 1, 2 and 3 weeks (43%-98% complete). Baselines and prediction limits are calculated using a 5 year historical period and serfling type regression. For the 2012/2013 season excess deaths coincided with the exceptionally long 18 week influenza season and were considerable (6,000+ excess above baseline) compared to other seasons.

Objectives: Severe influenza can lead to Intensive Care Unit (ICU) admission. We explored whether ICU data reflect influenza like illness (ILI) activity in the general population, and whether ICU respiratory infections can predict influenza epidemics. Methods: We calculated the time lag and correlation between ILI incidence (from ILI sentinel surveillance, based on general practitioners (GP) consultations) and percentages of ICU admissions with a respiratory infection (from the Dutch National Intensive Care Registry) over the years 2003–2011. In addition, ICU data of the first three years was used to build three regression models to predict the start and end of influenza epidemics in the years thereafter, one to three weeks ahead. The predicted start and end of influenza epidemics were compared with observed start and end of such epidemics according to the incidence of ILI. Results: Peaks in respiratory ICU admissions lasted longer than peaks in ILI incidence rates. Increases in ICU admissions occurred on average two days earlier compared to ILI. Predicting influenza epidemics one, two, or three weeks ahead yielded positive predictive values ranging from 0.52 to 0.78, and sensitivities from 0.34 to 0.51. Conclusions: ICU data was associated with ILI activity, with increases in ICU data often occurring earlier and for a longer time period. However, in the Netherlands, predicting influenza epidemics in the general population using ICU data was imprecise, with low positive predictive values and sensitivities. (aut.ref.)

BackgroundWe 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 findingsWe 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 influenza 1,956 (range 0-3,990). 15,845 years-of-life-lost were estimated for the pandemic; for an average seasonal epidemic 17,908. For 0-4 yrs of age the number of influenza-attributed deaths during the pandemic were higher than in any seasonal epidemic; 77 deaths (range 61-93) compared to 16 deaths (range 0-45). The ≥75 yrs of age showed a far below average number of deaths. Using pneumonia/influenza and respiratory/cardiovascular instead of all-cause deaths consistently resulted in relatively low total pandemic mortality, combined with high impact in the youngest age category.ConclusionThe pandemic had an overall moderate impact on mortality compared to 10 preceding seasonal epidemics, with higher mortality in young children and low mortality in the elderly. This resulted in a total number of pandemic deaths far below the average for seasonal influenza, and a total number of years-of-life-lost somewhat below average. Comparing pandemic and seasonal influenza mortality as in our study will help assessing the worldwide impact of the 2009 pandemic.

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.

There is evidence that HIV-positive injecting drug users benefit less than other risk groups from highly active antiretroviral therapy that has been available since 1996. In this multicentre European study the impact of the availability of highly active antiretroviral therapy on the progression rates to AIDS and death among injecting drug users with a documented date of HIV seroconversion is studied. After highly active antiretroviral therapy became available the risk of AIDS and death for injecting drug users decreased by 28% and 36%, which is less than has been reported for other risk groups.

Background: It is not known whether the risk of active tuberculosis disease varies with the length of time that individuals are infected with HIV. Objective: To study how, independently of CD4 T cell count, the risk of tuberculosis varies with the duration of HIV infection. Methods: Using Poisson regression analysis, the incidence of and risk factors for tuberculosis were studied in 683 injecting drug users (IDU) with a documented date of HIV seroconversion followed in seven cohorts in six European countries until 1998. Results: Overall incidence was 11.5/1000 person-years. Adjusted for CD4 T cell count and geographic region, the risk ratio (RR) for tuberculosis (both pulmonary and extrapulmonary), compared with the first 3 years of HIV infection, was 2.8 for years 4 to 6 of HIV infection [95% confidence interval (CI), 1.3-6.3], 1.2 for year 7 to 9 (95% Cl, 0.3-4.2) and 4.6 after 9 years (95% Cl, 1.4-15.0). The adjusted RR for geographic region was 13.1 (95% Cl, 4.3-40.0) for Amsterdam and 15.8 (95% Cl, 4.8-52.0) for the Valencian region of Spain compared with all other sites combined. Conclusion: The risk of tuberculosis is increased relatively early in HIV infection (year 4 to 6) and also later (after year 9) with possibly a relatively silent period between. As expected, IDU in Southern Europe have a substantially higher risk of tuberculosis than IDU in Northern and Central Europe. Amsterdam forms an exception for Northern Europe, with very high incidence rates. (C) 2003 Lippincott Williams Wilkins