Influenza A(H1N1) Oseltamivir Resistant Viruses in the Netherlands During the Winter 2007/2008

Background: Antiviral susceptibility surveillance in the Netherlands was intensified after the first reports about the emergence of influenza A(H1N1) oseltamivir resistant viruses in Norway in January, 2008. Methods: Within the existing influenza surveillance an additional questionnaire study was performed to retrospectively assess possible risk factors and establish clinical outcome of all patients with influenza virus A(H1N1) positive specimens. To discriminate resistant and sensitive viruses, fifty percent inhibitory concentrations for the neuramidase inhibitors oseltamivir and zanamivir were determined in a neuraminidase inhibition assay. Mutations previously associated with resistance to neuramidase inhibitors and M2 blockers (amantadine and rimantadine) were searched for by nucleotide sequencing of neuraminidase and M2 genes respectively. Results: Among 171 patients infected with A(H1N1) viruses an overall prevalence of oseltamivi resistance of 27% (95% CI: 20-34%) was found. None of influenza A(H1N1) oseltamivir resistant viruses tested was resistant against amantadine or zanamivir. Patient characteristics, underlying conditions, influenza vaccination, symptoms, complications, and exposure to oseltamivir and other antivirals did not differ significantly between patients infected with resistant and sensitive A(H1N1) viruses. Conclusion: In 2007/2008 a large proportion of influenza A(H1N1) viruses resistant to oseltamivir was detected. There were no clinical differences between patients infected with resistant and sensitive A(H1N1) viruses. Continuous monitoring of the antiviral drug sensitivity profile of influenza viruses is justified, preferably using the existing sentinel surveillance, however, complemented with data from the more severe end of the clinical spectrum. In order to act timely on emergencies of public health importance we suggest setting up a surveillance system that can guarantee rapid access to the latter. (aut. ref.

Estimation of acute and chronic Q fever incidence in children during a three-year outbreak in the Netherlands and a comparison with international literature

Background:  In the Dutch 2007-2009 Q fever outbreak Coxiella burnetii was transmitted aerogenically from dairy goat farms to those living in the surrounding areas. Relatively few children were reported. The true number of pediatric infections is unknown. In this study, we estimate the expected number of acute and chronic childhood infections. Methods:  As Coxiella was transmitted aerogenic to those living near infected dairy goat farms, we could use adult seroprevalence data to estimate infection risk for inhabitants, children and adults alike. Using Statistics Netherlands data we estimated the number of children at (high) risk for developing chronic Q fever. Literature was reviewed for childhood (0-15 years) Q fever reports and disease rates. We compared this with Dutch reported and our estimated data for 2007-2009. Results:  In The Netherlands epidemic, 44 children were reported (1.2 % of total notifications). The childhood incidence was 0.15 compared to 2.6 per 10,000 inhabitants for adults. No complications were reported. Based on the expected similarity in childhood and adult exposure we assume that 9.8 % of children in the high-risk area had Q fever infection, resulting in 1562 acute infections during the Q fever epidemic interval. Based on the prevalence of congenital heart disease, at least 13 children are at high risk for developing chronic Q fever. In medical literature, 42 case reports described 140 childhood Q fever cases with a serious outcome (four deaths). In chronic Q fever, cardiac infections were predominant. Four outbreaks were reported involving children, describing 11 childhood cases. 36 National and/or regional studies reported seroprevalences varying between 0 and 70 %. Conclusion:  In the 3-year Dutch epidemic, few childhood cases were reported, with pulmonary symptoms leading, and none with a serious presentation. With an estimated 13 high-risk children for chronic infection in the high exposure area, and probably forty in the whole country, we may expect several chronic Q fever complications in the coming years in paediatric practice

Chronic Q fever diagnosis—consensus guideline versus expert opinion

Chronic Q fever, caused by Coxiella burnetii, has high mortality and morbidity rates if left untreated. Controversy about the diagnosis of this complex disease has emerged recently. We applied the guideline from the Dutch Q Fe­ver Consensus Group and a set of diagnostic criteria pro­posed by Didier Raoult to all 284 chronic Q fever patients included in the Dutch National Chronic Q Fever Database during 2006–2012. Of the patients who had proven cas­es of chronic Q fever by the Dutch guideline, 46 (30.5%) would not have received a diagnosis by the alternative cri­teria designed by Raoult, and 14 (4.9%) would have been considered to have possible chronic Q fever. Six patients with proven chronic Q fever died of related causes. Until results from future studies are available, by which current guidelines can be modified, we believe that the Dutch lit­erature-based consensus guideline is more sensitive and easier to use in clinical practice

ICU respiratory admissions data for influenza severity surveillance?

ObjectiveIntensive 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.IntroductionWhile 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.MethodsWe 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.ResultsAnnually, 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.ConclusionsIn 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

Estimating severity of influenza epidemics from severe acute respiratory infections (SARI) in intensive care units.

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. 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. 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). 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

Estimating severity of influenza epidemics from severe acute respiratory infections (SARI) in intensive care units

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

Estimating severity of influenza epidemics from severe acute respiratory infections (SARI) in intensive care units.

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. 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. 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). 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

Observed incidence of Influenza Like Illness (ILI) according to the Sentinel General Practitioners registry (sentinel GP registry) and predicted according to Intensive Care Unit (ICU) data.

<p>Weeks where an influenza epidemic was detected are also shown. Figure (a), (b), and (c) predict one to three weeks ahead. Weeks marked with an *-sign indicate multiple detections of an influenza epidemic in one influenza year.</p

Percentage of medical Intensive Care Unit (ICU) admission with respiratory infection, and incidence of Influenza Like Illness (ILI) cases in the period 2003–2011.

<p>Incidence of ILI is plotted per 10,000 population per week. The ILI according to the full model is also plotted.</p