• Chlamydia psittaci: a relevant cause of community-acquired pneumonia in two Dutch hospitals.

      Spoorenberg, S M C; Bos, W J W; van Hannen, E J; Dijkstra, F; Heddema, E R; van Velzen-Blad, H; Heijligenberg, R; Grutters, J C; de Jongh, B M (2016-02)
      Of all hospitalised community-acquired pneumonias (CAPs) only a few are known to be caused by Chlamydia psittaci. Most likely the reported incidence, ranging from of 0% to 2.1%, is an underestimation of the real incidence, since detection of psittacosis is frequently not incorporated in the routine microbiological diagnostics in CAP or serological methods are used.
    • Comparing national infectious disease surveillance systems: China and the Netherlands.

      Vlieg, Willemijn L; Fanoy, Ewout B; van Asten, Liselotte; Liu, Xiaobo; Yang, Jun; Pilot, Eva; Bijkerk, Paul; van der Hoek, Wim; Krafft, Thomas; van der Sande, Marianne A; et al. (2017)
      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.
    • Estimating severity of influenza epidemics from severe acute respiratory infections (SARI) in intensive care units.

      van Asten, Liselotte; Luna Pinzon, Angie; de Lange, Dylan W; de Jonge, Evert; Dijkstra, Frederika; Marbus, Sierk; Donker, Gé A; van der Hoek, Wim; de Keizer, Nicolette F (2018-12-19)
      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.
    • Livestock-associated risk factors for pneumonia in an area of intensive animal farming in the Netherlands.

      Freidl, Gudrun S; Spruijt, Ineke T; Borlée, Floor; Smit, Lidwien A M; van Gageldonk-Lafeber, Arianne B; Heederik, Dick J J; Yzermans, Joris; van Dijk, Christel E; Maassen, Catharina B M; van der Hoek, Wim (2017)
      Previous research conducted in 2009 found a significant positive association between pneumonia in humans and living close to goat and poultry farms. However, as this result might have been affected by a large goat-related Q fever epidemic, the aim of the current study was to re-evaluate this association, now that the Q-fever epidemic had ended. In 2014/15, 2,494 adults (aged 20-72 years) living in a livestock-dense area in the Netherlands participated in a medical examination and completed a questionnaire on respiratory health, lifestyle and other items. We retrieved additional information for 2,426/2,494 (97%) participants from electronic medical records (EMR) from general practitioners. The outcome was self-reported, physician-diagnosed pneumonia or pneumonia recorded in the EMR in the previous three years. Livestock license data was used to determine exposure to livestock. We quantified associations between livestock exposures and pneumonia using odds ratios adjusted for participant characteristics and comorbidities (aOR). The three-year cumulative frequency of pneumonia was 186/2,426 (7.7%). Residents within 2,000m of a farm with at least 50 goats had an increased risk of pneumonia, which increased the closer they lived to the farm (2,000m aOR 1.9, 95% CI 1.4-2.6; 500m aOR 4.4, 95% CI 2.0-9.8). We found no significant associations between exposure to other farm animals and pneumonia. However, when conducting sensitivity analyses using pneumonia outcome based on EMR only, we found a weak but statistically significant association with presence of a poultry farm within 1,000m (aOR: 1.7, 95% CI 1.1-2.7). Living close to goat and poultry farms still constitute risk factors for pneumonia. Individuals with pneumonia were not more often seropositive for Coxiella burnetii, indicating that results are not explained by Q fever. We strongly recommend identification of pneumonia causes by the use of molecular diagnostics and investigating the role of non-infectious agents such as particulate matter or endotoxins.
    • Pitfalls of molecular diagnostic testing for Coxiella burnetii DNA on throat swabs.

      Buijs, Sheila B; Hermans, Mirjam H A; Agni, Nabila; de Vries, Maaike C; Hoepelman, Andy I M; Oosterheert, Jan Jelrik; Wever, Peter C (2019-07-01)
    • Prediction model for pneumonia in primary care patients with an acute respiratory tract infection: role of symptoms, signs, and biomarkers.

      Groeneveld, G H; van 't Wout, J W; Aarts, N J; van Rooden, C J; Verheij, T J M; Cobbaert, C M; Kuijper, E J; de Vries, J J C; van Dissel, J T (2019-11-20)
    • Space-time analysis of pneumonia hospitalisations in the Netherlands.

      Benincà, Elisa; van Boven, Michiel; Hagenaars, Thomas; van der Hoek, Wim (2017)
      Community acquired pneumonia is a major global public health problem. In the Netherlands there are 40,000-50,000 hospital admissions for pneumonia per year. In the large majority of these hospital admissions the etiologic agent is not determined and a real-time surveillance system is lacking. Localised and temporal increases in hospital admissions for pneumonia are therefore only detected retrospectively and the etiologic agents remain unknown. Here, we perform spatio-temporal analyses of pneumonia hospital admission data in the Netherlands. To this end, we scanned for spatial clusters on yearly and seasonal basis, and applied wavelet cluster analysis on the time series of five main regions. The pneumonia hospital admissions show strong clustering in space and time superimposed on a regular yearly cycle with high incidence in winter and low incidence in summer. Cluster analysis reveals a heterogeneous pattern, with most significant clusters occurring in the western, highly urbanised, and in the eastern, intensively farmed, part of the Netherlands. Quantitatively, the relative risk (RR) of the significant clusters for the age-standardised incidence varies from a minimum of 1.2 to a maximum of 2.2. We discuss possible underlying causes for the patterns observed, such as variations in air pollution.