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. 2017 Jul 13;12(7):e0180797.
doi: 10.1371/journal.pone.0180797. eCollection 2017.

Space-time analysis of pneumonia hospitalisations in the Netherlands

Elisa Benincà  1   2 , Michiel van Boven  1 , Thomas Hagenaars  2 , Wim van der Hoek  1
Affiliations

Space-time analysis of pneumonia hospitalisations in the Netherlands

Elisa Benincà et al. PLoS One. .

Abstract

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.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Time series of unspecified pneumonia (ICD-10 discharge diagnosis J18) hospitalisation cases plotted on a weekly basis.
Fig 2
Fig 2. Maps of incidence of unspecified pneumonia cases at municipality level for the years 2012, 2013 and 2014.
Black circles represent significant clusters (p<0.05) identified whilst imposing a 10% upper limit and choosing a non-overlapping criterion. The incidence intervals in the colorbar represent the quantiles of the pneumonia incidence in 2014.
Fig 3
Fig 3. Maps of age adjusted incidence of unspecified pneumonia cases at municipality level for the years 2012, 2013 and 2014.
Black circles represent significant clusters (p<0.05) identified whilst imposing a 10% upper limit and choosing a non-overlapping criterion. The clusters in Sat Scan have been adjusted by taking age classes as covariate in the analysis. The incidence intervals in the colorbar represent the quantiles of the pneumonia incidence in 2014.
Fig 4
Fig 4. Maps of seasonal age-adjusted incidence of unspecified pneumonia cases at municipality level.
Black circles represent significant clusters (p<0.05) identified whilst imposing a 10% upper limit. The clusters are calculated by taking all the seasonal cases of the three years and the incidence is calculated by averaging over three years. The clusters in SaTScan have been adjusted by taking age class as covariate in the analysis.
Fig 5
Fig 5. Map of the Netherlands divided in five regions.
Time series of age-adjusted incidence [x 100,000 per week] of unspecified pneumonia for the five regions of the Netherlands (b-f).
Fig 6
Fig 6. Wavelet power spectra of the age adjusted incidence for the five regions of the Netherlands.
Color codes represent wavelet power and areas inside the black contour lines correspond to 95% confidence regions where the power is higher than the power of red noise with the same autocorrelation coefficient as the data. Transparent areas on the left and right hand sides of the plots represent the cone of influence, which is a region where edge effects are important. A cluster tree has been constructed (a) based on the dissimilarity matrix of the wavelets power spectra.
See this image and copyright information in PMC

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