Research Article: Space-time analysis of pneumonia hospitalisations in the Netherlands

Date Published: July 13, 2017

Publisher: Public Library of Science

Author(s): Elisa Benincà, Michiel van Boven, Thomas Hagenaars, Wim van der Hoek, Jeffrey Shaman.


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.

Partial Text

Community acquired pneumonia (CAP) is one of the major causes of hospitalisation and death in developing as well as in developed countries. Alarmingly, the burden of pneumonia has been increasing [1, 2] and according to a recent study in England, this trend cannot be explained just by changes in demography, admission practices or diagnosis codes [2].

No consent was given because the data were analyzed anonymously.

The temporal dynamics of pneumonia hospitalisations display a strong seasonal pattern with peaks in winter and troughs in summer (Fig 1). This seasonal oscillatory behavior is observed in all years, although the difference in number of cases between winter and summer is less pronounced in 2014. In addition, there is a substantial increase in the total number of cases in 2014 compared with the previous years (see also Table 1).

Here, we provide a comprehensive analysis of space-time clustering of pneumonia hospitalisations by using two different statistical approaches. Our results show that pneumonia hospital admissions are strongly clustered both in space and in time. The spatial distribution of pneumonia is heterogeneous, and differences between high and low incidence areas can be more than an order of magnitude.

This study highlights that pneumonia hospital admissions are strongly clustered both in space and in time, and that even at a small spatial scale, differences in incidence can be substantial. It emphasizes that there is a need for more insight into the epidemiology of pneumonia, which is a prerequisite for devising prevention strategies. Although CAP is an important public health problem, remarkably little is known about its determinants. The identification of clear geographical clusters of high pneumonia incidence represents an important first step towards developing and testing hypotheses regarding these determinants.




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