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Epidemiology and Statistics ? a complex future?

The problems which concern epidemiologists today, and those anticipated to emerge in the future, encompass a wide spectrum of issues from genomics to the health impacts of global environment changes. However, amongst the many factors discussed, a recent paper on the ?Future of Epidemiology? (Ness em, 2009) does not identify the role of statistics as important in underpinning the ability of epidemiology to respond to the increasing complex modelling challenges facing the field. Arguably, there is a growing need to extend  traditional epidemiological paradigms and adopt a fully integrated systems approach to address the interactions between disease processes and the physical, ecological, environmental and social systems that may contribute to future disease burden. This emerging interdisciplinary research challenge requires understanding the behaviour of systems of complex, dynamic and uncertain interactions involving hierarchies of information at different spatial and temporal resolution. The modelling of such complex hierarchical systems and the quantification of uncertainty and risk within them requires exploitation of new advances in statistics. However, the gap between the ongoing developments in statistical methodology and modelling and the capacity of epidemiologists to recognise, understand and exploit the growing power of such technologies has increased very quickly in recent years. At the same time, there is always a clear need to appreciate the limitations of seductively ?bright new? statistical methodology for its own sake and for epidemiology and public health to remain true to its roots in the substantive practical objective of improving the health of populations. In this paper, we illustrate some of these issues and the complexity faced by contemporary epidemiological studies in the context of the epidemiological surveillance of dengue fever mosquitoes in one area of Brazil. In order to detect hot spots of mosquitoes two different traps were tested in three neighbourhoods in Rio de Janeiro, one designed to capture adult females and the other one the eggs. Both were evenly distributed over the area, in different dwellings. Adults and eggs were counted weekly, for 78 weeks. Firstly, the time trends over each area were analysed, with differing results among neighbourhoods, no time structure in one of the areas and the expected increase in mosquito density during summer in other places, with similar patterns for both types of traps. Observations were then aggregated over time and investigated using a generalised additive model (GAM): the spatial patterns of each trap were entirely different in all areas, in spite of the significance of the spatial term in some of the models. Subsequently a latent Gaussian Markov random field model was applied, but no spatial, temporal or spatio-temporal structured effects were detected. Only unstructured random terms both for space and for time were apparent for both types of traps. These analyses present public health professionals with some problems:
? Is the absence of any spatial effects due to: too large a distance between traps to detect spatial dependence (each neighbourhood was only 0.25km2 with 80 traps evenly distributed), or lack of power of the model, or the wrong model?
? Considering that in the GAM model the adult trap presented some spatial structure, how to interpret
it? Should we do that?
? Which type of trap should the Brazilian Health Ministry indicate to do the entomological surveillance?
(the cost of the egg trap is one tenth of that for the adult females).
This simple illustration emphasises that incorporation of increasingly sophisticated statistical methodology
into epidemiology should be seriously addressed in order to avoid on the one hand, a severe scientific
limitation in the complexity of contemporary public health studies and on the other, over (or mistaken?)
interpretation of the increased complexity of statistical models.

References
Ness, R.B.; Andrews, E.B.; Gaudino, J.A. et al (2009), ?The Future of Epidemiology?, Academic Medicine,
84(11)
:1631-1637.
 
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