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The need for updated spirometric reference values to be used on European populations is widely acknowledged, especially for subjects aged >70 yrs. Their reference values are generally based on extrapolations. The aim of the present study was to calculate reference values for lung function screening of healthy, never-smoking adults aged 18-80 yrs and to compare them with the most widely used reference equations. Results of screening spirometry of 8,684 healthy, never-smoking adults were used to calculate mean values and fifth percentiles of lung function variables. The European Community of Coal and Steel (ECCS) reference equations underestimate forced expiratory volume in one second (FEV(1)) and forced vital capacity (FVC). For example, in 50-yr-old males (height 175 cm), lower limits of normal for FEV(1) are underestimated by 198 mL, and for FVC by 210 mL. In 50-yr-old females (height 165 cm), lower limits of normal for FEV(1) are underestimated by 191 mL, and for FVC by 270 mL. The decline of FVC in elderly subjects is steeper than predicted by the ECCS. Reference equations derived from spirometry data locally collected in a practical setting by well-trained personnel might be more appropriate for everyday use than generally used equations based on data from scientific studies in the distant past.

In geographical epidemiology, disease counts are typically available in discrete spatial units and at discrete time-points. For example, surveillance data on infectious diseases usually consists of weekly counts of new infections in pre-defined geographical areas. Similarly, but on a different time-scale, cancer registries typically report yearly incidence or mortality counts in administrative regions. A major methodological challenge lies in building realistic models for space-time interactions on discrete irregular spatial graphs. In this paper, we will discuss an observation-driven approach, where past observed counts in neighbouring areas enter directly as explanatory variables, in contrast to the parameter-driven approach through latent Gaussian Markov random fields (Rue and Held, 2005) with spatio-temporal structure. The main focus will lie on the demonstration of the spread of influenza in Germany, obtained through the design and simulation of a spatial extension of the classical SIR model (Hufnagel et al., 2004).

We propose a stochastic model for the analysis of time series of disease counts as collected in typical surveillance systems on notifiable infectious diseases. The model is based on a Poisson or negative binomial observation model with two components: a parameter-driven component relates the disease incidence to latent parameters describing endemic seasonal patterns, which are typical for infectious disease surveillance data. An observation-driven or epidemic component is modeled with an autoregression on the number of cases at the previous time points. The autoregressive parameter is allowed to change over time according to a Bayesian changepoint model with unknown number of changepoints. Parameter estimates are obtained through the Bayesian model averaging using Markov chain Monte Carlo techniques. We illustrate our approach through analysis of simulated data and real notification data obtained from the German infectious disease surveillance system, administered by the Robert Koch Institute in Berlin. Software to fit the proposed model can be obtained from http://www.statistik.lmu.de/ approximately mhofmann/twins.

In modelling we usually endeavour to find a single 'best' model that explains the relationship between independent and dependent variables. Selection of a single model fails to take into account the prior uncertainty in the model space. The Bayesian model averaging (BMA) approach tackles this problem by considering the set of all possible models. We apply BMA approach to the estimation of the false negative fraction (FNF) in a particular case of a two-stage multiple screening test for bowel cancer. We find that after taking model uncertainty into consideration the estimate of the FNF obtained is largely dependent on the covariance structure of the priors. Results obtained when the Zellner g-prior for the prior variance is used is largely influenced by the magnitude of g.

The study of spatial variations in disease rates is a common epidemiological approach used to describe the geographical clustering of diseases and to generate hypotheses about the possible 'causes' which could explain apparent differences in risk. Recent statistical and computational developments have led to the use of realistically complex models to account for overdispersion and spatial correlation. However, these developments have focused almost exclusively on spatial modelling of a single disease. Many diseases share common risk factors (smoking being an obvious example) and, if similar patterns of geographical variation of related diseases can be identified, this may provide more convincing evidence of real clustering in the underlying risk surface. We propose a shared component model for the joint spatial analysis of two diseases. The key idea is to separate the underlying risk surface for each disease into a shared and a disease-specific component. The various components of this formulation are modelled simultaneously by using spatial cluster models implemented via reversible jump Markov chain Monte Carlo methods. We illustrate the methodology through an analysis of oral and oesophageal cancer mortality in the 544 districts of Germany, 1986–1990.

We apply a generalized Bayesian age–period–cohort (APC) model to a data-set on lung cancer mortality in West Germany, in the period 1952–1996. Our goal is to predict future death rates until the year 2010, separately for males and females. Since age and period are not measured on the same grid, we propose a generalized APC model where consecutive cohort parameters represent strongly overlapping birth cohorts. This approach results in a rather large number of parameters, where standard algorithms for statistical inference by Markov chain Monte Carlo methods turn out to be computationally intensive. We propose a more efficient implementation based on ideas of block sampling from the time series literature. We entertain two different formulations, penalizing either first or second differences of age, period and cohort parameters. To assess the predictive quality of both formulations, we first forecast the rates for the period 1987–1996 based on data until 1986. A comparison with the actual observed rates is made based on a predictive deviance criterion. Predictions of lung cancer mortality until 2010 are then reported and a modification of the formulation in order to include information on cigarette consumption is finally described.