Michaela Paul

Multivariate modelling of infectious disease surveillance data.

This paper describes a model-based approach to analyse multivariate time series data on counts of infectious diseases. It extends a method previously described in the literature to deal with possible dependence between disease counts from different pathogens. In a spatio-temporal context it is proposed to include additional information on global dispersal of the pathogen in the model. Two examples are given: the first describes an analysis of weekly influenza and meningococcal disease counts from Germany. The second gives an analysis of the spatio-temporal spread of influenza in the U.S.A., 1996-2006, using air traffic information. Maximum likelihood estimates in this non-standard model class are obtained using general optimization routines, which are integrated in the R package surveillance.

Bayesian bivariate meta-analysis of diagnostic test studies using integrated nested Laplace approximations.

For bivariate meta-analysis of diagnostic studies, likelihood approaches are very popular. However, they often run into numerical problems with possible non-convergence. In addition, the construction of confidence intervals is controversial. Bayesian methods based on Markov chain Monte Carlo (MCMC) sampling could be used, but are often difficult to implement, and require long running times and diagnostic convergence checks. Recently, a new Bayesian deterministic inference approach for latent Gaussian models using integrated nested Laplace approximations (INLA) has been proposed. With this approach MCMC sampling becomes redundant as the posterior marginal distributions are directly and accurately approximated. By means of a real data set we investigate the influence of the prior information provided and compare the results obtained by INLA, MCMC, and the maximum likelihood procedure SAS PROC NLMIXED. Using a simulation study we further extend the comparison of INLA and SAS PROC NLMIXED by assessing their performance in terms of bias, mean-squared error, coverage probability, and convergence rate. The results indicate that INLA is more stable and gives generally better coverage probabilities for the pooled estimates and less biased estimates of variance parameters. The user-friendliness of INLA is demonstrated by documented R-code.

Count data regression charts for the monitoring of surveillance time series

Control charts based on the Poisson and negative binomial distribution for monitoring time series of counts typically arising in the surveillance of infectious diseases are presented. The in-control mean is assumed to be time-varying and linear on the log-scale with intercept and seasonal components. If a shift in the intercept occurs the system goes out-of-control. Using the generalized likelihood ratio (GLR) statistic a monitoring scheme is formulated to detect on-line whether a shift in the intercept occurred. In the case of Poisson the necessary quantities of the GLR detector can be efficiently computed by recursive formulas. Extensions to more general alternatives e.g. containing an auto-regressive epidemic component are discussed. Using Monte Carlo simulations run-length properties of the proposed schemes are investigated and the Poisson scheme is compared to existing methods. The practicability of the charts is demonstrated by applying them to the observed number of salmonella hadar cases in Germany 2001-2006.

Predictive assessment of a non-linear random effects model for multivariate time series of infectious disease counts.

Infectious disease counts from surveillance systems are typically observed in several administrative geographical areas. In this paper, a non-linear model for the analysis of such multiple time series of counts is discussed. To account for heterogeneous incidence levels or varying transmission of a pathogen across regions, region-specific and possibly spatially correlated random effects are introduced. Inference is based on penalized likelihood methodology for mixed models. Since the use of classical model choice criteria such as AIC or BIC can be problematic in the presence of random effects, models are compared by means of one-step-ahead predictions and proper scoring rules. In a case study, the model is applied to monthly counts of meningococcal disease cases in 94 departments of France (excluding Corsica) and weekly counts of influenza cases in 140 administrative districts of Southern Germany. The predictive performance improves if existing heterogeneity is accounted for by random effects.

Modeling seasonality in space-time infectious disease surveillance data

Infectious disease data from surveillance systems are typically available as multivariate times series of disease counts in specific administrative geographical regions. Such databases are useful resources to infer temporal and spatiotemporal transmission parameters to better understand and predict disease spread. However, seasonal variation in disease notification is a common feature of surveillance data and needs to be taken into account appropriately. In this paper, we extend a time series model for spatiotemporal surveillance counts to incorporate seasonal variation in three distinct components. A simulation study confirms that the different types of seasonality are identifiable and that a predictive approach suggested for model selection performs well. Application to surveillance data on influenza in Southern Germany reveals a better model fit and improved one-step-ahead predictions if all three components allow for seasonal variation.

Statistical approaches to the monitoring and surveillance of infectious diseases for veterinary public health

This paper covers the aspect of using statistical methodology for the monitoring and surveillance of routinely collected data in veterinary public health. An account of the Farrington algorithm and Poisson cumulative sum schemes for the prospective detection of aberrations is given with special attention devoted to the occurrence of seasonality and spatial aggregation of the time series. Modelling approaches for retrospective analysis of surveillance counts are also described. To illustrate the applicability of the methodology in veterinary public health, data from the monitoring of rabies among fox in Hesse, Germany, are analysed.

Assessment of mitral valve area during percutaneous mitral valve repair using the MitraClip system: comparison of different echocardiographic methods.

Background—Quantification of the mitral valve area (MVA) is important to guide percutaneous mitral valve repair using the MitraClip system. However, little is known about how to best assess MVA in this specific situation. Methods and Results—Immediately before and after MitraClip implantation, comprehensive 3-dimensional (3D) transesophageal echocardiography data were acquired for MVA assessment by the pressure half-time method and by two 3D quantification methods (mitral valve quantification software and 3D quantification software). In addition, transmitral gradients by continuous-wave Doppler (dPmeanCW) were measured to indirectly assess MVA. Data are given as median (interquartile range). Thirty-three patients (39% women) with a median age of 77.1 years (12.4 years) were studied. Before intervention, the median MVAs by the pressure half-time method, mitral valve quantification software, and 3D quantification software were 4.4 cm2 (2.0 cm2), 4.7 cm2 (2.4 cm2), and 6.2 cm2 (2.4 cm2), respectively (P<0.001). After intervention, MVA was reduced to 1.9 cm2 (0.7 cm2), 2.1 cm2 (1.1 cm2), and 2.8 cm2 (1.1 cm2), respectively (P=0.001). The median values for dPmeanCW before and after intervention were 1.0 mm Hg (1.0 mm Hg) and 3.0 mm Hg (3.0 mm Hg; P<0.001), respectively. At discharge, the median dPmeanCW was 4.0 mm Hg (3.0 mm Hg). In multivariate regression analyses including body surface area, the 3 different MVA methods, and dPmeanCW, a post-dPmeanCW ≥5 mm Hg was the best independent predictor of an elevated transmitral gradient at discharge. Conclusions—Transmitral gradients by continuous-wave Doppler are quick, feasible in all patients, and superior to direct peri-interventional assessment of MVA. A postinterventional transmitral gradient by continuous-wave Doppler of ≥5 mm Hg best predicted elevated transmitral gradients at discharge.

Heat Waves, Incidence of Infectious Gastroenteritis, and Relapse Rates of Inflammatory Bowel Disease: A Retrospective Controlled Observational Study

OBJECTIVES:The objective of this study was to evaluate the effect of heat waves on flares of inflammatory bowel disease (IBD) and infectious gastroenteritis (IG).METHODS:In this retrospective controlled observational study, data from 738 IBD and 786 IG patients admitted to the University Hospital of Zurich in the years 2001–2005, as well as from 506 other noninfectious chronic intestinal inflammations, which were used as control, were collected. Climate data were obtained from the Swiss Federal Office for Meteorology and Climatology.RESULTS:The presence of a heat wave increased the risk of IBD flares by 4.6% (95% confidence interval (CI): 1.6–7.4%, P=0.0035) and of IG flares by 4.7% (95% CI: 1.8–7.4%, P=0.0020) for every additional day within a heat wave period. In the control group there was no significant effect (95% CI: −6.2–2.9%, P=0.53). Screening of alternative forms for the effect of heat waves suggested that for IG the effect is strongest when lagged by 7 days (risk increase per day: 7.2%, 95% CI: 4.6–9.7%, P<0.0001), whereas for IBD no such transformation was required. Other formulations with additive effects, interactions between heat waves and time of the year, and additional adjustments for daily average temperature did not show any improvement in model fit.CONCLUSIONS:In this retrospective controlled observational study, we found a substantial increase in hospital admissions because of flares of IBD and IG during heat wave periods. Whereas the effect on IG is strongest with a delay of 7 days, the effect on IBD flares is immediate, suggesting different mechanisms.

The Paediatric Palliative Screening Scale: Further validity testing:

Background: Paediatric palliative care is still often introduced late in the illness trajectory of children with life-limiting diseases. Translating palliative care into practice continues to be a challenge. Aim: To validate the Paediatric Palliative Screening Scale further by defining attributes that predict the need for palliative care in children between 1 and 18 years. Design: Proportional-odds logistic regression analysis was performed to investigate the relationship between the attributes of the Paediatric Palliative Screening Scale and the experts’ assessment of case vignettes with various combinations of different attribute characteristics. Estimates from regression analysis were transformed to empirical weightings of the Paediatric Palliative Screening Scale attribute characteristics. Setting/participants: Online questionnaires with case vignettes were sent to 33 paediatric palliative care experts from Europe, the United States, Canada, Australia and New Zealand. Results: The highest weightings among the five previously defined attributes were estimated life expectancy <12 months (40% of maximum score) and preferences of the child/parents received (24%). Trajectory of disease and impact on daily activities of the child, expected outcome of treatment directed at the disease and burden of treatment, and symptom or problem burden were weighted less. Conclusions: According to this second step of psychometric testing of the Paediatric Palliative Screening Scale, the strongest and most urgent necessity indicators for a palliative care approach are life expectancy and child/family preferences. These results are somewhat discrepant with results from the previous validation of the instrument as well as previous research findings.