Christel Faes

Human Salmonellosis: Estimation of Dose-Illness from Outbreak Data

The quantification of the relationship between the amount of microbial organisms ingested and a specific outcome such as infection, illness, or mortality is a key aspect of quantitative risk assessment. A main problem in determining such dose-response models is the availability of appropriate data. Human feeding trials have been criticized because only young healthy volunteers are selected to participate and low doses, as often occurring in real life, are typically not considered. Epidemiological outbreak data are considered to be more valuable, but are more subject to data uncertainty. In this article, we model the dose-illness relationship based on data of 20 Salmonella outbreaks, as discussed by the World Health Organization. In particular, we model the dose-illness relationship using generalized linear mixed models and fractional polynomials of dose. The fractional polynomial models are modified to satisfy the properties of different types of dose-illness models as proposed by Teunis et al. Within these models, differences in host susceptibility (susceptible versus normal population) are modeled as fixed effects whereas differences in serovar type and food matrix are modeled as random effects. In addition, two bootstrap procedures are presented. A first procedure accounts for stochastic variability whereas a second procedure accounts for both stochastic variability and data uncertainty. The analyses indicate that the susceptible population has a higher probability of illness at low dose levels when the combination pathogen-food matrix is extremely virulent and at high dose levels when the combination is less virulent. Furthermore, the analyses suggest that immunity exists in the normal population but not in the susceptible population.

The Effective Sample Size and an Alternative Small-Sample Degrees-of-Freedom Method

Correlated data frequently arise in contexts such as, for example, repeated measures and meta-analysis. The amount of information in such data depends not only on the sample size, but also on the structure and strength of the correlations among observations from the same independent block. A general concept is discussed, the effective sample size, as a way of quantifying the amount of information in such data. It is defined as the sample size one would need in an independent sample to equal the amount of information in the actual correlated sample. This concept is widely applicable, for Gaussian data and beyond, and provides important insight. For example, it helps explain why fixed-effects and random-effects inferences of meta-analytic data can be so radically divergent. Further, we show that in some cases the amount of information is bounded, even when the number of measures per independent block approaches infinity. We use the method to devise a new denominator degrees-of-freedom method for fixed-effects testing. It is compared to the classical Satterthwaite and Kenward–Roger methods for performance and, more importantly, to enhance insight. A key feature of the proposed degrees-of-freedom method is that it, unlike the others, can be used for non-Gaussian data, too. This article has supplementary material online.

Variational Bayesian inference for parametric and nonparametric regression with missing data

Bayesian hierarchical models are attractive structures for conducting regression analyses when the data are subject to missingness. However, the requisite probability calculus is challenging and Monte Carlo methods typically are employed. We develop an alternative approach based on deterministic variational Bayes approximations. Both parametric and nonparametric regression are considered. Attention is restricted to the more challenging case of missing predictor data. We demonstrate that variational Bayes can achieve good accuracy, but with considerably less computational overhead. The main ramification is fast approximate Bayesian inference in parametric and nonparametric regression models with missing data. Supplemental materials accompany the online version of this article.

Establishing the spread of bluetongue virus at the end of the 2006 epidemic in Belgium

Bluetongue (BT) was notified for the first time in several Northern European countries in August 2006. The first reported outbreaks of BT were confirmed in herds located near the place where Belgium, The Netherlands and Germany share borders. The disease was rapidly and widely disseminated throughout Belgium in both sheep and cattle herds. During the epidemic, case reporting by the Veterinary Authorities relied almost exclusively on the identification of herds with confirmed clinical infected ruminants. A cross-sectional serological survey targeting all Belgian ruminants was then undertaken during the vector-free season. The first objective of this study was to provide unbiased estimates of BT-seroprevalence for different regions of Belgium. Since under-reporting was suspected during the epidemic, a second goal was to compare the final dispersion of the virus based on the seroprevalence estimates to the dispersion of the confirmed clinical cases which were notified in Belgium, in order to estimate the accuracy of the case detection based on clinical suspicion. True within-herd seroprevalence was estimated based on a logistic-normal regression model with prior specification on the diagnostic tests sensitivity and specificity. The model was fitted in a Bayesian framework. Herd seroprevalence was estimated using a logistic regression model. To study the linear correlation between the BT winter screening data and the case-herds data, the linear predicted values for the herd prevalence were compared and the Pearson correlation coefficient was estimated. The overall herd and true within-herd seroprevalences were estimated at 83.3 (79.2-87.0) and 23.8 (20.1-28.1)%, respectively. BT seropositivity was shown to be widely but unevenly distributed throughout Belgium, with a gradient decreasing towards the south and the west of the country. The analysis has shown there was a strong correlation between the outbreak data and the data from the survey (r=0.73, p<0.0001). The case detection system based on clinical suspicion underestimated the real impact of the epidemic, but indicated an accurate spatial distribution of the virus at the end of the epidemic.

Seventy-five years of estimating the force of infection from current status data.

The force of infection, describing the rate at which a susceptible person acquires an infection, is a key parameter in models estimating the infectious disease burden, and the effectiveness and cost-effectiveness of infectious disease prevention. Since Muench formulated the first catalytic model to estimate the force of infection from current status data in 1934, exactly 75 years ago, several authors addressed the estimation of this parameter by more advanced statistical methods, while applying these to seroprevalence and reported incidence/case notification data. In this paper we present an historical overview, discussing the relevance of Muenchs work, and we explain the wide array of newer methods with illustrations on pre-vaccination serological survey data of two airborne infections: rubella and parvovirus B19. We also provide guidance on deciding which method(s) to apply to estimate the force of infection, given a particular set of data.

Establishment of reference values for novel urinary biomarkers for renal damage in the healthy population: are age and gender an issue?

Abstract Background: Recently, a lot of research has focused on the discovery of novel renal biomarkers. Among others, the urinary kidney injury molecule 1 (KIM-1) and neutrophil gelatinase-associated lipocalin (NGAL) have been proven to be promising biomarkers in a wide variety of renal pathologies. However, little is known about the normal concentrations in urine of healthy subjects. Therefore, the goal of our study is to establish reference values for urinary KIM-1, NGAL, N-acetyl-β-D-glucosamidase (NAG), and cystatin C in a healthy population, taking into account possible effects of age and gender. Methods: We collected urine samples from 338 healthy, nonsmoking subjects between 0 and 95 years old. Subjects with elevated α1-microglobulin values were excluded. Next to the urinary concentrations of KIM-1, NGAL, NAG, and cystatin C, we measured urinary creatinine and specific gravity to correct for urinary dilution. The possible effect of age and gender on the four urinary biomarkers was investigated, and the reference values were established. Results: For the absolute urinary concentrations of the biomarkers, age had a significant effect on all the biomarkers, except for cystatin C, whereas gender significantly affected all four of them, except for NAG. The normalization of biomarkers for creatinine and specific gravity had an effect on the correlation between the biomarkers on one hand and age and gender on the other. Conclusions: In conclusion, age and gender had different effects on KIM-1, NGAL, NAG, and cystatin C. Based on this knowledge, age- and gender-specific reference values for KIM-1, NGAL, NAG, and cystatin C were established.

Does Air Pollution Trigger Infant Mortality in Western Europe? A Case-Crossover Study

Background: Numerous studies show associations between fine particulate air pollutants [particulate matter with an aerodynamic diameter ≤ 10 μm (PM10)] and mortality in adults. Objectives: We investigated short-term effects of elevated PM10 levels on infant mortality in Flanders, Belgium, and studied whether the European Union (EU) limit value protects infants from the air pollution trigger. Methods: In a case-crossover analysis, we estimated the risk of dying from nontraumatic causes before 1 year of age in relation to outdoor PM10 concentrations on the day of death. We matched control days on temperature to exclude confounding by variations in daily temperature. Results: During the study period (1998–2006), PM10 concentration averaged 31.9 ± 13.8 μg/m3. In the entire study population (n = 2,382), the risk of death increased by 4% [95% confidence interval (CI), 0–8%; p = 0.045] for a 10-μg/m3 increase in daily mean PM10. However, this association was significant only for late neonates (2–4 weeks of age; n = 372), in whom the risk of death increased by 11% (95% CI, 1–22%; p = 0.028) per 10-μg/m3 increase in PM10. In this age class, infants were 1.74 (95% CI, 1.18–2.58; p = 0.006) times more likely to die on days with a mean PM10 above the EU limit value of 50 μg/m3 than on days below this cutoff. Conclusions: Even in an affluent region in Western Europe, where infant mortality is low, days with higher PM air pollution are associated with an increased risk of infant mortality. Assuming causality, the current EU limit value for PM10, which may be exceeded on 35 days/year, does not prevent PM10 from triggering mortality in late neonates.

Modeling Infectious Disease Parameters Based on Serological and Social Contact Data

Mathematical models for infectious diesease.- The static model.- The dynamic model.- The stochastic model.- Implementation of models in MATLAB.- Data sources for modelling infectious diseases.- Estimation from serological data.- Parametric models for teh prevalence and the force of infection.- Non-parametric approaches to model the prevalence and force of infection.- Semi-parametric approaches to model the prevalence and force of infection.- A Bayesian approach.- Modelling the prevalence and the force of infection direction from antibody levels.- Modelling multivariate serological data.- Estimation from other data sources.- Estimating mixing patterns and Ro in a heterogenous population.- Modelling in a homogeneous population.- Modelling in a heterogeneous population.- Modelling AIDS outbreak data.- Modelling hepatitis C among injection drug users.- Modelling dengue.- Modelling bovine herpes virus in cattle.

European Surveillance of Antimicrobial Consumption (ESAC): outpatient cephalosporin use in Europe (1997–2009)

BACKGROUND Data on 13 years of outpatient cephalosporin use were collected from 33 European countries within the European Surveillance of Antimicrobial Consumption (ESAC) project, funded by the European Centre for Disease Prevention and Control (ECDC), and analysed in detail. METHODS For the period 1997-2009, data on outpatient use of systemic cephalosporins aggregated at the level of the active substance were collected using the Anatomical Therapeutic Chemical (ATC)/defined daily dose (DDD) method (WHO, version 2011) and expressed in DDD per 1000 inhabitants per day (DID). For detailed analysis of trends over time, seasonal variation and composition of outpatient cephalosporin use in 33 European countries, we distinguished between first-generation (J01DB), second-generation (J01DC), third-generation (J01DD) and fourth-generation (J01DE) cephalosporins. RESULTS Total outpatient cephalosporin use in 2009 varied from 8.7 DID in Greece to 0.03 DID in Denmark. In general, use was higher in Southern and Eastern European countries than in Northern European countries. Total outpatient cephalosporin use increased over time by 0.364 (SD 0.473) DID between 1997 and 2009. Cephalosporin use increased for half of the countries. Low-consuming Northern European countries and the UK further decreased their use. Second-generation cephalosporins increased by >20% in seven countries (mainly cefuroxime), coinciding with a decrease in first-generation cephalosporins. Substantial parenteral use of third-generation substances (mainly ceftriaxone) was observed in France, Italy and the Russian Federation. CONCLUSIONS Since 1997, the use of the older (narrow-spectrum) cephalosporins decreased in favour of the newer (i.e. broad-spectrum) cephalosporins in most countries. Extreme variations between European countries in cephalosporin use over time suggest that they are to a large extent inappropriately used.

Eight Years of the Great Influenza Survey to Monitor Influenza-Like Illness in Flanders

In 2003, an internet-based monitoring system of influenza-like illness (ILI), the Great Influenza Survey (GIS), was initiated in Belgium. For the Flemish part of Belgium, we investigate the representativeness of the GIS population and assess the validity of the survey in terms of ILI incidence during eight influenza seasons (from 2003 through 2011). The validity is investigated by comparing estimated ILI incidences from the GIS with recorded incidences from two other monitoring systems, (i) the Belgian Sentinel Network and (ii) the Google Flu Trends, and by performing a risk factor analysis to investigate whether the risks on acquiring ILI in the GIS population are comparable with results in the literature. A random walk model of first order is used to estimate ILI incidence trends based on the GIS. Good to excellent correspondence is observed between the estimated ILI trends in the GIS and the recorded trends in the Sentinel Network and the Google Flu Trends. The results of the risk factor analysis are in line with the literature. In conclusion, the GIS is a useful additional surveillance network for ILI monitoring in Flanders. The advantages are the speed at which information is available and the fact that data is gathered directly in the community at an individual level.