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Flow cytometry is often employed in ecology to measure traits and population size of bacteria and phytoplankton. This technique allows measuring millions of particles in a relatively small amount of time. However, distinguishing between different populations is not a straightforward task. Gating is a process in the identification of particles measured in flow cytometry. Gates can either be created manually using known characteristics of these particles, or by using automated clustering techniques. Available automated techniques implemented in statistical packages for flow cytometry are primarily developed for medicinal applications, while only two exist for phytoplankton. cyanoFilter is an R package built to identify phytoplankton populations from flow cytometry data. The package also integrates gating functions from two other automated algorithms. It also provides a gating accuracy test function that can be used to determine the accuracy of a desired gating function if monoculture flowcytometry data is available. The central algorithm in the package exploits observed pigmentation and granularity of phytoplankton cells. We demonstrate how its performance depends on strain similarity, using a model system of six cyanobacteria strains. Using the same system, we compare the performance of the central gating function in the package to similar functions in other packages.

Biomarkers play a key role in the monitoring of disease progression. The time taken for an individual to reach a biomarker exceeding or lower than a meaningful threshold is often of interest. Due to the inherent variability of biomarkers, persistence criteria are sometimes included in the definitions of progression, such that only two consecutive measurements above or below the relevant threshold signal that "true" progression has occurred. In previous work, a novel approach was developed, which allowed estimation of the time to threshold using the parameters from a linear mixed model where the residual variance was assumed to be pure measurement error. In this paper, we extend this methodology so that serial correlation can be accommodated. Assuming that the Markov property holds and applying the chain rule of probabilities, we found that the probability of progression at each timepoint can be expressed simply as the product of conditional probabilities. The methodology is applied to a cohort of HIV positive individuals, where the time to reach a CD4 count threshold is estimated. The second application we present is based on a study on abdominal aortic aneurysms, where the time taken for an individual to reach a diameter exceeding 55 mm is studied. We observed that erroneously ignoring the residual correlation when it is strong may result in substantial overestimation of the time to threshold. The estimated probability of the biomarker reaching a threshold of interest, expected time to threshold, and confidence intervals are presented for selected patients in both applications.

Modelling travel behaviour has always been a major research area in transportation analysis. After the second World War, due to the rapid increase in car ownership and car use in Western Europe and the United States, several models have been developed by transportation planners. In the fifties and sixties, travel was assumed to be the result of four subsequent decisions that were modelled: trip generation, trip distribution, mode choice and the assignment of trips to the road network (Ruiter & Ben-Akiva, 1978). These original tripbased models have been extended to ensuing tour-based models (Daly et al., 1983) and activity-based models (Pendyala et al., 1995; Ben-Akiva & Bowman, 1998; Kitamura & Fujii, 1998; Arentze & Timmermans, 2000; Bhat et al., 2004). In tour-based models, trips are explicitly connected in tours, i.e. chains that start and end at the same home or work base. This is carried out by introducing spatial constraints, hereby dealing with the lack of spatial interrelationship which was so apparent in the traditional four-step trip-based model. In activity-based models, travel demand is derived from the activities that individuals and households need or wish to perform. Decisions with respect to travel are driven by a collection of activities that form an activity diary. Travel should therefore be modelled within the context of the entire agenda, or as a component of the activity scheduling decision. In this way, the relationship between travel and non-travel aspects is taken into account. The reason why people undertake trips is one of the key aspects to be modelled in an activity-based model. However, every working transportation model still exists of at least these original four components of trip generation, distribution, mode choice and assignment. In order to fully understand the structure of a traditional transportation model, we need to elaborate on it some more. As shown in Figure 1, trip generation encompasses both the modelling of production (P) and attraction (A) of trips for a certain region (zone). Production is mainly being modelled at the level of the household, incorporating household characteristics (income, car ownership, household composition, ...), features of the zone (land price, degree of urbanization) and accessibility of the zone, whereas attraction is modelled at zone level

In reliability theory, diagnostic accuracy, and clinical trials, the quantity [Formula: see text], also known as the Probabilistic Index (PI), is a common treatment effect measure when comparing two groups of observations. The quantity [Formula: see text], a linear transformation of PI known as the net benefit, has also been advocated as an intuitively appealing treatment effect measure. Parametric estimation of PI has received a lot of attention in the past 40 years, with the formulation of the Uniformly Minimum-Variance Unbiased Estimator (UMVUE) for many distributions. However, the non-parametric Mann–Whitney estimator of the PI is also known to be UMVUE in some situations. To understand this seeming contradiction, in this paper a systematic comparison is performed between the non-parametric estimator for the PI and parametric UMVUE estimators in various settings. We show that the Mann–Whitney estimator is always an unbiased estimator of the PI with univariate, completely observed data, while the parametric UMVUE is not when the distribution is misspecified. Additionally, the Mann–Whitney estimator is the UMVUE when observations belong to an unrestricted family. When observations come from a more restrictive family of distributions, the loss in efficiency for the non-parametric estimator is limited in realistic clinical scenarios. In conclusion, the Mann–Whitney estimator is simple to use and is a reliable estimator for the PI and net benefit in realistic clinical scenarios.

Censoring due to a limit of detection or limit of quantification happens quite often in many medical studies. Conventional approaches to deal with censoring when analyzing these data include, for example, the substitution method and the complete case (CC) analysis. More recently, maximum likelihood estimation (MLE) has been increasingly used. While the CC analysis and the substitution method usually lead to biased estimates, the MLE approach appears to perform well in many situations. This article proposes an MLE approach to estimate the association between two measurements in the presence of censoring in one or both quantities. The central idea is to use a copula function to join the marginal distributions of the two measurements. In various simulation studies, we show that our approach outperforms existing conventional methods (CC and substitution analyses). In addition, rank-based measures of global association such as Kendall's tau or Spearman's rho can be studied, hence, attention is not only confined to Pearson's product-moment correlation coefficient capturing solely linear association. We have shown in our simulations that our approach is robust to misspecification of the copula function or marginal distributions given a small association. Furthermore, we propose a straightforward MLE method to fit a (multiple) linear regression model in the presence of censoring in a covariate or both the covariate and the response. Given the marginal distribution of the censored covariate, our method outperforms conventional approaches. We also compare and discuss the performance of our method with multiple imputation and missing indicator model approaches.

Protection and safety authorities recommend the use of model averaging to determine the benchmark dose approach as a scientifically more advanced method compared with the no-observed-adverse-effect-level approach for obtaining a reference point and deriving health-based guidance values. Model averaging however highly depends on the set of candidate dose-response models and such a set should be rich enough to ensure that a well-fitting model is included. The currently applied set of candidate models for continuous endpoints is typically limited to two models, the exponential and Hill model, and differs completely from the richer set of candidate models currently used for binary endpoints. The objective of this article is to propose a general and wide framework of dose response models, which can be applied both to continuous and binary endpoints and covers the current models for both type of endpoints. In combination with the bootstrap, this framework offers a unified approach to benchmark dose estimation. The methodology is illustrated using two data sets, one with a continuous and another with a binary endpoint.

Objective: To construct a clinical prediction rule for coronary artery disease (CAD) presenting with chest pain in primary care. Study Design and Setting: Meta-Analysis using 3,099 patients from five studies. To identify candidate predictors, we used random forest trees, multiple imputation of missing values, and logistic regression within individual studies. To generate a prediction rule on the pooled data, we applied a regression model that took account of the differing standard data sets collected by the five studies. Results: The most parsimonious rule included six equally weighted predictors: age >= 55 (males) or >= 65 (females) (+1); attending physician suspected a serious diagnosis (+1); history of CAD (+1); pain brought on by exertion (+1); pain feels like "pressure" (+1); pain reproducible by palpation (-1). CAD was considered absent if the prediction score is = 2, it was 43.0% (95% CI: 35.8-50.4%). Conclusions: Clinical prediction rules are a key strategy for individualizing care. Large data sets based on electronic health records from diverse sites create opportunities for improving their internal and external validity. Our patient-level meta-analysis from five primary care sites should improve external validity. Our strategy for addressing site-to-site systematic variation in missing data should improve internal validity. Using principles derived from decision theory, we also discuss the problem of setting the cutoff prediction score for taking action. (C) 2016 Elsevier Inc. All rights reserved. This study was funded by Federal Ministry of Education and Research, Germany (BMBF grant no. FKZ 01GK0920).

BackgroundAccurate prediction of the course of an acute cough episode could curb antibiotic overprescribing, but is still a major challenge in primary care.AimThe authors set out to develop a new prediction rule for poor outcome (re-consultation with new or worsened symptoms, or hospital admission) in adults presenting to primary care with acute cough.Design and settingData were collected from 2604 adults presenting to primary care with acute cough or symptoms suggestive of lower respiratory tract infection (LRTI) within the Genomics to combat Resistance against Antibiotics in Community-acquired LRTI in Europe (GRACE; www.grace-lrti.org) Network of Excellence.MethodImportant signs and symptoms for the new prediction rule were found by combining random forest and logistic regression modelling. Performance to predict poor outcome in acute cough patients was compared with that of existing prediction rules, using the models’ area under the receiver operator characteristic curve (AUC), and any improvement obtained by including additional test results (C-reactive protein [CRP], blood urea nitrogen [BUN], chest radiography, or aetiology) was evaluated using the same methodology.ResultsThe new prediction rule, included the baseline Risk of poor outcome, Interference with daily activities, number of years stopped Smoking (> or or ConclusionThe new prediction rule outperforms all existing alternatives in predicting poor outcome in adult patients presenting to primary care with acute cough and could not be improved by including additional test results.

Background Despite declining trends maternal mortality remains an important public health issue in Mozambique. The delays to reach an appropriate health facility and receive care faced by woman with pregnancy related complications play an important role in the occurrence of these deaths. This study aims to examine the contribution of the delays in relation to the causes of maternal death in facilities in Mozambique. Methods Secondary analysis was performed on data from a national assessment on maternal and neonatal health that included in-depth maternal death reviews, using patient files and facility records with the most comprehensive information available. Statistical models were used to assess the association between delay to reach the health facility that provides emergency obstetric care (delay type II) and delay in receiving appropriate care once reaching the health facility providing emergency obstetric care (delay type III) and the cause of maternal death within the health facility. Results Data were available for 712 of 2,198 maternal deaths. Delay type II was observed in 40.4% of maternal deaths and delay type III in 14.2%.and 13.9% had both delays. Women who died of a direct obstetric complication were more likely to have experienced a delay type III than women who died due to indirect causes. Women who experienced delay type II were less likely to have also delay type III and vice versa. Conclusions The delays in reaching and receiving appropriate facility-based care for women facing pregnancy related complications in Mozambique contribute significantly to maternal mortality. Securing referral linkages and health facility readiness for rapid and correct patient management are needed to reduce the impact of these delays within the health system.

Maternal mortality remains very high in Mozambique, with estimates from 2015 showing a maternal mortality ratio of 489 deaths per 100,000 live births, even though the rates tend to decrease since 1990. Pregnancy related hemorrhage, gestational hypertension and diseases such as malaria and HIV/AIDS are amongst the leading causes of maternal death in Mozambique, and a significant number of these deaths occur within health facilities. Often, the analysis of data on maternal mortality involves the use of counts of maternal deaths as outcome variable. Previously we showed that a class of hierarchical zero-inflated models were very successful in dealing with overdispersion and clustered counts when analyzing data on maternal deaths and related risk factors within health facilities in Mozambique. This paper aims at providing additional insights over previous analyses and presents an extension of such models to account for spatial variation in a disease mapping framework of facility-based maternal mortality in Mozambique. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article. The present study received financial support from Universidade Eduardo Mondlane (UEM)/Vlaamse Interuniversitaire Raad (VLIR-UOS) DESAFIO Program. The authors would also like to acknowledge the Ministry of Health of Mozambique (MISAU) for providing the data. The authors would like to acknowledge the sponsors of this study: the Flemish Interuniversity Council (VLIR-UOS) and Universidade Eduardo Mondlane (UEM) through the DESAFIO Program. The authors would also like to acknowledge the Ministry of Health of Mozambique (MISAU) for providing the NMNH survey data.

This article is concerned with statistically and computationally efficient estimation in a hierarchical data setting with unequal cluster sizes and an AR(1) covariance structure. Maximum likelihood estimation for AR(1) requires numerical iteration when cluster sizes are unequal. A near optimal non-iterative procedure is proposed. Pseudo-likelihood and split-sample methods are used, resulting in computing weights to combine cluster size specific parameter estimates. Results show that the method is statistically nearly as efficient as maximum likelihood, but shows great savings in computation time. Financial support from the IAP research network #P7/06 of the Belgian Government (Belgian Science Policy) is gratefully acknowledged. The research leading to these results has also received funding from the European Seventh Framework programme FP7 2007–2013 under grant agreement No. 602552. The authors gratefully acknowledge support from the IWT-SBO ExaScience grant.

Airborne infectious diseases such as influenza are primarily transmitted from human to human by means of social contacts and thus easily spread within households. Epidemic models, used to gain insight in infectious disease spread and control, typically rely on the assumption of random mixing within households. Until now there was no direct empirical evidence to support this assumption. Here, we present the first social contact survey specifically designed to study contact networks within households. The survey was conducted in Belgium (Flanders and Brussels) in 2010-2011. We analyzed data from 318 households totaling 1266 individuals with household sizes ranging from 2 to 7 members. Exponential-family random graph models (ERGMs) were fitted to the within-household contact networks to reveal the processes driving contact between household members, both on weekdays and weekends. The ERGMs showed a high degree of clustering and, specifically on weekdays, decreasing connectedness with increasing household size. Furthermore, we found that the odds of a contact between father and child is smaller than for any other pair except for older siblings. Epidemic simulation results suggest that within-household contact density is the main driver of differences in epidemic spread between complete and empirical-based household contact networks. The homogeneous mixing assumption may therefore be an adequate characterization of the within-household contact structure for the purpose of epidemic simulation. However, ignoring the contact density when inferring from an epidemic model will result in biased estimates of within-household transmission rates. Further research on the implementation of within-household contact networks in epidemic models is necessary.Significance StatementHouseholds have a pivotal role in the spread of airborne infectious diseases. Households are bridging units between schools and workplaces, and social contacts within households are frequent and intimate, allowing for rapid disease spread. Infectious disease models typically assume that members of a household contact each other randomly. Until now there was no direct empirical evidence to support this assumption. In this paper, we present the first social contact survey specifically designed to study contact networks within households with young children. We investigate which factors drive contacts between household members on one particular day by means of a statistical model. Our results suggest the importance of connectedness within households over heterogeneity in number of contacts.

Facility-based maternal mortality remains an important public health problem in Mozambique. A number of factors associated with health system functioning can be described behind the occurrence of these deaths. This paper aimed to evaluate the magnitude of the health facility-based maternal mortality, its geographical distribution and to assess the health facility factors implicated in the occurrence of these deaths. A secondary analysis was done on data from the survey on maternal health needs performed by the Ministry of Health of Mozambique in 2008. During the study period 2.198 maternal deaths occurred out of 312.537 deliveries. According to the applied model the availability of Maternal and Child Health (MCH) nurses performing Emergency Obstetric Care functions was related to the reduction of facility-based maternal mortality by 40%. No significant effects were observed for the availability of medical doctors, surgical technicians and critical delivery room equipment. Impact statement Is largely known that the availability of skilled attendants assisting every delivery and providing Emergency Obstetric Care services during the pregnancy, labor and Childbirth is key for maternal mortality reduction. This study add the differentiation on the impact of different cadres of health services providers working on maternal and child health services on the facility based maternal mortality. In this setting the study proven the high impact of the midlevel skilled maternal and child health nurses on the reduction of maternal mortality. Another important add from this study is the use of facility based maternal mortality data to inform the management process of maternal healthcare services. The findings from this study have potential to impact on the decision of staffing prioritization in setting like the study setting. The findings support the policy choice to improve the availability of maternal and child health nurses.

Objectives: To assess whether persistence of antimicrobial resistance (i.e. non-susceptible resistance status) after treatment with penicillins or cephalosporins versus macrolides or tetracyclines differs and to compare the results obtained using routinely collected data with findings reported in prospective studies. Methods: Routinely collected microbiological data from 14 voluntary participating laboratories (2005) containing information on resistance status and individual antimicrobial consumption patterns (mid 2004-2005) were analysed using a generalised estimating equation (GEE) approach. The link function was adjusted to acknowledge that the proportion of resistant isolates in the population not treated with antibiotics [baseline resistance (BR)] is not necessarily zero. To optimise the comparability of this study with prospective studies, the analysis was repeated after removal of 14 isolates from patients who did not survive 2005. Results: BR estimates were unstable and their confidence intervals were wide, which called for a sensitivity analysis using an adjusted GEE model with three different BR estimates. All models indicated that the proportion of susceptible isolates differed by treatment group and increased significantly over time, with this increase being independent of treatment group. Persistence of resistance after exposure to macrolides or tetracyclines was approximately three times as long as after exposure to penicillins or cephalosporins. Conclusions: Resistance following treatment with macrolides or tetracyclines persists longer than following treatment with penicillins or cephalosporins, which confirms the findings from prospective studies and suggests the use of routinely collected data as a valuable alternative to determine such differences in persistence of resistance. a 2016 International Society for Chemotherapy of Infection and Cancer. Published by Elsevier Ltd. All rights reserved. The work was supported by the Methusalem Financing Programme of the Flemish government and the IAP Research Network P7/06 of the Belgian State (Belgian Science Policy). NH is supported by the University of Antwerp scientific chair in Evidence-Based Vaccinology, financed in 2009-16 by a gift from Pfizer. This study (IARG) was also supported by the Belgian National Council for the Promotion of Quality (Nationale Raad voor KwaliteitsPromotie; http://www.riziv.fgov.be/fr/inami/organes/Pages/cnpq.aspx).

BackgroundThe contraceptive prevalence rate in Mozambique was estimated as 11.3% in the last Demographic and Health Survey. The impact of family planning (FP) on women's health and on the reduction of maternal mortality is well known.MethodsAcknowledging the importance of user satisfaction in the utilisation of health services, exit interviews were used to assess women's satisfaction with FP services in Mozambique. The survey, conducted in 174 health facilities, was representative at the national level, covered all provinces, and both urban and rural areas.ResultsOverall, 86% of respondents were satisfied with FP services, but issues such as insufficient supplies of oral contraceptives and the low quality of healthcare provider/client interactions were given as reasons for women's dissatisfaction.ConclusionDefined actions at the level of health service provision are needed to tackle the identified issues and ensure improved satisfaction with, and better utilisation of, FP services in Mozambique.

Summary Assessing the consequences of biodiversity changes for ecosystem functioning requires separating the net effect of biodiversity from potential confounding effects such as the identity of the gained or lost species. Additive partitioning methods allow factoring out these species identify effects by comparing species’ functional contributions against the predictions of a null model under which functional contributions are independent of biodiversity. Classic additive partitioning methods quantify biodiversity effects based on a linear relationship between species deviations from the null model and their functional traits. However, based on ecological theory, non-linear relationships are also possible. Here we demonstrate how additive-partitioning methods can be extended to describe such non-linear relationships, and explain how non-linear biodiversity effects can be interpreted. We apply both linear and non-linear partitioning methods to the Cedar Creek biodiversity II experiment. Non-linear relationships were detected in the majority of plots, and increased with diversity. Non-linear partitioning thereby identified a convex relationship between species functional traits and their deviations from the null model, driven by strong positive effects of both species with low and high functional trait values trait values on ecosystem functioning. The presented non-linear extension of additive partitioning methods is therefore essential for revealing more complex biodiversity effects on ecosystem functioning, which are likely to occur in biodiversity experiments. This article is protected by copyright. All rights reserved.

This article examines the association between risky sexual behavior and HIV risk perception among miners. A cross-sectional survey (n = 293) was conducted with miners who worked in South African mines and lived in the Chokwe district of Gaza Province in southern Mozambique. The study used ordinary logistic and cumulative logistic models to understand what drives miners to HIV susceptibility. The study revealed that most miners were at risk of HIV infection as a result of risky sexual behavior. However, there was a strong negative association between risky sexual behavior and HIV risk perception. Seventy percent of the miners practicing risky sexual behavior reported low HIV risk perception. This demonstrates that miners tend to underestimate their HIV risk, which makes them more susceptible to contracting HIV. Risk perception among miners is related to a range of factors, both individual and environmental. Informed risk awareness is essential for these men to adopt preventive measures against HIV/AIDS. Therefore, it is important to consider cultural beliefs, as they influence the understanding of HIV risk perception. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The present study received financial support from Ministry of Science and Technology and Desafio/Vlaamse interuniversitaire raad VLIRUOS Program.

Since antimicrobial resistance (AMR) has been one of the major public health burdens over the last decade, it is of great importance to appropriately monitor and analyse AMR data. Isolate-based data within the EU have been routinely collected since 2010 and reported to EFSA on a yearly basis. AMR data are collected for several bacterial species, tested for susceptibility against different antimicrobials and minimum inhibitory concentration (MIC) is reported. For analysis purposes, a dichotomised version of the MIC values based on the epidemiological cut-off is used to represent different resistance patterns. This report describes various methods to analyse multi-drug resistance data, including the identification of structure to construct groups of isolates with similar resistance patterns or with similar MIC values. Multivariate classification trees and hierarchical cluster analysis after application of principal components and multiple correspondence analyses are applied aiming at group discovering. Latent class analysis is presented as an alternative model-based approach. The generalised estimating equations method is presented handling univariate and multivariate binary outcomes. Bayesian network analysis provides the user with a graphical representation of the underlying associations in the data to identify new co-resistance patterns. Models that deal with spatial distribution of resistant isolates, in combination with their evolution over time, are constructed for univariate and bivariate outcomes. Finally, pattern and source attributions tools are presented, providing, in addition to exploratory analyses, a logistic model to assess variables influencing certain resistance patterns. Source attribution is used to attribute resistance cases in humans to resistance observed in animal, human food consumption patterns and antimicrobial usage data. For illustration purposes, these methods are applied to a subset of the AMR data using an application developed with the R package “shiny”.

After a steep monotone rise with age, the seroprevalence profiles for human parvovirus B19 (PVB19) display a decrease or plateau between the ages of 20 and 40, in each of 5 European countries. We investigate whether this phenomenon is induced by waning antibodies for PVB19 and, if this is the case, whether secondary infections are plausible, or whether boosting may occur. Several immunological scenarios are tested for PVB19 by fitting different compartmental dynamic transmission models to serological data using data on social contact patterns. The social contact approach has already been shown informative to estimate transmission rates and the basic reproduction number for infections transmitted predominantly through nonsexual social contacts. Our results show that for 4 countries, model selection criteria favor the scenarios allowing for waning immunity at an age-specific rate over the assumption of lifelong immunity, assuming that the transmission rates are directly proportional to the contact rates. Different views on the evolution of the immune response to PVB19 infection lead to altered estimates of the age-specific force of infection and the basic reproduction number. The scenarios which allow for multiple infections during one lifetime predict a higher frequency of PVB19 infection in pregnant women and of associated fetal deaths. When prevaccination serological data are available, the framework developed in this paper could prove worthwhile to investigate these different scenarios for other infections as well, such as cytomegalovirus.

In the conventional linear mixed-effects model, four structures can be distinguished: fixed effects, random effects, measurement error and serial correlation. The latter captures the phenomenon that the correlation structure within a subject depends on the time lag between two measurements. While the general linear mixed model is rather flexible, the need has arisen to further increase flexibility. In addition to work done in the area, we propose the use of spline-based modeling of the serial correlation function, so as to allow for additional flexibility. This approach is applied to data from a pre-clinical experiment in dementia which studied the eating and drinking behavior in mice.

The aim of this paper is to gain a better understanding of the impact of simplification on a sequential model of activity-scheduling behavior which uses feature-selection methods. To that effect, the predictive performance of the Albatross model, which incorporates nine different facets of activity–travel behavior, based on the original full decision trees, is compared with the performance of the model based on trimmed decision trees. The results indicate that significantly smaller decision trees can be used for modeling the different choice facets of the sequential model system without losing much in predictive power. The performance of the models is compared at three levels: the choice-facet level, the activity-pattern level (comparing the observed and generated sequences of activities), and the trip-matrix level, comparing the correlation coefficients that determine the strength of the associations between the observed and the predicted origin–destination matrices. The results indicate that the model based on the trimmed decision trees predicts activity-diary schedules with a minimum loss of accuracy at the decision level. Moreover, the results indicate a slightly better performance at the activity-pattern and the trip-matrix level.

Antimicrobial resistance has become one of the main public health burdens of the last decades, and monitoring the development and spread of non-wild-type isolates has therefore gained increased interest. Monitoring is performed based on the minimum inhibitory concentration (MIC) values, which are collected through the application of dilution experiments. In order to account for the unobserved population heterogeneity of wild-type and non-wild-type isolates, mixture models are extremely useful. Instead of estimating the entire mixture globally, it was our major aim to provide an estimate for the wild-type first component only. The characteristics of this first component are not expected to change over time, once the wild-type population has been confidently identified for a given antimicrobial. With this purpose, we developed a new method based on the multinomial distribution, and we carry out a simulation study to study the properties of the new estimator. Because the new approach fits within the likelihood framework, we can compare distinct distributional assumptions in order to determine the most suitable distribution for the wild-type population. We determine the optimal parameters based on the AIC criterion, and attention is also paid to the model-averaged approach using the Akaike weights. The latter is thought to be very suitable to derive specific characteristics of the wild-type distribution and to determine limits for the wild-type MIC range. In this way, the new method provides an elegant means to compare distinct distributional assumptions and to quantify the wild-type MIC distribution of specific antibiotic-bacterium combinations.

Coenen, S (reprint author), Univ Antwerp, Vaccine & Infect Dis Inst VAXINFECTIO, Lab Med Microbiol, B-2020 Antwerp, Belgium. samuel.coenen@uantwerpen.be

Asymptotic properties of the parametric bootstrap procedure for maximum pseudolikelihood estimators and hypothesis tests are studied in the general framework of associated populations. The technique is applied to the analysis of toxicological experiments which, based on pseudolikelihood inference for clustered binary data, fits into this framework. It is shown that the bootstrap approximation can be used as an interesting alternative to the classical asymptotic distribution of estimators and test statistics. Finite sample simulations for clustered binary data models confirm the asymptotic theory and indicate some substantial improvements.

Background Evidence on the influence of comorbidity and comedication on clinical outcomes in patients with type 2 diabetes mellitus is scarce. Aim To ascertain the effect of five chronic diseases (joint disorder, respiratory disease, anaemia, malignancy, depression) and three chronically used drugs (non-steroid anti-inflammatory drugs [NSAIDs], corticosteroids, antidepressants) on treatment for hypoglycaemia in patients with type 2 diabetes. Design and setting Retrospective cohort study in a variety of practices across Flanders, Belgium. Method A retrospective cohort study was conducted, based on data from Intego, a general practice-based continuous morbidity registry. Multiple logistic regression analysis was used to predict the change in glycosylated haemoglobin (HbA1c) levels related to comorbidity, comedication, and a combination of both in 3416 patients with type 2 diabetes. Adjustments were made for age, sex, and diabetes-treatment group (diet, oral antidiabetic drugs, combination treatment, insulin). Results Concomitant joint and respiratory disorders, as well as the chronic use of NSAIDs and corticosteroids, either separately or in combination, were significantly associated with the worsening of HbA1c levels. Anaemia, depression, malignancy, and antidepressants had no statistically significant influence on the efficacy of treatment for hypoglycaemia. Conclusion The presence of some comorbid diseases or drug use can impede the efficacy of treatment for type 2 diabetes. This finding supports the need to develop treatment recommendations, taking into account the presence of both chronic comorbidity and comedication. Further research must be undertaken to ascertain the effect other combinations of chronic diseases have on the efficacy of treatment of this and other diseases.

When Bluetongue Virus Serotype 8 (BTV-8) was first detected in Northern Europe in 2006, several guidelines were immediately put into place with the goal to protect farms and stop the spreading of the disease. This however did not prevent further rapid spread of BTV-8 across Northern Europe. Using information on the 2006 Bluetongue outbreak in cattle farms in Belgium, a spatio-temporal transmission model was formulated. The model quantifies the local transmission of the disease between farms within a municipality, the short-distance transmission between farms across neighbouring municipalities and the transmission as a result of cattle movement. Different municipality-level covariates such as farm density, land composition variables, temperature and precipitation, were assessed as possibly influencing each component of the transmission process. Results showed a significant influence of the different covariates in each model component, particularly the significant effect of temperature and precipitation values in the number of infected farms. The model which allowed us to predict the dynamic spreading of BTV for different movement restriction scenarios, also affirmed the significant impact of cattle movement in the 2006 BTV outbreak pattern. Simulation results further showed the importance of considering the size of restriction zones in the formulation of guidelines for animal infectious diseases. Support from the IAP Research Network P7/06 of the Belgian State (Belgian Science Policy) and the Flemish Research Council (G064710). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conducting dietary exposure assessment (E) consists in combining deterministically or probabilistically food consumption figures (Q) with concentrations (C) of a given chemical substance in a number of foods or food categories. Occurrence data can be obtained either from control and monitoring programs or from a total diet Study (TDS). In both cases, data reported to be below the limit of detection (LOD), called “non-detects” or “left-censored data”, are likely to have a critical influence on the results of the assessment. Both the LOD and the limit of quantification (LOQ) are of special importance for exposure estimations in risk assessments as they determine the minimum value that can be detected and quantified, respectively.

The hepatitis C virus (HCV) and the human immunodeficiency virus (HIV) are a clear threat for public health, with high prevalences especially in high risk groups such as injecting drug users. People with HIV infection who are also infected by HCV suffer from a more rapid progression to HCV-related liver disease and have an increased risk for cirrhosis and liver cancer. Quantifying the impact of HIV and HCV co-infection is therefore of great importance. We propose a new joint mathematical model accounting for co-infection with the two viruses in the context of injecting drug users (IDUs). Statistical concepts and methods are used to assess the model from a statistical perspective, in order to get further insights in: (i) the comparison and selection of optional model components, (ii) the unknown values of the numerous model parameters, (iii) the parameters to which the model is most 'sensitive' and (iv) the combinations or patterns of values in the high-dimensional parameter space which are most supported by the data. Data from a longitudinal study of heroin users in Italy are used to illustrate the application of the proposed joint model and its statistical assessment. The parameters associated with contact rates (sharing syringes) and the transmission rates per syringe-sharing event are shown to play a major role. (C) 2013 Elsevier B.V. All rights reserved.

Background: Chest pain is a common complaint in primary care, with coronary heart disease (CHD) being the most concerning of many potential causes. Systematic reviews on the sensitivity and specificity of symptoms and signs summarize the evidence about which of them are most useful in making a diagnosis. Previous meta-analyses are dominated by studies of patients referred to specialists. Moreover, as the analysis is typically based on study-level data, the statistical analyses in these reviews are limited while meta-analyses based on individual patient data can provide additional information. Our patient-level meta-analysis has three unique aims. First, we strive to determine the diagnostic accuracy of symptoms and signs for myocardial ischemia in primary care. Second, we investigate associations between study-or patient-level characteristics and measures of diagnostic accuracy. Third, we aim to validate existing clinical prediction rules for diagnosing myocardial ischemia in primary care. This article describes the methods of our study and six prospective studies of primary care patients with chest pain. Later articles will describe the main results. less thanbrgreater than less thanbrgreater thanMethods/Design: We will conduct a systematic review and IPD meta-analysis of studies evaluating the diagnostic accuracy of symptoms and signs for diagnosing coronary heart disease in primary care. We will perform bivariate analyses to determine the sensitivity, specificity and likelihood ratios of individual symptoms and signs and multivariate analyses to explore the diagnostic value of an optimal combination of all symptoms and signs based on all data of all studies. We will validate existing clinical prediction rules from each of the included studies by calculating measures of diagnostic accuracy separately by study. less thanbrgreater than less thanbrgreater thanDiscussion: Our study will face several methodological challenges. First, the number of studies will be limited. Second, the investigators of original studies defined some outcomes and predictors differently. Third, the studies did not collect the same standard clinical data set. Fourth, missing data, varying from partly missing to fully missing, will have to be dealt with. Despite these limitations, we aim to summarize the available evidence regarding the diagnostic accuracy of symptoms and signs for diagnosing CHD in patients presenting with chest pain in primary care. less thanbrgreater than less thanbrgreater thanReview registration: Centre for Reviews and Dissemination (University of York): CRD42011001170 Funding Agencies|Federal Ministry of Education and Research (BMBF)|FKZ 01GK0920

Missing data often occur in regression analysis. Imputation, weighting, direct likelihood, and Bayesian inference are typical approaches for missing data analysis. The focus is on missing covariate data, a common complication in the analysis of sample surveys and clinical trials. A key quantity when applying weighted estimators is the mean score contribution of observations with missing covariate(s), conditional on the observed covariates. This mean score can be estimated parametrically or nonparametrically by its empirical average using the complete case data in case of repeated values of the observed covariates, typically assuming categorical or categorized covariates. A nonparametric kernel based estimator is proposed for this mean score, allowing the full exploitation of the continuous nature of the covariates. The performance of the kernel based method is compared to that of a complete case analysis, inverse probability weighting, doubly robust estimators and multiple imputation, through simulations. (C) 2011 Elsevier B.V. All rights reserved. This work has been funded by the IAP research network nr P6/03 of the Belgian Government (Belgian Science Policy). The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders (FWO) and the Flemish Government – department EWI.

The use of threshold values in order to diagnose individual subjects as being ‘susceptible’ or ‘infected or recovered/immune’ for a specific infection is virtually always prone to false positive, false negative or inconclusive classifications. Such misclassifications might lead to biased estimates for epidemiological parameters, such as the prevalence and the force of infection. In this article, we propose to estimate these epidemiological parameters directly from antibody titres, using an underlying mixture model. The method is applied to estimate the Salmonella serological prevalence in pigs and the age-dependent force of infection using serological data on the Varicella-Zoster virus (VZV) in humans. The threshold and direct method are compared through a simulation study.