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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.

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

Proposals to update the harmonised monitoring and reporting of antimicrobial resistance (AMR) from a public health perspective in Salmonella, Campylobacter coli, Campylobacter jejuni, Escherichia coli, Enterococcus faecalis, Enterococcus faecium and methicillin‐resistant Staphylococcus aureus (MRSA) from food‐producing animals and derived meat in the EU are presented in this report, accounting for recent trends in AMR, data collection needs and new scientific developments. Phenotypic monitoring of AMR in bacterial isolates, using microdilution methods for testing susceptibility and interpreting resistance using epidemiological cut‐off values is reinforced, including further characterisation of those isolates of E. coli and Salmonella showing resistance to extended‐spectrum cephalosporins and carbapenems, as well as the specific monitoring of ESBL/AmpC/carbapenemase‐producing E. coli. Combinations of bacterial species, food‐producing animals and meat, as well as antimicrobial panels have been reviewed and adapted, where deemed necessary. Considering differing sample sizes, numerical simulations have been performed to evaluate the related statistical power available for assessing occurrence and temporal trends in resistance, with a predetermined accuracy, to support the choice of harmonised sample size. Randomised sampling procedures, based on a generic proportionate stratified sampling process, have been reviewed and reinforced. Proposals to improve the harmonisation of monitoring of prevalence, genetic diversity and AMR in MRSA are presented. It is suggested to complement routine monitoring with specific cross‐sectional surveys on MRSA in pigs and on AMR in bacteria from seafood and the environment. Whole genome sequencing (WGS) of isolates obtained from the specific monitoring of ESBL/AmpC/carbapenemase‐producing E. coli is strongly advocated to be implemented, on a voluntary basis, over the validity period of the next legislation, with possible mandatory implementation by the end of the period; the gene sequences encoding for ESBL/AmpC/carbapenemases being reported to EFSA. Harmonised protocols for WGS analysis/interpretation and external quality assurance programmes are planned to be provided by the EU‐Reference Laboratory on AMR.

Recent studies suggest that a large proportion of new HIV-1 infections in mature epidemics occurs within discordant couples, making discordancy a major contributor to the spread of HIV/AIDS in Africa. This paper aims at assessing changes over a five-year period (2009-2015) on the (risk) factors associated with HIV serodiscordance among couples in Mozambique, using cross-sectional data from the INSIDA and IMASIDA surveys. The pooled data of both surveys were analyzed using a joint model for three parameters characterizing in a particular way disagreement and sero(con/dis)corance between the HIV statuses of couples, as introduced by Aerts et al.: the probability that the female partner is HIV positive, given that both partners differ in their HIV status, the probability that only one partner is HIV positive, given that at least one of the two partners is positive ("positive" serodiscordance), and the probability that both partners are negative given that at most one of the two partners is positive ("negative" seroconcordance). The results reveal similar significant factors and estimates as in Aerts et al. (HIV prevalence, union number for woman, STI for man, condom use by woman and wealth index), but the additional significant factors "condom use by man" (no use had a negative effect on the positive serodiscordance) and "union number for man" (for couples where the man has been married or co-habiting with a woman before had a decreased negative seroconcordance) were identified. The only factor that had a different effect over time (IMASIDA as compared to INSIDA) was the effect of "HIV prevalence of province" on the negative seroconcordance. The negative effect of a higher HIV prevalence was less pronounced in 2015 for negative seroconcordance. This study was financially supported by the Flemish Interuniversity Council (VLIR-UOS) in collaboration with Eduardo Mondlane University (UEM) through the DESAFIO Program, in Mozambique to AJCJ. Juga, AJC (corresponding author), Eduardo Mondlane Univ, Fac Sci, Dept Math & Informat, Maputo, Mozambique; Hasselt Univ, Data Sci Inst, I BioStat, Diepenbeek, Belgium. adelino.juga@gmail.com

Systematic assessment of QOL and care needs was applied in two gastroenterology departments to support “Cancer Care for the Whole Patient.” Patients with digestive cancer were asked to complete the Cancer Rehabilitation Evaluation System-Short Form (CARES-SF) at the start of treatment and 3 months later. Both times CARES data were processed, and summary reports on the retained insights were sent to the reference nurse for use in further follow-up of the patient. Patients’ and reference nurse’s experiences with the systematic CARES-assessment were explored with several survey questions and semi-structured interviews, respectively. The mean age of the 51 participants was 63 years (SD11.17), 52.9% was male. With the CARES-SF, a large variety of problems and care needs was detected. Problems most frequently experienced, and most burdensome for QOL are a mix of physical complaints, side effects from treatment, practical, relational, and psychosocial difficulties. Only for a limited number of experienced problems a desire for extra help was expressed. All patients positively evaluate the timing and frequency of the CARES-assessment. The majority believes that this assessment could contribute to the discussion of problems and needs with healthcare professionals, to get more tailored care. Reference nurses experienced the intervention as an opportunity to systematically explore patients’ well-being in a comprehensive way, leading to detection and discussion of specific problems or needs in greater depth, and more efficient involvement of different disciplines in care. Systematic QOL and needs assessment with the CARES-SF in oncology can contribute to more patient-centeredness and efficiency of care.

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).

In longitudinal studies of biomarkers, an outcome of interest is the time at which a biomarker reaches a particular threshold. The CD4 count is a widely used marker of human immunodeficiency virus progression. Because of the inherent variability of this marker, a single CD4 count below a relevant threshold should be interpreted with caution. Several studies have applied persistence criteria, designating the outcome as the time to the occurrence of two consecutive measurements less than the threshold. In this paper, we propose a method to estimate the time to attainment of two consecutive CD4 counts less than a meaningful threshold, which takes into account the patient-specific trajectory and measurement error. An expression for the expected time to threshold is presented, which is a function of the fixed effects, random effects and residual variance. We present an application to human immunodeficiency virus-positive individuals from a seroprevalent cohort in Durban, South Africa. Two thresholds are examined, and 95% bootstrap confidence intervals are presented for the estimated time to threshold. Sensitivity analysis revealed that results are robust to truncation of the series and variation in the number of visits considered for most patients. Caution should be exercised when interpreting the estimated times for patients who exhibit very slow rates of decline and patients who have less than three measurements. We also discuss the relevance of the methodology to the study of other diseases and present such applications. We demonstrate that the method proposed is computationally efficient and offers more flexibility than existing frameworks. Copyright © 2016 John Wiley & Sons, Ltd.

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.

A temporal trend analysis was performed on antimicrobial resistance data collected over 4 consecutive years (2011-2014) in the official Belgian antimicrobial resistance monitoring programme. Commensal Escherichia coli strains were isolated from faecal samples of four livestock categories (veal calves, young beef cattle, broiler chickens and slaughter pigs) and the trends of resistance profiles were analysed. The resistance prevalence remained high (>50%) during the study period for ampicillin in veal calves and chickens, for ciprofloxacin and nalidixic acid in chickens, for sulfamethoxazole in veal calves, chickens and pigs and for tetracycline in veal calves. Using logistic regression and Generalized Estimating Equation and after p value adjustment for multiple testing (Linear step-up method), statistically significant decreasing temporal trends were observed for several of the 11 tested antimicrobials in several livestock categories: in veal calves (10/11), in chickens (6/11) and in pigs (5/11). A significant increasing trend was observed for the prevalence of resistance to ciprofloxacin in chickens. Multi-resistance, considered as the resistance to at least three antimicrobials of different antibiotic classes, was observed in the four livestock categories but was significantly decreasing in veal calves, chickens and pigs. Overall, the prevalence of resistance and of multi-resistance was lowest in the beef cattle livestock category and highest in broiler chickens. These decreasing temporal trends of antimicrobial resistance might be due to a decrease of the total antimicrobial consumption for veterinary use in Belgium which was reported for the period between 2010 and 2013. The methodology and statistical tools developed in this study provide outputs which can detect shifts in resistance levels or resistance trends associated with particular antimicrobial classes and livestock categories. Such outputs can be used as objective evidence to evaluate the possible efficacy of measures taken by animal health authorities and stakeholders in the livestock sector to limit antimicrobial resistance occurrence.

Objectives The objective of this study was to explore the association between resistance and outpatient antibiotic use, expressed as either DDDs per 1000 inhabitants per day (DID) or packages per 1000 inhabitants per day (PID). Methods IMS Health data on outpatient penicillin and cephalosporin (β-lactam) and tetracycline, macrolide, lincosamide and streptogramin (TMLS) use, aggregated at the level of the active substance (WHO version 2011) expressed as DID and PID (2000–07) were linked to European Antimicrobial Resistance Surveillance System (EARSS) data on proportions of penicillin-non-susceptible Streptococcus pneumoniae (PNSP) and erythromycin-non-susceptible S. pneumoniae (ENSP) (2000–09). Combined data for 27 European countries were analysed with a generalized linear mixed model. Model fit for use in DID, PID or both and 0, 1 or 2 year time lags between use and resistance was assessed and predictions of resistance were made for decreasing use expressed as DID, PID or both. Results When exploring the association between β-lactam use and PNSP, the best model fit was obtained for use in PID without time lag. For the association between TMLS use and ENSP, the best model fit was obtained for use in both PID and DID with a 1 year time lag. PNSP and ENSP are predicted to decrease when use decreases in PID, but not when use decreases in DID. Conclusions Associations between outpatient antibiotic use and resistance and predictions of resistance were inconsistent whether expressing antibiotic use as DID or PID. We recommend that data on antibiotic use be expressed as PID and that time lags between use and resistance be considered when exploring these associations.

Current approaches such as inspections, audits, and end product testing cannot detect the distribution and dynamics of microbial contamination. Despite the implementation of current food safety management systems, foodborne outbreaks linked to fresh produce continue to be reported. A microbial assessment scheme and statistical modeling were used to systematically assess the microbial performance of core control and assurance activities in five Kenyan fresh produce processing and export companies. Generalized linear mixed models and correlated random-effects joint models for multivariate clustered data followed by empirical Bayes estimates enabled the analysis of the probability of contamination across critical sampling locations (CSLs) and factories as a random effect. Salmonella spp. and Listeria monocytogenes were not detected in the final products. However, none of the processors attained the maximum safety level for environmental samples. Escherichia coli was detected in five of the six CSLs, including the final product. Among the processing-environment samples, the hand or glove swabs of personnel revealed a higher level of predicted contamination with E. coli , and 80% of the factories were E. coli positive at this CSL. End products showed higher predicted probabilities of having the lowest level of food safety compared with raw materials. The final products were E. coli positive despite the raw materials being E. coli negative for 60% of the processors. There was a higher probability of contamination with coliforms in water at the inlet than in the final rinse water. Four (80%) of the five assessed processors had poor to unacceptable counts of Enterobacteriaceae on processing surfaces. Personnel-, equipment-, and product-related hygiene measures to improve the performance of preventive and intervention measures are recommended.

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.

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.

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.

We aimed to study the excess health-care expenditures for persons with a known positive isolate of Streptococcus pneumoniae. The data set was compiled by linking the database of the largest Belgian Sickness Fund with data obtained from laboratories reporting pneumococcal isolates. We analyzed the age-specific per-patient cumulative costs over time, using generalized estimating equations (GEEs). The mean structure was described by fractional polynomials. The quasi-likelihood under the independence model criterion was used to compare different correlation structures. We show for all age groups that the health-care costs incurred by diagnosed pneumococcal patients are significantly larger than those incurred by undiagnosed matched persons. This is not only the case at the time of diagnosis but also long before and after the time of diagnosis. These findings can be informative for the current debate on unrelated costs in health economic evaluation, and GEEs could be used to estimate these costs for other diseases. Finally, these results can be used to inform policy on the expected budget impact of preventing pneumococcal infections.

Nontyphoidal salmonellosis is the second most frequently reported zoonotic disease in the European Union (EU) and is considered to be a major threat to human health worldwide. The most reported Salmonella serovar in the EU is S. Enteritidis, mainly associated with egg contamination, followed by S. Typhimurium, with the latter being the most predominant serovar isolated from pork. These findings suggest that reducing the Salmonella contamination in the pork production might be a good strategy to prevent and control human salmonellosis in the EU. Recently, a quantitative microbial risk assessment (QMRA) has been developed to assess the risks for human salmonellosis due to home consumption of fresh minced pork meat in Belgium. (1) The newly developed risk model is called the METZOON model. In the current study, the METZOON model was used to evaluate the effectiveness of different hypothetical Salmonella mitigation strategies implemented at different stages of the minced pork production and consumption chain by means of a scenario analysis. To efficiently evaluate the mitigation strategies, model results were obtained by running simulations using the randomized complete block design. The effectiveness of a mitigation strategy is expressed using point and interval estimates of the effect size for dependent observations, expressed as the standardized difference in population means. The results indicate that the most effective strategies are taken during the slaughter processes of polishing, evisceration, and chilling, and during postprocessing, whereas interventions in the primary production and at the beginning of the slaughter process seem to have only a limited effect. Improving consumer awareness is found to be effective as well.

This retrospective case-control study assessed human papillomavirus 16 (HPV16) viral load and E2/E6 ratio as risk markers for cervical intraepithelial neoplasia (CIN) ≥2 lesions in HPV16-positive women in a routine liquid-based cytology setting. Triplex quantitative PCR for HPV16 E6, E2, and β-globin was done to determine the HPV16 load and the E2/E6 ratio, as a surrogate marker for integration, for women with a negative histologic endpoint (200 controls: 83 normal histology and 117 CIN1) and women with a ≥CIN2 endpoint (180 cases: 41 CIN2, 122 CIN3, and 17 invasive carcinoma). Our analysis showed a significantly higher HPV16 load in the case group, which was completely attributable to the high viral load of samples with invasive carcinoma as histologic endpoint. There was no significant difference in viral load between the other histologic groups. The E2/E6 ratio proved to be lower for the cases. However, the E2/E6 ratio indicated the presence of HPV integration in a considerable amount of control samples (44.3%), which suggests that HPV integration occurs early in the development of cancer and undermines the clinical value of viral integration. Overall, the intrinsic heterogeneous nature of the cervical cytology samples caused a substantial overlap of the HPV16 load and the E2/E6 ratio between controls and cases, which precludes the determination of cutoff values for risk prediction and hampers the clinical applicability in a cervical screening setting. (Cancer Epidemiol Biomarkers Prev 2009;18(11):2992–9)

According to the European Food Safety Authority, salmonellosis is still one of the main causes of infectious foodborne gastroenteritis in humans. Broilers are an important source of salmonellosis after eggs and pork. Between 1987 and 1999 the trend of human salmonellosis incidence in Belgium increased constantly. However, from 2000 until 2005 a decrease in human cases was observed, probably following the sanitary measures implemented in the Poultry breeder and laying sector. in order to decrease human infections it is essential to tackle the problem at the farm level to minimize cross-contamination from farm to fork. This paper seeks to answer two questions: (i) given the Salmonella status of the farm at a certain occasion (equal to the sampling time of the flock), what are the risk factors that the farm will be Salmonella positive at a following occasion? And (ii) what are the risk factors for a farm to be persistently positive for two consecutive flocks? We used surveillance data on 6824 broiler flocks studied for Salmonella infectivity from 2005 to 2006 in Belgium. The farms were tested regularly (3 weeks before slaughter of each broiler flock) for the presence of Salmonella based on multiple faecal samples per flock on a farm yielding clustered data. Generalized estimating equations, alternating logistic regression models, and random-intercept logistic regression models were employed to analyse these correlated binary data. Our results indicated that there are many factors that influence Salmonella risk in broiler flocks, and that they interact. Accounting for interactions between risk factors leads to an improved determination of those risk factors that increase infection with Salmonella. For the conditionai analysis, the risk factors found to increase the risk of Salmonella infection on a farm at a current occasion given the previous Salmonella status included: Salmonella infection of day-old chicks (of the current flock); a previously infected flock even though the farm was equipped with a hygiene place to change clothes prior to entering the broiler house; having temporary workmen when there was a separation between birds of different species; and separating birds of different species in the Walloon region relative to the Flanders region. Sanitary measures such as a cleaning and disinfecting procedure conducted by an external cleaning firm, applying the all-in all-out procedure, and hand washing decreased the risk despite their interaction with other factors. From the joint analysis, the most important factors identified for increased risk for persistent Salmonella on a farm involved the interaction between having temporary workmen when there were poultry or farmers in contact with foreign poultry or persons, and the interaction between having temporary workmen when there were poultry or farmers in contact with external poultry or persons. (C) 2009 Elsevier B.V. All rights reserved.

Frailty models are often used to study the individual heterogeneity in multivariate survival analysis. Whereas the shared frailty model is widely applied, the correlated frailty model has gained attention because it elevates the restriction of unobserved factors to act similar within clusters. Estimating frailty models is not straightforward due to various types of censoring. In this paper, we study the behavior of the bivariate-correlated gamma frailty model for type I interval-censored data, better known as current status data. We show that applying a shared rather than a correlated frailty model to cross-sectionally collected serological data on hepatitis A and B leads to biased estimates for the baseline hazard and variance parameters.

The numeral unit spread assessment pedigree (NUSAP) system was implemented to evaluate the quality of input parameters in a quantitative microbial risk assessment (QMRA) model for Salmonella spp. in minced pork meat. The input parameters were grouped according to four successive exposure pathways: (1) primary production (2) transport, holding, and slaughterhouse, (3) postprocessing, distribution, and storage, and (4) preparation and consumption. An inventory of 101 potential input parameters was used for building the QMRA model. The characteristics of each parameter were defined using a standardized procedure to assess (1) the source of information, (2) the sampling methodology and sample size, and (3) the distributional properties of the estimate. Each parameter was scored by a panel of experts using a pedigree matrix containing four criteria (proxy, empirical basis, method, and validation) to assess the quality, and this was graphically represented by means of kite diagrams. The parameters obtained significantly lower scores for the validation criterion as compared with the other criteria. Overall strengths of parameters related to the primary production module were significantly stronger compared to the other modules (the transport, holding, and slaughterhouse module, the processing, distribution, and storage module, and the preparation and consumption module). The pedigree assessment contributed to select 20 parameters, which were subsequently introduced in the QMRA model. The NUSAP methodology and kite diagrams are objective tools to discuss and visualize the quality of the parameters in a structured way. These two tools can be used in the selection procedure of input parameters for a QMRA, and can lead to a more transparent quality assurance in the QMRA.

Quantitative information on parents’ preferences regarding multiple vaccine injections and on work-loss due to vaccination is important to guide decision making on the use of combination vaccines for universal vaccination. Our survey in families of 1347 toddlers (18–24 months) and 1315 adolescents residing in Flanders, Belgium, revealed common attitudes in both age groups. The majority of parents would allow maximum two injections in one visit. 39% were not willing to pay anything to avoid a concomitant injection, whereas the remainder mentioned amounts around a median of €20. The responses were hardly influenced by the socioeconomic determinants studied and the concordance between the number of concomitant injections parents would allow and their willingness-to-pay assessed by an open-ended question was limited, which suggests that more sensitive quantification using other methods would be useful. Work-loss due to vaccination was assessed for infants only and was rare (4.5%).

To assess changes in infant vaccination coverage in Flanders since 1999, an EPI-survey was performed in 2005. The parents of 1354 children aged 18-24 months were interviewed at home and the vaccination documents were checked. Several factors possibly related to vaccination status were examined with parametric and non-parametric methods. The coverage rate of recommended vaccines, i.e. poliomyelitis, tetanus-diphtheria-pertussis, H. influenzae type b (Hib), hepatitis B, measles-mumps-rubella (MMR) and meningococcal C, reached at least 92.2%, which is a significant rise for MMR, hepatitis B and Hib since 1999. The vaccinating physician, the employment situation of the mother and the family income were significant predictive factors for having received all recommended vaccine doses (complete schedule), also when considering only doses that were according to minimal age and interval criteria (valid schedule). (c) 2007 Elsevier Ltd. All rights reserved.

SUMMARYThe objective of this study was to model the age–time-dependent incidence of hepatitis B while estimating the impact of vaccination. While stochastic models/time-series have been used before to model hepatitis B cases in the absence of knowledge on the number of susceptibles, this paper proposed using a method that fits into the generalized additive model framework. Generalized additive models with penalized regression splines are used to exploit the underlying continuity of both age and time in a flexible non-parametric way. Based on a unique case notification dataset, we have shown that the implemented immunization programme in Bulgaria resulted in a significant decrease in incidence for infants in their first year of life with 82% (79–84%). Moreover, we have shown that conditional on an assumed baseline susceptibility percentage, a smooth force-of-infection profile can be obtained from which two local maxima were observed at ages 9 and 24 years.

Summary. In industrialized countries, transmission of hepatitis C occurs primarily through injecting drug use. Transmission of hepatitis C in injecting drug users is mainly associated with the sharing of contaminated syringes/needles, although evidence for risk of hepatitis C infection through sharing of other injecting paraphernalia is increasing. In this paper, the independent effects of sharing paraphernalia other than syringes/needles have been estimated. The prevalence and force of infection were modelled using three serological data sets from drug users in three centres in Belgium as a function of the sharing behaviour. It was found that sharing of materials other than syringes/needles indeed seemed to contribute substantially to the spread of hepatitis C among injecting drug users.

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.

This study was aimed at the identification of location and household characteristics influencing the choice of keeping livestock or practising crop production in a post-conflict region in Central Africa. Two thousand eight hundred randomly selected families from Brazzaville (Congo) were surveyed. From these, 6% are both keeping livestock and producing crops, 3% are keeping livestock only, 24% are producing crops only and 67% are not producing crops nor keeping livestock. From these four groups, respectively, 135, 84, 246 and 245 households were interviewed to collect further data on household and location characteristics. Non-parametric and parametric techniques were compared as tools to analyse the groups. In the non-parametric classification tree method CART following variables were identified as being important for the engagement in keeping livestock or urban agriculture: keeping livestock before 1997, practising agriculture before 1997, property size, locality, income, availability of water, professional activity and level of instruction. Including surrogate variables resulted in extra variables: age, availability of electricity and sex. In the multinomial regression only the most important variables from the classification tree were withheld and other insights were obtained. The results of this research highlighted the shortcomings of multinomial regression. Fitting a full model containing all possible interactions becomes an impossible task with 20 explanatory variables. Using the classification tree information in a multinomial model appears the most appropriate solution, and this method is a useful tool for further work in the analysis of livestock and crop production systems. The importance of the historical component in the decision to practise crop production and keep livestock was demonstrated. Being involved in crop production develops roots, which cannot be destroyed by impediments such as periods of conflict. (C) 2003 Elsevier Ltd. All rights reserved.

Background Meta-analyses have become an essential tool in synthesizing evidence on clinical and epidemiological questions derived from a multitude of similar studies assessing the particular issue. Appropriate and accessible statistical software is needed to produce the summary statistic of interest. Methods Metaprop is a statistical program implemented to perform meta-analyses of proportions in Stata. It builds further on the existing Stata procedure metan which is typically used to pool effects (risk ratios, odds ratios, differences of risks or means) but which is also used to pool proportions. Metaprop implements procedures which are specific to binomial data and allows computation of exact binomial and score test-based confidence intervals. It provides appropriate methods for dealing with proportions close to or at the margins where the normal approximation procedures often break down, by use of the binomial distribution to model the within-study variability or by allowing Freeman-Tukey double arcsine transformation to stabilize the variances. Metaprop was applied on two published meta-analyses: 1) prevalence of HPV-infection in women with a Pap smear showing ASC-US; 2) cure rate after treatment for cervical precancer using cold coagulation. Results The first meta-analysis showed a pooled HPV-prevalence of 43% (95% CI: 38%-48%). In the second meta-analysis, the pooled percentage of cured women was 94% (95% CI: 86%-97%). Conclusion By using metaprop, no studies with 0% or 100% proportions were excluded from the meta-analysis. Furthermore, study specific and pooled confidence intervals always were within admissible values, contrary to the original publication, where metan was used. Electronic supplementary material The online version of this article (doi:10.1186/2049-3258-72-39) contains supplementary material, which is available to authorized users.

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

Objectives: The aim of this study was to measure differences in safety culture perceptions within Belgian acute hospitals and to examine variability based on language, work area, staff position, and work experience. Methods: The Hospital Survey on Patient Safety Culture was distributed to hospitals participating in the national quality and safety program (2007-2009). Hospitals were invited to participate in a comparative study. Data of 47,136 respondents from 89 acute hospitals were used for quantitative analysis. Percentages of positive response were calculated on 12 dimensions. Generalized estimating equations models were fitted to explore differences in safety culture. Results: Handoffs and transitions, staffing, and management support for patient safety were considered as major problem areas. Dutch-speaking hospitals had higher odds of positive perceptions for most dimensions in comparison with French-speaking hospitals. Safety culture scores were more positive for respondents working in pediatrics, psychiatry, and rehabilitation compared with the emergency department, operating theater, and multiple hospital units. We found an important gap in safety culture perceptions between leaders and assistants within disciplines. Administration and middle management had lower perceptions toward patient safety. Respondents working less than 1 year in the current hospital had more positive safety culture perceptions in comparison with all other respondents. Conclusions: Large comparative databases provide the opportunity to identify distinct high and low scoring groups. In our study, language, work area, and profession were identified as important safety culture predictors. Years of experience in the hospital had only a small effect on safety culture perceptions.

Movement of animals from one farm to another is a potential risk and can lead to the spreading of livestock diseases. Therefore, in order to implement effective control measures, it is important to understand the movement network in a given area. Using the SANITEL data from 2005 to 2009, around 2 million cattle movements in Belgium were traced. Exploratory analysis revealed different spatial structures for the movement of different cattle types: fattening calves are mostly moved to the Antwerp region, adult cattle are moved to different parts in Belgium. Based on these differences, movement of cattle would more likely cause a spread of disease to a larger number of areas in Belgium as compared to the fattening calves. A closer inspection of the spatial and temporal patterns of cattle movement using a weighted negative binomial model, revealed a significant short-distance movement of bovine which could be an important factor contributing to the local spreading of a disease. The model however revealed hot spot areas of movement in Belgium; four areas in the Walloon region (Luxembourg, Hainaut, Namur and Liege) were found as hot spot areas while East and West Flanders are important "receivers" of movement. This implies that an introduction of a disease to these Walloon regions could result in a spread toward the East and West Flanders regions, as what happened in the case of Bluetongue BTV-8 outbreak in 2006. The temporal component in the model also revealed a linear trend and short- and long-term seasonality in the cattle movement with a peak around spring and autumn. The result of this explorative analysis enabled the identification of "hot spots" in time and space which is important in enhancing any existing monitoring and surveillance system.

Background: Accurate estimates of the size of the drug-using populations are essential for evidence-based policy making. However, drug users form a ‘hidden’ population, necessitating the use of indirect methods to estimate population sizes. Methods: The benchmark-multiplier method was applied to estimate the population size of ever injecting drug users (ever-IDUs), aged 18–64 years, in Belgium using data from the national HIV/AIDS register and from a sero-behavioral study among injecting drug users. However, missing risk factor information and absence of follow-up of the HIV+/AIDS– cases, limits the usefulness of the Belgian HIV/AIDS register as benchmark. To overcome these limitations, statistical corrections were required. In particular, Imputation by Chained Equations was used to correct for the missing risk factor information whereas stochastic mortality modelling was applied to account for the mortality among the HIV+/AIDS– cases. Monte Carlo simulation was used to obtain confidence intervals, properly reflecting the uncertainty due to random error as well as the uncertainty associated with the two statistical corrections mentioned above. Results: In 2010, the prevalence (/1000) of ever-IDUs was estimated to be 3.5 with 95% confidence interval [2.5;4.8]. No significant time trends were observed for the period 2000–2010. Conclusions: To be able to estimate the ever-IDU population size using the Belgian HIV/AIDS register as benchmark, statistical corrections were required without which seriously biased estimates would result. By developing the improved methodology, Belgium is again able to provide ever-IDU population estimates, which are essential to assess the coverage of treatment and to forecast health care needs and costs.

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.

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

As discussed in previous chapters and illustrated by different methods, prevalence and force of infection (FOI) (and, as we will show in Chaps. 14 and 15, indirectly other parameters such as the basic reproduction number) are estimated from so-called seroprevalence data. Seroprevalence data are obtained by dichotomizing or trichotomizing disease-specific antibody levels using one or two threshold values, often provided by the test manufacturer. In particular, individuals are diagnosed as infected (left-censored age at infection, its value somewhere before the age at the time of the test) if their test result exceeds a certain threshold value τ s u and as being susceptible (right-censored age at infection) if their result falls below a possibly different threshold τ s l ≤τ s u . In case two different threshold values are used (τ s l

In the previous chapter, we discussed the order-restricted δ-clustering method for clustering subsets of genes with similar monotone dose-response profiles. In this chapter, we focus on a second approach in which clustering is based on information criteria (Lin et al. 2009 ; Liu et al. 2009a,b). As we mentioned in Chap. 9, for a dose-response experiment with K + 1 dose levels, after an initial filtering, there are a finite number of monotone dose-response ANOVA models which can be fitted to the data. For example, for a four dose-level experiment, there are, for each direction, seven dose-response profiles (listed in Table 9.1 and shown in Fig. 9.1 can be fitted.

The use of serological surveys is nowadays a common way to study the epidemiology of many infections. In case a single cross-sectional survey is available, one needs to assume that the disease is in steady state. While reasonable for some infections, as illustrated in earlier chapters, this assumption might be untenable for other situations. In this chapter we address methods to estimate age- and time-specific prevalence and force of infection from a series of prevalence surveys. Models such as the proportional hazards model of Nagelkerke et al. (1999) are discussed and illustrated on hepatitis A and tuberculosis data.

As discussed in Chap. 4, the use of serological surveys is one of the most common ways to investigate the epidemiology of infectious diseases and to estimate important parameters such as the force of infection. This chapter introduces basic statistical concepts and notation related to the analysis of serological and incidence data and briefly discusses several modeling issues.

So far the prevalence and the FOI were estimated within the frequentist framework. In this chapter we estimate the prevalence and the FOI within the Bayesian framework. We refer to Sect. B.5 of Appendix B for an introduction to Bayesian inference.

SUMMARYInjecting drug users (IDUs) account for most new HCV infections. The objectives of this study were: to estimate the force of infection for hepatitis C virus in IDUs within the interval-censoring framework and to determine the impact of risk factors such as frequency of injection, drug injected, sharing of syringes and time of first injection on the time to HCV infection. We used data from the Amsterdam Cohort Study collected in The Netherlands and focused on those individuals who were HCV negative upon entry into the study. Based on the results, the force of infection was found to vary with time of first injection. The risk of infection was higher in the first 3 years of an IDU's career, implying estimates based on single cross-sectional studies could be biased. Frequency of injection and type of drug injected were found to be highly significant predictors, whereas sharing syringes was not.

Background Crucial connections between sexual network structure and the distribution of HIV remain inadequately understood, especially in regard to the role of concurrency and age disparity in relationships, and how these network characteristics correlate with each other and other risk factors. Social desirability bias and inaccurate recall are obstacles to obtaining valid, detailed information about sexual behaviour and relationship histories. Therefore, this study aims to use novel research methods in order to determine whether HIV status is associated with age-disparity and sexual connectedness as well as establish the primary behavioural and socio-demographic predictors of the egocentric and community sexual network structures. Method/Design We will conduct a cross-sectional survey that uses a questionnaire exploring one-year sexual histories, with a focus on timing and age disparity of relationships, as well as other risk factors such as unprotected intercourse and the use of alcohol and recreational drugs. The questionnaire will be administered in a safe and confidential mobile interview space, using audio computer-assisted self-interview (ACASI) technology on touch screen computers. The ACASI features a choice of languages and visual feedback of temporal information. The survey will be administered in three peri-urban disadvantaged communities in the greater Cape Town area with a high burden of HIV. The study communities participated in a previous TB/HIV study, from which HIV test results will be anonymously linked to the survey dataset. Statistical analyses of the data will include descriptive statistics, linear mixed-effects models for the inter- and intra-subject variability in the age difference between sexual partners, survival analysis for correlated event times to model concurrency patterns, and logistic regression for association of HIV status with age disparity and sexual connectedness. Discussion This study design is intended to facilitate more accurate recall of sensitive sexual history data and has the potential to provide substantial insights into the relationship between key sexual network attributes and additional risk factors for HIV infection. This will help to inform the design of context-specific HIV prevention programmes.

The effectiveness of childhood immunization programs depends on the vaccination coverage actually achieved. Routinely collected coverage data are not always available, and comparability between countries is often compromised because of different data collection methods. In 2000, Gay developed a method to estimate trivalent vaccination coverage from readily available trivariate serological data on the basis of parametric assumptions related to the rate of seroconversion for each vaccine component and probabilities of natural exposure to infection. Gay's work was indirectly published in a paper by Altmann and Altmann, who derived exact solutions for the parameters on the basis of Gay's modeling equations. In this paper, we propose a general likelihood-based marginal model framework to extend Gay's model by relaxing two of its main assumptions. We use the Bahadur model for trivariate binary data to explicitly account for an association between the disease-specific exposure probabilities. We fit several correlation structures to measles, mumps, and rubella serology from Belgium and Ireland. For both countries, we estimate a small positive pairwise exposure correlation, which improves the fit to the data. However, the effect on the estimated vaccination coverage and its associated variability is fairly moderate. For both Belgium and Ireland, all models reveal that the vaccination coverage achieved during the first 15?years since the introduction of measles, mumps, and rubella immunization is insufficient to eliminate measles. Copyright (C) 2012 John Wiley & Sons, Ltd.

During their injecting career, injecting drug users (IDUs) are exposed to some infections, like hepatitis C virus (HCV) infection and human immunodeficiency virus (HIV) infection, due to their injecting behavioral risk factors, such as sharing syringes or other paraphernalia containing infected blood, or sexual behavior risk factors. If we consider that these IDUs might belong to a social network of people where these behavioral risk factors are spread, then HCV and HIV infections might be associated at both the individual and the population level. In this paper, we study the association between HCV and HIV infection at the population level using aggregate data. Our aim is to define a hierarchy of structured models with which the association between HCV and HIV infection at population level and the time trend of prevalence can be investigated. The data analyzed in the paper are diagnostic testing data, which consist of repeated crosssectional prevalence measurements from 1998 to 2006 for HCV and HIV infection, obtained from a sample of 515 drug treatment centers spread among the 20 regions in Italy, where subjects went for a serum diagnostic test. Since we do not have any individual data, it is not possible to relate these prevalence data to socio-demographic or behavioral risk data. Each region defines a cluster with repeated prevalence data for HCV and HIV infection over time. Several modeling approaches, such as generalized linear mixed models (GLMMs) and hierarchical Bayesian models are applied to the data. First, we test different covariance structures for the region-specific random effects in the GLMM context; second, a hierarchical Bayesian model is used to refit the best GLMM in order to obtain the posterior distribution for the parameters of primary interest. We found that the correlation at population level between HCV and HIV is approximately 0.68 and the prevalence of the two infections generally decreased over the years, compared to the situation in 1998.

The aim of the analysis presented in this paper is to study co-infection with hepatitis C virus (HCV) and human immunodeficiency virus (HIV) in injecting drug users (IDUs) using a joint modeling approach that makes use of multivariate statistical methods for current status data. Using marginal models, we estimate association measures between HCV and HIV infections at individual level, i.e., odds ratios and correlation coefficients, and we regress them against some risk factors, e.g., the length of the injecting career, the age at first injection, the ever sharing of syringes, and the frequency of current injecting. In addition, we fit random-effects models that take into account the individual heterogeneity in the acquisition of the infections. For our analysis, we use cross-sectional data from two independent serological surveys, one carried out in Italy (IT) in 2005 on 856 subjects, and the other in three Spanish (ES) cities, between 2001 and 2003, on 589 subjects. We found that the infections are positively associated within individuals, e.g., ORIT=2.56 with 95% confidence interval (CI) (1.43, 6.68) and ORES= 2.42, with 95% CI (1.41, 4.30). We found that the odds ratio and the correlation between HCV and HIV infections increase positively with the length of the injecting career. Moreover, they are found to be significantly positive in case IDUs have never shared syringes or report low injecting frequencies. The variance of the individual random effects is positive, e.g., σb 2=0.34 (0.14, 0.62), indicating that there is significant individual heterogeneity in the acquisition of the infections. Our results show that a significant association between HCV and HIV infections within IDUs is related to significant individual heterogeneity in the acquisition of the infections. Indeed, the association between these infections in IDUs who report ever sharing syringes is not significant, which can be explained by a higher homogeneity in their behaviors and, therefore, in their acquisition of the infections.

Resistance to antibiotics is a major public health problem and antibiotic use is being increasingly recognized as the main selective pressure driving this resistance. Yearly and quarterly data on outpatient antibiotic use were collected by the European Surveillance of Antimicrobial Consumption (ESAC) project for the period 1997-2009 from 33 and 27 European countries, respectively, and expressed in defined daily doses per 1000 inhabitants per day. Since repeated measures were taken for the countries, correlation has to be taken into account when analysing the data. This paper illustrates the application of mixed-effects models to the study of country-specific outpatient antibiotic use in Europe. Mixed models are useful in a wide variety of disciplines in the biomedical, physical and social sciences. In this application for outpatient antibiotic use, the linear mixed model is extended to a non-linear mixed model, allowing analysis of seasonal variation on top of a global trend, with country-specific effects for global mean use and amplitude, and trends over time in use and in amplitude. The 2005 data collection was funded by a grant from DG SANCO of the European Commission (Grant Agreement 2003211), whereas the 2006-09 data collection was funded by the ECDC (Grant Agreement 2007/001). This article is part of a JAC Supplement sponsored by the ECDC and the University of Antwerp.

Rapid laboratory diagnosis is critical for treating, managing, and preventing methicillin-resistant Staphylococcus aureus (MRSA) infections. We evaluated and compared the potential for MRSA detection of five chromogenic media, Brilliance MRSA agar (Oxoid), ChromID (bioMérieux), MRSA Select (Bio-Rad), CHROMagar (CHROMagar Microbiology), and BBL-CHROMagar (BD Diagnostics). Media were tested with log serial dilutions (10 0 to 10 6 CFU) of pure isolates of MRSA ( n = 60), non-MRSA ( n = 27), and defined mixtures thereof simulating clinical samples ( n = 84). Further evaluations were done on pre-enriched nasal and groin screening swabs ( n = 213) from 165 hospitalized patients. Randomized samples were spiral plated on each medium and independently scored by five investigators for characteristic colonies at 24 and 48 h of incubation. Confirmatory testing of up to five putative MRSA colonies recovered from each medium was done. The cumulative average sensitivity with isolates, mixtures, and clinical samples was the highest for Brilliance MRSA agar (97%) and similar for the other four media (≥92%). The cumulative average specificity was the highest for BBL-CHROMagar (99%), followed by MRSA Select (98%), CHROMagar (97%), ChromID (89%), and Brilliance MRSA agar (86%). All of the media detected MRSA at 10 and 1 CFU, although at these low loads, few MRSA samples harboring SCC mec type III or IV were misinterpreted as non-MRSA by investigators. False-positive results were mainly due to methicillin-resistant S. epidermidis . For an arbitrary MRSA prevalence of 5% and based on patient sample evaluations, the positive predictive values for BBL-CHROMagar and CHROMagar (∼84%) were the highest. The negative predictive values of all of the media were ≥92% for MRSA prevalences ranging from 5% to 30%. In conclusion, BBL-CHROMagar and CHROMagar gave the best overall results for detection of MRSA, irrespective of the sample concentration, investigator, or incubation period.

Kinetics of maternal rubella and varicella antibodies in 213 motherinfant pairs are described in a longitudinal study in Belgium. Blood samples are taken at 7 time points (week 36 of pregnancy, birth (cord), 1, 3, 6, 9, and 12 months), and analyzed for anti-rubella IgG and anti-varicella IgG by enzyme linked immunosorbent assay (ELISA). A generalized exponential model is used to analyse maternal antibody decay in infants. Model based, the mean duration of passive immunity is 2.1 months for rubella and 2.4 months for varicella. Infants are susceptible at young age for rubella, a disease with high vaccination coverage, as well as for varicella, an endemic disease in Western Europe.