Estimating seroconversion rates accounting for repeated infections by approximate Bayesian computation.

This study presents a novel approach for inferring the incidence of infections by employing a quantitative model of the serum antibody response. Current methodologies often overlook the cumulative effect of an individual's infection history, making it challenging to obtain a marginal distribution for antibody concentrations. Our proposed approach leverages approximate Bayesian computation to simulate cross-sectional antibody responses and compare these to observed data, factoring in the impact of repeated infections. We then assess the empirical distribution functions of the simulated and observed antibody data utilizing Kolmogorov deviance, thereby incorporating a goodness-of-fit check. This new method not only matches the computational efficiency of preceding likelihood-based analyses but also facilitates the joint estimation of antibody noise parameters. The results affirm that the predictions generated by our within-host model closely align with the observed distributions from cross-sectional samples of a well-characterized population. Our findings mirror those of likelihood-based methodologies in scenarios of low infection pressure, such as the transmission of pertussis in Europe. However, our simulations reveal that in settings of higher infection pressure, likelihood-based approaches tend to underestimate the force of infection. Thus, our novel methodology presents significant advancements in estimating infection incidence, thereby enhancing our understanding of disease dynamics in the field of epidemiology.

Patterns of antimicrobial resistance in Salmonella isolates from fattening pigs in Spain.

BACKGROUND: Swine are considered a major source of foodborne salmonellosis, a public health issue further complicated by the circulation of multidrug-resistant Salmonella strains that threaten the safety of the food chain. The current study aimed to identify patterns that can help to understand the epidemiology of antimicrobial resistance (AMR) in Salmonella in pigs in Spain through the application of several multivariate statistical methods to data from the AMR national surveillance programs from 2001 to 2017. RESULTS: A total of 1,318 pig Salmonella isolates belonging to 63 different serotypes were isolated and their AMR profiles were determined. Tetracycline resistance across provinces in Spain was the highest among all antimicrobials and ranged from 66.7% to 95.8%, followed by sulfamethoxazole resistance (range: 42.5% - 77.8%), streptomycin resistance (range: 45.7% - 76.7%), ampicillin resistance (range: 24.3% - 66.7%, with a lower percentage of resistance in the South-East of Spain), and chloramphenicol resistance (range: 8.5% - 41.1%). A significant increase in the percentage of resistant isolates to chloramphenicol, sulfamethoxazole, ampicillin and trimethoprim from 2013 to 2017 was observed. Bayesian network analysis showed the existence of dependencies between resistance to antimicrobials of the same but also different families, with chloramphenicol and sulfamethoxazole in the centre of the networks. In the networks, the conditional probability for an isolate susceptible to ciprofloxacin that was also susceptible to nalidixic acid was 0.999 but for an isolate resistant to ciprofloxacin that was also resistant to nalidixic acid was only 0.779. An isolate susceptible to florfenicol would be expected to be susceptible to chloramphenicol, whereas an isolate resistant to chloramphenicol had a conditional probability of being resistant to florfenicol at only 0.221. Hierarchical clustering further demonstrated the linkage between certain resistances (and serotypes). For example, a higher likelihood of multidrug-resistance in isolates belonging to 1,4,[5],12:i:- serotype was found, and in the cluster where all isolates were resistant to tetracycline, chloramphenicol and florfenicol, 86.9% (n = 53) of the isolates were Typhimurium. CONCLUSION: Our study demonstrated the power of multivariate statistical methods in discovering trends and patterns of AMR and found the existence of serotype-specific AMR patterns for serotypes of public health concern in Salmonella isolates in pigs in Spain.

Continuous Model Averaging for Benchmark Dose Analysis: Averaging Over Distributional Forms.

When estimating a benchmark dose (BMD) from chemical toxicity experiments, model averaging is recommended by the National Institute for Occupational Safety and Health, World Health Organization and European Food Safety Authority. Though numerous studies exist for Model Average BMD estimation using dichotomous responses, fewer studies investigate it for BMD estimation using continuous response. In this setting, model averaging a BMD poses additional problems as the assumed distribution is essential to many BMD definitions, and distributional uncertainty is underestimated when one error distribution is chosen a priori. As model averaging combines full models, there is no reason one cannot include multiple error distributions. Consequently, we define a continuous model averaging approach over distributional models and show that it is superior to single distribution model averaging. To show the superiority of the approach, we apply the method to simulated and experimental response data.

Measuring association among censored antibody titer data.

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.

An extended and unified modeling framework for benchmark dose estimation for both continuous and binary data.

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.

An explorative study on systematic assessment of QOL and care needs with the CARES-SF in the early follow-up of patients with digestive cancer.

PURPOSE: Systematic assessment of QOL and care needs was applied in two gastroenterology departments to support "Cancer Care for the Whole Patient." METHODS: 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. RESULTS: 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. CONCLUSIONS: Systematic QOL and needs assessment with the CARES-SF in oncology can contribute to more patient-centeredness and efficiency of care.

Random effects models for estimation of the probability and time to progression of a continuous biomarker.

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.

Household members do not contact each other at random: implications for infectious disease modelling.

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 into infectious disease spread and control, typically rely on the assumption of random mixing within households. Until now, there has been 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) from 2010 to 2011. We analysed data from 318 households totalling 1266 individuals with household sizes ranging from two to seven 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 older siblings and between father and child are smaller than for any other pair. The 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 simulations. However, ignoring the contact density when inferring based on an epidemic model will result in biased estimates of within-household transmission rates. Further research regarding the implementation of within-household contact networks in epidemic models is necessary.

Maternal death and delays in accessing emergency obstetric care in Mozambique.

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.

Bayesian estimation of multivariate normal mixtures with covariate-dependent mixing weights, with an application in antimicrobial resistance monitoring.

Bacteria with a reduced susceptibility against antimicrobials pose a major threat to public health. Therefore, large programs have been set up to collect minimum inhibition concentration (MIC) values. These values can be used to monitor the distribution of the nonsusceptible isolates in the general population. Data are collected within several countries and over a number of years. In addition, the sampled bacterial isolates were not tested for susceptibility against one antimicrobial, but rather against an entire range of substances. Interest is therefore in the analysis of the joint distribution of MIC data on two or more antimicrobials, while accounting for a possible effect of covariates. In this regard, we present a Bayesian semiparametric density estimation routine, based on multivariate Gaussian mixtures. The mixing weights are allowed to depend on certain covariates, thereby allowing the user to detect certain changes over, for example, time. The new approach was applied to data collected in Europe in 2010, 2012, and 2013. We investigated the susceptibility of Escherichia coli isolates against ampicillin and trimethoprim, where we found that there seems to be a significant increase in the proportion of nonsusceptible isolates. In addition, a simulation study was carried out, showing the promising behavior of the proposed method in the field of antimicrobial resistance.

Simulation-based evaluation of the linear-mixed model in the presence of an increasing proportion of singletons.

Data in medical sciences often have a hierarchical structure with lower level units (e.g. children) nested in higher level units (e.g. departments). Several specific but frequently studied settings, mainly in longitudinal and family research, involve a large number of units that tend to be quite small, with units containing only one element referred to as singletons. Regardless of sparseness, hierarchical data should be analyzed with appropriate methodology such as, for example linear-mixed models. Using a simulation study, based on the structure of a data example on Ceftriaxone consumption in hospitalized children, we assess the impact of an increasing proportion of singletons (0-95%), in data with a low, medium, or high intracluster correlation, on the stability of linear-mixed models parameter estimates, confidence interval coverage and F test performance. Some techniques that are frequently used in the presence of singletons include ignoring clustering, dropping the singletons from the analysis and grouping the singletons into an artificial unit. We show that both the fixed and random effects estimates and their standard errors are stable in the presence of an increasing proportion of singletons. We demonstrate that ignoring clustering and dropping singletons should be avoided as they come with biased standard error estimates. Grouping the singletons into an artificial unit might be considered, although the linear-mixed model performs better even when the proportion of singletons is high. We conclude that the linear-mixed model is stable in the presence of singletons when both lower- and higher level sample sizes are fixed. In this setting, the use of remedial measures, such as ignoring clustering and grouping or removing singletons, should be dissuaded.

Application of the Vertex Exchange Method to estimate a semi-parametric mixture model for the MIC density of Escherichia coli isolates tested for susceptibility against ampicillin.

In the last decades, considerable attention has been paid to the collection of antimicrobial resistance data, with the aim of monitoring non-wild-type isolates. This monitoring is performed based on minimum inhibition concentration (MIC) values, which are collected through dilution experiments. We present a semi-parametric mixture model to estimate the entire MIC density on the continuous scale. The parametric first component is extended with a non-parametric second component and a new back-fitting algorithm, based on the Vertex Exchange Method, is proposed. Our data example shows how to estimate the MIC density for Escherichia coli tested for ampicillin and how to use this estimate for model-based classification. A simulation study was performed, showing the promising behavior of the new method, both in terms of density estimation as well as classification.

Parametric overdispersed frailty models for current status data.

Frailty models have a prominent place in survival analysis to model univariate and multivariate time-to-event data, often complicated by the presence of different types of censoring. In recent years, frailty modeling gained popularity in infectious disease epidemiology to quantify unobserved heterogeneity using Type I interval-censored serological data or current status data. In a multivariate setting, frailty models prove useful to assess the association between infection times related to multiple distinct infections acquired by the same individual. In addition to dependence among individual infection times, overdispersion can arise when the observed variability in the data exceeds the one implied by the model. In this article, we discuss parametric overdispersed frailty models for time-to-event data under Type I interval-censoring, building upon the work by Molenberghs et al. (2010) and Hens et al. (2009). The proposed methodology is illustrated using bivariate serological data on hepatitis A and B from Flanders, Belgium anno 1993-1994. Furthermore, the relationship between individual heterogeneity and overdispersion at a stratum-specific level is studied through simulations. Although it is important to account for overdispersion, one should be cautious when modeling both individual heterogeneity and overdispersion based on current status data as model selection is hampered by the loss of information due to censoring.

Factors influencing risky sexual behaviour among Mozambican miners: a socio-epidemiological contribution for HIV prevention framework in Mozambique.

BACKGROUND: Information dealing with social and behavioural risk factors as well as their mechanisms among Mozambican migrants working in South African mines remains undocumented. This study aims to understand the various factors influencing HIV-related risk behaviours and the resulting HIV positive status of Mozambican miners employed by South African mines. This analysis was undertaken in order to inform a broader and more effective HIV preventive framework in Mozambique. METHOD: This study relied upon data sourced from the first Integrated Biological and Behavioural Survey among Mozambican miners earning their living in South African mines. It employs quantitative techniques using standard statistical tools to substantiate the laid-down objectives. The primary technique applied in this paper is the multivariable statistical method used in the formulation and application of a proximate determinants framework. RESULTS: The odds of reporting one sexual partner were roughly three times higher for miners working as perforators as opposed to other types of occupation. As well, the odds of condom use - always or sometimes - for miners in the 31-40 age group were three times higher than the odds of condom use in the 51+ age group. Miners with lower education levels were less likely to use condoms. The odds of being HIV positive when the miner reports use of alcohol or drugs (sometimes/always) is 0.32 times lower than the odds for those reporting never use of alcohol or drugs. And finally, the odds of HIV positive status for those using condoms were 2.16 times that of miners who never used condoms, controlling for biological and other proximate determinants. CONCLUSION: In Mozambique, behavioural theory emphasising personal behavioural changes is the main strategy to combat HIV among miners. Our findings suggest there is a need to change thinking processes about how to influence safer sexual behaviour. This is viewed to be the result of a person's individual decision, due to of the complexity of social and contextual factors that may also influence sexual behaviours. This only stresses the need for HIV prevention strategies to exclusively transcend individual factors while considering the broader social and contextual phenomena influencing HIV risk among Mozambican miners.

The magnitude and factors related to facility-based maternal mortality in Mozambique.

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.

A novel approach to estimation of the time to biomarker threshold: applications to HIV.

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.

Simulation-based evaluation of the performance of the F test in a linear multilevel model setting with sparseness at the level of the primary unit.

In a linear multilevel model, significance of all fixed effects can be determined using F tests under maximum likelihood (ML) or restricted maximum likelihood (REML). In this paper, we demonstrate that in the presence of primary unit sparseness, the performance of the F test under both REML and ML is rather poor. Using simulations based on the structure of a data example on ceftriaxone consumption in hospitalized children, we studied variability, type I error rate and power in scenarios with a varying number of secondary units within the primary units. In general, the variability in the estimates for the effect of the primary unit decreased as the number of secondary units increased. In the presence of singletons (i.e., only one secondary unit within a primary unit), REML consistently outperformed ML, although even under REML the performance of the F test was found inadequate. When modeling the primary unit as a random effect, the power was lower while the type I error rate was unstable. The options of dropping, regrouping, or splitting the singletons could solve either the problem of a high type I error rate or a low power, while worsening the other. The permutation test appeared to be a valid alternative as it outperformed the F test, especially under REML. We conclude that in the presence of singletons, one should be careful in using the F test to determine the significance of the fixed effects, and propose the permutation test (under REML) as an alternative.

Model averaging quantiles from data censored by a limit of detection.

In chemical risk assessment, it is important to determine the quantiles of the distribution of concentration data. The selection of an appropriate distribution and the estimation of particular quantiles of interest are largely hindered by the omnipresence of observations below the limit of detection, leading to left-censored data. The log-normal distribution is a common choice, but this distribution is not the only possibility and alternatives should be considered as well. Here, we focus on several distributions that are related to the log-normal distribution or that are seminonparametric extensions of the log-normal distribution. Whereas previous work focused on the estimation of the cumulative distribution function, our interest here goes to the estimation of quantiles, particularly in the left tail of the distribution where most of the left-censored data are located. Two different model averaged quantile estimators are defined and compared for different families of candidate models. The models and methods of selection and averaging are further investigated through simulations and illustrated on data of cadmium concentration in food products. The approach is extended to include covariates and to deal with uncertainty about the values of the limit of detection. These extensions are illustrated with (134) cesium measurements from Fukushima Prefecture, Japan. We can conclude that averaged models do achieve good performance characteristics in case no useful prior knowledge about the true distribution is available; that there is no structural difference in the performance of the direct and indirect method; and that, not surprisingly, only the true or closely approximating model can deal with extremely high percentages of censoring.

Exploring the association between resistance and outpatient antibiotic use expressed as DDDs or packages.

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 (ß-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 ß-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.

Measuring trends of outpatient antibiotic use in Europe: jointly modelling longitudinal data in defined daily doses and packages.

OBJECTIVES: To complement analyses of the linear trend and seasonal fluctuation of European outpatient antibiotic use expressed in defined daily doses (DDD) by analyses of data in packages, to assess the agreement between both measures and to study changes in the number of DDD per package over time. METHODS: Data on outpatient antibiotic use, aggregated at the level of the active substance (WHO version 2011) were collected from 2000 to 2007 for 31 countries and expressed in DDD and packages per 1000 inhabitants per day (DID and PID, respectively). Data expressed in DID and PID were analysed separately using non-linear mixed models while the agreement between these measurements was analysed through a joint non-linear mixed model. The change in DDD per package over time was studied with a linear mixed model. RESULTS: Total outpatient antibiotic and penicillin use in Europe and their seasonal fluctuation significantly increased in DID, but not in PID. The use of combinations of penicillins significantly increased in DID and in PID. Broad-spectrum penicillin use did not increase significantly in DID and decreased significantly in PID. For all but one subgroup, country-specific deviations moved in the same direction whether measured in DID or PID. The correlations are not perfect. The DDD per package increased significantly over time for all but one subgroup. CONCLUSIONS: Outpatient antibiotic use in Europe shows contrasting trends, depending on whether DID or PID is used as the measure. The increase of the DDD per package corroborates the recommendation to adopt PID to monitor outpatient antibiotic use in Europe.