Potential impact of nirsevimab and bivalent maternal vaccine against RSV bronchiolitis in infants: A population-based modelling study.

BACKGROUND: A new monoclonal antibody (nirsevimab; Beyfortus®) and a bivalent prefusion RSV vaccine (Abrysvo®) for maternal immunization have been approved recently. This is a modelling study to estimate the potential impact of different immunization programs with these products on RSV-bronchiolitis. METHODS: Population-based real-world data from primary care and hospitalizations were considered. RSV bronchiolitis dynamics in absence of these immunization scenarios were explained by a multivariate age-structured Bayesian model. Then, the potential impact was simulated under different assumptions including the most recent clinical trial data. Differences in endpoints, populations, and timeframes between trials make the two products' efficacy difficult to compare. RESULTS: A seasonal with catch-up program, assuming a constant effectiveness of 79.5 % during the first 5 months followed by a linear decay to 0 by month 10 with nirsevimab, would prevent between 5121 and 8846 RSV bronchiolitis per 100,000 infants-years. Assuming 77.3 % effectiveness with the same decay, between 976 and 1686 RSV-hospitalizations per 100,000 infants-years could be prevented depending on the uptake. A year-round maternal immunization program, with 51 % of effectiveness during the first 6 months followed by a linear decay to 0 by month 10 would prevent between 3246 and 5606 RSV bronchiolitis cases per 100,000 infants-years. Assuming 56.9 % effectiveness with the same decay, between 713 and 1231 RSV-hospitalizations per 100,000 infants-years could be prevented. CONCLUSIONS: Our results suggest that each strategy would effectively reduce RSV-bronchiolitis.

Evidence of the cooperative response of Blattella germanica gut microbiota to antibiotic treatment.

Gut microbiota plays a key role in host health under normal conditions. However, bacterial composition can be altered by external factors such as antibiotic (AB) intake. While there are many descriptive publications about the effects of AB on gut microbiota composition after treatment, the dynamics and interactions among the bacterial taxa are still poorly understood. In this work, we performed a longitudinal study of gut microbiome dynamics in B. germanica treated with kanamycin. The AB was supplied in three separate periods, giving the microbiota time to recover between each antibiotic intake. We applied two new statistical models, not focusing on pair-wise interactions, to more realistically study the interactions between groups of bacterial taxa and how some groups affect a single taxon. The first model provides information on the importance of a given genus, and the rest of the community, to define the abundance of that genus. The second model, on the other hand, provides details about the relationship between groups of bacteria, focusing on which community groups affect the taxa. These models help us to identify which bacteria are community-dependent in stress conditions, which taxa might be better adapted than the rest of the community, and which bacteria might be working together within the community to overcome the antibiotic. In addition, these models enable us to identify different bacterial groups that were separated in control conditions but were found together in treated conditions, suggesting that when the environment is more hostile (as it is under antibiotic treatment), the whole community tends to work together.

Assessment of human microbiota stability across longitudinal samples using iteratively growing-partitioned clustering.

Microbiome research is advancing rapidly, and every new study should definitively be based on updated methods, trends and milestones in this field to avoid the wrong interpretation of results. Most human microbiota surveys rely on data captured from snapshots-single data points from subjects-and have permitted uncovering the recognized interindividual variability and major covariates of such microbial communities. Currently, changes in individualized microbiota profiles are under the spotlight to serve as robust predictors of clinical outcomes (e.g. weight loss via dietary interventions) and disease anticipation. Therefore, novel methods are needed to provide robust evaluation of longitudinal series of microbiota data with the aim of assessing intrapersonally short-term to long-term microbiota changes likely linked to health and disease states. Consequently, we developed microbiota STability ASsessment via Iterative cluStering (µSTASIS)-a multifunction R package to evaluate individual-centered microbiota stability. µSTASIS targets the recognized interindividual variability inherent to microbiota data to stress the tight relationships observed among and characteristic of longitudinal samples derived from a single individual via iteratively growing-partitioned clustering. The algorithms and functions implemented in this framework deal properly with the sparse and compositional nature of microbiota data. Moreover, the resulting metric is intuitive and independent of beta diversity distance methods and correlation coefficients, thus estimating stability for each microbiota sample rather than giving nonconsensus magnitudes that are difficult to interpret within and between datasets. Our method is freely available under GPL-3 licensing. We demonstrate its utility by assessing gut microbiota stability from three independent studies published previously with multiple longitudinal series of multivariate data and respective metadata.

A Bayesian SIRS model for the analysis of respiratory syncytial virus in the region of Valencia, Spain.

We present a Bayesian stochastic susceptible-infected-recovered-susceptible (SIRS) model in discrete time to understand respiratory syncytial virus dynamics in the region of Valencia, Spain. A SIRS model based on ordinary differential equations has also been proposed to describe RSV dynamics in the region of Valencia. However, this continuous-time deterministic model is not suitable when the initial number of infected individuals is small. Stochastic epidemic models based on a probability of disease transmission provide a more natural description of the spread of infectious diseases. In addition, by allowing the transmission rate to vary stochastically over time, the proposed model provides an improved description of RSV dynamics. The Bayesian analysis of the model allows us to calculate both the posterior distribution of the model parameters and the posterior predictive distribution, which facilitates the computation of point forecasts and prediction intervals for future observations.

Impact of pharmacists' participation in a pharmacotherapy follow-up program.

OBJECTIVE: To evaluate the impact of a continuing pharmacy education (CPE) course on Spanish community pharmacists' participation in a pharmacotherapy follow-up program. DESIGN: Participation in a CPE course offered 4 times over a 4-year period via satellite teleconferencing was monitored and the data analyzed to determine the course's impact on community pharmacists' participation in a pharmacotherapy follow-up program. ASSESSMENT: Community pharmacists' participation in the pharmaceutical care CPE course had a slightly positive impact on their participation in the pharmacotherapy follow-up program. In the best profiles, there was a probability of 7.3% that participants would participate in the pharmacotherapy follow-up program. CONCLUSIONS: Completion of pharmaceutical care CPE courses did not have a significant impact on pharmacists' participation in a pharmacotherapy follow-up program.

epiModel: a system to build automatically systems of differential equations of compartmental type-epidemiological models.

In this paper we describe epiModel, a code developed in Mathematica that facilitates the building of systems of differential equations corresponding to type-epidemiological linear or quadratic models whose characteristics are defined in text files following an easy syntax. It includes the possibility of obtaining the equations of models involving age and/or sex groups.

Effectiveness of a videoconference training course on implementing pharmacy services.

OBJECTIVE: The aim of this research was to assess the effects of a series of four training courses comprised of 13 synchronous videoconferences on the implementation of cognitive services in Spanish community pharmacies. Setting A phone survey to continuing training course attendants. METHODS: A random sample of 225 pharmacists registered in a 2004 videoconference course was selected. The phone-survey questionnaire included quality perception elements rated on a 5-point Likert scale, and a series of questions used to identify position in the Rogers 5-step innovation-decision model. An algorithm was used to translate the questions into Rogers' categories. To discover determinants of attendants position in these categories, bivariate analysis, simple correspondence analysis, and logistic regressions were performed. MAIN OUTCOME MEASURE: Position in Rogers' diffusion of innovation steps regarding the adoption of pharmacotherapy follow-up. RESULTS: The perception of the course quality rated between good and very good for the majority of respondents. A significant association between having attended two or more of these four courses and the Persuasion/Decision step in Rogers's model appeared. No association was found between course attendance and the Implementation/Confirmation step of patient follow-up. Fifty percent of those who indicated they implemented the service reported following-up with less than 10 patients, and only 25% reported following up with more than 20 patients. CONCLUSIONS: Although participation in these courses was associated with higher steps in Rogers' model, significant association appeared only with Persuasion/Decision steps and not with the Implementation/Confirmation step, reflecting an attitude but not a performance change.