Epidemiological impact and cost-effectiveness of introducing vaccination against serogroup B meningococcal disease in France.

International audienceINTRODUCTION:Despite its low incidence in France, invasive serogroup B meningococcal disease remains a public health concern. A new vaccine against the disease, Bexsero(®), has been licensed in the EU. We studied the epidemiological impact and cost-effectiveness of routine vaccination using Bexsero(®) in order to inform the decision-making process regarding its potential inclusion in the vaccination schedule.METHODS:A multi-generational Markov model was used. Time horizon was set to 100 years. Five vaccination strategies were evaluated: infants at 3, 5, 6 and 13 months, toddlers at 13, 15 and 27 months and adolescents at 15 years provided 2 doses one month apart. A booster dose at 15 years old and a catch-up for 15 years old subjects during the first 15 years of the programme were added to the infant and toddler strategies. Costs per QALY gained were computed from a restricted societal perspective including direct costs only. Herd immunity was simulated in an alternative base-case scenario and sensitivity analyses.RESULTS:In the base-case analysis without herd immunity and with all cohorts vaccinated, at € 40 per vaccine dose, routine infant vaccination would provide the lowest cost per QALY gained (€ 380,973) despite only preventing 18% of cases. Under the assumption of herd immunity, the adolescent vaccination would provide the lowest cost per QALY gained (€ 135,902) preventing 24% of cases. Infant vaccination with a late booster and catch-up would prevent 51% of cases with a cost of € 188,511 per QALY gained.CONCLUSIONS:Given current meningococcal epidemiology in France and the available data on the protection provided by Bexsero(®), our modelling work showed that routine vaccination against serogroup B meningococcal disease is not cost-effective

The French Connection: The First Large Population-Based Contact Survey in France Relevant for the Spread of Infectious Diseases.

Empirical social contact patterns are essential to understand the spread of infectious diseases. To date, no such data existed for France. Although infectious diseases are frequently seasonal, the temporal variation of contact patterns has not been documented hitherto.COMES-F is the first French large-scale population survey, carried out over 3 different periods (February-March, April, April-May) with some participants common to the first and the last period. Participants described their contacts for 2 consecutive days, and reported separately on professional contacts when typically over 20 per day.2033 participants reported 38 881 contacts (weighted median [first quartile-third quartile]: 8[5-14] per day), and 54 378 contacts with supplementary professional contacts (9[5-17]). Contrary to age, gender, household size, holidays, weekend and occupation, period of the year had little influence on the number of contacts or the mixing patterns. Contact patterns were highly assortative with age, irrespective of the location of the contact, and gender, with women having 8% more contacts than men. Although most contacts occurred at home and at school, the inclusion of professional contacts modified the structure of the mixing patterns. Holidays and weekends reduced dramatically the number of contacts, and as proxies for school closure, reduced R0 by 33% and 28%, respectively. Thus, school closures could have an important impact on the spread of close contact infections in France.Despite no clear evidence for temporal variation, trends suggest that more studies are needed. Age and gender were found important determinants of the mixing patterns. Gender differences in mixing patterns might help explain gender differences in the epidemiology of infectious diseases

DeepBreath—automated detection of respiratory pathology from lung auscultation in 572 pediatric outpatients across 5 countries

Abstract The interpretation of lung auscultation is highly subjective and relies on non-specific nomenclature. Computer-aided analysis has the potential to better standardize and automate evaluation. We used 35.9 hours of auscultation audio from 572 pediatric outpatients to develop DeepBreath : a deep learning model identifying the audible signatures of acute respiratory illness in children. It comprises a convolutional neural network followed by a logistic regression classifier, aggregating estimates on recordings from eight thoracic sites into a single prediction at the patient-level. Patients were either healthy controls (29%) or had one of three acute respiratory illnesses (71%) including pneumonia, wheezing disorders (bronchitis/asthma), and bronchiolitis). To ensure objective estimates on model generalisability, DeepBreath is trained on patients from two countries (Switzerland, Brazil), and results are reported on an internal 5-fold cross-validation as well as externally validated (extval) on three other countries (Senegal, Cameroon, Morocco). DeepBreath differentiated healthy and pathological breathing with an Area Under the Receiver-Operator Characteristic (AUROC) of 0.93 (standard deviation [SD] ± 0.01 on internal validation). Similarly promising results were obtained for pneumonia (AUROC 0.75 ± 0.10), wheezing disorders (AUROC 0.91 ± 0.03), and bronchiolitis (AUROC 0.94 ± 0.02). Extval AUROCs were 0.89, 0.74, 0.74 and 0.87 respectively. All either matched or were significant improvements on a clinical baseline model using age and respiratory rate. Temporal attention showed clear alignment between model prediction and independently annotated respiratory cycles, providing evidence that DeepBreath extracts physiologically meaningful representations. DeepBreath provides a framework for interpretable deep learning to identify the objective audio signatures of respiratory pathology

Smoothed contact matrices according to Actual Period (right).

<p>Relative incidence of a new emerging infection in a completely susceptible population estimated from the matrix in regard (left).</p

Timeline of the study, showing the distribution of participants and contacts over time.

<p>The periods of inclusion were February, 20^th–March,17^th; April,1^st–April, 7^th; April,16^th–May, 14^th. The dot size is proportional to the log of participant’s number. (Design Period 1: 34 days; Design Period 2: 29 days)</p

3D representation of the base-case matrix without SPC (Supplementary Professional Contacts).

<p>3D representation of the base-case matrix without SPC (Supplementary Professional Contacts).</p

France social contact data

<p>Social contact data for France</p

Characteristics of contact (without SPC).

<p>Distribution of location, duration and frequency for all contacts (A) and physical contacts (B). Duration of contact according to frequency (C). Proportion of physical contacts according to duration (D), frequency (E) and location (F).</p

Smoothed contact matrices without SPC, for physical contacts only and with SPC(Supplementary Professional Contacts) (right).

<p>Relative incidence of a new emerging infection in a completely susceptible population estimated from the matrix in regard (left).</p