Contribution of vaginal culture to predict early onset neonatal infection in preterm prelabor rupture of membranes

International audienceBackground: Preterm prelabor rupture of membranes (PPROM) is a major cause of morbidity and mortality for both the mother and the newborn. The vaginal germ profile in PPROM is poorly known, particularly regarding the risk of early-onset neonatal infection (EONI). Objective: To determine microbiological risk factors for EONI in case of PPROM before 34 weeks of gestation (WG). Study Design: A retrospective single-center cohort of patients with PPROM before 34 W G from 2008 to 2016. Vaginal swabs were obtained at admission and at delivery as per usual care and were analyzed by Gram stain and culture for vaginal dysbiosisi.e lactobacilli depletion and/or presence of potential pathogens. Results: Among 268 cases of PPROM, 39 neonates had EONI 14.55 %; (95 %CI 0.11-0.19) Overall, vaginal samples culture was positive in 16.67 % (95 %CI 11.95 %-22.32 %) at the time of rupture and 24.76 % (95 %CI 19.02 %-31.23 %) at delivery, with no significant differences between EONI and no-EONI groups (p = 0.797 and 0.486, respectively), including for Group B Streptococci (GBS) and Escherichia coli. EONI was significantly associated with dysbiosis at the time of rupture (23.94 % versus 10.35 % in the absence of dysbiosis, p = 0.009) and at delivery (19.70 % versus 3.90 % if no dysbiosis, p < 0.001). Clinical intra-uterine infection was present in 78.5 % (n = 31) of the EONI group versus 37.2 % (n = 85) in the non-EONI group (p < 0.001) and chorioamnionitis and/or funisitis were found in 97.3 % and 91.9 %, respectively in the EONI group, versus 56.11 % and 53.96 %, respectively, in the non-EONI group (p < 0.001). Conclusion: Dysbiosis following rupture and at delivery, but not the presence of pathogens in the VS culture, was associated with the risk of EONI in case of PPROM

Analyse de données fonctionnelles pour la classification de profils épidémiques : application au paludisme

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Targeting Malaria Hotspots to Reduce Transmission Incidence in Senegal.

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A comprehensive repertoire of prokaryotic species identified in human beings

International audienceThe compilation of the complete prokaryotic repertoire associated with human beings as commensals or pathogens is a major goal for the scientific and medical community. The use of bacterial culture techniques remains a crucial step to describe new prokaryotic species. The large number of officially acknowledged bacterial species described since 1980 and the recent increase in the number of recognised pathogenic species have highlighted the absence of an exhaustive compilation of species isolated in human beings. By means of a thorough investigation of several large culture databases and a search of the scientific literature, we built an online database containing all human-associated prokaryotic species described, whether or not they had been validated and have standing in nomenclature. We list 2172 species that have been isolated in human beings. They were classified in 12 different phyla, mostly in the Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes phyla. Our online database is useful for both clinicians and microbiologists and forms part of the Human Microbiome Project, which aims to characterise the whole human microbiota and help improve our understanding of the human predisposition and susceptibility to infectious agents

Targeting hotspots to reduce transmission of malaria in Senegal: modeling of the effects of human mobility

Background: In central Senegal malaria incidences have declined in recent years in response to scaling-up of control measures, but now remains stable, making elimination improbable. Additional control measures are needed to reduce transmission. Methods: By using a meta-population mathematical model, we evaluated chemotherapy interventions targeting stable malaria hotspots, using a differential equation framework and incorporating human mobility, and fitted to weekly malaria incidences from 45 villages, over 5 years. Three simulated approaches for selecting intervention targets were compared: a) villages with at least one malaria case during the low transmission season of the previous year; b) villages ranked highest in terms of incidence during the high transmission season of the previous year; c) villages ranked based on the degree of connectivity with adjacent populations. Results: Our mathematical modeling, taking into account human mobility, showed that the intervention strategies targeting hotspots should be effective in reducing malaria incidence in both treated and untreated areas. Conclusions: Mathematical simulations showed that targeted interventions allow increasing malaria elimination potential

Comparison of Air Impaction and Electrostatic Dust Collector Sampling Methods to Assess Airborne Fungal Contamination in Public Buildings A BSTR ACT

International audienceMany ailments can be linked to exposure to indoor airborne fungus. However, obtaining a precise measurement of airborne fungal levels is complicated partly due to indoor air fluctuations and non-standardized techniques. Electrostatic dust collector (EDC) sampling devices have been used to measure a wide range of airborne analytes, including endotoxins, allergens, β-glucans, and microbial DNA in various indoor environments. In contrast, viable mold contamination has only been assessed in highly contaminated environments such as farms and archive buildings. This study aimed to assess the use of EDCs, compared with repeated air-impactor measurements, to assess airborne viable fungal flora in moderately contaminated indoor environments. Indoor airborne fungal flora was cultured from EDCs and daily air-impaction samples collected in an office building and a daycare center. The quantitative fungal measurements obtained using a single EDC significantly correlated with the cumulative measurement of nine daily air impactions. Both methods enabled the assessment of fungal exposure, although a few differences were observed between the detected fungal species and the relative quantity of each species. EDCs were also used over a 32-month period to monitor indoor airborne fungal flora in a hospital office building, which enabled us to assess the impact of outdoor events (e.g. ground excavations) on the fungal flora levels on the indoor environment. In conclusion , EDC-based measurements provided a relatively accurate profile of the viable airborne flora present during a sampling period. In particular, EDCs provided a more representative assessment of fungal levels compared with single air-impactor sampling. The EDC technique is also simpler than performing repetitive air-impaction measures over the course of several consecutive days. EDC is a versatile tool for collecting airborne samples and was efficient for measuring mold levels in indoor environments

Mathematical models for predicting human mobility in the context of infectious disease spread: introducing the impedance model

Abstract Background Mathematical models of human mobility have demonstrated a great potential for infectious disease epidemiology in contexts of data scarcity. While the commonly used gravity model involves parameter tuning and is thus difficult to implement without reference data, the more recent radiation model based on population densities is parameter-free, but biased. In this study we introduce the new impedance model, by analogy with electricity. Previous research has compared models on the basis of a few specific available spatial patterns. In this study, we use a systematic simulation-based approach to assess the performances. Methods Five hundred spatial patterns were generated using various area sizes and location coordinates. Model performances were evaluated based on these patterns. For simulated data, comparison measures were average root mean square error (aRMSE) and bias criteria. Modeling of the 2010 Haiti cholera epidemic with a basic susceptible–infected–recovered (SIR) framework allowed an empirical evaluation through assessing the goodness-of-fit of the observed epidemic curve. Results The new, parameter-free impedance model outperformed previous models on simulated data according to average aRMSE and bias criteria. The impedance model achieved better performances with heterogeneous population densities and small destination populations. As a proof of concept, the basic compartmental SIR framework was used to confirm the results obtained with the impedance model in predicting the spread of cholera in Haiti in 2010. Conclusions The proposed new impedance model provides accurate estimations of human mobility, especially when the population distribution is highly heterogeneous. This model can therefore help to achieve more accurate predictions of disease spread in the context of an epidemic

Spatio-temporal dynamic of malaria in Ouagadougou, Burkina Faso, 2011–2015

International audienceBackground: Given the scarcity of resources in developing countries, malaria treatment requires new strategies that target specific populations, time periods and geographical areas. While the spatial pattern of malaria transmission is known to vary depending on local conditions, its temporal evolution has yet to be evaluated. The aim of this study was to determine the spatio-temporal dynamic of malaria in the central region of Burkina Faso, taking into account meteorological factors. Methods: Drawing on national databases, 101 health areas were studied from 2011 to 2015, together with weekly meteorological data (temperature, number of rain events, rainfall, humidity, wind speed). Meteorological factors were investigated using a principal component analysis (PCA) to reduce dimensions and avoid collinearities. The Box-Jenkins ARIMA model was used to test the stationarity of the time series. The impact of meteorological factors on malaria incidence was measured with a general additive model. A change-point analysis was performed to detect malaria transmission periods. For each transmission period, malaria incidence was mapped and hotspots were identified using spatial cluster detection. Results: Malaria incidence never went below 13.7 cases/10,000 person-weeks. The first and second PCA components (constituted by rain/humidity and temperatures, respectively) were correlated with malaria incidence with a lag of 2 weeks. The impact of temperature was significantly non-linear: malaria incidence increased with temperature but declined sharply with high temperature. A significant positive linear trend was found for the entire time period. Three transmission periods were detected: low (16.8-29.9 cases/10,000 person-weeks), high (51.7-84.8 cases/10,000 person-weeks), and intermediate (26.7-32.2 cases/10,000 person-weeks). The location of clusters identified as high risk varied little across transmission periods. Conclusion: This study highlighted the spatial variability and relative temporal stability of malaria incidence around the capital Ouagadougou, in the central region of Burkina Faso. Despite increasing efforts in fighting the disease, malaria incidence remained high and increased over the period of study. Hotspots, particularly those detected for low transmission periods, should be investigated further to uncover the local environmental and behavioural factors of transmission, and hence to allow for the development of better targeted control strategies

Socioeconomic and environmental factors associated with malaria hotspots in the Nanoro demographic surveillance area, Burkina Faso

Abstract Background With limited resources and spatio-temporal heterogeneity of malaria in developing countries, it is still difficult to assess the real impact of socioeconomic and environmental factors in order to set up targeted campaigns against malaria at an accurate scale. Our goal was to detect malaria hotspots in rural area and assess the extent to which household socioeconomic status and meteorological recordings may explain the occurrence and evolution of these hotspots. Methods Data on malaria cases from 2010 to 2014 and on socioeconomic and meteorological factors were acquired from four health facilities within the Nanoro demographic surveillance area. Statistical cross correlation was used to quantify the temporal association between weekly malaria incidence and meteorological factors. Local spatial autocorrelation analysis was performed and restricted to each transmission period using Kulldorff’s elliptic spatial scan statistic. Univariate and multivariable analysis were used to assess the principal socioeconomic and meteorological determinants of malaria hotspots using a Generalized Estimating Equation (GEE) approach. Results Rainfall and temperature were positively and significantly associated with malaria incidence, with a lag time of 9 and 14 weeks, respectively. Spatial analysis showed a spatial autocorrelation of malaria incidence and significant hotspots which was relatively stable throughout the study period. Furthermore, low socioeconomic status households were strongly associated with malaria hotspots (aOR = 1.21, 95% confidence interval: 1.03–1.40). Conclusion These fine-scale findings highlight a relatively stable spatio-temporal pattern of malaria risk and indicate that social and environmental factors play an important role in malaria incidence. Integrating data on these factors into existing malaria struggle tools would help in the development of sustainable bottleneck strategies adapted to the local context for malaria control

Response to Letter to the Editor from Woolcott and Castilla-Bancayán: “Diabetes Increases Severe COVID-19 Outcomes Primarily in Younger Adults”

International audienceNo abstract availabl