Associations between pathogens in the upper respiratory tract of young children: interplay between viruses and bacteria

Aetiology of paediatric pneumonia after the introduction of pneumococcal conjugate vaccine

We describe the aetiology of community-acquired pneumonia in children before and after the introduction of the pneumococcal conjugate vaccination (PCV) programme in 2006.

Prospective studies were conducted in 2001–2002 (pre-vaccine) and 2009–2011 (post-vaccine) of children aged 0–16 years with radiologically confirmed pneumonia seen in hospital. Investigations included culture, serology, immunofluorescence antibody and urine antigen testing, with an increased use of PCR assays and expanded panels of pathogens in the post-vaccine study.

241 and 160 children were enrolled in the pre- and post-vaccine studies, respectively (73% aged <5 years). Identification of a causative pathogen was higher post-vaccination (61%) than pre-vaccination (48.5%) (p=0.019). Rates of bacterial infections were not different between post- and pre-vaccine studies (17.5% versus 24%, p=0.258). Viral (31%) and mixed (12.5%) infections were found more often post-vaccination (19.5%, p=0.021) than pre-vaccination (5%, p=0.015). Rates of identified pneumococcal infections were comparable between pre- and post-vaccine studies (14.7% versus 17.4%, p=0.557). Diagnosis of pneumococcal infection post-vaccination improved when PCR was used compared to culture (21.6% versus 6%, p=0.0004). Serotypes included in PCV13 but not PCV7 were identified in 75% (18 out of 24) post-vaccination.

Infection with nonvaccine pneumococcal serotypes continues to be a significant cause of pneumonia in children in the UK.

Aetiology of community-acquired pneumonia in children following a pneumococcal conjugate vaccination programmehttp://ow.ly/p9Wub

Multivariate approach for studying interactions between environmental variables and microbial communities

To understand the role of human microbiota in health and disease, we need to study effects of environmental and other epidemiological variables on the composition of microbial communities. The composition of a microbial community may depend on multiple factors simultaneously. Therefore we need multivariate methods for detecting, analyzing and visualizing the interactions between environmental variables and microbial communities. We provide two different approaches for multivariate analysis of these complex combined datasets: (i) We select variables that correlate with overall microbiota composition and microbiota members that correlate with the metadata using canonical correlation analysis, determine independency of the observed correlations in a multivariate regression analysis, and visualize the effect size and direction of the observed correlations using heatmaps; (ii) We select variables and microbiota members using univariate or bivariate regression analysis, followed by multivariate regression analysis, and visualize the effect size and direction of the observed correlations using heatmaps. We illustrate the results of both approaches using a dataset containing respiratory microbiota composition and accompanying metadata. The two different approaches provide slightly different results; with approach (i) using canonical correlation analysis to select determinants and microbiota members detecting fewer and stronger correlations only and approach (ii) using univariate or bivariate analyses to select determinants and microbiota members detecting a similar but broader pattern of correlations. The proposed approaches both detect and visualize independent correlations between multiple environmental variables and members of the microbial community. Depending on the size of the datasets and the hypothesis tested one can select the method of preference.