The estimated US burden of CAP is substantial, with >1.5 million unique adults being hospitalized annually, 100000 deaths occurring during hospitalization, and approximately 1 of 3 patients hospitalized with CAP dying within 1 year.

The advent of PCR has improved the identification of viruses in patients with communityacquired pneumonia (CAP). Several studies have used PCR to establish the importance of viruses in the aetiology of CAP.We performed a systematic review and meta-analysis of the studies that reported the proportion of viral infection detected via PCR in patients with CAP. We excluded studies with paediatric populations. The primary outcome was the proportion of patients with viral infection. The secondary outcome was short-term mortality.Our review included 31 studies. Most obtained PCR via nasopharyngeal or oropharyngeal swab. The pooled proportion of patients with viral infection was 24.5% (95% CI 21.5-27.5%). In studies that obtained lower respiratory samples in >50% of patients, the proportion was 44.2% (95% CI 35.1-53.3%). The odds of death were higher in patients with dual bacterial and viral infection (OR 2.1, 95% CI 1.32-3.31).Viral infection is present in a high proportion of patients with CAP. The true proportion of viral infection is probably underestimated because of negative test results from nasopharyngeal or oropharyngeal swab PCR. There is increased mortality in patients with dual bacterial and viral infection. @ERSpublications Viral infection is present in a high proportion of patients with community-acquired pneumonia

Our study demonstrated real-world, direct effectiveness of 13-valent pneumococcal conjugate vaccine against vaccine-type community-acquired pneumonia following introduction into a routine immunization program among adults aged ≥65 years, many of whom had immunocompromising and chronic medical conditions.

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Machine learning approaches to modeling of epidemiologic data are becoming increasingly more prevalent in the literature. These methods have the potential to improve our understanding of health and opportunities for intervention, far beyond our past capabilities. This article provides a walkthrough for creating supervised machine learning models with current examples from the literature. From identifying an appropriate sample and selecting features through training, testing, and assessing performance, the end-to-end approach to machine learning can be a daunting task. We take the reader through each step in the process and discuss novel concepts in the area of machine learning, including identifying treatment effects and explaining the output from machine learning models.