High Prevalence of Vaccine-Type Infections Among Children with Pneumococcal Pneumonia and Effusion After 13-Valent Pneumococcal Conjugate Vaccine Introduction in the Dominican Republic.

BACKGROUND: In 2013, the Dominican Republic introduced 13-valent pneumococcal conjugate vaccine (PCV13) using a 3-dose schedule (at 2, 4 and 12 months of age). We evaluated the impact of PCV13 on serotypes causing pneumococcal pneumonia with pleural effusion. METHODS: Surveillance data after PCV13 introduction (July 2014 to June 2016) were compared with data before PCV13 introduction (July 2009 to June 2011). Cases were defined as radiologic evidence of pneumonia with pleural effusion in a child aged <15 years. Pneumococcus was detected in pleural fluid by either culture or polymerase chain reaction, and serotyping was performed. The Ministry of Health's PCV13 uptake data for 2014-2016 were obtained. RESULTS: The prevalence of pneumococcus among cases was similar before and after PCV13 introduction (56.4% and 52.8%, respectively). The proportion of pneumococcal cases caused by vaccine serotypes was 86% for children <2 years old both before and PCV13 introduction. Compared with before PCV13, serotype 14 accounted for a smaller (28% vs 13%, respectively; P = .02) and serotype 1 for a larger (23% vs 37%; P = .09) proportion of pneumococcal cases after PCV13 introduction. National uptake for the first, second, and third PCV13 doses was 94%, 81%, and 28%, respectively, in 2014 and 75%, 61%, and 26% in 2015. DISCUSSION: While the decrease in pneumococcal pneumonia with pleural effusion caused by serotype 14 may reflect an early effect of PCV13 implementation, other vaccine serotypes, including serotype 1, are not well controlled. Better PCV13 coverage for all 3 doses is needed.

Standardization and denoising algorithms for mass spectra to classify whole-organism bacterial specimens.

MOTIVATION: Application of mass spectrometry in proteomics is a breakthrough in high-throughput analyses. Early applications have focused on protein expression profiles to differentiate among various types of tissue samples (e.g. normal versus tumor). Here our goal is to use mass spectra to differentiate bacterial species using whole-organism samples. The raw spectra are similar to spectra of tissue samples, raising some of the same statistical issues (e.g. non-uniform baselines and higher noise associated with higher baseline), but are substantially noisier. As a result, new preprocessing procedures are required before these spectra can be used for statistical classification. RESULTS: In this study, we introduce novel preprocessing steps that can be used with any mass spectra. These comprise a standardization step and a denoising step. The noise level for each spectrum is determined using only data from that spectrum. Only spectral features that exceed a threshold defined by the noise level are subsequently used for classification. Using this approach, we trained the Random Forest program to classify 240 mass spectra into four bacterial types. The method resulted in zero prediction errors in the training samples and in two test datasets having 240 and 300 spectra, respectively.