Background Infection with Streptococcus pneumoniae is a major cause of childhood morbidity and mortality worldwide, especially in low income countries where pneumococcal conjugate vaccines (PCVs) are ...still underused. In countries where PCVs have been introduced, much of their efficacy has resulted from their impact on nasopharyngeal carriage in vaccinated children. Understanding the epidemiology of carriage for S. pneumoniae and other common respiratory bacteria in developing countries is crucial for implementing appropriate vaccination strategies and evaluating their impact. Methods and Findings We have systematically reviewed published studies reporting nasopharyngeal or oropharyngeal carriage of S. pneumoniae, Haemophilus influenzae, Moraxella catarrhalis, Staphylococcus aureus, and Neisseria meningitidis in children and adults in low and lower-middle income countries. Studies reporting pneumococcal carriage for healthy children <5 years of age were selected for a meta-analysis. The prevalences of carriage for S. pneumoniae, H. influenzae, and M. catarrhalis were generally higher in low income than in lower-middle income countries and were higher in young children than in adults. The prevalence of S. aureus was high in neonates. Meta-analysis of data from young children before the introduction of PCVs showed a pooled prevalence estimate of 64.8% (95% confidence interval, 49.8%-76.1%) in low income countries and 47.8% (95% confidence interval, 44.7%-50.8%) in lower-middle income countries. The most frequent serotypes were 6A, 6B, 19A, 19F, and 23F. Conclusions In low and lower-middle income countries, pneumococcal carriage is frequent, especially in children, and the spectrum of serotypes is wide. However, because data are limited, additional studies are needed to adequately assess the impact of PCV introduction on carriage of respiratory bacteria in these countries.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background: The limited diagnostic accuracy of biomarkers in children at risk of a serious bacterial infection (SBI) might be due to the imperfect reference standard of SBI. We aimed to evaluate the ...diagnostic performance of a new classification algorithm for biomarker discovery in children at risk of SBI.
Methods: We used data from five previously published, prospective observational biomarker discovery studies, which included patients aged 0– <16 years: the Alder Hey emergency department (n = 1,120), Alder Hey pediatric intensive care unit (n = 355), Erasmus emergency department (n = 1,993), Maasstad emergency department (n = 714) and St. Mary's hospital (n = 200) cohorts. Biomarkers including procalcitonin (PCT) (4 cohorts), neutrophil gelatinase-associated lipocalin-2 (NGAL) (3 cohorts) and resistin (2 cohorts) were compared for their ability to classify patients according to current standards (dichotomous classification of SBI vs. non-SBI), vs. a proposed PERFORM classification algorithm that assign patients to one of eleven categories. These categories were based on clinical phenotype, test outcomes and C-reactive protein level and accounted for the uncertainty of final diagnosis in many febrile children. The success of the biomarkers was measured by the Area under the receiver operating Curves (AUCs) when they were used individually or in combination.
Results: Using the new PERFORM classification system, patients with clinically confident bacterial diagnosis (“definite bacterial” category) had significantly higher levels of PCT, NGAL and resistin compared with those with a clinically confident viral diagnosis (“definite viral” category). Patients with diagnostic uncertainty had biomarker concentrations that varied across the spectrum. AUCs were higher for classification of “definite bacterial” vs. “definite viral” following the PERFORM algorithm than using the “SBI” vs. “non-SBI” classification; summary AUC for PCT was 0.77 (95% CI 0.72–0.82) vs. 0.70 (95% CI 0.65–0.75); for NGAL this was 0.80 (95% CI 0.69–0.91) vs. 0.70 (95% CI 0.58–0.81); for resistin this was 0.68 (95% CI 0.61–0.75) vs. 0.64 (0.58–0.69) The three biomarkers combined had summary AUC of 0.83 (0.77–0.89) for “definite bacterial” vs. “definite viral” infections and 0.71 (0.67–0.74) for “SBI” vs. “non-SBI.”
Conclusion: Biomarkers of bacterial infection were strongly associated with the diagnostic categories using the PERFORM classification system in five independent cohorts. Our proposed algorithm provides a novel framework for phenotyping children with suspected or confirmed infection for future biomarker studies.
PDF