BACKGROUNDPneumococcal polysaccharide conjugate vaccines prevent pneumococcal disease in infants, but their efficacy against pneumococcal community-acquired pneumonia in adults 65 years of age or older is unknown. METHODSIn a randomized, double-blind, placebo-controlled trial involving 84,496 adults 65 years of age or older, we evaluated the efficacy of 13-valent polysaccharide conjugate vaccine (PCV13) in preventing first episodes of vaccine-type strains of pneumococcal community-acquired pneumonia, nonbacteremic and noninvasive pneumococcal community-acquired pneumonia, and invasive pneumococcal disease. Standard laboratory methods and a serotype-specific urinary antigen detection assay were used to identify community-acquired pneumonia and invasive pneumococcal disease. RESULTSIn the per-protocol analysis of first episodes of infections due to vaccine-type strains, community-acquired pneumonia occurred in 49 persons in the PCV13 group and 90 persons in the placebo group (vaccine efficacy, 45.6%; 95.2% confidence interval [CI], 21.8 to 62.5), nonbacteremic and noninvasive community-acquired pneumonia occurred in 33 persons in the PCV13 group and 60 persons in the placebo group (vaccine efficacy, 45.0%; 95.2% CI, 14.2 to 65.3), and invasive pneumococcal disease occurred in 7 persons in the PCV13 group and 28 persons in the placebo group (vaccine efficacy, 75.0%; 95% CI, 41.4 to 90.8). Efficacy persisted throughout the trial (mean follow-up, 3.97 years). In the modified intention-totreat analysis, similar efficacy was observed (vaccine efficacy, 37.7%, 41.1%, and 75.8%, respectively), and community-acquired pneumonia occurred in 747 persons in the PCV13 group and 787 persons in placebo group (vaccine efficacy, 5.1%; 95% CI, −5.1 to 14.2). Numbers of serious adverse events and deaths were similar in the two groups, but there were more local reactions in the PCV13 group. CONCLUSIONSAmong older adults, PCV13 was effective in preventing vaccine-type pneumococcal, bacteremic, and nonbacteremic community-acquired pneumonia and vaccinetype invasive pneumococcal disease but not in preventing community-acquired pneumonia from any cause. (Funded by Pfizer; CAPITA ClinicalTrials.gov number NCT00744263.)

Skeletal diseases and their surgical treatment induce severe pain. The innervation density of bone potentially explains the severe pain reported. Animal studies concluded that sensory myelinated A@-fibers and unmyelinated C-fibers are mainly responsible for conducting bone pain, and that the innervation density of these nerve fibers was highest in periosteum. However, literature regarding sensory innervation of human bone is scarce. This observational study aimed to quantify sensory nerve fiber density in periosteum, cortical bone, and bone marrow of axial and appendicular human bones using immunohistochemistry and confocal microscopy. Multivariate Poisson regression analysis demonstrated that the total number of sensory and sympathetic nerve fibers was highest in periosteum, followed by bone marrow, and cortical bone for all bones studied. Bone from thoracic vertebral bodies contained most sensory nerve fibers, followed by the upper extremity, lower extremity, and parietal neurocranium. The number of nerve fibers declined with age and did not differ between male and female specimens. Sensory nerve fibers were organized as a branched network throughout the periosteum. The current results provide an explanation for the severe pain accompanying skeletal disease, fracture, or surgery. Further, the results could provide more insight into mechanisms that generate and maintain skeletal pain and might aid in developing new treatment strategies.Perspective: This article presents the innervation of human bone and assesses the effect of age, gender, bone compartment and type of bone on innervation density. The presented data provide an explanation for the severity of bone pain arising from skeletal diseases and their surgical treatment.

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Failure to meet subject recruitment targets in clinical trials continues to be a widespread problem with potentially serious scientific, logistical, financial and ethical consequences. On the operational level, enrollment-related issues may be mitigated by careful site selection and by allocating monitoring or training resources proportionally to the anticipated risk of poor enrollment. Such procedures require estimates of the expected recruitment performance that are sufficiently reliable to allow centers to be sensibly categorized. In this study, we investigate whether information obtained from feasibility questionnaires can potentially be used to predict which centers will and which centers will not meet their enrollment targets by means of multivariable logistic regression analysis. From a large set of 59 candidate predictors, we determined the subset that is optimal for predictive purposes using Least Absolute Shrinkage and Selection Operator (LASSO) regularization. Although the extent to which the results are generalizable remains to be determined, they indicate that the prediction accuracy of the optimal model is only a marginal improvement over the intercept-only model, illustrating the difficulty of prediction in this setting.

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