When a new vaccine is introduced, it is critical to monitor trends in disease rates to ensure that the vaccine is effective and to quantify its impact. However, estimates from observational studies can be confounded by unrelated changes in healthcare utilization, changes in the underlying health of the population, or changes in reporting. Other diseases are often used to detect and adjust for these changes, but choosing an appropriate control disease a priori is a major challenge. The “synthetic controls” (causal impact) method, which was originally developed for website analytics and social sciences, provides an appealing solution. With this approach, potential comparison time series are combined into a composite and are used to generate a counterfactual estimate, which can be compared with the time series of interest after the intervention. We sought to estimate changes in hospitalizations for all-cause pneumonia associated with the introduction of pneumococcal conjugate vaccines (PCVs) in five countries in the Americas. Using synthetic controls, we found a substantial decline in hospitalizations for all-cause pneumonia in infants in all five countries (average of 20%), whereas estimates for young and middle-aged adults varied by country and were potentially influenced by the 2009 influenza pandemic. In contrast to previous reports, we did not detect a decline in all-cause pneumonia in older adults in any country. Synthetic controls promise to increase the accuracy of studies of vaccine impact and to increase comparability of results between populations compared with alternative approaches.

Because the real-world impact of new vaccines cannot be known before they are implemented in national programs, post-implementation studies at the population level are critical. Studies based on analysis of hospitalization rates of vaccine-preventable outcomes are typically used for this purpose. However, estimates of vaccine impact based on hospitalization data are particularly prone to confounding, as hospitalization rates are tightly linked to changes in the quality, access and use of the healthcare system, which often occur simultaneously with introduction of new vaccines. Here we illustrate how changes in healthcare delivery coincident with vaccine introduction can influence estimates of vaccine impact, using as an example reductions in infant pneumonia hospitalizations after introduction of the 10-valent pneumococcal conjugate vaccine (PCV10) in Brazil. To this end, we explore the effect of changes in several metrics of quality and access to public healthcare on trends in hospitalization rates before (2008–09) and after (2011–12) PCV10 introduction in 2010. Changes in infant pneumonia hospitalization rates following vaccine introduction were significantly associated with concomitant changes in hospital capacity and the fraction of the population using public hospitals. Importantly, reduction of pneumonia hospitalization rates after PCV10 were also associated with the expansion of outpatient services in several Brazilian states, falling more sharply where primary care coverage and the number of health units offering basic and emergency care increased more. We show that adjustments for unrelated (non-vaccine) trends commonly employed by impact studies, such as use of single control outcomes, are not always sufficient for accurate impact assessment. We discuss several ways to identify and overcome such biases, including sensitivity analyses using different denominators to calculate hospitalizations rates and methods that track changes in the outpatient setting. Employing these practices can improve the accuracy of vaccine impact estimates, particularly in evolving healthcare settings typical of low- and middle-income countries.

BACKGROUND Pneumococcal conjugate vaccines (PCVs) prevent invasive pneumococcal disease and pneumonia. However, some low- and middle-income countries have yet to introduce PCV into their immunization programs due, in part, to lack of certainty about the potential impact. Assessing PCV benefits is challenging because specific data on pneumococcal disease is often lacking, and it can be difficult to separate the effects of factors other than the vaccine that could also affect pneumococcal disease rates. METHODS We assess PCV impact by combining Bayesian model averaging with change point models to estimate the timing and magnitude of vaccine-associated changes, while controlling for seasonality and other covariates. We applied our approach to monthly time series of age-stratified hospitalizations related to pneumococcal infection in children under 5 years of age in the United States, Brazil, and Chile. RESULTS Our method accurately detected changes in data in which we knew true and noteworthy changes occurred, i.e. in simulated data and for invasive pneumococcal disease. Moreover, 24 months after the vaccine introduction, we detected reductions of 14%, 9%, and 9% in the U.S., Brazil, and Chile, respectively, in all-cause pneumonia (ACP) hospitalizations for age group 0 to <1 years of age. CONCLUSIONS Our approach provides a flexible and sensitive method to detect changes in disease incidence that occur after the introduction of a vaccine or other intervention, while avoiding biases that exist in current approaches to time trend analyses.

Molecular detection of viruses has been aided by high-throughput sequencing, permitting the genomic characterization of emerging strains. In this study, we comprehensively screened 500 respiratory secretions from children with upper and/or lower respiratory tract infections for viral pathogens. The viruses detected are described, including a divergent human parainfluenza virus type 4 from GS FLX pyrosequencing of 92 specimens. Complete full-genome characterization of the virus followed, using Single Molecule, Real-Time (SMRT) sequencing. Subsequent “primer walking” combined with Sanger sequencing validated the RS platform's utility in viral sequencing from complex clinical samples. Comparative genomics reveals the divergent strain clusters with the only completely sequenced HPIV4a subtype. However, it also exhibits various structural features present in one of the HPIV4b reference strains, opening questions regarding their lifecycle and evolutionary relationships among these viruses. Clinical data from patients infected with the strain, as well as viral prevalence estimates using real-time PCR, is also described.

Swine vesicular disease, an important viral disease affecting domestic pigs, is shown to have a single and recent origin in humans, leading us closer to a full understanding of the sudden emergence of this enigmatic veterinary disease, and exemplifying the sometimes overlooked risk of human to animal disease transfers.

The synergistic use of disparate data categories yields such unique detail, that the Greenland epidemic now serves as a model example for the epidemic emergence of HIV-1 in a society. This renders it suitable for testing of present and future sequence-based epidemiological methodologies.

BackgroundInfluenza viruses such as swine-origin influenza A(H1N1) virus (A(H1N1)pdm09) generate genetic diversity due to the high error rate of their RNA polymerase, often resulting in mixed genotype populations (intra-host variants) within a single infection. This variation helps influenza to rapidly respond to selection pressures, such as those imposed by the immunological host response and antiviral therapy. We have applied deep sequencing to characterize influenza intra-host variation in a transmission chain consisting of three cases due to oseltamivir-sensitive viruses, and one derived oseltamivir-resistant case.MethodsFollowing detection of the A(H1N1)pdm09 infections, we deep-sequenced the complete NA gene from two of the oseltamivir-sensitive virus-infected cases, and all eight gene segments of the viruses causing the remaining two cases.ResultsNo evidence for the resistance-causing mutation (resulting in NA H275Y substitution) was observed in the oseltamivir-sensitive cases. Furthermore, deep sequencing revealed a subpopulation of oseltamivir-sensitive viruses in the case carrying resistant viruses. We detected higher levels of intra-host variation in the case carrying oseltamivir-resistant viruses than in those infected with oseltamivir-sensitive viruses.ConclusionsOseltamivir-resistance was only detected after prophylaxis with oseltamivir, suggesting that the mutation was selected for as a result of antiviral intervention. The persisting oseltamivir-sensitive virus population in the case carrying resistant viruses suggests either that a small proportion survive the treatment, or that the oseltamivir-sensitive virus rapidly re-establishes itself in the virus population after the bottleneck. Moreover, the increased intra-host variation in the oseltamivir-resistant case is consistent with the hypothesis that the population diversity of a RNA virus can increase rapidly following a population bottleneck.

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