The success of 7-valent pneumococcal conjugate vaccination (PCV-7) introduced to the US childhood immunization schedule in 2000 was partially offset by increases in invasive pneumococcal disease (IPD) and pneumococcal carriage due to non-vaccine serotypes, in particular 19A, in the years that followed. A 13-valent conjugate vaccine (PCV-13) was introduced in 2010. As part of an ongoing study of the response of the Massachusetts pneumococcal population to conjugate vaccination, we report the findings from the samples collected in 2011, as PCV-13 was introduced.
We used multilocus sequence typing (MLST) to analyze 367 pneumococcal isolates carried by Massachusetts children (aged 3 months-7 years) collected during the winter of 2010–11 and used eBURST software to compare the pneumococcal population structure with that found in previous years.
One hundred and four distinct sequence types (STs) were found, including 24 that had not been previously recorded. Comparison with a similar sample collected in 2009 revealed no significant overall difference in the ST composition (p = 0.39, classification index). However, we describe clonal dynamics within the important replacement serotypes 19A, 15B/C, and 6C, and clonal expansion of ST 433 and ST 432, which are respectively serotype 22F and 21 clones.
While little overall change in serotypes or STs was evident, multiple changes in the frequency of individual STs and or serotypes may plausibly be ascribed to the introduction of PCV-13. This 2011 sample documents the initial impact of PCV-13 and will be important for comparison with future studies of the evolution of the pneumococcal population in Massachusetts.
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Pneumococcal conjugate vaccine; Streptococcus pneumoniae; Colonization; Molecular epidemiology; MLST
Limited information on age- and sex-specific estimates of influenza-associated death with different underlying causes is currently available. We regressed weekly age- and sex-specific US mortality outcomes underlying several causes between 1997 and 2007 to incidence proxies for influenza A/H3N2, A/H1N1, and B that combine data on influenzalike illness consultations and respiratory specimen testing, adjusting for seasonal baselines and time trends. Adults older than 75 years of age had the highest average annual rate of influenza-associated mortality, with 141.15 deaths per 100,000 people (95% confidence interval (CI): 118.3, 163.9), whereas children under 18 had the lowest average mortality rate, with 0.41 deaths per 100,000 people (95% CI: 0.23, 0.60). In addition to respiratory and circulatory causes, mortality with underlying cancer, diabetes, renal disease, and Alzheimer disease had a contribution from influenza in adult age groups, whereas mortality with underlying septicemia had a contribution from influenza in children. For adults, within several age groups and for several underlying causes, the rate of influenza-associated mortality was somewhat higher in men than in women. Of note, in men 50–64 years of age, our estimate for the average annual rate of influenza-associated cancer mortality per 100,000 persons (1.90, 95% CI: 1.20, 2.62) is similar to the corresponding rate of influenza-associated respiratory deaths (1.81, 95% CI: 1.42, 2.21). Age, sex, and underlying health conditions should be considered when planning influenza vaccination and treatment strategies.
age; influenza; mortality; sex; underlying cause
Human respiratory syncytial virus (RSV) is the leading cause of lower respiratory tract disease in infants and young children and an important respiratory pathogen in the elderly and immunocompromised. While population-wide molecular epidemiology studies have shown multiple cocirculating RSV genotypes and revealed antigenic and genetic change over successive seasons, little is known about the extent of viral diversity over the course of an individual infection, the origins of novel variants, or the effect of immune pressure on viral diversity and potential immune-escape mutations. To investigate viral population diversity in the presence and absence of selective immune pressures, we studied whole-genome deep sequencing of RSV in upper airway samples from an infant with severe combined immune deficiency syndrome and persistent RSV infection. The infection continued over several months before and after bone marrow transplant (BMT) from his RSV-immune father. RSV diversity was characterized in 26 samples obtained over 78 days. Diversity increased after engraftment, as defined by T-cell presence, and populations reflected variation mostly within the G protein, the major surface antigen. Minority populations with known palivizumab resistance mutations emerged after its administration. The viral population appeared to diversify in response to selective pressures, showing a statistically significant growth in diversity in the presence of pressure from immunity. Defining escape mutations and their dynamics will be useful in the design and application of novel therapeutics and vaccines. These data can contribute to future studies of the relationship between within-host and population-wide RSV phylodynamics.
IMPORTANCE Human RSV is an important cause of respiratory disease in infants, the elderly, and the immunocompromised. RSV circulating in a community appears to change season by season, but the amount of diversity generated during an individual infection and the impact of immunity on this viral diversity has been unclear. To address this question, we described within-host RSV diversity by whole-genome deep sequencing in a unique clinical case of an RSV-infected infant with severe combined immunodeficiency and effectively no adaptive immunity who then gained adaptive immunity after undergoing bone marrow transplantation. We found that viral diversity increased in the presence of adaptive immunity and was primarily within the G protein, the major surface antigen. These data will be useful in designing RSV treatments and vaccines and to help understand the relationship between the dynamics of viral diversification within individual hosts and the viral populations circulating in a community.
The average effect of an infectious disease intervention (e.g., a vaccine) varies across populations with different degrees of exposure to the pathogen. As a result, many investigators favor a per-exposure effect measure that is considered independent of the population level of exposure and that can be used in simulations to estimate the total disease burden averted by an intervention across different populations. However, while per-exposure effects are frequently estimated, the quantity of interest is often poorly defined, and assumptions in its calculation are typically left implicit. In this paper, we build upon work by Halloran and Struchiner (Epidemiology. 1995; 6:172-151) to develop a formal definition of the per-exposure effect and discuss conditions necessary for its unbiased estimation. With greater care paid to the parameterization of transmission models, their results can be better understood and can thereby be of greater value to decision-makers.
Epidemiologists aim to inform the design of public health interventions with evidence on the evolution, emergence and spread of infectious diseases. Sequencing of pathogen genomes, together with date, location, clinical manifestation and other relevant data about sample origins, can contribute to describing nearly every aspect of transmission dynamics, including local transmission and global spread. The analyses of these data have implications for all levels of clinical and public health practice, from institutional infection control to policies for surveillance, prevention and treatment. This review highlights the range of epidemiological questions that can be addressed from the combination of genome sequence and traditional ‘line lists’ (tables of epidemiological data where each line includes demographic and clinical features of infected individuals). We identify opportunities for these data to inform interventions that reduce disease incidence and prevalence. By considering current limitations of, and challenges to, interpreting these data, we aim to outline a research agenda to accelerate the genomics-driven transformation in public health microbiology.
Pneumococcal conjugate vaccines (PCVs) have been introduced worldwide. However, few developing countries have high-quality surveillance systems available for monitoring vaccine impact. We evaluated whether data from nasopharyngeal carriage studies can be used to accurately monitor post-PCV changes in the incidence of invasive pneumococcal disease (IPD) among children under 5 years of age. For various dates during 1991–2010, data on nasopharyngeal pneumococcal carriage and on IPD before and after administration of 7-valent PCV (PCV7) were available from England and Wales, the Netherlands, the Navajo and White Mountain Apache American Indian populations, and the US states of Massachusetts and Alaska. We estimated the change in carriage prevalence for each serotype in each study and then either calculated the average change (inverse variance-weighted) among vaccine and nonvaccine serotypes (model 1) or used mixed-effects models to estimate the change for each serotype individually, pooling serotype data within or between studies (models 2 and 3). We then multiplied these values by the proportion of IPD caused by each serotype during the pre-PCV7 period to obtain an estimate of post-PCV7 disease incidence. Model 1 accurately captured overall changes in IPD incidence following PCV7 introduction for most studies, while the more detailed models, models 2 and 3, were less accurate. Carriage data can be used in this simple model to estimate post-PCV changes in IPD incidence.
carriage; conjugate vaccine, pneumococcal; pneumococcus; surveillance; vaccine effectiveness; vaccines
Assessing the pandemic risk posed by specific non-human influenza A viruses is an important goal in public health research. As influenza virus genome sequencing becomes cheaper, faster, and more readily available, the ability to predict pandemic potential from sequence data could transform pandemic influenza risk assessment capabilities. However, the complexities of the relationships between virus genotype and phenotype make such predictions extremely difficult. The integration of experimental work, computational tool development, and analysis of evolutionary pathways, together with refinements to influenza surveillance, has the potential to transform our ability to assess the risks posed to humans by non-human influenza viruses and lead to improved pandemic preparedness and response.
influenza; pandemic; emergence; human; viruses
Background. We examined whether observed increases in antibiotic nonsusceptible nonvaccine serotypes after introduction of pneumococcal conjugate vaccine in the United States in 2000 were driven primarily by vaccine or antibiotic use.
Methods. Using active surveillance data, we evaluated geographic and temporal differences in serotype distribution and within-serotype differences during 2000–2009. We compared nonsusceptibility to penicillin and erythromycin by geography after standardizing differences across time, place, and serotype by regressing standardized versus crude proportions. A regression slope (RS) approaching zero indicates greater importance of the standardizing factor.
Results. Through 2000–2006, geographic differences in nonsusceptibility were better explained by within-serotype prevalence of nonsusceptibility (RS 0.32, 95% confidence interval [CI], .08–.55 for penicillin) than by geographic differences in serotype distribution (RS 0.71, 95% CI, .44–.97). From 2007–2009, serotype distribution differences became more important for penicillin (within-serotype RS 0.52, 95% CI, .11–.93; serotype distribution RS 0.57, 95% CI, .14–1.0).
Conclusions. Differential nonsusceptibility, within individual serotypes, accounts for most geographic variation in nonsusceptibility, suggesting selective pressure from antibiotic use, rather than differences in serotype distribution, mainly determines nonsusceptibility patterns. Recent trends suggest geographic differences in serotype distribution may be affecting the prevalence of nonsusceptibility, possibly due to decreases in the number of nonsusceptible serotypes.
antimicrobial resistance; erythromycin; invasive pneumococcal disease; penicillin; pneumococcus; Streptococcus pneumoniae
Whole-genome sequencing of pathogens has recently been used to investigate disease outbreaks and is likely to play a growing role in real-time epidemiological studies. Methods to analyze high-resolution genomic data in this context are still lacking, and inferring transmission dynamics from such data typically requires many assumptions. While recent studies have proposed methods to infer who infected whom based on genetic distance between isolates from different individuals, the link between epidemiological relationship and genetic distance is still not well understood. In this study, we investigated the distribution of pairwise genetic distances between samples taken from infected hosts during an outbreak. We proposed an analytically tractable approximation to this distribution, which provides a framework to evaluate the likelihood of particular transmission routes. Our method accounts for the transmission of a genetically diverse inoculum, a possibility overlooked in most analyses. We demonstrated that our approximation can provide a robust estimation of the posterior probability of transmission routes in an outbreak and may be used to rule out transmission events at a particular probability threshold. We applied our method to data collected during an outbreak of methicillin-resistant Staphylococcus aureus, ruling out several potential transmission links. Our study sheds light on the accumulation of mutations in a pathogen during an epidemic and provides tools to investigate transmission dynamics, avoiding the intensive computation necessary in many existing methods.
infectious diseases; epidemics; genetic distance; transmission routes
Importance and Objective
Prior influenza infection is a risk factor for invasive meningococcal disease. Quantifying the fraction of meningococcal disease attributable to influenza could improve understanding of viral-bacterial interaction and indicate additional health benefits to influenza immunization.
Design, Setting and Participants
A time series analysis of the association of influenza and meningococcal disease using hospitalizations in 9 states from 1989–2009 included in the State Inpatient Databases from the Agency for Healthcare Research and Quality and the proportion of positive influenza tests by subtype reported to the Centers for Disease Control. The model accounts for the autocorrelation of meningococcal disease and influenza between weeks, temporal trends, co-circulating respiratory syncytial virus, and seasonality. The influenza-subtype-attributable fraction was estimated using the model coefficients. We analyzed the synchrony of seasonal peaks in hospitalizations for influenza, respiratory syncytial virus, and meningococcal disease.
Results and Conclusions
In 19 of 20 seasons, influenza peaked≤2 weeks before meningococcal disease, and peaks were highly correlated in time (ρ = 0.95; P <.001). H3N2 and H1N1 peaks were highly synchronized with meningococcal disease while pandemic H1N1, B, and respiratory syncytial virus were not. Over 20 years, 12.8% (95% CI, 9.1–15.0) of meningococcal disease can be attributable to influenza in the preceding weeks with H3N2 accounting for 5.2% (95% CI, 3.0–6.5), H1N1 4.3% (95% CI, 2.6–5.6), B 3.0% (95% CI, 0.8–4.9) and pH1N1 0.2% (95% CI, 0–0.4). During the height of influenza season, weekly attributable fractions reach 59%. While vaccination against meningococcal disease is the most important prevention strategy, influenza vaccination could provide further protection, particularly in young children where the meningococcal disease vaccine is not recommended or protective against the most common serogroup.
The Janus cassette permits marker-free allelic replacement or knockout in streptomycin-resistant Streptococcus pneumoniae (pneumococcus) through sequential positive and negative selection. Spontaneous revertants of Janus can lead to high level of false-positives during negative selection, which necessitate a time-consuming post-selection screening process. We hypothesized that an additional counter-selectable marker in Janus would decrease the revertant frequency and reduce false-positives, since simultaneous reversion of both counter-selectable makers is much less likely. Here we report a modified cassette, Sweet Janus (SJ), in which the sacB gene from Bacillus subtilis conferring sucrose sensitivity is added to Janus. By using streptomycin and sucrose simultaneously as selective agents, the frequency of SJ double revertants was about 105-fold lower than the frequency of Janus revertants. Accordingly, the frequency of false-positives in the SJ-mediated negative selection was about 100-fold lower than what was seen for Janus. Thus, SJ enhances negative selection stringency and can accelerate allelic replacement in pneumococcus, especially when transformation frequency is low due to strain background or suboptimal transformation conditions. Results also suggested the sacB gene alone can function as a counter-selectable marker in the Gram-positive pneumococcus, which will have the advantage of not requiring a streptomycin-resistant strain for allelic replacement.
Recently, we developed a seasonal influenza prediction system that uses an advanced data assimilation technique and real-time estimates of influenza incidence to optimize and initialize a population-based mathematical model of influenza transmission dynamics. This system was used to generate and evaluate retrospective forecasts of influenza peak timing in New York City. Here we present weekly forecasts of seasonal influenza developed and run in real time for 108 cites in the United States during the recent 2012–2013 season. Reliable ensemble forecasts of influenza outbreak peak timing with leads of up to 9 weeks were produced. Forecast accuracy increased as the season progressed, and the forecasts significantly outperformed alternate, analog prediction methods. By Week 52, prior to peak for the majority of cities, 63% of all ensemble forecasts were accurate. To our knowledge, this is the first time predictions of seasonal influenza have been made in real time and with demonstrated accuracy.
Streptococcus pneumoniae (pneumococcus) is a significant pathogen that frequently colonizes the human nasopharynx. Environmental factors, including antimicrobial use and host immunity, exert selection on members of the nasopharyngeal population, and the dynamics of selection are influenced by the effective population size of the selected population, about which little is known. We measured here the variance effective population size (Ne) of pneumococcus in a mouse colonization model by monitoring the frequency change of two cocolonizing, competitively neutral pneumococcal strains over time. The point estimate of Ne during nasal carriage in 16 BALB/c mice was 133 (95% confidence interval [CI] = 11 to 203). In contrast, the lower-bound census population exhibited a mean of 5768 (95% CI = 2,515 to 9,021). Therefore, pneumococcal Ne during nasal carriage is substantially smaller than the census population. The Ne during day 1 to day 4 of colonization was comparable to the Ne during day 4 to day 8. Similarly, a low Ne was also evident for the colonization of pneumococcus in BALB/c mice exposed to cholera toxin 4 weeks prior to challenge and in another mouse strain (DO11.10 RAG−/−). We developed a mathematical model of pneumococcal colonization composed of two subpopulations with differential contribution to future generations. By stochastic simulation, this model can reproduce the pattern of observed pneumococcal Ne and predicts that the selection coefficients may be difficult to measure in vivo. We hypothesized that such a small Ne may reduce the effectiveness of within host selection for pneumococcus.
Streptococcus pneumoniae (pneumococcus) frequently colonizes the human nasopharynx and is an important cause of pneumonia, meningitis, sinusitis, and otitis media. The outer cell surface of pneumococcus may assume various degrees of negative charge depending on the polysaccharide capsule, of which more than 90 serotypes have been identified. The negative charge of capsular polysaccharides has been proposed to electrostatically repel pneumococci from phagocytic cells, and avoidance of phagocytosis correlates with higher carriage prevalence. We hypothesized that the surface charge of pneumococcus contributes to its success in nasopharyngeal carriage by modulating resistance to phagocyte-mediated killing. Here, we measured the surface charge (zeta potential) of laboratory-constructed strains that share a genetic background but differ in serotype and of clinical strains that differ in serotype and genetic background. A more negative surface charge correlated with higher resistance to nonopsonic killing by human neutrophils in vitro. In addition, a more negative zeta potential was associated with higher carriage prevalence in human populations before and after the widespread use of the pneumococcal conjugate vaccine PCV7. We also confirmed that capsule is the major determinant of net surface charge in clinical isolates with diverse backgrounds. We noted that exceptions exist to the idea that a higher magnitude of negative charge predicts higher prevalence. The results indicated that zeta potential is strongly influenced by pneumococcal capsule type but is unlikely to be the only important mechanism by which capsule interacts with host.
Understanding HIV transmission dynamics is critical to estimating the potential population-wide impact of HIV prevention and treatment interventions. We developed an individual-based simulation model of the heterosexual HIV epidemic in South Africa and linked it to the previously published Cost-Effectiveness of Preventing AIDS Complications (CEPAC) International Model, which simulates the natural history and treatment of HIV. In this new model, the CEPAC Dynamic Model (CDM), the probability of HIV transmission per sexual encounter between short-term, long-term and commercial sex worker partners depends upon the HIV RNA and disease stage of the infected partner, condom use, and the circumcision status of the uninfected male partner. We included behavioral, demographic and biological values in the CDM and calibrated to HIV prevalence in South Africa pre-antiretroviral therapy. Using a multi-step fitting procedure based on Bayesian melding methodology, we performed 264,225 simulations of the HIV epidemic in South Africa and identified 3,750 parameter sets that created an epidemic and had behavioral characteristics representative of a South African population pre-ART. Of these parameter sets, 564 contributed 90% of the likelihood weight to the fit, and closely reproduced the UNAIDS HIV prevalence curve in South Africa from 1990–2002. The calibration was sensitive to changes in the rate of formation of short-duration partnerships and to the partnership acquisition rate among high-risk individuals, both of which impacted concurrency. Runs that closely fit to historical HIV prevalence reflect diverse ranges for individual parameter values and predict a wide range of possible steady-state prevalence in the absence of interventions, illustrating the value of the calibration procedure and utility of the model for evaluating interventions. This model, which includes detailed behavioral patterns and HIV natural history, closely fits HIV prevalence estimates.
Please see later in the article for the Editors' Summary
The prospect of using whole genome sequence data to investigate bacterial disease outbreaks has been keenly anticipated in many quarters, and the large-scale collection and sequencing of isolates from cases is becoming increasingly feasible. While sequence data can provide many important insights into disease spread and pathogen adaptation, it remains unclear how successfully they may be used to estimate individual routes of transmission. Several studies have attempted to reconstruct transmission routes using genomic data; however, these have typically relied upon restrictive assumptions, such as a shared topology of the phylogenetic tree and a lack of within-host diversity. In this study, we investigated the potential for bacterial genomic data to inform transmission network reconstruction. We used simulation models to investigate the origins, persistence and onward transmission of genetic diversity, and examined the impact of such diversity on our estimation of the epidemiological relationship between carriers. We used a flexible distance-based metric to provide a weighted transmission network, and used receiver-operating characteristic (ROC) curves and network entropy to assess the accuracy and uncertainty of the inferred structure. Our results suggest that sequencing a single isolate from each case is inadequate in the presence of within-host diversity, and is likely to result in misleading interpretations of transmission dynamics – under many plausible conditions, this may be little better than selecting transmission links at random. Sampling more frequently improves accuracy, but much uncertainty remains, even if all genotypes are observed. While it is possible to discriminate between clusters of carriers, individual transmission routes cannot be resolved by sequence data alone. Our study demonstrates that bacterial genomic distance data alone provide only limited information on person-to-person transmission dynamics.
With the advent of affordable large-scale genome sequencing for bacterial pathogens, there is much interest in using such data to identify who infected whom in a disease outbreak. Many methods exist to reconstruct the phylogeny of sampled bacteria, but the resulting tree does not necessarily share the same structure as the transmission tree linking infected persons. We explored the potential of sampled genomic data to inform the transmission tree, measuring the accuracy and precision of estimated networks based on simulated data. We demonstrated that failing to account for within-host diversity can lead to poor network reconstructions - even with repeated sampling of each carrier, there is still much uncertainty in the estimated structure. While it may be possible to identify clusters of potential sources, identifying individual transmission links is not possible using bacterial sequence data alone. This work highlights potential limitations of genomic data to investigate transmission dynamics, lending support to methods unifying all available data sources.
The emergence of Neisseria gonorrhoeae with decreased susceptibility to extended spectrum cephalosporins raises the prospect of untreatable gonorrhoea. In the absence of new treatments, efforts to slow the increasing incidence of resistant gonococcus require insight into the factors that contribute to its emergence and spread. We assessed the relatedness between isolates in the USA and reconstructed likely spread of lineages through different sexual networks.
We sequenced the genomes of 236 isolates of N gonorrhoeae collected by the Centers for Disease Control and Prevention's Gonococcal Isolate Surveillance Project (GISP) from sentinel public sexually transmitted disease clinics in the USA, including 118 (97%) of the isolates from 2009–10 in GISP with reduced susceptibility to cefixime (cefRS) and 118 cefixime-susceptible isolates from GISP matched as closely as possible by location, collection date, and sexual orientation. We assessed the association between antimicrobial resistance genotype and phenotype and correlated phylogenetic clustering with location and sexual orientation.
Mosaic penA XXXIV had a high positive predictive value for cefRS. We found that two of the 118 cefRS isolates lacked a mosaic penA allele, and rechecking showed that these two were susceptible to cefixime. Of the 116 remaining cefRS isolates, 114 (98%) fell into two distinct lineages that have independently acquired mosaic penA allele XXXIV. A major lineage of cefRS strains spread eastward, predominantly through a sexual network of men who have sex with men. Eight of nine inferred transitions between sexual networks were introductions from men who have sex with men into the heterosexual population.
Genomic methods might aid efforts to slow the spread of antibiotic-resistant N gonorrhoeae through augmentation of gonococcal outbreak surveillance and identification of populations that could benefit from increased screening for aymptomatic infections.
American Sexually Transmitted Disease Association, Wellcome Trust, National Institute of General Medical Sciences, and National Institute of Allergy and Infectious Diseases, National Institutes of Health.
A critical question in tuberculosis control is why some strains of Mycobacterium tuberculosis are preferentially associated with multiple drug resistances. We demonstrate that M. tuberculosis strains from Lineage 2 (East Asian lineage and Beijing sublineage) acquire drug resistances in vitro more rapidly than M. tuberculosis strains from Lineage 4 (Euro-American lineage) and that this higher rate can be attributed to a higher mutation rate. Moreover, the in vitro mutation rate correlates well with the bacterial mutation rate in humans as determined by whole genome sequencing of clinical isolates. Finally, using a stochastic mathematical model, we demonstrate that the observed differences in mutation rate predict a substantially higher probability that patients infected with a drug susceptible Lineage 2 strain will harbor multidrug resistant bacteria at the time of diagnosis. These data suggest that interventions to prevent the emergence of drug resistant tuberculosis should target bacterial as well as treatment-related risk factors.
Whole genome sequencing of 616 asymptomatically carried pneumococci was used to study the impact of the 7-valent pneumococcal conjugate vaccine. Comparison of closely related isolates revealed the role of transformation in facilitating capsule switching to non-vaccine serotypes and the emergence of drug resistance. However, such recombination was found to occur at significantly different rates across the species, and the evolution of the population was primarily driven by changes in the frequency of distinct genotypes extant pre-vaccine. These alterations resulted in little overall effect on accessory genome composition at the population level, contrasting with the fall in pneumococcal disease rates after the vaccine’s introduction.