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Lipsitch M.,Harvard University | Lipsitch M.,Center for Communicable Disease Dynamics | Tchetgen Tchetgen E.,Harvard University | Tchetgen Tchetgen E.,Center for Communicable Disease Dynamics | And 3 more authors.

Noncausal associations between exposures and outcomes are a threat to validity of causal inference in observational studies. Many techniques have been developed for study design and analysis to identify and eliminate such errors. Such problems are not expected to compromise experimental studies, where careful standardization of conditions (for laboratory work) and randomization (for population studies) should, if applied properly, eliminate most such noncausal associations. We argue, however, that a routine precaution taken in the design of biologic laboratory experiments-the use of "negative controls"-is designed to detect both suspected and unsuspected sources of spurious causal inference. In epidemiology, analogous negative controls help to identify and resolve confounding as well as other sources of error, including recall bias or analytic flaws. We distinguish 2 types of negative controls (exposure controls and outcome controls), describe examples of each type from the epidemiologic literature, and identify the conditions for the use of such negative controls to detect confounding. We conclude that negative controls should be more commonly employed in observational studies, and that additional work is needed to specify the conditions under which negative controls will be sensitive detectors of other sources of error in observational studies. © 2010 by Lippincott Williams & Wilkins. Source

Mills H.L.,University of Bristol | Cohen T.,Center for Communicable Disease Dynamics | Cohen T.,Brigham and Womens Hospital | Colijn C.,University of Bristol
Journal of the Royal Society Interface

Individuals living with HIV experience a much higher risk of progression from latent M. tuberculosis infection to active tuberculosis (TB) disease relative to individuals with intact immune systems. A several-month daily course of a single drug during latent infection (i.e. isoniazid preventive therapy (IPT)) has proved in clinical trials to substantially reduce an HIV-infected individual's risk of TB disease. As a result of these findings and ongoing studies, the World Health Organization has produced strong guidelines for implementing IPT on a community-wide scale for individuals with HIV at risk of TB disease. To date, there has been limited use of IPT at a community-wide level. In this paper, we present a new co-network model for HIV and TB co-epidemics to address questions about how the population-level impact of community-wide IPT may differ from the individual-level impact of IPT offered to selected individuals. In particular, we examine how the effect of clustering of contacts within high-TB incidence communities may affect the rates of re-infection with TB and how this clustering modifies the expected population-level effects of IPT. We find that populations with clustering of respiratory contacts experience aggregation of TB cases and high numbers of re-infection events. While, encouragingly, the overall population-level effects of community-wide IPT appear to be sustained regardless of network structure, we find that in populations where these contacts are highly clustered, there is dramatic heterogeneity in the impact of IPT: in some sub-regions of these populations, TB is nearly eliminated, while in others, repeated re-infection almost completely undermines the effect of IPT. Our findings imply that as IPT programmes are brought to scale, we should expect local heterogeneity of effectiveness as a result of the complex patterns of disease transmission within communities. © 2011 The Royal Society. Source

Shepheard M.A.,Imperial College London | Fleming V.M.,University of Bath | Connor T.R.,Wellcome Trust Sanger Institute | Corander J.,University of Helsinki | And 3 more authors.

Background:Staphylococcus aureus exhibits tropisms to many distinct animal hosts. While spillover events can occur wherever there is an interface between host species, changes in host tropism only occur with the establishment of sustained transmission in the new host species, leading to clonal expansion. Although the genomic variation underpinning adaptation in S. aureus genotypes infecting bovids and poultry has been well characterized the frequency of switches from one host to another remains obscure. We sought to identify sustained switches in host tropism in the S. aureus population, both anthroponotic and zoonotic, and their distribution over the species phylogeny.Methodologies/Results:We have used a sample of 3042 isolates, representing 696 distinct MLST genotypes, from a well-established database (www.mlst.net). Using an empirical parsimony approach (AdaptML) we have investigated the distribution of switches in host association between both human and non-human (henceforth referred to as animal) hosts. We reconstructed a credible description of past events in the form of a phylogenetic tree; the nodes and leaves of which are statistically associated with either human or animal habitats, estimated from extant host-association and the degree of sequence divergence between genotypes. We identified 15 likely historical switching events; 13 anthroponoses and two zoonoses. Importantly, we identified two human-associated clade candidates (CC25 and CC59) that have arisen from animal-associated ancestors; this demonstrates that a human-specific lineage can emerge from an animal host. We also highlight novel rabbit-associated genotypes arising from a human ancestor.Conclusions:S. aureus is an organism with the capacity to switch into and adapt to novel hosts, even after long periods of isolation in a single host species. Based on this evidence, animal-adapted S. aureus lineages exhibiting resistance to antibiotics must be considered a major threat to public health, as they can adapt to the human population. © 2013 Shepheard et al. Source

Meric G.,University of Swansea | Miragaia M.,New University of Lisbon | De Been M.,University Utrecht | Yahara K.,University of Swansea | And 20 more authors.
Genome Biology and Evolution

The opportunistic pathogens Staphylococcus aureus and Staphylococcus epidermidis represent major causes of severe nosocomial infection, and are associated with high levels of mortality and morbidity worldwide. These species are both common commensals on the human skin and in the nasal pharynx, but are genetically distinct, differing at 24% average nucleotide divergence in 1,478 core genes. To better understand the genome dynamics of these ecologically similar staphylococcal species, we carried out a comparative analysis of 324 S. aureus and S. epidermidis genomes, including 83 novel S. epidermidis sequences. A reference pan-genome approach and whole genome multilocus-sequence typing revealed that around half of the genome was shared between the species. Based on a BratNextGen analysis, homologous recombination was found to have impacted on 40% of the core genes in S. epidermidis, but on only 24% of the core genes in S. aureus. Homologous recombination between the species is rare, with a maximum of nine gene alleles shared between any two S. epidermidis and S. aureus isolates. In contrast, there was considerable interspecies admixture of mobile elements, in particular genes associated with the SaPIn1 pathogenicity island, metal detoxification, and the methicillin-resistance island SCCmec. Our data and analysis provide a context for considering the nature of recombinational boundaries between S. aureus and S. epidermidis and, the selective forces that influence realized recombination between these species. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. Source

Siren J.,University of Helsinki | Siren J.,Aalto University | Hanage W.P.,Center for Communicable Disease Dynamics | Corander J.,University of Helsinki | And 2 more authors.
Molecular Biology and Evolution

Reconstruction of the past is an important task of evolutionary biology. It takes place at different points in a hierarchy of molecular variation, including genes, individuals, populations, and species. Statistical inference about population histories has recently received considerable attention, following the development of computational tools to provide tractable approaches to this very challenging problem. Here, we introduce a likelihood-based approach which generalizes a recently developed model for random fluctuations in allele frequencies based on an approximation to the neutral Wright-Fisher diffusion. Our new framework approximates the infinite alleles Wright-Fisher model and uses an implementation with an adaptive Markov chain Monte Carlo algorithm. The method is especially well suited to data sets harboring large population samples and relatively few loci for which other likelihood-based models are currently computationally intractable. Using our model, we reconstruct the global population history of a major human pathogen, Streptococcus pneumoniae. The results illustrate the potential to reach important biological insights to an evolutionary process by a population genetics approach, which can appropriately accommodate very large population samples. © The Author 2012. Source

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