Impact of Host Heterogeneity on the Efficacy of Interventions to Reduce Staphylococcus aureus Carriage

BACKGROUND Staphylococcus aureus is a common cause of bacterial infections worldwide. It is most commonly carried in and transmitted from the anterior nares. Hosts are known to vary in their proclivity for S. aureus nasal carriage and may be divided into persistent carriers, intermittent carriers, and noncarriers, depending on duration of carriage. Mathematical models of S. aureus to predict outcomes of interventions have, however, typically assumed that all individuals are equally susceptible to colonization. OBJECTIVE To characterize biases created by assuming a homogeneous host population in estimating efficacy of control interventions. DESIGN Mathematical model. METHODS We developed a model of S. aureus carriage in the healthcare setting under the homogeneous assumption as well as a heterogeneous model to account for the 3 types of S. aureus carriers. In both models, we calculated the equilibrium carriage prevalence to predict the impact of control measures (reducing contact and decolonization). RESULTS The homogeneous model almost always underestimates S. aureus transmissibility and overestimates the impact of intervention strategies in lowering carriage prevalence compared to the heterogeneous model. This finding is generally consistent regardless of changes in model setting that vary the proportions of various carriers in the population and the duration of carriage for these carrier types. CONCLUSIONS Not accounting for host heterogeneity leads to systematic and substantial biases in predictions of the effects of intervention strategies. Further understanding of the clinical impacts of heterogeneity through modeling can help to target control measures and allocate resources more efficiently

Antibiotics in agriculture and the risk to human health: how worried should we be?

The use of antibiotics in agriculture is routinely described as a major contributor to the clinical problem of resistant disease in human medicine. While a link is plausible, there are no data conclusively showing the magnitude of the threat emerging from agriculture. Here, we define the potential mechanisms by which agricultural antibiotic use could lead to human disease and use case studies to critically assess the potential risk from each. The three mechanisms considered are as follows 1: direct infection with resistant bacteria from an animal source, 2: breaches in the species barrier followed by sustained transmission in humans of resistant strains arising in livestock, and 3: transfer of resistance genes from agriculture into human pathogens. Of these, mechanism 1 is the most readily estimated, while significant is small in comparison with the overall burden of resistant disease. Several cases of mechanism 2 are known, and we discuss the likely livestock origins of resistant clones of Staphylococcus aureus and Enterococcus faecium, but while it is easy to show relatedness the direction of transmission is hard to assess in robust fashion. More difficult yet to study is the contribution of mechanism 3, which may be the most important of all

Missing Data in Marginal Structural Models: A Plasmode Simulation Study Comparing Multiple Imputation and Inverse Probability Weighting

BACKGROUND: The use of marginal structural models (MSMs) to adjust for time-varying confounding has increased in epidemiologic studies. However, in the setting of MSMs, recommendations for how best to handle missing data are contradictory. We present a plasmode simulation study to compare the validity and precision of MSMs estimates using complete case analysis (CC), multiple imputation (MI), and inverse probability weighting (IPW) in the presence of missing data on time-independent and time-varying confounders. MATERIALS AND METHODS: Simulations were based on a cohort substudy using data from the Osteoarthritis Initiative which estimated the marginal causal effect of intra-articular injection use on yearly changes in knee pain. We simulated 81 scenarios with parameter values varied on missing mechanisms (MCAR, MAR, and MNAR), percentages of missing (10%, 20%, and 30%), type of confounders (time-independent, time-varying, either or both), and analytical approaches (CC, IPW, and MI). The performance of CC, IPW, and MI methods was compared using relative bias, mean squared error of the estimates of interest, and empirical power. RESULTS: Across scenarios defined by missing data mechanism, extent of missing data, and confounder type, MI generally produced less biased estimates (range: 1.2%-6.7%) with better precision (range: 0.17-0.18) compared with IPW (relative bias: -5.3% to 8.0%; precision: 0.19-0.53). Empirical power was constant across the scenarios using MI. CONCLUSIONS: Under simple yet realistically constructed scenarios, MI seems to confer an advantage over IPW in MSMs applications

Stability of the pneumococcal population structure in Massachusetts as PCV13 was introduced

Background: 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. Methods: 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. Results: 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. Conclusions: 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. Electronic supplementary material The online version of this article (doi:10.1186/s12879-015-0797-z) contains supplementary material, which is available to authorized users

Media 1: Scaled diffraction calculation between tilted planes using nonuniform fast Fourier transform

Originally published in Optics Express on 14 July 2014 (oe-22-14-17331