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Find video protocols related to scientific articles indexed in Pubmed.
A Data-Driven Mathematical Model of CA-MRSA Transmission among Age Groups: Evaluating the Effect of Control Interventions.
PLoS Comput. Biol.
PUBLISHED: 11-01-2013
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Community associated methicillin-resistant Staphylococcus aureus (CA-MRSA) has become a major cause of skin and soft tissue infections (SSTIs) in the US. We developed an age-structured compartmental model to study the spread of CA-MRSA at the population level and assess the effect of control intervention strategies. We used Monte-Carlo Markov Chain (MCMC) techniques to parameterize our model using monthly time series data on SSTIs incidence in children (?19 years) during January 2004 -December 2006 in Maricopa County, Arizona. Our model-based forecast for the period January 2007-December 2008 also provided a good fit to data. We also carried out an uncertainty and sensitivity analysis on the control reproduction number, [Formula: see text] which we estimated at 1.3 (95% CI [1.2,1.4]) based on the model fit to data. Using our calibrated model, we evaluated the effect of typical intervention strategies namely reducing the contact rate of infected individuals owing to awareness of infection and decolonization strategies targeting symptomatic infected individuals on both [Formula: see text] and the long-term disease dynamics. We also evaluated the impact of hypothetical decolonization strategies targeting asymptomatic colonized individuals. We found that strategies focused on infected individuals were not capable of achieving disease control when implemented alone or in combination. In contrast, our results suggest that decolonization strategies targeting the pediatric population colonized with CA-MRSA have the potential of achieving disease elimination.
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Evaluation of Immunization Data Completeness Within a Large Community Health Care System Exchanging Data With a State Immunization Information System.
J Public Health Manag Pract
PUBLISHED: 09-19-2013
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Information systems are used by most states to maintain registries of immunization data both for monitoring population-level adherence and for use in clinical practice and research. Direct data exchange between such systems and electronic health record systems presents an opportunity to improve the completeness and quality of information available.
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A population based study of seasonality of skin and soft tissue infections: implications for the spread of CA-MRSA.
PLoS ONE
PUBLISHED: 01-18-2013
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Methicillin resistant Staphylococcus aureus (MRSA) is currently a major cause of skin and soft tissue infections (SSTI) in the United States. Seasonal variation of MRSA infections in hospital settings has been widely observed. However, systematic time-series analysis of incidence data is desirable to understand the seasonality of community acquired (CA)-MRSA infections at the population level. In this paper, using data on monthly SSTI incidence in children aged 0-19 years and enrolled in Medicaid in Maricopa County, Arizona, from January 2005 to December 2008, we carried out time-series and nonlinear regression analysis to determine the periodicity, trend, and peak timing in SSTI incidence in children at different age: 0-4 years, 5-9 years, 10-14 years, and 15-19 years. We also assessed the temporal correlation between SSTI incidence and meteorological variables including average temperature and humidity. Our analysis revealed a strong annual seasonal pattern of SSTI incidence with peak occurring in early September. This pattern was consistent across age groups. Moreover, SSTIs followed a significantly increasing trend over the 4-year study period with annual incidence increasing from 3.36% to 5.55% in our pediatric population of approximately 290,000. We also found a significant correlation between the temporal variation in SSTI incidence and mean temperature and specific humidity. Our findings could have potential implications on prevention and control efforts against CA-MRSA.
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The development and validation of a simulation tool for health policy decision making.
J Biomed Inform
PUBLISHED: 03-29-2010
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Computer simulations have been used to model infectious diseases to examine the outcomes of alternative strategies for managing their spread. Methicillin resistant Staphylococcus aureus (MRSA) skin and soft tissue infections have become prominent in many communities and efforts are underway to reduce the spread of this organism both in hospitals and communities. Currently, there are few tools for policy makers to use to examine the outcome of various choices when making decisions about MRSA. Using the example of MRSA, we describe, in this paper, a rigorous approach for development and validation of a tool that simulates the spread of MRSA infections. We used sensitivity analyses in a novel way and validated the simulation results against local data over time. Our approach for simulation development and validation is generalizeable to simulations of other diseases.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.