Diversity of Methicillin-Resistant Staphylococcus aureus (MRSA) Strains Isolated from Inpatients of 30 Hospitals in Orange County, California

There is a need for a regional assessment of the frequency and diversity of MRSA to determine major circulating clones and the extent to which community and healthcare MRSA reservoirs have mixed. We conducted a prospective cohort study of inpatients in Orange County, California, systematically collecting clinical MRSA isolates from 30 hospitals, to assess MRSA diversity and distribution. All isolates were characterized by spa typing, with selective PFGE and MLST to relate spa types with major MRSA clones. We collected 2,246 MRSA isolates from hospital inpatients. This translated to 91/10,000 inpatients with MRSA and an Orange County population estimate of MRSA inpatient clinical cultures of 86/100,000 people. spa type genetic diversity was heterogeneous between hospitals, and relatively high overall (72%). USA300 (t008/ST8), USA100 (t002/ST5) and a previously reported USA100 variant (t242/ST5) were the dominant clones across all Orange County hospitals, representing 83% of isolates. Fifteen hospitals isolated more t008 (USA300) isolates than t002/242 (USA100) isolates, and 12 hospitals isolated more t242 isolates than t002 isolates. The majority of isolates were imported into hospitals. Community-based infection control strategies may still be helpful in stemming the influx of traditionally community-associated strains, particularly USA300, into the healthcare setting. © 2013 Hudson et al

Quantitative methods used to evaluate impact of health promotion interventions to prevent HIV infections : a methodological systematic review protocol

Background: Combination prevention is currently considered the best approach to combat HIV epidemic. It is based upon the combination of structural, behavioral, and biomedical interventions. Such interventions are frequently implemented in a health-promoting manner due to their aims, the approach that was adopted, and their complexity. The impact evaluation of these interventions often relies on methods inherited from the biomedical field. However, these methods have limitations and should be adapted to be relevant for these complex interventions. This systematic review aims to map the evidence-based methods used to quantify the impact of these interventions and analyze how these methods are implemented. Methods: Three databases (Web of Science, Scopus, PubMed) will be used to identify impact evaluation studies of health promotion interventions that aimed at reducing the incidence or prevalence of HIV infection. Only studies based on quantitative design assessing intervention impact on HIV prevalence or incidence will be included. Two reviewers will independently screen studies based on titles and abstracts and then on the full text. The information about study characteristics will be extracted to understand the context in which the interventions are implemented. The information specific to quantitative methods of impact evaluation will be extracted using items from the Mixed Methods Appraisal Tool (MMAT), the guidelines for reporting Statistical Analyses and Methods in the Published Literature (SAMPL), and the guidelines for Strengthening The Reporting of Empirical Simulation Studies (STRESS). This review will be conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) statement. Discussion: The impact evaluation of HIV prevention interventions is a matter of substantial importance given the growing need for evidence of the effectiveness of these interventions, whereas they are increasingly complex. These evaluations allow to identify the most effective strategies to be implemented to fight the epidemic. It is therefore relevant to map the methods to better implement them and adapt them according to the type of intervention to be evaluated

Modelling the transmission of healthcare associated infections: a systematic review.

BACKGROUND: Dynamic transmission models are increasingly being used to improve our understanding of the epidemiology of healthcare-associated infections (HCAI). However, there has been no recent comprehensive review of this emerging field. This paper summarises how mathematical models have informed the field of HCAI and how methods have developed over time. METHODS: MEDLINE, EMBASE, Scopus, CINAHL plus and Global Health databases were systematically searched for dynamic mathematical models of HCAI transmission and/or the dynamics of antimicrobial resistance in healthcare settings. RESULTS: In total, 96 papers met the eligibility criteria. The main research themes considered were evaluation of infection control effectiveness (64%), variability in transmission routes (7%), the impact of movement patterns between healthcare institutes (5%), the development of antimicrobial resistance (3%), and strain competitiveness or co-colonisation with different strains (3%). Methicillin-resistant Staphylococcus aureus was the most commonly modelled HCAI (34%), followed by vancomycin resistant enterococci (16%). Other common HCAIs, e.g. Clostridum difficile, were rarely investigated (3%). Very few models have been published on HCAI from low or middle-income countries.The first HCAI model has looked at antimicrobial resistance in hospital settings using compartmental deterministic approaches. Stochastic models (which include the role of chance in the transmission process) are becoming increasingly common. Model calibration (inference of unknown parameters by fitting models to data) and sensitivity analysis are comparatively uncommon, occurring in 35% and 36% of studies respectively, but their application is increasing. Only 5% of models compared their predictions to external data. CONCLUSIONS: Transmission models have been used to understand complex systems and to predict the impact of control policies. Methods have generally improved, with an increased use of stochastic models, and more advanced methods for formal model fitting and sensitivity analyses. Insights gained from these models could be broadened to a wider range of pathogens and settings. Improvements in the availability of data and statistical methods could enhance the predictive ability of models