Frequent Undetected Ward-Based Methicillin-Resistant Staphylococcus aureus Transmission Linked to Patient Sharing Between Hospitals.

Background: Recent evidence suggests that hospital transmission of methicillin-resistant Staphylococcus aureus (MRSA) is uncommon in UK centers that have implemented sustained infection control programs. We investigated whether a healthcare-network analysis could shed light on transmission paths currently sustaining MRSA levels in UK hospitals. Methods: A cross-sectional observational study was performed in 2 National Health Service hospital groups and a general district hospital in Southeast London. All MRSA patients identified at inpatient, outpatient, and community settings between 1 November 2011 and 29 February 2012 were included. We identified genetically defined MRSA transmission clusters in individual hospitals and across the healthcare network, and examined genetic differentiation of sequence type (ST) 22 MRSA isolates within and between hospitals and inpatient or outpatient and community settings, as informed by average and median pairwise single-nucleotide polymorphisms (SNPs) and SNP-based proportions of nearly identical isolates. Results: Two hundred forty-eight of 610 (40.7%) MRSA patients were linked in 90 transmission clusters, of which 27 spanned multiple hospitals. Analysis of a large 32 patient ST22-MRSA cluster showed that 26 of 32 patients (81.3%) had multiple contacts with one another during ward stays at any hospital. No residential, outpatient, or significant community healthcare contacts were identified. Genetic differentiation between ST22 MRSA inpatient isolates from different hospitals was less than between inpatient isolates from the same hospitals (P ≤ .01). Conclusions: There is evidence of frequent ward-based transmission of MRSA brought about by frequent patient admissions to multiple hospitals. Limiting in-ward transmission requires sharing of MRSA status data between hospitals

Evidence for Community Transmission of Community-Associated but Not Health-Care-Associated Methicillin-Resistant Staphylococcus Aureus Strains Linked to Social and Material Deprivation: Spatial Analysis of Cross-sectional Data

Supporting information for a paper titled "Evidence for Community Transmission of Community-Associated but Not Health-Care-Associated Methicillin-Resistant Staphylococcus Aureus Strains Linked to Social and Material Deprivation: Spatial Analysis of Cross-sectional Data". This includes: an MS Word document that describes the modelling approach (S1 Methods), Summary statistics for area-level variables in 513 LSOAs within catchment areas of the hospital cohort (S1 Table), data from the 2011 England and Wales census that outlines population structure of 513 LSOAs within catchment areas of the hospital cohort. (S2 Table), Individual patient-level metadata (S1 Text), and LSOA-level aggregated metadata (S2 Text

Clonal variation in high- and low-level phenotypic and genotypic mupirocin resistance of MRSA isolates in south-east London

OBJECTIVES: Both low-level mupirocin resistance (LMR) and high-level mupirocin resistance (HMR) have been identified. The aim of this study was to determine the epidemiology of LMR and HMR in MRSA isolates at five hospitals that have used mupirocin for targeted decolonization as part of successful institutional control programmes. METHODS: All MRSA identified in three microbiology laboratories serving five central and south-east London hospitals and surrounding communities between November 2011 and February 2012 were included. HMR and LMR were determined by disc diffusion testing. WGS was used to derive multilocus sequence types (MLSTs) and the presence of HMR and LMR resistance determinants. RESULTS: Prevalence of either HMR or LMR amongst first healthcare episode isolates from 795 identified patients was 9.69% (95% CI 7.72-11.96); LMR was 6.29% (95% CI 4.70-8.21) and HMR was 3.40% (95% CI 2.25-4.90). Mupirocin resistance was not significantly different in isolates identified from inpatients at each microbiology laboratory, but was more common in genotypically defined 'hospital' rather than 'community' isolates (OR 3.17, 95% CI 1.36-9.30, P = 0.002). LMR was associated with inpatient stay, previous history of MRSA and age ≥65 years; HMR was associated with age ≥65 years and residential postcode outside London. LMR and HMR varied by clone, with both being low in the dominant UK MRSA clone ST22 compared with ST8, ST36 and ST239/241 for LMR and with ST8 and ST36 for HMR. V588F mutation and mupA carriage had high specificity (>97%) and area under the curve (>83%) to discriminate phenotypic mupirocin resistance, but uncertainty around the sensitivity point estimate was large (95% CI 52.50%-94.44%). Mutations in or near the mupA gene were found in eight isolates that carried mupA but were not HMR. CONCLUSIONS: Mupirocin resistance was identified in <10% of patients and varied significantly by clone, implying that changes in clonal epidemiology may have an important role in determining the prevalence of resistance in conjunction with selection due to mupirocin use

Maps for RR of HA- and CA-MRSA in LSOAs compared to the whole catchment area in disease mapping (unadjusted) models.

<p>Disease mapping models do not take into consideration the distribution and effect of risk factors for MRSA. These account for the observed and expected counts of HA- or CA-MRSA given the standardised age and gender population structure in each LSOA. HA-MRSA (A) was modelled considering unstructured random effects only (iid model). CA-MRSA (B) was modelled considering both unstructured and structured (spatial) random effects (BYM model). Cut-off values in figure legends correspond to quantiles for area-specific RRs of HA- and CA-MRSA, respectively.</p

Maps for RR of HA- and CA-MRSA in LSOAs compared to the whole catchment area, in ecological regression models accounting for area-specific quintile-stratified percentage of usual residents attending a hospital and households deprived in 2–4 dimensions (HA-MRSA) or 1–2 dimensions (CA- MRSA).

<p>Ecological regression models account for the distribution and effect of risk factors for MRSA in addition to the observed and expected counts of HA- or CA-MRSA given the standardised age and gender population structure in each LSOA. HA-MRSA (A) was modelled considering unstructured random effects only (iid model). CA-MRSA (B) was modelled considering both unstructured and structured (spatial) random effects (BYM model). Cut-off values in figure legends correspond to quantiles for area-specific RRs of HA- and CA-MRSA, respectively.</p

Map of London boroughs showing catchment areas for the hospital cohort.

<p>Shown in red from left to right, catchment areas for the hospital cohort were south of the river Thames and included the Southwark, Lambeth, and Lewisham boroughs.</p