Additional file 1: of Variability in contact precautions to control the nosocomial spread of multi-drug resistant organisms in the endemic setting: a multinational cross-sectional survey

Table S1. Country of workplace of the 213 survey participants (number of respondents per country). Table S2. Survey respondents affiliations (n = 213). Table S3. MRSA contact precaution measures according to professional background. Table S4. GRE contact precaution measures according to professional background. Table S5. ESBL-E. coli contact precaution measures according to professional background. Table S6. ESBL-non-E. coli contact precaution measures according to professional background. Table S7. CR-E. coli contact precaution measures according to professional background. Table S8. CRE contact precaution measures according to professional background. Table S9. MRD P. aeruginosa contact precaution measures according to professional background. Table S10. MRD A. baumannii contact precaution measures according to professional background. Table S11. Indication and specification for contact precautions (CP) and isolation (cont. Next page) after deduplication*. Table S12. Other specific requirements for CP, results after deduplication*. Table S13. Characteristics of respondents that indicated “unknown” compared to respondents that provided any other answer. (DOCX 78 kb

The National One Week Prevalence Audit of Universal Meticillin-Resistant <i>Staphylococcus aureus</i> (MRSA) Admission Screening 2012

<div><p>Introduction</p><p>The English Department of Health introduced universal MRSA screening of admissions to English hospitals in 2010. It commissioned a national audit to review implementation, impact on patient management, admission prevalence and extra yield of MRSA identified compared to “high-risk” specialty or “checklist-activated” screening (CLAS) of patients with MRSA risk factors.</p> <p>Methods</p><p>National audit May 2011. Questionnaires to infection control teams in all English NHS acute trusts, requesting number patients admitted and screened, new or previously known MRSA; MRSA point prevalence; screening and isolation policies; individual risk factors and patient management for <i>all</i> new MRSA patients and random sample of negatives.</p> <p>Results</p><p>144/167 (86.2%) trusts responded. Individual patient data for 760 new MRSA patients and 951 negatives. 61% of emergency admissions (median 67.3%), 81% (median 59.4%) electives and 47% (median 41.4%) day-cases were screened. MRSA admission prevalence: 1% (median 0.9%) emergencies, 0.6% (median 0.4%) electives, 0.4% (median 0%) day-cases. Approximately 50% all MRSA identified was new. Inpatient MRSA point prevalence: 3.3% (median 2.9%). 104 (77%) trusts pre-emptively isolated patients with previous MRSA, 63 (35%) pre-emptively isolated admissions to “high-risk” specialties; 7 (5%) used PCR routinely. Mean time to MRSA positive result: 2.87 days (±1.33); 37% (219/596) newly identified MRSA patients discharged before result available; 55% remainder (205/376) isolated post-result. In an average trust, CLAS would reduce screening by 50%, identifying 81% of all MRSA. “High risk” specialty screening would reduce screening by 89%, identifying 9% of MRSA.</p> <p>Conclusions</p><p>Implementation of universal screening was poor. Admission prevalence (new cases) was low. CLAS reduced screening effort for minor decreases in identification, but implementation may prove difficult. Cost effectiveness of this and other policies, awaits evaluation by transmission dynamic economic modelling, using data from this audit. Until then trusts should seek to improve implementation of current policy and use of isolation facilities.</p> </div

Dependence between observed weekly number of CDI – AR1-model.

<p>A and B: autocorrelation function (ACF) of normalized residuals of the AR1-model fitted to data of all hospitals (<b>A</b>) and of teaching hospitals only (<b>B</b>) including a fitted cubic representation of the CDI trend over time and seasonality. As in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0099860#pone-0099860-g002" target="_blank">figure 2</a>, the blue lines correspond to the threshold for significance of correlation (dependence) (p<0.05) between lagged weekly observations up to week 20. Crossing of this threshold by the AR1-model residuals at lag 2 suggests the AR1 structure (symptomatic cases primarily cause acquisition of <i>C. difficile</i> among patients admitted to hospital up to one week later and, to a lesser extent, to cases admitted beyond this time), does not fully explain the dependence structure between weekly observations.</p

AR(1) model fit teaching hospitals.

<p>Grey dots represent the weekly-observed CDI cases within the teaching hospitals from April 2008 to March 2012. X-axis: Week 0 corresponds to the first week of April 2008 and week 209 to the last week of March 2012. Blue line: fit of the AR(1) model with a cubic representation of the rate of change of CDI over time and seasonality.</p

Description of CDI data from 46 selected hospitals.

<p>Summary statistics of CDI cases reported to the English mandatory surveillance system by a selection of 46 hospitals from the period of April 2008 to March 2012. IQR = Interquartile range; p.w. = per week.</p

Observed weekly number of CDI per hospital over the four-year study period.

<p>Grey dots represent the weekly-observed CDI cases within all hospitals from April 2008 to March 2012. X-axis: Week 0 corresponds to the first week of April 2008 and week 209 to the last week of March 2012. Red line: the incidence trend over time illustrated by cubic smoothing spline fit (for illustration).</p