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Background Up to 40% of hospitalized patients receive unnecessary or inappropriately broad antibiotics despite a low risk of multidrug-resistant organism (MDRO) infection. Empiric standard spectrum antibiotic use would reduce extended-spectrum (ES) antibiotic exposure and future resistance. We evaluated whether computerized prescriber order entry prompts providing patient-specific MDRO risk estimates could reduce ES antibiotic use compared to routine stewardship practices in patients hospitalized with urinary tract infection (UTI). Methods This 59-hospital cluster randomized trial compared: 1) INSPIRE prompts providing patient-specific MDRO UTI risk estimates at order entry and recommended standard spectrum antibiotics for risk < 10% versus 2) routine stewardship practices. Prompt used an absolute MDRO risk algorithm based on a 140 hospital data set. Trial population included adults treated with antibiotics for UTI in ED or non-ICU wards in first 3 days of admission (empiric days); prompt was triggered if ES antibiotics were ordered. Prescribers received feedback on prompt response. Trial periods: 18-month Baseline (Apr 2017–Sept 2018); 6-month Phase-in (Oct 2018–Mar 2019); 15-month Intervention (Apr 2019 – June 2020). Primary outcome was ES antibiotic days of therapy (ES-DOT) per empiric day; secondary outcomes were a) vancomycin and b) anti-pseudomonal DOT per empiric day. Unadjusted, as-randomized analyses used generalized linear mixed effects models to assess differences in ES-DOT rates between the intervention vs baseline period across arms (difference in differences), while clustering by patient and hospital. Results Results: We randomized 59 hospitals in 12 states, with 87,749 and 66,996 non-ICU UTI admissions in baseline and intervention periods, respectively. Intervention group had a a 21% reduction in ES-DOT compared to routine care. Vancomycin and anti-pseudomonal DOT were similarly reduced in the intervention group by 17% and 23%, respectively (Table). Conclusion Conclusion: INSPIRE order entry prompts providing real-time, patient-specific MDRO risk estimates with recommendation to use standard spectrum antibiotics in low risk patients significantly reduced empiric ES prescribing in adults admitted with UTI. Disclosures Shruti K. Gohil, MD, MPH, Medline (Other Financial or Material Support, Co-Investigator in studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnycke (Other Financial or Material Support, Co-Investigator in studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Stryker (Sage) (Other Financial or Material Support, Co-Investigator in studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products) Edward Septimus, MD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products) Ken Kleinman, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products) Lauren Heim, MPH, Medline (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Stryker (Sage) (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product)Xttrium (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product) Syma Rashid, MD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Stryker (Sage) (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product)Xttrium (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product) Taliser R. Avery, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Kenneth Sands, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Julia Moody, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Kimberly N. Smith, MBA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Brandon Carver, BA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Caren Spencer-Smith, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Russell Poland, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Jason Hickok, MBA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Arjun Srinivasan, MD, Nothing to disclose John A. Jernigan, MD, MS, Nothing to disclose Jonathan B. Perlin, MD, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Richard Platt, MD, MSc, Medline (Research Grant or Support, Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Susan S. Huang, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Stryker (Sage) (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Xttrium (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)

Background Up to 40% of hospitalized patients receive unnecessary or inappropriately broad antibiotics despite a low risk of multidrug-resistant organism (MDRO) infection. Empiric standard spectrum antibiotic use would reduce extended-spectrum (ES) antibiotic exposure and future resistance. We evaluated whether computerized prescriber order entry prompts providing patient-specific MDRO risk estimates could reduce ES antibiotic use compared to routine stewardship practices in patients hospitalized with pneumonia. Methods This 59 hospital cluster-randomized trial compared: 1) INSPIRE prompts providing patient-specific MDRO pneumonia risk estimates at order entry and recommended standard spectrum antibiotics for risk < 10% versus 2) routine stewardship practices. Prompt used an absolute MDRO risk algorithm based on a 140 hospital data set. Trial population included adults treated with antibiotics for pneumonia in ED or non-ICU wards in first 3 days of admission (empiric days); prompt was triggered if ES antibiotics were ordered. Prescribers received feedback on prompt response. Trial periods: 18-month Baseline (Apr 2017–Sept 2018); 6-month Phase-in (Oct 2018–Mar 2019); 15-month Intervention (Apr 2019 – June 2020). Primary outcome was ES antibiotic days of therapy (ES-DOT) per empiric day; secondary outcomes were a) vancomycin and b) anti-pseudomonal DOT per empiric day. Unadjusted, as-randomized analyses used generalized linear mixed effects models to assess differences in ES-DOT rates between the intervention vs baseline period across arms (difference in differences), while clustering by patient and hospital. Results We randomized 59 hospitals in 12 states, with 59,897 and 51,486 non-ICU pneumonia admissions in baseline and intervention periods, respectively. Intervention group had a 33% reduction in ES-DOT compared to routine care. Vancomycin and anti-pseudomonal DOT were similarly reduced in the intervention group by 27% and 33%, respectively (Table). Conclusion INSPIRE order entry prompts providing real-time, patient-specific MDRO risk estimates with recommendation to use standard spectrum antibiotics in low risk patients significantly reduced empiric ES prescribing in adults admitted with pneumonia. Disclosures Shruti K. Gohil, MD, MPH, Medline (Other Financial or Material Support, Co-Investigator in studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnycke (Other Financial or Material Support, Co-Investigator in studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Stryker (Sage) (Other Financial or Material Support, Co-Investigator in studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products) Edward Septimus, MD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products) Ken Kleinman, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products) Lauren Heim, MPH, Medline (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Stryker (Sage) (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product)Xttrium (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product) Syma Rashid, MD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Stryker (Sage) (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product)Xttrium (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product) Taliser R. Avery, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Kenneth Sands, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Julia Moody, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Micaela H. Coady, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Kimberly N. Smith, MBA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Brandon Carver, BA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Caren Spencer-Smith, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Russell Poland, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Jason Hickok, MBA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Arjun Srinivasan, MD, Nothing to disclose John A. Jernigan, MD, MS, Nothing to disclose Jonathan B. Perlin, MD, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Richard Platt, MD, MSc, Medline (Research Grant or Support, Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Susan S. Huang, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Stryker (Sage) (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Xttrium (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)

Background ICU universal decolonization with daily chlorhexidine (CHG) baths plus mupirocin nasal decolonization reduces all-cause bloodstream infections (BSI) and MRSA clinical cultures. We assessed nasal iodophor, an antiseptic less susceptible to resistance, in place of mupirocin. Methods We conducted a cluster randomized non-inferiority trial in ICUs, comparing universal decolonization with: 1) Mupirocin-CHG: daily CHG baths and 5 days of twice daily nasal mupirocin, to 2) Iodophor-CHG: same regimen, substituting twice daily 10% povidone-iodine for mupirocin. All adult ICUs in a hospital were assigned to the same strategy. We compared each hospital’s outcomes during the 18-month intervention (Nov 2017-Apr 2019) to its own baseline (May 2015-Apr 2017), during which all hospitals used mupirocin-CHG. The primary outcome was ICU-attributable S. aureus clinical isolates. Secondary outcomes included ICU-attributable MRSA clinical isolates and all-cause BSI. As randomized and as treated analyses used unadjusted proportional hazards models assessing differences in outcomes between baseline and intervention periods across the two groups, accounting for clustering by hospital and patient. Results We randomized 137 hospitals with 233 ICUs in 18 states. There were 442,544 admissions in the baseline period and 349,262 in the intervention period. Median ICU length of stay was 4 days. ICU types included mixed medical surgical (56%), medical (9%), surgical (11%), cardiac (15%), and neurologic (9%). CHG adherence was similar in both arms (85%), but adherence was greater for mupirocin (90%) than iodophor (82%). Primary as-randomized results (Table, Figure) exceeded the non-inferiority margin in favor of mupirocin, for S. aureus clinical cultures (21% superiority, P< 0.001) and for MRSA clinical cultures (20% superiority, P< 0.001). The regimens had similar BSI hazards. Analyses of fully adherent patients are in progress. Figure - Primary and Secondary Outcomes of Mupirocin Iodophor Swap Out Trial Conclusion Universal iodophor-CHG was equivalent to mupirocin-CHG for ICU BSI prevention. Mupirocin-CHG was superior to iodophor-CHG for S. aureus and MRSA clinical isolates, potentially due to greater adherence to mupirocin. Disclosures Susan S. Huang, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Stryker (Sage) (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Xttrium (Other Financial or Material Support, Conducted studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products) Edward Septimus, MD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products) Ken Kleinman, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic products) Lauren Heim, MPH, Medline (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic and cleaning products)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Stryker (Sage) (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product)Xttrium (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product) Julia Moody, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Taliser R. Avery, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Syma Rashid, MD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Stryker (Sage) (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product)Xttrium (Other Financial or Material Support, Conducted clinical trials and studies in which participating hospitals and nursing homes received contributed antiseptic product) Katherine Haffenreffer, BS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Lauren Shimelman, BA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Caren Spencer-Smith, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Selsebil Sljivo, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Ed Rosen, BS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Russell Poland, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Micaela H. Coady, MS, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Eunice J. Blanchard, MSN RN, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Kimberly Reddish, DNP, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Brandon Carver, BA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Kimberly N. Smith, MBA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Jason Hickok, MBA, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Karen Lolans, BS, Medline (Research Grant or Support) Nadia Khan, BS, Medline (Research Grant or Support) John A. Jernigan, MD, MS, Nothing to disclose Kenneth Sands, MD, MPH, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Jonathan B. Perlin, MD, PhD, Medline (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product) Richard Platt, MD, MSc, Medline (Research Grant or Support, Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)Molnlycke (Other Financial or Material Support, Conducted studies in which participating hospitals received contributed antiseptic product)

BACKGROUND:Universal skin and nasal decolonisation reduces multidrug-resistant pathogens and bloodstream infections in intensive care units. The effect of universal decolonisation on pathogens and infections in non-critical-care units is unknown. The aim of the ABATE Infection trial was to evaluate the use of chlorhexidine bathing in non-critical-care units, with an intervention similar to one that was found to reduce multidrug-resistant organisms and bacteraemia in intensive care units. METHODS:The ABATE Infection (active bathing to eliminate infection) trial was a cluster-randomised trial of 53 hospitals comparing routine bathing to decolonisation with universal chlorhexidine and targeted nasal mupirocin in non-critical-care units. The trial was done in hospitals affiliated with HCA Healthcare and consisted of a 12-month baseline period from March 1, 2013, to Feb 28, 2014, a 2-month phase-in period from April 1, 2014, to May 31, 2014, and a 21-month intervention period from June 1, 2014, to Feb 29, 2016. Hospitals were randomised and their participating non-critical-care units assigned to either routine care or daily chlorhexidine bathing for all patients plus mupirocin for known methicillin-resistant Staphylococcus aureus (MRSA) carriers. The primary outcome was MRSA or vancomycin-resistant enterococcus clinical cultures attributed to participating units, measured in the unadjusted, intention-to-treat population as the HR for the intervention period versus the baseline period in the decolonisation group versus the HR in the routine care group. Proportional hazards models assessed differences in outcome reductions across groups, accounting for clustering within hospitals. This trial is registered with ClinicalTrials.gov, number NCT02063867. FINDINGS:There were 189 081 patients in the baseline period and 339 902 patients (156 889 patients in the routine care group and 183 013 patients in the decolonisation group) in the intervention period across 194 non-critical-care units in 53 hospitals. For the primary outcome of unit-attributable MRSA-positive or VRE-positive clinical cultures (figure 2), the HR for the intervention period versus the baseline period was 0·79 (0·73-0·87) in the decolonisation group versus 0·87 (95% CI 0·79-0·95) in the routine care group. No difference was seen in the relative HRs (p=0·17). There were 25 (

Background Universal decolonization with daily chlorhexidine (CHG) bathing with and without nasal decolonization has significantly reduced positive MRSA clinical cultures and bloodstream infections in adult ICUs in several clinical trials. We evaluated whether decolonization was similarly effective in a lower risk hospitalized population. Methods We conducted a 2 arm cluster-randomized trial involving a 1-year baseline period (April 2013–March 2014) and a 21-month intervention period (June 2014–February 2016). All noncritical care units in a hospital were assigned to the same strategy. These were (1) Routine Care: routine bathing product and frequency and (2) Decolonization: CHG for routine daily bathing (2% leave-on CHG) or showering (4% rinse-off CHG) for all patients plus mupirocin for 5 days for known MRSA. Universal ICU decolonization was in place in both arms by September 2013. Differences between the arms in the outcome rates between the baseline and intervention periods were assessed with proportional hazards models, using shared frailties to account for clustering by hospital. The primary analysis was as-randomized and unadjusted. Primary outcome was any MRSA or VRE clinical isolate attributable to the unit. Secondary outcome was all-cause bloodstream infections. Additional analyses adjusted for age, gender, race, Medicaid insurer, surgery, and comorbidities. Results We randomized 53 hospitals in 15 states. There were 194 adult units with 189,616 admissions in the baseline period and 340,350 in the intervention period. Common unit types included mixed medical surgical (30%), cardiac (20%), step-down (11%), medical (10%), surgical (10%), and oncology (4%). There were no significant differences between arms in the relative hazards for intervention vs. baseline for either outcome (Table and Figure). Adjusted analyses yielded similar results. Conclusion Universal daily CHG bathing or showering plus targeted mupirocin for MRSA+ patients in non-critical care units did not reduce the combination of positive MRSA and VRE clinical cultures or bloodstream infections due to all pathogens. Further analyses to assess for any differential effects in high-risk subpopulations will be important. Disclosures S. S. Huang, Sage Products: Receipt of contributed product, Conducting studies in which participating healthcare facilities are receiving contributed product (no contribution in submitted abstract), Participating healthcare facilities in my studies received contributed product; Xttrium Laboratories: Receipt of contributed product, Conducting studies in which participating healthcare facilities are receiving contributed product (no contribution in submitted abstract), Participating healthcare facilities in my studies received contributed product; Clorox: Receipt of contributed product, Conducting studies in which participating healthcare facilities are receiving contributed product (no contribution in submitted abstract), Participating healthcare facilities in my studies received contributed product; 3M: Receipt of contributed product, Conducting studies in which participating healthcare facilities are receiving contributed product (no contribution in submitted abstract), Participating healthcare facilities in my studies received contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; E. Septimus, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; K. Kleinman, Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; J. Moody, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; J. Hickok, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; L. Heim, Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; A. Gombosev, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; 3M: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; T. Avery, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; received research funds from Clorox, but Clorox has no role in the design K. Haffenreffer, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; receive research funds from Clorox, but Clorox has no role in the design; L. Shimelman, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; receive research funds from Clorox, but Clorox has no role in the design; M. K. Hayden, OpGen, Inc.: Receipt of donated laboratory services for project, Research support; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; R. A. Weinstein, Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; OpGen Inc.: Receipt of donated laboratory services for project, Research support; C. Spencer-Smith, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; R. E. Kaganov, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; M. V. Murphy, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; T. Forehand, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; J. Lankiewicz, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; M. H. Coady, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; received research funds from Clorox, but Clorox has no role in the design.; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; L. M. Portillo, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; J. Patel Sarup, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; J. Perlin, Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Clorox: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; R. Platt, Clorox: Receipt of contributed product, Conducting clinical studies in which participating healthcare facilities are receiving contributed product; receive research funds from Clorox, but Clorox has no role in the design; Molnlycke: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Sage Products: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product; Xttrium: Receipt of contributed product, Conducting studies in healthcare facilities that are receiving contributed product

Background: Universal skin and nasal decolonization reduces multidrug-resistant pathogens and bloodstream infections in intensive care units. The effect of universal decolonization on pathogens and infections in non-critical care units is unknown. Methods: A two-arm cluster-randomized trial was conducted in 53 hospitals in the Hospital Corporation of America Healthcare system. Hospitals were randomized and their participating non-critical care units assigned to either routine care or daily chlorhexidine bathing for all patients plus mupirocin for known methicillin-resistant Staphylococcus aureus (MRSA) carriers. The trial involved a 12-month baseline (Mar 2013-Feb 2014) and 21-month intervention (Jun 2014-Feb 2016). Primary outcome was MRSA or vancomycin-resistant enterococcus (VRE) clinical cultures attributed to participating units, and secondary outcome was all-pathogen bloodstream infection. Proportional hazards models assessed differences in outcome reductions across arms, accounting for clustering by hospital. Post-hoc subgroup analyses were performed. Findings: There were 189,081 patients in the baseline period and 339,902 patients in the intervention period across 194 non-critical care units in 53 hospitals. Hazard ratios for MRSA/VRE clinical isolates during intervention vs. baseline periods were 0·87 (CI:0·79, 0·95) for routine care, and 0·79 (CI:0·73, 0·87) for decolonization (p=0·17), and 0·96 (CI:0·85, 1·08) and 0·90 (0·81, 1·01) for all-pathogen bloodstream infection, respectively (p=0·44). Among patients with central venous catheters, midline catheters, or lumbar drains, the hazard ratio for MRSA/VRE clinical cultures was 1·17 (CI:1·00, 1·37) for routine care and 0·80 (CI:0·69, 0·92) for decolonization, (p=0·0004), and the hazard ratio for all-pathogen bloodstream infections was 1·13 (CI:0·96, 1·33) for routine care and 0·82 (CI:0·71, 0·94) for decolonization, p=0·004. Interpretation: Decolonization with universal chlorhexidine bathing and targeted mupirocin for MRSA carriers did not reduce multidrug-resistant organisms or all-pathogen bloodstream infection in all non-critical care patients, but patients with invasive devices experienced 37% reduction in MRSA/VRE and 31% reduction in bloodstream infections. Clinical Trial Registration Number: clinicaltrials.gov Identifier: NCT0206386 Funding Statement: This project was funded by the National Institutes of Health Common Fund and administered by the National Institute of Allergy and Infectious Diseases (UH2/UH3 AT007769‐01 (Huang)). Declaration of Interest: Sage Products and Molnlycke contributed antiseptic chlorhexidine product to this trial. Investigators are also conducting other studies in which contributed antiseptic product is provided to participating hospitals and nursing homes from Stryker (Sage Products) (KK, LH, MHC, MKH, RAW, SSH), 3M (LH, SSH), Clorox (CS, ES, JH, JM, JP, KH, KK, LH, LS, MHC, MKH, RAW, RP, SSH, TRA), Xttrium (LH, SSH), and Medline (CS, ES, JH, JM, JP, KH, KK, LH, LS, MHC, MKH, RAW, RP, SSH, TRA). Investigator-initiated grant funds were received from Clorox (MKH, MHC, LS, KH, RP). Companies contributing product or providing grant funds have no role in the design, conduct, analysis, or publication of the ABATE Infection Trial or other studies conducted by these investigators. All other authors have no disclosures. Ethics Approval Statement: Central IRB approval was obtained from Harvard Pilgrim Health Care with a waiver of informed consent. All participating hospitals formally ceded IRB oversight to the HPHC IRB, except for the designated medical center IRB (Chippenham & Johnston Willis Hospitals) that provided prisoner oversight for the trial.

Background Hospital-specific surgical site infection (SSI) performance following colon surgery and abdominal hysterectomies can impact hospitals' relative rankings around quality metrics used to determine financial penalties. Current SSI surveillance largely focuses on SSI detected at the operative hospital. Methods We performed a retrospective cohort study to assess the impact on hospitals' relative SSI performance rankings when SSI detected at nonoperative hospitals are included. We used data from a California statewide hospital registry to assess for evidence of SSI following colon surgery or abdominal hysterectomies performed 1 March 2011 through 30 November 2013 using previously validated claims-based SSI surveillance methods. Risk-adjusted hospital-specific rankings based on SSI detected at operative hospitals versus any California hospital were generated. Results Among 60059 colon surgeries at 285 hospitals and 64918 abdominal hysterectomies at 270 hospitals, 5921 (9.9%) colon surgeries and 1481 (2.3%) abdominal hysterectomies received a diagnosis code for SSI within the 30 days following surgery. Operative hospital surveillance alone would have missed 7.2% of colon surgery and 13.4% of abdominal hysterectomy SSIs. The proportion of an individual hospital's SSIs detected during hospitalizations at other hospitals varied widely. Including nonoperative hospital SSIs resulted in improved relative ranking of 11 (3.9%) colon surgery and 13 (4.8%) hysterectomy hospitals so that they were no longer in the worst performing quartile, mainly among hospitals with relatively high surgical volumes. Conclusions Standard SSI surveillance that mainly focuses on infections detected at the operative hospital causes varying degrees of SSI underestimation, leading to inaccurate assignment or avoidance of financial penalties for approximately 1 in 11-16 hospitals.

Objective.To estimate and compare the impact on healthcare costs of 3 alternative strategies for reducing bloodstream infections in the intensive care unit (ICU): methicillin-resistant Staphylococcus aureus (MRSA) nares screening and isolation, targeted decolonization (ie, screening, isolation, and decolonization of MRSA carriers or infections), and universal decolonization (ie, no screening and decolonization of all ICU patients).Design.Cost analysis using decision modeling.Methods.We developed a decision-analysis model to estimate the health care costs of targeted decolonization and universal decolonization strategies compared with a strategy of MRSA nares screening and isolation. Effectiveness estimates were derived from a recent randomized trial of the 3 strategies, and cost estimates were derived from the literature.Results.In the base case, universal decolonization was the dominant strategy and was estimated to have both lower intervention costs and lower total ICU costs than either screening and isolation or targeted decolonization. Compared with screening and isolation, universal decolonization was estimated to save $171,000 and prevent 9 additional bloodstream infections for every 1,000 ICU admissions. The dominance of universal decolonization persisted under a wide range of cost and effectiveness assumptions.Conclusions.A strategy of universal decolonization for patients admitted to the ICU would both reduce bloodstream infections and likely reduce healthcare costs compared with strategies of MRSA nares screening and isolation or screening and isolation coupled with targeted decolonization.

Author(s): Yokoe, Deborah S; Avery, Taliser R; Platt, Richard; Huang, Susan S | Abstract: BackgroundPublic reporting of surgical site infections (SSIs) by hospitals is largely limited to infections detected during surgical hospitalizations or readmissions to the same facility. SSI rates may be underestimated if patients with SSIs are readmitted to other hospitals. We assessed the impact of readmissions to other facilities on hospitals' SSI rates following primary total hip arthroplasty (THA) or total knee arthroplasty (TKA).MethodsThis was a retrospective cohort study of all patients who underwent primary THA or TKA at California hospitals between 1 January 2006 and 31 December 2009. SSIs were identified using ICD-9-CM diagnosis codes predictive of SSI assigned at any California hospital within 365 days of surgery using a statewide repository of hospital data that allowed tracking of patients between facilities. We used statewide data to estimate the fraction of each hospital's THA and TKA SSIs identified at the operative hospital versus other hospitals.ResultsA total of 91 121 THA and 121 640 TKA procedures were identified. Based on diagnosis codes, SSIs developed following 2214 (2.3%) THAs and 2465 (2.0%) TKAs. Seventeen percent of SSIs would have been missed by operative hospital surveillance alone. The proportion of hospitals' SSIs detected at nonoperative hospitals ranged from 0% to 100%. Including SSIs detected at nonoperative hospitals resulted in better relative ranking for 61% of THA hospitals and 61% of TKA hospitals.ConclusionsLimiting SSI surveillance to the operative hospital caused varying degrees of SSI underestimation and substantially impacted hospitals' relative rankings, suggesting that alternative methods for comprehensive postdischarge surveillance are needed for accurate benchmarking.

Background Because patients can remain colonized with vancomycin-resistant enterococci (VRE) for long periods of time, VRE may spread from one health care facility to another. Methods Using the Regional Healthcare Ecosystem Analyst, an agent-based model of patient flow among all Orange County, California, hospitals and communities, we quantified the degree and speed at which changes in VRE colonization prevalence in a hospital may affect prevalence in other Orange County hospitals. Results A sustained 10% increase in VRE colonization prevalence in any 1 hospital caused a 2.8% (none to 62%) average relative increase in VRE prevalence in all other hospitals. Effects took from 1.5 to >10 years to fully manifest. Larger hospitals tended to have greater affect on other hospitals. Conclusions When monitoring and controlling VRE, decision makers may want to account for regional effects. Knowing a hospital's connections with other health care facilities via patient sharing can help determine which hospitals to include in a surveillance or control program.

Both targeted decolonization and universal decolonization of patients in intensive care units (ICUs) are candidate strategies to prevent health care-associated infections, particularly those caused by methicillin-resistant Staphylococcus aureus (MRSA).We conducted a pragmatic, cluster-randomized trial. Hospitals were randomly assigned to one of three strategies, with all adult ICUs in a given hospital assigned to the same strategy. Group 1 implemented MRSA screening and isolation; group 2, targeted decolonization (i.e., screening, isolation, and decolonization of MRSA carriers); and group 3, universal decolonization (i.e., no screening, and decolonization of all patients). Proportional-hazards models were used to assess differences in infection reductions across the study groups, with clustering according to hospital.A total of 43 hospitals (including 74 ICUs and 74,256 patients during the intervention period) underwent randomization. In the intervention period versus the baseline period, modeled hazard ratios for MRSA clinical isolates were 0.92 for screening and isolation (crude rate, 3.2 vs. 3.4 isolates per 1000 days), 0.75 for targeted decolonization (3.2 vs. 4.3 isolates per 1000 days), and 0.63 for universal decolonization (2.1 vs. 3.4 isolates per 1000 days) (P=0.01 for test of all groups being equal). In the intervention versus baseline periods, hazard ratios for bloodstream infection with any pathogen in the three groups were 0.99 (crude rate, 4.1 vs. 4.2 infections per 1000 days), 0.78 (3.7 vs. 4.8 infections per 1000 days), and 0.56 (3.6 vs. 6.1 infections per 1000 days), respectively (P0.001 for test of all groups being equal). Universal decolonization resulted in a significantly greater reduction in the rate of all bloodstream infections than either targeted decolonization or screening and isolation. One bloodstream infection was prevented per 54 patients who underwent decolonization. The reductions in rates of MRSA bloodstream infection were similar to those of all bloodstream infections, but the difference was not significant. Adverse events, which occurred in 7 patients, were mild and related to chlorhexidine.In routine ICU practice, universal decolonization was more effective than targeted decolonization or screening and isolation in reducing rates of MRSA clinical isolates and bloodstream infection from any pathogen. (Funded by the Agency for Healthcare Research and the Centers for Disease Control and Prevention; REDUCE MRSA ClinicalTrials.gov number, NCT00980980).

Purpose In post-marketing drug safety surveillance, data mining can potentially detect rare but serious adverse events. Assessing an entire collection of drug–event pairs is traditionally performed on a predefined level of granularity. It is unknown a priori whether a drug causes a very specific or a set of related adverse events, such as mitral valve disorders, all valve disorders, or different types of heart disease. This methodological paper evaluates the tree-based scan statistic data mining method to enhance drug safety surveillance. Methods We use a three-million-member electronic health records database from the HMO Research Network. Using the tree-based scan statistic, we assess the safety of selected antifungal and diabetes drugs, simultaneously evaluating overlapping diagnosis groups at different granularity levels, adjusting for multiple testing. Expected and observed adverse event counts were adjusted for age, sex, and health plan, producing a log likelihood ratio test statistic. Results Out of 732 evaluated disease groupings, 24 were statistically significant, divided among 10 non-overlapping disease categories. Five of the 10 signals are known adverse effects, four are likely due to confounding by indication, while one may warrant further investigation. Conclusion The tree-based scan statistic can be successfully applied as a data mining tool in drug safety surveillance using observational data. The total number of statistical signals was modest and does not imply a causal relationship. Rather, data mining results should be used to generate candidate drug–event pairs for rigorous epidemiological studies to evaluate the individual and comparative safety profiles of drugs. Copyright © 2013 John Wiley & Sons, Ltd. key words—pharmacovigilance; drug safety surveillance; adverse events; data mining; scan statistics; clusters; pharmacoepidemiology

Purpose This study describes practical considerations for implementation of near real-time medical product safety surveillance in a distributed health data network. Methods We conducted pilot active safety surveillance comparing generic divalproex sodium to historical branded product at four health plans from April to October 2009. Outcomes reported are all-cause emergency room visits and fractures. One retrospective data extract was completed (January 2002–June 2008), followed by seven prospective monthly extracts (January 2008–November 2009). To evaluate delays in claims processing, we used three analytic approaches: near real-time sequential analysis, sequential analysis with 1.5 month delay, and nonsequential (using final retrospective data). Sequential analyses used the maximized sequential probability ratio test. Procedural and logistical barriers to active surveillance were documented. Results We identified 6586 new users of generic divalproex sodium and 43 960 new users of the branded product. Quality control methods identified 16 extract errors, which were corrected. Near real-time extracts captured 87.5% of emergency room visits and 50.0% of fractures, which improved to 98.3% and 68.7% respectively with 1.5 month delay. We did not identify signals for either outcome regardless of extract timeframe, and slight differences in the test statistic and relative risk estimates were found. Conclusions Near real-time sequential safety surveillance is feasible, but several barriers warrant attention. Data quality review of each data extract was necessary. Although signal detection was not affected by delay in analysis, when using a historical control group differential accrual between exposure and outcomes may theoretically bias near real-time risk estimates towards the null, causing failure to detect a signal. Copyright © 2013 John Wiley & Sons, Ltd.

Background: A range of strategies and approaches have been developed for preventing health care-associated infections. Understanding the variation in practices among facilities is necessary to improve compliance with existing programs and aid the implementation of new interventions. Methods: In 2009, HCA Inc administered an electronic survey to measure compliance with evidence-based infection prevention practices as well as identify variation in products or methods, such as use of special approach technology for central vascular catheters and ventilator care. Responding adult intensive care units (ICUs) were those considering participation in a clinical trial to reduce health care-associated infections. Results: Responses from 99 ICUs in 55 hospitals indicated that many evidenced-based practices were used consistently, including methicillin-resistant Staphylococcus aureus (MRSA) screening and use of contact precautions for MRSA-positive patients. Other practices exhibited wide variability including discontinuation of precautions and use of antimicrobial technology or chlorhexidine patches for central vascular catheters. MRSA decolonization was not a predominant practice in ICUs. Conclusion: In this large, community-based health care system, there was substantial variation in the products and methods to reduce health care-associated infections. Despite system-wide emphasis on basic practices as a precursor to adding special approach technologies, this survey showed that these technologies were commonplace, including in facilities where improvement in basic practices was needed. Copyright © 2013 by the Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Whether targeted or universal decolonization strategies for the control of methicillin-resistant Staphylococcus aureus (MRSA) select for resistance to decolonizing agents is unresolved. The REDUCE-MRSA trial (ClinicalTrials registration no. NCT00980980) provided an opportunity to investigate this question. REDUCE-MRSA was a 3-arm, cluster-randomized trial of either screening and isolation without decolonization, targeted decolonization with chlorhexidine and mupirocin, or universal decolonization without screening to prevent MRSA infection in intensive-care unit (ICU) patients. Isolates from the baseline and intervention periods were collected and tested for susceptibility to chlorhexidine gluconate (CHG) by microtiter dilution; mupirocin susceptibility was tested by Etest. The presence of the qacA or qacB gene was determined by PCR and DNA sequence analysis. A total of 3,173 isolates were analyzed; 2 were nonsusceptible to CHG (MICs, 8 μg/ml), and 5/814 (0.6%) carried qacA or qacB . At baseline, 7.1% of MRSA isolates expressed low-level mupirocin resistance, and 7.5% expressed high-level mupirocin resistance. In a mixed-effects generalized logistic regression model, the odds of mupirocin resistance among clinical MRSA isolates or MRSA isolates acquired in an ICU in intervention versus baseline periods did not differ across arms, although estimates were imprecise due to small numbers. Reduced susceptibility to chlorhexidine and carriage of qacA or qacB were rare among MRSA isolates in the REDUCE-MRSA trial. The odds of mupirocin resistance were no different in the intervention versus baseline periods across arms, but the confidence limits were broad, and the results should be interpreted with caution.

Background.Since hospitals in a region often share patients, an outbreak of methicillin-resistant Staphylococcus aureus (MRSA) infection in one hospital could affect other hospitals.Methods.Using extensive data collected from Orange County (OC), California, we developed a detailed agent-based model to represent patient movement among all OC hospitals. Experiments simulated MRSA outbreaks in various wards, institutions, and regions. Sensitivity analysis varied lengths of stay, intraward transmission coefficients (β), MRSA loss rate, probability of patient transfer or readmission, and time to readmission.Results.Each simulated outbreak eventually affected all of the hospitals in the network, with effects depending on the outbreak size and location. Increasing MRSA prevalence at a single hospital (from 5% to 15%) resulted in a 2.9% average increase in relative prevalence at all other hospitals (ranging from no effect to 46.4%). Single-hospital intensive care unit outbreaks (modeled increase from 5% to 15%) caused a 1.4% average relative increase in all other OC hospitals (ranging from no effect to 12.7%).Conclusion.MRSA outbreaks may rarely be confined to a single hospital but instead may affect all of the hospitals in a region. This suggests that prevention and control strategies and policies should account for the interconnectedness of health care facilities.

Summary Background Urinary tract infections (UTIs) are common health-care-associated infections. Bacteriuria commonly precedes UTI and is often treated with antibiotics, particularly in hospital intensive care units (ICUs). In 2013, a cluster-randomised trial (REDUCE MRSA Trial [Randomized Evaluation of Decolonization vs Universal Clearance to Eradicate MRSA]) showed that body surface decolonisation reduced all-pathogen bloodstream infections. We aim to further assess the effect of decolonisation on bacteriuria and candiduria in patients admitted to ICUs. Methods We did a secondary analysis of a three-group, cluster-randomised trial of 43 hospitals (clusters) with patients in 74 adult ICUs. The three groups included were either meticillin-resistant Staphylococcus aureus (MRSA) screening and isolation, targeted decolonisation (screening, isolation, and decolonisation of MRSA carriers) with chlorhexidine and mupirocin, and universal decolonisation (no screening, all patients decolonised) with chlorhexidine and mupirocin. Protocol included chlorhexidine cleansing of the perineum and proximal 6 inches (15·24 cm) of urinary catheters. ICUs within the same hospital were assigned the same strategy. Outcomes included high-level bacteriuria (≥50 000 colony forming units [CFU]/mL) with any uropathogen, high-level candiduria (≥50 000 CFU/mL), and any bacteriuria with uropathogens. Sex-specific analyses were specified a priori. Proportional hazards models assessed differences in outcome reductions across groups, comparing an 18-month intervention period to a 12-month baseline period. Findings 122 646 patients (48 390 baseline, 74 256 intervention) were enrolled. Intervention versus baseline hazard ratios (HRs) for high-level bacteriuria were 1·02 (95% CI 0·88–1·18) for screening or isolation, 0·88 (0·76–1·02) for targeted decolonisation, and 0·87 (0·77–1·00) for universal decolonisation (no difference between groups, p=0·26), with no sex-specific reductions (HRs for men: 1·09 [95% CI 0·85–1·40] for screening or isolation, 1·01 [0·79–1·29] for targeted decolonisation, and 0·78 [0·63–0·98] for universal decolonisation, p=0·12; HRs for women: 0·97 [0·80–1·17] for screening and isolation, 0·83 [0·70–1·00] for targeted decolonisation, and 0·93 [0·79–1·09] for universal decolonisation, p=0·49). HRs for high-level candiduria were 1·14 (0·95–1·37) for screening and isolation, 0·99 (0·83–1·18) for targeted decolonisation, and 0·83 (0·70–0·99) for universal decolonisation (p=0·05). Differences between sexes were due to reductions in men in the universal decolonisation group (HRs: 1·21 [95% CI 0·88–1·68] for screening or isolation, 1·01 [0·73–1·39] for targeted decolonisation, and 0·63 [0·45–0·89] for universal decolonisation, p=0·02). Bacteriuria with any CFU/mL was also reduced in men in the universal decolonisation group (HRs 1·01 [0·81–1·25] for screening or isolation, 1·04 [0·83–1·30] for targeted decolonisation, and 0·74 [0·61–0·90] for universal decolonisation, p=0·04). Interpretation Universal decolonisation of patients in the ICU with once a day chlorhexidine baths and short-course nasal mupirocin could be a potential preventive strategy in male patients because it significantly decreases candiduria and any bacteriuria, but not for women. Funding HAI Program from AHRQ, US Department of Health and Human Services as part of the Developing Evidence to Inform Decisions about Effectiveness (DEcIDE) program, CDC Prevention Epicenters Program.

Closing the Translation Gap: Toolkit-based Implementation of Universal Decolonization in Adult Intensive Care Units Reduces Central Line–associated Bloodstream Infections in 95 Community Hospitals Edward Septimus,1,2 Jason Hickok,1 Julia Moody,1 Ken Kleinman,3 Taliser R. Avery,3 Susan S. Huang,4 Richard Platt,3 and Jonathan Perlin1 1Hospital Corporation of America, Nashville, Tennessee; 2Texas AM 3Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts; and 4University of California, Irvine Health School of Medicine Background. Challenges exist in implementing evidence-based strategies, reaching high compliance, and achieving desired out- comes. The rapid adoption of a publicly available toolkit featuring routine universal decolonization of intensive care unit (ICU) patients may affect catheter-related bloodstream infections. Methods. Implementation of universal decolonization—treatment of all ICU patients with chlorhexidine bathing and nasal mupirocin—used a prerelease version of a publicly available toolkit. Implementation in 136 adult ICUs in 95 acute care hospitals across the United States was supported by planning and deployment tactics coordinated by a central infection prevention team using toolkit resources, along with coaching calls and engagement of key stakeholders. Operational and process measures derived from a common electronic health record system provided real-time feedback about performance. Healthcare-associated central line–associated bloodstream infections (CLABSIs), using National Healthcare Safety Network surveillance definitions and comparing the preimplementation period of January 2011 through December 2012 to the postimplementation period of July 2013 through February 2014, were assessed via a Poisson generalized linear mixed model regression for CLABSI events. Results. Implementation of universal decolonization was completed within 6 months. The estimated rate of CLABSI decreased by 23.5% (95% confidence interval, 9.8%–35.1%; P = .001). There was no evidence of a trend over time in either the pre- or postimplementation period. Adjusting for seasonality and number of beds did not materially affect these results. Conclusions. Dissemination of universal decolonization of ICU patients was accomplished quickly in a large community health system and was associated with declines in CLABSI consistent with published clinical trial findings. Keywords. universal decolonization; decolonization; healthcare-associated central line–associated bloodstream infections (CLABSI); quality improvement; learning health system. There has been increasing interest in improving the dissemination of best practices into routine clinical care. Despite frequent acknowledgment of the need for faster translation of evidence- based interventions into practice, it can be challenging to accomplish this. This is often the case even when the desired interventions are the product of pragmatic trials that are intended to test interventions under conditions of routine practice [1, 2]. Growing evidence suggests that universal decolonization of intensive care unit (ICU) patients reduces the risk of healthcare- associated bloodstream infections (BSIs). These include central line–associated bloodstream infections (CLABSIs), which comprise the majority of these

OBJECTIVETo assess the time-dependent exposure of California healthcare facilities to patients harboring methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE), extended-spectrum β-lactamase (ESBL)–producing Escherichia coli and Klebsiella pneumoniae, and Clostridium difficile infection (CDI) upon discharge from 1 hospital.METHODSRetrospective multiple-cohort study of adults discharged from 1 hospital in 2005–2009, counting hospitals, nursing homes, cities, and counties in which carriers were readmitted, and comparing the number and length of stay of readmissions and the number of distinct readmission facilities among carriers versus noncarriers.RESULTSWe evaluated 45,772 inpatients including those with MRSA (N=1,198), VRE (N=547), ESBL (N=121), and CDI (N=300). Within 1 year of discharge, MRSA, VRE, and ESBL carriers exposed 137, 117, and 45 hospitals and 103, 83, and 37 nursing homes, generating 58,804, 33,486, and 15,508 total exposure-days, respectively. Within 90 days of discharge, CDI patients exposed 36 hospitals and 35 nursing homes, generating 7,318 total exposure-days. Compared with noncarriers, carriers had more readmissions to hospitals (MRSA:1.8 vs 0.9/patient; VRE: 2.6 vs 0.9; ESBL: 2.3 vs 0.9; CDI: 0.8 vs 0.4; all PPPCONCLUSIONSPatients harboring antibiotic-resistant pathogens rapidly expose numerous facilities during readmissions; regional containment strategies are needed.Infect. Control Hosp. Epidemiol. 2015;36(11):1275–1282

We surveyed infection prevention programs in 16 hospitals for hospital-associated methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci, extended-spectrum β-lactamase, and multidrug-resistant Acinetobacter acquisition, as well as hospital-associated MRSA bacteremia and Clostridium difficile infection based on defining events as occurring >2 days versus >3 days after admission. The former resulted in significantly higher median rates, ranging from 6.76% to 45.07% higherInfect Control Hosp Epidemiol 2014;35(11):1417–1420

Norovirus outbreak may spread among hospitals via patient sharing. Within 30 days, it spread 4.1-17.5% of the time (R0 3.74). Highlights need to cooperatively track and control outbreaks, Background Because hospitals in a region are connected via patient sharing, a norovirus outbreak in one hospital may spread to others. Methods. We utilized our Regional Healthcare Ecosystem Analyst software to generate an agent-based model of all the acute care facilities in Orange County (OC), California and simulated various norovirus outbreaks in different locations, both with and without contact precautions. Results. At the lower end of norovirus reproductive rate (R0) estimates (1.64), an outbreak tended to remain confined to the originating hospital (≤6.1% probability of spread). However, at the higher end of R0 (3.74), an outbreak spread 4.1%–17.5% of the time to almost all other OC hospitals within 30 days, regardless of the originating hospital. Implementing contact precautions for all symptomatic cases reduced the probability of spread to other hospitals within 30 days and the total number of cases countywide, but not the number of other hospitals seeing norovirus cases. Conclusions. A single norovirus outbreak can continue to percolate throughout a system of different hospitals for several months and appear as a series of unrelated outbreaks, highlighting the need for hospitals within a region to more aggressively and cooperatively track and control an initial outbreak.

Objective.To determine rates of blood culture contamination comparing 3 strategies to prevent intensive care unit (ICU) infections: screening and isolation, targeted decolonization, and universal decolonization.Design.Pragmatic cluster-randomized trial.Setting.Forty-three hospitals with 74 ICUs; 42 of 43 were community hospitals.Patients.Patients admitted to adult ICUs from July 1, 2009, to September 30, 2011.Methods.After a 6-month baseline period, hospitals were randomly assigned to 1 of 3 strategies, with all participating adult ICUs in a given hospital assigned to the same strategy. Arm 1 implemented methicillin-resistantStaphylococcus aureus(MRSA) nares screening and isolation, arm 2 targeted decolonization (screening, isolation, and decolonization of MRSA carriers), and arm 3 conducted no screening but universal decolonization of all patients with mupirocin and chlorhexidine (CHG) bathing. Blood culture contamination rates in the intervention period were compared to the baseline period across all 3 arms.Results.During the 6-month baseline period, 7,926 blood cultures were collected from 3,399 unique patients: 1,099 sets in arm 1, 928 in arm 2, and 1,372 in arm 3. During the 18-month intervention period, 22,761 blood cultures were collected from 9,878 unique patients: 3,055 sets in arm 1, 3,213 in arm 2, and 3,610 in arm 3. Among all individual draws, for arms 1,2, and 3, the contamination rates were 4.1%, 3.9%, and 3.8% for the baseline period and 3.3%, 3.2%, and 2.4% for the intervention period, respectively. When we evaluated sets of blood cultures rather than individual draws, the contamination rate in arm 1 (screening and isolation) was 9.8% (N= 108 sets) in the baseline period and 7.5% (N= 228) in the intervention period. For arm 2 (targeted decolonization), the baseline rate was 8.4% (N= 78) compared to 7.5% (N= 241) in the intervention period. Arm 3 (universal decolonization) had the greatest decrease in contamination rate, with a decrease from 8.7% (N= 119) contaminated blood cultures during the baseline period to 5.1% (N= 184) during the intervention period. Logistic regression models demonstrated a significant difference across the arms when comparing the reduction in contamination between baseline and intervention periods in both unadjusted (P= .02) and adjusted (P= .02) analyses. Arm 3 resulted in the greatest reduction in blood culture contamination rates, with an unadjusted odds ratio (OR) of 0.56 (95% confidence interval [CI], 0.044-0.71) and an adjusted OR of 0.55 (95% CI, 0.43-0.71).Conclusion.In this large cluster-randomized trial, we demonstrated that universal decolonization with CHG bathing resulted in a significant reduction in blood culture contamination.

Objective As healthcare systems continue to expand and interconnect with each other through patient sharing, administrators, policy makers, infection control specialists, and other decision makers may have to take account of the entire healthcare ‘ecosystem’ in infection control. Materials and methods We developed a software tool, the Regional Healthcare Ecosystem Analyst (RHEA), that can accept user-inputted data to rapidly create a detailed agent-based simulation model (ABM) of the healthcare ecosystem (ie, all healthcare facilities, their adjoining community, and patient flow among the facilities) of any region to better understand the spread and control of infectious diseases. Results To demonstrate RHEA's capabilities, we fed extensive data from Orange County, California, USA, into RHEA to create an ABM of a healthcare ecosystem and simulate the spread and control of methicillin-resistant Staphylococcus aureus . Various experiments explored the effects of changing different parameters (eg, degree of transmission, length of stay, and bed capacity). Discussion Our model emphasizes how individual healthcare facilities are components of integrated and dynamic networks connected via patient movement and how occurrences in one healthcare facility may affect many other healthcare facilities. Conclusions A decision maker can utilize RHEA to generate a detailed ABM of any healthcare system of interest, which in turn can serve as a virtual laboratory to test different policies and interventions.

Hospital infection control strategies and programs may not consider control of methicillin-resistant Staphylococcus aureus (MRSA) in nursing homes in a county.Using our Regional Healthcare Ecosystem Analyst, we augmented our existing agent-based model of all hospitals in Orange County (OC), California, by adding all nursing homes and then simulated MRSA outbreaks in various health care facilities.The addition of nursing homes substantially changed MRSA transmission dynamics throughout the county. The presence of nursing homes substantially potentiated the effects of hospital outbreaks on other hospitals, leading to an average 46.2% (range, 3.3%-156.1%) relative increase above and beyond the impact when only hospitals are included for an outbreak in OC's largest hospital. An outbreak in the largest hospital affected all other hospitals (average 2.1% relative prevalence increase) and the majority (~90%) of nursing homes (average 3.2% relative increase) after 6 months. An outbreak in the largest nursing home had effects on multiple OC hospitals, increasing MRSA prevalence in directly connected hospitals by an average 0.3% and in hospitals not directly connected through patient transfers by an average 0.1% after 6 months. A nursing home outbreak also had some effect on MRSA prevalence in other nursing homes.Nursing homes, even those not connected by direct patient transfers, may be a vital component of a hospital's infection control strategy. To achieve effective control, a hospital may want to better understand how regional nursing homes and hospitals are connected through both direct and indirect (with intervening stays at home) patient sharing.

We calculated hospital-onset methicillin-resistant Staphylococcus aureus (HO-MRSA) rates for Orange County, California, hospitals using survey and state data. Numerators were variably defined as HO-MRSA occurring more than 48 hours (37%), more than 2 days (30%), and more than 3 days (33%) postadmission. Survey-reported denominators differed from state-reported patient-days. Numerator and denominator choices substantially impacted HO-MRSA rates.

Efforts to control life-threatening infections, such as with methicillin-resistant Staphylococcus aureus (MRSA), can be complicated when patients are transferred from one hospital to another. Using a detailed computer simulation model of all hospitals in Orange County, California, we explored the effects when combinations of hospitals tested all patients at admission for MRSA and adopted procedures to limit transmission among patients who tested positive. Called "contact isolation," these procedures specify precautions for health care workers interacting with an infected patient, such as wearing gloves and gowns. Our simulation demonstrated that each hospital's decision to test for MRSA and implement contact isolation procedures could affect the MRSA prevalence in all other hospitals. Thus, our study makes the case that further cooperation among hospitals-which is already reflected in a few limited collaborative infection control efforts under way-could help individual hospitals achieve better infection control than they could achieve on their own. © 2012 Project HOPE-The People-to-People Health Foundation, Inc.

This study describes practical considerations for implementation of near real-time medical product safety surveillance in a distributed health data network.We conducted pilot active safety surveillance comparing generic divalproex sodium to historical branded product at four health plans from April to October 2009. Outcomes reported are all-cause emergency room visits and fractures. One retrospective data extract was completed (January 2002-June 2008), followed by seven prospective monthly extracts (January 2008-November 2009). To evaluate delays in claims processing, we used three analytic approaches: near real-time sequential analysis, sequential analysis with 1.5 month delay, and nonsequential (using final retrospective data). Sequential analyses used the maximized sequential probability ratio test. Procedural and logistical barriers to active surveillance were documented.We identified 6586 new users of generic divalproex sodium and 43,960 new users of the branded product. Quality control methods identified 16 extract errors, which were corrected. Near real-time extracts captured 87.5% of emergency room visits and 50.0% of fractures, which improved to 98.3% and 68.7% respectively with 1.5 month delay. We did not identify signals for either outcome regardless of extract timeframe, and slight differences in the test statistic and relative risk estimates were found.Near real-time sequential safety surveillance is feasible, but several barriers warrant attention. Data quality review of each data extract was necessary. Although signal detection was not affected by delay in analysis, when using a historical control group differential accrual between exposure and outcomes may theoretically bias near real-time risk estimates towards the null, causing failure to detect a signal.

Rates of hospital-acquired infections, specifically methicillin-resistant Staphylococcus aureus (MRSA), are increasingly being used as indicators for quality of hospital hygiene. There has been much effort on understanding the transmission process at the hospital level; however, interhospital population-based transmission remains poorly defined. We evaluated whether the proportion of shared patients between hospitals was correlated with genetic similarity of MRSA strains from those hospitals. Using data collected from 30 of 32 hospitals in Orange County, California, multivariate linear regression showed that for each twofold increase in the proportion of patients shared between 2 hospitals, there was a 7.7% reduction in genetic heterogeneity between the hospitals’ MRSA populations (permutation P value = 0.0356). Pairs of hospitals that both served adults had more similar MRSA populations than pairs including a pediatric hospital. These findings suggest that concerted efforts among hospitals that share large numbers of patients may be synergistic to prevent MRSA transmission.

In post-marketing drug safety surveillance, data mining can potentially detect rare but serious adverse events. Assessing an entire collection of drug-event pairs is traditionally performed on a predefined level of granularity. It is unknown a priori whether a drug causes a very specific or a set of related adverse events, such as mitral valve disorders, all valve disorders, or different types of heart disease. This methodological paper evaluates the tree-based scan statistic data mining method to enhance drug safety surveillance.We use a three-million-member electronic health records database from the HMO Research Network. Using the tree-based scan statistic, we assess the safety of selected antifungal and diabetes drugs, simultaneously evaluating overlapping diagnosis groups at different granularity levels, adjusting for multiple testing. Expected and observed adverse event counts were adjusted for age, sex, and health plan, producing a log likelihood ratio test statistic.Out of 732 evaluated disease groupings, 24 were statistically significant, divided among 10 non-overlapping disease categories. Five of the 10 signals are known adverse effects, four are likely due to confounding by indication, while one may warrant further investigation.The tree-based scan statistic can be successfully applied as a data mining tool in drug safety surveillance using observational data. The total number of statistical signals was modest and does not imply a causal relationship. Rather, data mining results should be used to generate candidate drug-event pairs for rigorous epidemiological studies to evaluate the individual and comparative safety profiles of drugs.

Background.Hospitalized patients are at increased risk for acquisition of methicillin-resistant Staphylococcus aureus (MRSA). As hospital length of stay shortens, hospital-acquired MRSA events may be more likely to be detected after discharge.Objective.We assessed the impact of attributing MRSA cases discovered within 30 days after discharge to the most recent hospitalization and identified patient characteristics associated with MRSA detection after discharge.Design.Retrospective cohort study.Setting.Twenty-seven acute care hospitals in Orange County, California.Participants.Adult acute care admissions (2002–2007).Methods.Using a countywide hospital data set containing diagnostic codes with present-on-admission (POA) indicators, we identified the first admission with a MRSA code for each patient. This incident MRSA admission was defined as predischarge-detected (pre-DD) hospital-onset MRSA (HO-MRSA) when MRSA was not POA. If MRSA was POA and a prior admission occurred within 30 days, this prior admission was assigned postdischarge-detected (post-DD) HO-MRSA. We evaluated the impact of including post-DD HO-MRSA in the calculation of hospital HO-MRSA incidence using signed-rank tests and reviewed changes in hospital rankings. We conducted multivariate comparisons of patient characteristics of pre-DD versus post-DD HO-MRSA patients.Results.Among 1,217,253 at-risk hospitalizations, the inclusion of post-DD HO-MRSA tripled the median hospital HO-MRSA incidence, from 12.2 to 35.7 cases per 10,000 at-risk admissions (PConclusions.On the basis of administrative claims data, the inclusion of post-DD HO-MRSA significantly increased the estimated HO-MRSA incidence and altered hospital rankings. This finding underscores the limitations of single-facility data when deriving HO-MRSA incidence and rank.Infect Control Hosp Epidemiol 2012;33(2):114-121

Background Acute care facilities are connected via patient sharing, forming a network. However, patient sharing extends beyond this immediate network to include sharing with long-term care facilities. The extent of long-term care facility patient sharing on the acute care facility network is unknown. The objective of this study was to characterize and determine the extent and pattern of patient transfers to, from, and between long-term care facilities on the network of acute care facilities in a large metropolitan county. Methods/Principal Findings We applied social network constructs principles, measures, and frameworks to all 2007 annual adult and pediatric patient transfers among the healthcare facilities in Orange County, California, using data from surveys and several datasets. We evaluated general network and centrality measures as well as individual ego measures and further constructed sociograms. Our results show that over the course of a year, 66 of 72 long-term care facilities directly sent and 67 directly received patients from other long-term care facilities. Long-term care facilities added 1,524 ties between the acute care facilities when ties represented at least one patient transfer. Geodesic distance did not closely correlate with the geographic distance among facilities. Conclusions/Significance This study demonstrates the extent to which long-term care facilities are connected to the acute care facility patient sharing network. Many long-term care facilities were connected by patient transfers and further added many connections to the acute care facility network. This suggests that policy-makers and health officials should account for patient sharing with and among long-term care facilities as well as those among acute care facilities when evaluating policies and interventions.

Objective.Clostridium difficile infection (CDI) is associated with hospitalization and may cause readmission following admission for any reason. We aimed to measure the incidence of readmissions due to CDI.Design.Retrospective cohort study.Patients.Adult inpatients in Orange County, California, who presented with new-onset CDI within 12 weeks of discharge.Methods.We assessed mandatory 2000–2007 hospital discharge data for trends in hospital-associated CDI (HA-CDI) incidence, with and without inclusion of postdischarge CDI (PD-CDI) events resulting in rehospitalization within 12 weeks of discharge. We measured the effect of including PD-CDI events on hospital-specific CDI incidence, a mandatory reporting measure in California, and on relative hospital ranks by CDI incidence.Results.From 2000 to 2007, countywide hospital-onset CDI (HO-CDI) incidence increased from 15 per 10,000 to 22 per 10,000 admissions. When including PD-CDI events, HA-CDI incidence doubled (29 per 10,000 in 2000 and 52 per 10,000 in 2007). Overall, including PD-CDI events resulted in significantly higher hospital-specific CDI incidence, although hospitals had disproportionate amounts of HA-CDI occurring postdischarge. This resulted in substantial shifts in some hospitals' rankings by CDI incidence. In multivariate models, both HO and PD-CDI were associated with increasing age, higher length of stay, and select comorbidities. Race and Hispanic ethnicity were predictive of PD-CDI but not HO-CDI.Conclusions.PD-CDI events associated with rehospitalization are increasingly common. The majority of HA-CDI cases may be occurring postdischarge, raising important questions about both accurate reporting and effective prevention strategies. Some risk factors for PD-CDI may be different than those for HO-CDI, allowing additional identification of high-risk groups before discharge.Infect Control Hosp Epidemiol 2012;33(1):20-28

Objectives. We applied social network analyses to determine how hospitals within Orange County, California, are interconnected by patient sharing, a system which may have numerous public health implications. Methods. Our analyses considered 2 general patient-sharing networks: uninterrupted patient sharing (UPS; i.e., direct interhospital transfers) and total patient sharing (TPS; i.e., all interhospital patient sharing, including patients with intervening nonhospital stays). We considered these networks at 3 thresholds of patient sharing: at least 1, at least 10, and at least 100 patients shared. Results. Geographically proximate hospitals were somewhat more likely to share patients, but many hospitals shared patients with distant hospitals. Number of patient admissions and percentage of cancer patients were associated with greater connectivity across the system. The TPS network revealed numerous connections not seen in the UPS network, meaning that direct transfers only accounted for a fraction of total patient sharing. Conclusions. Our analysis demonstrated that Orange County's 32 hospitals were highly and heterogeneously interconnected by patient sharing. Different hospital populations had different levels of influence over the patient-sharing network.

Background/Aims When new medications are introduced, safety data are often sparse. Health plan data may be useful for active surveillance for adverse events. We conducted a 12-month pilot study to demonstrate the feasibility of prospective active surveillance using data from several large health insurers. This presentation will summarize logistical challenges and lessons learned.