Asymptomatic coronavirus disease 2019 (COVID-19) has been reported as a significant driver of COVID-19 outbreaks. Our hospital ward outbreak analysis suggests that comprehensive symptoms and signs assessment, in combination with adequate follow-up, allows a more precise determination of COVID-19 symptoms. Asymptomatic infection was quite uncommon among adults in this setting.

Cerebrospinal fluid shunt–associated surgical site infection surveillance for 3 months compared to 12 months after surgery captures 83% of cases with no significant differences in patient characteristics, surgery types, or pathogens. A shorter 3-month follow-up can reduce resource use and allow for more timely reporting of healthcare-associated infection rates for hospitals.

To assess the burden of respiratory virus coinfections with severe acute respiratory coronavirus virus 2 (SARS-CoV-2), this study reviewed 4,818 specimens positive for SARS-CoV-2 and tested using respiratory virus multiplex testing. Coinfections with SARS-CoV-2 were uncommon (2.8%), with enterovirus or rhinovirus as the most prevalent target (88.1%). Respiratory virus coinfection with SARS-CoV-2 remains low 1 year into the coronavirus disease 2019 (COVID-19) pandemic.

Background: In Alberta, Canada, surgical site infections (SSIs) following total hip (THR) and knee replacements (TKR) are reported using 2 data sources: infection prevention and control (IPC), which surveys all THR and TKR using NHSN definitions and the Canadian International Classification of Disease, Tenth Revision (ICD-10-CA) codes, and the National Surgical Quality Improvement Program (NSQIP), which uses a systematic sampling process that involves an 8-day cycle schedule, modified NHSN definitions and current procedural terminology (CPT) codes. We compared the similarities and discrepancies in THR/TKR SSI reporting. Methods: A retrospective multisite cohort study of IPC and NSQIP THR/TKR SSI data at 4 hospitals was performed. SSI data were collected between September 1, 2015, and March 31, 2018. Demographic information and complex and total SSIs reported by IPC and NSQIP were compared for both THR and TKR surgeries. To determine whether both data sources reported similar trends over time, total SSIs by quarter were compared. Univariate analyses using a t test for age and the χ2 test for gender for complex SSIs and total SSIs was performed. The Pearson correlation and the Shapiro-Wilk test were used to assess the THR and TKR trends between the 2 data sources. A P value of <.05 was considered significant. Results: Following the removal of duplicates and missing data, 7,549 IPC and 2,037 NSQIP patients, respectively, were compared. Age, gender, and other demographic parameters were not significantly different. Total THR and TKR SSIs per 100 procedures using NSQIP data were significantly higher than the same rates using IPC data: THR, 2.25 versus 0.92 (P < .05) and TKR, 3.43 versus 1.26 (P < .05). Both IPC and NSQIP data indicated increasing total THR SSI rates over time, but with different magnitudes (r = 0.658). For total TKR SSI, the IPC rate decreased, whereas the NSQIP rate increased over the same period (r = 0.374). When superficial SSIs were excluded, the rates reported between IPC and NSQIP data by hospital and by procedure type were more comparable, with trends toward higher rates reported by NSQIP for THR than for TKR: THR, 1.19 versus 0.68 (P = 0.15) and TKR, 0.92 versus 0.80 (P = .68). Conclusions: Different approaches used to monitor SSIs following surgeries may lead to different results and trend patterns. NSQIP reports total SSI rates that are significantly higher than the IPC Alberta orthopedic population predominantly as a result of increased identification of superficial SSIs. Because the diagnosis of superficial SSIs may be less reliable, SSI reporting should focus on complex infections.

Funding: None

Disclosures: None

Background: Hospital-acquired Clostridioides difficile infection (HA-CDI) rates are highly variable over time, posing problems for research assessing interventions that might improve rates. By understanding seasonality in HA-CDI rates and the impacts that other factors such as influenza admissions might have on these rates, we can account for them when establishing the relationship between interventions and infection rates. We assessed whether there were seasonal trends in HA-CDI and whether they could be accounted for by influenza rates. Methods: We assessed HA-CDI rates per 10,000 patient days, and the rate of hospitalized patients with influenza per 1,000 admissions in 4 acute-care facilities (n = 2,490 beds) in Calgary, Alberta, from January 2016 to December 2018. We used 4 statistical approaches in R (version 3.5.1 software): (1) autoregressive integrated moving average (ARIMA) to assess dependencies and trends in each of the monthly HA-CDI and influenza series; (2) cross correlation to assess dependencies between the HA-CDI and influenza series lagged over time; (3) Poisson harmonic regression models (with sine and cosine components) to assess the seasonality of the rates; and (4) Poisson regression to determine whether influenza rates accounted for seasonality in the HA-CDI rates. Results: Conventional ARIMA approaches did not detect seasonality in the HA-CDI rates, but we found strong seasonality in the influenza rates. A cross-correlation analysis revealed evidence of correlation between the series at a lag of zero (R = 0.41; 95% CI, 0.10–0.65) and provided an indication of a seasonal relationship between the series (Fig. 1). Poisson regression suggested that influenza rates predicted CDI rates (P < .01). Using harmonic regression, there was evidence of seasonality in HA-CDI rates (2 [2 df] = 6.62; P < .05) and influenza rates (2 [2 df] = 1,796.6; P < .001). In a Poisson model of HA-CDI rates with both the harmonic components and influenza admission rates, the harmonic components were no longer predictive of HA-CDI rates. Conclusions: Harmonic regression provided a sensitive means of identifying seasonality in HA-CDI rates, but the seasonality effect was accounted for by influenza admission rates. The relationship between HA-CDI and influenza rates is likely mediated by antibiotic prescriptions, which needs to be assessed. To improve precision and reduce bias, research on interventions to reduce HA-CDI rates should assess historic seasonality in HA-CDI rates and should account for influenza admissions.

Funding: None

Disclosures: None

Background: Inappropriate prescribing behavior can be associated with higher rates of antibiotic resistance, calling for detailed studies on how physicians make prescribing decisions. We conducted a mixed-methods study to investigate physician antibiotic prescribing behavior in a 141-bed pediatric hospital. Methods: We applied a mixed-methods research design. The quantitative phase was conducted over a 6-month period to identify cases of inappropriate prescribing. The qualitative phase comprised 22 qualitative interviews with clinical teaching units (CTU) and pediatric intensive care unit (PICU) team members (physicians and pharmacists). Two coders analyzed the data deductively using the theoretical domain framework (TDF), as well as the social determinants of antimicrobial prescribing (SDAP). Results: In 52.9% of the 36 identified cases in the CTU and 31.4% of the 37 cases in the PICU, an infectious diseases (ID) consultation occurred. Compliance rates with ID recommendations were 79% and 91% in the CTU and PICU, respectively. The CTU and PICU expressed appreciation for ID involvement when ID supported their de-escalation choices in complex cases and in cases in which less commonly known antibiotics were used. However, the ID service involvement was perceived as detrimental to antimicrobial prescribing decisions for CTU and PICU across 3 of the 4 SDAP domains (Fig. 1, qualitative research quotes). Relationship between clinicians: CTU physicians and pharmacists perceived ID involvement as negatively impacting the relationship of the team. Antimicrobial decisions were automatically defaulted to ID, whereas pharmacy involvement was disregarded and the decisions were delayed. Risk, fear, and emotion: These were experienced across all respondents’ groups that identified ID specialists’ egos and personalities as contrary to open collaborative discussion on antimicrobial decisions. (Mis)perception of the problem: ID physicians were identified as more conservative in their antimicrobial choices, leading to prolonged duration of treatment, broader choices, and longer hospitalizations. The CTU and pharmacy respondents felt that ID recommendations were inconsistent among physicians and deviated from guidelines with little justification. Conclusions: Although CTU and PICU teams tend to comply with ID prescribing recommendations and ID involvement with complicated cases, pharmacists, CTU physicians, and PICU physicians perceived ID consultations to negatively affect collaborative efforts for stewardship. These findings offer novel insights into how an ID service can improve its role to positively affect appropriate prescribing. CTU and PICU respondents called for a supportive and trusting relationship with the ID service as a major driver for behavioral change and enhanced stewardship.

Funding: None

Disclosures: None

Background:Clostridioides difficile infection (CDI) is the most common cause of infectious diarrhea in hospitalized patients. Probiotics have been studied as a measure to prevent CDI. Timely probiotic administration to at-risk patients receiving systemic antimicrobials presents significant challenges. We sought to determine optimal implementation methods to administer probiotics to all adult inpatients aged 55 years receiving a course of systemic antimicrobials across an entire health region. Methods: Using a randomized stepped-wedge design across 4 acute-care hospitals (n = 2,490 beds), the probiotic Bio-K+ was prescribed daily to patients receiving systemic antimicrobials and was continued for 5 days after antimicrobial discontinuation. Focus groups and interviews were conducted to identify barriers, and the implementation strategy was adapted to address the key identified barriers. The implementation strategy included clinical decision support involving a linked flag on antibiotic ordering and a 1-click order entry within the electronic medical record (EMR), provider and patient education (written/videos/in-person), and local site champions. Protocol adherence was measured by tracking the number of patients on therapeutic antimicrobials that received BioK+ based on the bedside nursing EMR medication administration records. Adherence rates were sorted by hospital and unit in 48- and 72-hour intervals with recording of percentile distribution of time (days) to receipt of the first antimicrobial. Results: In total, 340 education sessions with >1,800 key stakeholders occurred before and during implementation across the 4 involved hospitals. The overall adherence of probiotic ordering for wards with antimicrobial orders was 78% and 80% at 48 and 72 hours, respectively over 72 patient months. Individual hospital adherence rates varied between 77% and 80% at 48 hours and between 79% and 83% at 72 hours. Of 246,144 scheduled probiotic orders, 94% were administered at the bedside within a median of 0.61 days (75th percentile, 0.88), 0.47 days (75th percentile, 0.86), 0.71 days (75th percentile, 0.92) and 0.67 days (75th percentile, 0.93), respectively, at the 4 sites after receipt of first antimicrobial. The key themes from the focus groups emphasized the usefulness of the linked flag alert for probiotics on antibiotic ordering, the ease of the EMR 1-click order entry, and the importance of the education sessions. Conclusions: Electronic clinical decision support, education, and local champion support achieved a high implementation rate consistent across all sites. Use of a 1-click order entry in the EMR was considered a key component of the success of the implementation and should be considered for any implementation strategy for a stewardship initiative. Achieving high prescribing adherence allows more precision in evaluating the effectiveness of the probiotic strategy.

Funding: Partnerships for Research and Innovation in the Health System, Alberta Innovates/Health Solutions Funding: Award

Disclosures: None

Background: The association between antimicrobial use (AMU) and emergence of antimicrobial resistance is well documented. The Canadian Nosocomial Infection Surveillance Program (CNISP) has conducted sentinel surveillance of AMU at participating Canadian hospitals since 2009 resulting in the largest pan-Canadian hospital database of dispensed antimicrobials. Objectives: Describe interhospital variability of AMU across Canada. Methods: Hospitals submit annual AMU data based on patient days (PD). Antimicrobials were measured in defined daily doses (DDD) for adults using the WHO Anatomical Therapeutic Chemical (ATC) system. The AMU data among pediatric patients have been available since 2017 using days of therapy (DOT). Surveillance includes systemic antibacterial agents (J01 ATC codes), oral metronidazole, and oral vancomycin. AMU was assessed using quintiles, interquartile ranges (IQR), and relative IQRs (upper- and lower-quartile values divided by the median). Results: Between 2009 and 2018, 20–26 hospitals participated in adult surveillance each year (35 teaching hospitals and 3 nonteaching hospitals participated in ≥1 year). Over this period, overall AMU decreased by 13% at participating adult hospitals from 645 to 560 DDD per 1,000 PD. AMU varied substantially between hospitals, but this variability decreased over time (Fig. 1). In 2009, the IQRs for overall AMU spanned 309 DDD per 1,000 PD, and in 2018 it spanned only 103 DDD per 1,000 PD. This decrease in variability was due to large decreases in use among hospitals with high use in 2009–2010. Among hospitals in the highest use quintile in 2009–2010, AMU decreased, on average, 44 DDD per 1,000 PD each year. Among hospitals in the lowest use quintile in 2009–2010, AMU increased, on average, 6 DDD per 1,000 PD each year. In 2018, antibiotics with the largest absolute IQR variability were cefazolin (61–113 DDD per 1,000 PD), piperacillin-tazobactam (32–64 DDD per 1,000 PD), and vancomycin (24–49 DDD per 1,000 PD). Among antibiotics with ≥1 DDD per 1,000 PD, antibiotics with the largest relative IQR variability were tobramycin (0.3–6 DDD per 1,000 PD), cefadroxil (0.08–9 DDD per 1,000 PD), and linezolid (0.2–3 DDD per 1,000 PD). In 2018, the IQR for overall pediatric AMU (n = 7 teaching hospitals) was 426–581 DOT per 1,000 PD. Antibiotics with the largest IQRs were vancomycin (0.6–58 DOT per 1,000 PD), cefazolin (33–88 DOT per 1,000 PD), and tobramycin (3–57 DOT per 1,000 PD). Among antibiotics with ≥1 DOT per 1,000 PD in 2018, antibiotics with the largest relative IQRs were tobramycin (3–57 DOT per 1,000 PD), cefuroxime (1–6 DOT per 1,000 PD), and amoxicillin (8–42 DOT per 1,000 PD). Conclusions: There is wide variation in overall antibiotic use across hospitals. Variation between AMU at adult hospitals has decreased between 2009 and 2018; in 2018, antibiotics with the largest IQRs were cefazolin and piperacillin-tazobactam. Benchmarking AMU is crucial for informing antimicrobial stewardship efforts.

Funding: CNISP is funded by the Public Health Agency of Canada.

Disclosures: Allison McGeer reports funds to her institution from Pfizer and Merck for projects for which she is the principal investigator. She also reports consulting fees from Sanofi-Pasteur, Sunovion, GSK, Pfizer, and Cidara.

Johanna Blaak, W21C, University of Calgary; Rachel DiMaio, University of Calgary; Julia Kupis, University of Calgary; Ross Sweetzir, Cisco Systems; Conny Betuzzi, Alberta Children’s Hospital, Alberta Health Services; Corey Dowler, Alberta Children’s Hospital, Alberta Health Services; Krista McIntytre, Alberta Children’s Hospital, Alberta Health Services; Jaime Kaufman, University of Calgary; Greg Hallihan, University of Calgary; John Conly, Foothills Medical Centre; Joseph Vayalumkal, Alberta Childrens Hospital

Background: Interaction design offers a novel interventional strategy to enhance hand-hygiene compliance (HHC) and reduce hospital-acquired infections (HAIs) in the pediatric setting. A quality improvement initiative in collaboration with the University of Calgary and Alberta Health Services led to the implementation of a pilot project with sensor-embedded alcohol -based hand rub (ABHR) dispensers at a hematology-oncology and hematopoietic stem cell transplant unit at Alberta Children’s Hospital (ACH). Methods: Internet of things (IoT) sensors were installed in ABHR dispensers (n = 3) on the unit. Usage data were transmitted to a local server using an MQTT messaging protocol for 16 weeks. Real-time data visualization was presented on a central display next to the nursing station with 11 unique pediatric themes including dinosaurs, transportation, and Canadian animals. Data were collected with and without visualization, and frequency of use (FoU) was determined for both periods. Qualitative interviews with unit stakeholders (n = 13) were held to determine perceptions of the intervention. Results: During the first 8 weeks of the study period, the mean daily use without visualization was 47 times (SD, 14.5) versus 99 times (SD, 23.9) with visualization. When accounting for novelty, by removing the first week of data, the mean daily use was 92 (SD 19.6). The percentage increase from period 1 to period 2 was 96.6%, accounting for novelty. Qualitative interviews with stakeholders (n = 13) on the unit indicated that the intervention increased their personal awareness of hand hygiene (75%) and acted as a constant reminder to perform hand hygiene for everyone on the unit including nonclinical staff, patients, and family members (92%). Conclusions: These limited data suggest that interaction design may improve HH frequency and show promise as a tool for increased HH awareness and education. Interaction design provides a unique, innovative, and acceptable hand hygiene improvement strategy for staff, patients, and families in the pediatric inpatient setting.

Funding: None

Disclosures: None

Background: Surgical site infection (SSI) after cerebrospinal fluids (CSF) shunt surgery is thought to be acquired intraoperatively. Biomaterial-associated infection can present up to 1 year after surgery, but many national systems have shortened follow-up to 90 days. We compared 3- versus 12-month follow-up periods to determine the nature of case ascertainment in the 2 periods. Methods: Participants of any age with placement of an internal CSF shunt or revision surgical manipulation of an existing internal shunt identified in the Canadian Nosocomial Infection Surveillance Program (CNISP) participating hospitals between 2006 and 2018 were eligible. We excluded patients with external shunting devices or culture-positive CSF at the time of surgery. Patients were followed for 12 months after surgery for the primary outcome of a CSF infection with a positive CSF culture by review of laboratory and health records. Patients were categorized as adult (aged ≥18 years) or pediatric (aged < 18 years). The infection rate was expressed as the number of CSF shunt-associated infections divided by the number of shunt surgeries per 100 procedures. Results: In total, 325 patients (53% female) met inclusion criteria in 14 hospitals from 7 provinces were identified. Overall, 46.1% of surgeries were shunt revisions and 90.3% of shunts were ventriculoperitoneal. For pediatric patients, the median age was 0.7 years (IQR, 0.2–7.0). For adult patients, the median age was 47.9 years (IQR, 29.6–64.6). The SSI rates per 100 procedures were 3.69 for adults and 3.65 for pediatrics. The overall SSI rates per 100 procedures at 3 and 12 months were 2.74 (n = 265) and 3.48 (n = 323), respectively. By 3 months (90 days), 82% of infection cases were identified (Fig. 1). The median time from procedure to SSI detection was 30 days (IQR, 10–65). No difference was found in the microbiology of the shunt infections at 3- and 12-month follow-ups. The most common pathogens were coagulase-negative Staphylococcus (43.6 %), followed by S. aureus (24.8 %) and Propionibacterium spp (6.5 %). No differences in age distribution, gender, surgery type (new or revision), shunt type, or infecting organisms were observed when 3- and 12-month periods were compared. Conclusions: CSF-SSI surveillance for 3 versus 12 months would capture 82.0% (95% CI, 77.5–86.0) of cases, with no significant differences in the patient characteristics, surgery types, or pathogens. A 3-month follow-up can reduce resources and allow for more timely reporting of infection rates.

Funding: None

Disclosures: None

Background: In Alberta, Canada, surgical site infections (SSIs) following total hip and knee replacements (THRs and TKRs) are reported using the infection prevention and control (IPC) surveillance system, which surveys all THRs and TKRs using the NHSN definitions; and the National Surgical Quality Improvement Program (NSQIP), which uses different definitions and sampling strategies. Deterministic matching of patient data from these sources was used to examine the overlap and discrepancies in SSI reporting. Methods: A retrospective multisite cohort study of IPC and NSQIP superficial, deep, and organ-space THR/TKR SSI data collected 30 days postoperatively from September 1, 2015, to March 31, 2018 was undertaken. To identify patients with procedures captured by both IPC and NSQIP, data were cleaned, duplicates removed, and patients matched 1:1 using year of birth, procedure facility, type, side, date, and time. Positive and negative agreement were assessed, and the Cohen κ values were calculated. The definitions and data capture methods used by both IPC and NSQIP were also compared. Results: There were 7,549 IPC and 2,037 NSQIP patients, respectively, with 1,798 matched patients: IPC (23.8%) and NSQIP (88.3%). Moreover, 17 SSIs were identified by both IPC and NSQIP, including 9 superficial and 8 complex by IPC and 6 superficial and 11 complex by NSQIP. Also, 7 SSIs were identified only by IPC, of which 5 were superficial, and 36 SSIs were identified only by NSQIP, of which 28 were superficial (positive agreement, 0.44; negative agreement, 0.99; κ = .43). Excluding superficial SSIs, 7 SSIs were identified by both IPC and NSQIP; 3 were identified only by IPC; and 12 were identified only by NSQIP (positive agreement, 0.48; negative agreement, 1.00; κ = 0.48). Conclusions: THR/TKR SSI rates reported by IPC and NSQIP were not comparable in this matched dataset. NSQIP identifies more superficial SSIs. Variations in data capture methods and definitions accounted for most of the discordance. Both surveillance systems are critically involved with improving patient outcomes following surgery. However, stakeholders need to be aware of these variations, and education should be provided to facilitate an understanding of the differences and their interpretation. Future work should explore other surgical procedures and larger data sets.

Funding: None

Disclosures: None