Background: Socioeconomic barriers or divergent implementation of prevention measures may impact risk of healthcare-associated infections by racial groups. We utilized a previously studied cohort of patients to quantify disparities in central-line–associated bloodstream infection (CLABSI) risk by race accounting for inherent differences in risk related to device utilization. Methods: In a retrospective cohort of adult patients at 4 hospitals (range, 110–733 beds) from 2012 to 2017, we linked central-line data to patient encounter data: race, age, comorbidities, total parenteral nutrition (TPN), chemotherapy, CLABSI. Analysis was limited to patients with >2 central-line days and <3 concurrent central lines. Patient exposures were calculated for each central-line episode (defined by insertion and removal dates); analysis of central-line episode-specific risk of CLABSI among Black versus White patients adjusted for clinical factors, duration of central-line episode, and central-line risk category (ie, low: single port, dialysis or PICC; medium: single temporary or nontunneled; or high: any concurrent central-lines) in Cox proportional hazards regression of time to CLABSI. Results: In total, 526 CLABSIs occurred a median of 14 days after insertion among 57,642 central-line episodes in 32,925 patients. CLABSIs occurred in similar frequency across racial groups: 217 (1.7%) among Black patients, 256 (1.6%) among White patients, and 11 (1.6%) among Hispanic patients (also 42 among unknown or other race). Duration of central-line episode was similar between racial groups (median, 5 days). Black patients were less likely to have medium-risk central lines (34%) compared to white patients (RR, 0.82; 95% CI, 0.79–0.84), but they had a similar frequency of high-risk central lines (21%; RR, 1.0; 95% CI, 1.0–1.1). Compared with low-risk central lines, risk of CLABSI was increased among medium-risk central lines (RR, 1.3; 95% CI, 1.0–1.7) and high-risk central lines (RR, 2.2; 95% CI, 1.8–2.7). CLABSIs were more likely in TPN central lines (RR, 2.3; 95% CI, 1.9–2.7) than others, but they were not more likely among Black patients than White patients (RR, 0.9; 95% CI, 0.1–1.1). In survival analysis, there were 24,700 central-line episodes among Black patients compared to 26,648 episodes among White patients; adjusting for central-line risk and TPN, the risk of CLABSI was similar during the first 21 days of central-line use (adjusted hazard ratio, 1.08; 95% CI, 0.88–01.32) (Fig. 1). Conclusions: After accounting for central-line configuration, Black patients did not have a higher risk of CLABSI within 21 central-line days. Further evaluation is warranted to assess racial disparities in risks of other healthcare-associated infections and to determine whether a lack of CLABSI-specific racial disparities can be replicated in other regions and healthcare systems.

Disclosures: None

Background: Nursing home (NH) residents and staff were at high risk for COVID-19 early in the pandemic; several studies estimated seroprevalence of infection in NH staff to be 3-fold higher among CNAs and nurses compared to other staff. Risk mitigation added in Fall 2020 included systematic testing of residents and staff (and furlough if positive) to reduce transmission risk. We estimated risks for SARS-CoV-2 infection among NH staff during the first winter surge before widespread vaccination. Methods: Between February and May 2021, voluntary serologic testing was performed on NH staff who were seronegative for SARS-CoV-2 in late Fall 2020 (during a previous serology study at 14 Georgia NHs). An exposure assessment at the second time point covered prior 3 months of job activities, community exposures, and self-reported COVID-19 vaccination, including very recent vaccination (≤4 weeks). Risk factors for seroconversion were estimated by job type using multivariable logistic regression, accounting for interval community-incidence and interval change in resident infections per bed. Results: Among 203 eligible staff, 72 (35.5%) had evidence of interval seroconversion (Fig. 1). Among 80 unvaccinated staff, interval infection was significantly higher among CNAs and nurses (aOR, 4.9; 95% CI, 1.4–20.7) than other staff, after adjusting for race and interval community incidence and facility infections. This risk persisted but was attenuated when utilizing the full study cohort including those with very recent vaccination (aOR, 1.8; 95% CI, 0.9–3.7). Conclusions: Midway through the first year of the pandemic, NH staff with close or common resident contact continued to be at increased risk for infection despite enhanced infection prevention efforts. Mitigation strategies, prior to vaccination, did not eliminate occupational risk for infection. Vaccine utilization is critical to eliminate occupational risk among frontline healthcare providers.

Funding: None

Disclosures: None

Among 353 healthcare personnel in a longitudinal cohort in 4 hospitals in Atlanta, Georgia (May–June 2020), 23 (6.5%) had severe acute respiratory coronavirus virus 2 (SARS-CoV-2) antibodies. Spending >50% of a typical shift at the bedside (OR, 3.4; 95% CI, 1.2–10.5) and black race (OR, 8.4; 95% CI, 2.7–27.4) were associated with SARS-CoV-2 seropositivity.

Background: The movement of healthcare professionals (HCPs) induces an indirect contact network: touching a patient or the environment in one area, then again elsewhere, can spread healthcare-associated pathogens from 1 patient to another. Thus, understanding HCP movement is vital to calibrating mathematical models of healthcare-associated infections. Because long-term care facilities (LTCFs) are an important locus of transmission and have been understudied relative to hospitals, we developed a system for measuring contact patterns specifically within an LTCF. Methods: To measure HCP movement patterns, we used badges (credit-card–sized, programmable, battery-powered devices with wireless proximity sensors) worn by HCPs and placed in 30 locations for 3 days. Each badge broadcasts a brief message every 8 seconds. When received by other badges within range, the recipients recorded the time, source badge identifier, and signal strength. By fusing the data collected by all badges with a facility map, we estimated when and for how long each HCP was in any of the locations where instruments had been installed. Results: Combining the messages captured by all of our devices, we calculated the dwell time for each job type (eg, nurses, nursing assistants, physical therapists) in different locations (eg, resident rooms, dining areas, nurses stations, hallways, etc). Although dwell times over all job and area types averaged ∼100 seconds, the standard deviation was large (115 seconds), with a mean of maximums by job type of ∼450 seconds. For example, nursing assistants spent substantially more time in resident rooms and transitioned across rooms at a much higher rate. Overall, each distribution exhibits a power-law–like characteristic. By aggregating the data from devices with location data extracted from the floor plan, we were able to produce an explicit trace for each individual (identified only by job type) for each day and to compute cross-table transition probabilities by area for each job type. Conclusions: We developed a portable system for measuring contact patterns in long-term care settings. Our results confirm that frequent interactions between HCPs and LTC residents occur, but they are not uniform across job types or resident locations. The data produced by our system can be used to better calibrate mathematical models of pathogen spread in LTCs. Moreover, our system can be easily and quickly deployed to any healthcare settings to similarly inform outbreak investigations.

Funding: None

Disclosures: Scott Fridkin reports that his spouse receives a consulting fee from the vaccine industry.

Background: Certain nursing home (NH) resident care tasks have a higher risk for multidrug-resistant organisms (MDRO) transfer to healthcare personnel (HCP), which can result in transmission to residents if HCPs fail to perform recommended infection prevention practices. However, data on HCP-resident interactions are limited and do not account for intrafacility practice variation. Understanding differences in interactions, by HCP role and unit, is important for informing MDRO prevention strategies in NHs. Methods: In 2019, we conducted serial intercept interviews; each HCP was interviewed 6–7 times for the duration of a unit’s dayshift at 20 NHs in 7 states. The next day, staff on a second unit within the facility were interviewed during the dayshift. HCP on 38 units were interviewed to identify healthcare personnel (HCP)–resident care patterns. All unit staff were eligible for interviews, including certified nursing assistants (CNAs), nurses, physical or occupational therapists, physicians, midlevel practitioners, and respiratory therapists. HCP were asked to list which residents they had cared for (within resident rooms or common areas) since the prior interview. Respondents selected from 14 care tasks. We classified units into 1 of 4 types: long-term, mixed, short stay or rehabilitation, or ventilator or skilled nursing. Interactions were classified based on the risk of HCP contamination after task performance. We compared proportions of interactions associated with each HCP role and performed clustered linear regression to determine the effect of unit type and HCP role on the number of unique task types performed per interaction. Results: Intercept-interviews described 7,050 interactions and 13,843 care tasks. Except in ventilator or skilled nursing units, CNAs have the greatest proportion of care interactions (interfacility range, 50%–60%) (Fig. 1). In ventilator and skilled nursing units, interactions are evenly shared between CNAs and nurses (43% and 47%, respectively). On average, CNAs in ventilator and skilled nursing units perform the most unique task types (2.5 task types per interaction, Fig. 2) compared to other unit types (P < .05). Compared to CNAs, most other HCP types had significantly fewer task types (0.6–1.4 task types per interaction, P < .001). Across all facilities, 45.6% of interactions included tasks that were higher-risk for HCP contamination (eg, transferring, wound and device care, Fig. 3). Conclusions: Focusing infection prevention education efforts on CNAs may be most efficient for preventing MDRO transmission within NH because CNAs have the most HCP–resident interactions and complete more tasks per visit. Studies of HCP-resident interactions are critical to improving understanding of transmission mechanisms as well as target MDRO prevention interventions.

Funding: Centers for Disease Control and Prevention (grant no. U01CK000555-01-00)

Disclosures: Scott Fridkin, consulting fee, vaccine industry (spouse)

Background: The NHSN methods for central-line–associated bloodstream infection (CLABSI) surveillance do not account for additive CLABSI risk of concurrent central lines. Past studies were small and modestly risk adjusted but quantified the risk to be ~2-fold. If the attributable risk is this high, facilities that serve high-acuity patients with medically indicated concurrent central-line use may disproportionally incur CMS payment penalties for having high CLABSI rates. We aimed to build evidence through analysis using improved risk adjustment of a multihospital CLABSI experience to influence NHSN CLABSI protocols to account for risks attributed to concurrent central lines. Methods: In a retrospective cohort of adult patients at 4 hospitals (range, 110–733 beds) from 2012 to 2017, we linked central-line data to patient encounter data (age, comorbidities, total parenteral nutrition, chemotherapy, CLABSI). Analysis was limited to patients with >2 central-line days, with either a single central line or concurrence of no more than 2 central lines where insertion and removal dates overlapped by >1 day. Propensity-score matching for likelihood of concurrence and conditional logistic regression modeling estimated the risk of CLABSI attributed to concurrence of >1 day. To evaluate in Cox proportional hazards regression of time to CLABSIs, we also analyzed patients as unique central-line episodes: low risk (ie, ports, dialysis central lines, or PICC) or high risk (ie, temporary or nontunneled) and single versus concurrent. Results: In total, 64,575 central lines were used in 50,254 encounters. Among these patients, 517 developed a CLABSI; 438 (85%) with a single central line and 74 (15%) with concurrence. Moreover, 4,657 (9%) patients had concurrence (range, 6%–14% by hospital); of these, 74 (2%) had CLABSI, compared to 71 of 7,864 propensity-matched controls (1%). Concurrence patients had a median of 17 NHSN central-line days and 21 total central-line days. In multivariate modeling, patients with more concurrence (>2 of 3 of concurrent central-line days) had an higher risk for CLABSI (adjusted risk ratio, 1.62; 95% CI, 1.1–2.3) compared to controls. In survival analysis, 14,610 concurrent central-line episodes were compared to 31,126 single low-risk central-line episodes; adjusting for comorbidity, total parenteral nutrition, and chemotherapy, the daily excess risk of CLABSI attributable to the concurrent central line was ~80% (hazard ratio 1.78 for 2 high-risk or 2 low-risk central lines; hazard ratio 1.80 for a mix of high- and low-risk central lines) (Fig. 1). Notably, the hazard ratio attributed to a single high-risk line compared to a low-risk line was 1.44 (95% CI, 1.13–1.84). Conclusions: Since a concurrent central line nearly doubles the risk for CLABSI compared to a single low-risk line, the CDC should modify NHSN methodology to better account for this risk.

Funding: None

Disclosures: Scott Fridkin reports that his spouse receives consulting fees from the vaccine industry.

Background: Although antibiotic stewardship programs (ASP) are now required in nursing homes, assimilating and responding to data to improve prescribing in nursing homes is novel. Four Atlanta-based skilled nursing facilities (SNFs) began collaborating (EASIL: Emory Antibiotic Stewardship in Long-Term Care) to share standardized prescribing data to allow interfacility comparisons and action. Methods: After SNF ASPs were evaluated and trained, standardized prescribing logs were submitted (January 2019 to June 2019) including the following data: start date, treatment site, prescriber attribution of order (ie, SNF order vs hospital order) and monthly resident days. SNF-specific point estimates of usage rates were calculated as pooled means for all antibiotic starts, SNF-order starts, and days of therapy (DOT), by treatment site per 1,000 resident days. Duration of urinary tract infection (UTI) therapy was assessed by calculating percentage of SNF-UTI starts over recommended duration defined by the local treatment guideline. Rate ratios (RRs) of use were calculated to compare SNF-specific rates to the largest SNF. The 95% CIs were calculated using normal approximation. Results: Monthly starts ranged from 124 to 177, with a pooled mean of 7.8 antibiotic starts (any type), 4.5 SNF-order starts, and 1.2 SNF-UTI starts per 1,000 resident days. Approximately half of all starts were SNF starts (range, 43%–53%), and less than half of DOT were attributed to SNF starts (range, 35%–45%). Overall, SNF-order treatment sites were most often UTIs (29%), lower respiratory infections (17%), and skin and soft-tissue infections (17%). SNF-order UTI starts per 1,000 patient days varied at 1 SNF (SNF B RR, 1.57; 95% CI, 1.04–2.36). SNF-order UTI DOT per 1,000 patient days was more variable, with SNFs B and C having significantly higher rates (B RR, 1.49, 1.24, and 1.82; C RR, 5.42; 95% CI, 4.65–6.34) than SNF A (Fig. 1). The percentage of SNF-order UTI starts that were over recommended duration ranged from 8% (nitrofurantoin, SNF A) to 100% (fluoroquinolones, SNF C) (Fig. 1). Conclusions: Although UTIs are the single most common reason to prescribe antibiotics after arriving in a SNF, they account for a small fraction of overall starts and an even smaller fraction of DOT. We identified outlier prescribing by different SNFs using 3 metrics, suggesting that distinct corrective actions are necessary to target distinct prescribing challenges (starts, duration, and transitions of care).

Funding: None

Disclosures: Scott Fridkin reports that his spouse receives consulting fees from the vaccine industry.

Background: Current NHSN denominator reporting for central-line–associated bloodstream infection (CLABSI) counts each patient day with n central lines as 1 central-line day. The NHSN does not directly adjust for potential increased risk of CLABSI from concurrent central lines, but the current NHSN standardized infection ratio (SIR) methods may account for differences in concurrence by adjusting for location type. Objective: We examined differences in central-line concurrence by NHSN location type among CLABSI patients. Methods: In a retrospective cohort of adults with CLABSI at 4 hospitals from 2012 to 2017, we linked central-line data to encounter and CLABSI data. Central lines were considered concurrent if they overlapped for >1 day. We calculated proportion of patients with concurrence at both NHSN location and SIR group levels; risk ratios for concurrence between NHSN location types within each SIR group (ie,, locations defined by SIR models as equal “risk”) were determined. Results: In total, 930 CLABIs were identified from 19 NHSN-defined locations that map to 7 SIR groups. Most CLABSIs occurred in locations mapped to either of 2 SIR groups: wards (227, 16% concurrence) and ICUs (294, 33% concurrence). The ward group had 3 NHSN locations (median, 78 CLABSIs) with concurrence range 8% (medical-surgical ward) to 20% (surgical ward). The ICU group had 6 NHSN locations (median, 47.5 CLABSIs) and concurrence ranged from 20% (neurosurgical ICU) to 39% (medical ICU). Despite the noted variations, no risk ratio was statistically different within each SIR group (Table 1). Conclusions: In patients with CLABSIs, the frequency of concurrence varied up to 2-fold between location types within the current NHSN SIR groups, though not statistically significantly. Assessing whether this difference in magnitude persists in all patients with central lines is an important next step in refining risk adjustment methods to account for concurrent central-line use.

Funding: None

Disclosures: Scott Fridkin reports that his spouse receives consulting fees from the vaccine industry.

We have analyzed many discrimination learning difficulties as reflecting multiple stimulus control topographies (SCTs). Nevin & Grace's analysis offers new variables to consider in the design of stimulus-control shaping procedures and cross-setting generalization of newly established behavior. A multiple-SCT perspective also suggests that fixed-trial discrimination procedures may offer advantages for reconciling momentum theory and partial reinforcement extinction effects.