OBJECTIVE:To examine the relationship between race and ethnicity and central line-associated bloodstream infections (CLABSI) while accounting for inherent differences in CLABSI risk related to central venous catheter (CVC) type. DESIGN:Retrospective cohort analysis. SETTING:Acute care facilities within an academic healthcare system. PATIENTS:Adult inpatients from January 2012 through December 2017 with CVC present for ≥2 contiguous days. METHODS:We describe variability in demographics, comorbidities, CVC type/configuration, and CLABSI rate by patient's race and ethnicity. We estimated the unadjusted risk of CLABSI for each demographic and clinical characteristic and then modelled the effect of race on time to CLABSI, adjusting for total parenteral nutrition use and CVC type. We also performed exploratory analysis replacing race and ethnicity with social vulnerability index (SVI) metrics. RESULTS:32,925 patients with 57,642 CVC episodes met inclusion criteria, most of which (51,348, 89%) were among non-Hispanic White or non-Hispanic Black patients. CVC types differed between race/ethnicity groups. However, after adjusting for CVC type, configuration, and indication in an adjusted cox regression, the risk of CLABSI among non-Hispanic Black patients did not significantly differ from non-Hispanic White patients (adjusted hazard ratio [aHR] 1.19; 95% confidence interval [CI]: 0.94, 1.51). The odds of having a CLABSI among the most vulnerable SVI subset compared to the less vulnerable was no different (odds ratio [OR] 0.95; 95% CI: 0.75-1.2). CONCLUSIONS:We did not find a difference in CLABSI risk between non-Hispanic White and non-Hispanic Black patients when adjusting for CLABSI risk inherent in type and configuration of CVC.

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

Objectives: Estimate incidence of and risks for SARS-CoV-2 infection among nursing home staff in the state of Georgia during the 2020-2021 Winter COVID-19 Surge in the United States. Design: Serial survey and serologic testing at 2 time points with 3-month interval exposure assessment. Setting and Participants: Fourteen nursing homes in the state of Georgia; 203 contracted or employed staff members from those 14 participating nursing homes who were seronegative at the first time point and provided a serology specimen at second time point, at which time they reported no COVID-19 vaccination or only very recent vaccination (<= 4 weeks). Methods: Interval infection was defined as seroconversion to antibody presence for both nucleocapsid protein and spike protein. We estimated adjusted odds ratios (aORs) and 95% CIs by job type, using multivariable logistic regression, accounting for community-based risks including interval community incidence and interval change in resident infections per bed. Results: Among 203 eligible staff, 72 (35.5%) had evidence of interval infection. In multivariable analysis among unvaccinated staff, staff SARS-CoV-2 infection-induced seroconversion was significantly higher among nurses and certified nursing assistants accounting for race and interval infection incidence in both the community and facility (aOR 5.3, 95% CI 1.0-28.4). This risk persisted but was attenuated when using the full study cohort including those with very recent vaccination. Conclusions and Implications: Midway through the first year of the pandemic, job type continues to be associated with increased risk for infection despite enhanced infection prevention efforts including routine screening of staff. These results suggest that mitigation strategies prior to vaccination did not eliminate occupational risk for infection and emphasize critical need to maximize vaccine utilization to eliminate excess risk among front-line providers. (C) 2022 AMDA -The Society for Post-Acute and Long-Term Care Medicine.

Abstract Background Clostridioides difficile infection (CDI) incidence estimates vary between geographic regions; few studies have evaluated the impact of CDI test order frequency on estimated CDI incidence. We evaluated this impact in a sample of hospitals at two CDC Emerging Infections Program (EIP) sites. Methods Daily surveillance was conducted for diarrhea among inpatients at 5 acute care hospitals (2,379 beds) in EIP sites in NY (2 hospitals) and GA (3 hospitals) during two 10-workday periods in 2020 and 2021. Diarrhea onset, test orders, and specimen collection status were ascertained. Stools were tested by PCR/NAAT initially or after negative EIA toxin. Differences in diarrhea incidence, testing frequency, and CDI positivity across site, care locations and hospitals were compared using Wilcoxon rank sum test. Correlates of CDI testing and positivity were assessed using modified Poisson regression. Estimates of incidence using EIP methodology at 5 hospitals was compared between sites using Mantel-Hanzel summary rate ratio. Results Surveillance of 38,365 patient-days (PD) identified 860 diarrhea cases from 107 patient-care locations mapped to 26 unique NHSN defined location-types. Incidence of diarrhea was 22.4/1000 PD (medians 25.8 NY, 16.2 GA, P< 0.01); with similar proportions of diarrhea being hospital-onset (66%) and CDI positive (17%) by site. Overall, 35% were tested for CDI (21% NY, 49% GA, P< 0.01). Percent tested varied by NHSN location type (Figure). Regression models identified location-type (oncology, critical care), laxatives use, chemotherapy, and residing in EIP catchment area predictive of testing (Figure). Adjusting for these factors, NY was 49% less likely than GA to test (aRR 0.51, 95% CI 0.40-0.63). Simulation of EIP methods estimated NY had a 38% lower incidence of CDI than GA (summary rate ratio 0.62, 95% CI, 0.54-0.71). Figure Incidence of Diarrheal Episodes (A) and Proportions Tested (B) among Hospitalized Patients, 2021 (solid, ward; open, critical care; grey, oncology), and adjusted relative risk (solid circles) with 95% confidence intervals (whisker) of independent predictors of testing for CDI (C). Conclusion After adjusting for patient characteristics (e.g., location-type, laxative use), the likelihood of testing still differed between NY and GA sites; the magnitude of the differences in testing was similar to that observed in estimated CDI incidence. Testing practices likely influence surveillance data and is a consideration when comparing data across regions. Disclosures Scott Fridkin, MD, Pfizer: Grant/Research Support christopher J. Myers, MS, Infectious diseases, Pfizer: Grant/Research Support Udodirim N. Onwubiko, MBBS MPH, Pfizer: Grant/Research Support William C. Dube, MPH, Pfizer: Grant/Research Support Sahil Khanna, MBBS, MS, Pfizer: Grant/Research Support Joann M. Zamparo, MPH, Pfizer: Employee|Pfizer: Stocks/Bonds Frederick J. Angulo, DVM PhD, Pfizer Vaccines: Employee|Pfizer Vaccines: Stocks/Bonds Ghinwa Dumyati, MD, Pfizer: Grant/Research Support.

Crack cocaine is a highly addictive drug. To learn more about crack addiction, long-term crack smokers who had never met the DSM-IV criteria for lifetime cocaine dependence were compared with those who had. The study sample consisted of crack users (n=172) from the Dayton, Ohio, area who were interviewed periodically over 8 years. Data were collected on a range of variables including age of crack initiation, frequency of recent use, and lifetime cocaine dependence. Cocaine dependence was common with 62.8% of the sample having experienced it. There were no statistically significant differences between dependent and non-dependent users for age of crack initiation or frequency of crack use. In terms of sociodemographics, only race/ethnicity was significant, with proportionally fewer African-Americans than whites meeting the criteria for cocaine dependence. Controlling for sociodemographics, partial correlation analysis showed positive, statistically significant relationships between lifetime cocaine dependence and anti-social personality disorder, attention deficit/hyperactivity disorder, and lifetime dependence on alcohol, cannabis, amphetamine, sedative-hypnotics, and opioids. These results highlight the importance addressing race/ethnicity and comorbid disorders when developing, implementing, and evaluating interventions targeting people who use crack cocaine. Additional research is needed to better understand the role of race/ethnicity in the development of cocaine dependence resulting from crack use.

Dark chamber experiments were conducted to study the SOA formed from the oxidation of α-pinene and Δ-carene under different peroxy radical (RO2) fate regimes: RO2 + NO3, RO2 + RO2, and RO2 + HO2. SOA mass yields from α-pinene oxidation were <1 to ∼25% and strongly dependent on available OA mass up to ∼100 μg m-3. The strong yield dependence of α-pinene oxidation is driven by absorptive partitioning to OA and not by available surface area for condensation. Yields from Δ-carene + NO3 were consistently higher, ranging from ∼10-50% with some dependence on OA for <25 μg m-3. Explicit kinetic modeling including vapor wall losses was conducted to enable comparisons across VOC precursors and RO2 fate regimes and to determine atmospherically relevant yields. Furthermore, SOA yields were similar for each monoterpene across the nominal RO2 + NO3, RO2 + RO2, or RO2 + HO2 regimes; thus, the volatility basis sets (VBS) constructed were independent of the chemical regime. Elemental O/C ratios of ∼0.4-0.6 and nitrate/organic mass ratios of ∼0.15 were observed in the particle phase for both monoterpenes in all regimes, using aerosol mass spectrometer (AMS) measurements. An empirical relationship for estimating particle density using AMS-derived elemental ratios, previously reported in the literature for non-nitrate containing OA, was successfully adapted to organic nitrate-rich SOA. Observations from an NO3- chemical ionization mass spectrometer (NO3-CIMS) suggest that Δ-carene more readily forms low-volatility gas-phase highly oxygenated molecules (HOMs) than α-pinene, which primarily forms volatile and semivolatile species, when reacted with NO3, regardless of RO2 regime. The similar Δ-carene SOA yields across regimes, high O/C ratios, and presence of HOMs, suggest that unimolecular and multistep processes such as alkoxy radical isomerization and decomposition may play a role in the formation of SOA from Δ-carene + NO3. The scarcity of peroxide functional groups (on average, 14% of C10 groups carried a peroxide functional group in one test experiment in the RO2 + RO2 regime) appears to rule out a major role for autoxidation and organic peroxide (ROOH, ROOR) formation. The consistently substantially lower SOA yields observed for α-pinene + NO3 suggest such pathways are less available for this precursor. The marked and robust regime-independent difference in SOA yield from two different precursor monoterpenes suggests that in order to accurately model SOA production in forested regions the chemical mechanism must feature some distinction among different monoterpenes.

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: NoneDisclosures: None

AbstractObjective:We evaluated the impact of test-order frequency per diarrheal episodes on Clostridioides difficile infection (CDI) incidence estimates in a sample of hospitals at 2 CDC Emerging Infections Program (EIP) sites.Design:Observational survey.Setting:Inpatients at 5 acute-care hospitals in Rochester, New York, and Atlanta, Georgia, during two 10-workday periods in 2020 and 2021.Outcomes:We calculated diarrhea incidence, testing frequency, and CDI positivity (defined as any positive NAAT test) across strata. Predictors of CDI testing and positivity were assessed using modified Poisson regression. Population estimates of incidence using modified Emerging Infections Program methodology were compared between sites using the Mantel-Hanzel summary rate ratio.Results:Surveillance of 38,365 patient days identified 860 diarrhea cases from 107 patient-care units mapped to 26 unique NHSN defined location types. Incidence of diarrhea was 22.4 of 1,000 patient days (medians, 25.8 for Rochester and 16.2 for Atlanta; P < .01). Similar proportions of diarrhea cases were hospital onset (66%) at both sites. Overall, 35% of patients with diarrhea were tested for CDI, but this differed by site: 21% in Rochester and 49% in Atlanta (P < .01). Regression models identified location type (ie, oncology or critical care) and laxative use predictive of CDI test ordering. Adjusting for these factors, CDI testing was 49% less likely in Rochester than Atlanta (adjusted rate ratio, 0.51; 95% confidence interval [CI], 0.40–0.63). Population estimates in Rochester had a 38% lower incidence of CDI than Atlanta (summary rate ratio, 0.62; 95% CI, 0.54–0.71).Conclusion:Accounting for patient-specific factors that influence CDI test ordering, differences in testing practices between sites remain and likely contribute to regional differences in surveillance estimates.

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.

Abstract Background Extended-spectrum β-lactamase (ESBL)–producing Enterobacterales are frequent causes of urinary tract infections (UTIs). Severe infections caused by ESBL Enterobacterales are often treated with carbapenems, but optimal treatment for less severe infections such as UTIs is unclear. Methods This retrospective cohort study included patients admitted to 4 hospitals in an academic healthcare system with an ESBL UTI treated with either a noncarbapenem β-lactam (NCBL) or a carbapenem for at least 48 hours from 1 April 2014 to 30 April 2018. Those who received an NCBL were compared to those receiving a carbapenem, with a primary outcome of hospital length of stay (LOS) and secondary outcomes of clinical and microbiological response, days until transition to oral therapy, rate of relapsed infection, and rate of secondary infections with a multidrug-resistant organism. Results Characteristics were similar among patients who received carbapenems (n = 321) and NCBLs (n = 171). There was no difference in LOS for the NCBL group compared to the carbapenem group (13 days vs 15 days, P = .66). The NCBL group had higher rates of microbiologic eradication (98% vs 92%, P = .002), shorter time to transition to oral therapy (5 days vs 9 days, P < .001), shorter overall durations of therapy (7 days vs 10 days, P < .001), and lower rates of relapsed infections (5% vs 42%, P = .0003). Conclusions Patients treated with NCBLs had similar LOS, higher rates of culture clearance, and shorter durations of antibiotic therapy compared to patients treated with carbapenems, suggesting that treatment for ESBL UTIs should not be selected solely based on phenotypic resistance.

Abstract Objectives: To estimate prior severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection among skilled nursing facility (SNF) staff in the state of Georgia and to identify risk factors for seropositivity as of fall 2020. Design: Baseline survey and seroprevalence of the ongoing longitudinal Coronavirus 2019 (COVID-19) Prevention in Nursing Homes study. Setting: The study included 14 SNFs in the state of Georgia. Participants: In total, 792 SNF staff employed or contracted with participating SNFs were included in this study. The analysis included 749 participants with SARS-CoV-2 serostatus results who provided age, sex, and complete survey information. Methods: We estimated unadjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) for potential risk factors and SARS-CoV-2 serostatus. We estimated adjusted ORs using a logistic regression model including age, sex, community case rate, SNF resident infection rate, working at other facilities, and job role. Results: Staff working in high-infection SNFs were twice as likely (unadjusted OR, 2.08; 95% CI, 1.45–3.00) to be seropositive as those in low-infection SNFs. Certified nursing assistants and nurses were 3 times more likely to be seropositive than administrative, pharmacy, or nonresident care staff: unadjusted OR, 2.93 (95% CI, 1.58–5.78) and unadjusted OR, 3.08 (95% CI, 1.66–6.07). Logistic regression yielded similar adjusted ORs. Conclusions: Working at high-infection SNFs was a risk factor for SARS-CoV-2 seropositivity. Even after accounting for resident infections, certified nursing assistants and nurses had a 3-fold higher risk of SARS-CoV-2 seropositivity than nonclinical staff. This knowledge can guide prioritized implementation of safer ways for caregivers to provide necessary care to SNF residents.

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: Healthcare personnel (HCP) may be at increased risk for COVID-19, but differences in risk by work activities are poorly defined Centers for Disease Control and Prevention recommends cohorting hospitalized patients with COVID-19 to reduce in-hospital transmission of SARS-CoV-2, but it is unknown if occupational and non-occupational behaviors differ based on exposure to COVID-19 units Methods: We analyzed a subset of HCP from an ongoing CDC-funded SARSCoV- 2 serosurveillance study HCP were recruited from four Atlanta hospitals of different sizes and patient populations All HCP completed a baseline REDCap survey We used logistic regression to compare occupational activities and infection prevention practices among HCP stratified by exposure to COVID-19 units: low (0% of shifts), medium (1-49% of shifts) or high (≥50% of shifts) Results: Of 211 HCP enrolled (36% emergency department [ED] providers, 35% inpatient RNs, 17% inpatient MDs/APPs, 7% radiology technicians and 6% respiratory therapists [RTs]), the majority (79%) were female and the median age was 35 years Nearly half of the inpatient MD/APPs (46%) and RNs (47%) and over two-thirds of the RTs (67%) worked primarily in the ICU Aerosol generating procedures were common among RNs, MD/APPs, and RTs (26-58% performed ≥1), but rare among ED providers (0-13% performed ≥1) Compared to HCP with low exposure to COVID-19 units, those with medium or high exposure spent a similar proportion of shifts directly at the bedside and were about as likely to practice universal masking Being able to consistently social distance from co-workers was rare (33%);HCP with high exposure to COVID-19 units were less likely to report social distancing in the workplace compared to those with low exposure;however, this was not significantly different (OR 0 6;95% CI: 0 3, 1 1) Concerns about personal protective equipment in COVID-19 units were similar across levels of exposure (Table 1) Conclusion: The proportion of time spent in dedicated COVID-19 units did not appear to influence time HCP spend directly at the bedside or infection prevention practices (social distancing and universal masking) in the workplace Risk for SARSCoV- 2 infection in HCP may depend more on factors acting at the individual level rather than those related to location of work (Table Presented)

This cohort study examines rates of central line-associated bloodstream infection (CLABSI) among patients with single vs concurrent central venous catheters to determine variations in risk associated with use of multiple central venous catheters. Question Do current methods to measure performance of central line-associated bloodstream infection (CLABSI) prevention interventions adequately account for variations in patient risk associated with concurrent use of multiple central venous catheters (CVCs) that are medically indicated? Findings In this cohort study of 50 & x202f;254 patients at 4 hospitals, the risk for CLABSI associated with a second concurrent CVC was estimated to be approximately 80%, nearly 2-fold the risk of CLABSI for a patient with a single CVC. Meaning This finding suggests that risk for CLABSI associated with a second CVC is significant and of large magnitude, justifying efforts to modify methods of performance measurement involving CLABSI prevention. Importance National Healthcare Safety Network methods for central line-associated bloodstream infection (CLABSI) surveillance do not account for potential additive risk for CLABSI associated with use of 2 central venous catheters (CVCs) at the same time (concurrent CVCs); facilities that serve patients requiring high acuity care with medically indicated concurrent CVC use likely disproportionally incur Centers for Medicare & Medicaid Services payment penalties for higher CLABSI rates. Objective To quantify the risk for CLABSI associated with concurrent use of a second CVC. Design, Setting, and Participants This retrospective cohort study included adult patients with 2 or more days with a CVC at 4 geographically separated general acute care hospitals in the Atlanta, Georgia, area that varied in size from 110 to 580 beds, from January 1, 2012, to December 31, 2017. Variables included clinical conditions, central line-days, and concurrent CVC use. Patients were propensity score-matched for likelihood of concurrence (limited to 2 CVCs), and conditional logistic regression modeling was performed to estimate the risk of CLABSI associated with concurrence. Episodes of CVC were categorized as low or high risk and single vs concurrent use to evaluate time to CLABSI with Cox proportional hazards regression models. Data were analyzed from January to June 2019. Exposures Two CVCs present at the same time. Main Outcomes and Measures Hospitalizations in which a patient developed a CLABSI, allowing estimation of patient risk for CLABSI and daily hazard for a CVC episode ending in CLABSI. Results Among a total of 50 & x202f;254 patients (median [interquartile range] age, 59 [45-69] years; 26 & x202f;661 [53.1%] women), 64 & x202f;575 CVCs were used and 647 CLABSIs were recorded. Concurrent CVC use was recorded in 6877 patients (13.7%); the most frequent indications for concurrent CVC use were nutrition (554 patients [14.1%]) or hemodialysis (1706 patients [43.4%]). In the propensity score-matched cohort, 74 of 3932 patients with concurrent CVC use (1.9%) developed CLABSI, compared with 81 of 7864 patients with single CVC use (1.0%). Having 2 CVCs for longer than two-thirds of a patient's CVC use duration was associated with increased likelihood of developing a CLABSI, adjusting for central line-days and comorbidities (adjusted risk ratio, 1.62; 95% CI, 1.10-2.33; P = .001). In survival analysis adjusting for sex, receipt of chemotherapy or total parenteral nutrition, and facility, compared with a single CVC, the daily hazard for 2 low-risk CVCs was 1.78 (95% CI, 1.35-2.34; P < .001), while the daily hazard for 1 low-risk and 1 high-risk CVC was 1.80 (95% CI, 1.42-2.28; P < .001), and the daily hazard for 2 high-risk CVCs was 1.78 (95% CI, 1.14-2.77; P = .01). Conclusions and Relevance These findings suggest that concurrent CVC use is associated with nearly 2-fold the risk of CLABSI compared with use of a single low-risk CVC. Performance metrics for CLABSI should change to account for variations of this intrinsic patient risk among facilities to reduce biased comparisons and resultant penalties applied to facilities that are caring for more patients with medically indicated concurrent CVC use.

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

Peer comparison reduces unnecessary outpatient antibiotic prescribing, but no prescribing metric has been validated for inpatient comparison. We aimed to evaluate if an electronically derived antibiotic prescribing metric correlated with indicated antibiotic days in hospitalized patients. We previously created a hospitalist-specific adjusted antibiotic use metric (observed:expected [O:E]) for National Healthcare Safety Network-defined broad-spectrum antibiotics. From May-Oct 2019 at four Emory Healthcare hospitals, we identified outlier hospitalists prescribing in the top (high O:E) and bottom (low O:E) 15th percentile. We randomly selected 10 days of antibiotic administration from each outlier and reviewed days with > 2 days of consecutive days of antibiotics. For pneumonia, chronic obstructive pulmonary disease (COPD), or urinary tract infection (UTI) we determined if each day of antibiotics was indicated, assuming the diagnosis was accurate. We compared high vs. low O:E providers and used regression modeling to determine if the metric predicted indicated days of antibiotics. Among 997 days, 510 (51%) were from high and 487 (49%) from low O:E providers. High O:E providers had a greater proportion of days with > 2 prior days of antibiotics (60%) compared to low O:E providers (54%, p = 0.03). In the subset of days with > 2 prior days of antibiotics (n = 569), high O:E providers had more patient-days with longer hospital stays, diabetes and Charlson comorbidity index (CCI) >3, and fewer days supervising (resident/advanced practice provider, Table 1). The primary diagnosis was pneumonia, COPD exacerbation or UTI in 260 (25%) days; 91% were indicated based on duration with no difference between high and low O:E providers (88% vs. 94%, p = 0.1). After controlling for days of hospitalization, CCI, immunocompromised status, and supervisory role, a high O:E was not associated with indicated antibiotic use (OR 0.5, 95% CI 0.2 – 1.3). Description of days with a patient on greater than two days of antibiotics, comparing high- versus low-metric providers A high hospitalist antibiotic prescribing metric correlated with patients receiving > 2 consecutive days of antibiotics on any given day but did not predict unindicated antibiotic use for a subset of diagnoses. Evaluating indicated use by validating diagnoses may improve metric performance. Jessica Howard-Anderson, MD, Antibacterial Resistance Leadership Group (ARLG) (Other Financial or Material Support, The ARLG fellowship provides salary support for ID fellowship and mentored research training)

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: NoneDisclosures: Scott Fridkin reports that his spouse receives consulting fees 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)

Biomass burning (BB) is a large source of reactive compounds in the atmosphere. While the daytime photochemistry of BB emissions has been studied in some detail, there has been little focus on nighttime reactions despite the potential for substantial oxidative and heterogeneous chemistry. Here, we present the first analysis of nighttime aircraft intercepts of agricultural BB plumes using observations from the NOAA WP-3D aircraft during the 2013 Southeast Nexus (SENEX) campaign. We use these observations in conjunction with detailed chemical box modeling to investigate the formation and fate of oxidants (NO3, N2O5, O3, and OH) and BB volatile organic compounds (BBVOCs), using emissions representative of agricultural burns (rice straw) and western wildfires (ponderosa pine). Field observations suggest NO3 production was approximately 1 ppbv hr-1, while NO3 and N2O5 were at or below 3 pptv, indicating rapid NO3/N2O5 reactivity. Model analysis shows that >99% of NO3/N2O5 loss is due to BBVOC + NO3 reactions rather than aerosol uptake of N2O5. Nighttime BBVOC oxidation for rice straw and ponderosa pine fires is dominated by NO3 (72, 53%, respectively) but O3 oxidation is significant (25, 43%), leading to roughly 55% overnight depletion of the most reactive BBVOCs and NO2.