During 2016–2022, Medicare part D beneficiaries filled 8,674,460 clotrimazole-betamethasone dipropionate prescriptions. Annual rates were stable (30.9 prescriptions/1,000 beneficiary-years in 2022, enough for one in every 33 beneficiaries). Diagnostic testing was infrequent, particularly among internal medicine, family medicine, and general practitioners, suggesting potential opportunities to improve diagnostic and prescribing practices.

Background: Medicare claims are frequently used to study Clostridioides difficile infection (CDI) epidemiology. Categorizing CDI based on location of onset and potential exposure is critical in understanding transmission patterns and prevention strategies. While claims data are well-suited for identifying prior healthcare utilization exposures, they lack specimen collection and diagnosis dates to assign likely location of onset. Algorithms to classify CDI onset and healthcare association using claims data have been published, but the degree of misclassification is unknown. Methods: We linked patients with laboratory-confirmed CDI reported to four Emerging Infections Program (EIP) sites from 2016-2020 to Medicare beneficiaries using residence, birth date, sex, and hospitalization and/or healthcare exposure dates. Uniquely linked patients with fee-for-service Medicare A/B coverage and complete EIP case report forms were included. Patients with a claims CDI diagnosis code within ±28 days of a positive CDI test reported to EIP were categorized as hospital-onset (HO), long-term care facility onset (LTCFO), or community-onset (CO, either healthcare facility-associated [COHCFA] or community-associated [CA]) using a previously published algorithm based on claim type, ICD-10-CM code position, and duration of hospitalization (if applicable). EIP classifies CDI into these categories using positive specimen collection date and other information from chart review (e.g. admit/discharge dates). We assessed concordance of EIP and claims case classifications using Cohen’s kappa. Results: Of 10,002 eligible EIP-identified CDI cases, 7,064 were linked to a unique beneficiary; 3,451 met Medicare A/B fee-for-service coverage inclusion criteria. Of these, 650 (19%) did not have a claims diagnosis code ±28 days of the EIP specimen collection date (Table); 48% (313/650) of those without a claims diagnosis code were categorized by EIP as CA CDI. Among those with a CDI diagnosis code, concurrence of claims-based and EIP CDI classification was 68% (κ=0.56). Concurrence was highest for HO and lowest for COHCFA CDI. A substantial number of EIP-classified CO CDIs (30%, Figure) were misclassified as HO using the claims-based algorithm; half of these had a primary ICD-10 diagnosis code of sepsis (226/454; 50%). Conclusions: Evidence of CDI in claims data was found for 81% of EIP-reported CDI cases. Medicare classification algorithms concurred with the EIP classification in 68% of cases. Discordance was most common for community-onset CDI patients, many of whom were hospitalized with a primary diagnosis of sepsis. Misclassification of CO-CDI as HO may bias findings of claims-based CDI studies.

Background: Multiplex molecular tests for infectious diseases can provide highly sensitive results rapidly; however, these tests may more readily detect asymptomatic colonization. There are reports of non-FDA approved laboratory-developed multiplex tests for the diagnosis of urinary tract infections (UTI). Differentiating UTI from asymptomatic bacteriuria is challenging, especially in older adults. The increased sensitivity of multiplex tests may exacerbate this challenge. We sought to describe the use of multiplex testing for UTIs in Medicare claims. Methods: Multiplex testing was identified using carrier claims submitted by non-institutional providers using the Chronic Conditions Warehouse for 2016 – 2022. Because there are no CPT-4 codes specifying UTI multiplex testing, we included claims as described in Figure 1 and categorized claims based on the primary ICD-10-CM diagnosis. The payment amounts for line items related to testing for infectious agents were summed. Laboratories were counted using CLIA numbers listed on corresponding claims. Beneficiaries residing in a nursing home at the time of their claim were identified using stay information derived from the Minimum Dataset 3.0. For comparison, similar characteristics among carrier claims with a CPT-4 code indicating urine culture were also described. Results: Claims for unspecified multiplex molecular tests overall have increased, driven by increases in claims with a primary UTI diagnosis (from 8,521 in 2016 to 386,943 in 2022), while urine cultures have not (Figure 1). In 2022, 65% of all unspecified multiplex tests were linked to a diagnosis of UTI; UTI multiplex claims were associated with 647 laboratories. For UTI claims, the median cost per claim for line items related to multiplex testing was $589 compared to $13 for urine culture-related line items. Overall, 8% of UTI multiplex claims were for beneficiaries residing in a nursing home. Conclusions: Claims for non-FDA approved unspecified multiplex tests associated with a primary diagnosis of UTI have increased >45-times between 2016-2021 and have >45-times higher median costs than urine cultures. The use of this testing in the Medicare population, including nursing home residents, is of potential concern given that inappropriate treatment of asymptomatic bacteriuria has been described to be common in older adults. Research is needed to outline use cases where UTI multiplex testing may be beneficial. Appropriate use of diagnostic testing is important to minimize diagnostic errors and avoid unnecessary antibiotic use.

Background: The 2014 US National Strategy for Combating Antibiotic-Resistant Bacteria aimed to reduce inappropriate inpatient antibiotic use by 20% for monitored conditions, such as community-acquired pneumonia (CAP), by 2020. Clinical guidelines recommend treating uncomplicated CAP with a minimum of 5 days of antibiotic therapy. Total length of therapy (LOT) >7 days or >3 days after clinical improvement is rarely necessary. In a previous study estimating LOT in uncomplicated CAP patients, 71% of patients ≥65 years exceeded recommended duration of antibiotics in 2012–2013 (Yi et al, 2018). We evaluated annual trends in LOT in adults ≥65 years hospitalized with uncomplicated CAP from 2013 to 2020. Methods: We conducted a retrospective cohort study among patients in the CMS database with a primary diagnosis of bacterial or unspecified pneumonia using International Classification of Diseases 9th and 10th Revision codes, length of stay (LOS) of 2–10 days, discharged home with self-care, and not rehospitalized in the 3 days following discharge. Discharge home was used as a surrogate for clinical improvement. Because inpatient LOT is not available in CMS data, we used linear regression to model inpatient LOT as a function of LOS using data on CAP patients ≥65 years from the PINC AI healthcare database. Postdischarge LOT was based on prescriptions filled following discharge. Total LOT was calculated by summing estimated inpatient LOT and actual postdischarge LOT (Fig. 1). Total LOT >7 days and postdischarge LOT >3 days were considered indicators of likely excessive LOT. We reported trends in the proportion of patients with likely excessive LOT during the study period. Results: From 2013 through 2020, there were 400,928 uncomplicated CAP hospitalizations among patients aged ≥65 years. Patients were more likely to be female (55%), and they had a median age of 76 years and a median LOS of 3 days. The median total LOT decreased from 9.5 days in 2013 to 7.7 days in 2020. The proportion of patients with total LOT >7 days decreased from 68% in 2013 to 50% in 2020 (% change, −27%); the proportion with postdischarge LOT >3 days decreased from 73% in 2013 to 62% in 2020 (% change, −16%) (Fig. 2). Conclusions: Likely excessive total LOT for adults ≥65 years hospitalized with uncomplicated CAP decreased by 27% in 2020, a considerable improvement from 2013. However, the high proportion of patients with likely excessive postdischarge LOT in 2020 (62%) demonstrates the need for antibiotic stewardship to optimize prescribing at hospital discharge.

Disclosures: None

Despite understanding its impact on organizational effectiveness, practical guidance on how to train translational team (TT) leaders is lacking. Previously, we developed an evolutionary learning model of TT maturation consisting of three goal-directed phases: (1). team assembly (Formation); (2). conducting research (Knowledge Generation); and (3). dissemination and implementation (Translation). At each phase, the team acquires group-level knowledge, skills, and attitudes (KSAs) that enhance its performance. Noting that the majority of team-emergent KSAs are promoted by leadership behaviors, we examine the SciTS literature to identify the relevant behaviors for each phase. We propose that effective team leadership evolves from a hierarchical, transformational model early in team Formation to a shared, functional leadership model during Translation. We synthesized an integrated model of TT leadership, mapping a generic “functional leadership” taxonomy to relevant leadership behaviors linked to TT performance, creating an evidence-informed Leadership and Skills Enhancement for Research (LASER) training program. Empirical studies indicate that leadership behaviors are stable across time; to enhance leadership skills, ongoing reflection, evaluation, and practice are needed. We provide a comprehensive multi-level evaluation framework for tracking the growth of TT leadership skills. This work provides a framework for assessing and training relevant leadership behaviors for high-performance TTs.

Among nursing home outbreaks of coronavirus disease 2019 (COVID-19) with ≥3 breakthrough infections when the predominant severe acute respiratory coronavirus virus 2 (SARS-CoV-2) variant circulating was the SARS-CoV-2 δ (delta) variant, fully vaccinated residents were 28% less likely to be infected than were unvaccinated residents. Once infected, they had approximately half the risk for all-cause hospitalization and all-cause death compared with unvaccinated infected residents.

One in six nursing home residents and staff with positive SARS-CoV-2 tests ≥90 days after initial infection had specimen cycle thresholds (Ct) <30. Individuals with specimen Ct<30 were more likely to report symptoms but were not different from individuals with high Ct value specimens by other clinical and testing data.

Background: Previously, we reported decreasing postadmission urine-culture rates in hospitalized patients between 2012 and 2017, indicating a possible decrease in hospital-onset urinary tract infections or changes in diagnostic practices in acute-care hospitals (ACHs). In this study, we re-evaluated the trends using more recent data from 2017–2020 to assess whether new trends in hospital urine-culturing practices had emerged. Method: We conducted a longitudinal analysis of monthly urine-culture rates using microbiology data from 355 ACHs participating in the Premier Healthcare Database in 2017–2020. All cultures from the urinary tract collected on or before day 3 were defined as admission urine cultures and those collected on day 4 or later were defined as postadmission urine cultures. We included discharges from months where a hospital reported at least 1 urine culture with microbiology and antimicrobial susceptibility test results. Annual estimates of rates of admission culture and postadmission urine-culture rates were assessed using general estimating equation models with a negative binomial distribution accounting for hospital-level clustering and adjusting for hospital bed size, teaching status, urban–rural designation, discharge month, and census division. Estimated rate for each year (2018, 2019, and 2020) was compared to previous year’s estimated rate using rate ratios (RRs) and 95% confidence intervals (CIs) generated through the multivariable GEE models. Results: From 2017 to 2020, we included 8.7 million discharges and 1,943,540 urine cultures, of which 299,013 (15.4%) were postadmission urine cultures. In 2017–2020, unadjusted admission culture rates were 20.0, 19.6, 17.9, and 18.2 per 100 discharges respectively; similarly, unadjusted postadmission urine-culture rates were 8.6, 7.8, 7.0, and 7.5 per 1,000 patient days. In the multivariable analysis, adjusting for hospital characteristics, no significant changes in admission urine-culture rates were detected during 2017–2019; however, in 2020, admission urine-culture rates increased 6% compared to 2019 (RR, 1.06; 95% CI, 1.02–1.09) (Fig. 1). Postadmission urine-culture rates decreased 4% in 2018 compared to 2017 (RR, 0.96; 95% CI, 0.91–0.99) and 8% in 2019 compared to 2018 (RR, 0.92; 95% CI, 0.87–0.96). In 2020, postadmission urine-culture rates increased 10% compared to 2019 (RR, 1.10; 95% CI, 1.06–1.14) (Fig. 2). Factors significantly associated with postadmission urine-culture rates included discharge month and hospital bed size. For admission urine cultures, discharge month was the only significant factor. Conclusions: Between 2017–2019, postadmission urine-culture rates continued a decreasing trend, while admission culture rates remained unchanged. However, in 2020 both admission and postadmission urine culture rates increased significantly in comparison to 2019.

Funding: None

Disclosures: None

Previously reported associations between hospital-level antibiotic use and hospital-onset Clostridioides difficile infection (HO-CDI) were reexamined using 2012–2018 data from a new cohort of US acute-care hospitals. This analysis revealed significant positive associations between total, third-generation, and fourth-generation cephalosporin, fluoroquinolone, carbapenem, and piperacillin-tazobactam use and HO-CDI rates, confirming previous findings.

Background: Carbapenem-resistant Acinetobacter baumannii (CRAB) is an important cause of healthcare-associated infections with limited treatment options and high mortality. To describe risk factors for mortality, we evaluated characteristics associated with 30-day mortality in patients with CRAB identified through the Emerging Infections Program (EIP). Methods: From January 2012 through December 2017, 8 EIP sites (CO, GA, MD, MN, NM, NY, OR, TN) participated in active, laboratory-, and population-based surveillance for CRAB. An incident case was defined as patient’s first isolation in a 30-day period of A. baumannii complex from sterile sites or urine with resistance to ≥1 carbapenem (excluding ertapenem). Medical records were abstracted. Patients were matched to state vital records to assess mortality within 30 days of incident culture collection. We developed 2 multivariable logistic regression models (1 for sterile site cases and 1 for urine cases) to evaluate characteristics associated with 30-day mortality. Results: We identified 744 patients contributing 863 cases, of which 185 of 863 cases (21.4%) died within 30 days of culture, including 113 of 257 cases (44.0%) isolated from a sterile site and 72 of 606 cases (11.9%) isolated from urine. Among 628 hospitalized cases, death occurred in 159 cases (25.3%). Among hospitalized fatal cases, death occurred after hospital discharge in 27 of 57 urine cases (47.4%) and 21 of 102 cases from sterile sites (20.6%). Among sterile site cases, female sex, intensive care unit (ICU) stay after culture, location in a healthcare facility, including a long-term care facility (LTCF), 3 days before culture, and diagnosis of septic shock were associated with increased odds of death in the model (Fig. 1). In urine cases, age 40–54 or ≥75 years, ICU stay after culture, presence of an indwelling device other than a urinary catheter or central line (eg, endotracheal tube), location in a LTCF 3 days before culture, diagnosis of septic shock, and Charlson comorbidity score ≥3 were associated with increased odds of mortality (Fig. 2). Conclusion: Overall 30-day mortality was high among patients with CRAB, including patients with CRAB isolated from urine. A substantial fraction of mortality occurred after discharge, especially among patients with urine cases. Although there were some differences in characteristics associated with mortality in patients with CRAB isolated from sterile sites versus urine, LTCF exposure and severe illness were associated with mortality in both patient groups. CRAB was associated with major mortality in these patients with evidence of healthcare experience and complex illness. More work is needed to determine whether prevention of CRAB infections would improve outcomes.

Funding: None

Disclosures: None

Background: Studies on the effectiveness of hospital-based interventions often measure hospital-onset infections as the outcome of interest. However, hospital-associated infections may manifest after patient discharge (classified as hospital-associated community-onset, HACO), and the epidemiology may vary by antibiotic resistance (AR) profile. We examined the epidemiology and trends of HACO infections of AR and non–antibiotic-resistant (non-AR) bacteria. Methods: We included clinical community-onset (CO) cultures (obtained sooner than or on day 3 of hospitalization) yielding the bacterial species of interest among hospitalized patients in 260 hospitals in the Premier Healthcare Database from 2012 to 2017. HACO infections were defined as CO cultures in a patient who had a previous hospitalization in the same hospital within 30 days. We examined methicillin resistance among Staphylococcus aureus (MRSA), vancomycin resistance among Enterococcus spp (VRE), carbapenem resistance among Enterobacteriaceae (E. coli, Klebsiella spp, and Enterobacter spp) (CRE), extended-spectrum cephalosporin resistance suggestive of extended-spectrum β-lactamase (ESBL) production in Enterobacteriaceae, carbapenem resistance among Acinetobacter spp (CRAsp), and carbapenem resistance among Pseudomonas aeruginosa (CRPA). We described the proportion of CO infections that were HACO, the proportion of HACO infections from sterile sites, overall HACO rates, and annual trends for sensitive and resistant phenotypes. Generalized estimating equation regression models that accounted for hospital-level clustering were used to estimate annual trends controlling for hospital characteristics and month of discharge. Results: The rate of HACO infections by pathogen ranged from 0.78 to 38.76 per 10,000 hospitalizations; 7%–34% were sterile site infections (Table 1). For each bacterial pathogen, a significantly higher proportion of AR CO infections had a previous hospitalization compared to non-AR CO infections (all χ2, P < .05). The annual trends for AR and non-AR HACO infections between 2012 and 2017 were significantly decreasing for most pathogens, except ESBL HACO infections. Conclusions: Even when using a definition limited to readmission to the same hospital, HACO infections occur commonly with differing rates by pathogen and antibiotic resistance profile. Although these rates are decreasing for most of the pathogens studied, improving surveillance and identifying prevention strategies for these infections are necessary to further reduce the burden of hospital-associated infections.

Funding: None

Disclosures: None

Background: In recent years, the historic declines in the incidence of methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infections (BSIs) in the United States have slowed. We examined trends in the incidence of community-onset (CO) MRSA BSIs among hospitalized persons with and without substance-use diagnoses. Methods: Using data from >200 US hospitals reporting to the Premier Healthcare Database (PHD) during 2012–2017, we conducted a retrospective study among hospitalized persons aged ≥18 years. MRSA BSIs with substance use were defined as hospitalizations having both a blood culture positive for MRSA and at least 1 International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) or ICD-10-CM diagnostic code for substance use including opioids, cocaine, amphetamines, or other substances (excluding cannabis, alcohol, and nicotine). MRSA BSIs were considered community onset when a positive blood culture was collected within 3 days of admission. We assessed annual trends and described characteristics of CO MRSA BSI hospitalizations, stratified by substance use. Results: Of 20,049 MRSA BSIs from 2012 to 2017, 17,634 (88%) were CO. Overall, MRSA BSI incidence decreased 7%, from 178.5 to 166.2 per 100,000 hospitalizations during the study period; However, CO MRSA BSI rates remained stable (152.7 to 149.9 per 100,000 hospitalizations). Among CO MRSA BSIs, 1,838 (10%) were BSIs with substance-use diagnoses; the incidence of CO MRSA BSIs with substance use increased 236% (from 8.2 to 27.6 per 100,000 hospitalizations) and represented a greater proportion of the CO MRSA rate over the study period (Fig. 1). The incidence of CO MRSA BSIs without substance use decreased 15% (from 144.5 to 122.4 per 100,000 hospitalizations). Patients with CO MRSA BSIs with substance use were younger (median, 40 vs 65 years), more likely to be female (50% vs 40%), white (79% vs 69%), and to leave against medical advice (15% vs 1%). Among patients not leaving against medical advice, CO BSI patients with substance-use diagnoses had longer lengths of stay (median, 11 vs 9 days), lower in-hospital mortality (9% vs 14%), and higher hospitalization costs (median, $22,912 vs $17,468) compared to patients without substance-use diagnoses. Conclusions: Although the overall CO MRSA BSI rate remained unchanged from 2012 to 2017, infections with substance use diagnoses increased >3-fold, and infections without substance use diagnoses decreased. These data suggest that the emergence of MRSA associated with substance-use diagnoses threatens potential progress in reducing the incidence of CO MRSA infections. Additional strategies may be needed to prevent MRSA BSI in patients with substance-use diagnoses, and to maintain national progress in the reduction of MRSA infections overall.

Funding: None

Disclosures: None

Background: Most invasive methicillin-resistant Staphylococcus aureus (iMRSA) infections have onset in the community but are associated with healthcare exposures. More than 25% of cases with healthcare exposure occur in nursing homes (NHs) where facility-specific iMRSA rates vary widely. We assessed associations between nursing home characteristics and iMRSA incidence rates to help target prevention efforts in NHs. Methods: We used active, laboratory- and population-based surveillance data collected through the Emerging Infections Program during 2011–2015 from 25 counties in 7 states. NH-onset cases were defined as isolation of MRSA from a normally sterile site in a surveillance area resident who was in a NH within 3 days before the index culture. We calculated MRSA incidence (cases per NH resident day) using Centers for Medicare & Medicaid Services (CMS) skilled nursing facility cost reports and described variation in iMRSA incidence by NH. We used Poisson regression with backward selection, assessing variables for collinearity, to estimate adjusted rate ratios (aRRs) for NH characteristics (obtained from the CMS minimum dataset) associated with iMRSA rates. Results: Of 590 surveillance area NHs included in analysis, 89 (15%) had no NH-onset iMRSA infections. Rates ranged from 0 to 23.4 infections per 100,000 resident days. Increased rate of NH-onset iMRSA infection occurred with increased percentage of residents in short stay ≤30 days (aRR, 1.09), exhibiting wounds or infection (surgical wound [aRR, 1.08]; vascular ulcer/foot infection [aRR, 1.09]; multidrug-resistant organism infection [aRR, 1.13]; receipt of antibiotics [aRR, 1.06]), using medical devices or invasive support (ostomy [aRR, 1.07]; dialysis [aRR, 1.07]; ventilator support [aRR, 1.17]), carrying neurologic diagnoses (cerebral palsy [aRR, 1.14]; brain injury [aRR, 1.1]), and demonstrating debility (requiring considerable assistance with bed mobility [aRR, 1.05]) (Table). iMRSA rates decreased with increased percentage of residents receiving influenza vaccination (aRR, 0.96) and with the presence of any patients in isolation for any active infection (aRR, 0.83). Conclusions: iMRSA incidence varies greatly across nursing homes, with many NH patient and facility characteristics associated with NH-onset iMRSA rate differences. Some associations (short stay, wounds and infection, medical device use and invasive support) suggest that targeted interventions utilizing known strategies to decrease transmission may help to reduce infection rates, while others (neurologic diagnoses, influenza vaccination, presence of patients in isolation) require further exploration to determine their role. These findings can help identify NHs in other areas more likely to have higher rates of NH-onset iMRSA who could benefit from interventions to reduce infection rates.

Funding: None

Disclosures: None

Background: Epidemiological studies have utilized administrative discharge diagnosis codes to identify methicillin-resistant and methicillin-sensitive Staphylococcus aureus (MRSA and MSSA) infections and trends, despite debate regarding the accuracy of utilizing codes for this purpose. We assessed the sensitivity and positive predictive value (PPV) of MRSA- and MSSA-specific diagnosis codes, trends, characteristics, and outcomes of S. aureus hospitalizations by method of identification. Methods: Clinical micro biology results and discharge data from geographically diverse US hospitals participating in the Premier Healthcare Database from 2012–2017 were used to identify monthly rates of MRSA and MSSA. Positive MRSA or MSSA clinical cultures and/or a MRSA- or MSSA-specific International Classification of Diseases, Ninth/Tenth Revision, Clinical Modification (ICD-9/10 CM) diagnosis codes from adult inpatients (aged ≥18 years) were included as S. aureus hospitalizations. Septicemia was defined as a positive blood culture or a MRSA or MSSA septicemia code. Sensitivity and PPV for codes were calculated for hospitalizations where admission status was not listed as transfer; true infection was considered a positive clinical culture. Negative binominal regression models measured trends in rates of MRSA and MSSA per 1,000 hospital discharges. Results: We identified 168,634 MRSA and 148,776 MSSA hospitalizations in 256 hospitals; 17% of MRSA and 21% of MSSA were septicemia. Less than half of all S. aureus hospitalizations (49% MRSA, 46% MSSA) and S. aureus septicemia hospitalizations (37% MRSA, 38% MSSA) had both a positive culture and diagnosis code (Fig. 1). Sensitivity of MRSA codes in identifying positive cultures was 61% overall and 56% for septicemia, PPV was 62% overall and 53% for septicemia. MSSA codes had a sensitivity of 49% in identifying MSSA cultures and 52% for MSSA septicemia; PPV was 69% overall and 62% for septicemia. Despite low sensitivity, MRSA trends are similar for cultures and codes, and MSSA trends are divergent (Fig. 2). For hospitalizations with septicemia, mortality was highest among those with a blood culture only (31.3%) compared to hospitalizations with both a septicemia code and blood culture (16.6%), and septicemia code only (14.7%). Conclusions: ICD diagnosis code sensitivity and PPV for identifying infections were consistently poor in recent years. Less than half of hospitalizations have concordant microbiology laboratory results and diagnosis codes. Rates and trend estimates for MSSA differ by method of identification. Using diagnosis codes to identify S. aureus infections may not be appropriate for descriptive epidemiology or assessing trends due to significant misclassification.

Funding: None

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

Background: Microbiology data are utilized to quantify epidemiology and trends in pathogens, antimicrobial resistance, and bloodstream infections. Understanding variability and trends in rates of hospital-level blood culture utilization may be important for interpreting these findings. Methods: We used clinical microbiology results and discharge data to identify monthly blood culture rates from US hospitals participating in the Premier Healthcare Database during 2012–2017. We included all discharges from months where a hospital reported at least 1 blood culture with microbiology and antimicrobial susceptibility results. Blood cultures drawn on or before day 3 were defined as admission cultures (ACs); blood cultures collected after day 3 were defined as a postadmission cultures (PACs). The AC rate was defined as the proportion of all hospitalizations with an AC. The PAC rate was defined as the number of days with a PAC among all patient days. Generalized estimating equation regression models that accounted for hospital-level clustering with an exchangeable correlation matrix were used to measure associations of monthly rates with hospital bed size, teaching status, urban–rural designation, region, month, and year. The AC rates were modeled using logistic regression, and the PAC rates were modeled using a Poisson distribution. Results: We included 11.7 million hospitalizations from 259 hospitals, accounting for nearly 52 million patient days. The median annual hospital-level AC rate was 27.1%, with interhospital variation ranging from 21.1% (quartile 1) to 35.2% (quartile 3) (Fig. 1). Multivariable models revealed no significant trends over time (P = .74), but statistically significant associations between AC rates with month (P < .001) and region (P = .003), associations with teaching status (P = .063), and urban-rural designation (P = .083) approached statistical significance. There was no association with bed size (P = .38). The median annual hospital-level PAC rate was 11.1 per 1,000 patient days, and interhospital variability ranged from 7.6 (quartile 1) to 15.2 (quartile 3) (Fig. 2). Multivariable models of PAC rates showed no significant trends over time (P = .12). We found associations between PAC rates with month (P = .016), bed size (P = .030), and teaching status (P = .040). PAC rates were not associated with urban–rural designation (P = .52) or region (P = .29). Conclusions: Blood culture utilization rates in this large cohort of hospitals were unchanged between 2012 and 2017, though substantial interhospital variability was detected. Although both AC and PAC rates vary by time of year and potentially by teaching status, AC rates vary by geographic characteristics whereas PAC rates vary by bed size. These factors are important to consider when comparing rates of bloodstream infections by hospital.

Funding: None

Disclosures: None

We compared methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infections (BSIs) captured by culture-based surveillance and MRSA septicemia hospitalizations captured by administrative coding using statewide hospital discharge data in Connecticut from 2010 to 2018. Observed discrepancies between identification methods suggest administrative coding is inappropriate for assessing trends in MRSA BSIs.