During the coronavirus disease 2019 pandemic, mathematical modeling has been widely used to understand epidemiological burden, trends, and transmission dynamics, to facilitate policy decisions, and, to a lesser extent, to evaluate infection prevention and control (IPC) measures. This review highlights the added value of using conventional epidemiology and modeling approaches to address the complexity of healthcare-associated infections (HAI) and antimicrobial resistance. It demonstrates how epidemiological surveillance data and modeling can be used to infer transmission dynamics in healthcare settings and to forecast healthcare impact, how modeling can be used to improve the validity of interpretation of epidemiological surveillance data, how modeling can be used to estimate the impact of IPC interventions, and how modeling can be used to guide IPC and antimicrobial treatment and stewardship decision-making. There are several priority areas for expanding the use of modeling in healthcare epidemiology and IPC. Importantly, modeling should be viewed as complementary to conventional healthcare epidemiological approaches, and this requires collaboration and active coordination between IPC, healthcare epidemiology, and mathematical modeling groups.

Background: Diagnosis and management of suspected urinary tract infection (UTI) in outpatient settings has been shown to be suboptimal. We previously developed a set of stewardship metrics for UTIs based on electronic health record (EHR) data (Antimicrobial Stewardship & Healthcare Epidemiology 2022;2 suppl 1:S5–S6. doi:10.1017/ash.2022). A tier-based approach was used to more fully capture antibiotic use associated with genitourinary (GU) symptoms and diagnoses. Herein we report a preliminary analysis of validity and reliability of these metrics based on chart abstraction. Methods: The study cohort consisted of patients who visited Veterans Affairs emergency departments or primary care clinics between 2015 and 2022 and who had a GU diagnosis based on International Classification of Disease, Tenth Revision (ICD-10) codes, divided into 3 categories: tier 1 (antibiotics always indicated), tier 2 (antibiotics sometimes indicated), and tier 3 (antibiotics not indicated). Visits related to urological procedures, nontarget settings, or concomitant non-GU infections were excluded. Cases were randomly sampled for manual review from within 8 strata based on tier, use of antibiotics, and visit type. An infectious disease physician and pharmacist abstracted charts using a standardized data-collection instrument. Clinical judgments regarding diagnosis and treatment were recorded on a Likert scale without knowledge of how the patient was managed. The intraclass correlation coefficient (ICC) was used to estimate interrater reliability. Results: To date, 148 cases have been reviewed (50 by both reviewers). Mean (SD) age was 67.5 (15.3) years and 12.2% were female. In a majority of tier 1 and 2 visits in which antibiotics were given, the reviewers found evidence for GU infection (69.7%) and favored prescribing of antibiotics (60.6%) (Table). In contrast, most patients in the tier 3 category who received antibiotics were judged to have noninfectious conditions (eg, benign prostatic hypertrophy) and to not require antibiotics. In the subset of records examined by both reviewers, the interrater reliability of judgments of whether antibiotics were warranted was good (ICC = .704). Conclusions: This preliminary validation provides support for a tier-based approach for stewardship metrics for GU conditions that relies upon electronic data to identify patients for whom antibiotics are generally not indicated.

Disclosures: None

Background: Antibiotic resistance is pervasive in the Veterans’ Affairs (VA) healthcare system, with rates of fluoroquinolone and trimethoprim–sulfamethoxazole (TMP/SMX) resistance approaching 30% in E. coli urinary isolates. The efficacy of antimicrobial treatment is critically dependent on the susceptibility of the infecting pathogen; however, prescription decisions are often made empirically in practice. We analyzed susceptibility profiles of enteric gram-negative rods (Enterobacterales) from clinical urine cultures collected from ambulatory patients receiving care in VA clinics and emergency departments. Our goals were (1) to develop a predictive model to support choice of empiric antibiotics pending results of susceptibility testing and (2) to examine the relationship between past antibiotic exposures and susceptibility profiles to enhance understanding of antibiotic selective pressure. Methods: We obtained 265,076 positive cultures from 157,422 unique patients from 2015 to 2020. We trained random forest multinomial classifiers to estimate the risk of a positive urine culture isolate being resistant to the multinomial outcome: fluoroquinolone, TMP–SMX, cephalosporin, or any combination of these 3 agents. Data sources evaluated for model generation included demographics, comorbidities, trend and seasonal terms, treatment history for multiple antimicrobial treatments summarized using number of prescriptions in weekly intervals, and sample history summarized by number of resistant and susceptible cultures in weekly intervals. Using 5-fold cross validation, we assess the performance of the clinical prediction using the area under the receiver operating characteristic curve (AUC) for each multinomial outcome. In addition to prediction, we modeled the direct effect of treatment on resistance using multinomial group lasso (MGL). This method allows variable selection in variable groupings, such as all variables related to the fluoroquinolone treatment history, which allowed us to assess the effect of a patient’s complete course of treatment on resistance. Results: In cross-validation analysis, our random forest model was best at predicting outcomes with fluoroquinolone resistant phenotypes compared to non–fluoroquinolone-resistant phenotypes (Table 1). From MGL, we found that having a prescription for fluoroquinolone treatment 4–8 weeks prior to a urinalysis was positively associated with fluoroquinolone resistance and negatively associated with fluoroquinolone susceptible phenotypes (Fig. 1). Conclusions: Our results show that a patient’s sample and treatment history are highly predictive of a future resistance. Fluoroquinolone treatment is especially associated with increased risk of fluoroquinolone single- and multidrug resistances. A history of either fluoroquinolone or trimethoprim-sulfamethoxazole (TMP-SMX) treatment is a stronger indicator of a future resistant phenotype than cephalosporin or penicillin.

Funding: None

Disclosures: None

Background: Tracking antibiotic use is a core element of antimicrobial stewardship. We developed a set of metrics based on electronic health record data to support an outpatient stewardship initiative to improve management of urinary tract infections (UTIs) in Veterans’ Affairs (VA) emergency departments (EDs) and primary care clinics. Because UTI diagnostic codes only capture a portion of genitourinary (GU)-related antibiotic use, a tier-based approach was used to evaluate practices. Methods: Metrics were developed to target practices related to antibiotic prescribing and diagnostic testing (Table 1). GU conditions were divided into 3 categories: tier 1, conditions for which antibiotics are usually or always indicated; tier 2, conditions for which antibiotics are sometimes indicated; and tier 3, conditions for which antibiotics are rarely or never indicated (eg, benign prostatic hypertrophy with symptoms). Patients with visits related to urological procedures, nontarget providers, and concomitant non-GU infections were excluded. Descriptive analyses included calculation of the correlation matrix for the 7 metrics and the construction of box plots to display interfacility variability. Results: Metrics were calculated quarterly for 18 VA medical centers, including affiliated clinics, in a western VA network, from July 2018 to June 2020 (Table 1). Tier 3 GU conditions accounted for 1,276 of 11,840 (11%) of GU-related antibiotic use. Metrics 1 and 6b were strongly correlated with each other and were also positively correlated with metrics 2 and 5 (coefficients > 0.5) (Fig. 1). Substantial interfacility variation was observed (Fig. 2). Conclusions: Stewardship metrics for suspected or documented UTIs can identify opportunities for practice improvement. Broadly capturing GU conditions in addition to UTIs may enhance utility for performance feedback. Antibiotic prescribing for tier 3 GU conditions is analogous to unnecessary antibiotic use for acute, uncomplicated bronchitis and upper respiratory tract infections.

Funding: None

Disclosures: None

We performed a retrospective analysis of the changes in accuracy of International Classification of Diseases, Clinical Modification (ICD-CM) diagnosis codes for colectomy and hysterectomy surgical site infection surveillance. After the transition from ICD-CM ninth edition to tenth edition codes, there was no significant change in the accuracy of these codes for SSI surveillance.

Background: A multicenter audit-and-feedback intervention was conducted to improve management of acute respiratory infections (ARIs) including group A streptococcal (GAS) pharyngitis within 6 VA medical Centers (VAMCs). A relative reduction (24.8%) in azithromycin prescribing after the intervention was observed. Within these facilities during 2015–2018, 2,266 cases of GAS occurred, and susceptibility to erythromycin ranged from 55% to 70%. We evaluated whether prescribing a macrolide for GAS pharyngitis was associated with an increase in outpatient return visits. Methods: A cohort of ambulatory adults treated for GAS pharyngitis (years 2014–2019) at 6 VAMCs was created. Demographic, diagnostic, treatment, and revisit data were extracted from the Corporate Data Warehouse. GAS pharyngitis was defined by an acute pharyngitis diagnostic code combined with a GAS-positive rapid strep test or throat culture ≤3 days of index date. Antibiotic prescriptions were included if filled ≤3 days of index date and were classified as first line (penicillin/amoxicillin), second line (cephalexin/clindamycin), macrolides (azithromycin, clarithromycin, erythromycin), or other (remaining antibiotics). A return visit was defined as a new visit to primary care, urgent care, or the emergency department with a diagnostic code for an ARI ≤30 days from the index visit. Logistic regression was used to adjust for nonantibiotic covariates and to compare treatments. Results are reported as odds ratio (OR ± 95% CI; P value). Results: Of 12,666 patients with a diagnostic code for acute pharyngitis, 2,923 (23.1%) had GAS testing performed. Of those, 582 (19.9%) were GAS-positive and 460 (15.7%) received antibiotics. The mean age was 39.0 years (±SD, 11.7) and 73.7% were male. Antibiotics included penicillins for 363 patients (78.9%), cephalosporins for 21 (4.6%), clindamycin for 32 (7.0%), macrolides for 47 (10.2%), and other for 17 (3.9%). Penicillin allergy was documented in 48 patients (10.5%), and these patients received cephalosporins (18.8%), clindamycin (35.4%), macrolides (41.7%), and other antibiotics (4.2%). Return visits occurred in 47 cases (10.4%). Limited chart review indicated that 6 of 10 macrolide recipients (60.0%) with return visits had recurrence or unresolved symptoms. After adjustment for calendar month and facility, odds of a return visit for treatment with a macrolide relative to penicillins was 2.79 (OR, 1.19; 95% CI, ±6.56; P = .02). The audit-feedback intervention was not associated with ARI-related return visits (OR, 0.53; 95% CI, 0.26–1.06; P = .07). Conclusions: Return visit rates were higher for GAS pharyngitis patients treated with a macrolide than for those treated with penicillins. Macrolides were the most commonly prescribed non-penicillin therapy irrespective of penicillin allergy. Further work is necessary to determine the reason for the increase in return visits.

Funding: None

Disclosures: None

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: Audit-and-feedback interventions track clinician practice patterns for a targeted practice behavior. Audit and feedback of antibiotic prescribing data for acute respiratory infections (ARI) is an effective stewardship strategy that relies on administrative coding to identify eligible visits for audit. Diagnostic shifting is the misclassification of a patient’s diagnosis in response to audit and feedback and is a potential unintended consequence of audit and feedback. Objective: To develop a method to identify patterns consistent with diagnostic shifting including both positive shifting (improved diagnosis and documentation) and negative shifting (intentionally altering documentation of diagnosis to justify antibiotic prescribing), after implementation of an audit-and-feedback intervention to improve ARI management. Methods: We evaluated the intervention effect on diagnostic shifting within 12 University of Utah pediatric clinics (293 providers). Data included 66,827 ARI diagnoses: pneumonia, sinusitis, bronchitis, pharyngitis, upper respiratory infection (URI), acute otitis media (AOM), or serous otitis with effusion (OME). To determine whether rates of ARI diagnoses changed after the intervention, we developed logistic generalized estimating equation (GEE) models with robust sandwich standard error estimates to account for clinic-wise clustering. Outcomes included the change in each ARI diagnosis relative to the competing 6 diagnoses included in audit-and-feedback reports before and after intervention implementation. Models tested for a change in outcomes after the intervention (ie, diagnostic shift) after adjustment for month of diagnosis. For each diagnosis, we estimated the population attributable fraction (PAF) for antibiotic prescriptions due to combined shifts in diagnostic frequencies and prescription rates for each diagnosis. The PAF is the estimated fraction of antibiotic prescriptions that would have changed under a population-level intervention. Results: In month-adjusted analyses, diagnoses of pneumonia and OME decreased after the intervention: odds ratio (OR), 0.46 (95% CI, 0.31–0.68) and OR, 0.81 (95% CI, 0.67–0.99), respectively. In addition, URI diagnoses increased: OR, 1.05 (95% CI 1.00, 1.11). We did not detect changes in the diagnosis rates of sinusitis, AOM, bronchitis, and pharyngitis post intervention. The intervention effect on the PAF for antibiotics prescriptions was consistently positive but relatively small in magnitude. PAF was highest for URIs (PAF, 8.87%), followed by AOM (PAF, 3.56%) and sinusitis (PAF, 2.76%), and was lowest for pneumonia and bronchitis (PAF, 0.41% for both). Conclusions: Our analysis found minimal evidence overall of diagnostic shifting after a stewardship intervention using audit and feedback in these pediatric clinics. Small changes in diagnostic coding may reflect more appropriate diagnosis and coding, a positive effect of audit and feedback, rather than intentional negative diagnostic shift.

Funding: None

Disclosures: None

Background: Acute respiratory infections (ARIs) are a key target to improve antibiotic use in the outpatient setting. The Core Elements of Outpatient Antibiotic Stewardship provide a framework for improving antibiotic use, but data on safety and effectiveness of interventions to improve antibiotic use are limited. We report the impact of Core Elements implementation within Veterans’ Healthcare Administration clinics on antibiotic prescribing and patient outcomes. Methods: The intervention targeting treatment of uncomplicated ARIs (sinusitis, pharyngitis, bronchitis, and viral upper respiratory infections [URIs]) in emergency department and primary care settings was initiated within 10 sites between September 2017 and January 2018. The intervention was developed using the Core Elements and included local site champions, audit-and-feedback with peer comparison, and academic detailing. We evaluated the following outcomes: per-visit antibiotic prescribing rates overall and by diagnosis; appropriateness of treatment; 30-day ARI revisits; 30-day infectious complications (eg,, pneumonia,); 30-day adverse medication effects; 90-day Clostridium difficile infection (CDI); and 30-day hospitalizations. Multilevel logistic regression was used to calculate rate ratios (RR) with 95% CI for each outcome in the postintervention period (12 months) compared to the preintervention period (39–42 months). Results: There were 14,020 uncomplicated ARI visits before the intervention and 4,866 uncomplicated ARI visits after the intervention. The proportions of uncomplicated ARI visits with antibiotics prescribed were 59.17% before the intervention versus 44.34% after the intervention. A trend in reduced antibiotic prescribing for ARIs throughout the entire (before and after) observation period was evident (0.92; 95% CI, 0.90–0.94); however, a significant reduction in antibiotic prescribing after the intervention was identified (0.74; 95% CI, 0.59–0.93). Per-visit antibiotic prescribing rates decreased significantly for bronchitis and URI (0.54; 95% CI, 0.44–0.65), pharyngitis (0.76; 95% CI, 0.67–0.86), and sinusitis (0.92; 95% CI, 0.85–1.0). Appropriate therapy for pharyngitis increased (1.43; 95% CI, 1.21–1.68), but appropriate therapy for sinusitis remained unchanged (0.92; 95% CI, 0.85–1.0) after the intervention. Complications associated with antibiotic undertreatment were not different after the intervention: ARI-related revisit rates (1.01; 95% CI, 0.98–1.05) and infectious complications (1.01; 95% CI, 0.79–1.28). A potential benefit of improved antibiotic use included a reduction in visits for adverse medication effects (0.82; 95% CI, 0.72–0.94). Furthermore, 90-day CDI events were too sparse to model: preintervention incidence was 0.08% and postintervention incidence was 0.06%. Additionally, 30-day hospitalizations were significantly lower in the postintervention period (0.79; 95% CI, 0.72–0.87). Conclusions: Implementation of the Core Elements was safe and effective and was associated with reduced antibiotic prescribing rates for uncomplicated ARIs, improvements in diagnosis-specific appropriate therapy, visits for adverse antibiotic effects, and 30-day hospitalization rates. No adverse events were noted in ARI-related revisit rates or infectious complications. CDI rates were low and unchanged.

Funding: None

Disclosures: None

Background: Contamination of healthcare workers and patient environments likely play a role in the spread of antibiotic-resistant organisms. The mechanisms that contribute to the distribution of organisms within and between patient rooms are not well understood, but they may include movement patterns and patient interactions of healthcare workers. We used an innovative technology for tracking healthcare worker movement and patient interactions in ICUs. Methods: The Kinect system, a device developed by Microsoft, was used to detect the location of a person’s hands and head over time, each represented with 3-dimensional coordinates. The Kinects were deployed in 2 intensive care units (ICUs), at 2 different hospitals, and they collected data from 5 rooms in a high-acuity 20-bed cardiovascular ICU (unit 1) and 3 rooms in a 10-bed medical-surgical ICU (unit 2). The length of the Kinect deployment varied by room (range, 15–48 days). The Kinect data were processed to included date, time, and location of head and hands for all individuals. Based on the coordinates of the bed, we defined events indicating bed touch, distance 30 cm (1 foot) from the bed, and distance 1 m (3 feet) from the bed. The processed Kinect data were then used to generate heat maps showing density of person locations within a room and summarizing bed touches and time spent in different locations within the room. Results: The Kinect systems captured In total, 2,090 hours of room occupancy by at least 1 person within ~1 m of the bed (Table 1). Approximately half of the time spent within ~1 m from the bed was at the bedside (within ~30 cm). The estimated number of bed touches per hour when within ~1 m was 13–23. Patients spent more time on one side of the bed, which varied by room and facility (Fig. 1A, 1B). Additionally, we observed temporal variation in intensity measured by person time in the room (Fig. 1C, 1D). Conclusions: High occupancy tends to be on the far side (away from the door) of the patient bed where the computers are, and the bed touch rate is relatively high. These results can be used to help us understand the potential for room contamination, which can contribute to both transmission and infection, and they highlight critical times and locations in the room, with a potential for focused deep cleaning.

Funding: None

Disclosures: None

Background: The Press Ganey (PG) Medical Practice Survey is a commonly used questionnaire for measuring patient experience in healthcare. Our objective was to evaluate the PG surveys completed by caregivers of children presenting for urgent care evaluation of acute respiratory infections (ARIs) to determine any correlation with receipt of antibiotics during their visit. Methods: We evaluated responses to the PG urgent-care surveys for encounters of children <18 years presenting with ARIs (ie, sinusitis, bronchitis, pharyngitis, upper respiratory infection, acute otitis media, or serous otitis media with effusion) within 9 University of Utah urgent-care centers. Scores could range from 0 to 100. Because the distributions of scores followed right- skewed distribution with a high ceiling effect, we defined scores as dissatisfied with their care (≤25th percentile) and satisfied with their care (scores >25th percentile). Univariate and multivariable generalized mixed-effects logistic regression was used to assess correlates of patient dissatisfaction. Random intercepts were included for each provider to account for correlation within the same provider. Separate models were used for each PG component score. Multivariable models adjusted for receipt of antibiotics, age, gender, race, ethnicity, and provider type. Results: Overall, 388 of 520 responses (74.6%) indicated satisfaction and 132 responses (25.4%) indicated dissatisfaction. Among patients who did not receive antibiotics, 87 of 284 responses (30.6%) indicated dissatisfaction versus 45 of 236 (19.1%) who did receive antibiotics. Among patients who were dissatisfied with their clinician, raw clinician PG scores were higher among patients who received antibiotics (mean, 64.5; standard deviation [SD], 16.9) versus those who did not receive antibiotics (mean, 54.7; SD, 24.4; P = .015) (Table 1). In a multivariable analysis, receipt of antibiotics was associated with a reduction in patient dissatisfaction overall (odds ratio, 0.55; 95% CI, 0.36–0.85). Conclusions: Overall, most responses for patients seen for ARIs in pediatric urgent care were satisfied. However, a significantly higher proportion of responses for patients who did not receive antibiotics were dissatisfied than for those patients who received antibiotics. Antibiotic stewardship strategies to communicate appropriate prescribing while preserving patient satisfaction are needed in pediatric urgent-care settings.

Funding: None

Disclosures: None

Background: The Core Elements of Outpatient Antibiotic Stewardship provide a framework to improve antibiotic use, but cost-effectiveness data on interventions to improve antibiotic use are limited. Beginning in September 2017, an antibiotic stewardship intervention was launched in within 10 outpatient Veterans Healthcare Administration clinics. The intervention was based on the Core Elements and used an academic detailing (AD) and an audit and feedback (AF) approach to encourage appropriate use of antibiotics. The objective of this analysis was to evaluate the cost-effectiveness of the intervention among patients with uncomplicated acute respiratory tract infections (ARI). Methods: We developed an economic simulation model from the VA’s perspective for patients presenting for an index outpatient clinic visit with an ARI (Fig. 1). Effectiveness was measured as quality-adjusted life-years (QALYs). Cost and utility parameters for antibiotic treatment, adverse drug reactions (ADRs), and healthcare utilization were obtained from the published literature. Probability parameters for antibiotic treatment, appropriateness of treatment, antibiotic ADRs, hospitalization, and return ARI visits were estimated using VA Corporate Data Warehouse data from a total of 22,137 patients in the 10 clinics during 2014–2019 before and after the intervention. Detailed cost data on the development of the AD and AF materials and electronically captured time and effort for the National AD Service activities by specific providers from a national ARI campaign were used as a proxy for the cost estimate of similar activities conducted in this intervention. We performed 1-way and probabilistic sensitivity analyses (PSAs) using 10,000 second-order Monte Carlo simulations on costs and utility values using their means and standard deviations. Results: The proportion of uncomplicated ARI visits with antibiotics prescribed (59% vs 40%) was lower and appropriate treatment was higher (24% vs 32%) after the intervention. The intervention was estimated to cost $110,846 (2018 USD) over a 2-year period. Compared to no intervention, the intervention had lower mean costs ($880 vs $517) and higher mean QALYs (0.837 vs 0.863) per patient because of reduced inappropriate treatment, ADRs, and subsequent healthcare utilization, including hospitalization. In threshold analyses, the antibiotic stewardship strategy was no longer dominant if intervention cost was >$64,415,000 or the number of patients cared for was <3,672. In the PSA, the antibiotic stewardship intervention was dominant in 100% of the 10,000 Monte Carlo iterations (Fig. 2). Conclusions: In every scenario, the VA outpatient AD and AF antibiotic stewardship intervention was a dominant strategy compared to no intervention.

Funding: None

Disclosures: None