Background: Antimicrobial-resistant and healthcare-associated pathogens are a serious threat in the United States, accounting for over 3 million infections each year. Antimicrobial stewardship remains the strongest tool in reducing the over prescribing of antibiotics, the leading modifiable cause of resistance. Despite this, inappropriate antibiotic use is still common in the VA. Furthermore, little is known about the role health disparities play in antibiotic inappropriate antibiotic prescribing. Significance: This proposal is highly significant for Veterans and the goals of VA. Previous studies have shown that additional antimicrobial stewardship efforts are needed in VA outpatient settings. Additionally, this project will provide evidence on health disparities and system factors that can be targeted in interventions to improve VA outpatient antibiotic prescribing. This project is aligned with the priorities of our operation partners: the VA Antimicrobial Stewardship Task Force and the Office of Rural Health. Specific Aims: The goal of this proposal will be to evaluate the patient and system factors that drive inappropriate antibiotic prescribing at the Videoconferencing Antimicrobial Stewardship Team (VAST) study sites using integrated health disparities and antimicrobial stewardship frameworks. Aim 1: Determine the percentage of antibiotic over-prescribing and under-prescribing for acute respiratory infections that occur by Veteran's race/ethnicity in VAST sites. Aim 2: Determine the percentage of antibiotic over-prescribing and under-prescribing by Veteran's race/ethnicity for urinary tract infections in VAST sites. Aim 3: Explore the system factors that predict the rate of over/under prescription of antibiotics by race/ethnicity. Methodology: For Aim 1, a retrospective cohort design will include outpatients from the 16 VAST study sites with a diagnosis of acute respiratory infection. The percentage of over-prescribing, and under-prescribing will be determined using criteria from clinical practice guidelines. Multinomial logistic regression models will determine the likelihood of patients correctly treated, over, or under-prescribed antibiotics by race/ethnicity. For Aim 2, a randomly selected sample of outpatients presenting with a urinary tract infection will be examined using electronic medical reviews to determine whether the diagnosis and treatment of urinary tract infection was appropriate according to clinical practice guidelines. The ratio of appropriately diagnosed and prescribed events will be evaluated by race/ethnicity. Aim 3 will use the same cohorts described in Aims 1 & 2 to determine the difference in antibiotic prescribing practices by system factors. Hierarchical logistic regression models will determine the likelihood of patients being over or under-prescribed antibiotics. Candidate Background: Dr. Wilson has been a Research Health Scientist with the Center of Innovation for Complex Chronic Healthcare (CINCCH) at Edward Hi...