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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.

SUMMARYThis comprehensive review outlines the impact of military-relevant respiratory infections, with special attention to recruit training environments, influenza pandemics in 1918 to 1919 and 2009 to 2010, and peacetime operations and conflicts in the past 25 years. Outbreaks and epidemiologic investigations of viral and bacterial infections among high-risk groups are presented, including (i) experience by recruits at training centers, (ii) impact on advanced trainees in special settings, (iii) morbidity sustained by shipboard personnel at sea, and (iv) experience of deployed personnel. Utilizing a pathogen-by-pathogen approach, we examine (i) epidemiology, (ii) impact in terms of morbidity and operational readiness, (iii) clinical presentation and outbreak potential, (iv) diagnostic modalities, (v) treatment approaches, and (vi) vaccine and other control measures. We also outline military-specific initiatives in (i) surveillance, (ii) vaccine development and policy, (iii) novel influenza and coronavirus diagnostic test development and surveillance methods, (iv) influenza virus transmission and severity prediction modeling efforts, and (v) evaluation and implementation of nonvaccine, nonpharmacologic interventions.