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Background Treating patients with infections due to multidrug-resistant pathogens often requires substantial healthcare resources. The purpose of this study was to report estimates of the healthcare costs associated with infections due to multidrug-resistant bacteria in the United States (US). Methods We performed retrospective cohort studies of patients admitted for inpatient stays in the Department of Veterans Affairs healthcare system between January 2007 and October 2015. We performed multivariable generalized linear models to estimate the attributable cost by comparing outcomes in patients with and without positive cultures for multidrug-resistant bacteria. Finally, we multiplied these pathogen-specific, per-infection attributable cost estimates by national counts of infections due to each pathogen from patients hospitalized in a cohort of 722 US hospitals from 2017 to generate estimates of the population-level healthcare costs in the US attributable to these infections. Results Our analysis cohort consisted of 16 676 patients with community-onset infections and 172 712 matched controls and 8246 patients with hospital-onset infections and 66 939 matched controls. The highest cost was seen in hospital-onset invasive infections, with attributable costs (95% confidence intervals) ranging from $30 998 ($25 272–$36 724) for methicillin-resistant Staphylococcus aureus to $74 306 ($20 377–$128 235) for carbapenem-resistant (CR) Acinetobacter. The highest attributable costs for community-onset invasive infections were seen in CR Acinetobacter ($62 396; $20 370–$104 422). Treatment of these infections cost an estimated $4.6 billion ($4.1 billion–$5.1 billion) in 2017 in the US for community- and hospital-onset infections combined. Conclusions We found that antimicrobial-resistant infections led to substantial healthcare costs.

Carbapenem-resistant Klebsiella pneumoniae (CRKP) is an antibiotic resistance threat of the highest priority. Given the limited treatment options for this multidrug-resistant organism (MDRO), there is an urgent need for targeted strategies to prevent transmission. Here, we applied whole-genome sequencing to a comprehensive collection of clinical isolates to reconstruct regional transmission pathways and analyzed this transmission network in the context of statewide patient transfer data and patient-level clinical data to identify drivers of regional transmission. We found that high regional CRKP burdens were due to a small number of regional introductions, with subsequent regional proliferation occurring via patient transfers among health care facilities. While CRKP was predicted to have been imported into each facility multiple times, there was substantial variation in the ratio of intrafacility transmission events per importation, indicating that amplification occurs unevenly across regional facilities. While myriad factors likely influence intrafacility transmission rates, an understudied one is the potential for clinical characteristics of colonized and infected patients to influence their propensity for transmission. Supporting the contribution of high-risk patients to elevated transmission rates, we observed that patients colonized and infected with CRKP in high-transmission facilities had higher rates of carbapenem use, malnutrition, and dialysis and were older. This report highlights the potential for regional infection prevention efforts that are grounded in genomic epidemiology to identify the patients and facilities that make the greatest contribution to regional MDRO prevalence, thereby facilitating the design of precision interventions of maximal impact.

We described antibiotic use among inpatients with coronavirus disease 2019 (COVID-19). Most COVID-19 inpatients received antibiotic therapy. We also described hospital-wide antibiotic use during 2020 compared with 2019, stratified by hospital COVID-19 burden. While total antibiotic use decreased between years, certain antibiotic use increased with higher COVID-19 burden.

Background Balancing the control of SARS-CoV-2 transmission with the resumption of travel is a global priority. Current recommendations include mitigation measures before, during, and after travel. Pre- and post-travel strategies including symptom monitoring, antigen or nucleic acid amplification testing, and quarantine can be combined in multiple ways considering different trade-offs in feasibility, adherence, effectiveness, cost, and adverse consequences. Methods We used a mathematical model to analyze the expected effectiveness of symptom monitoring, testing, and quarantine under different estimates of the infectious period, test-positivity relative to time of infection, and test sensitivity to reduce the risk of transmission from infected travelers during and after travel. Results If infection occurs 0–7 days prior to travel, immediate isolation following symptom onset prior to or during travel reduces risk of transmission while traveling by 30–35%. Pre-departure testing can further reduce risk, with testing closer to the time of travel being optimal even if test sensitivity is lower than an earlier test. For example, testing on the day of departure can reduce risk while traveling by 44–72%. For transmission risk after travel with infection time up to 7 days prior to arrival at the destination, isolation based on symptom monitoring reduced introduction risk at the destination by 42–56%. A 14-day quarantine after arrival, without symptom monitoring or testing, can reduce post-travel risk by 96–100% on its own. However, a shorter quarantine of 7 days combined with symptom monitoring and a test on day 5–6 after arrival is also effective (97--100%) at reducing introduction risk and is less burdensome, which may improve adherence. Conclusions Quarantine is an effective measure to reduce SARS-CoV-2 transmission risk from travelers and can be enhanced by the addition of symptom monitoring and testing. Optimal test timing depends on the effectiveness of quarantine: with low adherence or no quarantine, optimal test timing is close to the time of arrival; with effective quarantine, testing a few days later optimizes sensitivity to detect those infected immediately before or while traveling. These measures can complement recommendations such as social distancing, using masks, and hand hygiene, to further reduce risk during and after travel.