ABSTRACT
Background. The CLUSTER trial assessed the impact of prospective identification of clusters coupled with a response protocol on the containment of hospital clusters. Methods. This 82-hospital CRT in 16 states compared clusters of bacterial and fungal healthcare pathogens using a statistical outbreak detection tool (WHONET-SaTScan) coupled with a standardized response protocol (automated cluster detection arm) compared to routine surveillance with the response protocol (control arm). Trial periods: 24 mo Baseline (2/17-1/19);5 mo Phase-in (2/19-6/ 19);30 mo Intervention (7/19-1/22). The primary outcome was the number of additional cases occurring after initial cluster detection. Analyses used generalized linear mixed models to assess differences in additional cases between the intervention vs baseline periods across arms, clustering by hospital. Results were assessed overall and, to account for the effect of COVID-19 on hospital operations, stratified into pre-COVID-19 (7/19-6/20) and during COVID-19 (7/20-1/22) intervention periods. We also assessed the probability that a patient was in a cluster. Results. In the baseline period, the automated cluster detection and control arms had 0.09 and 0.07 additional cluster cases/1000 admissions, respectively. The automated cluster detection arm had a 22% greater relative reduction in additional cluster cases in the intervention vs baseline period compared to control (P=0.5). Within the intervention period, the automated cluster detection arm had a significant 64% relative reduction pre-COVID-19 (P< 0.05) and a non-significant 6% relative reduction during COVID-19 (P=0.9) compared to control (Figure). When evaluating patient risk of being part of a cluster across the entire intervention period, the automated cluster detection arm had a significant 35% relative reduction vs control (P< 0.01). Conclusion. A statistical automated tool coupled with a response protocol improved cluster containment by 64% pre-COVID-19 but not during COVID-19;there were no significant differences between the arms when using the entire intervention period. Automated cluster detection may substantially improve outbreak containment in non-pandemic periods when infection prevention programs are able to optimize containment protocols. (Figure Presented).
ABSTRACT
Background. The profound changes wrought by COVID-19 on routine hospital operations may have influenced performance on hospital measures, including healthcare-associated infections (HAIs). Objective. Evaluate the association between COVID-19 surges and HAI or cluster rates Methods. Design: Prospective cohort study Setting. 148 HCA Healthcare-affiliated hospitals, 3/1/2020-9/30/2020, and a subset of hospitals with microbiology and cluster data through 12/31/2020 Patients. All inpatients Measurements. We evaluated the association between COVID-19 surges and HAIs, hospital-onset pathogens, and cluster rates using negative binomial mixed models. To account for local variation in COVID-19 pandemic surge timing, we included the number of discharges with a laboratory-confirmed COVID-19 diagnosis per staffed bed per month at each hospital. Results. Central line-associated blood stream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), and methicillin-resistant Staphylococcus aureus (MRSA) bacteremia increased as COVID-19 burden increased (P ≤ 0.001 for all), with 60% (95% CI, 23 to 108%) more CLABSI, 43% (95% CI, 8 to 90%) more CAUTI, and 44% (95% CI, 10 to 88%) more cases of MRSA bacteremia than expected over 7 months based on predicted HAIs had there not been COVID-19 cases. Clostridioides difficile infection (CDI) was not significantly associated with COVID-19 burden. Microbiology data from 81 of the hospitals corroborated the findings. Notably, rates of hospital-onset bloodstream infections and multidrug resistant organisms, including MRSA, vancomycin-resistant enterococcus and Gram-negative organisms were each significantly associated with COVID-19 surges (P < 0.05 for all). Finally, clusters of hospital-onset pathogens increased as the COVID-19 burden increased (P = 0.02). Limitations. Variations in surveillance and reporting may affect HAI data. Table 1. Effect of an increase in number of COVID-19 discharges on HAIs and hospital-onset pathogens Figure 1. Predicted mean HAI rates as COVID-19 discharges increase Predicted mean HAI rate by increasing monthly COVID-19 discharges. Panel a. CLABSI, Panel b, CAUTI Panel c. MRSA Bacteremia, Panel d. CDI. Data are stratified by small, medium and large hospitals. Figure 2. Monthly comparison of COVID discharges to clusters COVID-19 discharges and the number of clusters of hospital-onset pathogens are correlated throughout the pandemic. Conclusion. COVID-19 surges adversely impact HAI rates and clusters of infections within hospitals, emphasizing the need for balancing COVID-related demands with routine hospital infection prevention.
ABSTRACT
Background. At the outset of the COVID-19 pandemic, healthcare workers (HCWs) raised concerns about personal risks of acquiring infection during patient care. This led to more stringent infection prevention practices than CDC guidelines during a time of uncertainty about transmission and limited U.S. testing capacity. Hospitals were challenged to protect against true COVID-19 exposure risks, while avoiding use of unproven measures that could interfere with timely, high quality care. We evaluated hospital experiences with HCW COVID-19 exposure concerns impacting clinical workflow/management. Methods. We conducted a 32-question structured survey of hospital infection prevention leaders (one per hospital) from CDC Prevention Epicenters, University of California (CA) Health system, HCA Healthcare, and the Southern CA Metrics Committee between May-Dec, 2020. We assessed facility characteristics and COVID-19 exposure concerns causing changes in respiratory care, procedure delays/modifications, requests to change infection prevention processes, disruptions in routine medical care, and health impacts of PPE overuse. Percentages were calculated among respondents for each question. Results. Respondents represented 53 hospitals: 22 (42%) were small (< 200 beds), 14 (26%) medium (200-400 beds), and 17 (32%) large ( >400 beds) facilities. Of these, 11 (21%) provided Level 1 trauma care, and 22 (41%) provided highly immunocompromised patient care;75% had cared for a substantial number of COVID-19 cases before survey completion. Majority reported changes in respiratory care delivery (71%-87%), procedural delays (75%-87%), requests to change infection prevention controls/ protocols (58%-96%), and occupational health impacts of PPE overuse including skin irritation (98%) and carbon dioxide narcosis symptoms (55%) (Table). Conclusion. HCW concerns over work-related COVID-19 exposure contributed to practice changes, many of which are unsupported by CDC guidance and resulted in healthcare delivery delays and alterations in clinical care. Pandemic planning and response must include the ability to rapidly develop evidence to guide infection prevention practice.