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The Impact of COVID-19 on Healthcare-Associated Infections

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). We aimed to evaluate the association between COVID-19 surges and HAI and cluster rates. Methods In 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, 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. 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. There were 60% (95% CI, 23-108%) more CLABSI, 43% (95% CI, 8-90%) more CAUTI, and 44% (95% CI, 10-88%) more cases of MRSA bacteremia than expected over 7 months based on predicted HAIs had there not been COVID-19 cases. Clostridioides difficile infection 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. Finally, clusters of hospital-onset pathogens increased as the COVID-19 burden increased. 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.


Introduction
The COVID-19 pandemic placed extraordinary demands on the healthcare system, resulting in modifications in routine patient care practices that could have the potential to either increase or decrease risks for healthcare-associated infections (HAIs). Negative impacts may have resulted when usual efforts to monitor and prevent HAIs were redirected to the COVID-19 response. Enhanced isolation practices and the burden of increased personal protective equipment (PPE) requirements may have led to reduced focus on routine HAI prevention activities such as central line and urinary catheter care. Earlier studies suggest that these shifts in activities and supplies could be associated with an increase in HAI rates. [1,2] Simultaneously, infection prevention and control practices became more visible in healthcare systems. Hand hygiene was emphasized both inside and outside of healthcare facilities. [3] Training on donning and doffing of personal protective equipment was enhanced, and many hospitals saw increased compliance with contact precautions. It is possible that increased attention to standard infection prevention practices and the use of personal protective equipment impacted HAI rates in a beneficial direction, particularly the spread of multidrug resistant organisms (MDROs). [4,5] Increased attention to infection prevention practices may have balanced the additional pandemic-related burden on infection prevention resources. Understanding whether and how COVID-19 impacted HAI rates is essential to guide resources, policies, and practices during the next stages of the COVID-19 response.
A c c e p t e d M a n u s c r i p t 5

Study population and setting:
We conducted a prospective cohort study in 148 HCA Healthcare-affiliated hospitals. HAI events were assessed by the hospitals' infection preventionists on all patients admitted between March 1, 2020 and September 30, 2020. Hospital-onset bloodstream infections (BSI) and MDRO events were assessed in 81 hospitals with microbiology data available between March 1, 2020 and December 31, 2020. We used a spatial and temporal scan statistic to identify clusters in 40 of those hospitals ( Figure 1). [6][7][8][9] The 40 hospitals were a random sample of the 81 hospitals, balanced on hospital and intensive care unit census, average comorbidity count, length of stay and historical cluster data. [10] This study was approved by the Harvard Pilgrim Health Care institutional review board, and HCA Healthcare-affiliated hospitals and collaborating institutions delegated review.

Data sources and events:
Central line-associated blood stream infections (CLABSI), catheter-associated urinary tract infections Hospital-onset BSI was defined as a positive blood culture obtained on hospital day 3 or later and in an inpatient location. If the organism was on the NHSN list of common commensal organisms [12], we required 2 cultures of the same organism on the same or consecutive days. Hospital-onset MDROs were defined as clinical cultures growing an MDRO organism based on the CDC criteria [13] and obtained from any body site on hospital day 3 or later, excluding surveillance cultures. The In 40 hospitals, we identified clustering of organisms based on hospitals' microbiology data. Clusters were defined by statistically significant increases in organisms collected on hospital day 3 or later from a single ward or clinically related wards compared to a 2 year baseline time period. [6] Identification of clusters was based on matching of species and antimicrobial resistance profile when available. We used a statistically-based cluster detection tool, WHONET-SaTScan, to identify clusters, and parameters were based upon prior studies. [6,14] Statistical significance was measured using a recurrence interval, which estimates the likelihood that the cluster signal would occur by chance. [15] We used a threshold recurrence interval of 200 days, meaning that a cluster of this type of organism with the observed number and distribution of cases would be expected to occur by chance less than once per every 200 days.
For each facility and month, the number of COVID-19 patients per staffed bed was calculated by dividing the number of cases discharged from the facility with SARS-CoV-2, confirmed by polymerase chain reaction, per month by the number of beds the facility was approved to service. As we included COVID-19 patients discharged from facilities rather than admitted, we did not include lag time in the analysis. Covariates included hospital size as a categorical variable (small <200 beds, medium 200 to <300 beds, and large ≥300 beds). CLABSI and CAUTI models included the expected count as an offset, while MRSA bacteremia and CDI models included patient days as a covariate. We also evaluated chronologic calendar month to account for changes in process over time and use of contact precautions for MRSA and VRE in the models.
A c c e p t e d M a n u s c r i p t 7

Statistical analysis
We used negative binomial mixed models to account for within-hospital correlation across the repeated measures over time. Different models were developed for each event type. The data for the models included the monthly number of discharges of COVID-19 patients per staffed bed as the predictor. Results are presented as the relative rate in the event per 0.1 increase in the monthly discharges per staffed bed. Excess cases of HAIs were calculated as the difference between the observed number of events and the predicted number from the model, had there been 0 COVID-19 discharges across the study period. Facility level parameters limited to hospital size were included in the models. All statistical analyses were performed in SAS version 9.4.

Role of the funding source
This study was funded by the CDC Prevention Epicenter Program. The funder had no role in the design or conduct of the study; collection, management, analysis or interpretation of the data; preparation, review or approval of the manuscript; or decision to submit the manuscript for publication.

Results
The 148 hospitals ranged in size from 34 to 1013 beds and were located in 17 states. The hospitals had a total of 1,024,160 discharges between March 1, 2020 and September 30, 2020 (Table 1). They included 60 small facilities, 40 medium facilities and 48 large facilities.
Increased relative rates of CLABSI, CAUTI and MRSA bacteremia reported to NHSN were associated with increasing monthly COVID-19 discharges (Table 2). For each 0.1 increase in the monthly number of discharges of COVID-19 patients per staffed bed, there was a relative increase of 1.14 (95% CI, 1.09 to 1.19) for CLABSI, 1.09 (95% CI, 1.04 to 1.15) for CAUTI, and 1.09 (95% CI, 1.04 to 1.14) for MRSA bacteremia (Figure 2a-c). Larger hospital size A c c e p t e d M a n u s c r i p t 8 was independently associated with a greater number of HAI events. Over 7 months, there were 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 were expected based on the predicted number across the 148 hospitals. CDI relative rates, however, were not associated with increased monthly rates of COVID-19 discharges, 0.97 (95% CI, 0.93 to  Table   2). Hospital size was also independently associated with BSI and MDRO events.
Spatio-temporal scanning in 40 hospitals identified 101 clusters with a mean size of 3.8 isolates. Increased relative rates of clusters of hospital-onset pathogens were associated with increasing monthly rates of COVID-19 discharges per staffed bed. For each increase of 0.1 in the monthly number of discharges of COVID-19 patients per staffed bed, there was a A c c e p t e d M a n u s c r i p t 9 relative increase of 1.09 (95% CI, 1.01 to 1.18) in the occurrence of clusters (Table 2, Figure   3). The cluster isolates accounted for 16% of the excess BSI cases and 36% of the excess MDRO cases.

Discussion
This analysis of prospectively collected HAI and microbiology data in geographically diverse US hospitals confirmed that elevated HAI rates were temporally associated with increases in hospitalized COVID-19 patients. Furthermore, the number of clusters of hospital-onset pathogens increased during COVID-19 surges, suggesting increased healthcare-associated transmission as one possible mechanism to account for increases in HAIs. As the facilities included here represent a sample of hospitals across the United States with varying local pandemic pressures, this analysis supports the hypothesis that certain HAI rates are being adversely affected by the pandemic response. This highlights the critical importance of identifying strategies to ensure the sustainability of routine infection prevention programs even during periods of public health crises that require diversion of healthcare resources.
In the HCA Healthcare system and many other hospitals, HAI rates had been steadily declining prior to the COVID-19 pandemic. [16] Efforts in hospitals to reach zero HAIs focused attention on surveillance and infection prevention process measures. [17] However, as health systems were strained by COVID-19, HAI rates increased, demonstrating how community pandemic control impacts other patients beyond those infected by the pandemic pathogen. This study's finding that the number of clusters significantly increased is consistent with recent case reports of outbreaks during COVID-19 surges or on COVID-19 specialty units. [18,19] The additional burden of COVID-19 care, disrupting routine practice, may have contributed to the clustering of infections, including both lapses in routine infection prevention practice as well as transmission of healthcare-associated pathogens.
Additionally, during COVID-19 surges, many elective admissions were canceled, resulting in higher acuity patient populations. [20] A c c e p t e d M a n u s c r i p t However, this study supplements NHSN reported events dependent on adjudication, such as CLABSI or CAUTI, with microbiology-based analyses that minimize the potential impact of reduced infection preventionist effort available for HAI surveillance. Additionally, HAI rates may have been impacted by dynamic changes in the overall risk of HAIs within the inpatient population given the marked increase in acuity and decrease in elective admissions.
Although the per-patient risk of a hospital-onset infection remained very low, HAI rates increased during COVID-19 surges. Further research is necessary to elucidate the specific ways in which the COVID-19 burden is affecting HAI rates, but our results identify a need to build capacity in infection prevention and control. As hospitals and healthcare systems prepare for the next stages of the pandemic and recovery, this study emphasizes the need to remain focused on routine infection prevention.      A c c e p t e d M a n u s c r i p t  A c c e p t e d M a n u s c r i p t 21 Figure 3