Comparison of the Impact of COVID-19 on Veterans Affairs and Non-federal Hospitals: a Survey of Infection Prevention Specialists

Abstract

Background As the COVID-19 pandemic evolves, it is critical to understand characteristics that have allowed US healthcare systems, including the Veterans Affairs (VA) and non-federal hospitals, to mount an effective response in the setting of limited resources and unpredictable clinical demands generated by this system shock. Objective To compare the impact of and response to resource shortages to both VA and non-federal healthcare systems during the COVID-19 pandemic. Design Cross-sectional national survey administered April 2021 through May 2022. Participants Lead infection preventionists from VA and non-federal hospitals across the US. Main Measures Surveys collected hospital demographic factors along with 11 questions aimed at assessing the effectiveness of the hospital’s COVID response. Key Results The response rate was 56% (71/127) from VA and 47% (415/881) from non-federal hospitals. Compared to VA hospitals, non-federal hospitals had a larger average number of acute care (214 vs. 103 beds, p<.001) and intensive care unit (24 vs. 16, p<.001) beds. VA hospitals were more likely to report no shortages of personal protective equipment or medical supplies during the pandemic (17% vs. 9%, p=.03) and more frequently opened new units to care specifically for COVID patients (71% vs. 49%, p<.001) compared with non-federal hospitals. Non-federal hospitals more frequently experienced increased loss of staff due to resignations (76% vs. 53%, p=.001) and financial hardships stemming from the pandemic (58% vs. 7%, p<0.001). Conclusions In our survey-based national study, lead infection preventionists noted several distinct advantages in VA versus non-federal hospitals in their ability to expand bed capacity, retain staff, mitigate supply shortages, and avoid financial hardship. While these benefits appear to be inherent to the VA’s structure, non-federal hospitals can adapt their infrastructure to better weather future system shocks.

Identifying a competency improvement strategy for infection prevention and control professionals: A rapid systematic review and cluster analysis

“…Of the 46 studies included in the analysis, 14 were carried out in the United States [12–25], 11 in China [26–36], four in each of the United Kingdom [27, 37–39] and Switzerland [6, 7, 40, 41], three in Italy [42–44], two in South Africa [45, 46] and one in each of Germany [8], Saudi Arabia [47], Finland [48], the Netherlands [49], Australia [50], Ghana [51], Pakistan [52], and Singapore [53]. There were 20 cross‐sectional studies [8, 15–17, 24, 25, 31, 32, 34–37, 39–41, 49–52, 54], 15 qualitative studies [6, 7, 13, 14, 19, 21, 23, 26, 28, 29, 38, 42, 45, 46, 48], eight before–after studies [12, 20, 22, 27, 30, 33, 47, 53], two retrospective studies [18, 44] and one prospective observational study [43] (Table 2).…”

“…Of the 46 studies included in the analysis, 14 were carried out in the United States [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ], 11 in China [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], four in each of the United Kingdom [ 27 , 37 , 38 , 39 ] and Switzerland [ 6 , 7 , 40 , 41 ], three in Italy [ 42 , 43 , 44 ], two in South Africa [ 45 , 46 ] and one in each of Germany [ 8 ], Saudi Arabia [ 47 ], Finland [ 48 ], the Netherlands [ 49 ], Australia [ 50 ], Ghana [ 51 ], Pakistan [ 52 ], and Singapore [ 53 ]. There were 20 cross‐sectional studies [ 8 , 15 , 16 , 17 , 24 , 25 , 31 , 32 , 34 ...…”

Ten pillars for the expansion of health system infection prevention capacity

“…4 Their workload increased, augmented by additional reporting requirements connected to the pandemic, with some reporting up to a 500% increase in calls to IPs. 5 Many experienced IPs decided to leave the field or retire early 6 even before the pandemic, resulting in a high turnover [7][8][9] and exacerbating shortages. Moreover, there is a paucity of HEs or physicians with formal training to support infection prevention.…”

Artificial neural network based prediction of the lung tissue involvement as an independent in‐hospital mortality and mechanical ventilation risk factor in COVID‐19

“…10 Evolving pandemic waves have exerted significant burden on medical systems, which have struggled with high number of admissions, timely diagnostics, and accurate prediction of disease severity to optimize decision processes related to the need for hospital admission and mechanical ventilation. 12 The analysis of CT images requires expert radiologic assessment and time, both of which may be of limited availability during a pandemic. Furthermore, the extent of lung tissue involvement may vary because of human error, which reduces the accuracy and usefulness of CT radioimaging data for prediction models.…”