| Literature DB >> 33794312 |
Karen Jones1, Julia Mantey1, Laraine Washer2, Jennifer Meddings3, Payal K Patel4, Ana Montoya1, John P Mills5, Kristen Gibson1, Lona Mody6.
Abstract
BACKGROUND: Nursing home (NH) populations have borne the brunt of morbidity and mortality of COVID-19. We surveyed Michigan NHs to evaluate preparedness, staffing, testing, and adaptations to these challenges.Entities:
Keywords: Infection prevention and control; Pandemic; Preparedness; Staffing
Mesh:
Year: 2021 PMID: 33794312 PMCID: PMC8007185 DOI: 10.1016/j.ajic.2021.03.016
Source DB: PubMed Journal: Am J Infect Control ISSN: 0196-6553 Impact factor: 2.918
Pandemic response planning: management of personal protective equipment supplies and training
| Question | N (%) |
| Very well; plan addressed >90% of issues | 94/139 (67.6%) |
| Fair; plan addresses most but not all issues | 40/139 (28.8%) |
| Not very well; plan addressed <50% of issues | 2/139 (1.4%) |
| Not applicable; we did not have a Pandemic Response Plan for COVID-19 | 3/139 (2.2%) |
| Yes | 91/139 (65.5%) |
| No | 48/139 (34.5%) |
| Gowns | 76/91 (83.5%) |
| Alcohol-based sanitizer | 54/91 (59.3%) |
| N95 respirators | 47/91 (51.7%) |
| Masks (surgical) | 38/91 (41.8%) |
| Eye shields/goggles | 18/91 (19.8%) |
| Gloves | 15/91 (16.5%) |
| Other | 10/91 (11.0%) |
| Corporate | 52/91 (57.1%) |
| County/local health department | 47/91 (51.7%) |
| Community | 44/91 (48.4%) |
| State government | 28/91 (30.8%) |
| Local hospitals | 11/91 (12.1%) |
| Federal government | 11/91 (12.1%) |
| Other | 30/91 (33.0%) |
| Yes | 139/139 (100.0%) |
| No | 0 |
| Don't know | 0 |
| CDC | 120/139 (86.3%) |
| Corporate | 84/139 (60.4%) |
| State and/or local health department | 73/139 (52.5%) |
| Social media | 6/139 (4.3%) |
| Other | 12/139 (8.6%) |
| In-person, one-on-one training | 109/138 (80.0%) |
| Written policy/procedure, “read and sign” | 92/138 (66.7%) |
| In-person, group training | 86/138 (62.3%) |
| On-demand computer training (eg, modules) | 47/138 (34.1%) |
| Live virtual training (eg, Skype, Zoom) | 24/138 (17.4%) |
| Other | 3/138 (2.2%) |
| Perform random audits of PPE use with direct feedback to staff | 123/137 (89.8%) |
| Repeat education periodically (eg, monthly, staff meetings, etc.) | 117/137 (85.4%) |
| Use a trained staff “observer” to ensure PPE is used correctly | 112/137 (81.8%) |
| Other | 2/137 (1.5%) |
| No ongoing education or audits at this time | 6/137 (4.4%) |
Fig 1Organizations providing guidance relied on during the pandemic. Facilities referred to guidance from a variety of organizations during the pandemic. Federal organizations, such as CDC and CMS, were frequently reported to be relied upon the most.
Effects of COVID-19 on staff absences, resignations, and shortages
| Question | N (%) |
| None | 22/137 (16.1%) |
| 1-10 | 70/137 (51.1%) |
| > 10 | 45/137 (32.9%) |
| None | 35/137 (25.6%) |
| 1-10 | 89/137 (65.0%) |
| > 10 | 13/137 (9.5%) |
| Yes | 76/138 (55.1%) |
| No | 62/138 (44.9%) |
| Remaining staff volunteered to work extended hours | 60/76 (79.0%) |
| Non-clinical staff filled different roles | 46/76 (60.5%) |
| Remaining staff mandated to work extended hours | 36/76 (47.4%) |
| Agency/contracted staff | 27/76 (35.5%) |
| We didn't get additional help | 2/76 (2.6%) |
| Volunteers from the community | 1/76 (1.3%) |
| Other | 7/76 (9.2%) |
| Yes | 87/138 (63.0%) |
| No | 51/138 (37.0%) |
| Don't know | 0 |
Adaptations to restrictions and evaluation of communication with external stakeholders
| Question | N (%) |
| Phone calls | 133/136 (97.8%) |
| Videoconferencing | 131/136 (96.3%) |
| Window visits | 110/136 (80.9%) |
| Other (eg, social media, mail/letters) | 21/136 (15.4%) |
| Yes, and telemedicine was new to our facility with COVID-19 | 82/135 (60.7%) |
| Yes, and telemedicine has been used at our facility in the past | 14/135 (10.4%) |
| No, we do not use telemedicine | 38/135 (28.2%) |
| Don't know | 1/135 (0.7%) |
| Video | 41/96 (42.7%) |
| Telephone | 3/96 (3.1%) |
| By both video and telephone | 52/96 (54.2%) |
| Communication is very good | 71/136 (52.2%) |
| Communication is fair | 47/136 (34.6%) |
| Communication is poor | 18/136 (13.2%) |
| Communication is very good | 99/136 (72.8%) |
| Communication is fair | 34/136 (25.0%) |
| Communication is poor | 3/136 (2.2%) |
| Straightforward, uncomplicated, no issues for >90% of transfers | 105/131 (80.2%) |
| Somewhat more difficult to send these residents to the hospital | 19/131 (14.5%) |
| Not at all straightforward, issues with >50% of resident transfers | 7/131 (5.3%) |
| Yes | 47/136 (34.6%) |
| No | 89/136 (65.4%) |
| Yes | 46/136 (33.8%) |
| No | 90/136 (66.2%) |
| Staffing issues | 23/136 (16.9%) |
| Bed not available | 21/136 (15.4%) |
| Corporate policy not to accept patient | 22/136 (16.2%) |
| Not enough PPE | 20/136 (14.7%) |
| State guidance not to accept patient | 10/136 (7.4%) |
| Other | 38/136 (27.9%) |
| One negative COVID test | 47/135 (34.8%) |
| Two negative COVID tests >24 hours apart | 31/135 (23.0%) |
| No testing for COVID required but only accepting asymptomatic patients | 22/135 (16.3%) |
| None, accepting all possible/confirmed COVID patients | 13/135 (9.6%) |
| Other | 22/135 (16.3%) |
| Yes | 105/134 (78.4%) |
| No | 29/134 (21.6%) |
Predictors of staffing shortages and specific types of staff losses
| Predictors of overall staffing shortages | |||
| Staff shortages (any) | |||
| Facility characteristics | Any staff shortages | No staff shortages | |
| For-Profit | 50 (56.2) | 39 (43.8) | .86 |
| Non-Profit/Government | 24 (54.6) | 20 (45.5) | |
| < 50 beds | 10 (41.7) | 14 (58.3) | .072 |
| 51-100 beds | 24 (49.0) | 25 (51.0) | |
| 101-150 beds | 33 (67.4) | 16 (32.7) | |
| > 150 beds | 9 (56.3) | 7 (43.8) | |
| Yes | 37 (78.7) | 10 (21.3) | <.001 |
| No | 37 (41.6) | 52 (58.4) | |
| Yes | 55 (52.4) | 50 (47.6) | .35 |
| No | 18 (62.1) | 11 (37.9) | |
| Associations between Presence of COVID-19 Positive Patients and Specific Types of Staff Losses | |||
| COVID+ patients currently at facility | No COVID+ patients currently at facility | ||
| No | 10 (21.3) | 52 (58.4) | <.001 |
| Yes | 37 (78.7) | 37 (41.6) | |
| None | 2 (4.4) | 19 (21.6) | <.001 |
| 1-10 staff members | 18 (39.1) | 52 (59.1) | |
| >10 staff members | 26 (56.5) | 17 (19.3) | |
| None | 5 (10.9) | 29 (32.6) | <.001 |
| 1-10 staff members | 30 (65.2) | 58 (65.2) | |
| >10 staff members | 11 (23.9) | 2 (2.3) | |
| No | 6 (12.8) | 45 (50.6) | <.001 |
| Yes | 41 (87.2) | 44 (49.4) | |
| None | 0 (0.0) | 5 (5.6) | <.001 |
| Staff requiring time off only | 6 (12.8) | 40 (44.9) | |
| Staff resignations | 40 (87.0) | 44 (49.4) |
Significance determined using Pearson's chi2 test.
Significance determined using logistic regression.
Staff resignations are accompanied by staff requiring time off 80/84 instances (95%) for which all data points available.