| Literature DB >> 36006978 |
Wei Kong Wong1, Audrey Shuk Lan Chong2, Bing-Ling Kueh2, Amirul Mohd Sallehuddin Bin Mannan3, Muhammad Ubaidullah Arasy Bin Aziz4, Zhi-Yiu Hiang-Weang Lim5, Faulzan Bin Abdul Hamid6, Marcus Netto7, Bee Hwai Tan7.
Abstract
Approximately 1.29 million COVID-19 cases involving healthcare workers (HCWs) have been reported globally, leading to several hospitals conducting mass testing for early detection of infected HCWs. This study was conducted to report our experience and findings from the mass testing of HCWs from a public hospital in Sabah, Malaysia. The mass testing was conducted from 1st March 2020 to 30th June 2020, and involved self-reported data and laboratory results of 2089 HCWs. All HCWs who took at least two nasopharyngeal swabs for COVID-19 testing at two different time intervals during the study period were included. Throughout the mass testing period, various strategies such as practices of the new norm, daily temperature and symptom checking, wearing of appropriate personal protective equipment (PPE), identification of high-risk areas and travel declaration of staffs were within the hospital for prevention of COVID-19 transmission. We observed a small percentage of COVID-19 infected HCWs (n = 19, 0.91%) from the mass testing. Both symptomatic and asymptomatic COVID-19 HCWs were almost equal in number. A majority of those infected were nurses (n = 16, 0.77%) who had contact exposure to COVID-19 positive person or person under investigation (PUI) (n = 15, 0.72%). Four of the COVID-19 infected HCWs (n = 4/19, 21.05%) had no contact exposure. These HCWs were not identified through contact tracing. Fortunately, they were detected during the mass testing and were isolated promptly. In conclusion, mass testing of HCWs helped in early identification of COVID-19 infected HCWs not identified through contact tracing. Strategies such as stratified mass testing, strict compliance to new norm, appropriate PPE usage and identification of high-risk area were effective in the prevention of COVID-19 infection among HCWs.Entities:
Mesh:
Year: 2022 PMID: 36006978 PMCID: PMC9409526 DOI: 10.1371/journal.pone.0273326
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.752
Demographic characteristics of the HCWs.
| Variable |
| COVID +ve | COVID -ve |
|---|---|---|---|
|
| |||
|
| |||
| • | 1349 (64.58) | 10 (0.48) | 1339 (64.10) |
| • | 649 (31.07) | 5 (0.24) | 644 (30.83) |
| • | 91 (4.36) | 4 (0.19) | 87 (4.16) |
| • | 32 (IQR 28–37) | 34 (IQR 29–47) | 32 (IQR 28–37) |
|
| |||
| • | 342 (16.37) | 9 (0.43) | 333 (15.94) |
| • | 1747 (83.63) | 10 (0.48) | 1737 (83.15) |
|
| |||
| • | 1483 (70.99) | 19 (0.91) | 1464 (70.08) |
| • | 606 (29.01) | 0 | 606 (29.01) |
|
| |||
| • | 1581 (75.68) | 18 (0.86) | 1563 (74.82) |
| • | 508 (24.32) | 1 (0.05) | 507 (24.27) |
|
| |||
| • | 634 (30.35) | 11 (0.53) | 623 (29.82) |
| • | 1316 (63.00) | 8 (0.38) | 1308 (62.61) |
| • | 139 (6.65) | 0 | 139 (6.65) |
|
| |||
| • | 436 (20.87) | 2 (0.10) | 434 (20.78) |
| • | 725 (34.71) | 16 (0.77) | 709 (33.94) |
| • | 497 (23.79) | 0 | 497 (23.79) |
| • | 324 (15.51) | 1 (0.05) | 323 (15.46) |
| • | 107 (5.12) | 0 | 107 (5.12) |
|
| |||
| • | 1402 (67.11) | 4 (0.19) | 1398 (66.92) |
| • | 566 (27.09) | 14 (0.67) | 552 (26.42) |
| • | 75 (3.59) | 0 | 75 (3.59) |
| • | 27 (1.29) | 1 (0.05) | 26 (1.24) |
| • | 12 (0.57) | 0 | 12 (0.57) |
| • | 7 (0.34) | 0 | 7 (0.34) |
|
| |||
| • | 954 (45.67) | 10 (0.48) | 944 (45.19) |
| • | 149 (7.13) | 6 (0.29) | 143 (6.85) |
| • | 12 (0.57) | 0 | 12 (0.57) |
| • | 922 (44.14) | 3 (0.14) | 919 (43.99) |
| • | 34 (1.63) | 0 | 34 (1.63) |
| • | 18 (0.86) | 0 | 18 (0.86) |
|
| |||
| • | 1140 (54.57) | 4 (0.19) | 1136 (54.38) |
| • | 133 (6.37) | 3 (0.14) | 130 (6.22) |
| • | 440 (21.06) | 8 (0.38) | 432 (20.68) |
| • | 102 (4.88) | 4 (0.19) | 98 (4.69) |
| • | 274 (13.12) | 0 | 274 (13.12) |
*data is skewed to the right
Fig 1Daily cases in Sabah, daily swab of HCWs and new cases of HCWs.
Daily cases of COVID-19 in Sabah, daily swab test for HCWs and new cases of COVID-19 infected HCWs recorded between 1 March 2020 and 30 June 2020; majority of HCWs were infected during the first wave of COVID-19 in Sabah.
Univariate and multivariate logistic regression analysis showing odds ratio of factors associated with COVID-19 infection among the HCWs.
| Variable | Univariate logistic regression analysis | Multivariate logistic regression analysis | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Model 1 | Model 2 | ||||||||
| Crude OR | (95% CI) | p-value | Adjusted OR | (95% CI) | p-value | Adjusted OR | (95% CI) | p-value | |
|
| |||||||||
| • 51 and above | 6.16b | (1.89–20.03) | 0.944 | 7.77a | (1.92–31.44) | 0.014 | 8.29 | (2.36–29.12) | 0.001 |
| • 35–50 | 1.04 | (0.35–3.05) | 0.003 | 1.23 | (0.41–3.72) | 0.718 | 1.15 | (0.39–3.42) | 0.805 |
| • 18–34 (RC) | 1.00 | 1.00 | 1.00 | ||||||
|
| |||||||||
| • Yes | 4.70c | (1.89;11.64) | 0.001 | 2.63 | (0.93–7.42) | 0.068 | 2.76 | (1.03–7.39) | 0.043 |
| • No (RC) | 1.00 | 1.00 | 1.00 | ||||||
|
| |||||||||
| • Female | 2.10 x 10 7 | (0.00-NA) | 0.992 | - | - | - | - | - | - |
| • Male (RC) | 1.00 | ||||||||
|
| |||||||||
| • Clinical | 5.84 | (0.78–43.85) | 0.086 | 3.91 | (0.46–33.19) | 0.211 | - | - | - |
| • Non-clinical (RC) | 1.00 | 1.00 | |||||||
|
| |||||||||
| • COVID area | 2.89a | (1.15–7.21) | 0.023 | 1.84 | (0.68–5.03) | 0.233 | - | - | - |
| • Non COVID area (RC) | 1.00 | 1.00 | |||||||
|
| |||||||||
| • Doctor | 7.44 x 10 6 | (0.00-NA) | 0.997 | ||||||
| • Nurse | 3.65 x 10 7 | (0.00-NA) | 0.996 | - | - | - | - | - | - |
| • Allied health | 1.00 | (0.00-NA) | 1.000 | ||||||
| • Ancillary staff | 5.00 x 10 6 | (0.00-NA) | 0.997 | ||||||
| • Admin (RC) | 1.00 | ||||||||
|
| |||||||||
| • First contact exposure | 8.86c | (2.91–27.05) | <0.001 | 8.78 | (0.29–264.86) | 0.212 | |||
| • Second contact exposure | 0.00 | (0.00-NA) | 0.997 | 0.00 | (0.00-NA) | 0.997 | - | - | - |
| • Contact with PUI | 13.44a | (1.45–124.44) | 0.022 | 20.26 | (0.36–1130.03) | 0.143 | |||
| • Second degree contact | 0.00 | (0.00-NA) | 0.999 | 0.00 | (0.00-NA) | 0.999 | |||
| • Indirect contact | 0.00 | (0.00-NA) | 0.999 | 0.00 | (0.00-NA) | 0.999 | |||
| • No contact exposure (RC) | 1.00 | 1.00 | |||||||
|
| |||||||||
| • No | 3.96b | (1.42–11.06) | 0.009 | 8.62b | (1.61–46.22) | 0.012 | |||
| • Breach | 0.00 | (0.00-NA) | 0.999 | 0.00 | (0.00-NA) | 0.999 | - | - | - |
| • Not relevant | 0.31 | (0.09–1.12) | 0.074 | 2.29 | (0.26–20.58) | 0.459 | |||
| • Unknown | 0.00 | (0.00-NA) | 0.998 | 0.00 | (0.00-NA) | 0.999 | |||
| • Yes (RC) | 1.00 | 1.00 | |||||||
|
| |||||||||
| • High risk | 6.55a | (1.45;29.61) | 0.015 | 0.10 | (<0.01–4.88) | 0.246 | 4.06 | (0.85–19.48) | 0.080 |
| • Medium risk | 5.26b | (1.58;17.56) | 0.007 | 0.70 | (0.02–23.39) | 0.844 | 4.51 | (1.22–16.59) | 0.024 |
| • Low risk | 11.59c | (2.86;47.06) | 0.001 | 0.68 | (0.02–24.52) | 0.832 | 9.41 | (2.16–40.92) | 0.003 |
| • No identifiable risk (RC) | 1.00 | 1.00 | 1.00 | ||||||