| Literature DB >> 33153079 |
Jimmy Lee1, Vidhya Venugopal2, P K Latha2,3, Sharifah Badriyah Alhadad4,5,6, Clarence Hong Wei Leow7, Nicholas Yong De Goh7, Esther Tan1, Tord Kjellstrom8,9, Marco Morabito10,11, Jason Kai Wei Lee5,7,12,13.
Abstract
The need for healthcare workers (HCWs) to wear personal protective equipment (PPE) during the coronavirus disease 2019 (COVID-19) pandemic heightens their risk of thermal stress. We assessed the knowledge, attitudes, and practices of HCWs from India and Singapore regarding PPE usage and heat stress when performing treatment and care activities. One hundred sixty-five HCWs from India (n = 110) and Singapore (n = 55) participated in a survey. Thirty-seven HCWs from Singapore provided thermal comfort ratings before and after ice slurry ingestion. Differences in responses between India and Singapore HCWs were compared. A p-value cut-off of 0.05 depicted statistical significance. Median wet-bulb globe temperature was higher in India (30.2 °C (interquartile range [IQR] 29.1-31.8 °C)) than in Singapore (22.0 °C (IQR 18.8-24.8 °C)) (p < 0.001). Respondents from both countries reported thirst (n = 144, 87%), excessive sweating (n = 145, 88%), exhaustion (n = 128, 78%), and desire to go to comfort zones (n = 136, 84%). In Singapore, reports of air-conditioning at worksites (n = 34, 62%), dedicated rest area availability (n = 55, 100%), and PPE removal during breaks (n = 54, 98.2%) were higher than in India (n = 27, 25%; n = 46, 42%; and n = 66, 60%, respectively) (p < 0.001). Median thermal comfort rating improved from 2 (IQR 1-2) to 0 (IQR 0-1) after ice slurry ingestion in Singapore (p < 0.001). HCWs are cognizant of the effects of heat stress but might not adopt best practices due to various constraints. Thermal stress management is better in Singapore than in India. Ice slurry ingestion is shown to be practical and effective in promoting thermal comfort. Adverse effects of heat stress on productivity and judgment of HCWs warrant further investigation.Entities:
Keywords: KAP survey; PPE; climate change; mitigation strategies; worker protection
Mesh:
Year: 2020 PMID: 33153079 PMCID: PMC7663197 DOI: 10.3390/ijerph17218100
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Comparison of demographic details, roles, and location of workplaces among healthcare worker (HCW) respondents during the coronavirus disease 2019 (COVID-19) pandemic. (W = Wilcoxon rank-sum test statistics, χ2 = Pearson chi-square test statistics).
| Factors | India | Singapore |
|---|---|---|
| Age (years) | 31.0 (26.0–38.0) | 29.0 (27.0–33.0) |
| W = 3075.5, | ||
| Male gender | 64 (58%) | 20 (36.4%) * |
| χ2(1) = 10.9, | ||
| BMI (kg/m2) | 24.4 (22.5–26.6) | 22.0 (18.8–24.8) * |
| W = 3569, | ||
| WBGT (°C) | 30.2 (29.1–31.8) | 20.0 (19.3–26.05) *** |
| W = 5589.5, | ||
| Role | ||
| Medical | 45 (39%) | 21 (42%) |
| Nursing | 33 (29%) | 28 (56%) |
| Operations | 4 (4%) | 0 (0%) |
| Sanitary | 5 (4%) | 0 (0%) |
| Others | 28 (24%) | 1 (2%) |
| Location | ||
| Tentage | 2 (2%) | 26 (52%) |
| Fever Facility (FF) | 24 (21%) | 26 (52%) |
| Clean Area | 25 (22%) | 14 (28%) |
| Others | 64 (57%) | 2 (4%) |
* p < 0.05; *** p < 0.001, when compared with India. Data expressed in n (%) for categorical variables, mean (standard deviation (SD)) and median (interquartile range (IQR)) for continuous variables.
Figure 1HCW respondents’ responses (total) to statements on knowledge on effects of heat stress on a 5-point Likert scale: (1) strongly disagree; (2) disagree; (3) neither agree nor disagree; (4) agree; (5) strongly agree.
Figure 2HCW respondents’ responses (total) to statements on attitudes towards personal protective equipment (PPE) usage on a 5-point Likert scale: (1) strongly disagree; (2) disagree; (3) neither agree nor disagree; (4) agree; (5) strongly agree.
Figure 3Type of PPE worn by HCW respondents during their shifts in % of respondents in each country (** p < 0.01; *** p < 0.001).
Comparison of impact of PPE usage and related thermal stress, duration of usage, time taken for donning and doffing the PPE, whether or not PPE could be removed during breaks, availability of dedicated rest areas, and symptoms experienced due to heat stress (W = Wilcoxon rank-sum test statistics, χ2 = Pearson’s chi-square test statistics).
| Factors | India | Singapore |
|---|---|---|
| Days/week in PPE | 6 (5–6) | 5 (4–5) *** |
| W = 3854, | ||
| Hours/shift in PPE | 6 (5–8) | 8 (8–8.15) *** |
| W = 1516, | ||
| Working in A/C | 27 (25%) | 34 (62%) *** |
| χ2(1) = 20.29, | ||
| Dedicated rest area | 46 (42%) | 55 (100%) *** |
| χ2(1) = 53.91, | ||
| Time taken to don PPE (minutes) | 7 (5–10) | 3 (2–5) *** |
| W = 4850.5, | ||
| Remove PPE on breaks | 66 (60%) | 54 (98.2%) *** |
| χ2(1) = 25.06, | ||
| Symptoms in PPE | ||
| Headache | 36 (33%) | 12 (22%) |
| χ2(1) = 1.62, | ||
| Dizziness | 7 (6%) | 16 (29%) *** |
| χ2(1) = 13.95, | ||
| Thirst | 98 (85%) | 46 (92%) |
| χ2(1) = 0.8973, | ||
| Vomiting | 0 (0%) | 0 (0%) |
| Excessive sweating | 95 (86%) | 50 (90%) |
| χ2(1) = 0.3485, | ||
| Breathing difficulty | 27 (25%) | 7 (13%) |
| χ2(1) = 2.45, | ||
| Dehydration | 27 (23%) | 3 (6%) ** |
| χ2(1) = 7.75, | ||
| Exhaustion | 86 (78%) | 42 (76%) |
| χ2(1) = 0.452, | ||
| Wanting to go comfort zone | 94 (86%) | 42 (76%) |
| χ2(1) = 1.51, | ||
| Sick leave | 3 (3%) | 4 (8%) |
| Fisher’s exact: |
** p < 0.01; *** p < 0.001, when compared with India. Data expressed in n (%) for categorical variables and median (IQR) for continuous variables.