The impact of COVID-19 workload on psychological distress amongst Canadian intensive care unit healthcare workers during the 1st wave of the COVID-19 pandemic: A longitudinal cohort study

Daniel Pestana; Kyra Moura; Claire Moura; Taylor Mouliakis; Frédérick D’Aragon; Jennifer L Y Tsang; Alexandra Binnie

doi:10.1371/journal.pone.0290749

. 2024 Mar 7;19(3):e0290749. doi: 10.1371/journal.pone.0290749

The impact of COVID-19 workload on psychological distress amongst Canadian intensive care unit healthcare workers during the 1^st wave of the COVID-19 pandemic: A longitudinal cohort study

Daniel Pestana ^1,^2,^#, Kyra Moura ^3,^#, Claire Moura ^3,^#, Taylor Mouliakis ⁴, Frédérick D’Aragon ^5,⁶, Jennifer L Y Tsang ^7,^8,^*, Alexandra Binnie ¹

Editor: Juan Jesús García-Iglesias⁹

PMCID: PMC10919682 PMID: 38452002

Abstract

Intensive care unit healthcare workers (ICU HCW) are at risk of mental health disorders during emerging disease outbreaks. Numerous cross-sectional studies have reported psychological distress, anxiety, and depression amongst ICU HCW during the COVID-19 pandemic. However, few studies have followed HCW longitudinally, and none of these have examined the association between COVID-19 workload and mental health. We conducted a longitudinal cohort study of 309 Canadian ICU HCW from April 2020 to August 2020, during the 1^st wave of the COVID-19 pandemic. Psychological distress was assessed using the General Health Questionnaire 12-item scale (GHQ-12) at 3 timepoints: during the acceleration phase of the 1^st wave (T1), the deceleration phase of the 1^st wave (T2), and after the 1^st wave had passed (T3). Clinically relevant psychological distress, defined as a GHQ-12 score ≥ 3, was identified in 64.7% of participants at T1, 41.0% at T2, and 34.6% at T3. Psychological distress was not associated with COVID-19 workload at T1. At T2, psychological distress was associated with the number of COVID-19 patients in the ICU (odds ratio [OR]: 1.06, 95% confidence interval [CI]: 1.00, 1.13) while at T3, when COVID-19 patient numbers were low, it was associated with the number of weekly hospital shifts with COVID-19 exposure (OR: 1.33, 95% CI: 1.09, 1.64). When analyzed longitudinally in a mixed effects model, pandemic timepoint was a stronger predictor of psychological distress (OR: 0.24, 95% CI: 0.15, 0.40 for T2 and OR: 0.16, 95% CI: 0.09, 0.27 for T3) than COVID-19 workload. Participants who showed persistent psychological distress at T3 were compared with those who showed recovery at T3. Persistent psychological distress was associated with a higher number of weekly shifts with COVID-19 exposure (OR: 1.97, 95% CI:1.33, 3.09) but not with a higher number of COVID-19 patients in the ICU (OR: 0.86, 95% CI: 0.76, 0.95). In summary, clinically relevant psychological distress was observed in a majority of ICU HCW during the acceleration phase of the 1^st wave of the COVID-19 pandemic but decreased rapidly as the 1^st wave progressed. Persistent psychological distress was associated with working more weekly shifts with COVID-19 exposure but not with higher numbers of COVID-19 patients in the ICU. In future emerging disease outbreaks, minimizing shifts with direct disease exposure may help alleviate symptoms for individuals with persistent psychological distress.

Introduction

Mental health issues are common amongst healthcare workers (HCW) during emerging disease outbreaks [1–4]. Risk factors include increased workload, shortages of personal protective equipment (PPE), anxiety with respect to being infected, anxiety with respect to transmitting the infection to family and friends, and loss of social supports due to self-isolation and quarantine [5, 6]. During the 1^st wave of the COVID-19 pandemic, cross-sectional studies in multiple countries reported high rates of psychological morbidity amongst HCW, including psychological distress, anxiety, depression, and post-traumatic stress disorder (PTSD). A meta-analysis of 13 studies reported a pooled prevalence of 23.2% for symptoms of anxiety and 22.8% for symptoms of depression [7].

Intensive care unit (ICU) workers may be at higher risk of mental health issues during emerging disease outbreaks than other HCW. At the peak of the 1^st wave of the pandemic, a study of 1,058 French ICU HCW reported anxiety, depression and peritraumatic dissociation in 50.4%, 30.4%, and 32% of participants, respectively [8]. A separate study of 2,643 French ICU HCW reported clinically relevant psychological distress in 55% of respondents [9]. The prevalence was higher in “high intensity” areas of the country, where the number of COVID-19 ICU patients exceeded the number of baseline ICU beds. In a study of 709 ICU HCW in the UK, 45% met criteria for depression, PTSD, severe anxiety, or problem drinking and 13% reported frequent thoughts of suicidality or self-harm [10]. In all three studies, mental health issues were more common amongst females than males, and more common amongst nurses and nursing assistants than physicians.

Studies conducted after the peak of the 1^st wave have reported lower rates of psychological distress. A Canadian study of ICU HCW, conducted between June and August of 2020, reported psychological distress in only 18% of respondents [11]. However, few studies have followed ICU HCW longitudinally to examine changes in mental health over time [12, 13]. None of these, to our knowledge, have examined the relationship between COVID-19 workload and mental health.

In this longitudinal study, we followed Canadian ICU HCW from April to July 2020, during the 1^st wave of the pandemic, to identify variables associated with psychological distress as well as variables associated with persistence of psychological distress over time.

Materials and methods

Survey design

The study was designed as a 4-month, longitudinal survey of Canadian ICU HCW. It was built in an expedited fashion to capture data during the 1^st wave of the COVID-19 pandemic. At study entry (T1) in April 2020, participants provided baseline demographic data including sex, age, profession, and years of experience. Psychological well-being was assessed using the General Health Questionnaire 12-item scale (GHQ-12), a self-reported screening tool that focuses on breaks in normal functioning rather than life-long traits [14–16].

After enrolment, participants were asked to complete weekly surveys reporting their COVID-19 workload in the previous 7 days, including the number of hospital shifts worked, the number of hospital shifts worked with exposure to COVID-19 patients, and the maximum number of COVID-19 patients in the ICU in the previous week.

The GHQ-12 was repeated at two timepoints during the 16 week study: during the deceleration phase of the 1^st wave (T2), between June 1^st and June 7^th, 2020, and after the 1^st wave of the pandemic had passed (T3), between July 31^stand August 14^th, 2020.

Survey administration

The survey was administered online through the secure Qualtrics platform (www.qualtrics.com). The target population was Canadian ICU HCW, including physicians, registered nurses, respiratory therapists, and allied health professionals (i.e. physiotherapists, speech and language therapists, dieticians, pharmacists, clerks and healthcare assistants). The survey link was disseminated through online advertisements (S1 Appendix) to the membership of the Canadian Critical Care Trials Group (CCCTG), the Canadian Community ICU Research Network (CCIRNet), the Canadian Critical Care Society (CCCS), and the Critical Care Nurses Association, as well as through their associated social media groups. Distribution began on April 6^th, 2020 and enrollment closed on April 30^th, 2020 [17]. Anyone with access to the link could potentially enrol. Due to the broad distribution strategy, it was not possible to calculate a response rate.

Electronic consent (e-consent) was provided by participants prior to completion of the T1 survey. Withdrawal was allowed at any time and participants could request removal of their data. Participants who did not reply to two consecutive surveys were sent an email asking if they wished to continue in the study. During the 16 weeks of the study, 64 participants withdrew from further participation and 1 participant asked for their data to be removed.

To ensure data linkage, weekly surveys were distributed using unique email survey links. Each survey remained accessible for 7 days and a reminder email was sent 3 days after distribution. The T2 and T3 surveys were distributed in conjunction with the 9^th and 16^th weekly surveys on June 1^st, 2020 and July 31^st, 2020, respectively. Responses to the final survey closed on August 14^th, 2020.

Data analysis

Data were described as number and percentage for categorical variables and mean and standard deviation or median and interquartile range for continuous variables. Response categories on the GHQ-12 questionnaire were coded using the 0-0-1-1 scoring method [14]. A threshold of ≥ 3 points on the GHQ-12 has been identified as an appropriate cutoff for mental health diagnosis screening [15, 16] and a marker of clinically relevant psychological distress [2, 18].

Multivariable logistic regression was used to identify predictors of clinically relevant psychological distress (GHQ-12 ≥ 3). Variables included age, sex, profession, years of experience, and COVID-19 workload variables. Stepwise forwards and backwards regression was used to identify the model with the lowest Akaike Information Criterion (AIC) [19]. Sex and profession were included in all models because previous studies have shown a strong association between these variables and HCW mental health during emerging disease outbreaks [8–10]. For analysis of longitudinal data, a mixed effects model was created in which participant identity was included as a random effect, sex and profession as fixed effects, and age, years of experience, COVID-19 workload variables and pandemic timepoint as optional fixed effects. To deal with missingness, median weekly hospital shifts, median weekly hospital shifts with COVID-19 exposure, and the median number of patients in the ICU were used as variables. Stepwise forwards and backwards regression was used to identify the model with the lowest AIC. The minimal dataset is available as Supporting Information (S1 Dataset).

All analysis was performed using R software v 3.6.2 [20].

Ethical consideration

Ethics approval was obtained from the Hamilton Integrated Research Ethics Board (HiREB #10790) and the Centre Hospitalier Universitaire de Sherbrooke (#MP-31-2021-3704).

Results

Study enrolment began on April 6^th, 2020, during the acceleration phase of the 1^st wave of the pandemic in Canada. A total of 309 Canadian ICU healthcare workers (HCW) enrolled in the study by completing the e-consent and T1 survey. Although proof of institutional affiliation was not required, 129 respondents (42%) provided email addresses from Canadian hospitals, medical systems, or universities, representing 33 different institutions. A majority of participants were working in the province of Ontario (78%), with the remainder in Quebec, Alberta, Manitoba, the Atlantic provinces and British Columbia. Registered nurse (RN) was the most common profession (47.9%), followed by physician (26.5%), respiratory therapist (RT) (14.2%), and allied health professional (11.3%) (Table 1). The mean age of participants was 39.8 years (SD = 9.67) and a majority were female (73.5%).

Table 1. Participant demographics at T1, T2 and T3.

Timepoint	T1 N = 309	T2 N = 195	T3 N = 191	p-value
Age
Mean (SD)	39.8 (9.67)	41.0 (10.1)	41.4 (9.93)	0.165
Median [Min, Max]	39.0 [23.0, 63.0]	41.0 [23.0, 63.0]	41.0 [23.0, 63.0]
Sex
Female	227 (73.5%)	149 (76.4%)	144 (75.4%)	0.742
Male	82 (26.5%)	46 (23.6%)	47 (24.6%)
Role
Allied Health	35 (11.3%)	24 (12.3%)	25 (13.1%)	0.941
Physician	82 (26.5%)	52 (26.7%)	55 (28.8%)
Registered nurse	148 (47.9%)	97 (49.7%)	87 (45.5%)
Respiratory therapist	44 (14.2%)	22 (11.3%)	24 (12.6%)
Years of Experience
Mean (SD)	13.8 (9.38)	15.3 (9.73)	15.4 (9.70)	0.115
Median [Min, Max]	12.0 [1.00, 43.0]	13.0 [1.00, 40.0]	14.0 [1.00, 40.0]

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After enrolment, participants were asked to complete weekly surveys tracking their COVID-19 workload. The average number of surveys completed was 9.23 (SD = 5.22). The GHQ-12 survey was repeated at week 9 (T2), with 195/309 (63.1%) respondents, and at week 16 (T3) with 191/309 (61.8%) respondents. Overall, 217/309 (70.2%) participants responded to either the T2 or T3 survey and 169/309 completed both. Participant characteristics were similar at T1, T2 and T3 (Table 1).

COVID-19 workload

COVID-19 workload was tracked weekly during the 16 weeks of the study. Variables included the number of weekly hospital shifts, the number of weekly shifts with direct exposure to COVID-19 patients, and the maximum number of COVID-19 patients in the participant’s ICU. Fig 1 shows the change in COVID-19 workload variables over the 16 weeks of the study. Weekly hospital shifts were relatively constant (Fig 1A), while weekly hospital shifts with exposure to COVID-19 patients (Fig 1B) and the maximum number of COVID-19 patients in the ICU (Fig 1C) both declined, in keeping with the progression of the 1^st wave of the pandemic [17].

Fig 1 — (A) Weekly hospital shifts. (B) Weekly hospital shifts with exposure to COVID-19 patients. (C) The maximum number of COVID-19 patients admitted to the participant’s ICU. Study weeks are shown on the x axis and the frequency of each response is shown on the y axis. T1, T2 and T3 indicate the timing of the GHQ-12 surveys.

Prevalence of clinically relevant psychological distress

At T1, during the acceleration phase of the 1^st wave of the pandemic, 200 of 309 study participants (64.7%) scored ≥ 3 points on the General Health Questionnaire 12-item scale (GHQ-12), indicating clinically relevant psychological distress (Table 2). At T2, during the deceleration phase, 80/195 participants (41.0%) scored ≥ 3 points on the GHQ-12. At T3, after the 1^st wave had passed, 66/191 participants (34.6%) scored ≥ 3 points on the GHQ-12. Similar results were observed in the subgroup of 169 participants (54.7%) who completed all three GHQ-12 surveys (“complete participants”) (Table 2).

Table 2. Prevalence of clinically relevant psychological distress (GHQ-12 score ≥ 3) at T1, T2 and T3 amongst (A) all participants and (B) participants who completed all GHQ-12 surveys surveys (n = 169).

	Timepoint	T1	T2	T3
A	All participants	200/309 (64.7%)	80/195 (41.0%)	66/191 (34.6%)
B	Complete participants	105/169 (62.1%)	68/169 (40.2%)	53/169 (31.4%)
	Chi-Square P value	0.642	0.963	0.596

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Longitudinal change in GHQ-12 scores

Average GHQ-12 scores declined from T1 to T3 (Anova, p < 0.0001) (Fig 2A). At T1 the average GHQ-12 score was 3.85 (SD = 2.78), at T2 it was 2.65 (SD = 2.83), and at T3 it was 2.28 (SD = 3.01). A similar decline was seen amongst the 169 participants who completed the T1, T2 and T3 surveys (Anova, p < 0.0001) (Fig 2B).

Female participants reported higher GHQ-12 scores than male participants at all timepoints (Fig 2C). However, both sexes showed a significant decline in GHQ-12 scores from T1 to T3 (Fig 2C). Physicians, registered nurses, and respiratory therapists also showed a decrease in GHQ-12 scores between T1 and T3 (Fig 2C). Allied health professionals did not show a significant change in GHQ-12 scores; however, the number of participants in this category was small and it represented a diverse group of professionals (Fig 2D).

Variables predicting psychological distress

Multivariable logistic regression with stepwise variable selection was used to identify variables that predicted clinically relevant psychological distress (GHQ-12 score ≥ 3) at each timepoint (Table 3). Sex and profession were included in all models due to their known association with HCW mental health during emerging disease outbreaks [8–10, 21].

Table 3. Multivariate logistic regression models to predict clinically relevant psychological distress (GHQ-12 ≥ 3) at T1, T2, T3 and all timepoints.

Models were selected through stepwise forwards and backwards regression to obtain the lowest Akaike Information Criterion (AIC). In the final mixed effects model, participant ID was included as a random effect. OR: odds ratio. 95% CI: 95% confidence interval. AIC: Akaike Information Criterion. ROC: receiver operating curve. ICC: intraclass correlation coefficient. For weekly hospital shifts, the OR is the increase in risk per additional shift worked.

	T1		T2		T3		All timepoints
Variables	OR (95% CI)	p-value	OR (95% CI)	p-value	OR (95% CI)	p-value	OR (95% CI)	p-value
Sex: Male	0.66 (0.36, 1.20)	0.2	0.59 (0.24, 1.43)	0.3	0.36 (0.14, 0.89)	0.033	0.43 (0.22, 0.85)	0.016
Profession: Physician	—	—	—	—	—	—	—	—
Profession: RN	2.54 (1.34, 4.84)	0.005	1.21 (0.45, 3.30)	0.7	1.26 (0.54, 2.98)	0.6	1.69 (1.11, 2.58)	0.014
Profession: RT	1.45 (0.68, 3.14)	0.3	1.19 (0.35, 4.04)	0.8	0.82 (0.25, 2.48)	0.7	1.56 (0.65, 3.73)	0.32
Profession: Allied health	1.11 (0.48, 2.60)	0.8	1.92 (0.59, 6.36)	0.3	0.91 (0.29, 2.76)	0.9	1.54 (0.60, 3.92)	0.37
Median number of weekly hospital shifts			0.76 (0.59, 0.98)	0.037
Median number of weekly hospital shifts with COVID-19 exposure					1.33 (1.09, 1.64)	0.006
Median number of COVID-19 patients in the ICU			1.06 (1.00, 1.13)	0.070
Timepoint: T2							0.24 (0.15, 0.40)	<0.001
Timepoint: T3							0.16 (0.09, 0.27)	<0.001
No. of observations	309		166		190		695
AIC	392.5		230.0		239.7		862.7
Area under the ROC	0.64		0.64		0.68		0.69
ICC							0.40

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At T1, during the acceleration phase of the 1^st wave, the best regression model included only sex and profession, without any of the COVID-19 workload variables (Table 3). Female participants were at higher risk of psychological distress than male participants, and nurses were at higher risk than other professionals. At T2, during the deceleration phase of the 1^st wave, the best regression model included sex, profession, weekly hospital shifts, and the maximum number of COVID-19 patients in the participant’s ICU. Notably, more weekly hospital shifts was associated with decreased psychological distress (odds ratio (OR): 0.76, 95% confidence interval (CI): 0.59, 0.98). At T3, the best regression model included sex, profession, and weekly hospital shifts with direct exposure to COVID-19 patients (OR 1.33, 95% CI: 1.09, 1.64) (Table 3).

A mixed effects model was created to identify variables predictive of clinically relevant psychological distress over time (Table 3). In addition to sex and profession, age, years of experience, COVID-19 workload variables and pandemic timepoint (T1 vs T2 vs T3) were analysed as potential predictors. Given the longitudinal nature of the data, participant identity was included as a random effect. The best regression model included sex, profession, and pandemic timepoint, but not individual COVID-19 workload variables (Table 3).

Persistent psychological distress

Out of the 190 participants who reported psychological distress at T1, 122 responded to the T3 survey. Of these, 55/122 (45.1%) reported a GHQ-12 score ≥ 3 at T3, suggesting persistent psychological distress, whilst 67/122 (54.9%) reported a GHQ-12 score < 3. No statistically significant differences in demographics or COVID-19 workload variables were identified between individuals with persistent psychological distress vs those with recovery (Table 4). The Brief Resilience Coping Scale (BRCS) [22], a self-reported measure of resilience, was also applied at T3 and no difference in resilience was identified between the two groups.

Table 4. A comparison of participants with “persistent” psychological distress at T3 (GHQ-12 ≥ 3 at T1 and T3) versus those without (GHQ-12 score ≥ 3 at T1 and < 3 at T3).

Q-value: p-value adjusted to maintain false discovery rate ≤ 0.05. Variables in bold are statistically significant at the p = 0.05 level. BRCS: Brief Resilience Coping Scale.

Characteristic	Overall N = 122	Persistence N = 55	Recovery N = 67	p-value	q-value
GHQ-12 score at T1	5.2 (2.2)	5.7 (2.5)	4.8 (1.9)	0.029	0.2
GHQ-12 score at T3	2.9 (3.2)	5.9 (2.4)	0.5 (0.7)	<0.001	<0.001
Age, mean (SD)	41.0 (9.9)	41 (11)	40.9 (8)	>0.9	>0.9
Sex (% female)				0.08	0.3
Female	98 (80%)	48 (87%)	50 (75%)
Male	24 (20%)	7 (13%)	17 (25%)
Profession				0.2	0.4
Allied Health	12 (9.8%)	3 (5.5%)	9 (13%)
Physician	29 (24%)	14 (25%)	15 (22%)
Registered Nurse	65 (53%)	33 (60%)	32 (48%)
Respiratory therapist	16 (13%)	4 (9.1%)	11 (16%)
Resilience (BRCS)	15.1 (2.3)	15.0 (2.1)	15.2 (2.5)	0.6	0.8
Median weekly hospital shifts	3.3 (1.4)	3.5 (1.4)	3.1 (1.5)	0.13	0.4
Median weekly hospital shifts with COVID-19 exposure	1.2 (1.4)	1.4 (1.5)	1.0 (1.3)	0.055	0.3
Median maximum COVID-19 patients in ICU	6.4 (5.5)	5.72 (4.92)	7.04 (5.91)	0.2	0.4
Number of weekly responses	12.7 (3.0)	12.2 (3.2)	13.1 (2.8)	0.12	0.3

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Predictors of persistent psychological distress

Multivariable logistic regression was used to determine the association of age, sex, profession, years of experience and COVID-19 workload variables with persistent psychological distress. Workload variables were derived from all weekly survey responses and included the median number of weekly hospital shifts worked, the median number of weekly hospital shifts worked with exposure to COVID-19 patients, and the median number of COVID-19 patients in the participant’s ICU.

The best regression model is shown in Table 5. The model included sex, profession, median weekly hospital shifts with exposure to COVID-19 and median number of COVID-19 patients in the participant’s ICU. Weekly hospital shifts with exposure to COVID-19 patients was associated with persistent psychological distress. Conversely, male sex, RT or allied health profession, and the number of COVID-19 patients in the ICU were associated with recovery. The area under the receiver operating curve for the model was 0.74.

Table 5. Multivariable logistic regression to predict persistence of psychological distress at T3.

Variable	OR	95% CI	p-value
Profession: Physician	—	—	—
Profession: RN	0.60	0.20, 1.76	0.36
Profession: RT	0.16	0.03, 0.71	0.02
Profession: Allied health	0.17	0.03, 0.092	0.05
Sex: Male	0.27	0.08, 0.88	0.037
Weekly hospital shifts with exposure to COVID-19 patients	1.97	1.33, 3.09	0.002
Number of COVID-19 patients in the ICU	0.86	0.76, 0.95	0.005
Number of observations	121
AIC	158.4
AUROC	0.74

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OR: odds ratio. 95% CI: 95% confidence interval. AIC: Akaike Information Criterion. AUROC: Area Under the Receiver Operating Curve.

Discussion

Emerging disease outbreaks place a heavy toll on HCW. During the COVID-19 pandemic, multiple studies have documented psychological morbidity amongst frontline HCW, particularly amongst ICU HCW [8, 12, 13, 21, 23, 24]. However, few studies have monitored the mental health of HCW over time. In this study, psychological distress was assessed in a cohort of Canadian ICU HCW at three timepoints during the 1^st wave of the COVID-19 pandemic: the acceleration phase in April 2020, the deceleration phase in June 2020, and after the 1^st wave had passed in July-August 2020 [14]. At each timepoint, participants completed the General Health Questionnaire 12-item scale (GHQ-12), which has been widely used to assess HCW mental health during emerging disease outbreaks and is designed to assess breaks in normal functioning rather than life-long traits [14]. Although it does not diagnose specific psychiatric disease, the GHQ-12 has good specificity (74–88%) and sensitivity (74–80%) for mental health disorders, particularly anxiety and mood disorders [16, 25].

In April 2020, during the acceleration phase of the 1^st wave in Canada, over 60% of participants showed evidence of clinically relevant psychological distress. At this early stage of the pandemic, sex and profession were the best predictors of psychological distress while individual COVID-19 workload variables were not significant predictors. The high level of psychological distress observed at the beginning of the 1^st wave is consistent with other studies of ICU HCW at similar timepoints [8, 9, 24]. April 2020 was a time of great anxiety and uncertainty worldwide; COVID-19 coverage accounted for more than 50% of headlines in major online news sites [26] and a significant increase in psychological distress was observed in the general population at that time [27]. Moreover, news stories were reporting many infections and fatalities amongst HCW in China and Italy during their devastating 1^st waves [28, 29]. External factors, such as news exposure and uncertainty about the future course of the pandemic, may have had more impact on HCW mental health at the beginning of the 1^st wave than did individual COVID-19 workload.

In June 2020, during the deceleration phase of the 1^st wave, the prevalence of clinically relevant psychological distress decreased from 64.7% to 41.0% of participants. In addition to female sex and RN profession, the number of COVID-19 patients in the participant’s ICU was predictive of psychological distress. Unexpectedly, the number of weekly hospital shifts worked (with or without COVID-19 exposure) was protective against psychological distress. It is unclear, however, whether working more shifts had a positive impact on mental health or whether HCW with better mental health were willing to take on more shifts.

In July 2020, after the 1^st wave of the pandemic had passed, the prevalence of clinically relevant psychological distress decreased to 34.5%. In addition to sex and profession, the number of weekly hospital shifts with exposure to COVID-19 patients was predictive of clinically relevant psychological distress. COVID-19 exposure was low in July 2020, with a majority of participants reporting no workplace exposure in the previous week (Fig 1). In this context, ongoing exposure to COVID-19 was a significant predictor of mental health issues.

When data from T1, T2 and T3 were analysed longitudinally using a mixed effects model, the strongest predictor of clinically relevant psychological distress was pandemic timepoint. The inclusion of individual COVID-19 workload variables in the mixed effects model did not improve prediction. This suggests that the broader context of the pandemic had more impact on the psychological wellbeing of ICU HCW than did individual COVID-19 workload variables.

Factors predictive of persistent psychological distress were analysed by comparing participants who displayed psychological distress at both T1 and T3 with those who displayed psychological distress at T1 but not at T3. The strongest predictor of persistent psychological distress was the number of weekly shifts with COVID-19 exposure. This is an important modifiable risk factor that could be addressed by providing modified scheduling for ICU HCW experiencing persistent psychological distress. It could also be addressed by rotating ICU HCW between hospitals with high and low patient numbers to limit the number of shifts with disease exposure.

RTs and allied health professionals were more likely to show recovery than physicians, while registered nurses were similar. Studies have shown that physicians are reluctant to seek help for psychiatric illness due to stigma and concerns about licensing [30]. In addition, ICU physicians in Canada often work alone, without immediate peer support. Social isolation is associated with mental health disorders, so a lack of close contact with peers may have impacted physician recovery [31].

Surprisingly, a higher median number of COVID-19 patients in the ICU was associated with recovery from psychological distress. Since patient loads were universally low at T3, this finding suggests that individuals who experienced psychological distress in the context of high patient loads at the beginning of the 1^st wave were more likely to recover when patient loads decreased. Conversely, individuals who experienced psychological distress at T1 in the absence of high patient loads were less likely to recover.

One important variable that was not assessed in this study was pre-existing psychiatric disease. HCW suffer from anxiety and depression at rates that may exceed those of the general population [32]. Prior to the COVID-19 pandemic, symptoms of anxiety and depression were reported in 18% and 11–31% of American ICU nurses, respectively [33, 34]. Similarly, in France, symptoms of depression were reported in 18.8% of ICU physicians and 15.6% of ICU nurses and nursing assistants [35]. Although the GHQ-12 is designed to detect acute changes in mental health, it is possible that pre-existing mental illness contributed to the high rates of psychological distress detected in this study.

Only a few studies have monitored the mental health of ICU HCW longitudinally during the 1^st wave of the pandemic. A study of 526 ICU HCW in the UK reported a decrease in clinically relevant psychological distress, from 50% to 34.6% of participants, between the acceleration and deceleration phases of the 1^st wave of the pandemic [13]. Psychological distress was associated with concerns about being infected with COVID-19 and about infecting family members, however the investigators did not examine which variables were associated with persistence of psychological distress. A study of 786 frontline HCWs (not all of them from ICU) in the United States reported a similar decrease in psychological distress from 35.1% to 20.5% of participants between April and November 2020 [12]. Factors associated with persistent psychological distress were a history of psychiatric disease, fewer years of practice, the presence of post-surge stressors, and family-related concerns. Neither study evaluated the impact of COVID-19 workload on psychological distress.

Our study highlights the high rates of clinically relevant psychological distress amongst ICU HCW during the 1^st wave of the pandemic, the importance of pandemic timepoint as a predictor of psychological distress, and the association between the number of shifts worked with COVID-19 exposure and persistent psychological distress.

Strengths

This study has a number of strengths. Data was collected early in the 1^st wave of the pandemic, prior to most published studies. Longitudinal data was also collected, allowing for mental health to be monitored over time. Participants came from a large number of centres, allowing for comparison between participants with variable COVID-19 workloads. Weekly data collection for COVID-19 workload variables allowed for good data precision and minimized recall bias.

Limitations

This study has several limitations. Participants were self-selected and may not be representative of Canadian ICU HCW; however, the study was publicized as a study of COVID-19 infection and transmission rather than mental health, which may have mitigated self-selection bias. The study population was relatively small with significant loss to follow-up; however, no differences with respect to demographics were noted across study timepoints. Moreover, the prevalence of psychological distress was similar amongst participants who completed all 3 GHQ-12 surveys, suggesting that loss to follow-up did not significantly impact results. No data is available with respect to pre-existing psychiatric illness. Although the GHQ-12 is designed to measure breaks in usual functioning, individuals with pre-existing mental health issues may have been more likely to report psychological distress.

Conclusions

In this longitudinal study of Canadian ICU HCW, clinically relevant psychological distress decreased from 64.5% to 34.5% during the 1^st wave of the COVID-19 pandemic. The strongest predictor of clinically relevant psychological distress was pandemic timepoint, suggesting that external factors, such as news media coverage, played a significant role in HCW mental health. The number of weekly shifts with exposure to COVID-19 was associated with persistent psychological distress at the end of the study period.

Supporting information

S1 Appendix. Study advertisement.

(PDF)

pone.0290749.s001.pdf^{(58.6KB, pdf)}

S1 Dataset. Study data in excel format.

(XLSX)

pone.0290749.s002.xlsx^{(158.3KB, xlsx)}

S1 Checklist. Checklist for Reporting Results of Internet E-Surveys (CHERRIES).

(DOCX)

pone.0290749.s003.docx^{(19.8KB, docx)}

Acknowledgments

We thank the Canadian ICU professionals who generously participated in this study. We thank Dr Marc de Rosnay and Dr Ana Marreiros for helpful discussions.

Data Availability

All relevant non-identifying data are within the manuscript and its Supporting information files.

Funding Statement

This study was funded by an unrestricted research grant to JT from Mohawk Medbuy, a not-for-profit, shared services corporation (https://www.mohawkmedbuy.ca/). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0290749.r001

Decision Letter 0

Juan Jesús García-Iglesias

22 Jun 2023

PONE-D-23-09335The impact of COVID-19 workload on psychological distress amongst Canadian intensive care unit healthcare workers during the 1st wave of the COVID-19 pandemic: a longitudinal cohort study.PLOS ONE

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Reviewer #1: In this paper, the authors present a longitudinal study of mental health among Canadian healthcare workers (HCWs) from intensive care units (ICU). The population was studied longitudinally at the beginning, middle and end of the first wave of Covid in 2020. A total 309 HCWs participate in the first survey timepoint, and around 190 at each of the two subsequent rounds of survey. Overall, the authors identified several factors that contributed to the presence of psychological distress at the various timepoints, as well as identifying factors associated with the likelihood of recovery from the distress.

The authors are to be commended for mounting such a comprehensive project at such short notice. The results are interesting, and the paper is generally well written.

I have some comments for the authors’ consideration:

Methods : page 4, lines 109-111 : were the data about workload self-reported? Or were they retried from written hospital administrative data? If self-reported, this may be subject to bias and should be mentioned as a limitation.

Methods / survey administration: I appreciate that the authors provided additional methodological information in the supplementary material, but to facilitate the reading of the main paper, more detail is needed here, on two key points:

First, the authors should explain exactly how the survey was distributed – was it simply emailed to all registered members of the societies listed? Do the authors have any idea of the number of people who received the link? Could recipients forward the link to other people?

Second, it is unclear what exactly the participants were required to do. From reading the results on page 6, paragraph “Covid 19 workload”, and from my reading of the consent form provided in the supplementary material, it appears that the participants were asked to provide information about workload on a weekly basis. This is not clear from the methods, where the reader is given to understand that the participants were surveyed at 3 timepoints only (and not in between). The authors should clearly explain what was measured at the 3 timepoints T1, T2 and T3, and specify whether the participants also had to provide information about their workload every week. If so, please explain how this was done. It’s not explained in the supplementary methods either. I cannot understand fully, from the methods presented, whether the participants were individually identified and followed – i.e. are the 195 and 191 respondents who participated in the surveys at T2 and T3 respectively, a subset of the original 309 participants from T1? If they were all recording their workload weekly, that indicates a high drop-out rate, with about one third of the participants lost by T2.

Results, page 8, line 231/232: if weekly hospital shifts is associated with a protective effect, then it should be specified in Table 3 that the OR is the increase in risk per additional shift worked. Same remark for weeks shifts with Covid exposure.

Results, page 10, line 248 – do the authors mean that there are 190 unique individuals who were identified to have answered both surveys?

Table 4 – I don’t recall it being mentioned in the methods that the BRCS was administered – when was this tool administered to participants? This should be added to the methods section.

Table 5 – I think the title of this table is wrong, or else the authors have interpreted the data the wrong way around (unlikely). On page 11, lines 272-274, the authors report the factors associated with the likelihood of recovery from psychological distress, and these factors are what Table 5 purports to report. However, the authors report that male sex and a higher number of Covid patients in the ICU were associated with recovery – but this is ambiguous. According to the odds ratios reported in Table 5, both these factors have ORs that are associated with a protective effect, and therefore, a lower likelihood of the outcome. If the outcome modelled in the regression analysis is “recovery”, then male sex and more COVID cases in ICU “protect against” recovery and thus, are associated with a lower likelihood of recovery. This is incongruent with the result report on lines 273/274, where the authors state that physician profession and a higher number of weekly shifts with exposure to Covid patients were associated with persistence of psychological distress. Firstly, there is no odds ratio for the profession “physician”, indicating that this category was the reference category, so it cannot be associated with the outcome. There must a mistake somewhere. Secondly, the odds ratio associated with the number of hospital shifts with exposure is 1.97, indicating an approximately twofold increase in the risk of the outcome. This suggests that the outcome being modelled in the table is “persistent psychological distress” (i.e. lack of recovery), which is the opposite of what the table title indicates. So, overall, there is some work to be done here to ensure that the table title clearly indicates the outcome being modelled, to enable the reader to interpret the ORs in the right direction. Second, the authors must clarify the reference category used among the professions, and correct the result pertaining to physicians. The same comment applies to the discussion, because the authors discuss this result about physician recovery (page 12, line 335), but the reader does not see this result in the results section.

A key limitation of this study is that there is no information about baseline mental health among the respondents. Indeed, it has been reported that pre-pandemic, around 13% of physicians/surgeons and around 18% of registered nurses may have had diagnosed depression (DOI: 10.1097/JOM.0000000000002630). Therefore, we do not know how many of these professionals had mental health issues or signs and symptoms of burnout before the pandemic began.

Discussion, page 12, line 323: the authors state that “these participants appear to have been more susceptible to mental health issues at T3” – what is your basis for this assertion?

Limitations – as mentioned above, the authors might add the lack of knowledge about baseline mental health status, and possible bias in the reporting of workload. Also, since we are now more than three years later, the authors might mention how this information can be of use to the medical community now.

References: the authors should verify the format of the references – some still contain dates (e.g.refs 5,6, 7….), and some journal names are not presented correctly (e.g. ref 10…)

In summary: The main action points include a revision of the methods to make the exact procedures clearer; clarify the interpretation of Table 5 and the related text in the results and discussion; and add limitations as detailed above.

Reviewer #2: Thank you for the opportunity to review this manuscript. I commend the authors on conducting this important study and at such an early and chaotic phase of the pandemic. It is impressive that they had a relatively high follow-up rate as part of this longitudinal survey, which is notoriously challenging to achieve, especially in studies of healthcare providers.

I have included suggestions below for the authors to consider.

Abstract

- The abstract does not currently include statistical data from the multivariable analysis, although the findings are described in writing. Generally, the data are included for readers to take the strengths of any associations into consideration. Would suggest the authors include the few relevant data points numerically for findings that they have summarized in writing.

Introduction

- The Introduction is well-written and referenced well. Given that this is a Canadian study with a large proportion of participants from Ontario, perhaps the authors can consider summarizing other Canadian/ Ontario studies that have been published in the literature... numerous have been published that can be incorporated... a few of these listed below:

> PMID: 34940952

> PMID: 35167590

> PMID: 36627462

- The last paragraph of the Introduction section begins with "Only a few studies..." but these studies have not been cited. Please cite the studies you're referring to.

- The objective statement (last 2 sentences in the introduction) are detailed enough that they are similar to statements in the Methods. I would suggest a broad objective statement be included instead to summarize the overall purpose of the study, without getting into Methodology at this stage.

Materials and Methods

- The study is well described in this section.

Results

- What proportion of participants were from academic vs. community settings? This is important to note given my comment below regarding the authors' speculation about community ICU physicians.

- The authors describe the findings of the BRCS in Table 4 but did not indicate in the Methods that this instrument was also administered and do not discuss its findings in the Results. This is an interesting variable and would be of interest to readers to know about the outcomes on the resilience front. I would suggest discussing this instrument in the Methods and Results in more detail.

- Also regarding the BRCS, was this variable considered for inclusion in the MV analysis?

- Table 3 - I would suggest putting the most notable findings where a variable was predictive of the outcome in bold font for easier review by readers.

- Table 5 indicates that the physician profession was used as the reference variable for the analysis but the text states that the physician profession was predictive of persistence - can the authors please explain and provide the data that led to this conclusion? Was there another analysis in which the physician profession was not used as the reference variable and allowed the authors to draw this conclusion?

- The authors state that physician role was predictive of persistence of mental health issues - was the nursing profession (which showed the highest proportion of respondence with persistence) not predictive of persistence? If not, how do the authors explain this finding? Is it possible that this finding could be explained by multicollinearity between sex and the nursing profession (since most nurses were female)? I imagine that would influence the MV analysis and perhaps, it may not be appropriate to include both nursing profession and sex in the same model due to high collinearity issues?

Discussion

- As noted in my comment about the Introduction, it would be great if the authors could discuss their findings in the context of other Canadian-based studies.

- The first paragraph of the Discussion section is a reiteration of the Intro and Methods - I would propose this is not necessary and the Discussion should usually begin with a high-level summary of the study findings.

- The authors discuss the isolated work environment of community ICU physicians but did not state whether this variable specifically (being a community ICU physician) was an independent predictor of poorer mental health. Perhaps the MD group has a small sample size and would not allow for such an analysis. At a minimum, would suggest reporting the number of community vs. academic physicians in the sample.

Conclusion

- Here, the authors discuss 'external factors, such as news media coverage...' which they don't explicitly describe in the Discussion section as potential contributors to poorer mental health. I would suggest the authors discuss this in more detail in the discussion, so that they are not discussing a new concept in the Conclusion section.

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Reviewer #1: Yes: Fiona Ecarnot

Reviewer #2: No

**********

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PLoS One. 2024 Mar 7;19(3):e0290749. doi: 10.1371/journal.pone.0290749.r002

Author response to Decision Letter 0

8 Aug 2023

Thank you very much for allowing us to resubmit our manuscript. Please see our detailed to Response to Reviewers in the documents list.

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PLoS One. doi: 10.1371/journal.pone.0290749.r003

Decision Letter 1

Juan Jesús García-Iglesias

15 Aug 2023

The impact of COVID-19 workload on psychological distress amongst Canadian intensive care unit healthcare workers during the 1st wave of the COVID-19 pandemic: a longitudinal cohort study.

PONE-D-23-09335R1

Dear Dr. Tsang,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0290749.r004

Acceptance letter

Juan Jesús García-Iglesias

27 Feb 2024

PONE-D-23-09335R1

PLOS ONE

Dear Dr. Tsang,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Appendix. Study advertisement.

(PDF)

pone.0290749.s001.pdf^{(58.6KB, pdf)}

S1 Dataset. Study data in excel format.

(XLSX)

pone.0290749.s002.xlsx^{(158.3KB, xlsx)}

S1 Checklist. Checklist for Reporting Results of Internet E-Surveys (CHERRIES).

(DOCX)

pone.0290749.s003.docx^{(19.8KB, docx)}

Attachment

Submitted filename: Response to reviewers.docx

pone.0290749.s004.docx^{(29KB, docx)}

Data Availability Statement

All relevant non-identifying data are within the manuscript and its Supporting information files.

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PERMALINK

The impact of COVID-19 workload on psychological distress amongst Canadian intensive care unit healthcare workers during the 1st wave of the COVID-19 pandemic: A longitudinal cohort study

Daniel Pestana

Kyra Moura

Claire Moura

Taylor Mouliakis

Frédérick D’Aragon

Jennifer L Y Tsang

Alexandra Binnie

Roles

Abstract

Introduction

Materials and methods

Survey design

Survey administration

Data analysis

Ethical consideration

Results

Table 1. Participant demographics at T1, T2 and T3.

COVID-19 workload

Fig 1. Weekly COVID-19 workload data reported by participants during the 16 weeks of the study.

Prevalence of clinically relevant psychological distress

Table 2. Prevalence of clinically relevant psychological distress (GHQ-12 score ≥ 3) at T1, T2 and T3 amongst (A) all participants and (B) participants who completed all GHQ-12 surveys surveys (n = 169).

Longitudinal change in GHQ-12 scores

Fig 2. GHQ-12 scores at T1, T2 and T3 for (A) all participants, (B) complete participants (n = 169), (C) all participants subdivided by profession, (D) all participants subdivided by sex.

Variables predicting psychological distress

Table 3. Multivariate logistic regression models to predict clinically relevant psychological distress (GHQ-12 ≥ 3) at T1, T2, T3 and all timepoints.

Persistent psychological distress

Table 4. A comparison of participants with “persistent” psychological distress at T3 (GHQ-12 ≥ 3 at T1 and T3) versus those without (GHQ-12 score ≥ 3 at T1 and < 3 at T3).

Predictors of persistent psychological distress

Table 5. Multivariable logistic regression to predict persistence of psychological distress at T3.

Discussion

Strengths

Limitations

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Juan Jesús García-Iglesias

Roles

Author response to Decision Letter 0

Decision Letter 1

Juan Jesús García-Iglesias

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Acceptance letter

Juan Jesús García-Iglesias

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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The impact of COVID-19 workload on psychological distress amongst Canadian intensive care unit healthcare workers during the 1^st wave of the COVID-19 pandemic: A longitudinal cohort study