Introduction

The Covid-19 pandemic has led to an unprecedented strain on healthcare services globally. Considerable changes in healthcare delivery have necessarily taken place. These have included cessation of routine services, repurposing of clinical areas, redeployment of staff to unfamiliar clinical environments [1,2], and in some circumstances, the rationing of services [3]. The impact of these modified working conditions on safety culture and psychological well-being are poorly understood.

Traumatic events or adverse conditions during natural disasters, conflict, and pandemics may lead to burnout [4–6]. Burnout is defined as “a syndrome of exhaustion, depersonalization, and reduced professional efficacy” [7] and leads to poorer patient safety outcomes [8–10]. Burnout is composed of two elements: “exhaustion”, linked to excessive job demands; and “disengagement”, linked to insufficient job resources [11]. During the Covid-19 pandemic healthcare systems have faced rising demands and limited resources, as such, it is important to understand the corresponding rates of burnout.

Similarly, infectious disease outbreaks have well-documented effects on the psychological wellbeing of healthcare workers (HCWs). During the Severe Acute Respiratory Syndrome (SARS), H1N1 and Ebola outbreaks, studies showed that frontline HCWs were at higher risk of developing psychological sequelae, including chronic stress, anxiety, depression and post-traumatic stress disorder [12–17]. Various factors are understood to have contributed to this phenomenon, such as excessive workload, concerns about occupational exposure, or infection of HCWs’ families. In comparison to previous pandemics, the psychological impact of Covid-19 may be more significant and widespread, given the scale of the pandemic [18–20].

Objectives

This study aims to describe the prevalence and predictors of burnout, anxiety and depression in healthcare workers during the Covid-19 pandemic.

Methods

Setting

Countries selected for inclusion represented a range of Covid-19 mortality rates, health system design, economic development, and had regional coordinators that could adapt the questionnaire to the local context and champion distribution. See Table 1 for comparison of country settings.

Table 1

Comparison of National Health Settings [21–29].

	UK	Singapore	Poland
Population (millions)	66	5.7	38
GDP ($)	42,962	64,582	15,423
Healthcare spend (% of GDP)	9.6	4.4	6.5
Per capita spend on healthcare ($)	3,859	2,619	907
Physicians/1,000	2.8	2.3	2.4
Nurses & midwives/1,000	8.2	6.2	6.9
Covid-19 cases*	288,133	45,613	37,216
Covid-19 deaths*	44,650	26	1,562
Healthcare funding	Public	Co-funding	Public
Healthcare provision	Public	Co-delivery	Public
National Lockdown/Circuit breaker	23rd March 2020	7th April 2020	15th Mar 2020
Initial government policies	Workplace closures School closures Public space closures Travel restrictions Reduction in elective services Temporary Nightingale hospitals	Workplace closures School closures Public space closures Travel restrictions Reduction in elective services Satellite clinics in temporary worker accommodation	Travel restriction School closures Reduction in public event capacity Designation of ‘infectious disease’ hospitals
Start government response stringency index±	80	39	57
End government response stringency index§	71	78	51

*As of 11th July 2020.

^±Start of study period (27th Mar 2020).

^§End of study period (16^th June 2020).

The Government response stringency index is a composite measure, proposed by Hale et al, that integrates measures of ‘[Covid-19] containment and health’, ‘economic support’, and ‘[policy] stringency’ to form an overall index that can be used to compare government policy over time in different countries. The score represents the number and strictness of government policies and should not be interpreted as an ‘effectiveness score’[30].

UK

The UK has 66 million residents [21]. Healthcare is publicly funded through general taxation and provided free at point of delivery by the National Health Service. The gross domestic product (GDP) is $42,962 per capita [22], of which 9.6% ($3,859) is spent on healthcare [23]. The UK has 2.8 physicians and 8.2 nurses and midwifes per 1,000 people [24,25]. The first documented case of Covid-19 was on 29th January 2020 and a national lockdown was initiated on 23rd March 2020 that introduced workplace, public space and school closures. In order to increase clinical capacity measures were taken including the cessation of elective services, redeployment of staff, reconfiguration of hospitals and establishment of a series of temporary ‘Nightingale’ hospitals. As of 11 July 2020 the UK has had 288,133 Covid-19 infections and 44,650 related deaths [26].

Singapore

Singapore is a city-state of 5.7 million residents. 80% of hospital care is provided by the public sector [27] and is funded through a mixed-financing model of co-funding [28]. The GDP per capita is $64,582 [22], of which 4.4% ($2,619) is spent on healthcare [23]. Singapore has 2.3 physicians [24] and 6.2 nurses and midwives per 1,000 people [25]. Singapore reported its first case of Covid-19 on 23rd January 2020. In April, a national lockdown [29] was initiated that introduced workplace, public space, and school closures. In order to increase clinical capacity, non-urgent clinical procedures were reduced. As of 11 July 2020 Singapore has had 45,613 Covid-19 infections and 26 related deaths [26].

Poland

Poland has 38 million residents [21]. Healthcare is funded through the National Health Fund, general taxation and private insurance. The GDP per capita is $15,423 [22], of which, 6.5% ($907) is spent on healthcare [23]. Poland has 2.4 physicians and 6.9 nurses and midwives per 1,000 people [25,26]. On 4th March 2020, Poland reported its first case of Covid-19 and a national lockdown was initiated on 15th March 2020, which included border closures to foreign nationals and a quarantine for returning citizens. Twenty-three hospitals were repurposed into infectious diseases hospitals for patients with suspected or confirmed COVID-19 infection. A further 67 hospitals had an infectious disease ward available. As of 11 July 2020 Poland has had 37,216 Covid-19 infections and 1,562 related deaths [26].

Results

A total of 3,537 responses were received (Table 2). Amongst these, 2,544 (72%) of respondents were female and 923 male (26.1%). 684 (19.3%) responses were from doctors, 1,590 (45%) from nurses, 517 (14.6%) from other clinical staff (including healthcare support workers, allied health professionals, pharmacists etc), and 746 (21.1%) non-clinical staff. 765 responses were from the UK, 232 from Poland, 2,503 from Singapore, and 37 from other countries, which were excluded due to the low response rate for the purpose of analysis to minimise a response bias. During the pandemic, 766 (21.7%) clinical staff were redeployed as part of response measures and 777 (22%) respondents had received at least one test for SARS-CoV-2 infection.

Table 2

Respondent characteristics.

Covariates	Overall (n = 3,537)		UK (n = 765)	Poland (n = 232)	Singapore (n = 2,503)	Pearson chi-square
	n	%	%	%	%	p-value
Gender						<0.001
Male	923	26.1	28.9	8.6	26.4
Female	2544	71.9	69.9	90.5	71.3
Undisclosed	70	2.0	1.2	0.9	2.4
Role						<0.001
Non-clinical	746	21.1	14.1	1.7	25.3
Doctor	684	19.3	35.7	8.2	14.5
Nurse	1590	45.0	36.7	89.7	43.7
Other clinical staff	517	14.6	13.5	0.4	16.5
Base Specialty						<0.001
Medicine	1238	35.0	30.5	29.3	37.4
Surgery	412	11.7	21.4	15.5	7.4
Acute	388	11.0	16.9	16.8	8.7
Other specialty	1055	29.8	26.7	38.4	30.2
Non-clinical	433	12.2	4.6	0.0	15.9
Days Worked in Past Week						<0.001
5 or less	2651	75.0	76.0	84.9	74.0
6 or more	715	20.2	5.0	9.5	26.0
No response	171	4.8	19.1	5.6	0.0
Redeployed						<0.001
No	2771	78.3	62.8	89.7	82.1
Yes	766	21.7	37.3	10.3	17.9
Redeployed Specialty						<0.001
Covid GM	214	6.1	9.8	3.5	5.0
ITU/EM	208	5.9	17.9	1.3	2.6
Other	344	9.7	9.5	5.6	10.2
Treated Covid +ve patient in past week						<0.001
No	1994	56.4	34.4	54.3	63.4
Yes	853	24.1	54.1	17.7	15.3
Don’t know	242	6.8	7.1	28.0	4.8
Not applicable	448	12.7	4.4	0.0	16.5
Presence of Symptoms						<0.001
Asymptomatic	3158	89.3	85.4	93.5	90.1
Symptomatic	379	10.7	14.6	6.5	9.9
Testing Status						<0.001
Not tested	2760	78.0	72.9	68.5	80.4
Tested	777	22.0	27.1	31.5	19.6
Psychological Outcomes	n	% (95% CI)	% (95% CI)	% (95% CI)	% (95% CI)
Burnout	2364	67(65–68)	63 (60–66)	71(65–77)	68 (66–70)
Anxiety	701	20(18–21)	27 (24–30)	28 (22–33)	17 (15–18)
Depression	389	11(9–12)	12 (9–14)	14 (10–19)	10 (9–12)

GM: General medicine; ITU: Intensive treatment unit; EM: Emergency medicine.

The questionnaires had strong reliability as evident by their high α coefficients (HADS α = 0.90, OLBI α = 0.88, SAQ α = 0.94). In our study, 2,364 (67%, 95% CI 65%-68%) of respondents were identified as being at high risk of burnout, whilst 701 (20%, 95% CI 18%-21%) and 389 (11%, 95% CI 9%-12%) met the criteria for anxiety and depression, respectively. A number of respondents met criteria for more than one condition (Fig 1).

Fig 1

Venn diagram demonstrating prevalence of anxiety, depression and burnout in the sampled population.

This figure demonstrates the number of respondents meeting the OLBI criteria for burnout, the HADS criteria for anxiety and the HADS criteria for depression. The overlap of sets represent individuals meeting more than one criteria.

Burnout

On univariate analysis (Table 3), significant covariates included undisclosed gender, job role, base specialty, redeployment, having been tested for Covid-19, treatment of patients with Covid-19, SAQ score, anxiety and depression. There was no significant relationship between, country, symptoms of Covid-19, number of days worked and burnout.

Table 3

Logistic regression analysis with burnout as dependent variable.

Covariate	Univariate Analysis				Multivariate Analysis
	OR	p-value	95% CI		OR	p-value	95% CI
Base Specialty
Medicine	Baseline
Surgery	0.88	0.325	0.69	1.13
EM/ITU/Anaesthetics	0.84	0.193	0.65	1.09
Other specialty	0.81	0.021	0.67	0.97
Gender
Male	Baseline				Baseline
Female	1.15	0.107	0.97	1.37	0.94	0.618	0.75	1.19
Undisclosed	3.32	<0.001	2.03	5.44	1.39	0.295	0.75	2.59
Country
UK	Baseline				Baseline
Poland	1.36	0.058	0.99	1.86	1.30	0.196	0.87	1.95
Singapore	0.94	0.474	0.78	1.12	1.20	0.137	0.94	1.52
Role
Non-clinical	Baseline				Baseline
Doctor	1.42	0.006	1.11	1.81	2.10	<0.001	1.49	2.95
Nurse	1.54	<0.001	1.25	1.90	1.38	0.026	1.04	1.84
Other clinical staff	1.64	<0.001	1.27	2.13	2.02	<0.001	1.45	2.82
Days Worked in Past Week
5 or less	Baseline
6 or more	1.16	0.120	0.96	1.39
Redeployed
No	Baseline				Baseline
Yes	1.45	<0.001	1.22	1.73	1.27	0.035	1.02	1.58
Redeployed Specialty
Not redeployed	Baseline
Covid GM	1.28	0.113	0.94	1.74
ITU/EM	1.59	0.002	1.18	2.14
Other	1.49	0.001	1.17	1.89
Treated Covid +ve Patient in Past Week
No	Baseline
Yes	1.13	0.172	0.95	1.36
Don’t know	2.09	<0.001	1.59	2.75
Not applicable	1.04	0.726	0.82	1.32
Presence of Symptoms
Asymptomatic	Baseline
Symptomatic	0.80	0.085	0.62	1.03
Testing Status
Not tested	Baseline				Baseline
Tested	0.80	0.016	0.66	0.96	0.64	<0.001	0.51	0.82
SAQ
50th Percentile	Baseline				Baseline
25th Percentile	3.48	<0.001	2.92	4.15	2.43	<0.001	1.98	2.99
75th Percentile	0.29	<0.001	0.22	0.38	0.30	<0.001	0.22	0.40
Anxiety
Normal/Borderline	Baseline				Baseline
Abnormal	8.23	<0.001	6.86	9.87	4.87	<0.001	3.92	6.06
Depression
Normal/Borderline	Baseline				Baseline
Abnormal	10.06	<0.001	7.92	12.78	4.06	<0.001	3.04	5.42

OR: Odds ratio; EM: Emergency medicine; ITU: Intensive treatment unit; GM: General medicine.

On multivariate analysis (Table 3), the following predictors of burnout were: doctor role (OR 2.10; 95% CI 1.49–2.95), nursing role (OR 1.38; 95% CI 1.04–1.84), other clinical roles (OR 2.02; 95% CI 1.45–2.82), being redeployed (OR 1.27; 95% CI 1.02–1.58), SAQ score lower than the 25th percentile (OR 2.43; 95% CI 1.98–2.99), anxiety (OR 4.87; 95% CI 3.92–6.06) and depression (OR 4.06; 95% CI 3.04–5.42). Statistically significant factors that were inversely correlated with burnout included: being tested for Covid-19 (OR 0.64; 95% CI 0.51–0.82) and SAQ score higher than the 75th percentile (OR 0.30; 95% CI 0.22–0.40). SAQ score by psychological state is demonstrated in Fig 2.

Fig 2

Radar plot demonstrating SAQ subscale by psychological state.

This figure demonstrates the SAQ subscale scores by psychological outcome. Distance from the centre represents proportion of a subscale answered positively. A greater distance represents a more positive score. § patients may be represented in more than one series. §§ Not all subscales are weighted equally in calculating overall SAQ score, the area of the radar plot will therefore not represent the overall SAQ score.

Depression

Significant predictors of depression (Table 4) included: being redeployed (OR 1.44; 95% CI 1.07–1.95), SAQ scores lower than the 25th percentile (OR 2.29; 95% CI 1.73–3.02), burnout (OR 4.18; 95% CI 3.13–5.57) and anxiety (OR 5.13; 95% CI 3.90–6.73). Significant factors inversely correlated with depression included: female gender (OR 0.62; 95% CI 0.45–0.84) and doctor role (OR 0.60; 95% CI 0.38–0.96).

Table 4

Multivariate analyses with anxiety and depression as dependent variables.

Covariate	Multivariate Analysis for Anxiety				Multivariate Analysis for Depression
	OR	p-value	95% CI		OR	p-value	95% CI
Gender
Male	Baseline				Baseline
Female	1.47	0.004	1.13	1.91	0.62	0.002	0.45	0.84
Undisclosed	2.12	0.025	1.10	4.08	0.76	0.462	0.36	1.58
Country
UK	Baseline				Baseline
Poland	0.63	0.032	0.41	0.96	1.13	0.657	0.66	1.92
Singapore	0.48	<0.001	0.37	0.61	1.07	0.675	0.78	1.48
Role
Non-clinical	Baseline				Baseline
Doctor	1.13	0.511	0.78	1.63	0.60	0.031	0.38	0.96
Nurse	1.09	0.579	0.81	1.47	0.77	0.164	0.54	1.11
Other clinical staff	0.64	0.023	0.43	0.94	0.87	0.543	0.55	1.37
Redeployed
No	Baseline				Baseline
Yes	1.14	0.297	0.89	1.45	1.44	0.015	1.07	1.93
Testing Status
Not tested	Baseline				Baseline
Tested	1.28	0.055	1.00	1.64	1.18	0.29	0.87	1.61
SAQ
50th Percentile	Baseline				Baseline
25th Percentile	2.19	<0.001	1.74	2.76	2.29	<0.001	1.73	3.02
75th Percentile	0.88	0.41	0.66	1.19	0.74	0.185	0.48	1.15
Burnout
Low risk	Baseline				Baseline
High risk	4.89	<0.001	3.93	6.08	4.18	<0.001	3.13	5.57
Anxiety
Normal/Borderline					Baseline
Abnormal					5.13	<0.001	3.90	6.73
Depression
Normal/Borderline	Baseline
Abnormal	5.15	<0.001	3.91	6.78

OR: Odds ratio.

Discussion

In our study, 2,364 (67%) respondents were at high risk of burnout. Prior to the onset of Covid-19 studies reported rates of burnout in the UK of 31.5% [51] and 42% [52] for doctors and nurses, respectively. In Singapore figures were similar with 33% [53] and 51% [54] of nurses and doctors exhibiting symptoms of burnout, respectively. The higher rates observed in this study suggest that the Covid-19 pandemic, or changes as a result of the pandemic may have led to an increased rate of burnout amongst staff.

Our results demonstrate that clinical roles confer a higher burnout risk compared with non-clinical roles. This may be explained by the nature of these roles. Particular challenges might have included adapting to a new method of working, increased service demands, prolonged periods wearing personal protective equipment, feeling “powerless” to manage patients’ conditions, and a fear of becoming infected or infecting others [55]. Similar findings were seen in Toronto during the SARS epidemic, where HCWs that treated SARS patients had significantly higher levels of burnout than those that did not [6].

Staff who were redeployed to new clinical areas had a higher risk of burnout. This may be due to physical conditions such as spending prolonged periods wearing protective equipment or due to the stress of adapting to a new clinical environment. Moreover, areas that required redeployed staff, by definition, had (or anticipated having) demand in excess of resources, necessitating the reallocation of staff. The combination of these increased demands, limited resources, and the psychological stress of dealing with an unfamiliar disease in an unfamiliar environment may have led to increased rates of burnout. This hypothesis would be supported by by the demands-resources model of burnout [7,11].

Anxiety and depression were noted in 20% and 11% of respondents respectively. Respondents with anxiety or depression were likely to also have symptoms of burnout. This is a significant burden of psychological morbidity. This finding is consistent with a recent meta-analysis of studies in China and Singapore, which demonstrated that approximately 1 in 5 HCWs have experienced symptoms of anxiety (23.2%) or depression (22.8%) during Covid-19 [18]. Similarly, rates of depression (19.8%) were also seen in Italian HCWs, although with a lower prevalence of anxiety(8%) [19]. Our study found that anxiety was more prevalent than depression amongst HCWs. To our best knowledge, there has been no published work reporting rates of depression, anxiety or burnout in the UK or Poland during Covid-19.

Female gender was predictive of anxiety (OR 1.47), which is in keeping with previous findings during Covid-19 [56,57]. However female gender was also found to be inversely correlated with depression, which contrasts from previous research [56,57]. These findings may reflect differences in the sampled population, such as the proportion of redeployed staff or be related to the timing of sampling compared with the onset of the Covid-19 pandemic.

Burnout, anxiety and depression have a negative impact on staff and patient outcomes [9,58] as well as leading to workforce attrition [59]. The high rates seen during the Covid-19 pandemic risk compounding a pre-existing healthcare workforce crisis [60,61], which may inturn impact on patient outcomes during the recovery phase of the pandemic. Initiatives shown to have a positive effect on psychological wellbeing include: clear communication, access to personal protective equipment, adequate rest, and psychological support [57,62].

An unexpected finding was the inverse relationship between staff SARS-CoV-2 testing and mental health. Two possible explanations exist: 1. provision of testing is a proxy for a well-run organisation, staff feeling well supported feel positively about working conditions and perception of management and, in turn are less likely to develop adverse mental health outcomes. 2. Staff suffering from burnout, anxiety or depression were less likely to seek out testing, possibly due to disengagement, physical or psychological symptoms [63]. Irrespective of the cause, both explanations are important as they support the need for staff testing, in particular for staff groups identified at risk of Covid-19 or poor mental wellbeing.

Safety attitudes were significantly associated with psychological outcomes in this study. It cannot be determined whether safety attitude is a contributory factor for burnout, anxiety, and depression, or if these psychological states lead to poor safety attitudes. However, this is an important finding, as safety attitudes are both modifiable and independently associated with clinical outcomes [50,64,65]. The SAQ domains can be divided into net causes (teamwork, working conditions, safety climate subscales) and net effects (perception of management, job satisfaction, stress recognition) [66]. This suggests that in addition to supporting psychological wellbeing, initiatives that promote safety climate, working conditions, and teamwork may have benefits on safety attitudes and in turn psychological outcomes.

Strengths and limitations

There are some limitations to our approach. The countries investigated are well stratified by: Covid-19 death rate, gross domestic product and geographic region (Western Europe, Eastern Europe and Asia-Pacific). However, the use of convenience sampling (a combination of social media and targeted email communications), means it is difficult to estimate response rate, response bias, and external validity. However, this study recruited a large number of respondents in a multi-centre, international population with a diverse range of healthcare workers. The results are therefore likely to be internally valid and associations between covariates reliable. Our sample was 72% female, which is broadly in line with the demographics of the healthcare workforce in the countries studied [67,68]. There was wide variation in the number of respondents between countries and an overrepresentation of nurses in the Polish cohort, this alongside data on only a limited number of variables may have resulted in residual confounding on multivariate regression analyses.

While the OLBI has many good psychometric qualities, a clinical cut-off for when someone is considered “burned out” has been an issue of debate [69]. The cut-off values used in this study to describe prevalence are based on findings from a Swedish group as correlated with clinician-diagnosed burnout [70,71]. The same cut-off values have been adopted in multiple other studies [34–36]. Given the high prevalence of burnout in this sample, and a lack of universally agreed cut-offs when using the OLBI, to improve specificity we used the 75th percentile of burnout scores rather than (lower) cut-offs values for the purpose of regression analyses.

While investigating the prevalence of psychological findings during Covid-19 is important, it is unclear if findings are as a direct result of Covid-19. It is also unclear if acute derangements persist over time. Repeated measurements will be needed to identify any potential long-term effects of the Covid-19 pandemic.

Finally, the contextual differences between participating countries, may limit the extent to which direct comparisons can be drawn between study countries.

Future recommendations

Based on these findings, we outline several preliminary recommendations that may positively impact on the psychological health of HCWs and patient safety.

As highlighted by the association between safety attitudes and psychological outcomes, institutions should pay particular attention to safety culture during the Covid-19 pandemic. The use of patient safety teams, for example, can support the integration of human factors principles, such as effective communication, into organizational processes that will improve patient and staff safety [72]. The use of such teams during a time of organisational change can help “design, adapt and reconfigure work systems, maximize individual and team performance under high-risk, high-stakes environments, while minimizing the introduction of new significant safety risks or unintended consequences into the work system”. Similarly, institutions should boost and expand learning systems to capture risks and improvement opportunities, and leverage these to protect staff and patients. This is of particular importance given the limited evidence about the effects of Covid-19 on patients, staff and institutions [73].

Previous research has suggested that burnout is a precursor to depression [74], consequently, benefit may been seen from interventions to address burnout before the onset of depression or anxiety. At the individual level, evidence-based interventions include mindfulness, self-awareness exercises, and appreciative interviews [75]. At the organizational level, quality improvement projects that improve organizational communication and streamline workflows can reduce burnout rates [76].

Measures to mitigate harm arising from psychological distress following the Covid-19 pandemic are important to prevent long-term harm. Greenburg et al [77] proposed six evidence-based principles to support the mental health of HCWs following the Covid-19 pandemic: appropriate appreciation, investigating absences (for welfare reasons), conducting return to work interview, paying close attention to at-risk groups, continually monitoring staff, and helping HCWs make sense of their experience.

Further research is needed to investigate the mechanisms underlying the associations identified in this study. Interrupted time series or longitudinal studies would be appropriate to investigate chronicity of findings and generate hypotheses about causality. Alongside this, interventions to reduce psychological harm or improve safety culture should be evaluated for efficacy, with case control or ideally, randomized-controlled designs.

Conclusion

Our findings demonstrate a significant burden of burnout, anxiety, and depression amongst healthcare workers. A strong association was seen between SARS-CoV-2 testing, safety attitudes, gender, job role, redeployment and psychological state. These findings highlight the importance of targeted support services and proactive SARS-CoV-2 testing of healthcare workers.