Coping styles and mental health in response to societal changes during the COVID-19 pandemic

Abstract

Background:

Psychosocial responses to infectious disease outbreaks have the potential to inflict acute and longstanding mental health consequences. Early research across the globe has found wide ranging psychological responses to the current COVID-19 pandemic. Understanding how different coping styles can be effective in mitigating mental ill health would enable better tailored psychological support.

Aims:

The aim of this study was to gain an understanding of psychosocial responses to the COVID-19 pandemic, including depression, anxiety and distress, as well as effective coping styles in an Australian sample.

Method:

A sample of 1,495 adults, residing in Australia between April 3rd and May 3rd 2020, completed an online survey which measured psychological distress (Impact of Events Scale-Revised), depression, anxiety, stress (DASS-21), as well as coping strategies (Brief COPE).

Results:

47% of the respondents were experiencing some degree of psychological distress. Females experienced higher levels of depression, anxiety and stress than males. Coping strategies associated with better mental health were positive reframing, acceptance and humour. Conversely, self-blame, venting, behavioural disengagement and self-distraction were associated with poorer mental health.

Conclusion:

Rates of psychological symptoms amongst the Australian population are similar to those reported in other countries. Findings add to the growing literature demonstrating a gender disparity in the mental health impacts of COVID-19. Positive emotion focused coping strategies may be effective for reducing psychological symptoms. Understanding psychosocial responses including beneficial coping strategies are crucial to manage the current COVID-19 situation optimally, as well as to develop mental health response plans for future pandemics.

Keywords

COVID-19 depression anxiety mental health coping

Introduction

Psychosocial responses to infectious disease outbreaks have the potential to inflict acute and longstanding mental health consequences (Van Bortel et al., 2016). To reduce psychological distress, individuals can utilise a range of coping strategies. Some coping styles are ineffective and may exacerbate mental health problems while other coping styles may be effective at mitigating the nature and impact of these psychological responses. A better understanding of the psychosocial responses across the community and beneficial coping strategies are crucial to manage the current pandemic optimally, as well as develop mental health response plans for future pandemics.

Evidence from previous infectious disease outbreaks, including the 2003 severe acute respiratory syndrome (SARS), the 2009/2010 H1N1 influenza pandemic, and the 2012 Middle East respiratory syndrome reveal variable psychological symptoms including anxiety, fear, depression, anger, irritability, helplessness, grief and loss (Chew et al., 2020). The psychological consequences of infectious disease outbreaks are not limited to those infected, caregivers and healthcare workers. Rather, the mental health and well-being of the general population can be affected as a result of disruption to daily life, fear, uncertainty, stigmatisation and concerns relating to job and financial security (Chew et al., 2020).

The current COVID-19 pandemic is unique in relation to the number of countries affected, its high transmissibility; potential infectivity via people who are mildly symptomatic or asymptomatic; and the absence of a vaccine (at the time of writing). As of May 3rd 2020, COVID-19 has affected 3,349,786 people, with 238,628 reported deaths (COVID-19 National Incident Room Surveillance Team., 2020a). Within Australia, a multi-cultural island nation, there have been 6,784 cases of COVID-19 reported, with 89 deaths (as of May 3rd, 2020) (COVID-19 National Incident Room Surveillance Team., 2020b) have been a lower number of COVID-19 cases compared to other comparable high-income countries (COVID-19 National Incident Room Surveillance Team., 2020b). The immediate psychological impact of the COVID-19 outbreak has been measured using online surveys, primarily within China. A survey conducted in China two weeks after the initial outbreak of COVID-19 revealed that out of a total of 1,120 respondents more than half rated the psychological impact of outbreak as moderate or severe, 16.5% of respondents reported moderate to severe depressive symptoms; 28.8% of respondents reported moderate to severe anxiety symptoms; and 8.1% reported moderate to severe stress levels (Wang et al., 2020a). Levels of depression, anxiety and stress remained elevated and stable when the survey was repeated 4 weeks later (Wang et al., 2020b). Predictors of poorer mental health included female gender, student status, and COVID-19 related somatic symptoms.

In an attempt to reduce psychological distress, a range of coping strategies can be adopted, only some of which are effective (Mahmoud et al., 2012; Main et al., 2011). Coping strategies are characterised as dynamic responses to a specific situation, namely the COVID-19 pandemic, where an effort is made to reduce or avoid the effects of a stressor. Common coping strategies that have been adopted in response to previous pandemics include problem-focused coping (an active effort to manage the stressful situation by engaging in problem-focused efforts to alter the situation or seek alternatives (Stanislawski, 2019)); seeking social support; and, positive appraisal of the situation (construing the stressful situation in positive light (Chew et al., 2020; Stanislawski, 2019)). Coping strategies associated with better mental health outcomes during the current pandemic have not been reported.

The aim of the current study was to examine psychosocial responses within an Australian population and gain an understanding of coping strategies that are effective, as well as coping strategies that are ineffective for minimising psychological symptoms.

Materials and methods

We adopted a prospective online survey. The analysis presented in the current study is cross-sectional data based on a sample of the Australian public’s psychological responses to the COVID-19 pandemic from April 3rd to May 3rd. During this time, Australia was in a stage of enforced restrictions, including physical distancing, cancellation of mass gatherings as well as confinement and isolation for those who may have been exposed to people infected with COVID-19. Sampling strategies included social media advertising as well as snowball techniques to recruit members of the general public, aged 18 years and over living in Australia during the COVID-19 pandemic.

This study was approved by the Monash University Human Research Ethics Committee (MUHREC: 23963) and conforms to the provisions of the Declaration of Helsinki. All participants provided consent prior to commencing the survey.

Demographic information

Demographic information on the sample was collected including, age, gender, education, ethnicity, self-reported current diagnosis of a mental illness; government subsidies sought in the past month as a result of pandemic related employment changes and loneliness (question asked – ‘Compared to the month prior to the COVID-19 outbreak, have you felt an increased sense of loneliness over the past 7 days – Yes or No?’)

Psychological impact of COVID-19

The psychological impact of COVID-19 was measured using the Impact of Event Scale-Revised (IES-R). The IES-R is a self-administered 22 item questionnaire that has been extensively used for determining the extent of psychological impact after exposure to a public health crises, including infectious disease outbreaks (Reynolds et al., 2008; Wang et al., 2020a). The total IES-R score was divided into 0–23 (normal), 24–32 (mild/moderate psychological impact, scores in this range suggest PTSD is a clinical concern (Asukai et al., 2002)), 33–38 (moderate psychological impact, scores in this range represent a probable diagnosis of PTSD (Creamer et al., 2003)), and >39 (severe psychological impact) (Creamer et al., 2003).

Mental health status was measured using the Depression, Anxiety and Stress Scale (DASS-21) (Lovibond & Lovibond, 1995). The DASS-21 is a set of three self-report scales designed to measure the emotional states of depression, anxiety and stress. Questions 3, 5, 10, 13, 16, 17 and 21 formed the depression subscale. The total depression subscale score was divided into normal (0–4), mild depression (5–6), moderate depression (7–10), severe depression (11–13) and extremely severe depression (14+). Questions 2, 4, 7, 9, 15, 19 and 20 formed the anxiety subscale. The total anxiety subscale score was divided into normal (0–3), mild anxiety (4–5), moderate anxiety (6–7), severe anxiety (8–9) and extremely severe anxiety (10+). Questions 1, 6, 8, 11, 12, 14 and 18 formed the stress subscale. The total stress subscale score was divided into normal (0–7), mild stress (8–9), moderate stress (10–12), severe stress (13–16) and extremely severe stress (17+). The DASS has been previously used in research related to infectious disease outbreaks, including SARS (McAlonan et al., 2007) and early studies in China in relation to COVID-19 (Wang et al., 2020a).

Suicidal thoughts were measured using Beck Depression Inventory (BDI) suicide item (item 9) (Beck et al., 1996). The BDI suicide item assesses suicidal ideation and is rated on a 4-point scale, with higher scores indicating greater intent. The use of this single item as a brief, efficient screen for suicide risk has been previously demonstrated (Green et al., 2015).

Coping measures

The Brief COPE Inventory (Carver, 1997) is an abbreviated version of the original 60-item COPE inventory (Carver et al., 1989). The Brief COPE is a 28 item, self-report 4-point Likert instrument that was used to measure effective and ineffective ways to cope or minimise distress associated with the current COVID-19 pandemic. The Brief COPE has been validated to assess different types of stressors and to evaluate functionality or dysfunctionality of the use of certain strategies indifferent contexts (Meyer, 2001). The 28 items load onto 14 factors or coping styles: Self-distraction; Active Coping; Denial; Substance use; Use emotional support; Use of Instrumental support; Behavioural disengagement; venting; Positive reframing; Humour; Acceptance; Religion and Self-blame. The Brief COPE has been used to evaluate coping strategies during previous infectious disease outbreaks (Wong et al., 2005).

Data

Study data were collected and managed using REDCap (Research Electronic Data Capture), a secure, web-based software platform hosted at Monash University REDCap (Harris et al., 2009, 2019).

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Analysis strategy

We aimed to compare gender groups across a number of psychological ratings by reducing covariates or confounders imbalance (standardised mean differences, Table 2) through the propensity score weights (Austin, 2011). In this analysis, the rationale behind using propensity score weights is that there were still significant differences in some covariates characteristics between female and male respondents (>0.25) even after applying sampling weights for gender (Table 1). Therefore, we formulated the propensity scores for gender groups using 14 covariates, and then we calculated the standardised differences again after obtaining the propensity score weights (Table 2). Further, we obtained the final weights for group imbalance by multiplying the sampling weights by the propensity score weights. Furthermore, for each outcome in Table 3, we used the final weights to model the effects of gender (female vs male), adjusting for the 14 covariates. The final models are displayed in Table 3. Analyses for propensity score weights were performed with PSMATCH procedure in SAS (SAS institute, Cary NC).

Table 1.

Demographic and Psychological measures† by Gender.

Characteristic	Male		Female
Characteristic	Mean(%)	SE	Mean (%)	SE
Age (years)	44.3	1.024804	40.7	0.169196
Age category (18–24 years)	0.149573 (14.96%)	0.021411	0.155918 (15.59%)	0.004151
Age category (25–39 years)	0.294872 (29.49%)	0.027374	0.354286 (35.43%)	0.005473
Age category (40–54 years)	0.239316 (23.93%)	0.025614	0.293061 (29.31%)	0.005208
Age category (55–69 years)	0.200855 (20.08%)	0.024052	0.161633 (16.16%)	0.004212
Age category (70–85 years)	0.115385 (11.54%)	0.019180	0.035102 (3.51%)	0.002106
Ethnicity representation (Caucasian)	0.820513 (82.05%)	0.023038	0.831974 (83.20%)	0.004277
Ethnicity (Asian)	0.115385 (11.54%)	0.019180	0.086460 (8.65%)	0.003215
Current diagnosis of a mental illness (Yes vs. No)	0.291845 (29.18%)	0.027351	0.390701 (39.07%)	0.005581
Government subsidies or centre link access as a result of COVID-19 employment change	0.126126 (12.61%)	0.020464	0.134860 (13.49%)	0.003984
Loneliness (Yes vs. No)	0.463519 (46.35%)	0.030001	0.577396 (57.74%)	0.005662
Education (primary or secondary school vs university level – bachelor or above)	0.773504 (77.35%)	0.025128	0.782219 (78.22%)	0.004721
IES-R	13.767677	0.954943	21.864683	0.210698
IES-R category (normal range <24)	0.787879 (78.79%)	0.026691	0.606526 (60.65%)	0.006062
IES-R category (mild range 24–32)	0.085859 (8.59%)	0.018291	0.153551(15.36%)	0.004473
IES-R category (moderate range 33–38)	0.045455(4.54%)	0.013599	0.058541(5.86%)	0.002913
IES-R category (severe range >39)	0.080808(8.08%)	0.017794	0.181382(18.14%)	0.004781
DASS_21 Depression subscale	4.057143	0.290561	6.112988	0.068195
DASS_21 Anxiety subscale	1.531073	0.181202	3.147679	0.046908
DASS_21 Stress subscale	4.615819	0.287383	6.958904	0.058966
DASS_21 Depression Moderate to severe	0.194286 (19.42%)	0.027486	0.352693 (35.27%)	0.006219
DASS_21 Anxiety Moderate to severe	0.090395 (9.01%)	0.019807	0.206751 (20.68)%	0.005268
DASS_21 Stress Moderate to severe	0.101695 (10.17%)	0.020877	0.269758 (26.98%)	0.005771
Suicide ideation (BDI item 9)	0.136646 (13.66%)	0.024883	0.174112 (17.41%)	0.005141

†

Means and standard errors (in italics) are calculated with survey package in R.

Table 2.

Sample characteristic standardised mean differences between female and males.

	SMD (sample weighted)	SMD (Propensity score weighted)
Age	−0.34695	0.00814
Education: university	−0.02598	−0.02636
Current diagnosis of a mental illness (Yes vs No)	−0.25795	0.06958
IES-R category (normal range – scores <24)	0.46078	0.01816
IES-R category (mild range – scores 24–32)	−0.23806	−0.01932
IES-R category (moderate range 33–38)	−0.11188	−0.08942
IES-R category (severe range >39)	−0.31590	0.05131
Ethnicity representation (Caucasian)	−0.04561	0.05974
Ethnic representation (Asian)	0.20120	−0.02512
Government subsidies or centre link access as a result of COVID-19 employment change	−0.08194	−0.04046
DASS_21 depression moderate to severe category (7–14+)	−0.35866	0.01973
DASS_21 anxiety moderate to severe category (6–10+)	−0.32890	−0.00709
DASS_21 stress moderate to severe category(10–17+)	−0.45359	0.04275
Suicide ideation (BDI item 9)	−0.07924	−0.02924
Loneliness (Yes vs No)	−0.24881	−0.03885

SMD = standardised mean differences.

Table 3.

Weighted and adjusted gender (female vs male) effects for psychological measures.

Measure	Odds ratio^{† ‡}	LL 95%CI	UL 95% CI
DASS_21 depression moderate to severe category (7–14+)	1.054	0.514	2.164
DASS_21 anxiety moderate to severe category (6–10+)	1.316	0.586	2.956
DASS_21 stress moderate to severe category(10–17+)	1.047	0.507	2.162
Suicide ideation (BDI item 9)	1.120	0.563	2.228
Loneliness (Yes vs No)	1.103	0.699	1.740
IES-R category(normal range – scores<24)	0.905	0.512	1.600
IES-R category (mild range – scores 24–32)	1.024	0.508	2.064
IES-R category (moderate range 33–38)	1.30	0.478	3.701
IES-R category (severe range >39)	0.784	0.283	2.169

†

Weighted with both sampling and propensity weights.

‡

Adjusted.

Path analysis was performed on variables correlation matrix using structural equations model (SEM) to estimate simultaneous gender adjusted effects of coping (as measured by Brief COPE Inventory) on depression, anxiety and stress constructs (DASS-21). Analyses with latent variables path models are preferred for population-based studies because they allow for associative or parallel estimation of variables at the same time and fitting errors or disturbances of measurements as freely parameterised quantities. We used robust maximum likelihood, ML (lavaan package (Rosseel, 2012)) as the model estimator. The final model was assessed using multiple fit indices; Chi-square test(χ²), comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and standardised root mean square residual (SRMR). We opted not to use the modification indices (Chi-square estimates for addition or inclusion of parameters or path arrows), to improve the statistical fitness. All analyses were written in R (R Core Team).

Results

We received responses from 1,790 participants. Of these, 1,495 participants, all residing in Australia at the time of the survey, were included in the final analysis. The 295 excluded participants had missing data with more than 80% of survey items.

Demographic variables and psychological impact

The majority of respondents identified as female, n = 1,226 (81·6%); Caucasian n = 1,242 (82·7%); and were University educated with a Bachelors degree n = 730 (48·6%). The distribution of sociodemographic factors, depression, anxiety and stress scores as well as degree of psychological impact scores weighted by gender are presented in Table 1.

Analysis of covariate imbalance

Results from Table 2 suggest that weighting with the propensity score reduced covariate imbalance between the genders in such a way that made the psychological ratings or outcome means more comparable (all standardised mean differences <0·25).

Table 3 shows the weighted and adjusted effects of gender (female vs. male) on various psychological ratings/outcomes. The differences were reported on odds ratio scale with 95% confidence intervals. Higher odds of depression, anxiety, stress, suicide ideation and loneliness are reported for females than males.

Goodness of fit (path model)

The path coefficients, covariances and model fit indices are listed in Table 4. The Covariance matrix for latent Brief COPE domains is provided in Table 5. Overall, the hypothesised model is an acceptable fit to the observed data with a CFI of 0.93 and TLI of 0.92. In addition, both RMSEA (0.043: 90% CI (0.041, 0.045)) (MacCallum et al., 1996; Steiger and Lind, 1980) and SRMR (0.039) (square root of the difference between the observed covariance matrix and the predicted covariance matrix) supported the validity of the fitted hypotheses (Hu and Bentler, 1999).

Table 4.

Path model results (DASS and Brief COPE loadings, effects of COPE domains on DASS subscales, Goodness of fit).

DASS item-factor loadings (a = 0.95)
DASS depression (a = 0.92)		DASS anxiety (a = 0.89)		DASS stress (a = 0.85)
Item 3	0.673	Item 2	0.443	Item 1		0.711
Item 5	0.613	Item 4	0.404	Item 6		0.546
Item 10	0.803	Item 7	0.343	Item 8		0.605
Item 13	0.750	Item 9	0.472	Item 11		0.614
Item 16	0.771	Item 15	0.592	Item 12		0.766
Item 17	0.738	Item 19	0.487	Item 14		0.511
Item 21	0.699	Item 20	0.541	Item 18		0.588
Brief COPE item factor loadings (a = 0.84)
Self-distraction		Active coping		Denial
Item 1	0.534	Item 2	0.647	Item 3		0.458
Item 19	0.520	Item 7	0.816	Item 8		0.449
Substance use		Use of emotional support		Use of instrumental support
Item 4	0.796	Item 5	0.752	Item 10		0.766
Item 11	0.794	Item 15	0.752	Item 23		0.644
Behavioural disengagement		Venting		Positive reframing
Item 6	0.627	Item 9	0.627	Item 12		0.780
Item 16	0.552	Item 21	0·547	Item 17		0.816
Planning		Humour		Acceptance
Item 14	0.766	Item 18	0.918	Item 20		0.665
Item 25	0.719	Item 28	0.721	Item 24		0.718
Religion		Self-blame
Item 22	0.759	Item 13	0.829
Item 27	0.808	Item 26	0.546
Regression coefficients (β)
	DASS depression	p	DASS anxiety	p	DASS stress	p
Self-distraction	0.264	0.004	0.051	0.552	0.417	0.000
Active coping	0.006	0.967	0.169	0.268	−0.279	0.144
Denial	0.034	0.508	0.102	0.113	0.018	0.760
Substance use	0.032	0.316	0.056	0.149	0.058	0.141
Use of emotional support	− 0.182	0.090	−0.184	0.129	−0.064	0.617
Use of instrumental support	0.065	0.541	0.241	0.033	−0.009	0.938
Behavioural disengagement	0.246	0.010	0.294	0.007	0.071	0.520
Venting	0.099	0.254	0.097	0.302	0.219	0.028
Positive reframing	−0.204	0.001	−0.084	0.203	−0.116	0.117
Planning	−0.045	0.730	−0.086	0.547	0.326	0.061
Humour	−0.068	0.124	−0.040	0.365	−0.132	0.005
Acceptance	0.019	0.718	−0.149	0.007	−0.186	0.002
Religion	−0.019	0.571	0.041	0.257	−0.012	0.757
Self-blame	0.420	0.000	0.246	0.013	0.216	0.040
Gender (female)	0.228	0.000	0.343	0.000	0.316	0.000
Goodness – of – fit statistics
χ²(df)	2428.95(991)
CFI	0.93
TLI	0.92
RMSEA	0.043
SRMR	0.039

p values adjusted for multiplicity. In bold p < 0·05 denoting evidence against the null hypothesis of β = 0.

Table 5.

Covariance matrix for latent brief COPE domains.

	1	2	3	4	5	6	7	8	9	10	11	12	13
1. Self-distraction	−	−	−	−	−	−	−	−	−	−	−	−	−
2. Active coping	0.169	−	−	−	−	−	−	−	−	−	−	−	−
3. Denial	0.045	−0.022	−	−	−	−	−	−	−	−	−	−	−
4. Substance use	0.059	−0.030	0.051	−	−	−	−	−	−	−	−	−	−
5. Use of emotional support	0.188	0.243	0.020	0.039	−	−	−	−	−	−	−	−	−
6. Use of instrumental support	0.206	0.198	0.039	0.048	0.488	−	−	−	−	−	−	−	−
7. Behavioural disengagement	0.079	−0.119	0.148	0.140	−0.018	0.052	−	−	−	−	–	−	−
8. Venting	0.121	0.088	0.069	0.092	0.198	0.230	0.111	−	−	–	−	−	−
9. Positive reframing	0.176	0.357	−0.013	−0.011	0.216	0.184	−0.123	0.095	−	−	−	−	−
10. Planning	0.198	0.393	0.009	0.032	0.245	0.292	−0.010	0.192	0.359	−	−	−	−
11. Humour	0.164	0.116	0.053	0.117	0.189	0.202	0.061	0.137	0.243	0.183	−	−	−
12.·Acceptance	0.074	0.234	−0.094	−0.058	0.112	0.067	−0.148	0.031	0.301	0.213	0.115	−	−
13.·Religion	0.071	0.141	0.020	−0.032	0.141	0.138	−0.029	0.068	0.215	0.140	0.083	0.126	−
14.·Self-blame	0.148	−0.063	0.120	0.212	0.036	0.171	0.378	0.174	−0.113	0.142	0.070	−0.167	−0.007

Effects of COPE domains on DASS subscales

As shown in Table 4, the coping styles that were significantly associated with higher depression scores were self-distraction; behavioural disengagement and self-blame. Positive reframing was significantly associated with lower depression scores. Behavioural disengagement and self-blame, as well as use of instrumental support were significantly associated with higher anxiety scores. Acceptance as a coping style was associated significantly with lower anxiety scores. For stress scores, self-blame, venting and self-distraction were all significantly associated with higher stress scores. Humour and acceptance were the two coping styles associated with lower stress scores.

Discussion

Findings from our sample of 1,495 adults residing in Australia between April 3rd and May 3rd, demonstrated a range of psychological responses to the COVID-19 outbreak, with females reporting more severe psychological symptoms than males. Almost half of the respondents reported at least one PTSD symptom (Creamer et al., 2003). In relation to depression, anxiety and stress, 35% of females and 19% of males reported moderate to severe levels of depression; 27% of females and 10% of males reported moderate to severe levels of stress; 21% of females and 9% of males reported moderate to severe levels of anxiety. Suicidal thoughts were reported in 17% of females and 14% of males. Coping styles had differential effects on COVID-19 outbreak related depression, anxiety and stress. These findings provide crucial information about which coping styles are effective for mental health during the current pandemic.

Despite a lower number of COVID-19 cases in Australia compared to other comparable high-income countries, as well as a lower crude case fatality rate in Australia (1.3%) compared to the World Health Organization’s globally-reported rate (7.1%) (COVID-19, 2020), levels of depression, anxiety and stress reported in this sample are similar to early research, emerging from China (Wang et al., 2020a, 2020b), and higher than levels reported in Spain (Ozamiz-Etxebarria et al., 2020). The overall levels of psychological distress (IES-R) were lower in our sample, as compared to early reports from China measured 2 weeks after the initial outbreak of COVID-19 (Wang et al., 2020a), although longitudinal data emerging for China suggested a decline in rates of psychological distress from 2 weeks post-initial outbreak to 6 weeks after the initial outbreak (Wang et al., 2020b).

We observed higher rates of psychological distress, depression, anxiety and stress symptoms in females. This finding is consistent with early findings from China (Liu et al., 2020; Wang et al., 2020a, 2020b) and Spain (Ozamiz-Etxebarria et al., 2020). While these findings are consistent with pre-pandemic data reporting higher rates of depression and anxiety in females (Altemus et al., 2014), they also support the growing body of literature suggesting the current pandemic is having greater mental health impacts on women (Liu et al., 2020).

The high prevalence of suicidal thoughts (14–17%) observed in our study is based on response to a single item within the Beck Depression Inventory (BDI). Although this method has previously been validated as a screen for suicide within clinical samples (Green et al., 2015), it is less clear how to interpret positive scores within this non-clinical general population sample where the overall suicide prevalence would be lower (Johnston et al., 2009). Nevertheless, the frequency of suicidal thoughts in this sample is concerning, highlighting the need for specific resources to support safety during this pandemic.

Our findings show coping styles associated with better mental health were those classified as positive emotion focused coping styles (Stanislawski, 2019). Positive reframing was associated with lower depression scores. Positive reframing is an emotion-focused coping strategy and refers to the reinterpretation or reappraisal of a stressful event in positive terms (Stanislawski, 2019). Positive reframing has previously been reported as an effective strategy to reduce depression (Folkman & Lazarus, 1988; Horwitz et al., 2018). Acceptance, also a positive emotion-focused coping style (Stanislawski, 2019) defined as the capacity to learn to accept the reality of a stressful situation, was associated significantly with lower anxiety and stress scores. The use of humour to deal with negative emotions was also significantly associated with lower stress levels. Humour can facilitate coping and emotion regulation (Stanislawski, 2019). The use of humour as a coping style has also been associated with better mental health outcomes, for example in women exposed to multiple traumatic events in conflict zones (Saxon et al., 2017).

Conversely, we also identified ineffective coping styles associated with poorer mental health. Self-blame, which involves criticizing oneself for a perceived sense of responsibility in the situation was significantly associated with higher levels of depression, anxiety and stress. Self-blame has been associated with psychiatric comorbidities during previous infectious disease outbreaks (Sim et al., 2010). Behavioural disengagement was significantly associated with higher levels of depression and anxiety; use of instrumental support was associated with higher levels of anxiety; self-distraction was associated with higher levels of stress and depression; and, venting was associated with higher levels of stress. Collectively, these coping styles have been described as ‘less useful’ in previous coping models (Stanislawski, 2019).

Limitations and strengths

Findings from this study should be interpreted in light of a few limitations. The sample was predominantly females, Caucasian, and well educated, the sample also had a higher prevalence of self-reported mental illness (29–39%) relative to the general population (20% (Australian Bureau of Statistics, 2019)), limiting the generalisability of these results. Another potential limitation was that other factors that could potentially contribute to the associations between coping style and mental health, including personality variables, cognitive appraisal mechanisms, and past trauma were not examined and these factors could be important in the development of mental health resources and programs. The key strength of this study was the prospective study design and inclusion of well validated measures to assess psychosocial responses, and consideration of gender in our analysis.

Implications

Our findings can guide therapeutic resources for improving mental health during the current COVID-19 pandemic. To help cope with symptoms of depression recommendations could include trying to look for positives and reframing the current situation in a positive way, for example, focusing on what people can still do during a lockdown, rather than focusing on what they can no longer do. Encouraging people to accept the current situation may be recommended for people experiencing anxiety and stress. Humour is also effective for coping with stress and, where appropriate, could be incorporated into mental health resources, for example COVID-19 related memes on social media. Aspects of these strategies are also core components of more formal therapeutic interventions, such as cognitive behavioural therapy and acceptance and commitment therapy, pointing to the potential of these psychological treatments within a COVID-19 context. In conjunction with use of these positive emotion based coping strategies, maladaptive coping strategies, such as venting and self-blame, should be discouraged.

Conclusion

Findings from the current study indicated females experienced symptoms of mental ill health at a greater rate than males. Encouraging the use of positive emotion based coping strategies, such as positive reframing, humour and acceptance are effective for mental health and being cautious about the use of maladaptive coping strategies, such as venting and self-blame might be protective against outbreak-related increases in depression, anxiety, and stress. These findings will help inform the development of mental health programs.

The negative mental health impacts of this pandemics may continue, even if the numbers of infections reduce and restrictions are lifted. Following SARS, a range of adverse mental health symptoms, including post-traumatic stress disorder, depression, anxiety, feelings of loneliness and helplessness continued well beyond the easing of restrictions (Reynolds et al., 2008). To mitigate a mental health epidemic associated with the current COVID-19 pandemic, it is essential we learn from these findings and promote more effective coping styles across populations.

Footnotes

Conflict of interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Caroline Gurvich

References

Altemus

Sarvaiya

Neill Epperson

(2014). Sex differences in anxiety and depression clinical perspectives. Front Neuroendocrinol, 35(3), 320–330.

Asukai

Kato

Kawamura

Kim

Yamamoto

Kishimoto

Miyake

Nishizono-Maher

(2002). Reliability and validity of the Japanese-language version of the impact of event scale-revised (IES-R-J): Four studies of different traumatic events. The Journal of Nervous and Mental Disease, 190(3), 175–182.

Austin

P. C.

(2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46(3), 399–424.

Australian Bureau of Statistics. (2019). National Health Survey: First Results, 2017-18 (Report No. 4364.0.55.001). Australian Bureau of Statistics.

Beck

A. T.

Steer

R. A.

Brown

G. K.

(1996). Manual for the Beck Depression Inventory-II. Psychological Corporation.

Carver

C. S.

(1997). You want to measure coping but your protocol’s too long: Consider the brief COPE. International Journal of Behavioral Medicine, 4(1), 92–100.

Carver

C. S.

Scheier

M. F.

Weintraub

J. K.

(1989). Assessing coping strategies: A theoretically based approach. Journal of Personality and Social Psychology, 56(2), 267–283.

Chew

Q. H.

Wei

K. C.

Vasoo

Chua

H. C.

Sim

(2020). Narrative synthesis of psychological and coping responses towards emerging infectious disease outbreaks in the general population: Practical considerations for the COVID-19 pandemic. Singapore Medical Journal. 61(7), 350–356.

COVID-19. (2020). COVID-19, Australia: Epidemiology report 14 (Reporting week to 23:59 AEST 3 May 2020). Communicable Diseases Intelligence (2018), 44.

10.

COVID-19 National Incident Room Surveillance Team. (2020a). COVID-19, Australia: Epidemiology report 13 (Reporting week to 23:59 AEST 26 April 2020). Communicable Diseases Intelligence (2018), 44.

11.

COVID-19 National Incident Room Surveillance Team. (2020b). COVID-19, Australia: Epidemiology report 14 (Reporting week to 23:59 AEST 3 May 2020). Communicable Diseases Intelligence (2018), 44.

12.

Creamer

Bell

Failla

(2003). Psychometric properties of the impact of event scale - revised. Behaviour Research and Therapy 41(12), 1489–1496.

13.

Folkman

Lazarus

R. S.

(1988). The relationship between coping and emotion: Implications for theory and research. Social Science & Medicine, 26(3), 309–317.

14.

Green

K. L.

Brown

G. K.

Jager-Hyman

Cha

Steer

R. A.

Beck

A. T.

(2015). The predictive validity of the beck depression inventory suicide item. The Journal of Clinical Psychiatry, 76(12), 1683–1686.

15.

Harris

Taylor

Minor

B. L.

Elliott

Fernandez

O’Neal

McLeod

Delacqua

Kirby

Duda

S. N.

(2019). The REDCap consortium: Building an international community of software partners. Journal of Biomedical Informatics, 95, 103208.

16.

Harris

P. A.

Taylor

Thielke

Payne

Gonzalez

Conde

J.G.

(2009). Research electronic data capture (REDCap) – A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics, 42(2), 377–381.

17.

Horwitz

A. G.

Czyz

E. K.

Berona

King

C. A.

(2018) Prospective associations of coping styles with depression and suicide risk among psychiatric emergency patients. Behavior Therapy 49(2), 225–236.

18.

L. T.

Bentler

P. M.

(1999) Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.

19.

Johnston

A. K.

Pirkis

J. E.

Burgess

P. M.

(2009). Suicidal thoughts and behaviours among Australian adults: Findings from the 2007 National Survey of Mental Health and Wellbeing. Australian and New Zealand Journal of Psychiatry, 43(7), 635–643.

20.

Liu

Zhang

Wei

Jia

Shang

Sun

Zhou

Wang

Liu

(2020). Prevalence and predictors of PTSS during COVID-19 outbreak in China hardest-hit areas: Gender differences matter. Psychiatry Research, 287, 112921.

21.

Lovibond

S. H.

Lovibond

(1995). Manual for the depression anxiety stress scales. School of Psychology, University of New South Wales.

22.

MacCallum

R. C.

Browne

M. W.

Sugawara

H. M.

(1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 140–149.

23.

Mahmoud

J. S. R.

Staten

R. T.

Hall

L. A.

Lennie

T. A.

(2012) The relationship among young adult college students’ depression, anxiety, stress, demographics, life satisfaction, and coping styles. Issues in Mental Health Nursing 33(3), 149–156.

24.

Main

Zhou

Luecken

L. J.

Liu

(2011). Relations of SARS-related stressors and coping to Chinese college students’ psychological adjustment during the 2003 Beijing SARS epidemic. Journal of Counseling Psychology, 58(3), 410–423.

25.

McAlonan

G. M.

Lee

A. M.

Cheung

Tsang

K. W.

Sham

P. C.

Chua

S. E.

Wong

J. G.

(2007). Immediate and sustained psychological impact of an emerging infectious disease outbreak on health care workers. Canadian Journal of Psychiatry, 52(4), 241–247.

26.

Meyer

(2001). Coping with severe mental illness: Relations of the brief COPE with symptoms, functioning, and well-being. Journal of Psychopathology and Behavioral Assessment, 23(4), 265–277.

27.

Ozamiz-Etxebarria

Dosil-Santamaria

Picaza-Gorrochategui

Idoiaga-Mondragon

(2020). Stress, anxiety, and depression levels in the initial stage of the COVID-19 outbreak in a population sample in the northern Spain. Cadernos de Saúde Pública, 36(4), e00054020.

28.

Reynolds

D. L.

Garay

J. R.

Deamond

S. L.

Moran

M. K.

Gold

Styra

(2008). Understanding, compliance and psychological impact of the SARS quarantine experience. Epidemiology & Infection, 136(7), 997–1007.

29.

Rosseel

(2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36.

30.

Saxon

Makhashvili

Chikovani

Seguin

McKee

Patel

Bisson

Roberts

(2017). Coping strategies and mental health outcomes of conflict-affected persons in the Republic of Georgia. Epidemiology and Psychiatric Sciences, 26(3), 276–286.

31.

Sim

Huak Chan

Chong

P. N.

Chua

H. C.

Soon

S. W.

(2010). Psychosocial and coping responses within the community health care setting towards a national outbreak of an infectious disease. Journal of Psychosomatic Research, 68(2), 195–202.

32.

Stanislawski

(2019). The coping circumplex model: An integrative model of the structure of coping with stress. Frontiers in Psychology, 10, 694.

33.

Steiger

J. H.

Lind

J. C.

(1980). Statistically-based tests for the number of common factors. Paper presented at the annual spring meeting of the Psychometric Society, Iowa City, IA.

34.

Van Bortel

Basnayake

Wurie

Jambai

Koroma

A. S.

Muana

A. T.

Hann

Eaton

Martin

Nellums

L. B

. (2016). Psychosocial effects of an Ebola outbreak at individual, community and international levels. Bulletin of the World Health Organization, 94(3), 210–214.

35.

Wang

Pan

Wan

Tan

C. S.

R. C.

(2020a). Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China. International Journal of Environmental Research and Public Health,17(5), 1729.

36.

Wang

Pan

Wan

Tan

McIntyre

R. S.

Choo

F. N.

Tran

Sharma

V. K.

(2020b). A longitudinal study on the mental health of general population during the COVID-19 epidemic in China. Brain, Behavior, and Immunity, 87, 40–48.

37.

Wong

T. W.

Yau

J. K.

Chan

C. L.

Kwong

R. S.

S. M.

Lau

C. C.

Lau

F. L.

Lit

C. H.

(2005). The psychological impact of severe acute respiratory syndrome outbreak on healthcare workers in emergency departments and how they cope. European Journal of Emergency Medicine, 12(1), 13–18.