Emotional Disorders During the COVID-19 Outbreak in Spain: The Role of Sociodemographic Risk Factors and Cognitive Emotion Regulation Strategies

Abstract

Background

Cognitive emotion regulation strategies (CERS) play a transdiagnostic role in emotional disorders, but the role of these strategies in coping with emotions during the coronavirus disease 2019 (COVID-19) pandemic remains poorly understood.

Aims

To assess the presence of emotional disorders in Spain and the association to sociodemographic characteristics and CERS during the COVID-19 outbreak.

Method

Cross-sectional survey administered through an online platform. Sociodemographic variables and CERS (CERQ-Short) were collected and possible diagnoses of generalized anxiety disorder (GAD, GAD-7), major depression disorder (MDD; Patient Health Questionnaire–9 [PHQ-9]), panic attacks (PA; PHQ-PD), and panic disorders (PD; PHQ-PD) were assessed. Sociodemographic risk factors and CERS association to the possible diagnosis of emotional disorders were reported with hierarchical multivariate logistic regression analyses.

Results

A total of 1,753 respondents completed the questionnaire in Spain. Of these, most (76.8%) were female, with a mean (SD) age of 40.4 years (12.9). A high proportion of participants met diagnostic criteria for emotional disorders: 15.3% for GAD, 12.2% for MDD, 17.2% for PD, and 25.7% had experienced a PA. The contribution of sociodemographic variables to diagnoses of emotional disorders was modest, explaining from 3.1% to 5.7% of the variance; however, when CERS were added, the combination of sociodemographic and CERS explained from 15% to 29% of the variance. Rumination and catastrophizing were the most transdiagnostic maladaptive strategies and positive refocusing was another adaptive strategy.

Discussion

Although results from convenience samples should be handled with caution, the high prevalence of emotional disorders in this study suggests that the demand of mental health interventions will probably increase in Spain. Also, CERS play a clear role in the presence of these disorders.

Conclusion

Intervention programs should focus on training CERS in populations at high risk, focusing on the reduction of maladaptive CERS and the reinforce of other more adaptive CERS.

Keywords

cognitive emotion regulation strategies COVID-19 emotional disorders generalized anxiety disorder major depression disorder panic disorder

The coronavirus disease 2019 (COVID-19) outbreak presents a massive challenge to countries around the world, directly affecting the mental health of the general population in many countries (Gao et al., 2020; González-Sanguino et al., 2020; Sani et al., 2020; Wang et al., 2020). Among all, Spain is one of the countries most affected by the COVID-19 pandemic and a recent study found that 18.7% of the sample had symptoms of depression, 21.6% anxiety, and 15.8% posttraumatic stress disorder (PTSD; González-Sanguino et al., 2020). This finding is in line with other recent studies describing the impact of COVID-19 on mental health in China (Gao et al., 2020), in Italy (Sani et al., 2020), and in Australia (Newby et al., 2020), among others, where depression, anxiety, and PTSD symptoms are the most frequently observed findings. However, to date, there are no published data on the proportion of possible emotional disorders and panic attacks, which may provide a more accurate report of their prevalence when the pandemic arrived in Spain.

Some of these very recent studies conducted to assess mental health problems have focused on the coping strategies that may help people manage the effects of the confinement, the risk of contagion, or the associated psychological distress. In this regard, maladaptive cognitive emotion regulation strategies (CERS) have been associated with the onset and maintenance with emotional disorders (Aldao & Nolen-Hoeksema, 2010; Aldao, Nolen-Hoeksema, & Schweizer, 2010; Naragon-Gainey et al., 2017). Other studies have shown that some individuals use these strategies to cope with anxiety and depression (D’Avanzato et al., 2013). One of the major potential interests of studying the relationship between CERS and emotional disorders is the transdiagnostic role of these strategies (Aldao, Nolen-Hoeksema, & Schweizer, 2010; Hsu et al., 2015; Sloan et al., 2017). This transdiagnostic perspective suggests that the presence of certain maladaptive CERS underlies several different emotional disorders, including anxiety, depression, and eating disorders. Thus, targeting these maladaptive strategies could conceivably help prevent and treat all these disorders (Sloan et al., 2017).

To our knowledge, very few studies have assessed the effects of CERS as a coping mechanism for psychological distress during the current pandemic. For instance, in a sample of German patients, Jungmann and Witthöft (2020) found that the use of adaptive CERS (e.g., positive reappraisal, acceptance, positive refocusing, among others) reduced health-related anxiety levels. However, this has not been studied yet in Spain, one of the countries most affected by the COVID-19 pandemic. Thus, in the context of the mental health risks described above, we performed a cross-sectional survey with two main aims: (1) to study the presence of possible emotional disorders (generalized anxiety disorders [GAD], major depressive disorders [MDD], panic disorders [PD], and panic attacks [PA]) in a sample of volunteers in Spain during the government-imposed quarantine during the COVID-19 outbreak in March and April 2020 and (2) to determine the influence of sociodemographic variables and CERS as potential predictors of these possible diagnoses. This work will test the following hypotheses:

Hypothesis 1: Women, young people, single, low-income, and unemployed people will present the highest proportions of emotional disorders.

Hypothesis 2: Maladaptive CERS will predict positively several emotional disorders.

Hypothesis 3: Adaptive CERS will predict negatively several emotional disorders.

Hypothesis 4: CERS will explain a higher variance rather only the sociodemographic factors.

Hypothesis 5: Rumination and catastrophizing will be the most transdiagnostic variables.

Method

We conducted a cross-sectional survey using an online platform (SurveyMonkey). The national quarantine in Spain was declared on March 12, 2020. During the subsequent 4.5 weeks, the total confirmed number of deaths associated with COVID-19 increased from 87 to more than 20,000. This survey was available online from March 26, 2020 to April 25, 2020. Participation in the survey was completely voluntary and open to individuals from all over Spain. Of the 2,647 participants who started the survey, 1,753 (66.2%) fully completed all the assessment measures included in the survey. Most respondents were from the autonomous regions of Valencia (21.3%), Madrid (20.4%), Aragon (18%), Andalucía (%), (12.7%), and the rest from other regions of Spain (27,6%).

Data Collection

Generalized Anxiety Disorder–7 (GAD-7)

We used the validated Spanish version of the GAD-7 scale (García-Campayo et al., 2010), which has shown excellent psychometric properties (Moreno et al., 2019; Muñoz-Navarro, Cano-Vindel, Medrano, et al., 2017). Participants are asked to rate the frequency of anxiety symptoms during the past 2 weeks (scores 0–21), and a categorical diagnosis based on the GAD was obtained using an algorithm (score ≥2 on the first item plus three other questions).

Patient Health Questionnaire–9 (PHQ-9)

The PHQ-9 (Kroenke et al., 2001) was used to assess MDD. The scale comprises nine items evaluated on a 4-point Likert-type scale (score range: 0-27) and can be scored using a diagnostic algorithm based on Diagnostic and Statistical Manual of Mental Disorders (4th ed., DSM-IV) criteria for MDD: experiencing symptoms on most days as specified by one of the first two questions, plus symptoms on four other items. This algorithm was shown to have excelent psychometric properties for the diagnosis of MDD (sensitivity = .88 and specificity = .80) in a Spanish primary care sample (Muñoz-Navarro, Cano-Vindel, Moriana, et al., 2017).

Patient Health Questionnaire–Panic Disorder (PHQ-PD)

The PHQ-PD is the module that assesses DSM-IV-based panic disorder (PD). We used the validated Spanish-language version of this instrument (Muñoz-Navarro et al., 2016), which can be used to screen for panic attacks in the past 2 weeks, and includes an algorithm for the diagnosis of PD. In this validation study, the screening question yielded a sensitivity score of .83 (specificity = .66) and the modified algorithm of the original version yielded a sensitivity of .77 (specificity = .72), which makes the PHQ-PD a very useful tool to assess for panic attacks and panic disorder.

Cognitive Emotion Regulation Questionnaire–Short (CERQ-Short)

The CERQ-Short (Garnefski & Kraaij, 2006) is an abbreviated version of the CERQ. It evaluates nine different cognitive strategies that individuals use to regulate emotions when facing a negative event. The CERQ-Short evaluates the following strategies: rumination, catastrophizing, self-blame and other-blame, positive refocusing, acceptance, positive reappraisal, refocus on planning, and putting into perspective. We used the validated Spanish version of the CERQ-Short (Holgado-Tello et al., 2018), with slight with modifications in the instructions to accommodate its use in the context of the COVID-19 pandemic, for instance: “In the following questions, you are asked to indicate what you think when you experience negative or unpleasant emotions related to the crisis you are currently experiencing.” We assessed asymmetry and kurtosis for all nine strategies, finding that self-blame presented both asymmetry (>2) and kurtosis (>6; other data available on request). Thus, the self-blame strategy was deleted from the analyses.

Statistical Analyses

Data stratified for all the diagnosis (GAD, MDD, PA, and PD) are presented as numbers and percentages. For outcomes, chi-square tests were applied to determine whether sociodemographic variables (gender, age, marital status, educational level, level of income, and employment situation) were differentially associated with levels of PA and the proportion of possible diagnoses. The SPSS statistical software Version 26.0 (IBM Corp) was used to obtain standardized residuals, and post hoc tests were performed using chi-square tests on the squared residuals (degrees of freedom [df] = 1) using Bonferroni correction. Effects are reported by giving the corresponding z and p values.

Furthermore, stepwise multivariate logistic regression analyses were performed to assess sociodemographic risk factors and CERS. In these models, sociodemographic data (age, sex, education, marital status, employment status, and income) were entered in Step 1 and categorical clinical variables were entered jointly in Step 2. Dummy variables were created for the independent variables with a nominal or ordinal scale. We collapsed some sociodemographic variables, as follows: married: married and living with a partner; employed: full-time and part-time work; unemployed: unemployed searching for work, not searching for work and laid-off; incapacity to work: temporary and permanent incapacity to work. Statistical significance of the predictors was tested using the Wald test.

Ethical Aspects

Participation was completely voluntary, the survey was anonymous, and confidentiality of all information provided was assured. Before starting the survey, all participants were required to read the instructions and to provide informed consent and could abandon the survey at any time without giving reason. The study was approved by the clinical research ethics committee of the Hospital La Fe of Valencia, Spain (Code Protocol: PSICOGVID/001), and confidentiality of personal data was protected under the Spanish Data Protection Law.

Results

Sample Characteristics

Of the 1,753 completers, most were female (76.8%), with a mean (SD) age of 40.4 years (12.9). Most participants were married (37.9%) or single (33.6%), were university graduates (39.9%), or had a postgraduate educational level (39.5%). Most of the sample (60.2%) had a mean salary ranging from €12,000 to €24,000 (31.5%) or €24,000 to 36,000 (28.7%) and were working full-time (47.8%), while approximately every fifth person (22.9%) was unemployed.

Diagnosis of Emotional Disorders

Based on the diagnostic algorithms, a 15.3% met diagnostic criteria for GAD, a 12.2% for MDD, a 17.2% for PD and 25.7 for PA (see Table 1).

Table 1.

PHQ Diagnoses According to Gender and Age.

Sample characteristics			Gender^a					Age (years)
	Total		Female		Male			18–25		26–39		40–59		≥60
	n	%	n	%	n	%	p	n	%	n	%	n	%	n	%	p
	1,753	100	1,346	76.8	354	22.9		274	15.6	597	34.1	763	43.5	119	6.8
GAD-7
Without GAD	1,485	84.7	1,124	83.5	356	88.6		218	79.6	493	82.6	663	86.9	111	93.3
With GAD	268	15.3	222*	16.5	46	11.4	<.05	56***	20.4	104	17.4	100^*	13.1	8**	6.7	<.001
PHQ-9
Without MDD	1,539	87.8	1,173	87.1	362	90.0		223	81.4	513	85.9	686	89.9	117	98.3
With MDD	214	12.2	173	12.9	40	10.0	ns	51***	18.6	84	14.1	77*	10.1	2***	1.7	<.001
PHQ-PD (PA)
Without PA	1,302	74.3	953	70.8	347	86.3		184	67.2	424	71.0	591	77.5	103	86.6
With PA	451	25.7	393***	29.2	55	13.7	<.001	90**	32.8	173*	29.0	172**	22.5	16**	13.4	<.001
PHQ-PD
Without PD	1,452	82.8	1,079	80.2	370	92.0		203	74.1	481	80.6	665	85.8	113	95.0
With PD	301	17.2	267***	19.8	32	8.0	<.001	71***	25.9	116	19.4	108**	14.2	6***	5.0	<.001

Note. GAD-7 = Generalized Anxiety Disorder–7; PHQ-9 = Patient Health Questionnaire–9; PHQ-PD = Patient Health Questionnaire–panic disorder; GAD = generalized anxiety disorder; MDD = major depression disorder; PA = panic attack; PD = panic disorder.

Gender sample was 1.748, as five people indicating “diverse” were excluded from the analyses, as this subgroup was too small to offer reliable estimates.

p < .05. ^**p < .01. ^***p < .001.

Factors Associated With Emotional Disorders

Gender was significantly associated with GAD (p < .05) and PD (p < .001), as females presented a higher proportion of GAD (2.5, p < .001) and PD (5.5, p < .001) than males. No differences were found for MDD (see Table 1).

Age had a significant association to GAD (p < .001), MDD (p < .001), and PD (p < .001). The proportion of young adults (18–25 years) with GAD was higher (−2.6, p < .001) than that observed in the 40- to 59-year age-group (−2.2, p < .05) and people older than 60 years (−2.7, p < .01); more MDD (3.5, p < .001) than the 40- to 59-year age-group (−2.4, p < .05) and people older than 60 years (−3.6, p < .001), and more PD (4.2, p < .001) than the 40- to 59-year age-group (−2.9, p < .05) and people older than 60 years (−3.6, p < .01; see Table 1).

Marital status was significant for MDD (p < .001) and PD (p < .01). Singles presented more MDD (4.2, p < .001) than married people (−3.9, p < .001), and more PD (2.2, p < .05) than married people (−4, p < .001; see Table 2).

Table 2.

PHQ Diagnoses According to Marital Status and Educational Level.

Sample characteristics	Marital status											Educational level^a
	Married		Divorced		Widowed		Single		Living with partner			Basic education		Secondary education		Undergraduate		Bachelor		Postgraduate
	n	%	n	%	n	%	n	%	n	%	p	n	%	n	%	n	%	n	%	n	%	p
	664	37.9	144	8.2	13	0.7	612	34.9	320	18.3		43	2.5	96	5.5	222	12.7	699	39.9	692	39.5
GAD-7
Without GAD	577	86.9	126	87.5	10	76.9	504	82.4	268	84.8		32	74.4	75	78.1	179	80.6	585	83.7	613	88.6
With GAD	87	13.1	18	12.5	3	23.1	108	17.6	52	15.2	ns	11	25.6	21	21.9	43	19.4	114	16.3	79***	11.4	.001
PHQ-9
Without MDD	609	91.7	129	89.6	12	92.3	510	83.3	279	87.2		32	74.4	79	82.3	189	85.1	604	86.4	634	91.6
With MDD	55***	8.3	15	10.4	1	7.7	102***	16.7	41	12.8	<.001	11**	25.6	17	17.7	33	14.9	95	13.6	58***	8.4	<.001
PHQ-PD (PA)
Without PA	527	79.4	104	72.2	10	76.9	442	72.2	219	68.4		27	62.8	67	69.8	163	73.4	498	71.2	546	78.9
With PA	137***	20.6	40	27.8	3	23.1	170	27.8	101**	31.6	<.01	16	37.2	29	30.2	59	26.6	201*	28.8	146***	21.1	<.01
PHQ-PD
Without PD	581	87.5	120	83.3	12	92.3	490	80.1	249	77.8		33	78.0	73	76.0	179	80.6	565	80.8	601	86.8
With PD	83***	12.5	24	16.7	1	7.7	122*	19.9	71	22.2	.001	10	23.3	23	24.0	43	19.4	134	19.2	91***	13.2	<.01

Educational level sample was 1.752, as one “no schooling” was excluded from the analyses.

p < .05. ^**p < .01. ^***p < .001.

Educational level was significant for GAD (p < .001), MDD (p < .001), and PD (p < .01). Postgraduates presented less GAD (−3.6, p < .001), less MDD (−4, p < .001), and less PD (−3.6, p < .001) than people with basic education (2.7, p < .001; see Table 2).

Income level was significant for GAD (p < .001), MDD (p < .001), and PD (p < .001). People earning <€12,000 presented more GAD (2.4, p < .05) compared with those earning between €12,000 and €24,000 (2.8, p < .05) and more than €60,000 (−3.3, p < .001). People earning <€12,000 presented more MDD (2.6, p < .01) and presented more PD (3.8, p < .001) compared with people earning between €12,000 and €24,000 (3.3, p<.001) and more than €60,000 (−3.2, p < .01; see Table 3).

Table 3.

PHQ Diagnoses According to Level of Income.

Sample characteristics	Level of income
	<€12,000		€12,000–€24,000		€24,000–€36,000		€36,000–€60,000		>€60,000
	n	%	n	%	n	%	n	%	n	%	p
	250	14.3	553	31.5	503	28.7	322	18.4	125	7.1
GAD-7
Without GAD	199	79.6	449	81.2	436	86.7	292	90.7	109	87.2
With GAD	51*	20.4	104*	18.8	67	13.3	30	9.3	16***	12.8	<.001
PHQ-9
Without MDD	207	82.8	475	85.9	443	88.1	293	91.0	121	96.8
With MDD	43**	17.2	78	14.1	60	11.9	29	9.0	4	3.2	<.001
PHQ-PD (PA)
Without PA	165	66.0	388	70.2	391	77.7	251	78.0	121	96.8
With PA	85**	34.0	165**	29.8	112^	22.3	71	22.0	4**	3.2	<.001
PHQ-PD
Without PD	186	74.4	434	78.5	430	85.5	286	88.8	116	92.8
With PD	64***	25.6	119***	21.5	73	14.5	36	11.2	9**	7.2	<.001

p < .05. ^**p < .01. ^***p < .001.

Employment situation was significant for GAD (p < .001), MDD (p < .001), and PD (p < .001). Unemployed in search for work presented more GAD (3.9, p < .001) than full-time workers (−2.8, p < .01) and retired participants (−2.7, p < .001). The group of people unemployed in search for work presented more MDD (3.6, p < .001) than full-time workers (−3.4, p < .001) and retired participants (−2.7, p < .01). For PD, unemployed not searching for work (3.3, p < .001) and unemployed in search for work (2.9, p < .01) presented more PD than full-time workers (−4, p < .001) and retired people (−3, p < .01; see Table 4).

Table 4.

PHQ Diagnoses According to Employment Situation.

Sample characteristics	Employment situation
	Full-time work		Part-time work		Unemployed (in search for work)		Unemployed (not searching for work)		Laid-off		Temporary incapacity to work		Permanent incapacity to work		Retired
	n	%	n	%	n	%	n	%	n	%	n	%	n	%	n	%	p
	838	47.8	152	8.7	219	12.5	187	10.7	176	10.0	94	5.4	6	0.3	81	4.6
GAD-7
Without GAD	731	87.2	123	80.9	166	75.8	157	84.0	152	86.4	74	78.7	5	83.3	77	95.1
With GAD	107**	12.8	29	19.1	53***	24.2	30	16.0	24	13.6	20	21.3	1	16.7	4**	5.3	<.001
PHQ-9
Without MDD	759	90.6	122	86.2	176	80.4	159	85.0	154	87.5	77	83.9	4	66.7	79	97.5
With MDD	79***	9.4	18	13.8	43***	19.6	28	15.0	22	12.5	17	18.1	2	33.3	2**	2.5	<.001
PHQ-PD (PA)
Without PA	653	77.9	112	73.7	152	69.4	127	67.9	122	69.3	62	66.0	5	83.3	69	85.2
With PA	185***	22.1	40	26.3	67	30.6	60*	32.1	54	30.7	32	34.0	1	16.7	12*	14.8	<.01
PHQ-PD
Without PD	726	86.8	124	81.6	166	75.8	139	74.3	144	81.8	71	75.5	5	83.3	77	95.1
With PD	112***	13.4	28	18.4	53**	24.2	48***	25.7	32	18.2	23	24.5	1	16.7	4**	4.9	<.001

p < .05. **p < .01. ***p < .001.

Predictors of Generalized Anxiety Disorder, Major Depression Disorder, Panic Attacks, and Panic Disorders

Generalized Anxiety Disorder

In the first step, the sociodemographic variables accounted for 3.1% of the variance (R² = 0.031, χ² = 55.51, p < .001). The inclusion of CERS in the second step accounted for a total of 24% of the variance (R² = 0.24, χ² = 477.84, p < .001). The final model is presented in Table 5. Of the sociodemographic variables, only age and gender were significant predictors. Two variables—younger age (β = −0.02, p = .04) and female sex (β = −0.52, p = .02)—were significant predictors of GAD. Of the CERS, other-blaming (β = 0.12, p < .001), rumination (β = 0.46, p < .001), catastrophizing (β = 0.28, p < .001), and perspective taking (β = 0.13, p = 0.01) predicted positively GAD, whereas positive reappraisal (β = −0.24, p < .001) and positive refocusing (β = −0.36, p < .001) predicted GAD negatively.

Table 5.

Predictors of Emotional Disorders by Multivariable Logistic Regression Analyses.

Variable	GAD-7 (Generalized anxiety disorder)					PHQ-9 (Major depression disorder)
Variable	B	SE	Exp(B)	Wald	p	B	SE	Exp(B)	Wald	p
Age	−0.02	0.01	0.98	4.33	.04**	−0.02	0.01	0.98	4.48	.03**
Gender	−0.52	0.21	0.60	5.95	.02**	−0.27	0.21	0.76	1.66	.20
Educational level	−0.07	0.09	0.93	0.74	.39	−0.15	0.09	0.86	3.17	.08*
Income level	−0.14	0.08	0.87	2.68	.10	−0.10	0.08	0.91	1.29	.26
Marital status (Reference married)
Divorced	−0.12	0.33	0.89	0.13	.72	0.19	0.33	1.21	0.31	.58
Widowed	0.73	0.95	2.06	0.58	.45	−0.57	1.17	0.57	0.24	.62
Single	−0.20	0.21	0.82	0.96	.33	0.23	0.21	1.26	1.22	.27
Employment situation (Reference employed)
Unemployed	0.02	0.19	1.02	0.01	.91	0.16	0.19	1.17	0.67	.41
Medical	0.53	0.33	1.70	2.61	.11	0.77	0.32	2.17	5.99	.01**
Retired	−0.82	0.61	0.44	1.84	.18	−1.14	0.77	0.32	2.18	.14
Cognitive emotion regulation strategies
Other-blaming	0.12	0.03	1.13	11.97	.00***	0.08	0.04	1.09	5.50	.02**
Rumination	0.46	0.05	1.58	77.14	.00***	0.28	0.05	1.32	29.67	.00***
Catastrophizing	0.28	0.05	1.32	37.37	.00***	0.24	0.05	1.27	26.18	.00***
Acceptance	0.08	0.05	1.08	2.26	.13	0.07	0.05	1.07	1.65	.20
Perspective taking	0.13	0.05	1.14	6.45	.01**	0.13	0.05	1.14	6.19	.01**
Positive reappraisal	−0.24	0.05	0.79	26.33	.00***	−0.22	0.05	0.80	22.23	.00***
Plan focusing	0.06	0.05	1.06	1.16	.28	0.02	0.05	1.02	0.10	.75
Positive refocusing	−0.36	0.05	0.70	57.62	.00***	−0.20	0.05	0.82	19.54	.00***
	PHQ-PD (panic attack)					PHQ-PD (panic disorder)
Variable	B	SE	Exp(B)	Wald	p	B	SE	Exp(B)	Wald	p
Gender	−0.77	0.16	0.46	22.21	.00***	−0.88	0.20	0.41	18.86	.00***
Educational level	−0.09	0.07	0.92	1.74	.19	−0.11	0.07	0.90	2.21	.14
Income level	−0.14	0.06	0.87	5.41	.02**	−0.26	0.07	0.77	1.36	.00***
Marital status (Reference married)
Divorced	0.21	0.22	1.23	0.90	.34	0.08	0.26	1.09	0.10	.76
Widowed	−0.25	0.73	0.78	0.12	.73	−1.20	1.13	0.30	1.13	.29
Single	−0.28	0.15	0.76	3.32	.07*	−0.28	0.17	0.76	2.49	.12
Employment situation (Reference employed)
Unemployed	0.11	0.14	1.12	0.64	.42	0.18	0.16	1.20	1.34	.25
Incapacity to work	0.42	0.25	1.52	2.92	.09*	0.56	0.27	1.76	4.22	.04**
Retired	0.06	0.37	1.06	0.03	.87	−0.54	0.56	0.58	0.92	.34
Cognitive emotion regulation strategies
Other-blaming	−0.01	0.03	0.99	0.24	.62	−0.04	0.03	0.96	1.63	.20
Rumination	0.20	0.04	1.22	28.10	.00***	0.22	0.04	1.25	26.83	.00***
Catastrophizing	0.19	0.04	1.21	28.95	.00***	0.19	0.04	1.21	21.03	.00***
Acceptance	0.01	0.03	1.01	0.06	.82	−0.02	0.04	0.98	0.30	.59
Perspective taking	0.02	0.04	1.02	0.17	.68	0.08	0.04	1.08	3.34	.07*
Positive reappraisal	0.00	0.04	1.00	0.01	.92	−0.02	0.04	0.98	0.36	.55
Plan focusing	0.02	0.04	1.02	0.24	.62	0.06	0.05	1.06	1.69	.19
Positive refocusing	−0.14	0.03	0.87	18.66	.00***	−0.19	0.04	0.83	23.38	.00***

Note. GAD-7 = Generalized Anxiety Disorder–7; PHQ-9 = Patient Health Questionnaire–9; PHQ-PD = Patient Health Questionnaire–panic disorder.

p < .05. **p < .01. ***p < .001.

Major Depressive Disorder

Sociodemographic variables introduced in the first step accounted for 3.6% of the variance (R² = 0.036, χ² = 64.02, p < .001). When the CERS were entered in the second step, the total variance explained increased to 15% (R² = 0.150, χ² = 284.72, p < .001; see Table 5). In this case, the sociodemographic variables showed that several variables—younger age (β = −0.02, p = .03), low educational level (β = −0.15, p = .08), and incapacity to work (β = 0.77, p < .01)—were significant predictors of MDD. Of the CERS, other-blaming (β = .08, p = .02), rumination (β = 0.28, p < .001), catastrophizing (β = 0.24, p < .001), and perspective taking (β = 0.13, p = .01) predicted positively MDD, whereas positive reappraisal (β = −0.22, p < .001) and positive refocusing (β = −0.20,p < .001) predicted negatively MDD.

Panic attacks

For PA, the inclusion of sociodemographic variables in the first step accounted for 5% of the total variance (R² = 0.050, χ² = 89.79, p < .001). The CERS introduced in the second step explained 13% of the variance (R² = 0.130, χ² = 245.057, p < .001). The following sociodemographic variables were significant predictors of PA: younger age (β = −0.02, p < .001), female sex (β = −0.77, p < .001), low-income level (β = −0.14, p < .02), single (β = −0.28, p = .07), and working (β = −0.42, p = 0.09). Furthermore, maladaptive CERS rumination (β = 0.20, p < .001) and catastrophizing (β = 0.19, p < .001) predicted positively PA, whereas the adaptive strategy refocusing (β = −0.14, p < .001) predicted negatively PA (see Table 5).

Panic disorder

The sociodemographic variables introduced in the first step accounted for 5.7% of the variance in PD (R² = 0.057, χ² = 102.706, p < .001). The inclusion of CERS in step 2 explained 13% of the variance (R² = 0.126, χ² = 236.469, p < .001). The following sociodemographic variables were significant predictors of PD: younger age (β = −.02, p < .001), female sex (β = −0.88, p < .001), low-income level (β = −0.26, p < .02), and working (β = −0.56, p = .04). Furthermore, rumination (β = 0.22, p < .001), catastrophizing (β = 0.19, p < .001), and perspective taking (β = 0.08, p = .07) all predicted positively PD, whereas adaptive refocusing (β = −0.19, p <.001) predicted negatively PD (see Table 5).

Discussion

This cross-sectional survey, conducted during the government-imposed quarantine related to the COVID-19 outbreak in March and April 2020, reveals that a remarkably high proportion of the sample presented mental health issues. In this line, the hypotheses presented were confirmed, as women, young people, single, low income, and unemployed people presented the highest proportions of emotional disorders than the comparative. Also, sociodemographic variables were only moderate predictors for a diagnosis of an emotional disorder, explaining between 3.1% and 5.7% of the variance. However, adding CERS into the model increased the explanatory power, explaining 15% to 29% of the variance depending on the specific mental health outcome. Also, maladaptive CERS predicted positively several emotional disorders, whereas adaptive CERS predicted them negatively. In this line, rumination, catastrophizing and positive refocusing were the most transdiagnostic variables, as they were predictors in all the four emotional disorders. Thus, training these CERS in risk groups would be a potential target in prevention and treatment programs.

A similar study conducted earlier in a large population sample (n = 3,480) in Spain reported the presence of symptoms of depression, anxiety, and PTSD in 18.7%, 21.6%, and 15.8%, respectively, of the sample (González-Sanguino et al., 2020). These prevalence rates were somewhat higher than the rates observed in the present study, likely due to the use of very brief tools (e.g., the GAD-2 and PHQ-2, with only two items each) in the earlier study, which may have resulted in a higher rate of false positives study (Cano-Vindel et al., 2018). We used a diagnostic algorithm based on the complete set of indicators, an approach that may offer a more accurate estimate of prevalence. In line with this observation, the National Health Survey, conducted by the Spanish Ministry of Health, reported a prevalence of 6.7% for anxiety and depression; 9.1% and 9.2% for females, and 4% and 4.3% for males, respectively (Ministerio de sanidad Consumo y Bienestar, 2017). Previous research on the prevalence of emotional disorders in the general population in Spain (WHO World Mental Health Surveys) diagnosed by means of clinical interview (Composite International Diagnostic Interview [CIDI]) has shown that the prevalence of anxiety and mood disorders was 6.2% and 4.4%, respectively (Alonso et al., 2004; Haro et al., 2006). For PD, the European Study of the Epidemiology of Mental Disorders study found a prevalence of 0.8% in Europe and 0.6% in Spain (Haro et al., 2006). Thus, the proportion rates observed in the current study are much higher than these prevalence reports. This could be due to the convenience sample nature of our study or, more meaningfully, the effect of confinement.

Our findings show that females appear to be more affected by the current pandemic than males, with greater increases in prevalence of GAD and PD, a finding that is consistent with previous reports (Seedat et al., 2009). Nearly one third (29.2%) of the female participants in our survey reported experiencing a self-reported panic attack. While no differences in MDD were observed between males and females, a surprisingly high proportion of females—19.8% and 16.5%—met criteria for PD and GAD, respectively. This is generally in line with the study by Navarro-Mateu et al. (2017), who reported higher prevalence rates for women; nevertheless, the prevalence rate in our study obtained by screening tools was generally higher.

We also found that young adults (ages 18–25 years) and middle-age adults (ages 26–39 years) presented more emotional disorders (GAD, MDD, and PD) than older people. Among young adults, the proportion presenting PA (32.8%) was surprisingly high. Although the increased prevalence of these disorders in young adults has been previously reported (Patel et al., 2007), the prevalence rate of emotional disorders in our sample was substantially higher. This finding suggests that it is imperative to offer preventive measures in particular for young adults (Auerbach et al., 2016). In contrast, elderly and retired people were the least affected of all the subgroups, perhaps due their experience with previous crises, which may have provided them with adequate coping mechanisms (Muñoz-Navarro et al., 2021).

Regarding social support, PD was more prevalent in singles than in married people and those living with a nonmarried partner. Interestingly, people living with a nonmarried partner presented higher rates of PD than married people. This could be explained by higher perceived security in affective and socioeconomic areas for legally married persons. For instance, a study conducted in a large Spanish sample (>10,000 participants) examined the influence of gender and partner/marital status with regard to social instability, finding that a poorer mental health status was associated with poor stability among cohabiting women but not among married ones (Cortès-Franch et al., 2018). This suggests that marriage can function as a protective social support factor for stability, especially in times of crisis, which may also explain why single people may be more affected.

For level of education, our results indicate that a higher level of education was a protective factor against emotional disorders. We found that university graduates presented higher rates of GAD than those with a postgraduate educational level, while a higher proportion of people with basic education presented MDD and PD compared with postgraduate participants. These findings are consistent with previous research showing that a low educational level may be a risk factor for mental health problems (Carod-Artal, 2017). However, it is important to consider that the present study, based on an online survey, was composed of a large proportion of highly educated people who are also more skilled with new technologies. Consequently, our findings could underestimate the true proportion in the general population currently suffering from psychological problems.

Low income was also a significant risk factor. A family income level of less than €24,000 per year was a risk factor for PD, GAD, and MDD. Indeed, severe depressive symptoms were more prevalent in people earning less than €12,000, accounting for 15.2% of that group. Even more striking was the high rate of PA among people earning less than €12,000 (nearly 34%) or less than €24,000 (29.4%), while 25.6% and 21.5% of these groups presented PD, respectively. Interestingly, our findings show that a high salary (>€60,000/year) was a protective factor. As Wood et al (2013) showed in relation to the terrorist attack that took place on March 11, 2004, in the capital of Spain, people who experienced a panic attack during that incident were 3.7 times more likely to suffer from PD in the following year. Given the high proportion of people affected by these events, there is a clear need to implement preventive measures.

Finally, employment status was a predictor for emotional disorders, a finding that is related to income level. Full-time workers and retired people were less affected by psychological problems than unemployed people, a finding that is consistent with previous research (Carod-Artal, 2017). Interestingly, being laid-off did not affect the prevalence of emotional disorders, contrary to what would be expected, probably due to familiar social support and public economic protection.

The stepwise multivariate logistic regression models consistently revealed a substantial effect of CERS in the second step, accounting for 13% to 29% of the variance, while sociodemographic variables entered in the first step accounted for only a moderate proportion of variance (3.1% to 5.7%). As we have shown, some cognitive strategies, such as rumination and catastrophizing, are maladaptive, as these were associated with all the emotional disorders and PA. This is quite consistent with the literature as these strategies are related to emotional disorders and have been proposed as transdiagnostic mechanisms for emotional disorders (Aldao & Nolen-Hoeksema, 2010; Aldao, Nolen-Hoeksema, & Schweizer, 2010). Also, other-blaming was present for GAD and MDD, but not PA or PD.

With regard to adaptive strategies, we found that positive reappraisal and positive refocusing were present in GAD and MDD. However, only positive refocusing met criteria to be considered fully transdiagnostic in this study. More research is needed to better understand the moderators of these mechanisms. It is noteworthy that perspective taking was positively related to GAD, MDD, and PD. While perspective taking is usually considered an adaptive strategy, it actually had negative effects in the present study, may be because the threat due to infection is a real danger. This finding is consistent with some previous studies that have found that this may be a maladaptive strategy in some people with anxiety disorders (Nordahl et al., 2016). Possibly, perspective taking results in increased emotional distress, as it requires reflecting on one’s own position compared with others.

Study Limitations and Strengths

An important limitation of this study was the use of a convenience sample composed of volunteers who participated in an online survey (SurveyMonkey). Overall, the sample was younger and more highly educated than the general population. Consequently, the sample is not representative. In addition, people who were more affected by the crisis may have been more willing to participate in the study. Given the likely presence of self-selection effects, it is possible that prevalence rates are biased upward. However, given the enormity of the potential health and socioeconomic threat posed by the virus, the high prevalence rates observed in this study certainly seem plausible, a finding that is further supported by previous research showing that quarantines can produce negative psychological consequences, including PTSD, confusion, and anger (Brooks et al., 2020). Potential stressors during quarantine include quarantine duration, fear of infection, frustration, and boredom, among others. Future research should also evaluate the interactions between these risk factors and the CERS that may be protective against the onset of emotional disorders (Megías-Robles et al., 2019) to design preventive programs for selective population targets. Another limitation was the use of self-report measures rather than clinical interviews for the clinical diagnoses. However, it would be almost impossible to conduct clinical interviews for large samples in a short time, particularly during the quarantine.

Implications for Policies and Practice

The findings of this study represent a call for action in Spain and the entire world. Our data show a major impact of this global health crisis on mental health, and it seems probable that the resulting economic crisis may be even more harmful. As previous research has shown, the 2008 economic crisis had a severe negative impact on mental health in Spain (Gili et al., 2013; Navarro-Mateu et al., 2015; Roca et al., 2013). Clearly, there is a need to implement preventive and treatment strategies as well as to reinforce health care services in times of crisis. Indeed, primary care services should expect a rapid and significant increase in demand due to the increased prevalence of common mental health problems (Gili et al., 2013; Roca et al., 2009). Unfortunately, the availability of evidence-based psychological treatments for emotional disorders in the primary care setting in Spain and globally is scant (Alonso et al., 2018). For this reason, we believe that it is essential to strengthen primary care and community services to help patients with emotional disorders (González-Blanch et al., 2018; Muñoz-Navarro, Cano-Vindel, Ruiz-Rodríguez, et al., 2017). Preventive strategies (Auerbach et al., 2016) should be offered to the general population to help address the mental health crisis the world is currently facing (Galea et al., 2020). These preventive strategies could focus on training CERS to prevent and treat the onset and maintenance of emotional disorders caused by the COVID-19 pandemic.

Footnotes

Acknowledgements

The authors would like to thank all the collaborators who kindly helped in the sample recruitment process.

Declaration of Conflicting Interests

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

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was partially supported by the following projects: The Spanish Ministry of Economy, Industry and Competitiveness (PSI2017-84170-R) to P.F.-B. and Junta de Andalucía Regional Project (UMA18-FEDERJA-114) to P.F.-B. and R.C.

ORCID iDs

Roger Muñoz-Navarro

Antonio Cano Vindel

Pablo Fernández-Berrocal

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