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  • Published: 17 November 2021

Mental health concerns during the COVID-19 pandemic as revealed by helpline calls

  • Marius Brülhart   ORCID: orcid.org/0000-0001-5483-0219 1 , 2 ,
  • Valentin Klotzbücher   ORCID: orcid.org/0000-0001-9382-6757 3 ,
  • Rafael Lalive 1 , 2 &
  • Stephanie K. Reich 3  

Nature volume  600 ,  pages 121–126 ( 2021 ) Cite this article

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Mental health is an important component of public health, especially in times of crisis. However, monitoring public mental health is difficult because data are often patchy and low-frequency 1 , 2 , 3 . Here we complement established approaches by using data from helplines, which offer a real-time measure of ‘revealed’ distress and mental health concerns across a range of topics 4 , 5 , 6 , 7 , 8 , 9 . We collected data on 8 million calls from 19 countries, focusing on the COVID-19 crisis. Call volumes peaked six weeks after the initial outbreak, at 35% above pre-pandemic levels. The increase was driven mainly by fear (including fear of infection), loneliness and, later in the pandemic, concerns about physical health. Relationship issues, economic problems, violence and suicidal ideation, however, were less prevalent than before the pandemic. This pattern was apparent both during the first wave and during subsequent COVID-19 waves. Issues linked directly to the pandemic therefore seem to have replaced rather than exacerbated underlying anxieties. Conditional on infection rates, suicide-related calls increased when containment policies became more stringent and decreased when income support was extended. This implies that financial relief can allay the distress triggered by lockdown measures and illustrates the insights that can be gleaned from the statistical analysis of helpline data.

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The state of population mental health is difficult to measure. This could lead policymakers to neglect mental health issues relative to aspects that can be measured more easily—especially during fast-moving crisis situations 1 , 2 , 3 . We propose using helpline data as a source of real-time information on the state of public mental health. Helpline data have two main advantages. First, helpline calls can be considered as a manifestation of revealed distress and mental health concerns. Callers incur the mental and time cost of reaching out without having been prompted to do so. Therefore, helpline calls resemble clinical data by offering a measure of mental health that is unaffected by researchers’ study design and framing. Second, information about helpline calls is recorded digitally with daily frequency and covers a wide range of conversation topics.

Telephone helplines are well-established institutions for mental health protection and suicide prevention in many countries, and they offer support immediately, anonymously, cheaply and accessibly 10 , 11 , 12 . Some helplines specialize in particular issues such as suicide, children or violence against women. Suicide helplines, for example, have been shown to reduce suicide rates 13 , and call volumes of suicide prevention helplines have been shown to relate to the incidence of actual suicides 14 .

Using this approach in relation to the COVID-19 crisis, we documented the growth and composition of helpline calls as well as their pandemic-related determinants. Helplines take on particular relevance in a pandemic, when face-to-face contacts carry infection risks and may even be impossible owing to stay-at-home orders 4 , 5 , 6 , 7 , 8 , 9 . We collected data from 23 helplines in 14 European countries, the USA, China, Hong Kong, Israel and Lebanon. The total dataset covers 8 million individual calls made between 2019 and early 2021 (Extended Data Table   1 ). The panel structure of the data enables us to exploit differences in the timing of local infection waves and policy measures to isolate their separate effects on helpline calls. This is a first-order issue for policymakers, as interventions designed to contain infections might also affect mental health by exacerbating unemployment, financial stress, loneliness, relationship problems and pre-existing mental vulnerabilities. These are, in turn, well-recognized risk factors for suicide 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 .

We consider the analysis of helpline calls as a complement, and not a substitute, for established approaches. Mental health surveys 23 , 24 , 25 , 26 and suicide statistics 27 , 28 , 29 are highly informative, but they tend to be low frequency and available with a lag. Higher-frequency monitoring has been performed in the context of the COVID-19 pandemic on the basis of online searches as recorded by Google Trends 30 , 31 , 32 , 33 , by tracking visits to emergency departments 34 , 35 , and by monitoring calls to the police for help with domestic disputes 36 , 37 , 38 . Helpline data contribute a measurement tool that is both broadly available and well targeted on the mental health concerns of a particularly vulnerable segment of the population.

Increased call volumes across helplines

When we pool and size-weight the data for the 21 helplines for which we have daily data (Extended Data Table   1a, b ), we observe a peak call volume, reached six weeks after the outbreak of the pandemic, that exceeds the pre-pandemic level by 35% (95% confidence interval (CI): 22.6, 48.3; P  < 0.001) (Fig. 1a ). With the country-specific outbreak defined as the date when more than 1 SARS-CoV-2 infection per 100,000 inhabitants was recorded 39 , we see a significant increase of 13.5% (95% CI: 1.6, 25.5; P  = 0.027) for the first time in week 3. After the peak in week 6, volumes gradually decreased again, to 6.2% (95% CI: −0.2, 12.6; P  = 0.058) above pre-pandemic levels 39 by around week 11. When we instead define the starting point of the pandemic as the entry into force of the first shelter-in-place (SIP) order 40 , we observe an increase of 11.2% (95% CI: 3.1, 19.4; P  = 0.007) (Fig. 1b ) by week 2, steadily elevated call volumes from week 3 (+27%; 95% CI: 19.1, 35.0; P  < 0.001) until about week 8 (+22.6%; 95% CI: 15.2, 30.1; P  < 0.001), and a decrease thereafter 40 . The different time profiles are mainly explained by the fact that on average, SIP orders were issued 2 to 3 weeks after local outbreaks (Extended Data Fig. 1 , Extended Data Table 1 ).

figure 1

a , b , Estimated coefficients for week indicators with 95% confidence intervals. The dependent variable is ln(Calls). The sample includes daily data for 21 helplines during the period from 4 weeks before to 12 weeks after the event date in early 2020, and for 17 of the 21 helplines, the corresponding days in 2019. Average percentage change in call volumes relative to reference week 0. a , Week 0 is when the cumulative number of SARS-CoV-2 infections exceeded 1 per 100,000 population 39 . b , Week 0 is when SIP orders were introduced 40 . Results show data weighted by total number of calls recorded for each helpline during the sample period (black) and unweighted models (grey) ( Methods , equation ( 1 )).

Source data .

The gradual nature of the increase in call volumes could, to some extent, be a result of capacity constraints 4 . Several helplines initially had to leave some of the additional calls unanswered and only gradually managed to adjust capacity to the new level of demand. Because of this issue, the evolution of recorded aggregate call numbers should be interpreted as a lower-bound estimate of the true increase in the number of people who sought to call a helpline in the first wave of the pandemic. However, unanswered calls are not pre-screened, and call answering is thus a random process unrelated to the motives of the caller. Thus our data provide representative information on the reasons for calling even if some calls were left unanswered because of capacity constraints.

Caller issues and conversation topics

We analysed the reasons for calling using data on the 12 helplines for which we have call-level information on conversation topics and caller characteristics. Our main results relating to call topics are presented in Fig. 2 . Most pre-COVID-19 calls were made because of relationship issues (37%), loneliness (20%) or various fears and anxieties (13%) (Fig. 2a ). Women placed 61% of total calls, and 63% of calls were placed by people between 30 and 60 years of age. The breakdown by topic was fairly similar across helplines, with relationship issues being the most prevalent topic in 8 of the 10 helplines for which this category is defined (34% overall) (Extended Data Fig. 2 ). More than 90% of ‘calls’ were voice calls, but for some helplines our data also includes text-based (online chat) conversations. Between 49% and 81% of calls were placed by first-time or sporadic callers, both before and after the onset of the pandemic (Extended Data Table 2 ).

figure 2

a , Pre-pandemic shares of main non-exclusive helpline conversation topics by sex and age group, before cumulative SARS-CoV-2 infections 39 reached 1 per 100,000 population.  b , Estimated coefficients for the binary post-outbreak indicator variable with 95% confidence intervals. Separate linear probability regression models with the dependent variable set to ‘1’ for calls related to the indicated topic ( Methods , equation ( 2 )).

During the first wave of the pandemic, defined here as lasting until the end of June 2020, the composition of calls changed significantly. The biggest increase in calls was recorded in the category ‘fear’, with 2.4 percentage points (95% CI: 1.8, 2.9; P  < 0.001) (Fig. 2b ). This category includes calls made out of fear of infection with SARS-CoV-2.The other category of calls whose share increased during the first wave of the pandemic was ‘loneliness’ with 1.5 percentage points (95% CI: 1.1, 1.8; P  < 0.001) (Fig. 2b ). The share of all other conversation topics decreased during the first wave. Statistically significant relative decreases were observed for the topics ‘relationships’ (−2.5 percentage points; 95% CI: −3.2, −1.8; P  < 0.001), ‘livelihood’ (that is, economic worries, −0.6 percentage points; 95% CI: −0.9, −0.3; P  < 0.00), ‘violence’ (−0.3 percentage points; 95% CI: −0.5, −0.2; P  < 0.001) and ‘addiction’ (−0.3 percentage points; 95% CI: −0.4, −0.1; P  = 0.002) (Fig. 2b ). We detected no statistically significant change in the share of calls related to suicidal ideation (−0.1 percentage points; 95% CI: −0.3, −0.1; P  = 0.476 (two-sided t -test of difference) and P <  0.006 (two one-sided t -tests), against effect size <−0.35 and >0.35, respectively)) (Extended Data Fig. 3 ). These results show that the first wave of the pandemic and the associated measures led to a less than proportional increase in calls about domestic violence, addiction and suicidal ideation relative to the overall increase in calls.

When we break down post-pandemic changes in topic shares by gender and age group, we observe that the increase in fear-related calls was driven entirely by the over-30s, both male and female (between 2.1 and 3.1 percentage points; 95% CI: 1.5, 2.7 to 2.2, 4.0; P  < 0.001) (Extended Data Fig. 4 ). This is consistent with the fact that vulnerability to COVID-19 increases monotonically with age. The share of suicide-related calls placed by men under 30 fell particularly strongly (by 1.6 percentage points; 95% CI: −2.3, −0.9; P  < 0.001) (Extended Data Fig. 4 ). Conversely, the category of women under 30 stands out, with a 0.9 percentage points increase in the share of calls related to violence (95% CI: 0.2, 1.6; P  = 0.010) (Extended Data Fig. 4 ), despite the fact that it may well have been more difficult under stay-at-home orders to make helpline calls in situations of domestic violence.

For around one-third of the calls underlying our analysis of Fig. 2 , operators recorded more than one conversation topic (Extended Data Fig. 5a ). In particular, calls related to ‘violence’ and ‘livelihood’ also concerned ‘relationships’ (39% and 35%, respectively) (Extended Data Fig. 5b ), but combinations of all eight topics distinguished in our analyses were observed in the data. Dropping multiple-topic calls from the analysis left results almost unchanged (Extended Data Fig. 5c ).

Overall, our results suggest that the observed increase in helpline calls during the first wave of the COVID-19 pandemic was driven to a large extent by fears of the virus itself and by loneliness in the context of SIP orders, rather than by domestic violence, addiction or suicidal ideation.

Call dynamics during subsequent waves

For two of the largest helplines in our sample, Telefonseelsorge (Germany) and SOS Amitié (France), we received data up to 31 March 2021, enabling us to analyse helpline calls beyond the first wave of the pandemic. Figure 3 shows that call volumes increased again in the second half of 2020, in parallel with an increase in infections and a tightening of non-pharmaceutical interventions (NPIs). Whereas in Germany the volume of calls increased continuously into early 2021 (Fig. 3a ), in France it fell again after the peak in December 2020 (Fig. 3b ). These diverging patterns correlate with stronger upswings and downswings in both infections and the stringency of government measures in the two countries.

figure 3

a , c , Total number of daily helpline calls with seven-day moving average in black (right axis), government response stringency index in blue 40 , and seven-day moving average of newly confirmed SARS-CoV-2 infections per 10,000 population and day in red (left axis) 39 , for Germany (Telefonseelsorge) ( a ) and France (SOS Amitié) ( c ). Shaded areas indicate first wave (11 March 2020–30 June 2020) and subsequent waves (1 October 2020–31 March 2021). b , d , Estimated coefficients for binary variables denoting the two periods, and their associated 95% confidence intervals for Germany (Telefonseelsorge) ( b ) and France (SOS Amitié) ( d ), based on separate linear probability regression models with the dependent variable set to ‘1’ for calls related to the indicated topic ( Methods , equation ( 4 )).

Conversation topic patterns resemble each other both between the two helplines and between the two distinct periods of the pandemic. In Germany, calls due to loneliness increased by 1.4 percentage points (95% CI: 0.9, 2.0; P  < 0.016) in the first wave, and by 0.6 percentage points (95% CI: 0.1, 1.1; P  < 0.016) in subsequent waves (Fig. 3b ). In France, those increases were 2.0 percentage points (95% CI: 1.4,2.6; P  < 0.016) and 0.8 percentage points (95% CI: 0.4, 1.2; P  < 0.001), respectively (Fig. 3b ). During the first wave, the share of calls related to ‘fear’ (including the fear of infection) increased by 2.2 percentagepoints (95% CI: 1.4, 2.9; P  < 0.001) in Germany, and by 2.7 percentage points (95% CI: 2.0, 3.5; P  < 0.001) in France (Fig. 3b ). For France, we also observed a significant increase during subsequent waves, by 1.2 percentage points (95% CI: 0.8, 1.5; P  < 0.001) (Fig. 3b ). The share of calls concerning relationship issues decreased in Germany by 3.5 percentage points (95% CI: −4.4, −2.5; P  < 0.001) in the first wave and by 1.8 percentage points (95% CI: −2.3, −1.2; P  < 0.001) in subsequent waves (Fig. 3b ). Decreases were also observed for France: −2.6 percentage points (95% CI: −3.9, −1.2; P  < 0.001) in the first wave,and −1.1 percentage points (95% CI: −2.0, −0.3; P  < 0.001) during the subsequent waves (Fig. 3b ). Conversations were less likely to relate to suicidality during subsequent waves (−0.6 percentage points in Germany and −0.9 percentage points in France; 95% CI: −0.9, −0.3 and −1.0, −0.7; P  < 0.001) (Fig. 3b ).

Conversely, a larger proportion of calls in the second and third waves in France concerned physical health (+0.8 percentage points; 95% CI: 0.3, 1.2; P  = 0.001) (Fig. 3b ). This could be related to a larger share of the population being infected with SARS-CoV-2 or to health worries because of restricted or postponed access to treatment facilities and fewer opportunities for physical activity. Similar to the first wave, additional calls focused predominantly on issues linked directly with the pandemic: fear of infection, loneliness, and—new to subsequent waves—physical health.

Infection rates and policy measures

Helpline call data enable us to use panel data regression to isolate partial correlations between policy measures and indicators of mental health. A particularly informative empirical laboratory for this analysis are calls to the National Suicide Prevention Lifeline (hereafter referred to as Lifeline) in the USA. We have data for 2019, 2020 and early 2021, which enables us to exploit the considerable intranational (state-level) variation of epidemiological situations and policy measures observed within the USA. Thanks to coordination across the network of crisis centres that constitute the Lifeline through a common set of general guidelines, institutional and measurement issues that complicate comparisons across diverse sets of helplines and nations are less of a concern in this dataset. As a helpline focused on suicide, however, Lifeline does not enable us to track changes in the composition of mental health problems.

Our main findings are presented in Fig. 4 . The aggregate time trend reveals that during the first wave, calls to Lifeline were no higher than in the corresponding period of 2019 (around 32,000 weekly calls) (Fig. 4a ), but during subsequent waves they increased above pre-pandemic levels (more than 35,000 weekly calls in late 2020 and spring 2021). Figure 4b illustrates the heterogeneity in the time profiles of calls across states that we seek to ‘explain’ with state–week variation in our three explanatory variables: SARS-CoV-2 infection rates 39 (Fig. 4c ), NPIs as measured by the components ‘containment and closure policies’ summarized in the stringency index (Fig. 4d ), and the generosity of public compensation payments for labour costs (for example, furlough payments) as measured by the component ‘income support’ (Fig. 4e ) in the Oxford COVID-19 Government Response Tracker 40 .

figure 4

a , Total number of weekly calls routed to Lifeline centres by year, with three-week moving average (vertical axis is truncated). b , Deviation of logged calls from the time-averaged state-level mean (grey), with nationwide weekly average (black). c – e , Weekly average scores, with individual state values (grey). c , Newly confirmed SARS-CoV-2 infections 39 per 100,000 population (mean in red). d , Government response stringency index (mean in blue). e , Income support index 40 (mean in yellow). f , Estimated coefficients and associated 95% confidence intervals from sub-national panel model including state and week fixed effects. The dependent variable is ln(Lifeline calls + 1) and natural log values of the independent variables are used ( Methods , equation ( 5 )). g , Coefficient estimates for interaction terms with indicators for the two periods from January to August 2020 and September 2020 to March 2021, and associated 95% confidence intervals ( Methods , equation ( 6 )).

In Fig. 4f , we summarize our regression results based on data up to March 2021. For given policy measures, increases in SARS-CoV-2 infections were associated with statistically significant decreases in the number of calls to the suicide helpline (elasticity =  − 0.012, 95% CI: −0.023, −0.001; P  = 0.026) (Fig. 4f ). The estimated coefficient implies that a 10% increase in SARS-CoV-2 infections is associated with a 0.1% reduction in calls to the suicide helpline.

One interpretation of this result is that the pandemic itself attenuates suicidal anxieties, perhaps by shifting people’s focus towards the distress of others, or to their own fear of the pandemic. This interpretation is consistent with the evolution of calls to the US Disaster Distress Helpline, which was advertised for providing crisis counselling to people affected by COVID-19: calls to this helpline increased sharply during the initial phase of the pandemic (from around 500 to around 3,000 weekly calls) (Extended Data Fig. 6 ), suggesting some displacement of pre-existing anxieties by more proximate COVID-19-related sources of distress.

Policy interventions in the shape of more stringent state-level NPIs or more generous state-level income support measures were not found to have statistically significant effects on Lifeline calls (effect of NPI stringency: 0.020; 95% CI: −0.007, 0.047; P  = 0.155) (Fig. 4f ). Even though the data do not have the statistical power to reject the hypothesis of no effect, our estimates are consistent with stricter NPIs being followed by an increase in Lifeline calls and with income support policies having the opposite effect.

Figure 4g shows the estimated effects of the three explanatory variables separately for the first and subsequent waves of the pandemic, with the cut-off date placed at 1 September 2020. We find that the dampening effect on Lifeline calls of the pandemic itself (measured as the number of SARS-CoV-2 infections) increased over time (−0.022 during the second sub-period; 95% CI: −0.038, −0.006; P  = 0.006) (Fig. 4g ). The effects on Lifeline calls of more stringent NPIs or more generous income support, however, did not differ noticeably across waves of the pandemic. Together, these estimates confirm that the mental-health implications of the pandemic remained relatively stable across the first and subsequent waves. In Supplementary Tables 7 , 8 , we show that these qualitative results are robust across a range of panel regression specifications.

The pattern observed in US suicide helpline data is corroborated by a corresponding regression analysis based on the German and French helplines: all other things being equal, increasing SARS-CoV-2 infections and more generous income support policies were followed by falls in the number of helpline calls related to suicidality, with an elasticity of −0.024 (95% CI: −0.035, −0.014; P  < 0.001) and −0.020 (95% CI: −0.033, −0.006; P  = 0.004), respectively (Fig. 5 ). Conversely, more stringent NPIs were followed by more suicide-related calls (+0.035, 95% CI: 0.011, 0.060; P  = 0.005) (Fig. 5 ). These estimated effects are statistically significant and qualitatively consistent with those based on the Lifeline data.

figure 5

Coefficients from separate regression models by topic with 95% confidence intervals. The dependent variable is ln(Calls + 1), and natural log values of SARS-CoV-2 infections per 100,000 population 39 and policy indices 40 are used. The sample includes all calls to Telefonseelsorge (Germany) and SOS Amitié (France) for which at least one conversation topic was recorded, aggregated to daily totals from 1 January 2019 to 31 March 2021 ( Methods , equation ( 7 )).

Our findings suggest that public compensation payments for pandemic-induced losses not only reduce economic hardship but also have broader benefits: more generous income support leads to fewer calls due to fear (−0.042; 95% CI: −0.061, −0.024; P  < 0.001), loneliness (−0.024; 95% CI: −0.040, −0.008; P  = 0.003), physical health concerns (−0.026; 95% CI: −0.041, −0.011; P  = 0.001) and, as expected, economic anxiety (‘livelihood’; −0.016; 95% CI: −0.030, −0.002; P  = 0.023) (Fig. 5 ).

We drew on international helpline call data to shed light on a statistical blind spot of pandemic policy: mental health concerns and general distress of the population. Helpline calls increased during the pandemic, and this increase was driven primarily by concerns linked to the pandemic itself (such as fear of infection and loneliness). Conversely, on average, the share of calls due to other forms of distress, including suicidality, violence and addiction, decreased. The lack of an increase in the share of suicide-related calls is consistent with observed decreases in actual suicides during the early stages of the pandemic across several countries 28 . Underlying these general patterns are helpline-specific evolutions that are documented in detail in Supplementary Figs. 1 – 37 .

The panel structure of the data enables us to estimate multivariate models to disentangle the separate effects of the pandemic itself (SARS-CoV-2 infections), of the stringency of containment policies, and of the generosity of income support policies. We found that more stringent measures were associated with a higher number of calls due to fear, loneliness and suicidality, but that more generous income support had the opposite effect. This implies that compensation payments to workers and businesses affected economically by COVID-19, which were designed to preserve demand and productive capacity, have additional benefits in alleviating distress and mental health concerns.

Longitudinal helpline data offer an attractive complement to existing empirical approaches based on surveys, administrative and clinical data (such as suicide statistics, admissions to treatment centres and so on), and internet search data. Helpline data also have their limitations. One such issue is that call counts may be influenced not only by demand but also by supply, as capacity constraints can force operators to leave some calls unanswered. This may cause a downward bias in measures of increases in demand. However, as calls are not pre-screened, capacity constraints are unlikely to affect analyses of the composition of calls in terms of topics or caller characteristics.

Another limitation lies in our agnosticism about the representativeness of callers to helplines. We are aware of no rigorous evidence regarding the composition of the helpline caller population in terms of socio-demographic status, health, occupation, nationality and other factors. By focusing our analysis on changes in call volumes over time, we eliminate time-invariant specificities of the helpline caller population, which should remove a large proportion of any potential sample selection bias. Moreover, anecdotal evidence from helpline workers confirms that the caller population typically includes the most vulnerable members of society, which is the population of greatest interest in a study of distress and mental health concerns.

Helpline call data

Our sample of helplines includes large general crisis helplines and dedicated suicide prevention helplines, as well as some helplines that focus on specific groups such as children, parents or immigrants. Observations within helplines are self-selected, as they consist of callers to helplines. The selection of sample helplines was based on (1) an internet search of well-documented helplines, and (2) receiving data from those helplines. Out of 154 helplines that we contacted, we received data from 37 helplines. Where possible, we requested data from 1 January 2019 to the most recent available date, to enable a comparison of call patterns after the COVID-19 outbreak with call patterns at the same time of year before the pandemic. The information obtained from 23 helplines was of sufficiently detailed coverage and consistency to be included in our pooled analyses. Extended Data Table 1 lists the included helplines, grouped by the format in which the data were made available for this study.

The most detailed information was provided by the helplines in Extended Data Table 1a , where we received individual conversation-level data, including information on the callers’ sex and approximate age, as well as on the issues discussed during the conversation. From the three additional helplines in Extended Data Table 1b , we received aggregate time series of daily call volumes, with separate series by gender, age category and topic. Moreover, for the two helplines in Extended Data Table 1c , we received sub-national weekly series of call volumes across US states. In contrast to the data from helplines in Extended Data Table 1a, b , the number of calls in Extended Data Table 1c does not refer to answered calls and actual conversations, but to the raw number of calls routed to local centres.

Data processing and analysis were conducted according to the guidelines of the Internal Review Board (IRB) of the Faculty of Business and Economics at the University of Lausanne. Two considerations were important. First, all helplines guarantee anonymity to their callers, both towards their operators and towards the outside world. Names and addresses are never asked for, and caller numbers are hidden by the system. It is therefore impossible to identify callers even from the call-level data provided by a subset of helplines, and the anonymous information is not covered by data protection considerations. Second, all of the helplines we analysed inform callers that anonymous call data are collected for reporting and statistical purposes, whether explicitly in the terms and conditions or statutes, and/or implicitly in annual reports and online publications. The analysis of those data conforms with the aim of the Ethics Charter of the International Federation of Telephone Emergency Services (IFOTES), which aims to “(c)ollect and disseminate data gathered by the Branches in connection with the challenges facing Mental Health and Prevention of Suicide” and to “(a)ssist and encourage research carried out in these fields” 12 . The IRB exempted the study from a full review owing to the secondary nature of the data used.

Government response and epidemiological data

To measure the timing and intensity of government responses consistently across time, countries and sub-national regions, we rely on aggregate policy indices from the Oxford COVID-19 Government Response Tracker 40 . In particular, we use two policy indices, the government response stringency index and the income support index. The stringency index shows the strictness of containment policies and restrictions of personal freedom, and is based on an unweighted average of eight component scores for shelter-in-place requirements, workplace and public transport closures, restrictions on public events, gatherings, domestic and international travel, and information campaigns. The income support index reflects the availability of financial support and is constructed the index score using the ordinal measure and the flag for sectoral targeting to arrive at a value between 0 and 100 (following the definition of the stringency index). For the sub-national information on policies in US states, it is important to note that we use the total index scores, where, whenever national policies were more restrictive than those of individual states, the higher score is imputed. Data on the daily number of newly confirmed SARS-CoV-2 infections are taken from the JHU CSSE COVID-19 Dataset 39 .

Call volumes after the pandemic outbreak

For Fig. 1 , we combine the time-series data (Extended Data Table 1b ) with aggregates based on the call-level data (Extended Data Table 1a ) in a panel of daily call volumes for 21 helplines, covering the time up to 30 June 2020 if available. For four of the helplines (MIELI, SOS Détresse, Sahar and Muslimisches Seelsorgetelefon), no data were available for 2019. We then look exclusively on the period from 4 weeks before to 12 weeks after the country-specific event date in 2020, as well as, if available, the corresponding days in 2019. To summarize the overall dynamics, we estimate the following model:

The dependent variable is the natural logarithm of the number of calls to helpline h recorded on day t . We define the local outbreak as the date when (1) the cumulative number of SARS-CoV-2 infections in the population exceeded 1/100,000 or (2) when SIP orders were first introduced. For both versions, we define indicator variable Week τ , which is set ‘1’ for days in event week number τ in 2020. The model includes helpline fixed effects ξ h interacted with year, week-of-year and day-of-week indicators, summarized in the vector Θ t . The reference category is week 0 of the pandemic outbreak or SIP introduction, and the coefficient γ τ allows us to track the percentage deviation in daily calls, controlling for seasonal effects and secular trends. See Supplementary Table 1 and Extended Data Fig. 1 for details on event dates and call volumes for each of the 21 included helplines. Supplementary Table 2 contains numerical estimation results.

Helpline data on individual calls

To investigate changes in conversation topics (Fig. 2 ), we focus on the call-level data and combine information from 12 helplines (Extended Data Table 1a ) for which we have information on conversation topics and inferred caller characteristics. This yields a sample of up to 2.2 million calls. For each helpline, we categorize calls on the basis of the recorded information on the problems of callers and the topics discussed. Precise categorizations of call topics differ across helplines, but they are sufficiently similar to allow us to map them into to the following common, non-exclusive categories: loneliness (social isolation, entrapment), fear (general fear, anxiety disorder, fear of infection with SARS-Cov-2), suicidality (suicidal ideation, suicidal thoughts or plans, suicide attempts, suicidality of others), addiction (drugs, alcohol, other addictions), violence (physical violence and abuse, sexual harassment, rape), physical health (disease, long-term illness, disability), and two broad categories for livelihood (work situation, unemployment, financial problems, housing), and relationships (family life, parenting, marriage and intimate relationships, separation). Supplementary Tables 12 – 22 show the precise topic definitions for each helpline. As some topics are not recorded at all for some helplines, the sample size differs depending on which topic we look at: the largest sample includes data from 12 helplines, where we can distinguish calls related to suicide from calls concerning other issues (Extended Data Fig. 2 ). Recorded conversation topics can be non-exclusive. We document the joint distribution of topics in Extended Data Fig. 5a .

Additionally, we have coded the sex and age category of each caller, and (where possible) further characteristics such as marital status, living situation and occupational status. As helplines record age categories differently, our classification cannot be fully precise. Using the boundaries of available age groups, the group of callers below 30 includes only those that were recorded in an age group with an upper limit at or below 30. The same logic applies to the group of callers older than 60, and the middle category in some cases includes also individuals whose age is slightly below 30 years or above 60.

For Fig. 2 , we restrict the sample to calls recorded for the time from 1 January 2019 through 30 June 2020, where information on sex and age group of callers is available. When estimating the relative importance of a topic, we define the dependent variable T as equal to ‘1’ for call i to helpline h on day t if the conversation was related to the respective topic (Fear, Loneliness, Suicide, Addiction, Violence, Physical health, Livelihood, or Relationships), and zero for unrelated calls, where another topic was recorded. Calls without information on caller issues or conversation topics are not included. Based on the date when the cumulative number of SARS-CoV-2 infections per population exceeded 1/100,000 in the country of operation 39 , we define an indicator ‘Post outbreak’ and estimate a linear probability model as in equation ( 2 ):

The model includes the helpline indicator ξ h to account for time-invariant differences among helplines. We further add year, week-of-year and day-of-week indicators, summarized in the vector Θ t , interacted with the helpline fixed effects, to account for secular trends and for seasonal and day-of-week effects. Standard errors are clustered at the helpline–week level. Supplementary Table 3 contains numerical estimation results.

For the analysis of heterogeneous effects in Extended Data Fig. 4 , we estimate an alternative specification including individual caller characteristics and interaction terms. To illustrate the change in topics for different groups, we classify callers into six non-overlapping groups, denoted in the vectors Sex (male, female) and Age group (below 30, 30–60, above 60). In the model illustrated in equation ( 3 ), we interact the post-outbreak variable Post with all six group indicators, so that the coefficients represent the group-specific changes in topic shares:

For the main effects of caller sex and age groups, indicators for the reference group of male callers in the 30–60 age category are omitted. Supplementary Table 4 contains numerical estimation results.

For the analysis of the longer time horizon and subsequent waves in Fig. 3 , we focus on call-level data from Germany and France, from 1 January 2019 to 31 March 2021. We estimate a specification similar to the previous approach, separately for the two helplines and each topic. To distinguish the changes around the outbreak from later adjustments during the subsequent wave, we define two indicator variables W1 and W2 denoting two periods. The first covers the time from 11 March 2020, when the World Health Organization declared the outbreak a pandemic, to 30 June 2020, when the number of infections decreased again and containment measures were relaxed both in Germany and in France. The second period indicator is equal to one for the time after 1 October 2020. Equation ( 4 ) illustrates the estimated model:

As we analyse the two helplines separately, we do not include helpline fixed effects here, but capture secular trends and seasonal patterns through the inclusion of year, week-of-year, and day-of-week indicators summarized in the vector Θ t . Standard errors are clustered at the week level. See Supplementary Table 6 for numerical estimation results.

Call volumes across US states

The analysis of sub-national call volumes in Fig. 4 relies on data on weekly call volumes routed to Lifeline. The analysis is based on weekly call volumes for US states and territories over 116 weeks, starting in the week to 6 January 2019, and up to the week ending on Sunday, 21 March 2020 (Supplementary Fig. 37 ). Based on phone numbers, the state from which calls were placed can be inferred, even though internal migration means that this classification is subject to measurement error. While the Lifeline will serve any calls regardless of country of origin, its mission is to serve calls originating from the US and US territories. We focus on calls from 50 US states and DC. Calls originating from Canadian provinces, US territories, as well as those of other international or unknown origin are not considered, to maximize consistency and because of the limited availability of data on policy responses. The panel structure allows us to exploit the idiosyncratic variation within states j over time (weeks w ) while controlling for overall trends. We estimate a two-way error component model as illustrated in equation ( 5 ):

The dependent variable is the natural logarithm of the number of calls plus one, Infections is defined as one plus the sum of newly confirmed SARS-CoV-2 infections in week w per 100,000 population, while stringency and income support are calculated as weekly averages of the respective daily index scores. State fixed effects ξ j absorb all time-invariant factors, and our analysis is therefore based on the idiosyncratic within-state variation in call volumes over time. The inclusion of week indicators θ w allows us to capture all nation-wide and global effects and to focus solely on the relative differences in pandemic exposure and policy response. Standard errors are clustered at the state–month level.

To investigate the extent to which the relationship changed over time, we re-estimate the model as in equation ( 6 ). Here, we include the three main explanatory variables, interacted with two indicator variables that are set to “1” for the time period from 1 January to 31 August 2020, and for 1 September 2020 to 21 March 2021, respectively. Supplementary Tables 7 , 8 contain numerical estimation results from alternative specifications.

Call volumes in Germany and France

For the analysis in Fig. 5 , we combine the previous approaches and estimate the relationship between call volumes and the three variables as illustrated in equation ( 7 ), based on topic-specific call volumes to Telefonseelsorge (Germany) and SOS Amitié (France) during the time from 1 January 2019 to 31 March 2021.

In contrast to the sub-national panel of US states, here we do not include week fixed effects but capture secular trends and seasonal patterns through helpline fixed effects ξ h , interacted with year, week-of-year and day-of-week indicators summarized in the vector Θ t . Standard errors are clustered at the helpline–week level. Numerical estimation results are shown in Supplementary Table 9 .

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this paper.

Data availability

Data were provided by helplines for the sole purpose of this research project, subject to confidentiality agreements. The full data underlying specific parts of the analysis are available from the authors upon reasonable request and conditional on permission of the respective helplines. To obtain (updated) helpline data, researchers have to sign agreements with individual helplines—for further information, contact: [email protected] (Telefonseelsorge, Germany), [email protected] (SOS Amitié), [email protected] (De Luisterlijn), [email protected] (Nummer gegen Kummer), [email protected] (Tele-Onthaal), [email protected] (Telefonseelsorge, Austria), [email protected] (Hope Line), [email protected] (Telefono Amico), [email protected] (Zaupni Telefon Samarijan), [email protected] (Modrá linka), [email protected] (Sahar), [email protected] (SOS Voz Amiga), [email protected] (Muslimisches Seelsorgetelefon), [email protected] (Embrace Lifeline), [email protected] (SOS Détresse), [email protected] (Plavi Telefon), [email protected] (Samaritan Befrienders), [email protected] (Die Dargebotene Hand), [email protected] (MIELI), [email protected] (LESZ) and [email protected] (Lifeline and Disaster Distress Helpline). Data on infection rates and policy measures are publicly available online from the JHU CSSE COVID-19 Dataset at https://github.com/CSSEGISandData and the Oxford COVID-19 Government Response Tracker at https://github.com/OxCGRT .  Source data are provided with this paper.

Code availability

Files were collected in MS Excel 2016 and Notepad++ v7.9.5. Data preparation and analysis was carried out in Stata/SE 17.0, Do-files are available online at https://doi.org/10.5281/zenodo.5495830 .

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Acknowledgements

We thank the following people and helplines for sharing their expertise and for granting us access to their data: C. Hochhauser and A. Kesselring (Telefonseelsorge, Austria), J. Pots (Tele-Onthaal, Belgium), M. Kovacevic (Plavi Telefon, Bosnia and Herzegovina), R. Ma and W. Ni (Hope Line, China), H. Regnerova (Modrá linka, Czech Republic), H. Dumont (SOS Amitié, France), S. Winter (MIELI, Finland), L. Storch and B. Blömeke (Telefonseelsorge, Germany), M. I. Sagir (Muslimisches Seelsorgetelefon, Germany), H. Schütz (Nummer gegen Kummer, Germany), H.-Chia (Samaritan Befrienders, Hong Kong), E. Brandisz (LESZ, Hungary), Y. Levy (Sahar, Israel), M. Petra (Telefono Amico, Italy), P. Zeinoun (Embrace Lifeline, Lebanon), S. Hay (SOS Détresse, Luxembourg), J. Jakobs (De Luisterlijn, Netherlands), F. Paulino (SOS Voz Amiga, Portugal), K. Bogataj (Zaupni telefon Samarijan, Slovenia), S. Basler (Die Dargebotene Hand, Switzerland), A. Goldstein, J. Higgins, S. Murphy and J. Draper, Vibrant Emotional Health (National Suicide Prevention Lifeline and Disaster Distress Helpline, USA). We thank C. Efferson, E. Fehr, L. Keller, K. Kõlves, J. Vornberger and S. Métille for comments and suggestions. We are grateful to the Swiss National Science Foundation (NCCR LIVES—‘Overcoming Vulnerability: Life Course Perspectives’) for financial support.

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S.K.R., V.K., M.B. and R.L. collected data. R.L., M.B., V.K. and S.K.R. designed the empirical approach. V.K., M.B. and R.L. implemented the analysis. M.B., V.K., R.L. and S.K.R. wrote the paper.

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Extended data figures and tables

Extended data fig. 1 evolution of daily helpline call volumes during the first wave..

Sum of daily helpline contacts with seven-day moving average, January–June 2020 (black) and 2019 (light grey, not available for all helplines). Note that the vertical axes are truncated and not equal across panels, and the magnitudes of changes are thus not directly comparable. The solid red line shows the date of the pandemic outbreak, when more than 100 SARS-CoV-2 infections per 100,000 population have been recorded 39 , the dashed blue line shows the date when shelter-in-place requirements were first introduced in the country of operation 40 , see Supplementary Figs. 1 – 34 for details on individual helplines.

Source data

Extended data fig. 2 conversation topic shares by helpline..

Each cell shows the share of calls related to the conversation topic on the horizontal axis, in percent of all calls with the helpline indicated on the vertical axis. Full dataset, covering all calls for which at least on topic was recorded, from 1 January 2019 to the respective end of available data, see Extended Data Table 1a and Supplementary Figs. 1 – 34 .

Extended Data Fig. 3 Magnitude of post-outbreak changes and equivalence tests.

a , Coefficient estimates from linear probability models as in Fig. 2b , with 95% confidence intervals and equivalence bounds, defined as 5% of the pre-pandemic share of the respective topic, indicated by light blue vertical bars. b , Results from a normalized across conversation topics, with coefficient estimates and associated 95% confidence intervals, and equivalence bounds divided by the pre-pandemic share of calls related to the respective topic. c , Relevance tests, numerical coefficient estimate with corresponding equivalence bounds, with test statistics and p-values from two one-sided tests for equivalence.

Extended Data Fig. 4 Change in conversation topics by caller sex and age group.

Estimated coefficients for interaction terms of group indicators with binary post-outbreak variable, and associated 95% confidence intervals. Separate linear probability regression models with dependent variable set to one for calls related to the respective topic, see  Methods , equation ( 3 ) and Supplementary Table 4 .

Extended Data Fig. 5 Non-exclusive conversation topics.

Relation among conversation topics for calls included in the estimation sample underlying Fig. 2 , from 1 January 2019 to 30 June 2020, and where sex and age group of callers are observed. a , Distribution of recorded number of conversation topics per call, b , Overlap in conversation topics, where each row shows the distribution of second or further topics (horizontal axis), in percent of all calls that are related to one specific topic (vertical axis), c , Results from Fig. 2b , with alternative estimates based on a restricted sample of single-topic category calls, see  Methods , equation ( 2 ). Supplementary Table 5 contains the numerical estimates.

Extended Data Fig. 6 Disaster Distress Helpline.

a , Sum of weekly calls routed to centers by year with 3-week moving average, letters on the horizontal axis indicate calendar months. b , deviation of log calls from time-averaged state-level mean (gray), with overall weekly average (black). c , Estimated coefficients and associated 95% confidence intervals; sub-national panel model including state and week fixed effects. Dependent variable is ln(Disaster Distress calls + 1), and independent variables are measured in logs as well; see  Methods , equation ( 5 ). Supplementary Table 10 contains the numerical estimates. d , Coefficient estimates for interaction terms with indicators for the two periods from January–August 2020 and September 2020–March 2021, and associated 95% confidence intervals; see  Methods , equation ( 6 ). Supplementary Table 11 contains the numerical estimates.

Supplementary information

Supplementary information.

The Supplementary Information contains A, numerical estimation results underlying the main Figures and Extended Data Figures in Supplementary Tables, and B, Supplementary Notes with background information on individual helplines, as well as Supplementary Tables and Figures, organized alphabetically by country of operation.

Reporting Summary

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Brülhart, M., Klotzbücher, V., Lalive, R. et al. Mental health concerns during the COVID-19 pandemic as revealed by helpline calls. Nature 600 , 121–126 (2021). https://doi.org/10.1038/s41586-021-04099-6

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DOI : https://doi.org/10.1038/s41586-021-04099-6

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Research Article

Impact of COVID-19 pandemic on mental health: An international study

Roles Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

¶ ‡ ATG, MK and AK designed and implemented the study together. AK and MK should be considered joint senior authors.

Affiliation Division of Clinical Psychology & Intervention Science, Department of Psychology, University of Basel, Basel, Switzerland

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Roles Data curation, Formal analysis, Writing – original draft, Writing – review & editing

Affiliation Department of Health Sciences, European University Cyprus, Nicosia, Cyprus

Roles Investigation, Resources, Writing – review & editing

Affiliation Psychological Laboratory, Faculty of Public Health and Social Welfare, Riga Stradiņš University, Riga, Latvia

Affiliation Kore University Behavioral Lab (KUBeLab), Faculty of Human and Social Sciences, Kore University of Enna, Enna, Italy

Affiliation Department of Social Sciences, School of Humanities and Social Sciences, University of Nicosia, Nicosia, Cyprus

Affiliation Department of Nursing, Cyprus University of Technology, Limassol, Cyprus

Affiliation Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus

Affiliation Department of Psychological Counseling and Guidance, Faculty of Education, Hasan Kalyoncu University, Gaziantep, Turkey

Affiliation The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong

Affiliation Department of Psychology, Fundación Universitaria Konrad Lorenz, Bogotà, Columbia

Roles Conceptualization, Investigation, Resources, Writing – review & editing

Affiliation Faculty of Psychology, University of La Sabana, Chía, Columbia

Affiliation School of Applied Psychology, University College Cork, Cork, Ireland

Affiliation School of Psychology, University College Dublin, Dublin, Ireland

Affiliation Medical University Innsbruck, Innsbruck, Austria

Affiliation Department of Psychology, Babeş-Bolyai University (UBB), Cluj-Napoca, Romania

Affiliation Instituto Superior de Psicologia Aplicada (ISPA), Instituto Universitário; APPsyCI—Applied Psychology Research Center Capabilities & Inclusion, Lisboa, Portugal

Affiliation Faculdade de Psicologia, Alameda da Universidade, Universidade de Lisboa, Lisboa, Portugal

Affiliation LIP/PC2S, Université Grenoble Alpes, Grenoble, France

Affiliation Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Cadiz, Spain

Affiliation Instituto ACT, Madrid, Spain

Affiliation Department of Psychology, European University of Madrid, Madrid, Spain

Affiliation Department of Psychology and Sociology, University of Zaragoza, Zaragoza, Spain

Affiliation Vadaskert Child and Adolescent Psychiatric Hospital, Budapest, Hungary

Affiliation Private Pratice, Poland

Affiliation Department of Psychology, University of Jyväskylä, Jyväskylä, Finland

Affiliation Clinic for Psychiatry, Clinical Center of Montenegro, Podgorica, Montenegro

Affiliation Ljubljana University Medical Centre, Ljubljana, Slovania

Affiliation Département de Psychologie, Université du Québec à Trois-Rivières, Trois-Rivières, Canada

Affiliation Department of Psychiatry and Behavioral Science, Duke University, Durham, North Carolina, United States of America

Roles Conceptualization, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

Affiliation Department of Psychology, University of Cyprus, Nicosia, Cyprus

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  • Andrew T. Gloster, 
  • Demetris Lamnisos, 
  • Jelena Lubenko, 
  • Giovambattista Presti, 
  • Valeria Squatrito, 
  • Marios Constantinou, 
  • Christiana Nicolaou, 
  • Savvas Papacostas, 
  • Gökçen Aydın, 

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  • Published: December 31, 2020
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Table 1

The COVID-19 pandemic triggered vast governmental lockdowns. The impact of these lockdowns on mental health is inadequately understood. On the one hand such drastic changes in daily routines could be detrimental to mental health. On the other hand, it might not be experienced negatively, especially because the entire population was affected.

The aim of this study was to determine mental health outcomes during pandemic induced lockdowns and to examine known predictors of mental health outcomes. We therefore surveyed n = 9,565 people from 78 countries and 18 languages. Outcomes assessed were stress, depression, affect, and wellbeing. Predictors included country, sociodemographic factors, lockdown characteristics, social factors, and psychological factors.

Results indicated that on average about 10% of the sample was languishing from low levels of mental health and about 50% had only moderate mental health. Importantly, three consistent predictors of mental health emerged: social support, education level, and psychologically flexible (vs. rigid) responding. Poorer outcomes were most strongly predicted by a worsening of finances and not having access to basic supplies.

Conclusions

These results suggest that on whole, respondents were moderately mentally healthy at the time of a population-wide lockdown. The highest level of mental health difficulties were found in approximately 10% of the population. Findings suggest that public health initiatives should target people without social support and those whose finances worsen as a result of the lockdown. Interventions that promote psychological flexibility may mitigate the impact of the pandemic.

Citation: Gloster AT, Lamnisos D, Lubenko J, Presti G, Squatrito V, Constantinou M, et al. (2020) Impact of COVID-19 pandemic on mental health: An international study. PLoS ONE 15(12): e0244809. https://doi.org/10.1371/journal.pone.0244809

Editor: Joel Msafiri Francis, University of the Witwatersrand, SOUTH AFRICA

Received: October 3, 2020; Accepted: December 16, 2020; Published: December 31, 2020

Copyright: © 2020 Gloster et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work was supported by grants from the Swiss National Science Foundation awarded to Andrew T. Gloster (PP00P1_ 163716/1 & PP00P1_190082). The funder provided support in the form of salaries for authors [ATG], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section. One of the authors is employed by a commercial affiliation: Private Pratice, Poland. This affiliation provided support in the form of salaries for authors [BK], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: One of the authors is employed by a commercial affiliation: Private Pratice, Poland. This affiliation provided support in the form of salaries for author BK, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials. No other authors have competing interests to declare.

Introduction

The COVID-19 global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) virus triggered governmentally mandated lockdowns, social distancing, quarantines and other measures in the interest of public health. The mandated lockdowns abruptly and dramatically altered people’s daily routines, work, travel, and leisure activities to a degree unexperienced by most people living outside of war zones. Simultaneously, the highly contagious, yet invisible virus transformed previously neutral situations to perceived potentially dangerous ones: social interaction, touching one’s face, going to a concert, shaking someone’s hand, and even hugging grandparents. Given these changes and looming threat, increases in anxiety and depression can be expected [ 1 ]. Indeed, common psychological reactions to previous quarantines include post-traumatic symptoms, confusion, and anger [ 2 ], though these data stem from quarantines of specific regions or a subgroup of exposed people, such as medical professionals. It therefore remains an empirical question whether such patterns are consistent when entire populations across the globe are simultaneously affected.

For most people, it stands to reason that governmentally mandated lockdowns decrease their activity levels and the number of stimuli experienced compared to pre-lockdown levels. The impact of reducing activities, stimuli and routines on the population is unknown, but various analogue situations can be used to make predictions, like death of a spouse [ 3 ]; hearing loss [ 4 ]; job loss [ 5 ]; long duration expeditions [ 6 ]; poor acculturation [ 7 ]; and even ageing when combined with loneliness [ 8 ]. Each of these situations is associated with increases in psychological distress. This reduction of stimulations may lead to boredom and reductions in reinforcement, which has been associated with depression [ 9 ]. The sum total of these literatures, and some evidence from country specific studies on COVID-19 suggests that for some people, the mental distress in the form of stress, depression, and negative affect are likely reactions to the lockdown; therefore, people’s wellbeing is likely to suffer. Indeed, increased loneliness, social isolation, and living alone are associated with increased mortality [ 10 ]–the exact effect that mandated lockdown and social distancing rules aimed to counteract.

Alternately, the planned slowing down of daily routines can be beneficial. For example, vacations and weekends are highly sought-after–if not always achieved–periods of relaxation and stress reduction [ 11 ]. Likewise, some religious and spiritual traditions encourage simplicity, mindfulness, and solitude with the goal of increasing wellbeing [ 12 ]. It is therefore conceivable that for some people the lockdown could offer a reprieve from daily hassles and stress and even lead to increases in wellbeing. It is therefore equally important to identify protective factors that can buffer against the negative effects of the lockdown.

Although nearly all people around the globe have been subject to some form of lockdown measures to contain the COVID-19 response, variations exist with respect to how each person is confined, even within a single country. For instance, during the COVID-19 pandemic some people were allowed to go to work, whereas others were required to work exclusively from home. For various reasons, some people had difficulty obtaining some basic supplies. Further, some were thrust into the situation of taking care of others (e.g., children, due to closing of schools). Finally, some people lost income as a result of the lockdown, and this is a known risk-factor for poor mental health [ 13 , 14 ]. Finally, a lockdown may be experienced differently the longer it continues and potentially when in confined spaces [ 2 ]. All of these lockdown-specific features may have an impact on one’s mental health, but to date it remains inadequately explored.

As the risk of the pandemic continues, it is important to understand to what degree the virus-induced uncertainty and the lockdown-induced changes in daily routines impact stress, depression, affect, and wellbeing. Towards this end, it is important to identify factors that can mitigate potential negative psychological effects of pandemics and lockdowns. Various social and psychological factors have been identified in other contexts that may also help build resilience in large-scale pandemics such as COVID-19. On the social level, one such candidate is social support, which has repeatedly been found to positively impact mental health and wellbeing [ 15 – 18 ]. Another social factor is the family climate and family functioning, which clearly impacts people’s mental health [ 19 , 20 ]. Psychological factors such as mindfulness and psychologically flexible response styles (as opposed to rigid and avoidant response styles) are behavioral repertoires that have previously been shown to buffer the impact of stress and facilitate wellbeing [ 21 – 24 ].

Given the scope of the COVID-19 pandemic, it is crucial to better understand how a pandemic and associated lockdowns impact on mental health. Thus, the aim of this study was to determine mental health outcomes and to examine known predictors of outcomes to identify psychological processes and contextual factors that can be used in developing public health interventions. It can be assumed, but remains untested, that those with risks in social-demographic factors, living conditions, social factors and psychological factors have more severe reactions to the lockdown. We therefore tested whether outcomes of stress, depression, affect, and wellbeing were predicted by country of residence, social demographic characteristics, COVID-19 lockdown related predictors, social predictors, and psychological predictors.

Participants

The inclusion criteria were ≥18 years of age and ability to read one of the 18 languages (English, Greek, German, French, Spanish, Turkish, Dutch, Latvian, Italian, Portuguese, Finnish, Slovenian, Polish, Romanian, Hong Kong, Hungarian, Montenegrin, & Persian.). There were no exclusion criteria. People from all countries were eligible to participate.

Ethics approval was obtained from the Cyprus National Bioethics Committee (ref.: EEBK EΠ 2020.01.60) followed by site approvals from different research teams involved in data collection. All participants provided written informed consent prior to completing the survey (computer-based, e.g., by clicking “yes”).

A population based cross-sectional study was conducted in order to explore how people across the world reacted to the COVID-19. The anonymous online survey was distributed using a range of methods. Universities emailed the online survey to students and academic staff and also posted the survey link to their websites. In addition, and in order to broaden the sample to older age groups and to those with different socio-demographic characteristics, the survey was disseminated in local press (e.g., newspapers, newsletters, radio stations), in social media (e.g., Facebook, Twitter, etc.), in professional networks, local hospitals and health centers and professional groups’ email lists (e.g., medical doctors, teachers, engineers, psychologists, government workers), and to social institutions in the countries (e.g., churches, schools, cities/townships, clubs, etc.).

Data were collected for two months between 07th April and 07th June 2020. The majority of countries where data were collected had declared a state of emergency for COVID-19 during this time.

Well validated and established measures were used to assess constructs. When measures did not already exist in a language, they were subject to forward and backward translation procedures. Well-validated measures of predictors and outcomes and items measuring COVID-19 related characteristics were selected after a consensus agreement among the members of this study.

Respondents’ countries were coded and entered as predictors.

Socio-demographic status.

Participants responded to questions related to their socio-demographic characteristics including their age, gender, country of residence, marital status, employment status, educational level, whether they have children as well as their living situation.

Lockdown variables.

Participants responded to questions related to lockdown including length of lockdown, whether they need to leave home for work, any change in their finances, whether they were able to obtain basic supplies, the amount of their living space confined in during the lockdown. They were also asked whether they, their partner, or a significant other was diagnosed with COVID-19.

Social factors.

Social factors were measured using the Brief Assessment of Family Functioning Scale (BAFFS; [ 25 ]) and the Oslo Social Support Scale (OSSS; [ 26 ]). The BAFFS items are summed to produce a single score with higher scores indicating worse family functioning. The OSSS items are summed up and provide three levels types of social support: low (scored 3–8), moderate (scored 9–11) and high (scored 12–14).

Psychological factors.

Psychological factors including mindfulness and psychological flexibility. Mindfulness was measured using the Cognitive Affective Mindfulness Scale (CAMS; [ 27 ]). The CAMS produces a single score with higher scores indicating better mindfulness qualities. Psychological flexibility (e.g., hold one’s thoughts lightly, be accepting of one’s experiences, engage in what is important to them despite challenging situations) was measured using the Psyflex scale [ 28 ]. The Psyflex produces a single score with higher scores indicating better psychological flexibility qualities.

Stress was measured using the Perceived Stress Scale (PSS; [ 29 ]). The PSS assesses an individual’s appraisal of how stressful situations in their life are. Items ask about people’s feelings and thoughts during the last month. A total score is produced, with higher scores indicating greater overall distress.

Depression.

Depressive symptomatology was assessed using two items from the disengagement subscale of the Multidimensional State Boredom Scale (MSBS; [ 30 ]). These items assessed wanting to do pleasurable things but not finding anything appealing (i.e., boredom), as well as wasting time. Based on concepts of reinforcement deprivation (i.e., lack of access to or engagement with positive stimuli) that is known to contribute to depression, we added an item that measured how rewarding or pleasurable people found the activities that they were engaging in (i.e., reinforcement). Higher scores indicated higher depressive symptomatology.

Positive affect/ negative affect.

The Positive And Negative Affect Scale (PANAS) was used to measure affect [ 31 ]. The original version of the questionnaire was used with five additional items: bored, confused, angry, frustrated and lonely. All items were scored on a 5-point Likert type scale, ranging from 1 = very little/not at all to 5 = extremely and summed up so that higher scores in the positive-related items indicating higher positive affect and higher scores in the negative-related items indicating higher negative affect. In order to capture additional dimensions of negative affect believed to be relevant to the COVID-19 lockdowns, we additionally added five items: bored, confused, angry, frustrated, lonely.

Wellbeing was assessed using the Mental Health Continuum Short Form (MHC-SF; [ 32 ]); which assesses three aspects of wellbeing: emotional, psychological, and social. The MHC-SF produces a total score and scores for each of the three aspects of wellbeing. The MHC-SF can also be scored to produce categories of languishing (i.e., low levels of emotional, psychological, and social well-being), flourishing (i.e., high levels of emotional psychological and social well-being almost every day), and moderately mentally healthy (in between languishing and flourishing).

Statistical analysis

The mean and standard deviation was calculated for dependent variables that follow the normal distribution while the median and interquartile range (IQR) were computed for non-normally distributed data. Bivariable association between an outcome variable and each predictor was investigated with ANOVA test for categorical predictor and univariable linear regression for numerical predictor. Linear mixed-effect model with random effect for country was performed to consider simultaneously several predictors in the same model and to account for the variation in outcome variable between countries. Four separate linear mixed-effect models were used for each outcome variable, one for each set of socio-demographic, lockdown, social and psychosocial predictors and multicollinearity for each set of predictors was investigated with the variation inflation criterion (VIF). Standardized regression coefficients were computed as effect size indices to measure the strength of the association between predictor variables and outcome variables. The comparison between the country mean and overall mean for each outcome variable was estimated though a linear regression model with dependent variable the mean centering outcome and predictor the country. Cohen’s d effect size of the standardize difference between country mean and the overall mean was computed as a measure of the magnitude of the difference between the two means.

The whole sample was used in linear mixed-effect models while for the comparison of country mean to the overall mean was used the sample from countries with sample size ≥100. The R packages lme4 and effect sizes were used for fitting the linear mixed effect model and to compute the standardized regression coefficients of the linear mixed effect models [ 33 ]. Significance test and confidence intervals were calculated at a significance level of 0.05. The following cut-off values were used for the evaluation of the effect sizes: ‘tiny’ ≤0.05, ‘very small’ from 0.05 to ≤0.10, ‘small’ from 0.10 to ≤ 0.20, ‘medium’ from 0.20 to ≤ 0.30, ‘large’ from 0.30 to ≤ 0.40 and ‘very large’ > 0.40 [ 34 ].

Descriptive

Participants were n = 9,565 people from 78 countries. See supporting information for a participation flowchart ( S1 Appendix ). The countries with the largest samples were: Latvia (n = 1285), Italy (n = 962), Cyprus (n = 957), Turkey (n = 702), Switzerland (n = 550), Hong Kong (n = 516), Colombia (n = 485), Ireland (n = 414), Austria (n = 368), Romania (n = 339), Portugal (n = 334), France (n = 313), Spain (n = 296), Germany (n = 279), Hungary (n = 273), Greece (n = 270), USA (n = 268), Finland (n = 157), Montenegro (n = 147), Poland (n = 135), United Kingdom (n = 100), Slovenia (n = 77), and Canada (n = 60). The remaining countries are listed in the supporting information ( S1 Table ).

Outcome variables

The means, standard deviations, and where appropriate percentage of participants within categories of the five outcome variables can be seen in Table 1 .

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https://doi.org/10.1371/journal.pone.0244809.t001

Predictor variables

A full list of countries can be found in the supporting information ( S1 Table ).

The mean age was 36.9 (13.3) years. A majority of participants were female (77.7%), approximately a fifth male (22.0%), and small minority identified as other (0.3%). More than half of the respondents were either in a relationship (25.7%) or married (36.1%), almost a third were single (30.8%), and the rest were either divorced (5%), widower (1.1%) or other (1.3%). Participants indicated that they lived: alone (14.6%), with both parents (20.8%), one parent (5.1%), with their own family including partner and children (54.1%), or with friends or roommates (5.5%). Less than half of respondents had children (40.8%). Approximately half of the participants were working full time (53.4%), almost a fifth were working part-time (17.5%), 23.2% were unemployed and a small minority were either on parental leave (2.2%) or retired (3.7%).

COVID-19 lockdown variables.

At the time of responding, participants were in lockdown or self-isolation for a median of 5.0 (3.0 IQR) weeks. Most people indicated that they had not been infected with COVID-19 (88.0%), a small minority indicated they had been infected (1.4%) and the rest had symptoms but were unsure (10.6%). Similar patterns were seen with reported infection rates of partners (no: 92.2%, yes: 0.7%, unsure: 7.1%) and of people close to them (no: 86.0%; yes: 5.6%; unsure: 8.4%). With respect to leaving the house for work, almost half (47.7%) indicated that this never occurred, 7.7% indicated leaving only once, whereas an almost equal number indicated leaving a couple times per week (23.7%) or more than three times per week (21.0%). Nearly all participants indicated they were able to obtain all the basic supplies they needed (93.5%). Participants reported having a median inner living space of 90.0 square meters (80.0 IQR) and median outdoor space of 20.0 square meters (192.1 IQR). Finally, with respect to finances, more than half indicated that their financial situation remained about the same (57.9%), a minority indicated it improved (8.9%), and a third reported that their finances had gotten worse (33.3%).

Social and psychological predictors.

Mean values of the other predictors (i.e., social predictors and psychological predictors) can be seen in Table 1 .

Multivariate analyses

Results of multivariate analyses for the outcome of stress can be seen in Table 2 . The largest protective factor against stress was social support (high support vs low support (-3.35, 95%CI, -3.39 to -2.92), with a very large effect size). Positive predictors of stress with large effect sizes were being female (2.42, 95%CI, 2.07 to 2.77) and worsening of finances (2.32, 95%CI, 1.68 to 2.96), whereas psychological flexibility buffered this response (-0.65, 95%CI, -0.69 to -0.62). Higher education levels were also associated with lower levels of stress, with a large effect size (see Table 2 ). Moderate effect sizes for predictors associated with less stress were older age (-0.13, 95%CI, -0.14, -0.11) and mindfulness (-0.69, 95%CI, -0.74, -0.64). Moderate effect sizes of predictors associated with more stress were worse family functioning (0.98, 95%CI, 0.90, 1.06) and not being able to obtain all basic supplies (1.82 95%CI, 1.12, 2.52).

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https://doi.org/10.1371/journal.pone.0244809.t002

Differences in reported levels of stress across countries were largely negligible, with the exception of two countries that reported higher levels of stress (Hong Kong (2.85, 95%CI, 2.22, 3.49) and Turkey (2.47, 95%CI, 1.93, 3.02)) and two that reported lower levels of stress (Portugal (-2.50, 95%CI, -3.29, -1.71) and Montenegro (-3.30, 95%CI, -4.49, -2.11)) than the average stress level across all countries. See supporting information for information on each country ( S2 – S6 Tables).

Results of multivariate analyses for the outcome of depression can be seen in Table 3 . The strongest predictor of depression was social support, such that high (-1.30, 95%CI, -1.44, -1.16) and medium levels (-0.73, 95%CI, -0.85, -0.62) of social support were protective against depression (relative to low levels) with a very large and large effect sizes, respectively. The only other large effect size was for psychological flexibility, which also served in a protective manner (-0.20, 95%CI, -0.22, -0.19). Moderate effect sizes of predictors associated with less depression symptoms were also observed for higher education levels (see Table 3 ). Moderate effect sizes of predictors associated with more depression were worse family functioning (0.29, 95%CI, 0.27, 0.32) and not being able to obtain all basic supplies (0.49, 95%CI, 0.27, 0.70).

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The amount of depression symptoms reported on average within countries was similar for most countries with the exception of one country with lower reported levels than average with a large effect size (Austria (-0.71, 95%CI, -0.95, -0.47)) and one with higher levels than average with a large effect size (USA (0.85, 95%CI, 0.58, 1.13)). See supporting information for information on each country ( S2 – S6 Tables).

Results of multivariate analyses for the outcome of affect can be seen in Table 4 . With respect to positive affect, social support (high support vs low support (5.69, 95%CI, 5.23, 6.16) and psychological flexibility (0.77, 95%CI, 0.74, 0.81) were both predictors with very large effect sizes. Interestingly, those who left their house more than three times per week had higher levels of positive affect than those that did not leave their house for work (1.68, 95%CI, 1.18, 2.17), with a medium effect size. Higher education levels were associated with higher levels of positive affect with a medium to large effect size (see Table 4 , PANAS-Positive).

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https://doi.org/10.1371/journal.pone.0244809.t004

The amount of positive affect reported on average within countries was similar for most countries with the exception of one country with lower reported levels than average with a large effect size (Finland (-2.96, 95%CI, -4.19, -1.73)) and one with higher reported levels than average with a large effect size (Portugal (2.96, 95%CI, 2.12, 3.80)). See supporting information for information on each country ( S2 – S6 Tables).

With respect to negative affect, social support (high support vs low support (-2.74, 95%CI, -3.2, -2.29) and psychological flexibility (-0.62, 95%CI, -0.66, -0.58) were again the strongest associated predictors, with large effects. Higher education levels were also associated with lower levels of negative affect, with a medium effect (see Table 4 , PANAS-Negative). Higher levels of negative affect were noted, with medium effect sizes, for the predictors: worsening of finances (1.75, 95%CI, 1.10, 2.40) and not being able to obtain all basic supplies (1.6, 95%CI, 0.89, 2.31).

The amount of negative affect reported on average within countries was similar for most countries with the exception of few countries with lower reported negative affect levels than average with a very large effect sizes (Switzerland (-4.96, 95%CI, -5.91, -4.01), Germany (-4.70, 95%CI, -6.03, -3.37) & Austria (-6.49, 95%CI, -7.65, -5.33)) and one with a large effect size (Montenegro (-3.56, 95%CI, -5.39, -1.73). The average amount of negative affect was higher than average in two countries, with very large effects size (Turkey (5.75, 95%CI, 4.92, 6.59) & Finland (7.57, 95%CI, 5.80, 9.34)). See supporting information for information on each country ( S2 – S6 Tables).

Results of multivariate analyses for the outcome of wellbeing can be seen in Table 5 . Once again, social support (high support vs low support (13.20, 95%CI, 12.39, 14.01)) and psychological flexibility (1.42, 95%CI, 1.34, 1.49) were the predictors with the largest effect sizes (very large) on wellbeing. Higher education levels were associated with higher levels of wellbeing with a medium to large effect sizes (see Table 5 ). Medium negative effect sizes were noted for family functioning (-1.98, 95%CI, -2.12, -1.83) and inability to obtain all basic supplies (-3.27, 95%CI, -4.67, -1.87). Two medium positive effect sizes were observed: mindfulness (0.95, 95%CI, 0.86–1.04) and living with friends/roommates ((3.04, 95%CI, 1.59, 4.48), relative to living alone).

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The level of wellbeing reported on average within countries was similar for most countries with the exception of three countries with higher levels with large effect sizes (Austria (4.95, 95%CI, 3.55, 6.34), Finland (5.24, 95%CI, 3.10, 7.38), & Portugal (4.59, 95%CI, 3.12, 6.05)) and two countries with lower levels of wellbeing than average with large (Italy (-4.36, 95%CI, -11.06, 2.35)) and very large effect sizes (Hong Kong (-6.84, 95%CI, -8.02, -5.66)). See supporting information for information on each country ( S2 – S6 Tables).

The COVID-19 is the largest pandemic in modern history. This study assessed nearly 10,000 participants across many countries to examine the impact of the pandemic and resultant governmental lockdown measures on mental health. During the height of the lockdown, the pandemic was experienced as at least moderately stressful for most people, and 11% reported the highest levels of stress. Symptoms of depression were also high, including 25% of the sample indicating that the things they did were not reinforcing, 33% reporting high levels of boredom, and nearly 50% indicating they wasted a lot of time. Consistent with symptoms of stress and depression, 10% of participants were psychologically languishing. These results suggest that there is a subgroup of people who are especially suffering and that in about 50% of the respondents’ levels of mental health was only moderate. Previous studies have found that along with low levels, even moderate levels of mental health (which consists of only moderate levels of emotional, psychological, and social well-being) are associated with increased subsequent disability, productivity loss, and healthcare use [ 35 – 37 ]. Not everyone was suffering, however, as evidenced by the nearly 40% of participants who reported levels of mental health consistent with flourishing. The present results, while serious, do not point to more severe reactions observed in previous samples of selective quarantined individuals or groups [ 2 ]. Perhaps the previously reported distress in these groups is prevented when an entire country or world is in lockdown so that the feeling emerges that “everyone is in it together”.

Importantly, a handful of predictors emerged that consistently predicted all outcomes: Social support, education level, finances, access to basic needs, and the ability to respond psychologically flexible. The consistency of results examining predictors is noteworthy, both in terms of the consistently strong predictors (e.g., social support, education, psychological flexibly, as well as loss of income and lack of access to necessities) and in terms of the other predictors that were either not predictive or only weakly so. All predictors were chosen based on theoretical ties to the outcomes, previous findings, and studies on quarantines [ 2 ].

A novel finding was that people who left their house three or more times per week reported more positive affect than those that left their house less often. It is possible that these people experienced more variation, which contributed to positive affect. It is also possible they experienced a greater sense of normality. Future studies are encouraged to further investigate possible mechanisms through which this result unfolds.

Overall, these patterns did not differ substantially between countries. Although some differences did emerge, they were mostly inconsistent across outcomes. Three countries fared worse on two outcomes each: Hong Kong (stress & wellbeing); Turkey (stress & negative affect); and Finland (lower positive affect and higher negative affect)–though participants in Finland also reported higher levels of wellbeing than average. Two countries had more favorable outcomes than the average levels across all countries: Portugal (lower stress and higher wellbeing) and Austria (lower depression and higher wellbeing). The differences observed are likely due to a combination of chance, sampling, nation specific responses to the COVID-19 pandemic, cultural differences, and other factors playing out in the countries (e.g., political unrest [ 38 ]). If replicated, future studies are encouraged to examine possible mechanisms of these outcomes.

This study provides valuable insights on several levels. First, it documents the mental health outcomes across a broad sample during the COVID-19 global pandemic. Second, it informs about the conditions and resilience factors (social support, education, and psychological flexibility) and risk factors (loss of income and inability to get basic supplies) that affect mental health outcomes. Third, these factors can be used in future public health responses are being made, including those that require large scale lockdowns or quarantines. That is, public health officials should direct resources to identifying and supporting people with poor social support, income loss, and potentially lower levels of education and provide a strategy to mitigate special risks in these subpopulations. The importance of social support needs to be made clear to the public and to the degree possible mechanisms that can contribute to social support should be supported. Further, psychological flexibility is a trainable set of skills that has repeatedly been shown to ameliorate suffering [ 22 , 39 ]; and can be widely distributed with modern technological intervention tools such as digital, internet, or virtual means [ 40 ]. We do not claim, however, that psychological flexibility is the only factor that can be used for interventions. Instead, it is a recognized transdiagnostic factor assessed in this study and one that is feasible to be targeted and modified by interventions and prevention [ 41 – 43 ].

This study is limited by several important factors. First, the results are based on cross sectional analysis and correlations. As such, causation cannot be inferred and any delayed impact of the pandemic and lockdown on peoples’ mental health was not captured. Second, all results of this survey were obtained via self-report questionnaires, which can be subject to retrospective response bias. Third, although the sample was large and based on varied recruitment sources, it was not representative of the population and undersampled people who suffered most from the pandemic (i.e., front line health care professionals, people in intensive care, etc.) or people without internet access, etc. Finally, the country-specific incidence rates and lockdown measures differed across countries. These were not assessed, but future studies are encouraged to investigate how such factors impact mental health outcomes.

These limitations notwithstanding, based on nearly 10,000 international participants, this study found that approximately 10% of the population was languishing during or shortly after the lockdown period. These finding have implications for public health initiatives. First, officials are urged to attend to, find, and target people who have little social support and/ or whose finances have worsened as a result of the measures. Second, public health interventions are further urged to target psychological processes such as psychological flexibility in general to potentially help buffer other risk factors for mental health. Likewise, availability of social support and information about where to get support and remain connected are needed. These recommendations should become part of public health initiatives designed to promote mental health in general, and should equally be considered when lockdowns or physical distancing are prescribed during a pandemic.

Supporting information

S1 table. list of all countries included in the data set..

https://doi.org/10.1371/journal.pone.0244809.s001

S2 Table. Geodemographic predictors for Perceived Stress Scale.

https://doi.org/10.1371/journal.pone.0244809.s002

S3 Table. Geodemographic predictors for MSBS–depression.

https://doi.org/10.1371/journal.pone.0244809.s003

S4 Table. Geodemographic predictors for PANAS positive.

https://doi.org/10.1371/journal.pone.0244809.s004

S5 Table. Geodemographic predictors for PANAS negative.

https://doi.org/10.1371/journal.pone.0244809.s005

S6 Table. Geodemographic predictors for MHCSF—mental health continuum.

https://doi.org/10.1371/journal.pone.0244809.s006

S1 Appendix. Participation flowchart.

https://doi.org/10.1371/journal.pone.0244809.s007

Acknowledgments

We wish to thank the following people for their work in helping to implement the study: Spyros Demosthenous, Christiana Karashali, Diamanto Rovania (University of Cyprus); Maria Antoniade (European University of Cyprus); Ioanna Menoikou (Cyprus University of Technology); Elias Ioannou (University of Nicosia); Sonja Borner, Victoria Firsching-Block, Alexander Fenn (University of Basel); Cristīne Šneidere, Ingrīda Trups-Kalne, Lolita Vansovica, Sandra Feldmane, (Riga Stradiņš University); David Nilsson (Lund University); Miguel A. Segura-Vargas (Fundación Universitaria Konrad Lorenz); Claudia Lenuţa Rus, Catalina Otoiu, Cristina Vajaean (Babes-Bolyai University). We further wish to thank Fabio Coviello and Sonja Borner (University of Basel) for their help in preparing the manuscript.

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  • Open access
  • Published: 11 April 2023

Effects of the COVID-19 pandemic on mental health, anxiety, and depression

  • Ida Kupcova 1 ,
  • Lubos Danisovic 1 ,
  • Martin Klein 2 &
  • Stefan Harsanyi 1  

BMC Psychology volume  11 , Article number:  108 ( 2023 ) Cite this article

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The COVID-19 pandemic affected everyone around the globe. Depending on the country, there have been different restrictive epidemiologic measures and also different long-term repercussions. Morbidity and mortality of COVID-19 affected the mental state of every human being. However, social separation and isolation due to the restrictive measures considerably increased this impact. According to the World Health Organization (WHO), anxiety and depression prevalence increased by 25% globally. In this study, we aimed to examine the lasting effects of the COVID-19 pandemic on the general population.

A cross-sectional study using an anonymous online-based 45-question online survey was conducted at Comenius University in Bratislava. The questionnaire comprised five general questions and two assessment tools the Zung Self-Rating Anxiety Scale (SAS) and the Zung Self-Rating Depression Scale (SDS). The results of the Self-Rating Scales were statistically examined in association with sex, age, and level of education.

A total of 205 anonymous subjects participated in this study, and no responses were excluded. In the study group, 78 (38.05%) participants were male, and 127 (61.69%) were female. A higher tendency to anxiety was exhibited by female participants (p = 0.012) and the age group under 30 years of age (p = 0.042). The level of education has been identified as a significant factor for changes in mental state, as participants with higher levels of education tended to be in a worse mental state (p = 0.006).

Conclusions

Summarizing two years of the COVID-19 pandemic, the mental state of people with higher levels of education tended to feel worse, while females and younger adults felt more anxiety.

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Introduction

The first mention of the novel coronavirus came in 2019, when this variant was discovered in the city of Wuhan, China, and became the first ever documented coronavirus pandemic [ 1 , 2 , 3 ]. At this time there was only a sliver of fear rising all over the globe. However, in March 2020, after the declaration of a global pandemic by the World Health Organization (WHO), the situation changed dramatically [ 4 ]. Answering this, yet an unknown threat thrust many countries into a psycho-socio-economic whirlwind [ 5 , 6 ]. Various measures taken by governments to control the spread of the virus presented the worldwide population with a series of new challenges to which it had to adjust [ 7 , 8 ]. Lockdowns, closed schools, losing employment or businesses, and rising deaths not only in nursing homes came to be a new reality [ 9 , 10 , 11 ]. Lack of scientific information on the novel coronavirus and its effects on the human body, its fast spread, the absence of effective causal treatment, and the restrictions which harmed people´s social life, financial situation and other areas of everyday life lead to long-term living conditions with increased stress levels and low predictability over which people had little control [ 12 ].

Risks of changes in the mental state of the population came mainly from external risk factors, including prolonged lockdowns, social isolation, inadequate or misinterpreted information, loss of income, and acute relationship with the rising death toll. According to the World Health Organization (WHO), since the outbreak of the COVID-19 pandemic, anxiety and depression prevalence increased by 25% globally [ 13 ]. Unemployment specifically has been proven to be also a predictor of suicidal behavior [ 14 , 15 , 16 , 17 , 18 ]. These risk factors then interact with individual psychological factors leading to psychopathologies such as threat appraisal, attentional bias to threat stimuli over neutral stimuli, avoidance, fear learning, impaired safety learning, impaired fear extinction due to habituation, intolerance of uncertainty, and psychological inflexibility. The threat responses are mediated by the limbic system and insula and mitigated by the pre-frontal cortex, which has also been reported in neuroimaging studies, with reduced insula thickness corresponding to more severe anxiety and amygdala volume correlated to anhedonia as a symptom of depression [ 19 , 20 , 21 , 22 , 23 ]. Speaking in psychological terms, the pandemic disturbed our core belief, that we are safe in our communities, cities, countries, or even the world. The lost sense of agency and confidence regarding our future diminished the sense of worth, identity, and meaningfulness of our lives and eroded security-enhancing relationships [ 24 ].

Slovakia introduced harsh public health measures in the first wave of the pandemic, but relaxed these measures during the summer, accompanied by a failure to develop effective find, test, trace, isolate and support systems. Due to this, the country experienced a steep growth in new COVID-19 cases in September 2020, which lead to the erosion of public´s trust in the government´s management of the situation [ 25 ]. As a means to control the second wave of the pandemic, the Slovak government decided to perform nationwide antigen testing over two weekends in November 2020, which was internationally perceived as a very controversial step, moreover, it failed to prevent further lockdowns [ 26 ]. In addition, there was a sharp rise in the unemployment rate since 2020, which continued until July 2020, when it gradually eased [ 27 ]. Pre-pandemic, every 9th citizen of Slovakia suffered from a mental health disorder, according to National Statistics Office in 2017, the majority being affective and anxiety disorders. A group of authors created a web questionnaire aimed at psychiatrists, psychologists, and their patients after the first wave of the COVID-19 pandemic in Slovakia. The results showed that 86.6% of respondents perceived the pathological effect of the pandemic on their mental status, 54.1% of whom were already treated for affective or anxiety disorders [ 28 ].

In this study, we aimed to examine the lasting effects of the COVID-19 pandemic on the general population. This study aimed to assess the symptoms of anxiety and depression in the general public of Slovakia. After the end of epidemiologic restrictive measures (from March to May 2022), we introduced an anonymous online questionnaire using adapted versions of Zung Self-Rating Anxiety Scale (SAS) and Zung Self-Rating Depression Scale (SDS) [ 29 , 30 ]. We focused on the general public because only a portion of people who experience psychological distress seek professional help. We sought to establish, whether during the pandemic the population showed a tendency to adapt to the situation or whether the anxiety and depression symptoms tended to be present even after months of better epidemiologic situation, vaccine availability, and studies putting its effects under review [ 31 , 32 , 33 , 34 ].

Materials and Methods

This study utilized a voluntary and anonymous online self-administered questionnaire, where the collected data cannot be linked to a specific respondent. This study did not process any personal data. The questionnaire consisted of 45 questions. The first three were open-ended questions about participants’ sex, age (date of birth was not recorded), and education. Followed by 2 questions aimed at mental health and changes in the will to live. Further 20 and 20 questions consisted of the Zung SAS and Zung SDS, respectively. Every question in SAS and SDS is scored from 1 to 4 points on a Likert-style scale. The scoring system is introduced in Fig.  1 . Questions were presented in the Slovak language, with emphasis on maintaining test integrity, so, if possible, literal translations were made from English to Slovak. The questionnaire was created and designed in Google Forms®. Data collection was carried out from March 2022 to May 2022. The study was aimed at the general population of Slovakia in times of difficult epidemiologic and social situations due to the high prevalence and incidence of COVID-19 cases during lockdowns and social distancing measures. Because of the character of this web-based study, the optimal distribution of respondents could not be achieved.

figure 1

Categories of Zung SAS and SDS scores with clinical interpretation

During the course of this study, 205 respondents answered the anonymous questionnaire in full and were included in the study. All respondents were over 18 years of age. The data was later exported from Google Forms® as an Excel spreadsheet. Coding and analysis were carried out using IBM SPSS Statistics version 26 (IBM SPSS Statistics for Windows, Version 26.0, Armonk, NY, USA). Subject groups were created based on sex, age, and education level. First, sex due to differences in emotional expression. Second, age was a risk factor due to perceived stress and fear of the disease. Last, education due to different approaches to information. In these groups four factors were studied: (1) changes in mental state; (2) affected will to live, or frequent thoughts about death; (3) result of SAS; (4) result of SDS. For SAS, no subject in the study group scored anxiety levels of “severe” or “extreme”. Similarly for SDS, no subject depression levels reached “moderate” or “severe”. Pearson’s chi-squared test(χ2) was used to analyze the association between the subject groups and studied factors. The results were considered significant if the p-value was less than 0.05.

Ethical permission was obtained from the local ethics committee (Reference number: ULBGaKG-02/2022). This study was performed in line with the principles of the Declaration of Helsinki. All methods were carried out following the institutional guidelines. Due to the anonymous design of the study and by the institutional requirements, written informed consent for participation was not required for this study.

In the study, out of 205 subjects in the study group, 127 (62%) were female and 78 (38%) were male. The average age in the study group was 35.78 years of age (range 19–71 years), with a median of 34 years. In the age group under 30 years of age were 34 (16.6%) subjects, while 162 (79%) were in the range from 31 to 49 and 9 (0.4%) were over 50 years old. 48 (23.4%) participants achieved an education level of lower or higher secondary and 157 (76.6%) finished university or higher. All answers of study participants were included in the study, nothing was excluded.

In Tables  1 and 2 , we can see the distribution of changes in mental state and will to live as stated in the questionnaire. In Table  1 we can see a disproportion in education level and mental state, where participants with higher education tended to feel worse much more than those with lower levels of education. Changes based on sex and age did not show any statistically significant results.

In Table  2 . we can see, that decreased will to live and frequent thoughts about death were only marginally present in the study group, which suggests that coping mechanisms play a huge role in adaptation to such events (e.g. the global pandemic). There is also a possibility that living in times of better epidemiologic situations makes people more likely to forget about the bad past.

Anxiety and depression levels as seen in Tables  3 and 4 were different, where female participants and the age group under 30 years of age tended to feel more anxiety than other groups. No significant changes in depression levels based on sex, age, and education were found.

Compared to the estimated global prevalence of depression in 2017 (3.44%), in 2021 it was approximately 7 times higher (25%) [ 14 ]. Our study did not prove an increase in depression, while anxiety levels and changes in the mental state did prove elevated. No significant changes in depression levels go in hand with the unaffected will to live and infrequent thoughts about death, which were important findings, that did not supplement our primary hypothesis that the fear of death caused by COVID-19 or accompanying infections would enhance personal distress and depression, leading to decreases in studied factors. These results are drawn from our limited sample size and uneven demographic distribution. Suicide ideations rose from 5% pre-pandemic to 10.81% during the pandemic [ 35 ]. In our study, 9.3% of participants experienced thoughts about death and since we did not specifically ask if they thought about suicide, our results only partially correlate with suicidal ideations. However, as these subjects exhibited only moderate levels of anxiety and mild levels of depression, the rise of suicide ideations seems unlikely. The rise in suicidal ideations seemed to be especially true for the general population with no pre-existing psychiatric conditions in the first months of the pandemic [ 36 ]. The policies implemented by countries to contain the pandemic also took a toll on the population´s mental health, as it was reported, that more stringent policies, mainly the social distancing and perceived government´s handling of the pandemic, were related to worse psychological outcomes [ 37 ]. The effects of lockdowns are far-fetched and the increases in mental health challenges, well-being, and quality of life will require a long time to be understood, as Onyeaka et al. conclude [ 10 ]. These effects are not unforeseen, as the global population suffered from life-altering changes in the structure and accessibility of education or healthcare, fluctuations in prices and food insecurity, as well as the inevitable depression of the global economy [ 38 ].

The loneliness associated with enforced social distancing leads to an increase in depression, anxiety, and posttraumatic stress in children in adolescents, with possible long-term sequelae [ 39 ]. The increase in adolescent self-injury was 27.6% during the pandemic [ 40 ]. Similar findings were described in the middle-aged and elderly population, in which both depression and anxiety prevalence rose at the beginning of the pandemic, during the pandemic, with depression persisting later in the pandemic, while the anxiety-related disorders tended to subside [ 41 ]. Medical professionals represented another specific at-risk group, with reported anxiety and depression rates of 24.94% and 24.83% respectively [ 42 ]. The dynamic of psychopathology related to the COVID-19 pandemic is not clear, with studies reporting a return to normal later in 2020, while others describe increased distress later in the pandemic [ 20 , 43 ].

Concerning the general population, authors from Spain reported that lockdowns and COVID-19 were associated with depression and anxiety [ 44 ]. In January 2022 Zhao et al., reported an elevation in hoarding behavior due to fear of COVID-19, while this process was moderated by education and income levels, however, less in the general population if compared to students [ 45 ]. Higher education levels and better access to information could improve persons’ fear of the unknown, however, this fact was not consistent with our expectations in this study, as participants with university education tended to feel worse than participants with lower education. A study on adolescents and their perceived stress in the Czech Republic concluded that girls are more affected by lockdowns. The strongest predictor was loneliness, while having someone to talk to, scored the lowest [ 46 ]. Garbóczy et al. reported elevated perceived stress levels and health anxiety in 1289 Hungarian and international students, also affected by disengagement from home and inadequate coping strategies [ 47 ]. Wathelet et al. conducted a study on French University students confined during the pandemic with alarming results of a high prevalence of mental health issues in the study group [ 48 ]. Our study indicated similar results, as participants in the age group under 30 years of age tended to feel more anxious than others.

In conclusion, we can say that this pandemic changed the lives of many. Many of us, our family members, friends, and colleagues, experienced life-altering events and complicated situations unseen for decades. Our decisions and actions fueled the progress in medicine, while they also continue to impact society on all levels. The long-term effects on adolescents are yet to be seen, while effects of pain, fear, and isolation on the general population are already presenting themselves.

The limitations of this study were numerous and as this was a web-based study, the optimal distribution of respondents could not be achieved, due to the snowball sampling strategy. The main limitation was the small sample size and uneven demographic distribution of respondents, which could impact the representativeness of the studied population and increase the margin of error. Similarly, the limited number of older participants could significantly impact the reported results, as age was an important risk factor and thus an important stressor. The questionnaire omitted the presence of COVID-19-unrelated life-changing events or stressors, and also did not account for any preexisting condition or risk factor that may have affected the outcome of the used assessment scales.

Data Availability

The datasets generated and analyzed during the current study are not publicly available due to compliance with institutional guidelines but they are available from the corresponding author (SH) on a reasonable request.

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We would like to provide our appreciation and thanks to all the respondents in this study.

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Ida Kupcova, Lubos Danisovic & Stefan Harsanyi

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IK and SH have produced the study design. All authors contributed to the manuscript writing, revising, and editing. LD and MK have done data management and extraction, SH did the data analysis. Drafting and interpretation of the manuscript were made by all authors. All authors read and approved the final manuscript.

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Ethical permission was obtained from the Ethics Committee of the Institute of Medical Biology, Genetics and Clinical Genetics, Faculty of Medicine, Comenius University in Bratislava (Reference number: ULBGaKG-02/2022). The need for informed consent was waived by the Ethics Committee of the Institute of Medical Biology, Genetics and Clinical Genetics, Faculty of Medicine, Comenius University in Bratislava due to the anonymous design of the study. This study did not process any personal data and the dataset does not contain any direct or indirect identifiers of participants. This study was performed in line with the principles of the Declaration of Helsinki. All methods were carried out following the institutional guidelines.

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Kupcova, I., Danisovic, L., Klein, M. et al. Effects of the COVID-19 pandemic on mental health, anxiety, and depression. BMC Psychol 11 , 108 (2023). https://doi.org/10.1186/s40359-023-01130-5

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Introduction

Members of the COVID-19 Mental Health Measurement Working Group are working to ensure that measurement of mental health measures is a key part of large-scale national and international data collections relative to COVID-19 and then conducting research studies on mental health during the pandemic. 

Read more about our current measurements. Datasets that we have access to or experience with, or are collaborating with: 

  • About the survey
  • Obtain data access
  • Media reports
  • Contact:  Elizabeth Stuart

About the survey  

Request access to data for research  

Contact:  Calliope Holingue  and Kira Riehm 

About the survey: An interdisciplinary collaboration between faculty at Bloomberg School of Public Health and  University of Texas Tyler Department of Political Science  surveyd a statewide random sample of Texas residents (N=1197), with follow-ups planned. Measures include items on beliefs about COVID-19, preventive behaviors and social distancing, mental distress, employment changes due to the COVID-19 pandemic, substance use, partner violence, and gun ownership. 

Data available upon request

Contact:  Renee M. Johnson

Check back soon for more information. 

Find more information about the datasets, measurements, and other information here.

Research Publications

Kira E Riehm, MSc, Calliope Holingue, PhD, Emily J Smail, BSc, Arie Kapteyn, PhD, Daniel Bennett, PhD, Johannes Thrul, PhD, Frauke Kreuter, PhD, Emma E McGinty, PhD, Luther G Kalb, PhD, Cindy B Veldhuis, PhD, Renee M Johnson, PhD, M Daniele Fallin, PhD, Elizabeth A Stuart, PhD, Trajectories of Mental Distress Among U.S. Adults During the COVID-19 Pandemic,  Annals of Behavioral Medicine , 2021;, kaaa126,  https://doi.org/10.1093/abm/kaaa126

Riehm, K.E., Brenneke, S.G., Adams, L.B., Gilan, D., Lieb, K., Kunzler, A.M., Smail, E.J., Holingue, C., Stuart, E.A., Kalb, L.G. and Thrul, J., 2021. Association between psychological resilience and changes in mental distress during the COVID-19 pandemic.  Journal of Affective Disorders ,  282 , pp.381-385.

Holingue, C., Kalb, L.G., Riehm, K.E., Bennett, D., Kapteyn, A., Veldhuis, C.B., Johnson, R.M., Fallin, M.D., Kreuter, R., Stuart, E.A., and Thrul, J. (2020)  Mental Distress in the United States at the beginning of the COVID-19 Pandemic.     American Journal of Public Health. DOI  https://ajph.aphapublications.org/doi/10.2105/AJPH.2020.305857

 Liu, Y., Finch, B.K., Brenneke, S.G., Thomas, K., and  Le, PT.D. (2020)  Perceived Discrimination and Mental Distress Amid the COVID-19 Pandemic: Evidence From the Understanding America Study . 2020 Oct;59(4):481-492. doi: 10.1016/j.amepre.2020.06.007. Epub 2020 Jul 6.

Holingue, C., Badillo-Goicoechea, E., **Riehm, K.E., Veldhuis, C., Thrul, J., Johnson, R.M., Fallin, D.M., Kreuter, F.,  Stuart, E.A.,  and Kalb, L.G. (2020). Mental Distress during the COVID-19 Pandemic among US Adults without a Pre-existing Mental Health Condition: Findings from American Trend Panel Survey. Forthcoming in  Preventive Medicine.  Available online 3 August 2020.  https://doi.org/10.1016/j.ypmed.2020.106231 .

Riehm, K.E. ., Holingue, C., Kalb, L.G., Bennett, D., Kapteyn, A., Jiang, Q., Veldhuis,C., Johnson, R.M., Fallin, M.D., Kreuter, F., Stuart, E.A., and Thrul, J. (2020).  Associations Between Media Exposure and Mental Distress Among U.S. Adults at the Beginning of the COVID-19 Pandemic  American Journal of Preventive Medicine. DOI:https://doi.org/10.1016/j.amepre.2020.06.008.

Kreuter, F., Barkay, N., Bilinski, A., Bradford, A., Chiu, S., Eliat, R., Fan, J., Galili, T., Haimovich, D., Kim, B., LaRocca, S., Li, Y., Morris, K., Presser, S., Sarig, T., Salomon, J.A., Stewart, K.,  Stuart, E.A.,  and Tibshirani, R. (2020).  Partnering with a global platform to inform research and public policy making. Survey Research Methods : Journal of the European Survey Research Association  14(2) : Survey Research Methods during the COVID-19 crisis : 159-163.

McGinty, E.E ., Presskreischer, R., Han, H., and Barry, C.L. (2020).  Psychological distress and loneliness reported by US adults in 2018 and April 2020 . Journal of the American Medical Association. Published online June 3, 2020.

Read our research on: Immigration & Migration | Podcasts | Election 2024

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Mental health and the pandemic: what u.s. surveys have found.

research paper on covid 19 and mental health

The coronavirus pandemic has been associated with worsening mental health among people in the United States and around the world . In the U.S, the COVID-19 outbreak in early 2020 caused widespread lockdowns and disruptions in daily life while triggering a short but severe economic recession that resulted in widespread unemployment. Three years later, Americans have largely returned to normal activities, but challenges with mental health remain.

Here’s a look at what surveys by Pew Research Center and other organizations have found about Americans’ mental health during the pandemic. These findings reflect a snapshot in time, and it’s possible that attitudes and experiences may have changed since these surveys were fielded. It’s also important to note that concerns about mental health were common in the U.S. long before the arrival of COVID-19 .

Three years into the COVID-19 outbreak in the United States , Pew Research Center published this collection of survey findings about Americans’ challenges with mental health during the pandemic. All findings are previously published. Methodological information about each survey cited here, including the sample sizes and field dates, can be found by following the links in the text.

The research behind the first item in this analysis, examining Americans’ experiences with psychological distress, benefited from the advice and counsel of the COVID-19 and mental health measurement group at Johns Hopkins Bloomberg School of Public Health.

At least four-in-ten U.S. adults (41%) have experienced high levels of psychological distress at some point during the pandemic, according to four Pew Research Center surveys conducted between March 2020 and September 2022.

A bar chart showing that young adults are especially likely to have experienced high psychological distress since March 2020

Young adults are especially likely to have faced high levels of psychological distress since the COVID-19 outbreak began: 58% of Americans ages 18 to 29 fall into this category, based on their answers in at least one of these four surveys.

Women are much more likely than men to have experienced high psychological distress (48% vs. 32%), as are people in lower-income households (53%) when compared with those in middle-income (38%) or upper-income (30%) households.

In addition, roughly two-thirds (66%) of adults who have a disability or health condition that prevents them from participating fully in work, school, housework or other activities have experienced a high level of distress during the pandemic.

The Center measured Americans’ psychological distress by asking them a series of five questions on subjects including loneliness, anxiety and trouble sleeping in the past week. The questions are not a clinical measure, nor a diagnostic tool. Instead, they describe people’s emotional experiences during the week before being surveyed.

While these questions did not ask specifically about the pandemic, a sixth question did, inquiring whether respondents had “had physical reactions, such as sweating, trouble breathing, nausea, or a pounding heart” when thinking about their experience with the coronavirus outbreak. In September 2022, the most recent time this question was asked, 14% of Americans said they’d experienced this at least some or a little of the time in the past seven days.

More than a third of high school students have reported mental health challenges during the pandemic. In a survey conducted by the Centers for Disease Control and Prevention from January to June 2021, 37% of students at public and private high schools said their mental health was not good most or all of the time during the pandemic. That included roughly half of girls (49%) and about a quarter of boys (24%).

In the same survey, an even larger share of high school students (44%) said that at some point during the previous 12 months, they had felt sad or hopeless almost every day for two or more weeks in a row – to the point where they had stopped doing some usual activities. Roughly six-in-ten high school girls (57%) said this, as did 31% of boys.

A bar chart showing that Among U.S. high schoolers in 2021, girls and LGB students were most likely to report feeling sad or hopeless in the past year

On both questions, high school students who identify as lesbian, gay, bisexual, other or questioning were far more likely than heterosexual students to report negative experiences related to their mental health.

A bar chart showing that Mental health tops the list of parental concerns, including kids being bullied, kidnapped or abducted, attacked and more

Mental health tops the list of worries that U.S. parents express about their kids’ well-being, according to a fall 2022 Pew Research Center survey of parents with children younger than 18. In that survey, four-in-ten U.S. parents said they’re extremely or very worried about their children struggling with anxiety or depression. That was greater than the share of parents who expressed high levels of concern over seven other dangers asked about.

While the fall 2022 survey was fielded amid the coronavirus outbreak, it did not ask about parental worries in the specific context of the pandemic. It’s also important to note that parental concerns about their kids struggling with anxiety and depression were common long before the pandemic, too . (Due to changes in question wording, the results from the fall 2022 survey of parents are not directly comparable with those from an earlier Center survey of parents, conducted in 2015.)

Among parents of teenagers, roughly three-in-ten (28%) are extremely or very worried that their teen’s use of social media could lead to problems with anxiety or depression, according to a spring 2022 survey of parents with children ages 13 to 17 . Parents of teen girls were more likely than parents of teen boys to be extremely or very worried on this front (32% vs. 24%). And Hispanic parents (37%) were more likely than those who are Black or White (26% each) to express a great deal of concern about this. (There were not enough Asian American parents in the sample to analyze separately. This survey also did not ask about parental concerns specifically in the context of the pandemic.)

A bar chart showing that on balance, K-12 parents say the first year of COVID had a negative impact on their kids’ education, emotional well-being

Looking back, many K-12 parents say the first year of the coronavirus pandemic had a negative effect on their children’s emotional health. In a fall 2022 survey of parents with K-12 children , 48% said the first year of the pandemic had a very or somewhat negative impact on their children’s emotional well-being, while 39% said it had neither a positive nor negative effect. A small share of parents (7%) said the first year of the pandemic had a very or somewhat positive effect in this regard.

White parents and those from upper-income households were especially likely to say the first year of the pandemic had a negative emotional impact on their K-12 children.

While around half of K-12 parents said the first year of the pandemic had a negative emotional impact on their kids, a larger share (61%) said it had a negative effect on their children’s education.

research paper on covid 19 and mental health

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About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

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How COVID-19 shaped mental health: from infection to pandemic effects

Brenda w. j. h. penninx.

1 Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

2 Amsterdam Public Health, Mental Health Program and Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, The Netherlands

Michael E. Benros

3 Biological and Precision Psychiatry, Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark

4 Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

Robyn S. Klein

5 Departments of Medicine, Pathology & Immunology and Neuroscience, Center for Neuroimmunology & Neuroinfectious Diseases, Washington University School of Medicine, St. Louis, MO, USA

Christiaan H. Vinkers

The Coronavirus Disease 2019 (COVID-19) pandemic has threatened global mental health, both indirectly via disruptive societal changes and directly via neuropsychiatric sequelae after SARS-CoV-2 infection. Despite a small increase in self-reported mental health problems, this has (so far) not translated into objectively measurable increased rates of mental disorders, self-harm or suicide rates at the population level. This could suggest effective resilience and adaptation, but there is substantial heterogeneity among subgroups, and time-lag effects may also exist. With regard to COVID-19 itself, both acute and post-acute neuropsychiatric sequelae have become apparent, with high prevalence of fatigue, cognitive impairments and anxiety and depressive symptoms, even months after infection. To understand how COVID-19 continues to shape mental health in the longer term, fine-grained, well-controlled longitudinal data at the (neuro)biological, individual and societal levels remain essential. For future pandemics, policymakers and clinicians should prioritize mental health from the outset to identify and protect those at risk and promote long-term resilience.

In 2019, the COVID-19 outbreak was declared a pandemic by the World Health Organization (WHO), with 590 million confirmed cases and 6.4 million deaths worldwide as of August 2022 (ref. 1 ). To contain the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across the globe, many national and local governments implemented often drastic restrictions as preventive health measures. Consequently, the pandemic has not only led to potential SARS-CoV-2 exposure, infection and disease but also to a wide range of policies consisting of mask requirements, quarantines, lockdowns, physical distancing and closure of non-essential services, with unprecedented societal and economic consequences.

As the world is slowly gaining control over COVID-19, it is timely and essential to ask how the pandemic has affected global mental health. Indirect effects include stress-evoking and disruptive societal changes, which may detrimentally affect mental health in the general population. Direct effects include SARS-CoV-2-mediated acute and long-lasting neuropsychiatric sequelae in affected individuals that occur during primary infection or as part of post-acute COVID syndrome (PACS) 2 —defined as symptoms lasting beyond 3–4 weeks that can involve multiple organs, including the brain. Several terminologies exist for characterizing the effects of COVID-19. PACS also includes late sequalae that constitute a clinical diagnosis of ‘long COVID’ where persistent symptoms are still present 12 weeks after initial infection and cannot be attributed to other conditions 3 .

Here we review both the direct and indirect effects of COVID-19 on mental health. First, we summarize empirical findings on how the COVID-19 pandemic has impacted population mental health, through mental health symptom reports, mental disorder prevalence and suicide rates. Second, we describe mental health sequalae of SARS-CoV-2 virus infection and COVID-19 disease (for example, cognitive impairment, fatigue and affective symptoms). For this, we use the term PACS for neuropsychiatric consequences beyond the acute period, and will also describe the underlying neurobiological impact on brain structure and function. We conclude with a discussion of the lessons learned and knowledge gaps that need to be further addressed.

Impact of the COVID-19 pandemic on population mental health

Independent of the pandemic, mental disorders are known to be prevalent globally and cause a very high disease burden 4 – 6 . For most common mental disorders (including major depressive disorder, anxiety disorders and alcohol use disorder), environmental stressors play a major etiological role. Disruptive and unpredictable pandemic circumstances may increase distress levels in many individuals, at least temporarily. However, it should be noted that the pandemic not only resulted in negative stressors but also in positive and potentially buffering changes for some, including a better work–life balance, improved family dynamics and enhanced feelings of closeness 7 .

Awareness of the potential mental health impact of the COVID-19 pandemic is reflected in the more than 35,000 papers published on this topic. However, this rapid research output comes with a cost: conclusions from many papers are limited due to small sample sizes, convenience sampling with unclear generalizability implications and lack of a pre-COVID-19 comparison. More reliable estimates of the pandemic mental health impact come from studies with longitudinal or time-series designs that include a pre-pandemic comparison. In our description of the evidence, we, therefore, explicitly focused on findings from meta-analyses that include longitudinal studies with data before the pandemic, as recently identified through a systematic literature search by the WHO 8 .

Self-reported mental health problems

Most studies examining the pandemic impact on mental health used online data collection methods to measure self-reported common indicators, such as mood, anxiety or general psychological distress. Pooled prevalence estimates of clinically relevant high levels of depression and anxiety symptoms during the COVID-19 pandemic range widely—between 20% and 35% 9 – 12 —but are difficult to interpret due to large methodological and sample heterogeneity. It also is important to note that high levels of self-reported mental health problems identify increased vulnerability and signal an increased risk for mental disorders, but they do not equal clinical caseness levels, which are generally much lower.

Three meta-analyses, pooling data from between 11 and 61 studies and involving ~50,000 individuals or more 13 – 15 , compared levels of self-reported mental health problems during the COVID-19 pandemic with those before the pandemic. Meta-analyses report on pooled effect sizes—that is, weighted averages of study-level effect sizes; these are generally considered small when they are ~0.2, moderate when ~0.5 and large when ~0.8. As shown in Table 1 , meta-analyses on mental health impact of the COVID-19 pandemic reach consistent conclusions and indicate that there has been a heterogeneous, statistically significant but small increase in self-reported mental health problems, with pooled effect sizes ranging from 0.07 to 0.27. The largest symptom increase was found when using specific mental health outcome measures assessing depression or anxiety symptoms. In addition, loneliness—a strong correlate of depression and anxiety—showed a small but significant increase during the pandemic ( Table 1 ; effect size = 0.27) 16 . In contrast, self-reported general mental health and well-being indicators did not show significant change, and psychotic symptoms seemed to have decreased slightly 13 . In Europe, alcohol purchase decreased, but high-level drinking patterns solidified among those with pre-pandemic high drinking levels 17 . When compared to pre-COVID levels, no change in self-reported alcohol use (effect size = −0.01) was observed in a recent meta-analysis summarizing 128 studies from 58 (predominantly European and North American) countries 18 .

Pooled effect sizes from meta-analyses comparing mental health symptoms before versus during the first year of the COVID-19 pandemic in the general population

What is the time trajectory of self-reported mental health problems during the pandemic? Although findings are not uniform, various large-scale studies confirmed that the increase in mental health problems was highest during the first peak months of the pandemic and smaller—but not fully gone—in subsequent months when infection rates declined and social restrictions eased 13 , 19 , 20 . Psychological distress reports in the United Kingdom increased again during the second lockdown period 15 . Direct associations between anxiety and depression symptom levels and the average number of daily COVID-19 cases were confirmed in the US Centers for Disease Control and Prevention (CDC) data 21 . Studies that examined longer-term trajectories of symptoms during the first or even second year of the COVID-19 pandemic are more sparse but revealed stability of symptoms without clear evidence of recovery 15 , 22 . The exception appears to be for loneliness, as some studies confirmed further increasing trends throughout the first COVID-19 pandemic year 22 , 23 . As most published population-based studies were conducted in the early time period in which absolute numbers of SARS-CoV2-infected individuals were still low, the mental health impacts described in such studies are most likely due to indirect rather than direct effects of SARS-CoV-2 infection. However, it is possible that, in longer-term or later studies, these direct and indirect effects may be more intertwined.

The extent to which governmental policies and communication have impacted on population mental health is a relevant question. In cross-country comparisons, the extent of social restrictions showed a dose–response relationship with mental health problems 24 , 25 . In a review of 33 studies worldwide, it was concluded that governments that enacted stringent measures to contain the spread of COVID-19 benefited not only the physical but also the mental health of their population during the pandemic 26 , even though more stringent policies may lead to more short-term mental distress 25 . It has been suggested that effective communication of risks, choices and policy measures may reduce polarization and conspiracy theories and mitigate the mental health impact of such measures 25 , 27 , 28 .

In sum, the general pattern of results is that of an increase in mental health symptoms in the population, especially during the first pandemic months, that remained elevated throughout 2020 and early 2021. It should be emphasized that this increase has a small effect size. However, even a small upward shift in mental health problems warrants attention as it has not yet shown to be returned to pre-pandemic levels, and it may have meaningful cumulative consequences at the population level. In addition, even a small effect size may mask a substantial heterogeneity in mental health impact, which may have affected vulnerable groups disproportionally (see below).

Mental disorders, self-harm and suicide

Whether the observed increase in mental health problems during the COVID-19 pandemic has translated into more mental disorders or even suicide mortality is not easy to answer. Mental disorders, characterized by more severe, disabling and persistent symptoms than self-reported mental health problems, are usually diagnosed by a clinician based on the International Classification of Diseases, 10th Revision (ICD-10) or the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) criteria or with validated semi-structured clinical interviews. However, during the COVID-19 pandemic, research systematically examining the population prevalence of mental disorders has been sparse. Unfortunately, we can also not strongly rely on healthcare use studies as the pandemic impacted on healthcare provision more broadly, thereby making figures of patient admissions difficult to interpret.

On a global scale and based on imputations and modeling from survey data of self-reported mental health problems, the Global Burden of Disease (GBD) study 29 estimated that the COVID-19 pandemic has led to a 28% (95% uncertainty interval (UI): 25–30) increase in major depressive disorders and a 26% (95% UI: 23–28) increase in anxiety disorders. It should be noted that these estimations come with high uncertainty as the assumption that transient pandemic-related increases in mental symptoms extrapolate into incident mental disorders remains disputable. So far, only four longitudinal population-based studies have measured and compared current mental (that is, depressive and anxiety) disorder prevalence—defined using psychiatric diagnostic criteria—before and during the pandemic. Of these, two found no change 30 , 31 , one found a decrease 32 and one found an increase in prevalence of these disorders 33 . These studies were local, limited to high-income countries, often small-scale and used different modes of assessment (for example, online versus in-person) before and during the pandemic. This renders these observational results uncertain as well, but their contrast to the GBD calculations 29 is striking.

Time-series analysis of monthly suicide trends in 21 middle-income to high-income countries across the globe yielded no evidence for an increase in suicide rates in the first 4 months of the pandemic, and there was evidence of a fall in rates in 12 countries 34 . Also in the United States, there was a significant decrease in suicide mortality in the first pandemic months but a slight increase in mortality due to drug overdose and homicide 35 . A living systematic review 36 also concluded that, throughout 2020, there was no observed increase in suicide rates in 20 studies conducted in North America, Europe and Asia. Analyses of electronic health record data in the primary care setting showed reduced rates of self-harm during the first COVID-19 pandemic year 37 . In contrast, emergency department visits for self-harm behavior were unchanged 38 or increased 39 . Such inconsistent findings across healthcare settings may reflect a reluctance in healthcare-seeking behavior for mental healthcare issues. In the living systematic review, eight of 11 studies that examined service use data found a significant decrease in reported self-harm/suicide attempts after COVID lockdown, which returned to pre-lockdown levels in some studies with longer follow-up (5 months) 36 .

In sum, although calculations based on survey data predict a global increase of mental disorder prevalence, objective and consistent evidence for an increased mental disorder, self-harm or suicide prevalence or incidence during the first pandemic year remains absent. This observation, coupled with the only small increase in mental health symptom levels in the overall population, may suggest that most of the general population has demonstrated remarkable resilience and adaptation. However, alternative interpretations are possible. First, there is a large degree of heterogeneity in the mental health impact of COVID-19, and increased mental health in one group (for example, due to better work–family balance and work flexibility) may have masked mental health problems in others. Various societal responses seen in many countries, such as community support activities and bolstering mental health and crisis services, may have had mitigating effects on the mental health burden. Also, the relationship between mental health symptom increases during stressful periods and its subsequent effects on the incidence of mental disorders may be non-linear or could be less visible due to resulting alternative outcomes, such as drug overdose or homicide. Finally, we cannot rule out a lag-time effect, where disorders may take more time to develop or be picked up, especially because some of the personal financial or social consequences of the COVID pandemic may only become apparent later. It should be noted that data from low-income countries and longer-term studies beyond the first pandemic year are largely absent.

Which individuals are most affected by the COVID-19 pandemic?

There is substantial heterogeneity across studies that evaluated how the COVID pandemic impacted on mental health 13 – 15 . Although our society as a whole may have the ability to adequately bounce back from pandemic effects, there are vulnerable people who have been affected more than others.

First, women have consistently reported larger increases in mental health problems in response to the COVID-19 pandemic than men 13 , 15 , 29 , 40 , with meta-analytic effect sizes being 44% 15 to 75% 13 higher. This could reflect both higher stress vulnerability or larger daily life disruptions due to, for example, increased childcare responsibilities, exposure to home violence or greater economic impact due to employment disruptions that all disproportionately fell to women 41 , thereby exacerbating the already existing pre-pandemic gender inequalities in depression and anxiety levels. In addition, adolescents and young adults have been disproportionately affected compared to younger children and older adults 12 , 15 , 29 , 40 . This may be the result of unfavorable behavioral and social changes (for example, school closure periods 42 ) during a crucial development phase where social interactions outside the family context are pivotal. Alarmingly, even though suicide rates did not seem to increase at the population level, studies in China 43 and Japan 44 indicated significant increases in suicide rates in children and adolescents.

Existing socio-cultural disparities in mental health may have further widened during the COVID pandemic. Whether the impact is larger for individuals with low socio-economic status remains unclear, with contrasting meta-analyses pointing toward this group being protected 15 or at increased risk 40 . Earlier meta-analyses did not find that the mental health impact of COVID-19 differed across Europe, North America, Asia and Oceania 13 , 14 , but data are lacking from Africa and South America. Nevertheless, a large-scale within-country comparison in the United States found that the mental health of Black, Hispanic and Asian respondents worsened relatively more during the pandemic compared to White respondents. Moreover, White respondents were more likely to receive professional mental healthcare during the pandemic, and, conversely, Black, Hispanic, and Asian respondents demonstrated higher levels of unmet mental healthcare needs during this time 45 .

People with pre-existing somatic conditions represent another vulnerable group in which the pandemic had a greater impact (pooled effect size of 0.25) 13 . This includes people with conditions such as epilepsy, multiple sclerosis or cardiometabolic disease as well as those with multiple comorbidities. The disproportionate impact may reflect this group’s elevated COVID-19 risk and, consequently, more perceived stress and fear of infection, but it could also reflect disruptions of regular healthcare services.

Healthcare workers faced increased workload, rapidly changing and challenging work environments and exposure to infections and death, accompanied by fear of infecting themselves and their families. High prevalences of (subthreshold) depression (13% 46 ), depressive symptoms (31% 47 ), (subthreshold) anxiety (16% 46 ), anxiety symptoms (23% 47 ) and post-traumatic stress disorder (~22% 46 , 47 ) have been reported in healthcare workers. However, a meta-analysis did not find a larger mental health impact of the pandemic as compared to the general population 40 , and another meta-analysis (of 206 studies) found that the mental health status of healthcare workers was similar to or even better than that of the general population during the first COVID year 48 . However, it is important to note that these meta-analyses could not differentiate between frontline and non-frontline healthcare workers.

Finally, individuals with pre-existing mental disorders may be at increased risk for exacerbation of mental ill-health during the pandemic, possibly due to disease history–illustrating a higher genetic and/or environmental vulnerability–but also due to discontinuity of mental healthcare. Already before the pandemic, mental health systems were under-resourced and disorganized in most countries 6 , 49 , but a third of all WHO member states reported disruptions to mental and substance use services during the first 18 months of the pandemic 50 , with reduced, shortened or postponed appointments and limited capacity for acute inpatient admissions 51 , 52 . Despite this, there is no clear evidence that individuals with pre-existing mental disorders are disproportionately affected by pandemic-related societal disruptions; the effect size for pandemic impact on self-reported mental health problems was similar in psychiatric patients and the general population 13 . In the United States, emergency visits for ten different mental disorders were generally stable during the pandemic compared to earlier periods 53 . In a large Dutch study 22 , 54 with multiple pre-pandemic and during-pandemic assessments, there was no difference in symptom increase among patients relative to controls (see Fig. 1 for illustration). In absolute terms, however, it is important to note that psychiatric patients show much higher symptom levels of depression, anxiety, loneliness and COVID-fear than healthy controls. Again, variation in mental health changes during the pandemic is large: next to psychiatric patients who showed symptom decrease due to, for example, experiencing relief from social pressures, there certainly have been many patients with symptom increases and relapses during the pandemic.

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Object name is nihms-1848557-f0001.jpg

Trajectories of mean depressive symptoms (QIDS score), anxiety symptoms (BAI score), loneliness (De Jong questionnaire score) and Fear of COVID-19 score before and during the first year of the COVID-19 pandemic in healthy controls (blue line, n = 378) and in patients with depressive and/or anxiety disorders (red line, n = 908). The x -axis indicates time with one pre-COVID assessment (averaged over up to five earlier assessments conducted between 2006 and 2019) and 11 online assessments during April 2020 through February 2021. Symbols indicate the mean score during the assessment with 95% CIs. As compared to pre-COVID assessment scores, the figure shows a statistically significant increase of depression and loneliness symptoms during the first pandemic peak (April 2020) in healthy controls but not in patients (for more details, see refs. 22 , 54 ). Asterisks indicate where subsequent wave scores differ from the prior wave scores ( P < 0.05). The figure also illustrates the stability of depressive and anxiety symptoms during the first COVID year, a significant increase in loneliness during this period and fluctuations of Fear of COVID-19 score that positively correlate with infection rates in the Netherlands. Raw data are from the Netherlands Study of Depression and Anxiety (NESDA), which were re-analyzed for the current plots to illustrate differences between two groups (healthy controls versus patients). BAI, Beck Anxiety Inventory; QIDS, Quick Inventory of Depressive Symptoms.

Impact of COVID-19 infection and disease on mental health and the brain

Not only the pandemic but also COVID-19 itself can have severe impact on the mental health of affected individuals and, thus, of the population at large. Below we describe acute and post-acute neuropsychiatric sequelae seen in patients with COVID-19 and link these to neurobiological mechanisms.

Neuropsychiatric sequelae in individuals with COVID-19

Common symptoms associated with acute SARS-CoV-2 infection include headache, anosmia (loss of sense of smell) and dysgeusia (loss of sense of taste). The broader neuropsychiatric impact is dependent on infection severity and is very heterogeneous ( Table 2 ). It ranges from no neuropsychiatric symptoms among the large group of asymptomatic COVID-19 cases to milder transient neuropsychiatric symptoms, such as fatigue, sleep disturbance and cognitive impairment, predominantly occurring among symptomatic patients with COVID-19 (ref. 55 ). Cognitive impairment consists of sustained memory impairments and executive dysfunction, including short-term memory loss, concentration problems, word-finding problems and impaired daily problem-solving, colloquially termed ‘brain fog’ by patients and clinicians. A small number of infected individuals become severely ill and require hospitalization. During hospital admission, the predominant neuropsychiatric outcome is delirium 56 . Delirium occurs among one-third of hospitalized patients with COVID-19 and among over half of patients with COVID-19 who require intensive care unit (ICU) treatment. These delirium rates seem similar to those observed among individuals with severe illness hospitalized for other general medical conditions 57 . Delirium is associated with neuropsychiatric sequalae after hospitalization, as part of post-intensive care syndrome 58 , in which sepsis and inflammation are associated with cognitive dysfunction and an increased risk of a broad range of psychiatric symptoms, from anxiety to depression and psychotic symptoms with hallucinations 59 , 60 .

Acute and post-acute neuropsychiatric sequelae of COVID-19 disease as described in empirical studies *

A subset of patients with COVID-19 develop PACS 61 , which can include neuropsychiatric symptoms. A large meta-analysis summarizes 51 studies involving 18,917 patients with a mean follow-up of 77 days (range, 14–182 days) 62 . The most prevalent neuropsychiatric symptom associated with COVID-19 was sleep disturbance, with a pooled prevalence of 27.4%, followed by fatigue (24.4%), cognitive impairment (20.2%), anxiety symptoms (19.1%), post-traumatic stress symptoms (15.7%) and depression symptoms (12.9%) ( Table 2 ). Another meta-analysis that assessed patients 12 weeks or more after confirmed COVID-19 diagnosis found that 32% experienced fatigue, and 22% experienced cognitive impairment 63 . To what extent neuropsychiatric symptoms are truly unique for patients with COVID remains unclear from these meta-analyses, as hardly any study included well-matched controls with other types of respiratory infections or inflammatory conditions.

Studies based on electronic health records have examined whether higher levels of neuropsychiatric symptoms truly translate into a higher incidence of clinically overt mental disorders 64 , 65 . In a 1-year follow-up using the US Veterans Affairs database, 153,848 survivors of SARS-CoV-2 infection exhibited an increased incidence of any mental disorder with a relative risk of 1.46 and, specifically, 1.35 for anxiety disorders, 1.39 for depressive disorders and 1.38 for stress and adjustment disorders, compared to a contemporary group and a historical control group ( n = 5,859,251) 65 . In absolute numbers, the incident risk difference attributable to SARS-CoV-2 for mental disorders was 64 per 1,000 individuals. Taquet et al. 64 analyzed electronic health records from the US-based TriNetX network with over 81 million patients and 236,379 COVID-19 survivors followed for 6 months. In absolute numbers, 6-month incidence of hospital contacts related to diagnoses of anxiety, affective disorder or psychotic disorder was 7.0%, 4.5% and 0.4%, respectively. Risks of incident neurological or psychiatric diagnoses were directly correlated with COVID-19 severity and increased by 78% when compared to influenza and by 32% when compared to other respiratory tract infections. In contrast, a medical record study involving 8.3 million adults confirmed that neuropsychiatric disorders were significantly elevated among COVID-19 hospitalized individuals but to a similar extent as in hospitalized patients with other severe respiratory disease 66 . In line with this, a study using language processing of clinical notes in electronic health records did not find an increase in fatigue, mood and anxiety symptoms among COVID-19 hospitalized individuals when compared to hospitalized patients for other indications and adjusted for sociodemographic features and hospital course 67 . It is important to note that research based only on hospital records might be influenced by increased health-seeking behavior that could be differential across care settings or by increased follow-up by hospitals of patients with COVID-19 (compared to patients with other conditions).

Consequently, whether PACS symptoms form a unique pattern due to specific infection with SARS-CoV-2 remains debatable. Prospective case–control studies that do not rely on hospital records but measure the incidence of neuropsychiatric symptoms and diagnoses after COVID-19 are still scarce, but they are critical for distinguishing causation and confounding when characterizing PACS and the uniqueness of neuropsychiatric sequalae after COVID-19 (ref. 68 ). Recent studies with well-matched control groups illustrate that long-term consequences may not be so unique, as they were similar to those observed in patients with other diseases of similar severity, such as after acute myocardial infarction or in ICU patients 56 , 66 . A first prospective follow-up study of COVID-19 survivors and control patients matched on disease severity, age, sex and ICU admission found similar neuropsychiatric outcomes, regarding both new-onset psychiatric diagnosis (19% versus 20%) and neuropsychiatric symptoms (81% versus 93%). However, moderate but significantly worse cognitive outcomes 6 months after symptom onset were found among survivors of COVID-19 (ref. 69 ). In line with this, a longitudinal study of 785 participants from the UK Biobank showed small but significant cognitive impairment among individuals infected with SARS-CoV-2 compared to matched controls 70 .

Numerous psychosocial mechanisms can lead to neuropsychiatric sequalae of COVID-19, including functional impairment; psychological impact due to, for example, fear of dying; stress of being infected with a novel pandemic disease; isolation as part of quarantine and lack of social support; fear/guilt of spreading COVID-19 to family or community; and socioeconomic distress by lost wages 71 . However, there is also ample evidence that neurobiological mechanisms play an important role, which is discussed below.

Neurobiological mechanisms underlying neuropsychiatric sequelae of COVID-19

Acute neuropsychiatric symptoms among patients with severe COVID-19 have been found to correlate with the level of serum inflammatory markers 72 and coincide with neuroimaging findings of immune activation, including leukoencephalopathy, acute disseminated encephalomyelitis, cytotoxic lesions of the corpus callosum or cranial nerve enhancement 73 . Rare presentations, including meningitis, encephalitis, inflammatory demyelination, cerebral infarction and acute hemorrhagic necrotizing encephalopathy, have also been reported 74 . Hospitalized patients with frank encephalopathies display impaired blood-brain barrier (BBB) integrity with leptomeningeal enhancement on brain magnetic resonance images 75 . Studies of postmortem specimens from patients who succumbed to acute COVID-19 reveal significant neuropathology with signs of hypoxic damage and neuroinflammation. These include evidence of BBB permeability with extravasation of fibrinogen, microglial activation, astrogliosis, leukocyte infiltration and microhemorrhages 76 , 77 . However, it is still unclear to what extent these findings differ from patients with similar illness severity due to acute non-COVID illness, as these brain effects might not be virus-specific effects but rather due to cytokine-mediated neuroinflammation and critical illness.

Post-acute neuroimaging studies in SARS-CoV-2-recovered patients, as compared to control patients without COVID-19, reveal numerous alterations in brain structure on a group level, although effect sizes are generally small. These include minor reduction in gray matter thickness in the various regions of the cortex and within the corpus collosum, diffuse edema, increases in markers of tissue damage in regions functionally connected to the olfactory cortex and reductions in overall brain size 70 , 78 . Neuroimaging studies of post-acute COVID-19 patients also report abnormalities consistent with micro-structural and functional alterations, specifically within the hippocampus 79 , 80 , a brain region critical for memory formation and regulating anxiety, mood and stress responses, but also within gray matter areas involving the olfactory system and cingulate cortex 80 . Overall, these findings are in line with ongoing anosmia, tremors, affect problems and cognitive impairment.

Interestingly, despite findings mentioned above, there is little evidence of SARS-CoV-2 neuroinvasion with productive replication, and viral material is rarely found in the central nervous system (CNS) of patients with COVID-19 (refs. 76 , 77 , 81 ). Thus, neurobiological mechanisms of SARS-CoV-2-mediated neuropsychiatric sequelae remain unclear, especially in patients who initially present with milder forms of COVID-19. Symptomatic SARS-CoV-2 infection is associated with hypoxia, cytokine release syndrome (CRS) and dysregulated innate and adaptive immune responses (reviewed in ref. 82 ). All these effects could contribute to neuroinflammation and endothelial cell activation ( Fig. 2 ). Examination of cerebrospinal fluid in patients with neuroimaging findings revealed elevated levels of pro-inflammatory, BBB-destabilizing cytokines, including interleukin-6 (IL-6), IL-1, IL-8 and mononuclear cell chemoattractants 83 , 84 . Whether these cytokines arise from the periphery, due to COVID-19-mediated CRS, or from within the CNS, is unclear. As studies generally lack control patients with other severe illnesses, the specificity of such findings to SARS-CoV-2 also remains unclear. Systemic inflammatory processes, including cytokine release, have been linked to glial activation with expression of chemoattractants that recruit immune cells, leading to neuroinflammation and injury 85 . Cerebrospinal fluid concentrations of neurofilament light, a biomarker of neuronal damage, were reportedly elevated in patients hospitalized with COVID-19 regardless of whether they exhibited neurologic diseases 86 . Acute thromboembolic events leading to ischemic infarcts are also common in patients with COVID-19 due to a potentially increased pro-coagulant process secondary to CRS 87 .

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(1) Elevation of BBB-destabilizing cytokines (IL-1β and TNF) within the serum due to CRS or local interactions of mononuclear and endothelial cells. (2) Virus-induced endotheliitis increases susceptibility to microthrombus formation due to platelet activation, elevation of vWF and fibrin deposition. (3) Cytokine, mononuclear and endothelial cell interactions promote disruption of the BBB, which may allow entry of leukocytes expressing IFNg into the CNS (4), leading to microglial activation (5). (6) Activated microglia may eliminate synapses and/or express cytokines that promote neuronal injury. (7) Injured neurons express IL-6 which, together with IL-1β, promote a ‘gliogenic switch’ in NSCs (8), decreasing adult neurogenesis. (9) The combination of microglial (and possibly astrocyte) activation, neuronal injury and synapse loss may lead to dysregulation of NTs and neuronal circuitry. IFNg, interferon-g; NSC, neural stem cell; NT, neurotransmitter; TJ, tight junction; TNF, tumor necrosis factor; vWF, von Willebrand factor.

It is also unclear whether hospitalized patients with COVID-19 may develop brain abnormalities due to hypoxia or CRS rather than as a direct effect of SARS-CoV-2 infection. Hypoxia may cause neuronal dysfunction, cerebral edema, increased BBB permeability, cytokine expression and onset of neurodegenerative diseases 88 , 89 . CRS, with life-threatening levels of serum TNF-α and IL-1 (ref. 90 ) could also impact BBB function, as these cytokines destabilize microvasculature endothelial cell junctional proteins critical for BBB integrity 91 . In mild SARS-CoV-2 infection, circulating immune factors combined with mild hypoxia might impact BBB function and lead to neuroinflammation 92 , as observed during infection with other non-neuroinvasive respiratory pathogens 93 . However, multiple studies suggest that the SARS-CoV-2 spike protein itself may also induce venous and arterial endothelial cell activation and endotheliitis, disrupt BBB integrity or cross the BBB via adoptive transcytosis 94 – 96 .

Reducing neuropsychiatric sequelae of COVID-19

The increased risk of COVID-19-related neuropsychiatric sequalae was most pronounced during the first pandemic peak but reduced over the subsequent 2 years 64 , 97 . This may be due to reduced impact of newer SARS-CoV-2 strains (that is, Omicron) but also protective effects of vaccination, which limit SARS-CoV-2 spread and may, thus, prevent neuropsychiatric sequalae. Fully vaccinated individuals with breakthrough infections exhibit a 50% reduction in PACS 98 , even though vaccination does not improve PACS-related neuropsychiatric symptoms in patients with a prior history of COVID-19 (ref. 99 ). As patients with pre-existing mental disorders are at increased risk of SARS-CoV-2 infection, they deserve to be among the prioritization groups for vaccination efforts 100 .

Adequate treatment strategies for neuropsychiatric sequelae of COVID-19 are needed. As no specific evidence-based intervention yet exists, the best current treatment approach is that for neuropsychiatric sequelae arising after other severe medical conditions 101 . Stepped care—a staged approach of mental health services comprising a hierarchy of interventions, from least to most intensive, matched to the individual’s need—is efficacious with monitoring of mental health and cognitive problems. Milder symptoms likely benefit from counseling and holistic care, including physiotherapy, psychotherapy and rehabilitation. Individuals with moderate to severe symptoms fulfilling psychiatric diagnoses should receive guideline-concordant care for these disorders 61 . Patients with pre-existing mental disorders also deserve special attention when affected by COVID-19, as they have shown to have an increased risk of COVID-19-related hospitalization, complications and death 102 . This may involve interventions to address their general health, any unfavorable socioenvironmental factors, substance abuse or treatment adherence issues.

Lessons learned, knowledge gaps and future challenges

Ultimately, it is not only the millions of people who have died from COVID-19 worldwide that we remember but also the distress experienced during an unpredictable period with overstretched healthcare systems, lockdowns, school closures and changing work environments. In a world that is more and more globalized, connectivity puts us at risk for future pandemics. What can be learned from the last 2 years of the COVID-19 pandemic about how to handle future and longstanding challenges related to mental health?

Give mental health equal priority to physical health

The COVID-19 pandemic has demonstrated that our population seems quite resilient and adaptive. Nevertheless, even if society as a whole may bounce back, there is a large group of people whose mental health has been and will be disproportionately affected by this and future crises. Although various groups, such as the WHO 8 , the National Health Commission of China 103 , the Asia Pacific Disaster Mental Health Network 104 and a National Taskforce in India 105 , developed mental health policies early on, many countries were late in realizing that a mental health agenda deserves immediate attention in a rapidly evolving pandemic. Implementation of comprehensive and integrated mental health policies was generally inconsistent and suboptimal 106 and often in the shadow of policies directed at containing and reducing the spread of SARS-CoV-2. Leadership is needed to convey the message that mental health is as important as physical health and that we should focus specific attention and early interventions on those at the highest risk. This includes those vulnerable due to factors such as low socioeconomic status, specific developmental life phase (adolescents and young adults), pre-existing risk (poor physical or somatic health and early life trauma) or high exposure to pandemic-related (work) changes—for example, women and healthcare personnel. This means that not only should investment in youth and reducing health inequalities remain at the top of any policy agenda but also that mental health should be explicitly addressed from the start in any future global health crisis situation.

Communication and trust is crucial for mental health

Uncertainty and uncontrollability during the pandemic have challenged rational thinking. Negative news travels fast. Communication that is vague, one-sided and dishonest can negatively impact on mental health and amplify existing distress and anxiety 107 . Media reporting should not overemphasize negative mental health impact—for example, putative suicide rate increases or individual negative experiences—which could make situations worse than they actually are. Instead, communication during crises requires concrete and actionable advice that avoids polarization and strengthens vigilance, to foster resilience and help prevent escalation to severe mental health problems 108 , 109 .

Rapid research should be collaborative and high-quality

Within the scientific community, the topic of mental health during the pandemic led to a multitude of rapid studies that generally had limited methodological quality—for example, cross-sectional designs, small or selective sampling or study designs lacking valid comparison groups. These contributed rather little to our understanding of the mental health impact of the emerging crisis. In future events that have global mental health impact, where possible, collaborative and interdisciplinary efforts with well-powered and well-controlled prospective studies using standardized instruments will be crucial. Only with fine-grained determinants and outcomes can data reliably inform mental health policies and identify who is most at risk.

Do not neglect long-term mental health effects

So far, research has mainly focused on the acute and short-term effects of the pandemic on mental health, usually spanning pandemic effects over several months to 1 year. However, longer follow-up of how a pandemic impacts population mental health is essential. Can societal and economic disruptions after the pandemic increase risk of mental disorders at a later stage when the acute pandemic effects have subsided? Do increased self-reported mental health problems return to pre-pandemic levels, and which groups of individuals remain most affected in the long-term? We need to realize that certain pandemic consequences, particularly those affecting income and school/work careers, may become visible only over the course of several years. Consequently, we should maintain focus and continue to monitor and quantify the effects of the pandemic in the years to come—for example, by monitoring mental healthcare use and suicide. This should include specific at-risk populations (for example, adolescents) and understudied populations in low-income and middle-income countries.

Pay attention to mental health consequences of infectious diseases

Even though our knowledge on PACS is rapidly expanding, there are still many unanswered questions related to who is at risk, the long-term course trajectories and the best ways to intervene early. Consequently, we need to be aware of the neuropsychiatric sequelae of COVID-19 and, for that matter, of any infectious disease. Clinical attention and research should be directed toward alleviating potential neuropsychiatric ramifications of COVID-19. Next to clinical studies, studies using human tissues and appropriate animal models are pivotal to determine the CNS region-specific and neural-cell-specific effects of SARS-CoV-2 infection and the induced immune activation. Indeed, absence of SARS-CoV-2 neuroinvasion is an opportunity to learn and discover how peripheral neuroimmune mechanisms can contribute to neuropsychiatric sequelae in susceptible individuals. This emphasizes the importance of an interdisciplinary approach where somatic and mental health efforts are combined but also the need to integrate clinical parameters after infection with biological parameters (for example, serum, cerebrospinal fluid and/or neuroimaging) to predict who is at risk for PACS and deliver more targeted treatments.

Prepare mental healthcare infrastructure for pandemic times

If we take mental health seriously, we should not only monitor it but also develop the resources and infrastructure necessary for rapid early intervention, particularly for specific vulnerable groups. For adequate mental healthcare to be ready for pandemic times, primary care, community mental health and public mental health should be prepared. In many countries, health services were not able to meet the population’s mental health needs before the pandemic, which substantially worsened during the pandemic. We should ensure rapid access to mental health services but also address the underlying drivers of poor mental health, such as mitigating risks of unemployment, sexual violence and poverty. Collaboration in early stages across disciplines and expertise is essential. Anticipating disruption to face-to-face services, mental healthcare providers should be more prepared for consultations, therapy and follow-up by telephone, video-conferencing platforms and web applications 51 , 52 . The pandemic has shown that an inadequate infrastructure, pre-existing inequalities and low levels of technological literacy hindered the use and uptake of e-health, both in healthcare providers and in patients across different care settings. The necessary investments can ensure rapid upscaling of mental health services during future pandemics for those individuals with a high mental health need due to societal changes, government measures, fear of infection or infection itself.

Even though much attention has been paid to the physical health consequences of COVID-19, mental health has unjustly received less attention. There is an urgent need to prepare our research and healthcare infrastructures not only for adequate monitoring of the long-term mental health effects of the COVID-19 pandemic but also for future crises that will shape mental health. This will require collaboration to ensure interdisciplinary and sound research and to provide attention and care at an early stage for those individuals who are most vulnerable—giving mental health equal priority to physical health from the very start.

Acknowledgements

The authors thank E. Giltay for assistance on data analyses and production of Fig. 1 . B.W.J.H.P. discloses support for research and publication of this work from the European Union’s Horizon 2020 research and innovation programme-funded RESPOND project (grant no. 101016127).

Competing interests

The authors declare no conflicts of interest.

COVID-19 and mental health: A review of the existing literature

Affiliation.

  • 1 Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Pondicherry, 605 006, India. Electronic address: [email protected].
  • PMID: 32302935
  • PMCID: PMC7151415
  • DOI: 10.1016/j.ajp.2020.102066

The COVID-19 pandemic is a major health crisis affecting several nations, with over 720,000 cases and 33,000 confirmed deaths reported to date. Such widespread outbreaks are associated with adverse mental health consequences. Keeping this in mind, existing literature on the COVID-19 outbreak pertinent to mental health was retrieved via a literature search of the PubMed database. Published articles were classified according to their overall themes and summarized. Preliminary evidence suggests that symptoms of anxiety and depression (16-28%) and self-reported stress (8%) are common psychological reactions to the COVID-19 pandemic, and may be associated with disturbed sleep. A number of individual and structural variables moderate this risk. In planning services for such populations, both the needs of the concerned people and the necessary preventive guidelines must be taken into account. The available literature has emerged from only a few of the affected countries, and may not reflect the experience of persons living in other parts of the world. In conclusion, subsyndromal mental health problems are a common response to the COVID-19 pandemic. There is a need for more representative research from other affected countries, particularly in vulnerable populations.

Keywords: Anxiety; COVID-19; Depression; Public health; Stress.

© 2020 Elsevier B.V. All rights reserved.

Publication types

  • Systematic Review
  • Anxiety* / epidemiology
  • Anxiety* / etiology
  • Anxiety* / prevention & control
  • Coronavirus Infections*
  • Depression* / epidemiology
  • Depression* / etiology
  • Depression* / prevention & control
  • Pneumonia, Viral*
  • Sleep Wake Disorders* / epidemiology
  • Sleep Wake Disorders* / etiology
  • Sleep Wake Disorders* / prevention & control
  • Stress, Psychological* / epidemiology
  • Stress, Psychological* / etiology
  • Stress, Psychological* / prevention & control

The Bronfenbrenner Center for Translational Research

Do COVID Infections Impact Mental Health?

A growing body of research finds covid can lead to mental health problems..

Posted February 12, 2024 | Reviewed by Devon Frye

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  • A new systematic review finds that COVID infections increase the risk of mental health disorders.
  • Women, people with lower incomes, and those with more severe infections are at greater risk.
  • Scientists are still trying to understand the biological mechanisms at play.

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More than four years after the first humans became infected with the SARS-CoV-2 virus, scientists understand more than ever before about how COVID-19 affects people. Beyond respiratory symptoms, COVID can lead to nausea, vomiting, diarrhea, rashes, and even eye infections. We’ve also learned that people can experience long COVID, which involves continued symptoms for months or even years after they initially get sick.

Now, new data is shedding light on how COVID affects mental health. A systematic review published this month in the journal BMC Psychiatry takes a careful look at the long-term mental health effects of COVID-19. In the analysis, Iranian public health researchers pooled the data from hundreds of studies that used validated assessment tools for depression , anxiety , and sleep disorders to quantify mental health disorders among patients with long COVID.

“Initially, research was centered on analyzing the psychosocial reaction to the COVID pandemic and the effects of various preventive measures, including lockdowns, school closures, and travel restrictions, on the mental health outcomes of the general population,” they wrote. “However, recent studies have expanded this scope to include not only the effects of pandemic-related circumstances but also the impact of COVID-19 as a debilitating disease.”

To look at depression rates, the researchers combined the data from 143 studies with more than 7.7 million participants with long COVID. They found that 23 percent of participants experienced depression, compared with approximately 4 percent of the general population at any given time.

For anxiety, researchers combined the data from 132 studies with more than 9.3 million participants with long COVID and found 23 percent reported symptoms of anxiety. Comparatively, about 4 percent of the general population experiences an anxiety disorder at any given time.

To quantify sleep problems, the researchers combined data from 27 studies with more than 15,000 participants and found 45 percent of patients with long COVID experienced sleep problems, compared with approximately 15 percent of the general population who report trouble sleeping at any given time.

Clearly, people with long COVID are significantly more likely to experience mental health problems. Researchers stressed the study’s focus on the mental health status of people infected with COVID, not how the experience of living through a global pandemic affected the mental health of the broader society.

The analysis was also able to identify risk factors for developing mental health disorders as part of a COVID infection. Older people, women, people with lower socioeconomic status, and people with pre-existing mental health conditions were all more likely to develop mental health disorders when they were infected with COVID. In addition, people with more severe initial infections were at a greater risk of developing mental health problems months later.

What’s going on here? Medical experts don’t yet fully understand how COVID affects mental health. There is some evidence that the virus invades the central nervous system and damages neurons. Another theory is that COVID causes problems with blood vessels and blood flow, causing indirect harm to the brain. In addition, COIVD causes an inflammatory response in the body that can disrupt neurotransmitter activity and lead to oxidative stress in the cells, which aggravates mental health symptoms.

The take-home message: A large and growing body of evidence demonstrates people infected with COVID are significantly more likely to experience mental health problems compared to the general population.

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Mental Health Care During the COVID-19 Era Remains Inaccessible to Many Distressed U.S. Adults

National trends underscore a public health need to broaden outpatient mental health care access to more distressed, older, and unemployed adults.

U.S. adults experienced considerable psychological distress and adverse mental health effects as a result of the COVID-19 pandemic according to a study by researchers at Columbia University Mailman School of Public Health and Vagelos College of Physicians and Surgeons. Based on insurance claims, mental health care provider surveys, and electronic health records, the research further revealed a decline in in-person outpatient mental health visits during the acute phase of the pandemic. Findings are reported in the Annals of Internal Medicine .

“The trends and patterns we observed in the United States align with reports globally concluding that several mental health problems, including depression, and generalized anxiety disorder, have become more prevalent during than before the pandemic,” said Mark Olfson , MD, MPH, professor of Epidemiology at Columbia Mailman School of Public Health, and Dollard Professor of Psychiatry, Medicine & Law at Vagelos College of Physicians and Surgeons .

To characterize the psychological distress experienced, determine the level of outpatient mental health care, and describe patterns of in-person versus telemental health care, the researchers studied the responses of adults from the Medical Expenditure Panel Surveys by the Agency for Healthcare Research and Quality Component, a nationally representative survey of over 85,000 people. Psychological distress was measured with a six-point scale range and outpatient mental health care use was determined via computer-assisted personal interviews.

The rate of serious psychological distress among adults increased from 3.5 percent to 4.2 percent from 2018 to 2021. While outpatient mental health care increased overall as well—from 11.2 percent to 12.4 percent—the rate among adults with serious psychological distress decreased from 46.5 percent to 40.4 percent. Young adults aged 18 to 44 years significantly increased outpatient mental health care but this pattern was not observed for middle-aged adults aged 45 to 64 years and older adults aged >65 years. Similarly, more employed adults reported outpatient mental health treatment care compared to the unemployed.

In 2021, 33 percent of mental health outpatients received at least one video visit. The likelihood of receiving in-person, telephone, or video mental health care varied across sociodemographic groups; percentages of video care were higher for younger adults than for middle-aged or older adults, women compared with men, college graduates compared with adults with less education, the seriously distressed, lower-income, unemployed, and rural patients.

“Thanks to a rapid pivot to telemental health care, there was an overall increase during the pandemic of adults receiving outpatient mental health care in the United States.  However, the percentage of adults with serious psychological distress who received outpatient mental health treatment significantly declined.  Several groups also had difficulty accessing telemental health care including older individuals and those with lower incomes and less education,” observed Olfson. “These patterns underscore critical challenges to extend the reach and access of telemental health services via easy-to-use and affordable service options.” 

“Increasing our understanding of the patterns we observed in terms of access to outpatient mental health care including in-person, telephone-administered, and internet-administered outpatient mental health services could inform ongoing public policy discussions and clinical interventions,” noted Olfson. “Identifying low-cost means of connecting lower-income patients to telemental health should be a priority, as well as increasing public investment to make access to high-speed broadband universal.”

“The national profile of adults who receive outpatient mental health care via telemental health—the younger adult, the employed, higher-income, and privately insured adults, raises concerns about disparities in access to virtual mental health care,” said Olfson. “Unless progress is made in reducing these barriers, primary care clinicians will continue to encounter challenges in connecting their older, unemployed, and lower-income patients to video-delivered outpatient mental health care.”

Co-authors are Chandler McClellan and Samuel H. Zuvekas, Agency for Healthcare Research and Quality; Melanie Wall, Columbia Mailman School of Public Health; and Carlos Blanco, National Institute on Drug Abuse.

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  • Published: 21 February 2024

The mediating role of coping styles in the relationship between fear of COVID-19 and mental health problems: a cross-sectional study among nurses

  • Nurul Huda 1 ,
  • Malissa Kay Shaw 2 ,
  • Hsiu Ju Chang 3 ,
  • Suci Tuty Putri 4 &
  • Satriya Pranata 5  

BMC Public Health volume  24 , Article number:  545 ( 2024 ) Cite this article

Metrics details

Fear of being infected by coronavirus disease 2019 (COVID-19) could trigger mental health problems among nurses at the frontline. In such a situation, coping strategies are needed to deal with the imminent threat. The purpose of this study was to test the mediating effects of coping on relationships of fear of COVID-19 with anxiety, depression and post-traumatic syndrome among nurses who were in contact with COVID-19 patients. A cross-sectional and correlational research design was used to recruit a sample of 278 nurses who treated COVID-19 patients in four government referral hospitals in Indonesia. A bootstrap resampling procedure was used to test the significance of the total and specific indirect effects of coping on relationships of Fear of COVID-19 with anxiety, depression and post-traumatic syndrome. The nurses reported moderate levels of fear of COVID-19, considerable anxiety and depression, and a moderate level of coping. We found coping to be significantly negatively correlated with the reported levels of anxiety, depression and post-traumatic syndrome ( p  < 0.001). Coping mediated relationships of fear of COVID-19 on depression, anxiety and post-traumatic syndrome after controlling for relevant confounders for each dependent variable. This shows that enacting coping mechanisms is important to achieve an adaptive effect on nurses' mental health. Proper assessments and interventions should be tailored and implemented for nurses who have contact with COVID-19 patients to facilitate their use of coping strategies when needed in stressful situations.

Peer Review reports

Introduction

The coronavirus disease 2019 (COVID-19) have been known as the biggest health challenge and crisis the world has experienced. Nurses as frontline healthcare staff have been one of the most vulnerable groups to contracting COVID-19 since they have been constantly in contact with the COVID-19 threat. In this situation, nurses play an important role in dealing with COVID-19 cases and subsequent complications which commonly require hospitalization [ 1 , 2 ]. In Indonesia, there were 4,272,421 infected cases of COVID-19 in 2022, and the probability of transferring the infection to healthcare personnel was quite high [ 3 ]. Fear of being infected could trigger mental health problems among healthcare staff, especially nurses [ 4 , 5 ].

Previous research found that during this pandemic period, the prevalence of mental health problems increased significantly among adults to 2.78% [ 6 ]. However, mental health problems are not only suffered by the general population but also by nurses who are in charge of the frontline among healthcare workers [ 7 ]. During this pandemic, the nurse’s working conditions were often harsh. In addition to being overworked, they were also at higher risk of contracting COVID-19, putting their lives and their families at risk while they fulfilled their duties [ 1 , 8 ]. These conditions have contributed to the mental health problems experienced by nurses [ 1 ]. They have had to deal with the fear of contracting COVID-19, anxiety, depression, post traumatic syndrome (PTSD) and other emotional states stimulated by directly observing patients’ suffering and death [ 1 , 9 ]. In addition, experiences of uncertainty and stigmatization have made many nurses reluctant to work and contemplate resignation [ 9 ]. Hence, it is not surprising that the levels of stress, anxiety, PTSD and depression symptoms among nurses were higher compared to other healthcare professionals which could have long-term psychological consequences [ 10 ].

Distress is acknowledged when stressful events have exceeded a person’s limited resources and may endanger their well-being. In such a situation, coping strategies are needed to deal with the imminent threat [ 11 ]. Coping involves an effort to manage adaptation demands and associated emotions [ 12 ]. During this pandemic, the use of certain coping strategies has been related to multiple outcomes including symptoms of depression and anxiety [ 13 , 14 ]. A previous study found that coping was briefly proven to be a mediator of the relationship between nurses’ stressors and psychological distress [ 15 , 16 , 17 ]. However, to our best knowledge, the mediating role that coping plays in the relationships of fear of COVID-19 with anxiety and depression among nurses has never been studied.

Mental health problems among nurses have been proven to be linked to negative outcomes including physical and psychological problems which may deteriorate the quality of patients’ care [ 9 ]. In addition, hospitals effectiveness and productivity may be reduced due to staff’s intentions to leave [ 1 ]. Therefore, it is essential to study nurses’ mental health problems during this pandemic and explore the factors determining the negative impact on nurses’ mental health. This study aimed to analyze the mental health problems among nurses, including fear of COVID-19, depression, and anxiety, identify the determinants of mental health problems as well as the mediating role of coping strategies.

Design, setting and population

A cross-sectional and correlational research design was used in this study. The patient inclusion criteria were nurses who had contact with COVID-19 patients, and the ability to speak the Indonesian language and fill out questionnaires. The exclusion criteria for this study were having been diagnosed with a major psychiatric morbidity, such as schizophrenia, or having a severe medical condition. Potential participants were recruited from inpatient wards, emergency wards and intensive care wards of 4 general government hospital in Indonesia. Data were collected between May and October 2022. The sample was composed of 278 nurses. Sampling and data collection were carried out face-to-face.

Data collection and instruments

Sociodemographic and clinical characteristic information.

Information concerning gender, age, marital status, religion, level of education, type of ward, and employment status were collected from participants.

Fear of COVID-19 scale

Participants’ fear of COVID-19 was assessed using the Fear of COVID-19 scale (FCV-19). The FCV-19S questionnaire consists of 7 items and scores range from 1 (strongly disagree) to 5 (agree). The total score, ranging between 7 and 35, is determined by adding up all item scores. The higher scores indicate a greater fear of COVID-19 and the lower scores designate a minor fear of COVID-19. Previous research found that this scale has solid psychometric properties, including high internal consistency with a Cronbach alpha value of 0.82 [ 18 ]. The Indonesian version of the FCV-19 scale showed good internal consistency with the value of Cronbach’s alpha being 0.87 [ 19 ].

Depression, Anxiety, and Stress Scale (DASS)

The DASS is comprised of three subscales of DASS-Stress, DASS-Anxiety and DASS Depression to, respectively, investigate stress, anxiety, and depression experienced 1 week before the survey [ 19 ]. Each subscale contains of 14 questions with a Likert scale from 0 to 3: 0, does not apply to me at all; 1, applies to me to some degree or some of the time; 2, applies to me to a considerable degree or a good part of the time; and 3, applies to me very much or most of the time. Scores for depression, anxiety and stress are calculated by summing the scores for the relevant items of each subscale. A higher score indicates a higher severity of each subscale. Cutoff points for the presence of indicators of stress, anxiety and depression are 14, 7 and 10, respectively. The DASS Indonesia version shows good reliability with α = 0.95 [ 19 ].

Brief Coping Orientation to Problems Experienced Inventory (Brief COPE)

The Brief COPE is the most commonly used measure to identify the nature of coping strategies implemented by individuals and explores 14 coping strategies. Every strategy has two items. It contains 28 items and is rated on a four-point Likert scale. In this study, Brief Cope consists of 14 coping strategies including acceptance, religion, planning, humour, positive reframing, instrumental support, active coping and emotional support, self-distraction, venting, self-blame, behavioural disengagement, denial and substance use [ 20 ]. A higher score represents greater coping strategies used by respondents. The Indonesian version of the Brief COPE exhibites good reliability with α = 0.825 [ 21 ].

The Impact of Event Scale-Revised (IES-R)

The Impact of Event Scale-Revised (IES-R) is one of the most commonly used metrics of post-traumatic stress disorder symptomatology. The IES-R questionnaire was validated by a previous study. The Indonesian IES-R revealed a Cronbach's alpha of 0.90 for the test and 0.92 for the retest for the total score [ 22 ]. The IES-R consists of 22 questions, each of which can receive a minimum score of 0 or a maximum score of 4. On a five-point Likert scale, the respondents indicate how often they experienced each symptom over the previous 7 days, ranging from 0 (not at all) to 4 (very much). The overall score is between 0 and 88 [ 23 ]. According to Horowitz et al. (1979) [ 24 ], a score of > 24 suggests some PTSD symptoms, > 33 is the optimal cutoff threshold, and > 37 indicates immune system suppression and severe PTSD.

Data analysis

The collected data were managed and analyzed by IBM SPSS Statistics for Windows version 21 (IBM Corporation, Armonk, NY, USA). Descriptive statistics and frequency distribution were performed to analyse the participants' demographics and clinical characteristics. The Kolmogorov–Smirnov method was used to test for normal distribution. As the assumption of normality was rejected, the parametric tests were then used to analyze the data. The Mann–Whitney U and Kruskal–Wallis U tests were used to explore determinant factors [ 25 ], particularly demography factors, which influence anxiety, depression and post traumatic syndrome among nurses rather than correlations. This resulted in the identification of the relevant covariates that were included in the mediation analysis. All statistical tests were 2-sided with the level of significance set at 0.05.

The PROCESS Macro for SPSS version. 4.2 was used for testing the mediating effects of coping. We chose 10,000 bootstrap samples as recommended by Hayes [ 26 ]. The bootstrapping method is preferred since it can provide accurate indirect effects of coefficients for the asymmetric distribution of the data as found in the present study [ 26 , 27 ]. Furthermore, to control type 1 errors, this method can own higher statistical power [ 26 ]. Fear of COVID-19 was included as an independent variable. Copings functioned as a mediator. Anxiety and depression were analyzed as dependent variables. The indirect effect test was conducted to confirm the mediating effect. The indirect effect is statistically significant at 0.05 if the 95% confidence interval (CI) does not encompass zero. Mediation was considered successful if the indirect effect was at a significant level. On this occasion, the direct effect remained significant (partial mediation) or may have been diminished (complete mediation). A higher indirect effect score indicates the more important mediator [ 26 ].

Ethical considerations

This study was approved by the Ethical Committee Board of the Faculty of Nursing at the University of Riau in Indonesia (IRB No: 430/ UN.19.5.1.8/KEPK.FKp/2022). Informed consent to participate in this study was obtained from all patients.

Response rate

Out of the 350 participants that we approached to participate in this study, 330 of them willing participated. The reasons for refusal to participate included limited time or no particular reason offered. 52 questionnaires were excluded from the data analysis because of incomplete data. In total, 278 questionnaires were used for the final data analysis, resulting in a final response rate of 84.2%. By using a mediated analysis with a medium effect size, this sample size fulfilled the minimal sample size and achieved a power of 0.8.

Socio-demographic characteristics

As shown in Table  1 , respondents were primarily female (70.9%), married (74.1%), Muslim (94.6%), and had graduated with a bachelor of nursing degree (53.2%). The mean age of respondents was 32.69 (SD = 6.0) years. Regarding their employment status, 58.3% of the participants were volunteers. Regarding the type of ward they work in 58.3%, 27.3% and 14.4% of the respondents were in charge of the inward unit, emergency unit and intensive care unit, respectively. The mean working time length since the first visit to the hospital was 6.71 (SD = 5.31). The mean score of fear of COVID-19 was 20.26 (SD = 6.00). Based on the categorical data 7.6%, 11.5% and 17.7% of participants had depression, post-traumatic syndrome disorder and anxiety symptoms, respectively.

Differences in socio-demographic and clinical characteristics regarding depression, anxiety and QOL

Results indicated that depression was associated with employment status (x 2  = 13,458, p  = 0.001). Anxiety was associated with marital status and employment status (x 2  = 2.547, p  = 0.011 and x 2  = 14.242, p  = 0.001, respectively). Furthermore, the post-traumatic syndrome was related to the type of ward and employment status (x 2  = 7.57, p  = 0.023 and x 2  = 13, 19, p  = 0.001, respectively). All of these associated factors were used as confounders in the subsequent mediation analyses.

Coping as a mediator

A regression analysis was used to test the hypothesis that coping mediated the effects of fear of COVID-19 on depression and anxiety after controlling employment status for depression and marital and employment status for anxiety.

Results showed that after controlling for relevant confounders for each dependent variable, fear of COVID-19 had significant effects on depression, with coping having an indirect effect among them (Fig.  1 ). However, the relationship between fear of COVID-19 and depression noticeably decreased but did not show significant results after controlling for coping. These results indicated that coping fully mediated the relationship between fear of COVID-19 and depression. In addition, coping was also a significant mediator in the association of fear of COVID-19 with anxiety (Fig.  1 ). The relationship between fear of COVID-19 and anxiety showed depletion but still proved significant results which underpinned the hypothesis of partial mediation.

figure 1

Mediating effects of coping on relationships of fear of COVID-19 with depression and anxiety. Regression coefficients (a, b, c, c’) are standardized with standard errors in parentheses. The 95% confidence intervals are displayed in brackets. c = total effect; c’ = direct effect; ab = indirect effect. Confounders for depression include employment status and confounders for anxiety include employment status and marital status

Figure  2 presents the results of the analysis regarding coping as a mediator between fear of Covid 19 and post-traumatic stress disorder. Results indicated that fear of Covid 19 was a significant predictor of coping (B = 0,37, p  < 0.01) and coping was a significant predictor for PTSD (B = 0,72, p  < 0,001). Fear of COVID was also a significant predictor of PTSD (B = 0.37, p  < 0.01). Moreover, after controlling for relevant confounders for each dependent variable, the results revealed that coping exerted an indirect effect on the dependent variables. These relationships decreased but remained significant thus supporting the hypothesis regarding partial mediation. These results indicated that coping partially mediated the relationship between fear of Covid-19 and PTSD. Moreover, from the statistical results, it can be concluded that only patients with PTSD experience mental health problems. If the PTSD variable is removed, fear of Covid and mental health problems will no longer be related. These results show that PTSD plays a major role in the incidence of mental health problems and fear of COVID. Coping is not proven to be a mediator between fear of Covid 19 on depression and anxiety if PTSD variables are excluded.

figure 2

Mediating effects of coping on relationships of fear of Covid with PTSD. Regression coefficients (a, b, c, c’) are standardized with standard errors in parentheses. The 95% confidence intervals are displayed in brackets. c = total effect; c’ = direct effect; ab = indirect effect. Confounders for PTSD include the type of ward and employment status

The major purpose of this study was to test the mediating effects of coping strategies on the relationships between fear of COVID-19 and emotional symptoms (anxiety and depression) and post-traumatic syndrome disorder after controlling for relevant confounding factors. We found that coping fully mediated the relationships of fear of COVID-19 with depression. In addition, coping also partially mediated the relationship between fear of COVID-19 and anxiety and also the relationship between fear of COVID-19 to post-traumatic syndrome disorder. Fear of COVID-19 was the most prevalent mental health problem among nurses, followed by anxiety, post-traumatic syndrome disorder and depression. Previous studies in similar situations such as the SARS pandemic also found fear of infection was a common problem and was one of the main stressors among nurses [ 1 , 18 ]. In addition, similar research identified that fear of COVID-19 was a significant predictor of depression and anxiety in nurses, accounting for 34% of variance in depression and 62% in anxiety [ 28 , 29 ].

The results of this study also found that frontline nurses were vulnerable to PTSD. Only patients with PTSD experienced mental health problems. If the PTSD variable is removed, fear of Covid and mental health problems will no longer be related. These results show that PTSD plays a major role in the incidence of mental health problems and fear of COVID. PTSD is an anxiety disorder that makes sufferers remember traumatic occurrences. Nurses are probably more vulnerable to PTSD due to closer and more frequent contact with COVID-19 [ 30 ]. These results are in line with a previous study that found more than 4 in every 10 nurses screened positive for higher levels of PTSD during the COVID-19 pandemic [ 31 ]. Previous studies also found that half of frontline nurses experienced PTSD and approximately 41% of them had severe emotional fatigue [ 31 , 32 ]. This could explain why one to two years after the infection outbreaks, nurses still experienced higher levels of mental health problems and PTSD. Constantly feeling afraid and haunted by traumatic occurrences might trigger depression. Therefore, immediate interventions to protect nurses against traumatic events of this pandemic should be undertaken [ 31 ].

As expected, coping was a mediator between fear of COVID-19 and depression, anxiety and PTSD. Less use of coping strategies could increase depression, anxiety and PTSD [ 33 ]. During the COVID-19 pandemic, a sudden rise in the number of critically ill patients and the burden of decision-making affected stress and anxiety. Some studies have reported the incidence of PTSD following a sense of uncertainty and fear of disease transmission to oneself and others [ 31 , 34 ]. These findings are similar to previous research which found that coping was a significant mediator among stressors such as fear of COVID-19 on depression and anxiety in a sample of nurses [ 9 ]. Coping is the individual continuous effort to adapt one’s behaviour and cognition to cope with stressful demands beyond one’s resources [ 35 ]. Individual coping styles are vital for influencing individual stress levels which can directly affect the strength of response to the stressor [ 33 ]. When faced with stress, individuals with appropriate coping strategies have positive thoughts and solutions (e.g. constructive actions). Therefore, developing appropriate coping strategies for nurses in the context of the COVID‐19 pandemic could be a crucial factor for nurses' mental health and reduce the risk of PTSD. Hence, Nurses should be provided with information regarding coping skills and treatment for anxiety, depression, PTSD and other mental health disorders.

Nurses were at a high risk of stress-related mental illness during this pandemic since they were the key frontline healthcare workers in hospitals. Anxiety and depression are the most common psychological problems encountered among nurses who were in contact with COVID-19 [ 1 , 36 ]. The unprecedented nature of the disease and the continuous growth in the number of cases and deaths are likely to continue to cause depression and anxiety among nurses. Additionally, the prolonged fear of infecting their family and children increases healthcare workers’ stress and leads to depression and anxiety [ 28 , 37 , 38 ]. An appropriate coping strategy makes people more skilled in coping with such pressures in life and work, which subsequently reduces anxiety and depression. Although nurses felt that COVID-19 is a roller-coaster transitional journey, the use of various coping strategies has been found to effectively reduce their depression and anxiety [ 5 ]. The results of this study further support coping styles representing a potential predictive factor of depression and anxiety among nurses who were in contact with COVID-19 patients. With the implementation of appropriate coping skills and therapeutic interventions, nurses will be able to let go of the negative impacts that the COVID-19 pandemic has caused and reintegrate into their roles as caring and entrusted healthcare providers [ 9 ]. Additionally, social support plays an important role in reducing anxiety and depression, especially among nurses [ 39 , 40 ]. Moreover, the power of spirituality in conjuncture with social support may reinforces someone's coping when facing problems [ 40 , 41 ]. Spirituality can produce more sincerity about one’s problems and make it easier to accept the challenges they face in life [ 41 , 42 ]. Integrating social support and spirituality into coping strategies will assist in the reduction of stress and depression [ 41 ].

In this study, we found that almost half of the nurses feel fear of COVID-19. These findings are similar to two previous studies which assessed the prevalence of COVID-19 fear using a similar instrument to the Fear of COVID-19 Scale [ 18 ]. Previous research estimated that 45.2% of nurses feared COVID-19 while 18.1% had a particularly strong fear associated with COVID-19 [ 18 ]. However, no cutoff points were mentioned for these prevalence estimates. During the COVID-19 pandemic, the fear experienced was about either being infected or infecting others, a fear that greatly impacted frontline healthcare workers, such as nurses. Longstanding fear may stimulate the behavioural immune system, which usually leads to aversive emotions, cognition and behavioural responses [ 4 , 28 ]. In addition, prolonged exposure to fear may lead to emotional and distress-related disorders [ 4 ]. Hence, as fear may help in explaining several of these consequences, it is important to understand what creates this fear and what the predictors are.

The findings from our study have significant implications. In clinical situations, hospital management must regularly assess nurses’ coping styles and support them in choosing and practising appropriate cope. Such assessment may identify a need for support and assistance in coping more readily. To cope psychologically with the mental health problems of the COVID-19 epidemic, nurses must create new ways of coping or change their preexisting routines. In addition, it is important for a public mental health response to address PTSD, especially post-pandemic and even longer term. Early detection of PTSD among vulnerable populations, including nurses, is important to improve post-pandemic mental health and recovery. Our findings also point to the importance of developing interventions to increase nurses’s coping abilities with hospital management’s support. This kind of intervention is believed to directly reduce the mental health problems among nurses and reduce PTSD [ 1 , 43 ]. With the implementation of healthy coping skills and therapeutic interventions, nurses will soon be able to let go of the negative impacts the COVID-19 pandemic has caused and reintegrate into their roles as caring and entrusted healthcare professionals [ 9 ].

To the best of our knowledge, this is the first study which investigated the role of coping as a mediator in the relationships of fear of COVID-19 with depression, PTSD and anxiety among nurses in Indonesia. The strength of this study is that we involved a large sample of nurses from 4 regions in Indonesia. However, this study has several limitations that should be recognized when generalizing the results. First, this study was only applied in the western part of Indonesia, and it is not possible to generalize the findings to all nurses in the eastern part of Indonesia. Second, this study design was cross-sectional, which prevented any identification of the direction of causation. Other similar studies need to be conducted in other countries for comparison.

Conclusions

We found that coping was a mediator of the relationship of fear of COVID-19 with anxiety and depression. These results highlight the importance of coping among nurses who have had contact with COVID-19 patients. The hospital management system must address the regular assessment of coping strategies among nurses to detect the type of coping used. Appropriate interventions must be implemented to reduce depression and anxiety.

Availability of data and materials

Not applicable.

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This research was supported by Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Universitas Riau through Dana DIPA Universitas Riau PIU ADB AKSI International Collaborative scheme 2022 No: 1334/UN19.5.1.3/PT.01.03/2022.

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Nurul Huda &  Erwin

College of Arts and Sciences, University of Health Sciences and Pharmacy in St. Louis, St. Louis, MO, United States

Malissa Kay Shaw

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Hsiu Ju Chang

Department of Nursing, Faculty of Sport and Health Education, Universitas Pendidikan Indonesia, Bandung, Indonesia

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Satriya Pranata

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Nurul Huda conceptualized, designed, wrote the first draft and framework as well as evaluated the data. Malissa Kay Shaw and Hsiu Ju Chang conceptualized, interpreted the data and supervised. Erwin, Suci Tuty Putri, and Satriya Pranata conceptualized and interpreted the data. The published version of the manuscript has been read and approved by all authors.

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Huda, N., Shaw, M.K., Chang, H.J. et al. The mediating role of coping styles in the relationship between fear of COVID-19 and mental health problems: a cross-sectional study among nurses. BMC Public Health 24 , 545 (2024). https://doi.org/10.1186/s12889-024-17863-w

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  • Fear of COVID-19
  • Mental health

BMC Public Health

ISSN: 1471-2458

research paper on covid 19 and mental health

ORIGINAL RESEARCH article

A participatory study of college students’ mental health during the first year of the covid-19 pandemic.

Chulwoo Park&#x;

  • Department of Public Health and Recreation, San José State University, San José, CA, United States

Introduction: The COVID-19 pandemic has negatively impacted college students’ mental health and wellbeing. Even before the pandemic, young adults reported high mental health morbidity. During the pandemic, young adult college students faced unprecedented challenges, including campus closure and a pivot to fully online education.

Methods: This study employed a novel participatory approach to a Course-based Undergraduate Research Experience (CURE) in an introductory epidemiology course to examine factors students considered important regarding their experience during the pandemic. Two groups of undergraduate students enrolled in this course (one in Fall 2020 and another in Spring 2021) and participated in the CURE. A sub-group of these students continued after the class and are authors of this article. Through repeated cross-sectional surveys of college students’ peer groups in northern California in October 2020 and March 2021, this student/faculty collaborative research team evaluated depression, anxiety, suicidal ideation and several other topics related to mental health among the students’ young adult community.

Results: There was a high prevalence of anxiety (38.07% in October 2020 and 40.65% in March 2021), depression (29.85% in October 2020 and 27.57% in March 2021), and suicidal ideation (15.94% in October 2020 and 16.04% in March 2021). In addition, we identified the significant burden of loneliness for college students, with 58.06% of students reporting feeling lonely at least several days in the past two weeks. Strategies that students used to cope with the pandemic included watching shows, listening to music, or playing video games (69.01%), sleeping (56.70%), taking breaks (51.65%), and connecting with friends (52.31%) or family (51.21%). Many reported distressing household experiences: more than a third reporting loss of a job or income (34.27%) in the first year of the pandemic. We explain the participatory research approach and share empirical results of these studies.

Discussion: We found this participatory CURE approach led to novel, experience-based research questions; increased student motivation; real-world benefits such as combatting imposter syndrome and supporting graduate school intentions; integration of teaching, research, and service; and development of stronger student-faculty relationships. We close with recommendations to support student wellbeing and promote student engagement in research.

Introduction

The global COVID-19 pandemic has been devastating for many communities. In the United States, while young adults have had lower mortality rates from COVID-19 than older adults, this age group has experienced massive disruptions to their education, living situations, and livelihoods. Throughout the pandemic, young adults have consistently reported the highest levels of depression and anxiety of any age group ( 1 ) and experienced the highest unemployment rates of any age group ( 2 ).

Young adult college students have faced unprecedented challenges, including campus closure and a pivot to fully online education. National surveys of college students from March through May 2020, at the start of the pandemic, showed that two-thirds of college students were very or extremely concerned about how long the pandemic would last, and a similar proportion experienced an increase in financial stress ( 3 ). In surveys of California college students, a majority reported that they changed their living situation, nearly half lost work, and 40% took on household caregiving responsibilities ( 4 ). Over 70% of California college students reported missing class because of personal stress ( 4 ).

According to a large, multi-campus study in the summer of 2020, first generation college students were more likely than continuing generation college students to experience financial hardships, food and housing insecurity, encounter technological barriers to online education, and to experience adverse mental health outcomes ( 5 ). Black, Latinx, and low-income students were also more likely to experience financial hardships, increased caregiving responsibilities, and have inadequate access to technology to support online learning ( 4 ).

While these burdens have been well-documented in a series of reports and articles, little research has been conducted by and for diverse college students, centering students’ concerns about their experiences during the pandemic. For example, although previous research measured mental health among diverse college students in Israel ( 6 ) and the United States ( 7 ), these studies did not emphasize students engagement in the study design and data collection, or provide an opportunity for students to draw on their experiential knowledge of mental health to generate research questions. Such participatory research has the potential to identify new aspects of wellbeing that have not been previously described, through engaging students to reflect on their experiences in a way that supports meaning-making and purposeful action for improving student wellbeing. The present study aims to fill this gap by describing a Course-based Undergraduate Research Experience (CURE) to conduct participatory research on college student wellbeing during the COVID-19 pandemic at a large, diverse public university in northern California.

This article employs a novel structure: while presenting findings from an empirical epidemiologic study, it documents the participatory methods and presents qualitative evaluative comments from members of the collaborative research team. In addition, using a participatory CURE approach, we illustrate the impacts of conducting this epidemiology research. We share the empirical results of this study which, though limited in their generalizability, produce valid estimates of the burden of various mental health challenges in the peer group of the student research partners. We argue that participatory CURE approaches are an under-utilized methodology in public health and social science disciplines. We encourage more wide-spread adoption focusing on adolescent mental health in school and university settings.

Participatory course-based undergraduate research experience

CUREs are a form of experiential learning where students gain hands-on research experience within a credit-bearing course. CUREs share five key attributes: they (1) engage students in scientific research, (2) emphasize collaboration, (3) produce new knowledge, (4) focus on broadly relevant topics, and (5) are scaffolded or iterative, allowing for multiple learning opportunities ( 8 – 10 ). While originally promoted in STEM education as a way to scale student involvement in research ( 11 , 12 ), CUREs have been increasingly recognized as high impact practices for diverse college students to increase retention, promote a sense of belonging in higher education, and enhance diversity in the academic pipeline ( 9 ). Empirical research is scant in social sciences and public health on the use of CUREs ( 10 ).

Because of their emphasis on engagement, focus on topics relevant to students, orientation toward students as collaborators, and scaffolded learning opportunities, CUREs are a natural fit for participatory research methodologies. Participatory methodologies have been employed in fields as diverse as education, international development, and public health for the past five decades. While the specific methods used in these fields vary, they share a common approach, centering partnership, mutual learning, application/action, and real world impact ( 13 ). Within the field of public health, the most commonly used participatory research approaches are Community-Based Participatory Research (CBPR), Participatory Action Research (PAR), and Youth Participatory Action Research (YPAR) ( 14 ).

Participatory research methodologies are collaborative and seek to equitably involve academic and non-academic research partners through all phases of a study, from identification of a problem to research design and implementation to analyzing and disseminating the study results. Many participatory studies aim to improve health and health equity ( 15 ). Participatory methodologies build on the resources and strengths of community members or participants, valuing lived experience in addition to other forms of knowledge. These methodologies often result in context-specific research and action projects. In addition, participatory methods often use multiple strategies for dissemination of knowledge produced through a study, including forms that are most accessible to community members as well as more traditional academic products or policy/advocacy reports ( 16 ).

The present study employed a participatory approach to a CURE study within a public health course. We considered college students as the participants or community of interest, situating the faculty member teaching the course as the “academic partner,” recognizing that, in reality, often participatory research partners occupy multiple, complex positions within a study ( 17 ). The CURE design encouraged student co-researchers to draw on the knowledge gained not only in their academic studies, but also from their lived experiences as members of the affected population. In addition, this participatory CURE provided an opportunity for continued involvement in this study after the course had concluded. Indeed, student co-researchers participated in all aspects of the study, including the writing of this manuscript. While all authors participated in the research and writing, we chose to italicize reflections of individual research team members. While there is rich history to this multi-voiced approach in qualitative research ( 18 ), this approach is less common in quantitative public health research, and we are not aware of any CURE studies that incorporate student voice as explicitly. We hope that this innovative approach deepens the reader’s experience in learning about this participatory course-based study and provides context to the experience of college students during the COVID-19 pandemic.

Materials and methods

Study design.

The study took place at a large, diverse, urban public university in northern California. Two linked cross-sectional studies were designed, implemented, and analyzed by college students as part of a CURE embedded in a required junior-level public health introductory epidemiology course. The course instructor (last author) encouraged students to think about their own lives, what they were learning about the pandemic through their classes, news and social media, and the experiences and concerns of their family and friends in order to identify topics that they wanted to explore through a survey.

Working in small groups, students discussed what topics they were generally interested in and then conducted literature reviews to identify what was already known on the topics they selected. With guidance from the instructor, students then developed survey questions on their topic, which were integrated into a single survey assessing their peers’ experiences during the COVID-19 pandemic. When topics examined constructs where standardized scales were available (e.g., depression), the instructor encouraged students to use these standard scales; when topics were novel or no prior scale could be found, original survey questions were developed based on student experiences and perspectives.

The present article describes the work of two different classes, with a focus on results related broadly to mental health. In the fall semester, the class had 25 students working in five teams; in the spring semester, the class had 26 students working in six teams. Each class produced one survey, which pulled together the research questions developed by each of the student teams: one survey conducted during fall semester of 2020 (“October 2020 survey”) and one survey conducted during spring semester of 2021 (“March 2021 survey”). Topics fell broadly under the heading of wellness during COVID-19. Both surveys assessed demographics and mental health. In this pre-vaccine era, students in the fall semester also examined attitudes toward COVID-19 vaccination, employment characteristics, access to personal protective equipment and social distancing at work, and coping strategies that participants were using to deal with the pandemic. In the spring, students added questions focusing on different aspects of mental health, food insecurity, adverse household experiences during the pandemic, as well as use of legal and illicit drugs. The rest of this article focuses on the survey constructs related to mental health. Both study protocols were written by students, edited by the faculty member, and approved by the University’s Institutional Review Board.

The student-faculty collaboration continued after the class ended with five undergraduate students (authors 3–7) joining the instructor, a colleague (first author), and a graduate student (second author) in further data analysis and dissemination. These continuing students led a process where they identified which findings they thought were important to share with the college community and their peers and disseminated these findings to university stakeholders through on-campus presentations, email to the director of the campus health center and other campus leaders, and through a blog. 1 The study team also collaborated on developing conference abstracts, which they presented in November 2021 at the American Public Health Association annual meeting, and in writing the present article.

Data collection

Both surveys employed a non-probability sampling design, disseminating a link to an anonymous online Qualtrics xm (Qualtrics International Inc., Provo, UT) survey through email and social media. The October 2020 survey was opened on October 14, 2020 and closed on November 4, 2020; the March 2021 survey was opened on March 23, 2021 and closed on April 13, 2021.

Each class developed their own sampling strategy and inclusion/exclusion criteria. For the October 2020 survey, the class decided to include all adults over the age of 18 who were California residents. This decision was made because many of the students were in their first semester at the university after transferring from community college and students were concerned that if they restricted participants to college students at their university, they might not be able to obtain a sufficiently large sample to examine the questions of interest. However, in documenting where they distributed the survey link, it was apparent that most people who received the survey link were contacted through campus lists (e.g., class lists, student organizations and clubs, and sports teams) and the sample was predominantly college students. For the March 2021 survey, the class decided to only include students over the age of 18 at the university where the study took place. Similar strategies were employed to disseminate the survey link.

Both surveys began by assessing the eligibility criteria. If a participant did not meet either of the criteria, they were taken to the end of the survey. If a participant met the criteria, they were asked a series of demographic questions including their age, gender identity, sexual orientation, and racial or ethnic identity.

Both surveys used the Patient Health Questionnaire-4 (PHQ-4) to assess depression and anxiety. These two constructs were coded according to the scale conventions with a total score ≥ 3 for questions 1 and 2 suggesting anxiety and a total score ≥ 3 for questions 3 and 4 suggesting depression ( 19 ). The PHQ-4 consists of a 2-item depression scale (PHQ-2) and a 2-item anxiety scale (GAD-2) and has good psychometric properties to measure depression and anxiety in the general population ( 19 , 20 ).

Both surveys included item 9 of the PHQ-9, which assesses thoughts of self-harm or suicidal ideation. This single item has been found to be a strong predictor of future suicide attempt or completion in large, population-based studies ( 21 , 22 ). Consistent with other studies of college student mental health, we used this single item as a proxy for suicidal ideation ( 23 – 25 ). We classified responses of “not at all” to this question as not having suicidal ideation and responses of “several days” or more frequently as having suicidal ideation to make it a binary variable.

The October 2020 survey assessed experiences of loneliness by asking how often participants felt lonely or isolated. This question was modified from the CES-D ( 26 ), which uses a reference time of 7 days, to use the same 2 week time frame as the PHQ-4 and PHQ-9 item 9. In addition, the survey asked “What are some of the ways you are coping with the COVID-19 pandemic?” and offered the following options: Taking breaks; Sleeping; Mindfulness practice or breathing exercises; Using alcohol or cannabis; Using other drugs; Exercise or spending time outdoors; Art, music, journaling, or another creative expression; Connecting with friends; Connecting with family; Watching shows, listening to music, or playing video games; Taking care of a pet; or Other (specify).

The March 2021 survey asked participants “Overall, how would you say your mental health has been since the start of the pandemic?” with the options of reporting that their mental health had gotten worse, stayed the same, gotten better, or that they were unsure. For participants who reported their mental health had gotten worse, they were asked the follow up question “Do you believe that your worsening mental health is due to the pandemic?” with options to state “Yes, largely due to the pandemic and its associated challenges,” “Somewhat, the pandemic has contributed, but there are other factors, too,” “No, my experience is not really because of the pandemic,” or “Unsure.” In addition, this survey asked participants “In the last 2 weeks, how often have you felt that you were on top of things?” with response options mirroring those available for the PHQ survey items. The March 2021 survey also assessed whether participants or members of their household had any of several distressing experiences during the COVID-19 pandemic.

Statistical methods

First, we described the demographic characteristics of the population. We then estimated the overall prevalence of each primary outcome and the distribution of these outcomes by demographic characteristic, testing for differences. Second, we assessed differences in the level of mental health pathology (anxiety, depression, and suicidal ideation) across the two time periods by using a qui-square test. We used generalizable linear regression models with robust standard errors to examine hypothesized relationships between variables. All analyses were performed using Stata/MP 14.2 (StataCorp, College Station, TX).

Study findings

There were 457 and 245 survey responses, respectively, in October 2020 and March 2021. After excluding respondents who did not meet eligibility criteria or who did not provide answers to any of the survey questions after opening the survey, we were left with analytic samples of 394 participants in October 2020 and 222 participants in March 2021, resulting in a total analytic sample of 616. Demographic characteristics of the samples are provided in Table 1 and show similar distributions by gender, sexual orientation, and race across the two different time frames. More than 70% of participants were female, three-fourths were heterosexual, the majority were Asian or Latinx, and ~75% were aged 18–25.

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Table 1 . Mental health status during COVID-19.

Anxiety and depression were common in this primarily young adult population ( Table 1 ). In October 2020, 38.07% met the criteria for anxiety and 29.85% met the criteria for depression; in March 2021, 40.65% met the criteria for anxiety and 27.57% met the criteria for depression. Suicidal ideation was also high with 15.94% of participants in October 2020 and 16.04% in March 2021 reporting suicidal thoughts. The prevalence of anxiety was higher among women compared to men in both time periods (43.94% vs. 20%, χ 2 (2) = 19.1, p  < 0.001 in October 2020 and 44.83% vs. 20.45%, χ 2 (2) = 13.31, p  = 0.003 in March 2021). Compared to heterosexual participants, non-heterosexual participants (combining gay, lesbian, bisexual, mostly heterosexual, and other) showed significantly higher prevalence in anxiety (54.41% vs. 34.78%, χ 2 (1) = 9.16, p  = 0.002), depression (55.88% vs. 24.53%, χ 2 (1) = 25.91, p  < 0.001), and suicidal ideation (33.82% vs. 11.8%, χ 2 (1) = 20.02, p  < 0.001) in October 2020. Participants aged 18–25 showed significantly higher prevalence of suicidal ideation compared to older adults (18.18% vs. 8.25%, χ 2 (1) = 4.91, p  = 0.027) in October 2020.

Examining differences in mental health outcomes across the two time periods, we found that the prevalence of anxiety, depression, and suicidal ideation were not significantly different (chi-square tests, respectively, p  = 0.533, 0.555, 0.975). We present data on the comorbidity of these mental health outcomes in Figure 1 . Among participants who answered all questions in the PHQ-4 and item 9 of the PHQ-9 across the two different time periods ( N  = 601), 50 of them (8.32%) were classified as meeting the criteria for all three outcomes ( Figure 1 ).

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Figure 1 . Comorbidity of anxiety, depression, and suicidal ideation. This graph displays the proportion of participants classified with each of the three mental health disorders in October 2020 and March 2021 (Total: 601).

In addition to anxiety, depression, and suicidal ideation, the October 2020 survey assessed loneliness and coping strategies that students were employing to manage the challenges of the pandemic. More than half the participants reported loneliness for several days, more than half the days, or nearly every day ( Table 2 ). Participants provided multiple answers for their coping strategies. The most common coping strategy was watching shows, listening to music, or playing video games (69.01%). More than half of the participants reported sleeping (56.70%), taking breaks (51.65%), connecting with friends (52.31%), and connecting with family (51.21%) to help them manage the pandemic. Other coping strategies are described in Table 2 .

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Table 2 . Additional mental health survey results from October 2020 ( N = 394) and March 2021 ( N = 222).

Using a generalizable linear regression model with robust standard errors, we found that students who experienced anxiety or depression were less likely to report taking breaks (Prevalence Ratio 0.73, 95% CI 0.56, 0.97, p  = 0.029 for anxiety and PR 0.68, 95% CI 0.49, 0.94, p  = 0.021 for depression) than students without anxiety or depression. Students who experienced depression, suicidal ideation, or reported feeling lonely nearly every day were less likely to report connecting with family (PR 0.69, 95% CI 0.49, 0.95, p  = 0.027 for depression; PR 0.49, 95% CI 0.30, 0.80, p  = 0.004 for suicidal ideation; PR 0.32, 95% CI 0.15, 0.67, p  = 0.003 for loneliness nearly every day). Those who reported feeling lonely nearly every day had more than twice the prevalence of coping by sleeping than students who did not report feeling lonely (PR 2.25, 95% CI 1.19, 4.23, p  = 0.012).

The March 2021 survey asked participants to report on their overall mental health: almost half of the participants’ reported that their mental health had become worse since the pandemic (46.40%), with 96.05% reporting that this was due fully or partially to the COVID-19 pandemic ( Table 2 ). Participants reported feeling on top of things for several days (43.40%), more than half the days (21.23%), and nearly every day (8.49%). Student researchers also inquired about various distressing experiences that participants or someone in their household may have had because of the COVID-19 pandemic in the March 2021 survey ( Table 2 ). Almost half of the participants reported that they or someone in their household had experienced mental health problems (43.95%). Additional stressors reported included loss of a job or income (34.27%), working without PPE or social distancing (21.77%), becoming sick with COVID-19 (19.35%), delaying necessary medical care (12.50%), experiencing violence or abuse (5.24%), reducing work to care for children (4.84%), and experiencing eviction, foreclosure, or being required to move to save money (3.63%).

Reflections on the research process

Through use of this participatory CURE, the student-faculty research team was able to obtain answers to original research questions of interest to the team members, campus stakeholders, and the broader public health community. Topics across the two semesters of the CURE were different, reflecting the CURE principle that new research questions and directions be generated each semester, in collaboration with the students in the course. In fact, there is an expectation in CURE research that work in a CURE is “unlikely to look the same from year to year” ( 27 ).

Students reported increased motivation to work on this participatory CURE than projects for other courses. One student reported: “ the prospect of collecting meaningful data and promoting change through our analysis motivated us to choose a topic that would interest us for the next 4+ months. We were all very interested in mental health and knew we would find shifts in wellness during COVID-19 because of our own experiences .” Another student reflected that the participatory CURE “ allowed me the chance to practically apply concepts we were learning in class. ” The graduate research assistant shared, “ I started this work at a time where I was feeling uninspired in my internship and struggling with motivation in grad school. Collaborating with this team helped me cope with my own isolation and loneliness. ”

While highly motivating, working on research that had potential for application outside the classroom context also made students feel nervous. One student shared that she experienced imposter syndrome and felt her “ excitement being quickly overtaken by anxiousness and doubt in my capability to keep up with the rest of the team .” The regular meeting structure, conversations with her peer co-researchers, and candid talks with the graduate student assistant working on the study all helped allay this student’s concerns. She noted that the faculty created “ an open space to speak out, for us to ask questions ” and encouraged students to “ step outside of our comfort zone .” Another student researcher shared “ The level of professionalism required for this project was honestly such a new experience for everybody involved! ”

As others have reported, participating in a CURE made some students more interested in pursuing graduate education. One student co-researcher shared, “ I really enjoyed how the curriculum changed to reflect that semester’s cohort. Working on this study and actively exploring epidemiology made me much more interested in public health. Now I want to pursue an MPH with a concentration in epidemiology/biostatistics. ”

As with other participatory methodologies, this study made use of the participant’s lived experiences to guide the research questions, strengthening their relevance. For example, loneliness was a topic that students selected because it resonated with their own experiences. One student shared “ I already had an idea what isolation can do to a person’s mental wellbeing from my job working at a 55+ aged community for skilled nursing and memory care… I witnessed elderly residents’ mental health decline from isolation because they were unable to receive social support from their family due to the lockdown protocol…. I was also isolating myself in my room and encountering loneliness myself. When speaking to other people my age they reported they were also experiencing similar feelings. I wondered how these experiences were being felt by my own community of college students. ” Working on this topic allowed this student to draw on her professional experience, her personal experience, and her academic knowledge. Students were similarly encouraged to decide for themselves what they thought the most valuable means of disseminating the study findings would be and selected a public-facing blog where they could write findings in lay language and post videos of short professional presentations of the research findings.

As other faculty engaged in CUREs have reported, the faculty collaborating in this participatory CURE reported that it was highly satisfying ( 28 ). One faculty member shared: “ I find doing student-partnered research incredibly meaningful. I love having the opportunity to more closely integrate my teaching, research, and mentorship. While I sometimes feel like the stakes for a CURE are higher than for other teaching approaches and it requires a deeper investment of time, that additional work is offset by the joy of seeing students motivated to do work for the science itself rather than for a grade. ” The other faculty member pointed out that employing a participatory CURE approach was an effective way to “ pursue research productivity and teaching effectiveness at the same time. ”

These perspectives highlight the potential for transformative experiences for students and faculty engaged in participatory CURE, especially on topics related to adolescent mental health and wellbeing in schools and universities. The Course-Based Undergraduate Research Experiences Network (CUREnet) database provides details on 25 CUREs across 24 campuses in multiple countries; despite the diverse topics and contexts, all of the courses are in STEM fields ( 29 ). This article extends the CURE literature by providing an example of a CURE within the public health field.

In our study of diverse young adults, we found that 46.93% had evidence of clinical levels of either anxiety or depression. This is consistent with the Healthy Minds Study from Fall 2020, which sampled over 30,000 college students on campuses across the country and found 47% met criteria for depression and/or anxiety disorders ( 30 ). While the Healthy Minds Study used different measures for anxiety and depression than we used in our study, these data are also similar to the findings from the U.S. Census Household Pulse survey, which, like the present study, used the PHQ-4. Throughout the pandemic, the Household Pulse survey has found that young adults ages 18–24 have the highest level of anxiety and depression of any age group ( 31 ). In December, midway between our two surveys, 56.2% of 18–24-year old’s surveyed reported symptoms of anxiety and/or depression. Depression and anxiety were more common in households that had experienced job loss and among racial and ethnic minority populations ( 31 ).

The level of depression in our study was slightly higher than has been reported in this specific student population previously and higher than most prior studies of college student mental health ( 32 ), likely reflecting the increase in depression in the population during the COVID-19 pandemic ( 33 ). However, these prior studies used the PHQ-9, a different measure of depression, rather than the PHQ-4, and thus observed differences might also reflect these different scales.

Compared to the Fall 2020 Healthy Minds Study, our participants reported more suicidal ideation in both time periods (Healthy Minds found 13% past year suicidal ideation vs. 15.94% and 16.04% past 2 weeks suicidal ideation in our study) ( 30 ). Our participants reported less loneliness than students in the Healthy Minds Study (41.94% of participants in our study reported not feeling lonely at all vs. 34% of students in the Healthy Minds Study reported feeling isolated from others hardly ever) ( 30 ). Suicidal ideation and loneliness are critical factors to track as even before the pandemic, suicide was the second leading cause of death in young people and the social isolation brought on by the pandemic is expected to exacerbate this problem ( 34 ).

Similar to surveys of Canadian students in the early months of the pandemic, over half of students in our October 2020 survey reported connecting with family or friends to help them cope with the pandemic and students who used these social strategies had better mental health ( 35 ). While a similar proportion of participants also reported using exercise to cope (our study: 46.81% vs. Canadian study 54.5%), twice as many participants in our study used mindfulness (27.69%) compared to students in the Canadian study (12.0%). More than half of our participants reported sleeping to cope, compared to just 17.5% of Canadian students.

The adverse mental health impacts of the COVID-19 pandemic are likely to be felt for years after the pandemic ends ( 36 ). Recognizing the extensive mental health challenges faced by young adult college students, we suggest that Universities proactively employ universal approaches to improving mental health, rather than relying on counseling and psychological services within health centers to treat all students who could potentially benefit from mental health care ( 37 ). Universal approaches target mental health interventions to all students through multiple, overlapping strategies rather than rely on the typical client/therapist mental health care model. Such approaches might focus on building the skills of resilience, which research shows can be actively taught ( 38 ). To combat loneliness and improve general emotional wellbeing, universities might also consider programming specifically aimed at reducing loneliness, including pedagogy training for faculty on teaching strategies that promote connections between students ( 39 ).

The strengths of this study include its participatory design, use of valid and reliable scales in addition to novel constructs, and good sample sizes for the research questions examined. The study quality benefits from the strong voice of students, members of the population under study, and the collaborative nature of the study. Survey questions reflected students’ interests, representing community members’ interests as well. From the students’ perspective, this study closed the gap between knowledge learned in the course and actual research and application. From the course instructors’ perspective, the CURE synergizes teaching and research, and require high commitment, creativity, investigativeness, and critical analysis ( 40 ).

Limitations

The study findings are limited by the non-probability sampling approach, decreasing generalizability. As students in the CURE were public health majors and minors, it is likely that the study sample overrepresented students in this field of study compared to other disciplines. Students were also mostly juniors and seniors, who might have different experiences than first-year students ( 41 ). However, our empirical findings are very similar to contemporaneous larger studies, such as the non-representative Healthy Minds Study and the nationally representative Household Pulse Survey. Regarding data from two time periods, the study represents a repeated cross-sectional survey rather than a longitudinal design and so changes in variables do not reflect changes at the individual level, but rather at the population level. Although the respondents of the first survey in October 2020 were not the same respondents of the second survey in March 2021 survey, the two samples were drawn from overlapping source populations and by comparing results in the two time periods, we could observe how mental health among this college student population changed. In addition, we did not specify a priori how we would evaluate the process or outcomes related to using this participatory CURE approach. Future participatory CURE studies would be strengthened from a priori specification of the design to assess process and CURE outcomes such as student motivation and graduate school intentions.

Course-Based Undergraduate Research Experiences are ripe locations for integrating participatory research approaches, such as Community-Based Participatory Research. This participatory CURE gave rise to a deeper understanding of college students’ mental health burden and highlighted both areas of risk and factors that are protective for this population. Students who engage in CUREs can be strong advocates for the application of research findings, bringing their youthful passion to solve complex problems. In the case of these findings, we hope that colleges and universities take seriously their obligation to serve and protect their student population by increasing mental health services, given the high need in this young adult population. While CUREs have been slow to be adopted outside of STEM fields, this study adds to a growing body of literature demonstrating the feasibility of CUREs in public health and other social sciences.

Data availability statement

The datasets presented in this article are not readily available because of concerns that privacy of research participants may be compromised when variables are combined. Requests to access the datasets should be directed to [email protected] .

Ethics statement

The studies involving human participants were reviewed and approved by San José State University IRB approval (IRB protocol tracking numbers 20251 and 21069). Exempt registration was received. Waiver of signed consent was approved. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author contributions

MW developed the CURE and taught the course. KN, DP, HM, TA, and the PH 161 cohorts of Fall 2020 and Spring 2021 designed the study and collected the data. CP, MMF, TME, KN, DP, HM, TA, and MW conducted initial data analysis. CP further analyzed the data and produced the tables and figures. CP, MW, KN, TME, and MMF conceived of the manuscript. MW drafted the manuscript, with contributions from KN, TME, CP, and MMF. All authors contributed to the article and approved the submitted version.

This work was supported by the Thoracic Foundation.

Acknowledgments

The authors would like to thank students from Worthen’s PH 161 Epidemiology classes in Fall 2020 and Spring 2021 at San José State University.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: course-based undergraduate research experience, depression, anxiety, loneliness, COVID-19, participatory research

Citation: Park C, McClure Fuller M, Echevarria TM, Nguyen K, Perez D, Masood H, Alsharif T and Worthen M (2023) A participatory study of college students’ mental health during the first year of the COVID-19 pandemic. Front. Public Health . 11:1116865. doi: 10.3389/fpubh.2023.1116865

Received: 05 December 2022; Accepted: 02 March 2023; Published: 21 March 2023.

Reviewed by:

Copyright © 2023 Park, McClure Fuller, Echevarria, Nguyen, Perez, Masood, Alsharif and Worthen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Miranda Worthen, [email protected]

† ORCID: Chulwoo Park, https://orcid.org/0000-0003-0667-6549 Miranda Worthen, https://orcid.org/0000-0002-6494-7098

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

research paper on covid 19 and mental health

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Study reveals insights on COVID-19 pandemic-related drinking and mental health

People who maintained their drinking habits had lower prevalence of mental health issues compared to those who abstained or whose drinking patterns changed

By the University at Buffalo 

New research from the University at Buffalo provides the most comprehensive assessment to date of drinking patterns during the COVID-19 pandemic and their association with four clinically prevalent mental health disorders in the U.S.

The study, published in March in the journal Alcohol and Alcoholism , looked at alcohol consumption among more than 3,600 U.S. residents, and examined associations between drinking patterns and anxiety, depression, stress and post-traumatic stress disorder (PTSD).

“We found that people who kept their drinking patterns had lower prevalence of mental health issues than abstainers or those whose drinking pattern changed,” said study first author Yihua Yue, an epidemiology and environmental health Ph.D. student in UB’s School of Public Health and Health Professions. “However, increased alcohol use and binge drinking were associated with higher odds of mental health disorders, highlighting the co-existence of alcohol overconsumption and mental health problems as a public health concern of the negative COVID-19 impacts.”

The research team included undergraduate students, and high school students from Nichols school and Williamsville East High School, who were actively involved in all aspects of the study, including assisting with a poster presentation at the Public Health Partnership Conference, where the study’s findings were highlighted.

Study participants provided information on four measures of alcohol consumption during the pandemic: drinking frequency, number of drinks per occasion, change in alcohol consumption since the COVID-19 pandemic, and binge drinking frequency. Self-reported symptoms of four mental health disorders were also obtained. The prevalence of symptoms of anxiety (23%), depression (27%), stress (51%) and PTSD (22%) was identified from May through August 2020, when a stay-at-home policy was in effect for several months.

During the pandemic, a significant portion of study participants altered their drinking habits, with 23% of respondents increasing alcohol consumption and 8% decreasing; 4% reported binge drinking weekly or more.

The study also broke out social determinants to, for the first time, identify groups that have stronger associations of alcohol consumption with mental health outcomes.

“We found that women, members of underrepresented groups and people who were worried about money who either increased their alcohol use or engaged in binge drinking during the pandemic experienced worse mental health,” Yue said.

People who did not have any financial concerns tended to drink more often.

Interestingly, drinkers had lower odds of all mental health symptoms, and the odds decreased with increased frequency. Individuals who drank weekly or more often, and those who had 10 drinks or more per month, had significantly lower odds of depression, the study found. But, participants who reported binge drinking had higher odds of reporting depression and stress.

Study senior author Lina Mu, M.D ., associate professor of epidemiology and environmental health in the School of Public Health and Health Professions, cautions, however, “It is important to interpret the data carefully and we must note that this study does not draw causal inferences.”

Yue said it’s possible that other factors that were not measured in the study may coexist with alcohol use and mental health symptoms. For example, socially active individuals may regularly consume moderate amounts of alcohol and display greater resilience when facing mental health challenges. It is, then, their mental resilience — not their alcohol use — that protects them from mental health problems.

Moreover, she added, people with financial stability may be more inclined to engage in moderate alcohol consumption as a social activity and may have better access to resources that help them manage stress and address mental health. On the other hand, individuals experiencing financial difficulties might be more susceptible to excessive alcohol consumption as a coping mechanism for stress or mental health problems, potentially worsening their mental health conditions.

“During the pandemic, people went through so many behavioral changes,” Mu said. “It is very critical to understand how those changes influence mental health so that we can guide the public to be better prepared in preventing mental health problems. This is a very complicated issue, and we are still at the early stage of addressing it.”

Beth A. Smith, M.D., professor of psychiatry and pediatrics in the Jacobs School of Medicine and Biomedical Sciences at UB, and chief of behavioral health for Kaleida Health, is a co-author on the paper.

The study included researchers from Vanderbilt, University of St. Andrews, UCLA, University of Southern California, Shanxi Medical University and University of Wisconsin, Madison.

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