Increase in Mental Disorders During the COVID-19 Pandemic—The Role of Occupational and Financial Strains
An analysis of the German National Cohort (NAKO) Study
Background: Numerous studies have reported an increase in mental disorders during the COVID-19 pandemic, but the exact reasons for this development are not well understood. In this study we investigate whether pandemic-related occupational and financial changes (e.g., reduced working hours, working from home, financial losses) were associated with increased symptoms of depression and anxiety compared with the situation before the pandemic.
Methods: We analyzed data from the German National Cohort (NAKO) Study. Between May and November 2020, 161 849 study participants answered questions on their mental state and social circumstances. Their responses were compared with data from the baseline survey before the pandemic (2014–2019). Linear fixed-effects models were used to determine whether individual changes in the severity of symptoms of depression (PHQ-9) or anxiety (GAD-7) were associated with occupational/
financial changes (controlling for various covariates).
Results: The prevalence of moderate or severe symptoms of depression and anxiety increased by 2.4% and 1.5%, respectively, during the COVID-19 pandemic compared with the preceding years. The mean severity of the symptoms rose slightly. A pronounced increase in symptoms was observed among those who became unemployed during the pandemic (+ 1.16 points on the depression scale, 95% confidence interval [0.91; 1.41], range 0–27). Increases were also seen for reduced working hours with no short-time allowance, increased working hours, working from home, insecurity regarding employment, and financial strain. The deterioration in mental health was largely statistically explained by the occupational and financial changes investigated in the model.
Conclusion: Depressive symptoms and anxiety disorders increased slightly in the study population during the first year of the COVID-19 pandemic. Occupational and financial difficulties were an essential contributory factor. These strains should be taken into account both in the care of individual patients and in the planning of targeted prevention measures.
The COVID-19 pandemic is a global crisis, impacting the living and working conditions of large numbers of people. Hence, it was suspected early on that the frequency of mental disorders and diseases could increase. (1, 2, 3). From various countries studies with measurements before and during the pandemic have become available, indicating that the state of mental health of the population did indeed deteriorate compared to the situation before the pandemic (4, 5, 6, 7, 8, 9). Multiple potential risk factors for the increase in mental disorders have been discussed. These include social isolation, working from home, home schooling, fear of infection with the SARS-CoV-2 virus, actual infections, and lifestyle changes, such as increased substance use (e.g. alcohol), among others (10, 11, 12, 13, 14). Moreover, collective social crises in general have the potential to put a strain on the mental health of people, even on those who are not directly impacted by their negative effects (15, 16). Furthermore, early empirical data suggest that pandemic-related changes in economic status and employment are associated with deterioration in mental health during the pandemic (17, 18, 19). Factors such fear of becoming unemployed, financial worries and work-related strain due to increased working hours are known risk factors for various types of mental disorders (20, 21, 22, 23). Since economic life was also impaired as a result of the measures that had to be taken to contain the spread of the virus, such strains may have increased and influenced the prevalence of mental health problems. This would be in line with the experiences made during previous economic crises, such as the 2008 global financial crisis, when a population-level mental health decline was observed in many countries (24).
What specific changes actually exert an effect on mental health is difficult to determine, because research into mediating risk factors has rarely been based on longitudinal studies. For this reason, we analyzed data of the prospective, population-based German National Cohort (NAKO) Study to find out to what extent pandemic-related changes were associated with an increase in depressive and anxiety-related symptoms during the first wave of the COVID-19 pandemic (6). Our study focused on work-related changes and changes in income. In addition, sociodemographic, health-related and COVID-19-specific factors were taken into account as covariates. This research approach aimed to identify specific risks that should receive special attention in the provision of care during the current and in future crises and which could be the focus of supporting, mental health-promoting preventive actions, in addition to the required infection control measures.
The population-based German National Cohort (NAKO) Study is with 205 185 participants Germany’s largest epidemiological cohort study. The baseline survey was conducted in 18 study centers in 13 German federal states (“Länder”) between 2014 and 2019. Random samples of the general population aged 20 to 69 years were drawn by the local registration offices and then used to recruit the study participants. The mean response rate was 18% (25). At each of the study centers, an ethics committee approval was available and the respondents agreed in writing to participate in the study after an informed consent discussion. A detailed description of the study design can be found in earlier publications (25, 26, 27).
A special survey was conducted upon the onset of the COVID-19 pandemic (6). Participants with e-mail addresses received a link to an online survey, while the remaining participants were sent paper questionnaires. In both cases, the mailing took place between 30 April 2020 and 15 May 2020. Individuals known to be deceased or to have withdrawn their consent were excluded. A total of 197 834 individuals were contacted; of these 161 892 responded up to and including 30 November 2020 (response rate: 81.8%). 105 questionnaires of the special survey were not used because the dates of completion were invalid. Thus, the analyses below are based on 161 787 individuals for whom one observation before and one during the pandemic were available.
During the baseline survey, sociodemographic data were obtained by means of a standardized interview and mental health information from a touchscreen questionnaire. In the COVID-19 special survey, online questionnaires and mailed paper questionnaires were used. The variables selected for the survey are outlined below and more detailed descriptions are provided in the eMethods section.
Mental disorders: Symptoms of depression were obtained using the Patient Health Questionnaire (PHQ-9) which surveyed the frequency of occurrence of nine symptoms over the last two weeks (28). Responses were added up to create a total score (range: 0–27, high values corresponded to high severity). Moderate or severe symptoms of depression were defined as scores ≥= 10. Anxiety was measured using the Generalized Anxiety Disorder Scale-7 (GAD-7) which measures the frequency of occurrence of seven symptoms of generalized anxiety disorder during the last four weeks (in the NAKO version) (29). Here, again, a total was calculated (range: 0–21) and moderate or severe symptoms of anxiety were defined as scores ≥= 10.
Work-related changes: Three indicators were used. Changes in employment situation were identified by first determining the employment status at both measurement points based on the workforce concept and then differentiating between employed, unemployed and inactive (pension, retirement, study, other) persons (30). Furthermore, pandemic-related work-specific changes were queried directly and combined with the employment status. In the follow-up survey, the options listed below were added to the category “working“: change of job, increase in working hours, reduction in working hours with and without short-time allowance, loss of job. In the “unemployed“ category, it was differentiated between unemployment before and due to the pandemic situation. Change in perceived job insecurity was the second indicator measured at both time points (31). Its four-item response scale was dichotomized and inactive persons were assigned to the “no job insecurity” category. The third indicator was the response to the question whether due to the pandemic working from home was required on all or some days.
Financial changes: As part of the COVID-19 special survey, the respondents reported whether their household‘s financial situation had improved, worsened, or remained the same since the start of the pandemic.
Covariates: A variety of characteristics were included in the study as potential confounders: age, gender, type of household, high-risk contact with a SARS-CoV-2 infected person, own SARS-CoV-2 infection, and self-reported health.
First, PHQ-9 and GAD-7 means as well as rates of occurrence of moderate or severe symptoms of depression or anxiety (cut-off ≥= 10) were calculated across the survey years. Given the differences in the sociodemographic characteristics of the respondents in the survey years of the baseline survey, adjustments were made for age, gender and study center. Changes in mental health were analyzed using linear panel data models with fixed effects (FE models). FE models look at intrapersonal changes of the dependent variable—in this case the symptoms— over time and relate them to changes in an independent variable, such as employment status. The intraindividual comparison ensures that effect estimates of FE models are controlled for both observed and unobserved time-constant confounders (characteristics with the same expression at all measurement points, such as gender) (32). For each outcome, three models were calculated. The first model estimated the individual changes in depression/anxiety during the pandemic in comparison with the baseline survey without adjustment. The second model included covariates (see above), complemented by indicators for occupational and financial changes in the final model. Missing values for dependent and independent variables were imputed, using a predictive mean matching method (eTable 1) (33). In addition, because negative occupational and/or financial changes may have different effects in men and women, separate models were calculated by gender.
For the sensitivity analyses, both symptom scales were dichotomized (cut-off ≥= 10) to find out whether there were changes in the proportions of moderate or severe symptoms. Associations were investigated using a multi-level model with Poisson distribution and robust standard errors to calculate the relative risks (RR) for the probability of mental disease during the pandemic. Furthermore, the primary analysis of the changes in mean symptom severity was repeated, but in a stepwise fashion, adding the main effect, occupational changes, financial strains, and finally the covariates to uncover correlations among the dependent variables and to evaluate whether adjusting for covariates has changed the main results.
All calculations were performed using the statistical software package Stata 16.1 MP (64-bit, StataCorp LLC, College Station, TX, USA).
Table 1 shows the characteristics of the NAKO study participants at both survey times. Between the two surveys, the participants had advanced in age by almost three years on average, which corresponds to the average interval between the baseline survey and the special survey. The mean severity of symptoms of depression and anxiety increased slightly during this period. In addition, the proportion of participants with moderate or severe symptoms of depression and anxiety disorder increased from 7.1% to 9.5% (+2.4) and 4.8% to 6.3% (+1.5), respectively.
Figure 1 graphically depicts the means of the symptom scales and the frequencies of moderate or severe symptoms for each of the years of the baseline survey (2014–2019) and for the COVID-19 special survey (2020).
Table 2 shows the results of the FE models with the correlation measures for all variables in the final model. A pronounced increase in severity on the PHQ-9 and GAD-7 scales was observed among those who became unemployed during the pandemic. For example, the scale value for depressive symptoms increase on average by 1.16 scale points. Likewise, a pandemic-related deterioration of the financial situation, job insecurity, change to working from home, increased working hours, as well as reduced working hours with no short-time allowance were associated with an increase in symptoms. In contrast, mental health improved with reduced working hours, when a short-time allowance was received, as well as with a change to inactive status. In general, mental health deteriorated especially in younger and middle-aged groups and in the presence of poor self-reported health.
Figure 2 shows the change in mental health before and during the pandemic after stepwise control for covariates and independent variables. A comparison of the models shows that the mean symptom increase from the baseline survey to the COVID-19 survey, which is still discernable in Model 2, becomes significant after statistical control of occupational and financial changes. Thus, this increase is statistically almost entirely explained by these factors.
The eFigure shows the measures of association for occupational and financial changes in respect to mental health, stratified by gender. In general, the correlations were consistent for both sexes. However, in women increased working hours and a deteriorated financial situation had a greater negative impact on mental health. In contrast, becoming unemployed during the pandemic was more strongly associated with poorer mental health among men.
eTable 2 shows results of a Poisson regression analysis based on cut-off values for moderate or severe mental disorders. While there are only minor deviations from the FE model, the effects of protective factors are less pronounced. Sensitivity analyses of the main effects of work-related changes—without simultaneously taking financial strain into account—showed that reduced working hours (with and without short-time allowance) generally affected mental health, consistent with a mediating effect of increased financial strain (eTable 3, eTable 4). An analysis of the main effects of financial and occupational changes without adjustment for covariates yielded comparable results (eTable 3, eTable 4).
This study investigated whether, and to what extent, occupational and financial changes due to the COVID-19 pandemic in spring and summer of 2020 were associated with increased severity of symptoms of depression and anxiety in 161 787 participants of the German National Cohort (NAKO) Study. Mental health was affected by pandemic-related job loss, reduced working hours with no short-time allowance, increased working hours, change to working from home, increased job insecurity, and deterioration of the financial situation. Overall, the increase in mental health problems was found significantly reduced after statistical control for work-related changes and financial strain. This suggests that the mean increase in symptom severity during the pandemic was largely due to an increase in occupational and financial strains. In general, this finding supports the importance of a stable employment and income situation for the mental health of individuals, not only in times of crisis.
For the most part, the results are in line with the current state of research. In particular, unemployment, perceived job insecurity and financial strain are established risk factors for impaired mental health (20, 21, 22). Likewise, previous studies have demonstrated a slight increase in depressive symptoms associated with long working hours (34). Interestingly, we were able to show that the opposite, i.e. reduced working hours, were also associated with increased symptoms. However, these symptoms did not increase when short-time allowance was received and no financial strain was reported. This suggests that short-time allowance as a resource may have been effective. During the pandemic, other forms of social security, such as unemployment benefits, produced similar effects (35, 36). Also worth mentioning is the finding that working from home was also associated with increased mental symptoms. This association was previously known primarily from data of cross-sectional studies (37). As expected, our analysis also found known gender differences, with a higher prevalence of mental symptoms among women. However, the strength of the associations between mental symptoms and changes in professional and everyday life was similar for both sexes.
Limitations and strengths
Methodological limitations should be considered, when interpreting the results of our study. It is important to note that the NAKO sample is not representative of the general population in Germany (27). Even though the study participants were randomly selected, recruitment was limited to 18 centers in Germany. In addition, the baseline survey participation rate is low. While the low participation rate is unlikely to have an impact on measures of association, it prevents the generalization of incidence/prevalence rates to the total population of Germany. In terms of content, the study is limited by the fact that only a small number of changes were assessed due to the limited scope of the COVID-19 survey. Our study’s focus on professional life and financial situation means that other changes, for example in the areas of private contacts and recreational activities, are disregarded. Apart from the high significance of the studied social changes for the increase in mental health problems, the moderate R-squared value (<0.2) suggests that the model variables explain only a limited proportion of the total symptom severity variance over time. Another limitation of the study design is that the COVID-19 survey was conducted during and shortly after the first wave of the pandemic in spring and summer of 2020. It is conceivable that the deterioration of mental health leveled off over the further course of the pandemic or—quite the opposite—progressed even further.
The strengths of the study include the standardized pre-post measurement of depressive symptoms and generalized anxiety as well as the longitudinal study design in combination with FE modelling, which assessed intraindividual changes in mental health during the pandemic. Its estimate is robust to time-constant confounders. Another advantage of our study is the large population-based sample. Many previous studies have focused on subpopulations, such as the elderly, making it difficult to produce generalizable statements.
Our study shows that pandemic-related financial and occupational changes were associated with increased mental strain on the study participants. Whether this association persists in the long term remains to be determined by future studies. However, it is becoming obvious that the mental health of the population requires special attention in times of social crises. Our results suggest that the economic and work-related fallout of the pandemic may lead to an increased need for psychotherapeutic services. Key individual risk factors for need of psychotherapy include experiences of job loss and job insecurity as well as financial strain, but also strain due to changes how the work is organized (e.g., reduced working hours or working from home). Thus, these factors could be used to make adjustments aimed at cushioning crisis-related effects on the health of the population. Partial results of this analysis suggest that social security measures could help mitigate negative effects of the pandemic on mental health.
Annette Peters, Miriam Engels, Börge Schmidt, Karin H. Greiser,
Barbara Bohn, Steffi Riedel-Heller, André Karch, Rafael Mikolajczyk,
Gérard Krause, Olga Lang, Leo Panreck, Marcella Rietschel,
Hermann Brenner, Beate Fischer, Claus-Werner Franzke, Sylvia Gastell, Bernd Holleczek, Karl-Heinz Jöckel, Rudolf Kaaks, Thomas Keil,
Alexander Kluttig, Oliver Kuß, Nicole Legath, Michael Leitzmann,
Wolfgang Lieb, Claudia Meinke-Franze, Karin B. Michels, Nadia Obi,
Tobias Pischon, Insa Feinkohl, Susanne Rospleszcz, Tamara Schikowski, Matthias B. Schulze, Andreas Stang, Henry Völzke, Stefan N. Willlich, Kerstin Wirkner, Hajo Zeeb, Wolfgang Ahrens
Affiliations of the additional authors
This project was conducted with data of the German National Cohort (NAKO) Study (www.nako.de). The German National Cohort (NAKO) Study has been funded by the German Federal Ministry of Education and Research (BMBF, Bundesministerium für Bildung und Forschung) (grant number 01ER1301A/B/C und 01ER1511D), the Federal States (“Länder”) and the Helmholtz Association, as well as the participating universities and institutes of the Leibniz Association. The analysis was developed within the context of the MethodCoV method platform, which has been funded by the Federal Ministry of Education and Research (BMBF) (grant number 01KX2021). We thank all participants and staff members of the German National Cohort (NAKO) Study.
Compliance with ethical guidelines
All participants received detailed information and gave their informed consent to participate in the study in writing. The study program was carried out in accordance with national law and the 1975 Declaration of Helsinki (in the current, revised version).
Conflict of interest statement
Prof. Berger is honorary spokesman of the Expert Group “Neurological and Psychiatric Diseases” of the German National Cohort (NAKO) Study.
Prof. Dragano received third-party funding from the Federal Institute for Occupational Safety and Health for a study on COVID-19 and Occupation in the German National Cohort (NAKO) Study.
The remaining authors declare no conflict of interest.
Manuscript received on 8 November 2021; revised version accepted on 4 February 2022
Translated from the original German by Ralf Thoene, MD.
Prof. Dr. phil. Nico Dragano
Institut für Medizinische Soziologie
Centre for Health and Society
Medizinische Fakultät der Heinrich-Heine-Universität Düsseldorf
Moorenstraße 5, 40225 Düsseldorf, Germany
Cite this as:
Dragano N, Reuter M, Peters A, Engels M, Schmidt B, Greiser KH, Bohn B,
Riedel-Heller S, Karch A, Mikolajczyk R, Krause G, Lang O, Panreck L, Rietschel M, Brenner H, Fischer B, Franzke CW, Gastell S, Holleczek B, Jöckel KH, Kaaks R, Keil T, Kluttig A, Kuß O, Legath N, Leitzmann M, Lieb W, Meinke-Franze C, Michels KB,
Obi N, Pischon T, Feinkohl I, Rospleszcz S, Schikowski T, Schulze MB, Stang A,
Völzke H, Willlich SN, Wirkner K, Zeeb H, Ahrens W, Berger K:
Increase in mental disorders during the COVID-19 pandemic—the role of occupational and financial strains. An analysis of the German National Cohort (NAKO) Study.
Dtsch Arztebl Int 2022; 119: 179–87. DOI: 10.3238/arztebl.m2022.0133
eReferences, eMethods, eTable, eFigure:
*2 Additional authors have contributed to this publication. They are listed under “cite this as” and at the end of this article together with their affiliations.
Institute of Medical Sociology, Center for Health and Society, Medical Faculty of the Heinrich Heine University of Düsseldorf, Düsseldorf, Germany: Prof. Dr. phil. Nico Dragano,
Dr. PH Marvin Reuter
Institute of Epidemiology and Social Medicine, Münster, Germany: Prof. Dr. med.
Institute of Epidemiology, Helmholtz Center Munich, Germany (AP, OL, SR)
Institute of Medical Sociology, Center for Health and Society, Medical Faculty, HHU Düsseldorf, Germany (ME)
Chair of Epidemiology, IBE, LMU München, Germany (AP, SR)
Dep. of Environ. Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA (AP)
IMIBE, Medical Faculty, University Duisburg-Essen, University Hospital Essen, Germany (BS, KHJ, AS)
DKFZ, Division of Cancer Epidemiology, Heidelberg, Germany (KHG, RK)
NAKO e. V. Heidelberg, Germany (BB, LP)
ISAP, University of Leipzig, Germany (SRH)
Institute of Epidemiology and Social Medicine, University of Münster, Germany (AKa, NL)
Institute of Medical Epidemiology, Biometrics and Informatics, University of Halle-Wittenberg, Halle (Saale), Germany (RM, AKlu)
Department of Epidemiology, Helmholtz Center for Infection Research, Braunschweig, Germany (GK)
Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany (MR)
DKFZ, Division of Clinical Epidemiology and Aging Research, Heidelberg, Germany (HB, BH)
Department of Epidemiology and Preventive Medicine, University of Regensburg, Germany (BF, ML)
Institute for Prevention and Tumor Epidemiology, Freiburg Medical Center, Medical Faculty, University of Freiburg, Germany (CWF, KBM)
NAKO Study Center Berlin-South/Brandenburg, Germany. Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (SG)
Saarland Cancer Registry, Saarbrücken, Germany (BH)
Institute for Social Medicine, Epidemiology, and Health Economics, Charité – Universitätsmedizin Berlin, Germany (TK, SNW)
Institute for Clinical Epidemiology and Biometry, University of Würzburg, Germany (TK)
State Institute of Health, LGL, Erlangen, Germany (TK)
Institute of Biometry and Epidemiology, DDZ, Leibniz Center for Diabetes Research, Heinrich Heine University Düsseldorf, Germany (OK)
Institute of Epidemiology, University of Kiel, Germany (WL)
Institute of Community Medicine, University Medicine Greifswald, Gemany (CMF, HV)
Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany (NO)
Research Group Molecular Epidemiology, MDC, Berlin, Germany (TP, IF)
Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany (TP)
Biobank Technology Platform, MDC, Berlin, Germany (TP)
Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Core Facility Biobank, Berlin, Germany (TP)
IUF gGmbH, Düsseldorf, Germany (TS)
Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany (BS)
Institute for Nutritional Research, University of Potsdam, Germany (MBS)
IMISE, Medical Faculty. University of Leipzig, Germany (KW)
LIFE, University of Leipzig, Germany (KW)
BIPS, Bremen, Germany (HZ, WA)
Health Sciences Bremen, University of Bremen, Germany (HZ)
DZIF, Site Hannover-Braunschweig, Germany (GK)
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