Submitted:
15 December 2023
Posted:
18 December 2023
You are already at the latest version
Abstract
Keywords:
1. Introduction
2. Mental illness and labour market outcomes - a review of the evidence
3. Materials and Methods
4. The problem of the burden of depressive disorders in developed economies
5. Results
- a)
- variable YLD (DD)rateF:
- b)
- variable YLD (DD)rateM:
6. Conclusions
7. Discussion
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Depressive disorder (depression), Fact sheets, WHO. https://www.who.int/news-room/fact-sheets/detail/depression (accessed on 10 October 2023).
- Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2020. Available from https://vizhub.healthdata.org/gbd-results/ (accessed on 12 October 2023).
- Hasin, D.S., Sarvet, A.L., Meyers, J.L., Saha, T.D., Ruan, W.J., Stohl, M., Grant, B.F.. Epidemiology of adult DSM-5 major depressive disorder and its specifiers in the United States. JAMA psychiatry 2018, 75, 336–346. [CrossRef]
- Liu, Q., He, H., Yang, J., Feng, X., Zhao, F., Lyu, J. Changes in the global burden of depression from 1990 to 2017: Findings from the Global Burden of Disease study. Journal of psychiatric research 2020, 126, 134–140. [CrossRef]
- 5. A New Benchmark for Mental Health Systems: Tackling the Social and Economic Costs of Mental Ill-Health, OECD Health Policy Studies, OECD Publishing, Paris, 2021. [CrossRef]
- de Oliveira, C., Saka, M., Bone, L., Jacobs, R. The role of mental health on workplace productivity: A critical review of the literature. Applied health economics and health policy 2023, 21, 167–193. [CrossRef]
- Hakulinen, C., Elovainio, M., Arffman, M., Lumme, S., Pirkola, S., Keskimäki, I., ... Böckerman, P. Mental disorders and long-term labour market outcomes: Nationwide cohort study of 2 055 720 individuals. Acta Psychiatrica Scandinavica 2019, 140, 371–381. [CrossRef]
- Harvey, S.B., Henderson, M., Lelliott, P., Hotopf, M. Mental health and employment: Much work still to be done. The British Journal of Psychiatry 2009, 194, 201–203. [CrossRef]
- Banerjee, S., Chatterji, P., Lahiri, K. Identifying the mechanisms for workplace burden of psychiatric illness. Medical care 2014, 112-120. [CrossRef]
- Maske, U.E., Buttery, A.K., Beesdo-Baum, K., Riedel-Heller, S., Hapke, U., Busch, M.A. Prevalence and correlates of DSM-IV-TR major depressive disorder, self-reported diagnosed depression and current depressive symptoms among adults in Germany. Journal of affective disorders 2016, 190, 167–177. [CrossRef]
- Cornwell, K., Forbes, C., Inder, B., Meadows, G. Mental illness and its effects on labour market outcomes. The journal of mental health policy and economics 2009, 12, 107–118.
- Frijters, P., Johnston, D.W., Shields, M.A. The effect of mental health on employment: Evidence from Australian panel data. Health economics 2014, 23, 1058–1071. [CrossRef]
- Banerjee, S., Chatterji, P., Lahiri, K. Effects of psychiatric disorders on labor market outcomes: A latent variable approach using multiple clinical indicators. Health economics 2017, 26, 184–205. [CrossRef]
- Marlowe, J.F. Depression's surprising toll on worker productivity. Employee benefits journa 2002, 27, 16–21.
- Johnston, D.A., Harvey, S.B., Glozier, N., Calvo, R.A., Christensen, H., Deady, M. The relationship between depression symptoms, absenteeism and presenteeism. Journal of affective disorders 2019, 256, 536–540. [CrossRef]
- Santini, Z.I., Thygesen, L.C., Koyanagi, A., Stewart-Brown, S., Meilstrup, C., Nielsen, L., ... Ekholm, O. Economics of mental wellbeing: A prospective study estimating associated productivity costs due to sickness absence from the workplace in Denmark. Mental Health & Prevention 2022, 28, 200247. [CrossRef]
- Stewart, W.F., Ricci, J.A., Chee, E., Hahn, S. R., Morganstein, D. Cost of lost productive work time among US workers with depression. Jama 2003, 289, 3135–3144. [CrossRef]
- Vigo, D., Thornicroft, G., Atun, R. Estimating the true global burden of mental illness. The Lancet Psychiatry 2016, 3, 171–178. [CrossRef]
- Sander, C., Dogan-Sander, E., Fischer, J.E., Schomerus, G. Mental health shame and presenteeism: Results from a German online survey. Psychiatry Research Communications 2023, 3, 100102. [CrossRef]
- Brouwers, E.P. Social stigma is an underestimated contributing factor to unemployment in people with mental illness or mental health issues: Position paper and future directions. BMC psychology 2020, 8, 1–7. [CrossRef]
- Bonaccio, S., Connelly, C.E., Gellatly, I.R., Jetha, A., Martin Ginis, K.A. The participation of people with disabilities in the workplace across the employment cycle: Employer concerns and research evidence. Journal of Business and Psychology 2020, 35, 135–158. [CrossRef]
- Cybula-Fujiwara, A., Merecz-Kot, D., Walusiak-Skorupa, J., Marcinkiewicz, A., Wiszniewska, M. Pracownik z chorobą psychiczną–możliwości i bariery w pracy zawodowej. Medycyna Pracy 2015, 66, 57–69. [CrossRef]
- Giermanowska, E. Niepełnosprawni. Ukryty segment polskiego rynku pracy. Prakseologia 2016, 158, 275–298.
- Brunner, B., Igic, I., Keller, A.C., Wieser, S. Who gains the most from improving working conditions? Health-related absenteeism and presenteeism due to stress at work. The European Journal of Health Economics 2019, 20, 1165–1180. [CrossRef]
- Sørensen, J.K., Framke, E., Pedersen, J., Alexanderson, K., Bonde, J.P., Farrants, K., ... Rugulies, R. Work stress and loss of years lived without chronic disease: An 18-year follow-up of 1.5 million employees in Denmark. European Journal of Epidemiology 2022, 37, 389–400. [CrossRef]
- Burns, R.A., Butterworth, P., Anstey, K.J. An examination of the long-term impact of job strain on mental health and wellbeing over a 12-year period. Social psychiatry and psychiatric epidemiology 2016, 51, 725–733=. [CrossRef]
- Limmer, A., Schütz, A. Interactive effects of personal resources and job characteristics on mental health: A population-based panel study. International archives of occupational and environmental health 2021, 94, 43–53. [CrossRef]
- Grossman, M. The Demand for Health: A Theoretical and Empirical Investigation. National Bureau of Economay Research and Columbia University Press: New York, NY, USA, 1972, 1-111.
- Nikunlaakso, R., Reuna, K., Oksanen, T., Laitinen, J. Associations between accumulating job stressors, workplace social capital, and psychological distress on work-unit level: A cross-sectional study. BMC Public Health 2023, 23, 1–9. [CrossRef]
- Luppa, M., Heinrich, S., Angermeyer, M.C., König, H.H., Riedel-Heller, S.G. Cost-of-illness studies of depression: A systematic review. Journal of affective disorders 2007, 98(1-2), 29-43. [CrossRef]
- Koopmanschap, M., Burdorf, A., Lötters, F. Work absenteeism and productivity loss at work. In: Loisel, P., Anema, J. (eds) Handbook of Work Disability. Springer, New York, NY, 2013. [CrossRef]
- Rost, K.M., Meng, H., Xu, S. Work productivity loss from depression: Evidence from an employer survey. BMC health services research 2014, 14, 1–9. [CrossRef]
- Van den Hout, W.B. The value of productivity: Human-capital versus friction-cost method. Annals of the rheumatic diseases 2010, 69(Suppl 1), i89-i91. [CrossRef]
- Zhang, W., Bansback, N., Anis, A.H. Measuring and valuing productivity loss due to poor health: A critical review. Social science & medicine 2011, 72, 185–192. [CrossRef]
- Zhang, W., Sun, H., Woodcock, S., Anis, A.H. Valuing productivity loss due to absenteeism: Firm-level evidence from a Canadian linked employer-employee survey. Health economics review 2017, 7, 1–14. [CrossRef]
- Koopmanschap, M.A., Rutten, F.F., Van Ineveld, B.M., Van Roijen, L. The friction cost method for measuring indirect costs of disease. Journal of health economics 1995, 14, 171–189. [CrossRef]
- Krol, M., Brouwer, W. How to estimate productivity costs in economic evaluations. Pharmacoeconomics 2014, 32, 335–344. [CrossRef]
- Hanly, P., Ortega Ortega, M., Pearce, A., de Camargo Cancela, M., Soerjomataram, I., Sharp, L. Estimating Global Friction Periods for Economic Evaluation: A Case Study of Selected OECD Member Countries. PharmacoEconomics 2023, 1-9. [CrossRef]
- Schultz, A. B., Chen, C. Y., & Edington, D. W. The cost and impact of health conditions on presenteeism to employers: A review of the literature. Pharmacoeconomics 2009, 27, 365–378. [CrossRef]
- Brouwer, W., Verbooy, K., Hoefman, R., van Exel, J. Production Losses due to Absenteeism and Presenteeism: The Influence of Compensation Mechanisms and Multiplier Effects. PharmacoEconomics 2023, 41, 1103–1115. [CrossRef]
- Łyszczarz, B., Sowa, K. Production losses due to mortality associated with modifiable health risk factors in Poland. The European Journal of Health Economics 2022, 1-13. [CrossRef]
- Murray, C.J., Lopez, A.D. Measuring the global burden of disease. New England Journal of Medicine 2013, 369, 448–457. [CrossRef]
- (accessed on 10 October 2023).
- Global Burden of Disease Health Financing Collaborator Network Produced Estimates for Gross Domestic Product (GDP) from 1960–2050. Estimates Are Reported as GDP Per Person in Constant 2021 Purchasing-Power Parity-Adjusted (PPP) Dollars. Available online: https://ghdx.healthdata.org/record/ihme-data/global-gdp-per-capita-1960-2050-fgh-2021 (accessed on 12 October 2023).
- Survey of U.S. Workers Reveals Impact on Productivity from Depression, The Center for Workplace Mental Health. https://workplacementalhealth.org/mental-health-topics/depression/survey-of-u-s-workers-reveals-impact-on-productivi (accessed on 10 October 2023).
- Jakubowska, A., Bilan, S., Werbiński, J. Chronic diseases and labour resources:" Old and new" European Union member states. Journal of International Studies 2021, 14. [CrossRef]
- Gutiérrez-Rojas, L., Porras-Segovia, A., Dunne, H., Andrade-González, N., Cervilla, J.A. Prevalence and correlates of major depressive disorder: A systematic review. Brazilian Journal of Psychiatry 2020, 42, 657–672. [CrossRef]
- Huijts, T., Stornes, P., Eikemo, T.A., Bambra, C., HiNews Consortium. Prevalence of physical and mental non-communicable diseases in Europe: Findings from the European Social Survey (2014) special module on the social determinants of health. The European Journal of Public Health 2017, 27(suppl_1), 8-13. [CrossRef]
- Lim, G.Y., Tam, W.W., Lu, Y., Ho, C.S., Zhang, M.W., Ho, R.C. Prevalence of depression in the community from 30 countries between 1994 and 2014. Scientific reports 2018, 8, 2861. [CrossRef]
- Arias-de la Torre, J., Vilagut, G., Ronaldson, A., Serrano-Blanco, A., Martín, V., Peters, M., ... Alonso, J. Prevalence and variability of current depressive disorder in 27 European countries: A population-based study. The Lancet Public Health 2021, 6, e729–e738. [CrossRef]
- Rai, D., Zitko, P., Jones, K., Lynch, J., Araya, R. Country- and individual-level socioeconomic determinants of depression: Multilevel cross-national comparison. The British Journal of Psychiatry 2013, 202, 195–203. [CrossRef]
- Nikunlaakso, R., Reuna, K., Selander, K., Oksanen, T., Laitinen, J. Synergistic Interaction between Job Stressors and Psychological Distress during the COVID-19 Pandemic: A Cross-Sectional Study. International Journal of Environmental Research and Public Health 2022, 19, 13991. [CrossRef]
- . [CrossRef]
- Santomauro, D.F., Herrera, A.M.M., Shadid, J., Zheng, P., Ashbaugh, C., Pigott, D.M., ... Ferrari, A.J. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. The Lancet 2021, 398, 1700–1712. [CrossRef]
- Twenge, J.M., Joiner, T.E. US Census Bureau-assessed prevalence of anxiety and depressive symptoms in 2019 and during the 2020 COVID-19 pandemic. Depression and anxiety 2020, 37, 954–956. [CrossRef]
- Ettman, C.K., Abdalla, S.M., Cohen, G.H., Sampson, L., Vivier, P.M., Galea, S. Prevalence of depression symptoms in US adults before and during the COVID-19 pandemic. JAMA network open 2020, 3, e2019686–e2019686. [CrossRef]
- Żołnierczyk-Zreda, D. Zaburzenia depresyjne pracujących Polaków w okresie pandemii COVID-19 (lata 2019–2022). Medycyna Pracy 2023, 74, 41–51. [CrossRef]






| Location name | YLD (DD) 2019 | Change in the period 1990-2019 (in %) | ||||||
| Rate* | % of total YLD | Rate* | % of total YLD | |||||
| F | M | F | M | F | M | F | M | |
| High SDI | 855,0 | 530,8 | 5,4% | 4,2% | 9,3% | 9,8% | -4,4% | -7,4% |
| High-middle SDI | 783,5 | 460,2 | 6,0% | 4,4% | 7,4% | 12,5% | -6,3% | -2,3% |
| Middle SDI | 698,6 | 433,2 | 5,9% | 4,6% | 17,2% | 23,1% | -2,0% | 3,5% |
| Low-middle SDI | 755,8 | 495,7 | 6,5% | 5,3% | 9,8% | 15,3% | 1,7% | 10,2% |
| Low SDI | 669,2 | 456,0 | 6,5% | 5,3% | 1,2% | 3,4% | 6,7% | 12,5% |
| Average | SD | GDP per cap., 20191 | YLD(DD) rateM, 20193 | YLD(DD) rateF, 20193 | %ΔYLD(DD) rateM, 1990-2019 | %ΔYLD(DD) rateF, 1990-2019 | |
| GDP per cap., 2019 | 47389 | 19147 | 1,000 | 0,111 | -0,090 | -0,234 | -0,170 |
| YLD(DD)rateM, 2019 | 514,8 | 136,6 | 0,111 | 1,000 | 0,826* | 0,123 | 0,069 |
| YLD(DD)rateF, 2019 | 920,8 | 200,4 | -0,090 | 0,826* | 1,000 | 0,047 | 0,046 |
| %ΔYLD(DD)rateM, 1990-2019 | 2,2% | 12,2% | -0,234 | 0,123 | 0,047 | 1,000 | 0,929* |
| %ΔYLD(DD)rateF, 1990-2019 | -0,2% | 12,5% | -0,170 | 0,069 | 0,046 | 0,929* | 1,000 |
| EU Member States | Groups of EU |
YLD(DD)rate, Female |
EU Member States | Groups of EU | YLD(DD)rate, Male | ||||
| 2019 | Change. 1990-2019. in % | 2019 | Change. 1990-2019. in % | ||||||
| 1 | Greece | EU-14 | 1578.9 | 3.0% | 1 | Greece | EU-14 | 940.8 | 8.5% |
| 2 | Spain | EU-14 | 1543.6 | 31.1% | 2 | Ireland | EU-14 | 830.0 | 5.7% |
| 3 | Sweden | EU-14 | 1413.6 | -2.7% | 3 | Lithuania | EU-CEE | 785.2 | -2.3% |
| 4 | Portugal | EU-14 | 1401.0 | -11.4% | 4 | Finland | EU-14 | 784.3 | -26.1% |
| 5 | Ireland | EU-14 | 1244.2 | 3.4% | 5 | Latvia | EU-CEE | 731.6 | -11.9% |
| 6 | Finland | EU-14 | 1215.6 | -20.1% | 6 | Spain | EU-14 | 717.6 | 10.8% |
| 7 | France | EU-14 | 1191.5 | -12.1% | 7 | Portugal | EU-14 | 706.8 | -7.3% |
| 8 | Lithuania | EU-CEE | 1160.4 | -6.5% | 8 | France | EU-14 | 698.5 | -9.4% |
| 9 | Italy | EU-14 | 1118.1 | -0.4% | 9 | Belgium | EU-14 | 684.1 | 15.8% |
| 10 | Belgium | EU-14 | 1076.8 | 13.8% | 10 | Sweden | EU-14 | 668.3 | -5.7% |
| 11 | Netherlands | EU-14 | 1069.9 | -6.6% | 11 | Netherlands | EU-14 | 648.3 | -1.8% |
| 12 | Latvia | EU-CEE | 1066.4 | -13.3% | 12 | Estonia | EU-CEE | 633.1 | -23.5% |
| 13 | Denmark | EU-14 | 1057.6 | -28.9% | 13 | Germany | EU-14 | 608.4 | 6.0% |
| 14 | Estonia | EU-CEE | 1039.2 | -25.8% | 14 | Italy | EU-14 | 584.2 | -5.6% |
| 15 | Germany | EU-14 | 995.3 | 15.8% | 15 | Denmark | EU-14 | 581.8 | -22.8% |
| 16 | Slovenia | EU-CEE | 948.2 | -19.4% | 16 | Luxembourg | EU-14 | 556.7 | -19.1% |
| 17 | Luxembourg | EU-14 | 923.2 | -21.5% | 17 | Austria | EU-14 | 514.8 | -18.0% |
| 18 | Malta | - | 918.4 | -0.2% | 18 | Malta | - | 486.8 | -1.8% |
| 19 | Austria | EU-14 | 916.9 | -18.1% | 19 | Croatia | EU-CEE | 462.9 | -12.4% |
| 20 | Cyprus | - | 914.6 | 0.0% | 20 | Cyprus | - | 462.7 | 0.1% |
| 21 | Croatia | EU-CEE | 849.7 | -16.2% | 21 | Slovenia | EU-CEE | 460.5 | -20.7% |
| 22 | Czechia | EU-CEE | 849.7 | -15.2% | 22 | Czechia | EU-CEE | 449.5 | -6.9% |
| 23 | Hungary | EU-CEE | 802.6 | -23.4% | 23 | Hungary | EU-CEE | 446.2 | -20.5% |
| 24 | Slovakia | EU-CEE | 794.2 | -7.3% | 24 | Poland | EU-CEE | 445 | 5.2% |
| 25 | Bulgaria | EU-CEE | 775.2 | -10.9% | 25 | Romania | EU-CEE | 416.2 | 4.6% |
| 26 | Romania | EU-CEE | 749.7 | -2.9% | 26 | Bulgaria | EU-CEE | 378.3 | -8.0% |
| 27 | Poland | EU-CEE | 560.4 | -2.6% | 27 | Slovakia | EU-CEE | 373.2 | -10.5% |
| Variable | Variance (σt2) | F-Snedecor test |
H0 - homogeneity of variances |
|
| 1990 | 2019 | p-value | ||
| YLD(DD)rate, F | 66144,5 | 60622,7 | 0,8258 | confirmed |
| YLD(DD)rate, M | 27504,9 | 22628,1 | 0,6224 | confirmed |
| Parameters of a normal distribution | ||||||||
| Parameters | Mean EU-14 | Mean EU-CEE | Test t | Brown–Forsythe test | ||||
| t | df | p | Brn-Fors F(1.df) | df Brn-Fors | p Brn-Fors | |||
| YLD(DD)rate F, 1990 | 1262,9 | 1017,0 | 2,625 | 23 | 0,0151 | 0,000 | 23 | 0,9838 |
| YLD(DD)rate F, 2019 | 1196,2 | 872,3 | 4,073 | 23 | 0,0005 | 0,991 | 23 | 0,3299 |
| Parameters of a non-normal distribution | ||||||||
| Mann-Whitney U test | ||||||||
| Sum.rangyasnyiEU-14 | Sum.rangyasnyiEU-CEE | U | Z | p | ||||
| YLD(DD)rate M, 1990 | 722,7 | 569,5 | 222,0000 | 103,0000 | 37,00000 | 2,162423 | 0,030586 | |
| YLD(DD)rate M, 2019 | 680,3 | 507,4 | 231,0000 | 94,0000 | 28,00000 | 2,655127 | 0,007928 | |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).