Marieke F A van Hoffen1, Jos W R Twisk2, Martijn W Heymans2, Johan de Bruin3, Catelijne I Joling3, Corné A M Roelen4. 1. 1 Department of Research and Development, ArboNed Occupational Health Service, Utrecht, The Netherlands 2 Department of Epidemiology and Biostatistics, VU Medical Center, VU University, Amsterdam, The Netherlands marieke.van.hoffen@arboned.nl. 2. 2 Department of Epidemiology and Biostatistics, VU Medical Center, VU University, Amsterdam, The Netherlands. 3. 1 Department of Research and Development, ArboNed Occupational Health Service, Utrecht, The Netherlands. 4. 1 Department of Research and Development, ArboNed Occupational Health Service, Utrecht, The Netherlands 2 Department of Epidemiology and Biostatistics, VU Medical Center, VU University, Amsterdam, The Netherlands 3 Department of Health Sciences, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Abstract
BACKGROUND: Recently, a three-item screener, derived from the 16-item distress scale of the Four-Dimensional Symptom Checklist (4DSQ), was used to measure psychological distress in sicklisted employees. The aim of the present study was to investigate the ability of the 16-item distress scale and three-item distress screener to identify non-sicklisted employees at risk of sickness absence (SA) due to mental disorders. METHODS: Prospective cohort study including 4877 employees working in distribution and transport. The 4DSQ distress scale was distributed at baseline in November 2010. SA diagnosed within the International Classification of Diseases -10 chapter F was defined as mental SA and retrieved from an occupational health register during 2-year follow-up. The area under the receiver operating characteristic curve (AUC) was used to discriminate between workers with ('cases') and without ('non-cases') mental SA during follow-up. RESULTS: A total of 2782 employees (57%) were included in complete cases analysis; 73 employees had mental SA during 2-year follow-up. Discrimination between cases and non-cases was similar for the 16-item distress scale (AUC = 0.721; 95% CI, 0.622-0.823) and the three-item screener (AUC = 0.715; 95% CI, 0.615-0.815). CONCLUSION: Healthcare providers could use the three-item distress screener to identify non-sicklisted employees at risk of future mental SA.
BACKGROUND: Recently, a three-item screener, derived from the 16-item distress scale of the Four-Dimensional Symptom Checklist (4DSQ), was used to measure psychological distress in sicklisted employees. The aim of the present study was to investigate the ability of the 16-item distress scale and three-item distress screener to identify non-sicklisted employees at risk of sickness absence (SA) due to mental disorders. METHODS: Prospective cohort study including 4877 employees working in distribution and transport. The 4DSQ distress scale was distributed at baseline in November 2010. SA diagnosed within the International Classification of Diseases -10 chapter F was defined as mental SA and retrieved from an occupational health register during 2-year follow-up. The area under the receiver operating characteristic curve (AUC) was used to discriminate between workers with ('cases') and without ('non-cases') mental SA during follow-up. RESULTS: A total of 2782 employees (57%) were included in complete cases analysis; 73 employees had mental SA during 2-year follow-up. Discrimination between cases and non-cases was similar for the 16-item distress scale (AUC = 0.721; 95% CI, 0.622-0.823) and the three-item screener (AUC = 0.715; 95% CI, 0.615-0.815). CONCLUSION: Healthcare providers could use the three-item distress screener to identify non-sicklisted employees at risk of future mental SA.
Authors: Ellen J M Bakker; Jos H A M Kox; Harald S Miedema; Sita Bierma-Zeinstra; Jos Runhaar; Cécile R L Boot; Allard J van der Beek; Pepijn D D M Roelofs Journal: BMC Nurs Date: 2018-06-22
Authors: Ana Monteiro Pereira; Pedro Teques; Evert Verhagen; Vincent Gouttebarge; Pedro Figueiredo; João Brito Journal: BMJ Open Sport Exerc Med Date: 2021-10-11
Authors: Marieke F A van Hoffen; Giny Norder; Jos W R Twisk; Corné A M Roelen Journal: Int Arch Occup Environ Health Date: 2020-05-11 Impact factor: 3.015