Support for the higher-order factor structure of the WHODAS 2.0 self-report version in a Dutch outpatient psychiatric setting.
Dimensionality
Functioning
Measurement
Mental healthcare
Norm scores
WHODAS 2.0
Journal
Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation
ISSN: 1573-2649
Titre abrégé: Qual Life Res
Pays: Netherlands
ID NLM: 9210257
Informations de publication
Date de publication:
Oct 2021
Oct 2021
Historique:
accepted:
12
05
2021
pubmed:
13
6
2021
medline:
26
11
2021
entrez:
12
6
2021
Statut:
ppublish
Résumé
Previous studies of the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) interview version suggested a second-order model, with a general disability factor and six factors on a lower level. The goal of this study is to investigate if we can find support for a similar higher-order factor structure of the 36-item self-report version of the WHODAS 2.0 in a Dutch psychiatric outpatient sample. We aim to give special attention to the differences between the non-working group sample and the working group sample. Additionally, we intend to provide preliminary norms for clinical interpretation of the WHODAS 2.0 scores in psychiatric settings. Patients seeking specialized ambulatory treatment, primarily for depressive or anxiety symptoms, completed the WHODAS 2.0 as part of the initial interview. The total sample consisted of 770 patients with a mean age of 37.5 years (SD = 13.3) of whom 280 were males and 490 were females. Several factorial compositions (i.e., one unidimensional model and two second-order models) were modeled using confirmatory factor analysis (CFA). Descriptive statistics, model-fit statistics, reliability of the (sub)scales, and preliminary norms for interpreting test scores are reported. For the non-working group, the second-order model with a general disability factor and six factors on a lower level, provided an adequate fit. Whereas, for the working group, the second-order model with a general disability factor and seven factors on a lower level seemed more appropriate. The WHODAS 2.0 36-item self-report form showed adequate levels of reliability. Percentile ranks and normalized T-scores are provided to aid clinical evaluations. Our results lend support for a factorial structure of the WHODAS 2.0 36-item self-report version that is comparable to the interview version. While we conjecture that a seven-factor solution might give a better reflection of item content and item variance, further research is needed to assess the clinical relevance of such a model. At this point, we recommend using the second-order structure with six factors that matches past findings of the interview form.
Identifiants
pubmed: 34117613
doi: 10.1007/s11136-021-02880-8
pii: 10.1007/s11136-021-02880-8
pmc: PMC8481147
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2939-2949Informations de copyright
© 2021. The Author(s).
Références
Douglas, H., Georgiou, A., & Westbrook, J. (2017). Social participation as an indicator of successful aging: An overview of concepts and their associations with health. Australian Health Review, 41, 455–462. https://doi.org/10.1071/AH16038
doi: 10.1071/AH16038
pubmed: 27712611
Carver, L., Beamish, R., Phillips, S., & Villeneuve, M. (2018). A scoping review: Social participation as a cornerstone of successful aging in place among rural older adults. Geriatrics, 3, 75. https://doi.org/10.3390/geriatrics3040075
doi: 10.3390/geriatrics3040075
pmcid: 6371105
Cuijpers, P. (2019). Targets and outcomes of psychotherapies for mental disorders: an overview. World Psychiatry, 18, 276–285.
doi: 10.1002/wps.20661
pubmed: 31496102
pmcid: 6732705
World Health Organization. (2002). Towards a common language for functioning, disability and health: ICF—The international classification of functioning, disability and health. World Health Organization.
Üstün, T. B., Kosstanjsek, N., Chatterji, S., & Rehm, J. (Eds.). (2010). Measuring health and disability: Manual for WHO disability assessment schedule WHODAS 2.0. World Health Organization.
Obbarius, A., van Maasakkers, L., Baer, L., Clark, D. M., Crocker, A. G., de Beurs, E., Emmelkamp, P. M. G., et al. (2017). Standardization of health outcomes assessment for depression and anxiety: Recommendations from the ICHOM Depression and Anxiety Working Group. Quality of Life Research, 26, 3211–3225. https://doi.org/10.1007/s11136-017-1659-5
doi: 10.1007/s11136-017-1659-5
pubmed: 28786017
pmcid: 5681977
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Publishing.
doi: 10.1176/appi.books.9780890425596
Gold, L. H. (2014). DSM-5 and the assessment of functioning: The World Health Organization Disability Assessment Schedule 20 (WHODAS 20). The Journal of the American Academy of Psychiatry and the Law, 42, 173–181.
pubmed: 24986344
Üstün, B., Chatterji, S., Kostanjsek, N., Rehm, J., Kennedy, C., Epping-Jordan, J., Saxena, S., von Korff, M., & Pull, C. (2010). Developing the World Health Organization disability assessment schedule 2.0. Bulletin of the World Health Organization, 88, 815–823. https://doi.org/10.2471/BLT.09.067231
doi: 10.2471/BLT.09.067231
pubmed: 21076562
pmcid: 2971503
Federici, S., Bracalenti, M., Meloni, F., & Luciano, J. V. (2017). World Health Organization disability assessment schedule 2.0: An international systematic review. Disability and Rehabilitation, 39, 2347–2380. https://doi.org/10.1080/09638288.2016.1223177
doi: 10.1080/09638288.2016.1223177
pubmed: 27820966
Cuijpers, P., Juan, L., Hofmann, S. G., & Andersson, G. (2010). Self-reported versus clinician-rated symptoms of depression as outcome measures in psychotherapy research on depression: A meta-analysis. Clinical Psychology Review, 30, 768–778. https://doi.org/10.1016/j.cpr.2010.06.001
doi: 10.1016/j.cpr.2010.06.001
pubmed: 20619943
Garin, O., Ayuso-Mateos, J., Almansa, J., Nieto, M., Chatterji, S., Vilagut, G., Alonso, J., et al. (2010). Validation of the “World Health Organization Disability Assessment Schedule, WHODAS-2” in patients with chronic diseases. Health and Quality of Life Outcomes, 8, 51. https://doi.org/10.1186/1477-7525-8-51
doi: 10.1186/1477-7525-8-51
pubmed: 20482853
pmcid: 2893517
American Psychiatric Association. (2014). Handboek voor de classificatie van psychische stoornissen (1st ed.). Boom uitgevers.
Akwa GGZ. 2020. Vroege opsporing van psychische klachten en aandoeningen in de volwassen bevolking, 9.10.13 Screeningsinstrumenten naar functionele beperkingen. https://www.ggzstandaarden.nl/generieke-modules/vroege-opsporing-van-psychische-klachten-en-aandoeningen-in-de-volwassen-bevolking/introductie
Konecky, B., Meyer, E. C., Marx, B. P., Kimbrel, N. A., & Morissette, S. B. (2014). Using the WHODAS 2.0 to assess functional disability associated with DSM5 mental disorders. American Journal of Psychiatry, 171, 818–819. https://doi.org/10.1176/appi.ajp.2014.14050587
doi: 10.1176/appi.ajp.2014.14050587
pubmed: 25082488
Cella, D., Yount, S., Rothrock, N., Gershon, R., Cook, K., Reeve, B., Ader, D., Fries, J. F., Bruce, B., & Rose, M. (2007). The patient-reported outcomes measurement information system (PROMIS). Medical Care, 45, S3–S11. https://doi.org/10.1097/01.mlr.0000258615.42478.55
doi: 10.1097/01.mlr.0000258615.42478.55
pubmed: 17443116
pmcid: 2829758
Wahl, I., Löwe, B., Bjorner, J. B., Fischer, F., Langs, G., Voderholzer, U., Aita, S. A., Bergemann, N., Brähler, E., & Rose, M. (2014). Standardization of depression measurement: A common metric was developed for 11 self-report depression measures. Journal of Clinical Epidemiology, 67, 73–86. https://doi.org/10.1016/j.jclinepi.2013.04.019
doi: 10.1016/j.jclinepi.2013.04.019
pubmed: 24262771
Schalet, B. D., Cook, K. F., Choi, S. W., & Cella, D. (2014). Establishing a common metric for self-reported anxiety: Linking the MASQ, PANAS, and GAD-7 to PROMIS Anxiety. Journal of Anxiety Disorders, 28, 88–96. https://doi.org/10.1016/j.janxdis.2013.11.006
doi: 10.1016/j.janxdis.2013.11.006
pubmed: 24508596
RCore Team. (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48, 1–36.
doi: 10.18637/jss.v048.i02
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., et al. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686 .
doi: 10.21105/joss.01686
Revelle, W. (2018). psych: Procedures for psychological, psychometric, and personality research. Northwestern University.
Jorgensen, T. D., Pornprasertmanit, S., Schoemann, A. M., & Rosseel, Y. 2019. semTools: Useful tools for structural equation modeling. R package version 0.5-2
Epskamp, S. 2019. semplot: Path diagrams and visual analysis of various SEM Packages’ Output.
Willse, J.T. 2018. CTT: Classical test theory functions.
Torchiano, M. (2020). Effsize: Efficient effect size computation. Viena: R Foundation for Statistical Computing. https://doi.org/10.5281/zenodo.1480624
doi: 10.5281/zenodo.1480624
McNeish, D. (2017). Thanks coefficient alpha, we’ll take it from here. Psychological Methods. https://doi.org/10.1037/met0000144
doi: 10.1037/met0000144
pubmed: 29265846
Antony, M. M., & Barlow, D. H. (Eds.). (2010). Handbook of assessment and treatment planning for psychological disorders (2nd ed.). New York: The Guilford Press.
Black, R. A., Yang, Y., Beitra, D., & McCaffrey, S. (2015). Comparing fit and reliability estimates of a psychological instrument using second-order CFA, bifactor, and essentially tau-equivalent (coefficient alpha) models via AMOS 22. Journal of Psychoeducational Assessment, 33, 451–472. https://doi.org/10.1177/0734282914553551
doi: 10.1177/0734282914553551
Meijer, R., Niessen, S., & Boevé, A. (2015). Rapporteren van Subtestscores in de Klinische Praktijk. De Psycholoog, 50, 34–42.
Nunnally, J., & Bernstein, I. (1968). Psychometric theory. American Educational Research Journal. https://doi.org/10.2307/1161962
doi: 10.2307/1161962
Lance, C. E., Butts, M. M., & Michels, L. C. (2006). What did they really say? Organizational Research Methods, 9, 202–220. https://doi.org/10.1177/1094428105284919
doi: 10.1177/1094428105284919
Brown, T. (2015). Confirmatory factor analysis for applied research. New York: Guilford Press.
Li, C. H. (2016). Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares. Behavior Research Methods, 48, 936–949. https://doi.org/10.3758/s13428-015-0619-7
doi: 10.3758/s13428-015-0619-7
pubmed: 26174714
Reeve, B. B., Hays, R. D., Bjorner, J. B., Cook, K. F., Crane, P. K., Teresi, J. A., Thissen, D., et al. (2007). Psychometric evaluation and calibration of health-related quality of life item banks. Medical Care, 45, S22–S31. https://doi.org/10.1097/01.mlr.0000250483.85507.04
doi: 10.1097/01.mlr.0000250483.85507.04
pubmed: 17443115
Schreiber, J. B., Stage, F. K., King, J., Nora, A., & Barlow, E. A. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. Journal of Educational Research, 99, 323–338. https://doi.org/10.3200/JOER.99.6.323-338
doi: 10.3200/JOER.99.6.323-338
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). New York: Routledge Academic.
Good, D. V., Jo, M., Good, B. J., & Nassi, A. J. (1983). Patient requests in primary health care settings: Development and validation of a research instrument. Journal of Behavioral Medicine, 6, 151–168. https://doi.org/10.1007/BF00845378
doi: 10.1007/BF00845378
pubmed: 6620371