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Does providing personalized depression risk information lead to increased psychological distress and functional impairment? Results from a mixed-methods randomized controlled trial

Published online by Cambridge University Press:  04 November 2020

JianLi Wang*
Affiliation:
Institute of Mental Health Research, University of Ottawa, Ottawa, Canada Shandong Key Laboratory of Behavioral Medicine, School of Mental Health, Jining Medical University, Jining, China Faculty of Medicine, School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada Department of Psychiatry, Faculty of Medicine, University of Ottawa, Ottawa, Canada
Heidi Eccles
Affiliation:
Institute of Mental Health Research, University of Ottawa, Ottawa, Canada
Molly Nannarone
Affiliation:
Institute of Mental Health Research, University of Ottawa, Ottawa, Canada
Norbert Schmitz
Affiliation:
Douglas Mental Health Research Institute, McGill University, Montreal, Canada Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Canada
Scott Patten
Affiliation:
Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Canada Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Canada
Bonnie Lashewicz
Affiliation:
Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Canada
*
Author for correspondence: JianLi Wang, E-mail: [email protected]

Abstract

Background

Multivariable risk algorithms (MVRP) predicting the personal risk of depression will form an important component of personalized preventive interventions. However, it is unknown whether providing personalized depression risk will lead to unintended psychological harms. The objectives of this study were to evaluate the impact of providing personalized depression risk on non-specific psychological distress and functional impairment over 12 months.

Methods

A mixed-methods randomized controlled trial was conducted in 358 males and 354 females who were at high risk of having a major depressive episode according to sex-specific MVRPs, and who were randomly recruited across Canada. Participants were assessed at baseline, 6 and 12 months.

Results

Over 93% of participants were interested in knowing their depression risk. The intervention group had a greater reduction in K10 score over 12 months than the control group; complete-case analysis found a significant between-group difference in mean K10 change score (d = 1.17, 95% CI 0.12–2.23) at 12 months. Participants in the intervention group also reported significantly less functional impairment in the domains of home and work/school activities, than did those in the control group. A majority of the qualitative interviewees commented that personalized depression risk information does not have a negative impact on physical and mental health.

Conclusions

This study found no evidence that providing personalized depression risk information will lead to worsening psychological distress, functional impairment, and absenteeism. Provision of personalized depression risk information may have positive impacts on non-specific psychological distress and functioning.

Trial registration

ClinicalTrials.gov NCT02943876

Type
Original Article
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

American Psychiatric Association (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Arlington, VA: American Psychiatric Association.Google Scholar
Anderson, K. M., Wilson, P. W., Odell, P. M., & Kannel, W. B. (1991). An updated coronary risk profile. A statement for health professionals. Circulation, 83(1), 356362. doi:10.1161/01.CIR.83.1.356.CrossRefGoogle ScholarPubMed
Bellón, J. Á., Conejo-Cerón, S., Moreno-Peral, P., King, M., Nazareth, I., Martín-Pérez, C., … de Dios Luna, J. (2013). Preventing the onset of major depression based on the level and profile of risk of primary care attendees: Protocol of a cluster randomised trial (the predictD-CCRT study). BMC Psychiatry, 13, 171. doi:10.1186/1471-244X-13-171. https://doi.org/10.1186/1471-244X-13-171.CrossRefGoogle Scholar
Bellón, J. Á., Conejo-Cerón, S., Moreno-Peral, P., King, M., Nazareth, I., Martín-Pérez, C., … De Dios Luna, J. (2016). Intervention to prevent major depression in primary care: A cluster randomized trial. Annals of Internal Medicine, 164, 656665. doi:10.7326/M14-2653.CrossRefGoogle ScholarPubMed
Bellon, J. A., de Dios Luna, J., King, M., Moreno-Kustner, B., Nazareth, I., Monton-Franco, C., … Torres-Gonzalez, F. (2011). Predicting the onset of major depression in primary care: International validation of a risk prediction algorithm from Spain. Psychological Medicine, 41(10), 20752088. doi:10.1017/S0033291711000468.CrossRefGoogle ScholarPubMed
Bellón, J. Á., Moreno-Peral, P., Moreno-Küstner, B., Motrico, E., Aiarzagüena, J. M., Fernández, A., … Amezcua, M. (2014). Patients’ opinions about knowing their risk for depression and what to do about it. The predictD-qualitative study. PLoS One, 9(3), e92008. doi:10.1371/journal.pone.0092008.CrossRefGoogle Scholar
Berg, B. (2004). Qualitative research methods for the social sciences. Boston: Pearson Education.Google Scholar
Chondros, P., Davidson, S., Wolfe, R., Gilchrist, G., Dowrick, C., Griffiths, F., … Gunn, J. (2018). Development of a prognostic model for predicting depression severity in adult primary patients with depressive symptoms using the diamond longitudinal study. Journal of Affective Disorders, 227, 854860. doi:10.1016/j.jad.2017.11.042.CrossRefGoogle ScholarPubMed
Collins, F. S., Green, E. D., Guttmacher, A. E., & Guyer, M. S. (2003). A vision for the future of genomics research. Nature, 422, 835847. doi:10.1038/nature01626.CrossRefGoogle ScholarPubMed
Edwards, A. G. K., Naik, G., Ahmed, H., Elwyn, G. J., Pickles, T., Hood, K., & Playle, R. (2013). Personalised risk communication for informed decision making about taking screening tests. Cochrane Database of Systematic Reviews, 2013(2), 196. doi:10.1002/14651858.CD001865.pub3.Google Scholar
French, D. P., Cameron, E., Benton, J. S., Deaton, C., & Harvie, M. (2017). Can communicating personalised disease risk promote healthy behaviour change? A systematic review of systematic reviews. Annals of Behavioral Medicine, 51, 718729. doi:10.1007/s12160-017-9895-z.CrossRefGoogle ScholarPubMed
French, D. P., Olander, E. K., Chisholm, A., & Mc Sharry, J. (2014). Which behaviour change techniques are most effective at increasing older adults’ self-efficacy and physical activity behaviour? A systematic review. Annals of Behavioral Medicine, 48, 225234. doi:10.1007/s12160-014-9593-z.CrossRefGoogle ScholarPubMed
Friedrich, M. J. (2017). Depression is the leading cause of disability around the world. JAMA, 317(15), 1517. doi:10.1001/jama.2017.3826.Google ScholarPubMed
Grant, B. F., Hasin, D. S., Stinson, F. S., Dawson, D. A., Patricia Chou, S., June Ruan, W., & Huang, B. (2005). Co-occurrence of 12-month mood and anxiety disorders and personality disorders in the US: Results from the national epidemiologic survey on alcohol and related conditions. Journal of Psychiatric Research, 39(1), 19. doi:10.1016/j.jpsychires.2004.05.004.CrossRefGoogle ScholarPubMed
Jorm, A., Patten, S. B., Brugha, T. S., & Mojtabi, R. (2017). Has increased provision of treatment reduced the prevalence of common mental disorders? Review of the evidence from four countries. World Psychiatry, 16(1), 9099. doi:10.1002/wps.20388.CrossRefGoogle ScholarPubMed
Kessler, R. C., Andrews, G., Colpe, L. J., Hiripi, E., Mroczek, D. K., Normand, S. L. T., … Zaslavsky, A. M. (2002). Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychological Medicine, 32(6), 959976. doi:10.1017/S0033291702006074.CrossRefGoogle ScholarPubMed
Kessler, R., & Mroczek, D. (1994). Scoring of the UM-CIDI short forms. Ann Arbor: The University of Michigan Institute for Social Research/Survey Research Center.Google Scholar
King, M., Walker, C., Levy, G., Bottomley, C., Royston, P., Weich, S., … Nazareth, I. (2008). Development and validation of an international risk prediction algorithm for episodes of major depression in general practice attendees: The PredictD study. Archives of General Psychiatry, 65(12), 13681376. doi:10.1001/archpsyc.65.12.1368.CrossRefGoogle ScholarPubMed
Lloyd-Jones, D. M. (2010). Cardiovascular risk prediction: Basic concepts, current status, and future directions. Circulation, 121(15), 17681777. doi:10.1161/CIRCULATIONAHA.109.849166.CrossRefGoogle ScholarPubMed
Mauvais-Jarvis, F., Merz, N. B., Barnes, P., Brinton, R., Carrero, J.-J., DeMeo, D. L., … Suzuki, A. (2020). Sex and gender: Modifiers of health, disease, and medicine. The Lancet, 396(10250), 565582.CrossRefGoogle Scholar
Patten, S. B., Williams, J. V. A., Lavorato, D. H., Wang, J. L., McDonald, K., & Bulloch, A. G. M. (2015). Descriptive epidemiology of major depressive disorder in Canada in 2012. Canadian Journal of Psychiatry. Revue Canadienne de Psychiatrie, 60(1), 2330. doi:10.1177/070674371506000106.CrossRefGoogle ScholarPubMed
Sandelowski, M., & Leeman, J. (2012). Writing usable qualitative health research findings. Qualitative Health Research, 22(10), 14041413. doi:10.1177/1049732312450368.CrossRefGoogle ScholarPubMed
Sheridan, S. L., Viera, A. J., Krantz, M. J., Ice, C. L., Steinman, L. E., Peters, K. E., … Lungelow, D. (2010). The effect of giving global coronary risk information to adults: A systematic review. Archives of Internal Medicine, 170(3), 230239. doi:10.1001/archinternmed.2009.516.CrossRefGoogle ScholarPubMed
STATACorp (2019). STATA Software, Release 16. College Station.Google Scholar
Twisk, J., Bosman, L., Hoekstra, T., Rijnhart, J., Welten, M., & Heymans, M. (2018). Different ways to estimate treatment effects in randomised controlled trials. Contemporary Clinical Trials Communications, 10(March), 8085. doi:10.1016/j.conctc.2018.03.008.Google Scholar
Wang, J. L., Adair, C., Fick, G., Lai, D., Evans, B., Perry, B. W., … Addington, D. (2007). Depression literacy in Alberta: Findings from a general population sample. Canadian Journal of Psychiatry, 52(7), 442449. doi:10.1177/070674370705200706.CrossRefGoogle ScholarPubMed
Wang, J. L., MacQueen, G., Patten, S., Manuel, D., Lashewicz, B., & Schmitz, N. (2019). A randomized controlled trial to examine the impacts of disclosing personalized depression risk information on the outcomes of individuals who are at high risk of developing major depression: A research protocol. BMC Psychiatry, 19(1), 285. doi:10.1186/s12888-019-2270-2279. https://doi.org/10.1186/s12888-019-2270-9.CrossRefGoogle ScholarPubMed
Wang, J. L., Manuel, D., Williams, J., Schmitz, N., Gilmour, H., Patten, S., … Birney, A. (2013). Development and validation of prediction algorithms for major depressive episode in the general population. Journal of Affective Disorders, 151(1), 3945. doi:10.1016/j.jad.2013.05.045.CrossRefGoogle ScholarPubMed
Wang, J. L., Patten, S. B., Williams, J. V. A., Currie, S., Beck, C. A., Maxwell, C. J., & El-Guebaly, N. (2005). Help-seeking behaviours of individuals with mood disorders. Canadian Journal of Psychiatry, 50(10), 652659. doi:10.1177/070674370505001012.CrossRefGoogle ScholarPubMed
Wang, J. L., Sareen, J., Patten, S., Bolton, J., Schmitz, N., & Birney, A. (2014). A prediction algorithm for first onset of major depression in the general population: Development and validation. Journal of Epidemiology and Community Health, 68(5), 418424. doi:10.1136/jech-2013-202845.CrossRefGoogle ScholarPubMed
Zipkin, D. A., Umscheid, C. A., Keating, N. L., Allen, E., Aung, K., Beyth, R., … Feldstein, D. A. (2014). Evidence-based risk communication: A systematic review. Annals of Internal Medicine, 161(4), 270280. doi: 10.7326/M14-0295.CrossRefGoogle ScholarPubMed
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