Advertisement

Risk Prediction Models for Depression in Community-Dwelling Older Adults

      Highlights

      • What is the primary question addressed by this study?
        Can we reliably identify older individuals at risk for depression?
      • What is the main finding of this study?
        Using selected information, the Manto Risk Prediction Models can predict the risk for developing of depression 2 years later with satisfactory accuracy. The streamlined Manto Risk Prediction Model is available for free public use through a web-based risk calculator (https://manto.unife.it/).
      • What is the meaning of the finding?
        Estimating an individual's risk for developing late-life depression allows to target prevention strategies at an individual and a population-level.

      Abstract

      Objective

      To develop streamlined Risk Prediction Models (Manto RPMs) for late-life depression.

      Design

      Prospective study.

      Setting

      The Survey of Health, Ageing and Retirement in Europe (SHARE) study.

      Participants

      Participants were community residing adults aged 55 years or older.

      Measurements

      The outcome was presence of depression at a 2-year follow up evaluation. Risk factors were identified after a literature review of longitudinal studies. Separate RPMs were developed in the 29,116 participants who were not depressed at baseline and in the combined sample of 39,439 of non-depressed and depressed subjects. Models derived from the combined sample were used to develop a web-based risk calculator.

      Results

      The authors identified 129 predictors of late-life depression after reviewing 227 studies. In non-depressed participants at baseline, the RPMs based on regression and Least Absolute Shrinkage and Selection Operator (LASSO) penalty (34 and 58 predictors, respectively) and the RPM based on Artificial Neural Networks (124 predictors) had a similar performance (AUC: 0.730–0.743). In the combined depressed and non-depressed participants at baseline, the RPM based on neural networks (35 predictors; AUC: 0.807; 95% CI: 0.80–0.82) and the model based on linear regression and LASSO penalty (32 predictors; AUC: 0.81; 95% CI: 0.79–0.82) had satisfactory accuracy.

      Conclusions

      The Manto RPMs can identify community-dwelling older individuals at risk for developing depression over 2 years. A web-based calculator based on the streamlined Manto model is freely available at https://manto.unife.it/ for use by individuals, clinicians, and policy makers and may be used to target prevention interventions at the individual and the population levels.

      Key Words

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to The American Journal of Geriatric Psychiatry
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Horackova K
        • Kopecek M
        • Machů V
        • et al.
        Prevalence of late-life depression and gap in mental health service use across European regions.
        Eur Psychiatry. 2019; 57: 19-25https://doi.org/10.1016/j.eurpsy.2018.12.002
        • Andreas S
        • Schulz H
        • Volkert J
        • et al.
        Prevalence of mental disorders in elderly people: The European MentDis-ICF65+ study.
        Br J Psychiatry. 2017; 210: 125-131https://doi.org/10.1192/bjp.bp.115.180463
        • Alexopoulos GS.
        Mechanisms and treatment of late-life depression.
        Transl Psychiatry. 2019; 9https://doi.org/10.1038/s41398-019-0514-6
        • Andreescu C
        • Ajilore O
        • Aizenstein HJ
        • et al.
        Disruption of neural homeostasis as a model of relapse and recurrence in late-life depression.
        Am J Geriatr Psychiatry. 2019; https://doi.org/10.1016/j.jagp.2019.07.016
        • Laird KT
        • Krause B
        • Funes C
        • et al.
        Psychobiological factors of resilience and depression in late life.
        Transl Psychiatry. 2019; 9https://doi.org/10.1038/s41398-019-0424-7
        • Alexopoulos GS.
        Depression in the elderly.
        Lancet. 2005; 365: 1961-1970https://doi.org/10.1016/S0140-6736(05)66665-2
        • Köhler CA
        • Evangelou E
        • Stubbs B
        • et al.
        Mapping risk factors for depression across the lifespan: an umbrella review of evidence from meta-analyses and Mendelian randomization studies.
        J Psychiatr Res. 2018; 103: 189-207https://doi.org/10.1016/j.jpsychires.2018.05.020
        • Van Agtmaal MJM
        • Houben AJHM
        • Pouwer F
        • et al.
        Association of microvascular dysfunction with late-life depression: a systematic review and meta-analysis.
        JAMA Psychiatry. 2017; 74: 729-739https://doi.org/10.1001/jamapsychiatry.2017.0984
        • Sonsin-Diaz N
        • Gottesman RF
        • Fracica E
        • et al.
        Chronic systemic inflammation is associated with symptoms of late-life depression: the ARIC Study.
        Am J Geriatr Psychiatry. 2020; 28: 87-98https://doi.org/10.1016/j.jagp.2019.05.011
        • Milaneschi Y
        • Lamers F
        • Berk M
        • et al.
        Depression heterogeneity and its biological underpinnings: toward immunometabolic depression.
        Biol Psychiatry. 2020; 88: 369-380https://doi.org/10.1016/j.biopsych.2020.01.014
        • Alexopoulos GS
        • Morimoto SS.
        The inflammation hypothesis in geriatric depression.
        Int J Geriatr Psychiatry. 2011; 26: 1109-1118https://doi.org/10.1002/gps.2672
        • Lutz J
        • Van Orden KA
        • Bruce ML
        • et al.
        Social disconnection in late life suicide: an NIMH workshop on state of the research in identifying mechanisms, treatment targets, and interventions.
        Am J Geriatr Psychiatry. 2021; https://doi.org/10.1016/j.jagp.2021.01.137
        • Belvederi Murri M
        • Grassi L
        • Caruso R
        • et al.
        Depressive symptom complexes of community-dwelling older adults: a latent network model.
        Mol Psychiatry. 2021; https://doi.org/10.1038/S41380-021-01310-Y
        • Meeks TW
        • Vahia IV
        • Lavretsky H
        • et al.
        A tune in “a minor” can “b major”: a review of epidemiology, illness course, and public health implications of subthreshold depression in older adults.
        J Affect Disord. 2011; 129: 126-142https://doi.org/10.1016/j.jad.2010.09.015
        • Belvederi Murri M
        • Amore M
        • Respino M
        • et al.
        The symptom network structure of depressive symptoms in late-life: results from a European population study.
        Mol Psychiatry. 2018; 1https://doi.org/10.1038/s41380-018-0232-0
        • Cole MG
        • Dendukuri N.
        Risk factors for depression among elderly community subjects: a systematic review and meta-analysis.
        Am J Psychiatry. 2003; 160: 1147-1156https://doi.org/10.1176/appi.ajp.160.6.1147
        • Bernardini F
        • Attademo L
        • Cleary SD
        • et al.
        Risk prediction models in psychiatry: toward a new frontier for the prevention of mental illnesses.
        J Clin Psychiatry. 2016; (press)https://doi.org/10.4088/JCP.15r10003
        • Cattelani L
        • Belvederi Murri M
        • Chesani F
        • et al.
        Risk prediction model for late life depression: development and validation on three large European datasets.
        IEEE J Biomed Heal informatics. 2018; PP: 9https://doi.org/10.1109/JBHI.2018.2884079
        • Cuijpers P
        • Smit F
        • Patel V
        • et al.
        Prevention of depressive disorders in older adults: an overview.
        Psych J. 2015; 4: 3-10https://doi.org/10.1002/pchj.86
        • Hu MX
        • Turner D
        • Generaal E
        • et al.
        Exercise interventions for the prevention of depression: a systematic review of meta-analyses.
        BMC Public Health. 2020; 20https://doi.org/10.1186/s12889-020-09323-y
        • Biesheuvel-Leliefeld KEM
        • Kok GD
        • Bockting CLH
        • et al.
        Effectiveness of psychological interventions in preventing recurrence of depressive disorder: meta-analysis and meta-regression.
        J Affect Disord. 2015; 174: 400-410https://doi.org/10.1016/j.jad.2014.12.016
        • Almeida OP.
        Prevention of depression in older age.
        Maturitas. 2014; 79: 136-141https://doi.org/10.1016/j.maturitas.2014.03.005
        • Xu Z
        • Zhang Q
        • Li W
        • et al.
        Individualized prediction of depressive disorder in the elderly: a multitask deep learning approach.
        Int J Med Inform. 2019; https://doi.org/10.1016/j.ijmedinf.2019.103973
        • Okamoto K
        • Harasawa Y.
        Prediction of symptomatic depression by discriminant analysis in Japanese community-dwelling elderly.
        Arch Gerontol Geriatr. 2011; 52: 177-180https://doi.org/10.1016/j.archger.2010.03.012
        • Bogner HR
        • Morales KH
        • Reynolds CF
        • et al.
        Course of depression and mortality among older primary care patients.
        Am J Geriatr Psychiatry. 2012; 20: 895-903https://doi.org/10.1097/JGP.0b013e3182331104
        • Collins GS
        • Reitsma JB
        • Altman DG
        • et al.
        Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.
        BMJ. 2015; https://doi.org/10.1136/bmj.g7594
        • Börsch-Supan A
        • Brandt M
        • Hunkler C
        • et al.
        Data resource profile: the survey of health, ageing and retirement in Europe (share).
        Int J Epidemiol. 2013; 42: 992-1001https://doi.org/10.1093/ije/dyt088
        • Prince MJ
        • Reischies F
        • Beekman ATF
        • et al.
        Development of the EURO-D scale - A European Union initiative to compare symptoms of depression in 14 European centres.
        Br J Psychiatry. 1999; 174: 330-338https://doi.org/10.1192/bjp.174.4.330
        • Pagán-Rodríguez R
        • Pérez S.
        Depression and self-reported disability among older people in Western Europe.
        J Aging Health. 2012; 24: 1131-1156https://doi.org/10.1177/0898264312453070
        • Meier L
        • Van De Geer S
        • Bühlmann P.
        The group lasso for logistic regression.
        J R Stat Soc Ser B Stat Methodol. 2008; 70: 53-71https://doi.org/10.1111/j.1467-9868.2007.00627.x
        • Van Calster B
        • McLernon DJ
        • Van Smeden M
        • et al.
        Calibration: the Achilles heel of predictive analytics.
        BMC Med. 2019; 17https://doi.org/10.1186/s12916-019-1466-7
      1. Palumbo P, Cattelani L, Chesani F, et al. Manto online risk calculator for late life depression. Available at: www.manto.unife.it. Published 2021. Accessed September 16, 2021.

        • Wynants L
        • Van Smeden M
        • McLernon DJ
        • et al.
        Three myths about risk thresholds for prediction models.
        BMC Med. 2019; 17: 1-7https://doi.org/10.1186/s12916-019-1425-3
        • Brettschneider C
        • Heddaeus D
        • Steinmann M
        • et al.
        Cost-effectiveness of guideline-based stepped and collaborative care versus treatment as usual for patients with depression - A cluster-randomized trial.
        BMC Psychiatry. 2020; 20: 1-14https://doi.org/10.1186/s12888-020-02829-0
        • Smit F
        • Ederveen A
        • Cuijpers P
        • et al.
        Opportunities for cost-effective prevention of late-life depression: an epidemiological approach.
        Arch Gen Psychiatry. 2006; 63: 290-296https://doi.org/10.1001/ARCHPSYC.63.3.290
        • Belvederi Murri M
        • Caruso R
        • Ounalli H
        • et al.
        The relationship between demoralization and depressive symptoms among patients from the general hospital: network and exploratory graph analysis: demoralization and depression symptom network.
        J Affect Disord. 2020; https://doi.org/10.1016/j.jad.2020.06.074
        • Lee YY
        • Stockings EA
        • Harris MG
        • et al.
        The risk of developing major depression among individuals with subthreshold depression: a systematic review and meta-analysis of longitudinal cohort studies.
        Psychol Med. 2019; https://doi.org/10.1017/s0033291718000557
        • Cremers G
        • Taylor E
        • Hodge L
        • et al.
        Effectiveness and acceptability of low-intensity psychological interventions on the well-being of older adults: a systematic review.
        Clin Gerontol. 2019; https://doi.org/10.1080/07317115.2019.1662867
        • Blanken TF
        • Borsboom D
        • Penninx BW
        • et al.
        Network outcome analysis identifies difficulty initiating sleep as a primary target for prevention of depression: a 6-year prospective study.
        Sleep. 2019; : 1-6https://doi.org/10.1093/sleep/zsz288
        • Alexopoulos GS
        • Raue PJ
        • Banerjee S
        • et al.
        Comparing the streamlined psychotherapy “Engage” with problem-solving therapy in late-life major depression. A randomized clinical trial.
        Mol Psychiatry. 2020; https://doi.org/10.1038/s41380-020-0832-3
        • Alexopoulos GS
        • O'Neil R
        • Banerjee S
        • et al.
        “Engage” therapy Prediction of change of late-life major depression.
        J Affect Disord. 2017; 221: 192-197https://doi.org/10.1016/j.jad.2017.06.037
        • Solomonov N
        • Bress JN
        • Sirey JA
        • et al.
        Engagement in socially and interpersonally rewarding activities as a predictor of outcome in “Engage” behavioral activation therapy for late-life depression.
        Am J Geriatr Psychiatry. 2019; https://doi.org/10.1016/j.jagp.2018.12.033
      2. Task Force on Community Preventive Services. Interventions to Reduce Depression Among Older Adults: Home-Based Depression Care Management. Guide to Community Preventive Services. Published 2007. Available at: https://www.thecommunityguide.org/findings/mental-health-interventions-reduce-depression-among-older-adults-home. Accessed September 16, 2021.

        • Steinman L
        • Cristofalo M
        • Snowden M.
        Implementation of an evidence-based depression care management program (PEARLS): perspectives from staff and former clients.
        Prev Chronic Dis. 2012; 9 (Available at:) (Accessed September 16, 2021.)
        • Casado B
        • Quijano L
        • Stanley M
        • Cully J
        • et al.
        Healthy IDEAS: implementation of a depression program through community-based case management.
        Gerontologist. 2008; 48: 828-838https://doi.org/10.1093/GERONT/48.6.828
      3. Substance Abuse and Mental Health Services Administration. Promoting Emotional Health and Preventing Suicide: A Toolkit for Senior Centers. Available at: https://store.samhsa.gov/product/Promoting-Emotional-Health-and-Preventing-Suicide/SMA15-4416. Published 2015. Accessed September 16, 2021.

        • Malkin G
        • Hayat T
        • Amichai-Hamburger Y
        • et al.
        How well do older adults recognise mental illness? A literature review.
        Psychogeriatrics. 2019; 19: 491-504https://doi.org/10.1111/psyg.12427
        • MacKenzie CS
        • Pagura J
        • Sareen J.
        Correlates of perceived need for and use of mental health services by older adults in the collaborative psychiatric epidemiology surveys.
        Am J Geriatr Psychiatry. 2010; 18: 1103-1115https://doi.org/10.1097/JGP.0b013e3181dd1c06
        • Alexopoulos GS
        • Manning K
        • Kanellopoulos D
        • et al.
        Cognitive control, reward-related decision making and outcomes of late-life depression treated with an antidepressant.
        Psychol Med. 2015; 45: 3111-3120https://doi.org/10.1017/S0033291715001075
        • Cattelani L
        • Chesani F
        • Palmerini L
        • et al.
        A rule-based framework for risk assessment in the health domain.
        Int J Approx Reason. 2020; 119: 242-259https://doi.org/10.1016/j.ijar.2019.12.018