IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i23p8934-d454363.html
   My bibliography  Save this article

A Network Analysis of Major Depressive Disorder Symptoms and Age- and Gender-Related Differences in People over 65 in a Madrid Community Sample (Spain)

Author

Listed:
  • Miguel Ángel Castellanos

    (Methodology in Behavioral Sciences Department, Campus de Somosaguas, School of Psychology, Psychobiology, Complutense University of Madrid, Ctra. de Húmera, s/n, 28223 Pozuelo de Alarcón, Madrid, Spain)

  • Berta Ausín

    (Evaluation and Clinical Psychology Department, Campus de Somosaguas, School of Psychology, Personality, Complutense University of Madrid, Ctra. de Húmera, s/n, 28223 Pozuelo de Alarcón, Madrid, Spain)

  • Sara Bestea

    (Methodology in Behavioral Sciences Department, Campus de Somosaguas, School of Psychology, Psychobiology, Complutense University of Madrid, Ctra. de Húmera, s/n, 28223 Pozuelo de Alarcón, Madrid, Spain)

  • Clara González-Sanguino

    (Evaluation and Clinical Psychology Department, Campus de Somosaguas, School of Psychology, Personality, Complutense University of Madrid, Ctra. de Húmera, s/n, 28223 Pozuelo de Alarcón, Madrid, Spain)

  • Manuel Muñoz

    (Evaluation and Clinical Psychology Department, Campus de Somosaguas, School of Psychology, Personality, Complutense University of Madrid, Ctra. de Húmera, s/n, 28223 Pozuelo de Alarcón, Madrid, Spain)

Abstract

Major depressive disorder (MDD) is one of the most prevalent conditions among mental disorders in individuals over 65 years. People over 65 who suffer from MDD are often functionally impaired, chronically physically ill, and express cognitive problems. The concordance between a clinician-assessed MDD diagnosis in a primary care setting and MDD assessed with a structured clinical interview in older adults is only approximately 18%. Network analysis may provide an alternative statistical technique to better understand MDD in this population by a dimensional approach to symptomatology. The aim of this study was to carry out a network analysis of major depressive disorder (MDD) in people over 65 years old. A symptom network analysis was conducted according to age and gender in 555 people over 65, using a sample from the MentDis_ICF65+ Study. The results revealed different networks for men and women, and for the age groups 65–74 and 75–84. While depressive mood stood out in women, in men the network was more dispersed with fatigue or loss of energy and sleep disturbances as the main symptoms. In the 65–74 age group, the network was complex; however, in the 75–84 age group, the network was simpler with sleep disturbances as the central symptom. The gaps between the networks indicate the different characteristics of MDD in the elderly, with variations by gender and age, supporting the idea that MDD is a complex dynamic system that has unique characteristics in each person, rather than a prototypical classification with an underlying mental disorder. These unique characteristics can be taken into account in the clinical practice for detection and intervention of MDD.

Suggested Citation

  • Miguel Ángel Castellanos & Berta Ausín & Sara Bestea & Clara González-Sanguino & Manuel Muñoz, 2020. "A Network Analysis of Major Depressive Disorder Symptoms and Age- and Gender-Related Differences in People over 65 in a Madrid Community Sample (Spain)," IJERPH, MDPI, vol. 17(23), pages 1-13, December.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:23:p:8934-:d:454363
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/23/8934/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/17/23/8934/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Epskamp, Sacha & Cramer, Angélique O.J. & Waldorp, Lourens J. & Schmittmann, Verena D. & Borsboom, Denny, 2012. "qgraph: Network Visualizations of Relationships in Psychometric Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i04).
    2. Angélique O J Cramer & Claudia D van Borkulo & Erik J Giltay & Han L J van der Maas & Kenneth S Kendler & Marten Scheffer & Denny Borsboom, 2016. "Major Depression as a Complex Dynamic System," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-20, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhou, Jianhua & Zhang, Lulu & Gong, Xue, 2023. "Longitudinal network relations between symptoms of problematic internet game use and internalizing and externalizing problems among Chinese early adolescents," Social Science & Medicine, Elsevier, vol. 333(C).
    2. Nadja Bodner & Laura Bringmann & Francis Tuerlinckx & Peter Jonge & Eva Ceulemans, 2022. "ConNEcT: A Novel Network Approach for Investigating the Co-occurrence of Binary Psychopathological Symptoms Over Time," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 107-132, March.
    3. María Guillot-Valdés & Alejandro Guillén-Riquelme & Juan Carlos Sierra & Gualberto Buela-Casal, 2022. "Network and Exploratory Factorial Analysis of the Depression Clinical Evaluation Test," IJERPH, MDPI, vol. 19(17), pages 1-26, August.
    4. Inken Höller & Dajana Schreiber & Fionneke Bos & Thomas Forkmann & Tobias Teismann & Jürgen Margraf, 2022. "The Mereology of Depression—Networks of Depressive Symptoms during the Course of Psychotherapy," IJERPH, MDPI, vol. 19(12), pages 1-13, June.
    5. M. Marsman & K. Huth & L. J. Waldorp & I. Ntzoufras, 2022. "Objective Bayesian Edge Screening and Structure Selection for Ising Networks," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 47-82, March.
    6. Srebrenka Letina & Tessa F. Blanken & Marie K. Deserno & Denny Borsboom, 2019. "Expanding Network Analysis Tools in Psychological Networks: Minimal Spanning Trees, Participation Coefficients, and Motif Analysis Applied to a Network of 26 Psychological Attributes," Complexity, Hindawi, vol. 2019, pages 1-27, February.
    7. Georgia Mangion & Melanie Simmonds-Buckley & Stephen Kellett & Peter Taylor & Amy Degnan & Charlotte Humphrey & Kate Freshwater & Marisa Poggioli & Cristina Fiorani, 2022. "Modelling Identity Disturbance: A Network Analysis of the Personality Structure Questionnaire (PSQ)," IJERPH, MDPI, vol. 19(21), pages 1-17, October.
    8. Xiao Yang & Nilam Ram & Scott D. Gest & David M. Lydon-Staley & David E. Conroy & Aaron L. Pincus & Peter C. M. Molenaar, 2018. "Socioemotional Dynamics of Emotion Regulation and Depressive Symptoms: A Person-Specific Network Approach," Complexity, Hindawi, vol. 2018, pages 1-14, November.
    9. Michael J. Brusco & Douglas Steinley & Ashley L. Watts, 2022. "Disentangling relationships in symptom networks using matrix permutation methods," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 133-155, March.
    10. Denny Borsboom, 2022. "Possible Futures for Network Psychometrics," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 253-265, March.
    11. Jayawickreme, Nuwan & Mootoo, Candace & Fountain, Christine & Rasmussen, Andrew & Jayawickreme, Eranda & Bertuccio, Rebecca F., 2017. "Post-conflict struggles as networks of problems: A network analysis of trauma, daily stressors and psychological distress among Sri Lankan war survivors," Social Science & Medicine, Elsevier, vol. 190(C), pages 119-132.
    12. Yi-Lung Chen & Hsing-Ying Ho & Ray C. Hsiao & Wei-Hsin Lu & Cheng-Fang Yen, 2020. "Correlations between Quality of Life, School Bullying, and Suicide in Adolescents with Attention-Deficit Hyperactivity Disorder," IJERPH, MDPI, vol. 17(9), pages 1-12, May.
    13. Kan, Kees-Jan & van der Maas, Han L.J. & Levine, Stephen Z., 2019. "Extending psychometric network analysis: Empirical evidence against g in favor of mutualism?," Intelligence, Elsevier, vol. 73(C), pages 52-62.
    14. Sacha Epskamp, 2020. "Psychometric network models from time-series and panel data," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 206-231, March.
    15. de Boer, Nina Sofie, 2020. "Exploring the Long-Term Health Consequences of ADHD using a Multivariable Mendelian Randomization Network Approach," Thesis Commons c4wz5, Center for Open Science.
    16. Don Watson & Manfred Krug & Claus-Christian Carbon, 2022. "The relationship between citations and the linguistic traits of specific academic discourse communities identified by using social network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1755-1781, April.
    17. Matt Crum & Nikhil Ram-Mohan & Michelle M Meyer, 2019. "Regulatory context drives conservation of glycine riboswitch aptamers," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-24, December.
    18. Shinsuke Ohnuki & Yoshikazu Ohya, 2018. "High-dimensional single-cell phenotyping reveals extensive haploinsufficiency," PLOS Biology, Public Library of Science, vol. 16(5), pages 1-23, May.
    19. Payton J. Jones & Patrick Mair & Thorsten Simon & Achim Zeileis, 2020. "Network Trees: A Method for Recursively Partitioning Covariance Structures," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 926-945, December.
    20. Simon Foster & Meichun Mohler-Kuo, 2020. "The proportion of non-depressed subjects in a study sample strongly affects the results of psychometric analyses of depression symptoms," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-13, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:23:p:8934-:d:454363. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.