Protective and risk factors associated with symptoms of anxiety and depression: analysis of latent profiles and predictors
DOI:
https://doi.org/10.21615/cesp.7166Keywords:
Risk Factor's, protective factors, anxiety, depression, emotional disorders, emotional regulation, latent profilesAbstract
The prevalence of anxiety and depression symptoms poses a problem for health systems. These conditions share symptoms, etiological and maintenance factors. Risk factors and protective factors for their development have been found in various research. This research aimed at identifying latent profiles for anxious and depressive symptomatology and analyzing possible risk factors and protective factors (sociodemographic variables and trait emotion regulation strategies) in a sample of 632 participants aged between 18 and 65 (M= 31,04; SD= 10,14), who were residents in the City of Buenos Aires and Buenos Aires suburbs (Argentina) and were administered a sociodemographic questionnaire, Beck Anxiety Inventory, Beck Depression Inventory and Emotion Regulation Questionnaire. Three profiles characterized by their symptomatic severity were identified. Through a multinomial logistic regression, it was found that resorting mainly to cognitive reappraisal, being between 40 and 49 years old and having a medium-low, medium or medium-high income were protective factors, while feminine genre and resorting to expressive suppression were risk factors. These results highlight the importance of considering protective and risk factors when designing intervention programs to reduce the severity of anxiety and depression symptoms and improving people's mental health.
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References
Agresti, A. (2018). An introduction to categorical data analysis. John Wiley & Sons.
Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52(3), 317-332. https://doi.org/10.1007/BF02294359
Aldao, A., Gee, D. G., De Los Reyes, A., & Seager, I. (2016). Emotion regulation as a transdiagnostic factor in the development of internalizing and externalizing psychopathology: Current and future directions. Development and Psychopathology, 28(4), 927–946. https://doi.org/10.1017/S0954579416000638
Aldao, A., Nolen-Hoeksema, S., & Schweizer, S. (2010). Emotion-regulation strategies across psychopathology: A meta-analytic review. Clinical psychology review, 30(2), 217–237. https://doi.org/10.1016/j.cpr.2009.11.004
American Psychological Association. (2010). Ethical principles of psychologists and code of conduct. https://www.apa.org/ethics/code/principles.pdf
Andreotti, C., Thigpen, J. E., Dunn, M. J., Watson, K., Potts, J., Reising, M. M., Robinson, K. E., Rodriguez, E. M., Roubinov, D., Luecken, L., & Compas, B. E. (2013). Cognitive reappraisal and secondary control coping: associations with working memory, positive and negative affect, and symptoms of anxiety/depression. Anxiety, stress, and coping, 26(1), 20–35. https://doi.org/10.1080/10615806.2011.631526
Ávila Baray, H. L. (2006) Introducción a la Metodología de la Investigación. Edición electrónica. Cuauhtémoc, Instituto Tecnológico de Cd. Cuauhtémoc. http://www.eumed.net.
Barry, V., Stout, M. E., Lynch, M. E., Mattis, S., Tran, D. Q., Antun, A., Ribeiro, M. J., Stein, S. F., & Kempton, C. L. (2020). The effect of psychological distress on health outcomes: A systematic review and meta-analysis of prospective studies. Journal of health psychology, 25(2), 227–239. https://doi.org/10.1177/1359105319842931
Beck, A. T., Epstein, N., Brown, G., & Steer, R. A. (1988). An inventory for measuring clinical anxi-ety: Psychometric properties. Journal of Consulting and Clinical Psychology, 56(6), 893-897. https://doi.org/10.1037/0022-006X.56.6.893
Beck, A. T., Steer, R. A. & Brown, G. K. (2006). Inventario de Depresión de Beck (BDI-II), Manual. Paidós.
Berking, M., Wirtz, C. M., Svaldi, J., & Hofmann, S. G. (2014). Emotion regulation predicts symptoms of depression over five years. Behaviour Research and Therapy, 57, 13–20. https://doi.org/10.1016/j.brat.2014.03.003
Berlin, K. S., Williams, N. A., & Parra, G. R. (2014). An introduction to latent variable mixture modeling (part 1): Overview and cross-sectional latent class and latent profile analyses. Journal of pediatric psychology, 39(2), 174-187. https://doi.org/10.1093/jpepsy/jst084
Boersma, K., Södermark, M., Hesser, H., Flink, I. K., Gerdle, B., & Linton, S. J. (2019). Efficacy of a transdiagnostic emotion-focused exposure treatment for chronic pain patients with comorbid anxiety and depression: a randomized controlled trial. Pain, 160(8), 1708–1718. https://doi.org/10.1097/j.pain.0000000000001575
Brenlla, M. E., & Rodríguez, C. M. (2006). Adaptación argentina del Inventario de Depresión de Beck (BDI-II). Paidós.
Bullis, J. R., Boettcher, H., Sauer‐Zavala, S., Farchione, T. J., & Barlow, D. H. (2019). What is an emotional disorder? A transdiagnostic mechanistic definition with implications for assessment, treatment, and prevention. Clinical psychology: Science and practice, 26(2), e12278. https://doi.org/10.1111/cpsp.12278
Campbell-Sills, L., Barlow, D. H., Brown, T. A., & Hofmann, S. G. (2006). Effects of suppression and acceptance on emotional responses of individuals with anxiety and mood disorders. Behaviour research and therapy, 44(9), 1251-1263. https://doi.org/10.1016/j.brat.2005.10.001
Carlucci, L., Saggino, A., & Balsamo, M. (2021). On the efficacy of the unified protocol for transdiagnostic treatment of emotional disorders: A systematic review and meta-analysis. Clinical Psychology Review, 87, Article 101999. https://doi.org/10.1016/j.cpr.2021.101999
Celeux, G., & Soromenho, G. (1996). An entropy criterion for assessing the number of clusters in a mixture model. Journal of classification, 13(2), 195-212. https://doi.org/10.1007/BF01246098
Chen, Y., Peng, Y., Xu, H., & O´Brien, W. H. (2018). Age differences in stress and coping: Problem-focused strategies mediate the relationship between age and positive affect. The International Journal of Aging & Human Development, 86(4), 347–363. https://doi.org/10.1177/0091415017720890
Cía, A. H., Stagnaro, J. C., Aguilar Gaxiola, S., Vommaro, H., Loera, G., Medina-Mora, M. E., Sustas, S., Benjet, C., & Kessler, R. C. (2018). Lifetime prevalence and age-of-onset of mental disorders in adults from the Argentinean Study of Mental Health Epidemiology. Social psychiatry and psychiatric epidemiology, 53(4), 341–350. https://doi.org/10.1007/s00127-018-1492-3
Clark S. L., Muthén B. (2009). Relating Latent Class Analysis Results to Variables Not Included in the Analysis. http://www.statmodel.com/download/relatinglca.pdf
Cludius, B., Mennin, D., & Ehring, T. (2020). Emotion regulation as a transdiagnostic process. Emotion, 20(1), 37–42. https://doi.org/10.1037/emo0000646
Contractor, A. A., Elhai, J. D., Fine, T. H., Tamburrino, M. B., Cohen, G., Shirley, E., Chan, P. K., Liberzon, I., Galea, S., & Calabrese, J. R. (2015). Latent profile analyses of posttraumatic stress disorder, depression and generalized anxiety disorder symptoms in trauma-exposed soldiers. Journal of Psychiatric Research, 68, 19–26. https://doi.org/10.1016/j.jpsychires.2015.05.014
Dattani, S., Ritchie, H., & Roser, M. (2021). Mental Health. Our World in Data. https://ourworldindata.org/mental-health
Doran, C. M., & Kinchin, I. (2017). A review of the economic impact of mental illness. Australian Health Review, 43(1), 43-48. https://doi.org/10.1071/AH16115
Dryman, M. T., & Heimberg, R. G. (2018). Emotion regulation in social anxiety and depression: a systematic review of expressive suppression and cognitive reappraisal. Clinical psychology review, 65, 17–42. https://doi.org/10.1016/j.cpr.2018.07.004
Dutt, A. J., Gabrian, M., & Wahl, H. W. (2018). Developmental Regulation and Awareness of Age-Related Change: A (Mostly) Unexplored Connection. The journals of gerontology. Series B, Psychological sciences and social sciences, 73(6), 934–943. https://doi.org/10.1093/geronb/gbw084
El-Habil, A. M. (2012). An application on multinomial logistic regression model. Pakistan journal of statistics and operation research, 8(2), 271-291. http://dx.doi.org/10.18187/pjsor.v8i2.234
Etchevers, M. J., Garay, C. J., Putrino, N., Grasso, J., Helmich, N., & Rojas, L. (2021). Relevamiento del estado psicológico de la población argentina. https://www.psi.uba.ar/opsa/informes/opsa_salud_mental_poblacion_argentina_2022.pdf
Etchevers, M. J., Garay, C. J., Putrino, N. I., Helmich, N., & Lunansky, G. (2021). Argentinian mental health during the COVID-19 pandemic: a screening study of the general population during two periods of quarantine. Clinical Psychology in Europe, 3(1), 1-17. https://doi.org/10.32872/cpe.4519
Esponda, G. M., Hartman, S., Qureshi, O., Sadler, E., Cohen, A., & Kakuma, R. (2020). Barriers and facilitators of mental health programmes in primary care in low-income and middle-income countries. The lancet. Psychiatry, 7(1), 78–92. https://doi.org/10.1016/S2215-0366(19)30125-7
Ferguson, S. L., G. Moore, E. W., & Hull, D. M. (2020). Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers. International Journal of Behavioral Development, 44(5), 458-468. https://doi.org/10.1177/0165025419881721
Fernández, R. S., Crivelli, L., Guimet, N. M., Allegri, R. F., & Pedreira, M. E. (2020). Psychological distress associated with COVID-19 quarantine: Latent profile analysis, outcome prediction and mediation analysis. Journal of affective disorders, 277, 75–84. https://doi.org/10.1016/j.jad.2020.07.133
Fernández, R. S., Crivelli, L., Guimet, N. M., Allegri, R. F., Picco, S., & Pedreira, M. E. (2022). Psychological distress and mental health trajectories during the COVID-19 pandemic in Argentina: a longitudinal study. Scientific reports, 12(1), 5632. https://doi.org/10.1038/s41598-022-09663-2
Fuller, H. R., & Huseth-Zosel, A. (2021). Lessons in resilience: Initial coping among older adults during the COVID-19 pandemic. The Gerontologist, 61(1), 114–125. https://doi.org/10.1093/geront/gnaa170
Fusar-Poli, P., Solmi, M., Brondino, N., Davies, C., Chae, C., Politi, P., Borgwardt, S., Lawrie, S. M., Parnas, J., & McGuire, P. (2019). Transdiagnostic psychiatry: a systematic review. World psychiatry: official journal of the World Psychiatric Association (WPA), 18(2), 192–207. https://doi.org/10.1002/wps.20631
Global Burden Disease (GBD). (2016) Disease and Injury Incidence and Prevalence Collaborators (2017). Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet, 390(10100), 1211–1259. https://doi.org/10.1016/S0140-6736(17)32154-2
Groen, R. N., Ryan, O., Wigman, J. T. W., Riese, H., Penninx, B. W. J. H., Giltay, E. J., Wichers, M., & Hartman, C. A. (2020). Comorbidity between depression and anxiety: assessing the role of bridge mental states in dynamic psychological networks. BMC medicine, 18(1), 308. https://doi.org/10.1186/s12916-020-01738-z
Gross, J. J. (1998). The emerging field of emotion regulation: An integrative review. Review of general psychology, 2(3), 271-299. https://doi.org/10.1037%2F1089-2680.2.3.271
Gross, J. J. (2001). Emotion regulation in adulthood: Timing is everything. Current Directions in Psychological Science, 10, 214–219. https://doi.org/10.1111/1467-8721.00152
Gross, J. J. (2015a). Emotion Regulation: Current Status and Future Prospects. Psychological Inquiry, 26(1), 1–26. https://doi.org/10.1080/1047840X.2014.940781
Gross, J.J. (2015b). The extended process model of emotion regulation: Elaborations, applications, and future directions. Psychological Inquiry, 26(1), 130-137. https://doi.org/10.1080/1047840X.2015.989751
Gross, J. J., & John, O. P. (2003). Individual differences in two emotion regulation processes: implications for affect, relationships, and well-being. Journal of Personality and Social Psychology,85(2), 348-362. https://doi.org/10.1037/0022-3514.85.2.348
Hashimoto, E. M., Ortega, E. M. M., Cordeiro, G. M., Suzuki, A. K., & Kattan, M. W. (2019). The multinomial logistic regression model for predicting the discharge status after liver transplantation: estimation and diagnostics analysis. Journal of applied statistics, 47(12), 2159–2177. https://doi.org/10.1080/02664763.2019.1706725
Heeren, A., Jones, P. J., & McNally, R. J. (2018). Mapping network connectivity among symptoms of social anxiety and comorbid depression in people with social anxiety disorder. Journal of affective disorders, 228, 75–82. https://doi.org/10.1016/j.jad.2017.12.003
Heller, A. S., Johnstone, T., Peterson, M. J., Kolden, G. G., Kalin, N. H., & Davidson, R. J. (2013). Increased prefrontal cortex activity during negative emotion regulation as a predictor of depression symptom severity trajectory over 6 months. JAMA psychiatry, 70(11), 1181–1189. https://doi.org/10.1001/jamapsychiatry.2013.2430
Hiilamo, A., Shiri, R., Kouvonen, A., Mänty, M., Butterworth, P., Pietiläinen, O., Lahelma, E., Rahkonen, O., & Lallukka, T. (2019). Common mental disorders and trajectories of work disability among midlife public sector employees - A 10-year follow-up study. Journal of affective disorders, 247, 66–72. https://doi.org/10.1016/j.jad.2018.12.127
Kashdan, T. B., & Steger, M. F. (2006). Expanding the Topography of Social Anxiety: An Experience-Sampling Assessment of Positive Emotions, Positive Events, and Emotion Suppression. Psychological Science, 17(2), 120-128. https://doi.org/10.1111/j.1467-9280.2006.01674.x
Khan, A. J., Maguen, S., Straus, L. D., Nelyan, T. C., Gross, J. J., & Cohen, B. E. (2021). Expressive suppression and cognitive reappraisal in veterans with PTSD: Results from the mind your heart study. Journal of Affective Disorders, 283, 278-284. https://doi.org/10.1016/j.jad.2021.02.015
Koppelman, F. S., & Wen, C. H. (1998). Alternative nested logit models: structure, properties and estimation. Transportation Research Part B: Methodological, 32(5), 289-298. https://doi.org/10.1016/S0191-2615(98)00003-4
Layous, K., Chancellor, J., & Lyubomirsky, S. (2014). Positive activities as protective factors against mental health conditions. Journal of Abnormal Psychology, 123(1), 3–12. https://doi.org/10.1037/a0034709
Jacobson, N. C., & Newman, M. G. (2017). Anxiety and depression as bidirectional risk factors for one another: A meta-analysis of longitudinal studies. Psychological bulletin, 143(11), 1155–1200. https://doi.org/10.1037/bul0000111
The jamovi Project. (2022). jamovi. (Version 2.3) [Computer Software]. https://www.jamovi.org
John, O. P., & Gross, J. J. (2004). Healthy and unhealthy emotion regulation: Personality processes, individual differences, and life span development. Journal of Personality, 72(6), 1301–1333. https://doi.org/10.1111/j.1467-6494.2004.00298.x
Jungert, T., Holm, K., Iotti, N. O., & Longobardi, C. (2021). Profiles of bystanders' motivation to defend school bully victims from a self-determination perspective. Aggressive behavior, 47(1), 78–88. https://doi.org/10.1002/ab.21929
Kraiss, J. T., Ten Klooster, P. M., Moskowitz, J. T., & Bohlmeijer, E. T. (2020). The relationship between emotion regulation and well-being in patients with mental disorders: A meta-analysis. Comprehensive psychiatry, 102, 152189. https://doi.org/10.1016/j.comppsych.2020.152189
Kirwan, M., Pickett, S. M., & Jarrett, N. L. (2017). Emotion regulation as a moderator between anxiety symptoms and insomnia symptom severity. Psychiatry Research, 254, 40–47. https://doi.org/10.1016/j.psychres.2017.04.028
Lilienfeld, S. O., Smith, S. F., & Watts, A. L. (2016). Diagnosis and classification. En H. S. Friedman (Ed.), Encyclopedia of Mental Health (2nd ed., pp. 34–40). Academic Press.
Mansilla, M. (2000). Etapas del desarrollo humano. Revista de Investigación en Psicología, 3(2), 105-116. https://doi.org/10.15381/rinvp.v3i2.4999
Martinsen, K. D., Rasmussen, L. M. P., Wentzel-Larsen, T., Holen, S., Sund, A. M., Løvaas, M. E. S., Patras, J., Kendall, P. C., Waaktaar, T., & Neumer, S.-P. (2019). Prevention of anxiety and depression in school children: Effectiveness of the transdiagnostic EMOTION program. Journal of Consulting and Clinical Psychology, 87(2), 212–219. https://doi.org/10.1037/ccp0000360
McLachlan, G. J., Peel, D., & Bean, R. W. (2003). Modelling high-dimensional data by mixtures of factor analyzers. Computational Statistics & Data Analysis, 41(3-4), 379-388. https://doi.org/10.1016/S0167-9473(02)00183-4
McNally, R. J., Mair, P., Mugno, B. L., & Riemann, B. C. (2017). Co-morbid obsessive-compulsive disorder and depression: a Bayesian network approach. Psychological medicine, 47(7), 1204–1214. https://doi.org/10.1017/S0033291716003287
McRae, K., & Gross, J. J. (2020). Emotion regulation. Emotion, 20(1), 1–9. https://doi.org/10.1037/emo0000703
Montero, I., & León, O. G. (2007). A guide for naming research studies in Psychology. International Journal of clinical and Health psychology, 7(3), 847-862. https://www.redalyc.org/pdf/337/33770318.pdf
Muthén, B. O. (2001). Latent variable mixture modelling. En G.A. Marcoulides, & R. E. Schumacker (Eds.), New developments and techniques in structural equation modelling (pp. 1–33). Lawrence Erlbaum Associates.
Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14(4), 535–569. https://doi.org/10.1080/10705510701575396
Otzen, T., & Manterola, C. (2017). Técnicas de Muestreo sobre una Población a Estudio. International journal of morphology, 35(1), 227-232. http://dx.doi.org/10.4067/S0717-95022017000100037
Pagano, A., & Vizioli, N. (2021a). Estabilidad temporal y validez discriminante del Inventario de Ansiedad de Beck. LIBERABIT. Revista Peruana De Psicología, 27(1), e450. https://doi.org/10.24265/liberabit.2021.v27n1.03
Pagano, A. E., & Vizioli, N. A. (2021b). Adaptación del Cuestionario de Regulación Emocional (ERQ) en población adulta de la Ciudad Autónoma de Buenos Aires y el Conurbano Bonaerense. Revista psicodebate: psicología, cultura y sociedad., 21(1), 18-32. http://dx.doi.org/10.18682/pd.v21i1.3881
R Core Team. (2021). R: A Language and environment for statistical computing. (Version 4.1) [Computer software]. https://cran.r-project.org. (R packages retrieved from MRAN snapshot 2022-01-01).
Ranganathan, P., Aggarwal, R., & Pramesh, C. S. (2015). Common pitfalls in statistical analysis: Odds versus risk. Perspectives in clinical research, 6(4), 222–224. https://doi.org/10.4103/2229-3485.167092
Ranganathan, P., Pramesh, C. S., & Aggarwal, R. (2017). Common pitfalls in statistical analysis: Logistic regression. Perspectives in clinical research, 8(3), 148–151. https://doi.org/10.4103/picr.PICR_87_17
Ruiz-Rodríguez, P., Cano-Vindel, A., Muñoz, R., Medrano, L., Moriana, J. A., Buiza, C., Jiménez, G., Gonzáles Blanch, C., & Grupo de Investigación PsicAP. (2017). Impacto económico y carga de los trastornos mentales comunes en España: una revisión sistemática y crítica. Ansiedad y Estrés, 23(2-3), 118-123. https://doi.org/10.1016/j.anyes.2017.10.003
Sakiris, N., & Berle, D. (2019). A systematic review and meta-analysis of the Unified Protocol as a transdiagnostic emotion regulation based intervention. Clinical Psychology Review, 72, Article 101751. https://doi.org/10.1016/j.cpr.2019.101751
Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 6(2), 461-464. https://www.jstor.org/stable/pdf/2958889.pdf?_=1468598111327
Sheppes, G., Suri, G., & Gross, J. J. (2015). Emotion regulation and psychopathology. Annual review of clinical psychology, 11, 379–405. https://doi.org/10.1146/annurev-clinpsy-032814-112739
Sclove, S. L. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52(3), 333–343. https://doi.org/10.1007/BF02294360
Sloan, E., Hall, K., Moulding, R., Bryce, S., Mildred, H., & Staiger, P. K. (2017). Emotion regulation as a transdiagnostic treatment construct across anxiety, depression, substance, eating and borderline personality disorders: A systematic review. Clinical psychology review, 57, 141–163. https://doi.org/10.1016/j.cpr.2017.09.002
Spurk, D., Hirschi, A., Wang, M., Valero, D., & Kauffeld, S. (2020). Latent profile analysis: A review and “how to” guide of its application within vocational behavior research. Journal of Vocational Behavior, 120, 103445. https://doi.org/10.1016/j.jvb.2020.103445
Stagnaro, J. C., Cía, A., Vázquez, N., Vommaro, H., Nemirovsky, M., Serfaty, E., Sustas, S. E., Medina Mora, M. E., Benjet, C., Aguilar-Gaxiola, S., & Kessler, R. (2019). Estudio epidemiológico de salud mental en población general de la República Argentina. Vertex Revista Argentina De Psiquiatría, 29(142), 275–299. https://revistavertex.com.ar/ojs/index.php/vertex/article/view/256
Stellern, J., Xiao, K. B., Grennell, E., Sanches, M., Gowin, J. L., & Sloan, M. E. (2023). Emotion regulation in substance use disorders: a systematic review and meta-analysis. Addiction, 118(1), 30–47. https://doi.org/10.1111/add.16001
Sullivan, P. F., Kessler, R. C., & Kendler, K. S. (1998). Latent class analysis of lifetime depressive symptoms in the national comorbidity survey. The American journal of psychiatry, 155(10), 1398–1406. https://doi.org/10.1176/ajp.155.10.1398
Troy, A. S., Wilhelm, F. H., Shallcross, A. J., & Mauss, I. B. (2010). Seeing the silver lining: Cognitive reappraisal ability moderates the relationship between stress and depressive symptoms. Emotion, 10(6), 783–795. https://doi.org/10.1037/a0020262
Vizioli, N. A., & Pagano, A. E. (2020). Adaptación del Inventario de Ansiedad de Beck en población de Buenos Aires. Interacciones, e171. https://doi.org/10.24016/2020.v6n3.171
Vizioli, N. A., & Pagano, A. E. (2022a). Inventario de Ansiedad de Beck: validez estructural y fiabilidad a través de distintos métodos de estimación en población argentina. Acta Colombiana de Psicología, 25(1), 28-41. https://doi.org/10.14718/ACP.2022.25.1.3
Vizioli, N. A., & Pagano, A. E. (2022b). Estabilidad Temporal, Validez Convergente e Incremental de la Versión Argentina del Cuestionario de Regulación Emocional (ERQ). Psychologia, 16(1), 71-81. https://revistas.usb.edu.co/index.php/Psychologia/article/view/5397
World Medical Association. (2013). Declaration of Helsinki. Ethical principles for medical research involving human subjects. JAMA Network, 310(20), 2191-2194. https://doi.org/10.1001/jama.2013.281053
Zuckerman, M. (1999). Vulnerability to psychopathology: A biosocial model. American Psychological Association. https://doi.org/10.1037/10316-000
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