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Disentangling trait, occasion-specific, and accumulated situational effects of psychological distress in adulthood: evidence from the 1958 and 1970 British birth cohorts

Published online by Cambridge University Press:  08 January 2020

B. S. Scarpato ,
W. Swardfager ,
M. Eid ,
G.B. Ploubidis  and
H. Cogo-Moreira Open the ORCID record for H. Cogo-Moreira [Opens in a new window]
B. S. Scarpato
Affiliation:
Department of Psychiatry, Universidade Federal de São Paulo (UNIFESP), Sao Paulo, Brazil
W. Swardfager
Affiliation:
Department of Pharmacology & Toxicology, University of Toronto, Toronto, Canada Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Canada
M. Eid
Affiliation:
Department of Educational Science and Psychology, Freie Universität Berlin, Berlin, Germany
G.B. Ploubidis
Affiliation:
Centre for Longitudinal Studies, UCL Institute of Education, University College London, London, UK
H. Cogo-Moreira*
Affiliation:
Department of Psychiatry, Universidade Federal de São Paulo (UNIFESP), Sao Paulo, Brazil Centre for Longitudinal Studies, UCL Institute of Education, University College London, London, UK
*
Author for correspondence: H. Cogo-Moreira, E-mail: [email protected]
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Abstract

Background

The trajectories of psychological distress differ between individuals, but these differences can be difficult to understand because the measures contain both consistent and situational features; however, in longitudinal studies these sources of information can be disentangled. In addition to occasion-specific features, interindividual differences can be decomposed into two sources of information: trait and carry-over effects between neighboring occasions that are not related to the trait (i.e. accumulated situational effects).

Methods

To disentangle these three sources of variance throughout adulthood, the consistency (trait and accumulated situational effects) and occasion specificity of nine indicators of psychological distress from the Malaise Inventory were examined in two birth cohorts, the 1958 National Child Development Study (NCDS58), and the 1970 British Cohort Study (BCS70).

Results

The scale was administered at ages 23, 33, 42, and 50 in NCDS58 (n = 7147), and at ages 26, 30, 34, and 42 in BCS70 (n = 6859). For each psychological symptom, more variance was consistent than occasion-specific. The majority of the consistency was due to trait variance as opposed to accumulated situational effects, indicating that an individual predisposed to be distressed at the beginning of the study remained more likely to be distressed over the whole period. Symptoms of rage were notably more consistent among males than females in both cohorts (78.1% and 81.3% variance explained by trait in NCDS58 and BCS70, respectively), and among females in the NCDS58 (69%).

Conclusions

Symptoms of psychological distress exhibited high stability throughout adulthood, especially among men, due mostly to interindividual trait differences.

Keywords

Autoregressive effectcohort studydepressive symptomslatent state-trait modelpsychological distress
Type
Original Article
Information
Psychological Medicine , Volume 51 , Issue 5 , April 2021 , pp. 804 - 814
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Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press

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References

1958 National Child Development Study. (n.d.). Retrieved from Centre for Longitudinal Studies web site: https://cls.ucl.ac.uk/clsstudies/1958-national-child-development-study/.Google Scholar
1970 British Cohort Study (n.d.). Retrieved from Centre for Longitudinal Studies web site: https://cls.ucl.ac.uk/cls-studies/1970-british-cohort-study/.Google Scholar
Anastasi, A. (1983). Traits, states, and situations: A comprehensive view. In Wainer, H. & Messick, S. (Eds.), Principles of modern psychological measurement (pp. 345–356). Hillsdale, NJ: Erlbaum.Google Scholar
Asparouhov, T., & Muthén, B. O. (2010). Weighted least squares estimation with missing data. Mplus Technical Appendix, pp. 1–10. Retrieved from Statmodel website: https://www.statmodel.com/download/GstrucMissingRevision.pdf.Google Scholar
Bhagwagar, Z., & Cowen, P. J. (2008). ‘It's not over when it's over’: Persistent neurobiological abnormalities in recovered depressed patients. Psychological Medicine, 38(3), 307–313.10.1017/S0033291707001250CrossRefGoogle Scholar
Bollen, K. A., & Curran, P. J. (2004). Autoregressive latent trajectory (ALT) models a synthesis of two traditions. Sociological Methods & Research, 32, 336–383.10.1177/0049124103260222CrossRefGoogle Scholar
Cole, D., Jacquez, F., Truss, A., Pineda, A., Weitlauf, A., Tilghman-Osborne, C., … Maxwell, M. (2009). Gender differences in the longitudinal structure of cognitive diatheses for depression in children and adolescents. Journal of Clinical Psychology, 65, 1312–1326.10.1002/jclp.20631CrossRefGoogle ScholarPubMed
Cole, D. A., Martin, J. M., Jacquez, F. M., Tram, J. M., Zelkowitz, R., Nick, E. A., & Rights, J. D. (2017). Time-varying and time-invariant dimensions of depression in children and adolescents: Implications for cross-informant agreement. Journal of Abnormal Psychology, 126, 635–651.10.1037/abn0000267CrossRefGoogle ScholarPubMed
Cole, D. A., Martin, N. C., & Steiger, J. H. (2005). Empirical and conceptual problems with longitudinal trait-state models: Introducing a trait-state-occasion model. Psychological Methods, 10, 3–20.10.1037/1082-989X.10.1.3CrossRefGoogle ScholarPubMed
Cole, D. A., Nolen-Hoeksema, S., Girgus, J., & Paul, G. (2006). Stress exposure and stress generation in child and adolescent depression: A latent trait-state-error approach to longitudinal analyses. Journal of Abnormal Psychology, 115(1), 40–51.10.1037/0021-843X.115.1.40CrossRefGoogle ScholarPubMed
Conway, C. C., Hipwell, A. E., & Stepp, S. D. (2017). Seven-year course of borderline personality disorder features: Borderline pathology is as unstable as depression during adolescence. Clinical Psychological Science, 5, 742–749.10.1177/2167702617691546CrossRefGoogle ScholarPubMed
Conway, C. C., Rutter, L. A., & Brown, T. A. (2016). Chronic environmental stress and the temporal course of depression and panic disorder: A trait-state-occasion modeling approach. Journal of Abnormal Psychology, 125, 53–63.10.1037/abn0000122CrossRefGoogle ScholarPubMed
Curran, P. J., Howard, A. L., Bainter, S. A., Lane, S. T., & Mcginley, J. S. (2014). The separation of between-person and within-person components of individual change over time: A latent curve model with structured residuals. Journal of Consulting and Clinical Psychology, 82, 879–894.10.1037/a0035297CrossRefGoogle ScholarPubMed
de Kruif, M., Spijker, A. T., & Molendijk, M. L. (2016). Depression during the perimenopause: A meta-analysis. Journal of Affective Disorders, 206, 174–180.10.1016/j.jad.2016.07.040CrossRefGoogle ScholarPubMed
Dodgeon, B., Elliott, J., Johnson, J., & Shepherd, P. (2008). National Child Development Study user guide 2006: User guide to the Biomedical Survey 2002–2004 Dataset. Retrieved from Centre for Longitudinal Studies website: http://doc.ukdataservice.ac.uk/doc/5594/mrdoc/pdf/guide_to_the_ncds_biomedical_dataset.pdf.Google Scholar
Donnellan, M. B., Kenny, D. A., Trzesniewski, K. H., Lucas, R. E., & Conger, R. D. (2012). Using trait–state models to evaluate the longitudinal consistency of global self-esteem from adolescence to adulthood. Journal of Research in Personality, 46, 634–645.10.1016/j.jrp.2012.07.005CrossRefGoogle Scholar
Eid, M. (1996). Longitudinal confirmatory factor analysis for polytomous item responses: Model definition and model selection on the basis of stochastic measurement theory. Methods of Psychological Research – Online, 1, 65–85.Google Scholar
Eid, M., Holtmann, J., Santangelo, P., & Ebner-Priemer, U. (2017). On the definition of latent-state-trait models with autoregressive effects. European Journal of Psychological Assessment, 33, 285–295.10.1027/1015-5759/a000435CrossRefGoogle Scholar
Elliott, J., & Shepherd, P. (2006). Cohort profile: 1970 British Birth Cohort (BCS70). International Journal of Epidemiology, 35, 836–843.10.1093/ije/dyl174CrossRefGoogle Scholar
Elliott, J., Johnson, J., & Shepherd, P. (2009). National child development study: Revised region variables. London. Retrieved from Centre for Longitudinal Studies web site: https://cls.ucl.ac.uk/wp-content/uploads/2017/07/NCDS-Revised-Region-Variables1.pdf.Google Scholar
Forero, C. G., & Maydeu-Olivares, A. (2009). Estimation of IRT graded response models: Limited versus full information methods. Psychological Methods, 14(3), 275–299.10.1037/a0015825CrossRefGoogle ScholarPubMed
Fried, E. I., Epskamp, S., Nesse, R. M., Tuerlinckx, F., & Borsboom, D. (2016). What are ‘good’ depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis. Journal of Affective Disorders, 189, 314–320.10.1016/j.jad.2015.09.005CrossRefGoogle Scholar
Furnham, A., & Cheng, H. (2015). The stability and change of malaise scores over 27 years: Findings from a nationally representative sample. Personality and Individual Differences, 79, 30–34.10.1016/j.paid.2015.01.027CrossRefGoogle Scholar
Geiser, C., & Lockhart, G. (2012). A comparison of four approaches to account for method effects in latent state-trait analyses. Psychological Methods, 17, 255–283.10.1037/a0026977CrossRefGoogle ScholarPubMed
Grant, G., Nolan, M., & Ellis, N. (1990). A reappraisal of the Malaise inventory. Social Psychiatry and Psychiatric Epidemiology, 25, 170–178.Google ScholarPubMed
Hagemann, D., Hewig, J., Seifert, J., Naumann, E., & Bartussek, D. (2005). The latent state–trait structure of resting EEG asymmetry: Replication and extension. Psychophysiology, 42, 740–752.10.1111/j.1469-8986.2005.00367.xCrossRefGoogle Scholar
Hellhammer, J., Fries, E., Schweisthal, O. W., Schlotz, W., Stone, A. A., & Hagemann, D. (2007). Several daily measurements are necessary to reliably assess the cortisol rise after awakening: State- and trait components. Psychoneuroendocrinology, 32(1), 80–86.10.1016/j.psyneuen.2006.10.005CrossRefGoogle ScholarPubMed
Hertzog, C., & Nesselroade, J. R. (1987). Beyond autoregressive models: Some implications of the trait-state distinction for the structural modeling of developmental change. Child Development, 58, 93–109.10.2307/1130294CrossRefGoogle ScholarPubMed
Hirst, M. A., & Bradshaw, J. R. (1983). Evaluating the Malaise inventory: A comparison of measures of stress. Journal of Psychosomatic Research, 27, 193–199.10.1016/0022-3999(83)90022-3CrossRefGoogle ScholarPubMed
Jenkins, A. (2013). Learning and the lifecourse: Gaining new qualifications in adulthood. London: Institute of Education.Google Scholar
Kamata, A., & Bauer, D. J. (2008). A note on the relation between factor analytic and item response theory models. Structural Equation Modeling, 15(1), 136–153. https://doi.org/10.1080/10705510701758406.CrossRefGoogle Scholar
Kendall, A. D., Zinbarg, R. E., Mineka, S., Bobova, L., Prenoveau, J. M., Revelle, W., & Craske, M. G. (2015). Prospective associations of low positive emotionality with first onsets of depressive and anxiety disorders: Results from a 10-wave latent trait-state modeling study. Journal of Abnormal Psychology, 124, 933–943.10.1037/abn0000105CrossRefGoogle ScholarPubMed
Kessler, R. C., Birnbaum, H., Bromet, E., Hwang, I., Sampson, N., & Shahly, V. (2010). Age differences in major depression: Results from the National Comorbidity Survey Replication (NCS-R). Psychological Medicine, 40, 225.10.1017/S0033291709990213CrossRefGoogle Scholar
Kessler, R. C., Zhao, S., Blazer, D. G., & Swartz, M. (1997). Prevalence, correlates, and course of minor depression and major depression in the national comorbidity survey. Journal of Affective Disorders, 45, 19–30.10.1016/S0165-0327(97)00056-6CrossRefGoogle ScholarPubMed
Keyes, K. M., Nicholson, R., Kinley, J., Raposo, S., Stein, M. B., Goldner, E. M., & Sareen, J. (2014). Age, period, and cohort effects in psychological distress in the United States and Canada. American Journal of Epidemiology, 179, 1216–1227.10.1093/aje/kwu029CrossRefGoogle ScholarPubMed
Kovacs, M., Obrosky, S., & George, C. (2016). The course of major depressive disorder from childhood to young adulthood: Recovery and recurrence in a longitudinal observational study. Journal of Affective Disorders, 203, 374–381. https://doi.org/10.1016/j.jad.2016.05.042.CrossRefGoogle Scholar
Labaka, A., Goñi-Balentziaga, O., Lebeña, A., & Pérez-Tejada, J. (2018). Biological sex differences in depression: A systematic review. Biological Research for Nursing, 20, 383–392.10.1177/1099800418776082CrossRefGoogle ScholarPubMed
LaGrange, B., Cole, D. A., Jacquez, F., Ciesla, J., Dallaire, D., Pineda, A., … Felton, J. (2011). Disentangling the prospective relations between maladaptive cognitions and depressive symptoms. Journal of Abnormal Psychology, 120, 511–527.10.1037/a0024685CrossRefGoogle ScholarPubMed
Li, Z., Vidorreta, M., Katchmar, N., Alsop, D. C., Wolf, D. H., & Detre, J. A. (2018). Effects of resting state condition on reliability, trait specificity, and network connectivity of brain function measured with arterial spin labeled perfusion MRI. NeuroImage, 173, 165–175.10.1016/j.neuroimage.2018.02.028CrossRefGoogle ScholarPubMed
Little, R. J. (1992). Regression with missing X's: A review. Journal of the American Statistical Association, 87, 1227–1237.Google Scholar
Maeng, L. Y., & Milad, M. R. (2015). Sex differences in anxiety disorders: Interactions between fear, stress, and gonadal hormones. Hormones and Behavior, 76, 106–117.CrossRefGoogle ScholarPubMed
Marsh, H. W. (1998). Pairwise deletion for missing data in structural equation models: Nonpositive definite matrices, parameter estimates, goodness of fit, and adjusted sample sizes. Structural Equation Modeling: A Multidisciplinary Journal, 5, 22–36.CrossRefGoogle Scholar
McGee, R., Williams, S., & Silva, P. A. (1986). An evaluation of the Malaise inventory. Journal of Psychosomatic Research, 30, 147–152.CrossRefGoogle ScholarPubMed
Mehl, M. R., & Conner, T. S. (Eds.) (2012). Handbook of research methods for studying daily life. New York/London: Guilford Press.Google Scholar
Meyer, J. H., Wilson, A. A., Sagrati, S., Miler, L., Rusjan, P., Bloomfield, P. M., … Houle, S. (2009). Brain monoamine oxidase a binding in major depressive disorder. Archives of General Psychiatry, 66(12), 1304.CrossRefGoogle ScholarPubMed
Millsap, R. E. (2010). Testing measurement invariance using item response theory in longitudinal data: An introduction. Child Development Perspectives, 4, 5–9.CrossRefGoogle Scholar
Morgan, H., & Taylor, K. (2018). BCS70 technical report: age 46 survey. Retrieved from Centre for Longitudinal Studies web site: https://cls.ucl.ac.uk/wp-content/uploads/2019/03/BCS46_Technical-Report_FINAL.pdf.Google Scholar
Muthén, L. K., & Muthén, B. O. (2017). MPLUS (Version 8.2) [Computer software]. Statmodel. Retrieved from website: https://www.statmodel.com/index.shtml.Google Scholar
Ormel, J. (1983). Neuroticism and well-being inventories: Measuring traits or states? Psychological Medicine, 13(1), 165–176.CrossRefGoogle ScholarPubMed
Ormel, J., & Schaufeli, W. B. (1991). Stability and change in psychological distress and their relationship with self-esteem and locus of control: A dynamic equilibrium model. Journal of Personality and Social Psychology, 60(2), 288–299. doi: 10.1037/0022-3514.60.2.288CrossRefGoogle ScholarPubMed
Parry, W. (2013). Experiences of physical activity at age 10 in the 1970 British Cohort Study (CLS Cohort Studies Working paper 2013/6). London. Retrieved from Center for Longitudinal Studies website: https://www.bibliography.cls.ucl.ac.uk/shared/get-file.ashx?itemtype=document&id=1707.Google Scholar
Pettit, J. W., Lewinsohn, P. M., Seeley, J. R., Roberts, R. E., & Yaroslavsky, I. (2010). Developmental relations between depressive symptoms, minor hassles, and major events from adolescence through age 30 years. Journal of Abnormal Psychology, 119, 811–824.CrossRefGoogle ScholarPubMed
Peugh, J. L., & Enders, C. K. (2004). Missing data in educational research: A review of reporting practices and suggestions for improvement. Review of Educational Research, 74, 525–556.CrossRefGoogle Scholar
Ploubidis, G. B., McElroy, E., & Moreira, H. C. (2019). A longitudinal examination of the measurement equivalence of mental health assessments in two British birth cohorts. Longitudinal and Life Course Studies, 10(4), 471–489. doi: 10.1332/175795919x15683588979486.CrossRefGoogle Scholar
Ploubidis, G. B., Sullivan, A., Brown, M., & Goodman, A. (2017). Psychological distress in mid-life: Evidence from the 1958 and 1970 British birth cohorts. Psychological Medicine, 47, 291–303.CrossRefGoogle ScholarPubMed
Power, C., & Elliott, J. (2006). Cohort profile: 1958 British birth cohort (National Child Development Study). International Journal of Epidemiology, 35, 34–41.CrossRefGoogle Scholar
Prenoveau, J. M. (2016). Specifying and interpreting latent state–trait models with autoregression: An illustration. Structural Equation Modeling, 23, 731–749.CrossRefGoogle Scholar
Rodgers, B., Pickles, A., Power, C., Collishaw, S., & Maughan, B. (1999). Validity of the malaise inventory in general population samples. Social Psychiatry and Psychiatric Epidemiology, 34, 333–341.CrossRefGoogle ScholarPubMed
Salk, R. H., Hyde, J. S., & Abramson, L. Y. (2017). Gender differences in depression in representative national samples: Meta-analyses of diagnoses and symptoms. Psychological Bulletin, 143, 783–822.CrossRefGoogle ScholarPubMed
Sargent, P. A., Kjaer, K. H., Bench, C. J., Rabine, E. A., Messa, C., Meyer, J., … Cowen, P. J. (2000). Brain serotonin1A receptor binding measured by positron emission tomography with [11C]WAY-100635. Archives of General Psychiatry, 57(2), 174.CrossRefGoogle Scholar
Schermelleh-Engel, K., Kerwer, M., & Klein, A. G. (2014). Evaluation of model fit in nonlinear multilevel structural equation modeling. Methods of Psychological Research Online, 8, 23–74.Google Scholar
Schmitt, J. M., & Steyer, R. (1993). A latent state-trait model (not only) for social desirability. Personality and Individual Differences, 14, 519–529.CrossRefGoogle Scholar
Steyer, R. (1988). Experiment, Regression und Kausalität. Die logische Struktur kausaler Regressionsmodelle [Experiment, regression, and causality. The logical structure of causal regression models] (Unpublished Habilitation thesis). University of Trier, Trier, Germany.Google Scholar
Steyer, R., Ferring, D., & Schmitt, M. (1992). States and traits in psychological assessment. European Journal of Psychological Assessment, 8, 79–98.Google Scholar
Steyer, R., Majcen, A. M., Schwenkmezger, P., & Buchner, A. (1989). A latent state-trait anxiety model and its application to determine consistency and specificity coefficients. Anxiety Research, 1, 281–299.CrossRefGoogle Scholar
Steyer, R., Mayer, A., Geiser, C., & Cole, D. (2015). A theory of states and traits – revised. Annual Review of Clinical Psychology, 11, 71–98.CrossRefGoogle ScholarPubMed
Steyer, R., Schmitt, M., & Eid, M. (1999). Latent state-trait theory and research in personality and individual differences. European Journal of Personality, 13, 389–408.3.0.CO;2-A>CrossRefGoogle Scholar
Stout, C., Wight, M. A., & Bruhn, J. G. (1969). The Cornell medical index in disability evaluation. Journal of Epidemiology & Community Health, 23, 251–254.CrossRefGoogle ScholarPubMed
Takane, Y., & de Leeuw, J. (1987). On the relationship between item response theory and factor analysis of discretized variables. Psychometrika, 52, 393–408.CrossRefGoogle Scholar
TNS BMRB (2013) Technical Report of the 1970 British Cohort Study: 2008-2009 Survey. Retrieved from Centre for Longitudinal Studies web site: http://www.cls.ioe.ac.uk/page.aspx?&sitesectionid=846&sitesectiontitle=Technical+Report.Google Scholar
TNS BMRB (2014) Technical Report of the 1958 National Child Development Study: Age 55 survey. Retrieved from Centre for Longitudinal Studies web site: https://cls.ucl.ac.uk/wp-content/uploads/2017/07/NCDS-Age-55-Sweep-Technical-Report-FINAL.pdf.Google Scholar
Wang, L., Zhou, C., Zhu, D., Wang, X., Fang, L., Zhong, J., … Xie, P. (2016). Serotonin-1A receptor alterations in depression: A meta-analysis of molecular imaging studies. BMC Psychiatry, 16(1), 1–9.CrossRefGoogle ScholarPubMed
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