Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-22T15:36:07.608Z Has data issue: false hasContentIssue false

Longitudinal course of depressive symptoms in adulthood: linear stochastic differential equation modeling

Published online by Cambridge University Press:  30 August 2012

T. Rosenström*
Affiliation:
IBS, Unit of Personality, Work and Health Psychology, University of Helsinki, Finland
M. Jokela
Affiliation:
IBS, Unit of Personality, Work and Health Psychology, University of Helsinki, Finland
M. Hintsanen
Affiliation:
IBS, Unit of Personality, Work and Health Psychology, University of Helsinki, Finland Helsinki Collegium for Advanced Studies, University of Helsinki, Finland
L. Pulkki-Råback
Affiliation:
IBS, Unit of Personality, Work and Health Psychology, University of Helsinki, Finland
N. Hutri-Kähönen
Affiliation:
Department of Pediatrics, Tampere University and Tampere University Hospital, Finland
L. Keltikangas-Järvinen
Affiliation:
IBS, Unit of Personality, Work and Health Psychology, University of Helsinki, Finland
*
*Address for correspondence: T. Rosenström, IBS, Unit of Personality, Work and Health Psychology, University of Helsinki (Siltavuorenpenger 1 A), PO Box 9, 00014, Helsinki, Finland. (Email: [email protected])

Abstract

Background

Although many studies have addressed the topic of stability versus change in depressive symptoms, few have further decomposed the change to continuous accumulation versus non-systematic state fluctuations or measurement errors. This further step requires a longitudinal follow-up and an appropriate stochastic model; it would, for example, evaluate the hypothesis that women accumulate more susceptibility events than men.

Method

A linear stochastic differential equation model was estimated for a 16-year longitudinal course of depressive symptoms in the Young Finns community sample of 3596 participants (1832 women, 1764 men). This model enabled us to decompose the variance in depression symptoms into a stable trait, cumulative effects and state/error fluctuations.

Results

Women showed higher mean levels and higher variance of depressive symptoms than men. In men, the stable trait accounted for the majority [61%, 90% confidence interval (CI) 48.9–69.2] of the total variance, followed by cumulative effects (23%, 90% CI 9.9–41.7) and state/error fluctuations (16%, 90% CI 5.6–23.2). In women, the cumulative sources were more important than among men and accounted for 44% (90% CI 23.6–58.9) of the variance, followed by stable individual differences (32%, 90% CI 18.5–54.2) and state fluctuations (24%, 90% CI 19.1–27.3).

Conclusions

The results are consistent with previous observations that women suffer more depression than men, and have more variance in depressive symptoms. We also found that continuously accumulating effects are a significant contributor to between-individual differences in depression, especially for women. Although the accumulating effects are often confounded with non-systematic state fluctuations, the latter are unlikely to exceed 27% of the total variance of depressive symptoms.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bartlett, MS (1946). On the theoretical specification and sampling properties of autocorrelated time-series. Supplement to the Journal of the Royal Statistical Society 8, 2741.CrossRefGoogle Scholar
Beck, AT, Steer, RA, Brown, GK (1996). Manual for the Beck Depression Inventory-II. Psychological Corporation: San Antonio, TX.Google Scholar
Bienvenu, OJ, Davydow, DS, Kendler, KS (2011). Psychiatric ‘diseases’ versus behavioral disorders and degree of genetic influence. Psychological Medicine 41, 3340.CrossRefGoogle ScholarPubMed
Boker, S, Neale, M, Maes, H, Wilde, M, Spiegel, M, Brick, T, Spies, J, Estabrook, R, Kenny, S, Bates, T, Mehta, P, Fox, J (2011). OpenMx: an open source extended structural equation modeling framework. Psychometrika 76, 306317.CrossRefGoogle ScholarPubMed
Bollen, KA (1989). Structural Equations with Latent Variables. John Wiley & Sons, Inc.: New York.CrossRefGoogle Scholar
Brown, GW, Harris, TO (2008). Depression and the serotonin transporter 5-HTTLPR polymorphism: a review and a hypothesis concerning gene–environment interaction. Journal of Affective Disorders 111, 112.CrossRefGoogle Scholar
Caspi, A, Hariri, AR, Holmes, A, Uher, R, Moffitt, TE (2010). Genetic sensitivity to the environment: the case of the serotonin transporter gene and its implications for studying complex diseases and traits. American Journal of Psychiatry 167, 509527.CrossRefGoogle Scholar
Cicchetti, D, Rogosh, FA (1996). Equifinality and multifinality in developmental psychopathology. Development and Psychopathology 8, 597600.CrossRefGoogle Scholar
Cole, DA, Martin, NC (2005). The longitudinal structure of the Children's Depression Inventory: testing a latent trait-state model. Psychological Assessment 17, 144155.CrossRefGoogle ScholarPubMed
Cramer, AOJ, Borsboom, D, Aggen, SH, Kendler, KS (2012). The pathoplasticity of dysphoric episodes: differential impact of stressful life events on the pattern of depressive symptom inter-correlations. Psychological Medicine 42, 957965.CrossRefGoogle ScholarPubMed
Efron, B, Tibshirani, RJ (1993). An Introduction to the Bootstrap. Chapman & Hall/CRC: Boca Raton.CrossRefGoogle Scholar
Ekström, J (2009). An empirical polychoric correlation. Ph.D. thesis (http://escholarship.org/uc/item/2tw0b313). Accessed 17 April 2012.Google Scholar
Elovainio, M, Keltikangas-Järvinen, L, Pulkki-Råback, L, Kivimäki, M, Puttonen, S, Räsänen, L, Mansikkaniemi, K, Viikari, L, Raitakari, OT (2006). Depressive symptoms and C-reactive protein: the Cardiovascular Risk in Young Finns Study. Psychological Medicine 36, 797805.CrossRefGoogle ScholarPubMed
Enders, CK (2001). The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data. Psychological Methods 6, 352370.CrossRefGoogle ScholarPubMed
Hankin, BL, Abramson, LY, Moffitt, TE, Silva, PA, McGee, R, Angell, KE (1998). Development of depression from preadolescence to young adulthood: emerging gender differences in a 10-year longitudinal study. Journal of Abnormal Psychology 107, 128140.CrossRefGoogle Scholar
Hankin, BL, Fraley, RC, Lahey, BB, Waldman, ID (2005). Is depression best viewed as a continuum or discrete category? A taxometric analysis of childhood and adolescent depression in a population-based sample. Journal of Abnormal Psychology 114, 96108.CrossRefGoogle ScholarPubMed
Hyde, JS, Mezulis, AH, Abramson, LY (2008). The ABCs of depression: integrating affective, biological, and cognitive models to explain the emergence of the gender difference in depression. Psychological Review 115, 291313.CrossRefGoogle ScholarPubMed
Jokela, M, Singh-Manoux, A, Shipley, MJ, Ferrie, JE, Gimeno, D, Akbaraly, TN, Head, J, Elovainio, M, Marmot, MG, Kivimäki, M (2011). Natural course of recurrent psychological distress in adulthood. Journal of Affective Disorders 130, 454461.CrossRefGoogle ScholarPubMed
Kass, RE, Raftery, AE (1995). Bayes factors. Journal of the American Statistical Association 90, 773795.CrossRefGoogle Scholar
Kendler, KS, Gatz, M, Gardner, CO, Pedersen, NL (2006). Personality and major depression: a Swedish longitudinal population-based twin study. Archives of General Psychiatry 63, 11131120.CrossRefGoogle ScholarPubMed
Kenny, DA, Zautra, A (1995). The trait-state-error model for multiwave data. Journal of Consulting and Clinical Psychology 63, 5259.CrossRefGoogle ScholarPubMed
Kessler, RC, Berglund, P, Demler, O, Jin, R, Koretz, D, Merikangas, KR, Rush, AJ, Walters, EE, Wang, PS (2003). The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). Journal of the American Medical Association 289, 30953105.CrossRefGoogle ScholarPubMed
Klenke, A (2008). Probability Theory: A Comprehensive Course. Springer-Verlag: London.CrossRefGoogle Scholar
Kirsch, I, Deacon, BJ, Huedo-Medina, TB, Scoboria, A, Moore, TJ, Johnson, BT (2008). Initial severity and antidepressant benefits: a meta-analysis of data submitted to the Food and Drug Administration. PLoS Medicine 5, e45.CrossRefGoogle ScholarPubMed
Mathers, CD, Loncar, D (2006). Projections of global mortality and burden of disease from 2002 to 2030. PLoS Medicine 3, e442.CrossRefGoogle ScholarPubMed
Monroe, SM, Harkness, KL (2005). Life stress, the ‘kindling’ hypothesis, and the recurrence of depression: considerations from a life stress perspective. Psychological Review 112, 417445.CrossRefGoogle ScholarPubMed
Mueller, TI, Leon, AC, Keller, MB, Solomon, DA, Endicott, J, Coryell, W, Warshaw, M, Maser, JD (1999). Recurrence after recovery from major depressive disorder during 15 years of observational follow-up. American Journal of Psychiatry 156, 10001006.CrossRefGoogle ScholarPubMed
Murray, CJL, Lopez, AD (1997). Alternative projections of mortality and disability by cause 1990–2020: global burden of disease study. Lancet 349, 14981504.CrossRefGoogle ScholarPubMed
Muthén, B, Kaplan, D, Hollis, M (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika 52, 431462.CrossRefGoogle Scholar
Nolen-Hoeksema, S, Girgus, JS (1994). The emergence of gender differences in depression during adolescence. Psychological Bulletin 115, 424443.CrossRefGoogle ScholarPubMed
Øksendal, BK (2003). Stochastic Differential Equations: An Introduction with Applications. Springer-Verlag: Berlin.CrossRefGoogle Scholar
Oud, JHL, Delsing, MJMH (2010). Continuous time modeling of panel data by means of SEM. In Longitudinal Research with Latent Variables (ed. Montfort, K., Oud, J. and Satorra, A.), pp. 201244. Springer-Verlag: Berlin.CrossRefGoogle Scholar
Oud, JHL, Jansen, RARG (2000). Continuous time state space modeling of panel data by means of SEM. Psychometrika 65, 199215.CrossRefGoogle Scholar
Pulkki-Råback, L, Kivimäki, M, Ahola, K, Joutsenniemi, K, Elovainio, M, Rossi, H, Puttonen, S, Koskinen, S, Isometsä, E, Lönnqvist, J, Virtanen, M (2012). Living alone and antidepressant medication use: a prospective study in a working-age population. BMC Public Health, 12, 236.CrossRefGoogle Scholar
Raitakari, OT, Juonala, M, Rönnemaa, T, Keltikangas-Järvinen, L, Räsänen, L, Pietikäinen, M, Hutri-Kähönen, N, Taittonen, L, Jokinen, E, Marniemi, J, Jula, A, Telama, R, Kähönen, M, Lehtimäki, T, Åkerblom, HK, Viikari, JSA (2008). Cohort profile: the Cardiovascular Risk in Young Finns study. International Journal of Epidemiology 37, 12201226.CrossRefGoogle ScholarPubMed
Sihvo, S, Isometsä, E, Kiviruusu, O, Hämäläinen, J, Suvisaari, J, Perälä, J, Pirkola, S, Saarni, S, Lönnqvist, J (2008). Antidepressant utilisation patterns and determinants of short-term and non-psychiatric use in the Finnish general adult population. Journal of Affective Disorders 110, 94105.CrossRefGoogle ScholarPubMed
Solomon, A, Haaga, DAF, Arnow, BA (2001). Is clinical depression distinct from subthreshold depressive symptoms? A review of the continuity issue in depression research. Journal of Nervous and Mental Disease 189, 498506.CrossRefGoogle ScholarPubMed
Sullivan, PF, Neale, MC, Kendler, KS (2000). Genetic epidemiology of major depression: review and meta-analysis. American Journal of Psychiatry 157, 15521562.CrossRefGoogle ScholarPubMed
Uher, R, Farmer, A, Maier, W, Rietschel, M, Hauser, J, Marusic, A, Mors, O, Elkin, A, Williamson, RJ, Schmael, C, Henigsberg, J, Perez, J, Mendlewicz, J, Janzing, JGE, Zobel, A, Skibinska, M, Kozel, D, Stamp, AS, Bajs, M, Placentino, A, Barreto, M, McGuffin, P, Aitchison, KJ (2008). Measuring depression: comparison and integration of three scales in the GENDEP study. Psychological Medicine 38, 289300.CrossRefGoogle ScholarPubMed
Weissman, MM, Bland, RC, Canino, GJ, Faravelli, C, Greenwald, S, Hwu, HG, Joyce, PR, Karam, EG, Lee, CK, Leelouch, J, Lépine, JP, Newman, SC, Rubio-Stibec, M, Wells, JE, Wickramaratne, PJ, Wittchen, H, Yeh, EK (1996). Cross-national epidemiology of major depression and bipolar disorder. Journal of the American Medical Association 276, 293299.CrossRefGoogle ScholarPubMed
Zhang, T, Meaney, MJ (2010). Epigenetics and the environmental regulation of the genome and its function. Annual Review of Psychology 61, 439466.CrossRefGoogle ScholarPubMed