Depressive symptoms in the Belgian population: disentangling age and cohort effects
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Although the association between age and depression has been previously demonstrated, uncertainty remains because of the confounding relationship existing between age and cohort. A study by Yang (J Health Soc Behav 48(1):16, 2007) has evidenced important cohort effects and age-by-cohort interactions in depressive symptoms among US citizens. A crucial limitation, however, is that this study confines itself to elderly population. The objective of the present study is to bring further clarification to the association between age, cohort membership and depressive symptoms, by analyzing a sample with a wider age range.
The Panel Study of Belgian Households is a prospective longitudinal survey, following adults ages 25–74, annually from 1992 to 2002. Missing data were replaced using multiple imputation, allowing for a complete dataset (N = 7,000) at each wave. Respondents were classified into one of five birth cohorts: 1918–1927; 1928–1937; 1938–1947; 1948–1957; 1958–1967. Frequency of depressive symptoms was reported using a modified version of the Health and Daily Living form. Growth curve modeling was used to determine the effect of age and cohort on depression trajectory.
All cohorts differed significantly from one another, with recent cohorts always obtaining the highest mean HDL-depression score. The intensity of depressive symptoms increases linearly with age, but significant age-by-cohorts interactions were detected, indicating that the relationship between age and depression varies across cohorts. No evidence of a WW2 effect was found.
The association between age and depression has to take cohort membership into account. Cohort replacement effects explain the increase in depression in Belgium.
KeywordsDepressive symptoms Age effects Cohort effects Growth curve modelling Belgium
The first author is thankful to the Fonds québécois de la recherche sur la société et la culture (FQRSC) from which she received a doctoral funding and to the Québec inter-university center for social statistics (QICSS) which allowed her stay at Ghent University and led to the realization of this project.
- 4.Kessler R, Birnbaum H, Shahly V, Bromet E, Hwang I, McLaughlin K, Sampson N, Andrade L, de Girolamo G, Demyttenaere K (2009) Age differences in the prevalence and co-morbidity of dsm-iv major depressive episodes: results from the who world mental health survey initiative. Depress Anxiety 0:1–14Google Scholar
- 32.Bracke P, Wauterickx N (2003) Complaints of depression in a representative sample of the belgian population. Arch Pub Health 61(5):223–247Google Scholar
- 34.McKnight PE, McKnight KM, Sidani S, Figueredo AJ (2007) Missing data: a gentle introduction. The Guilford Press, New YorkGoogle Scholar
- 35.Rubin DB (1996) Multiple imputation after 18 + years. J Am Stat Assoc 91(434):473–489Google Scholar
- 36.Schafer J (1997) Analysis of incomplete multivariate data. Chapman & Hall/CRCGoogle Scholar
- 38.Verbeke G, Molenberghs G (2000) Linear mixed models for longitudinal data. Springer, New YorkGoogle Scholar
- 41.Moos RH, Cronkite RC, Billings AG, Finney JW (1985) Health and daily living form manual, revised version. Social Ecological Laboratory, Veterans Administration and Stanford University Medical Centers, StanfordGoogle Scholar
- 43.Bracke P (1996) Geslachtsverschillen in depressief gedrag in een representatieve steekproef van de vlaamse bevolking: De validiteit van een zelf-rapportageschaal. Arch Pub Health 54(7-8):275–300Google Scholar
- 50.Raudenbush SW, Bryk AS (2002) Hierarchical linear models: applications and data analysis methods. Advanced quantitative techniques in the social sciences, vol 1, 2nd edn. Sage Publications, Thousand OaksGoogle Scholar
- 51.Singer J, Willett J (2003) Applied longitudinal data analysis: modeling change and event occurrence. Oxford University Press, USAGoogle Scholar
- 54.Ehrenberg A (1998) La fatigue d’être soi : Dépression et société. O. Jacob, ParisGoogle Scholar
- 55.Schwartz B (2004) The tyranny of choice. Scientific American Mind, pp 71–75Google Scholar
- 57.Horwitz A, Wakefield J (2009) The medicalization of sadness: how psychiatry transformed a natural emotion into a mental disorder. Salute e Società 8(2):49–66Google Scholar