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Depressive symptoms in the Belgian population: disentangling age and cohort effects

Abstract

Objective

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.

Methods

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.

Results

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.

Conclusion

The association between age and depression has to take cohort membership into account. Cohort replacement effects explain the increase in depression in Belgium.

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Fig. 1

Notes

  1. 1.

    Imputation models including 143 depression variables (13 items measured at 11 time points) become practically impossible to estimate.

  2. 2.

    Selection models (simultaneously modeling the dependent variable and the missingness process) or pattern-mixture models (PMM) (distinguishing respondents by their missingness pattern) were developed to deal with data where the missingness process is not at random (MNAR). These models require a rigorous and relatively complex sensitivity analysis [38], as their results can be highly dependent on untestable assumptions about the missingness model (for selection models) or the choice for so-called identifying restrictions (for PMM).

  3. 3.

    Age must be measured on the same scale for respondents from different cohorts, as we want to estimate pure cohort effects, i.e. under control for the differences in the age composition of the cohorts [47:405]. Thus, grand-median centering of the age variable is preferred over cohort-median centering.

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Acknowledgments

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.

Author information

Correspondence to Marie-Christine Brault.

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Brault, M., Meuleman, B. & Bracke, P. Depressive symptoms in the Belgian population: disentangling age and cohort effects. Soc Psychiatry Psychiatr Epidemiol 47, 903–915 (2012). https://doi.org/10.1007/s00127-011-0398-0

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Keywords

  • Depressive symptoms
  • Age effects
  • Cohort effects
  • Growth curve modelling
  • Belgium