European Journal of Epidemiology

, Volume 20, Issue 1, pp 113–120

The problem of attrition in a Finnish longitudinal survey on depression

Article

Abstract

A cohort of all school children aged 16 years in 1983 (n=2194, 96.7%) in Tampere, Finland were studied at 16, 22 and 32 years of age by self-reported questionnaires. The non-response pattern was considered by modelling the individual response probability by panel year and gender. Gender and school performance at age 16 years were the most important predictors of non-response. They explained away the effect of all other variables at 16 and 22 years, except for earlier non-response at age 22. However, the ability of the models to predict non-respondents was very poor. The effect of attrition for the estimation of depression prevalence was evaluated first by longitudinal weighting methods used commonly in survey studies and then by Markov chain Monte Carlo (MCMC) simulation of the missing depression status. Under the missing-at-random assumption (MAR), both applied correction methods gave estimates of roughly the same size and did not significantly differ from the observed prevalence of depression. No indication of informative missingness was found. We therefore conclude that attrition does not seriously bias the estimation of depression prevalence in the data. In general, non-response models, which are needed to correct for informative missingness, are likely to have poor ability to predict non-response. Therefore, the plausibility of the MAR assumption is important in the presence of attrition.

Keywords

Attrition Depression Longitudinal study Non-response models 

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References

  1. Eaton, W, Anthony, J, Tepper, S, Dryman, A 1992Psychopathology and attrition in the epidemiologic catchment area surveys.Am J Epidemiol13510511059Google Scholar
  2. Koivusilta, L, Rimpelä, A, Andres, V 2003Health behaviours and health in adolescence as predictors of educational level in adulthood: A follow-up study from Finland.Soc Sci Med5577593CrossRefGoogle Scholar
  3. Oldehinkel, A, Wittchen, H, Schuster, P 1999Prevalence, 20-month incidence and outcome of unipolar depressive disorders in an community sample of adolescents.Psychol Med29655668CrossRefGoogle Scholar
  4. Pietilä, A-M, Järvelin, M-R 1995Health and social standing of young men viewed in light of information on their childhood and adolescence.Int J Nurs Stud32545555CrossRefGoogle Scholar
  5. de, Graaf R, Bijl, R, Smit, F, Ravelli, A, Vollebergh, W 2000Psychiatric and socioeconomic predictors of attrition in a longitudinal study: The Netherlands mental health survey and incidence study (NEMESIS).Am J Epidemiol15210391047CrossRefGoogle Scholar
  6. Little, R, Rubin, D 2002Statistical analysis with missing data, 2nd edn.WileyNew YorkGoogle Scholar
  7. Aro, H, Palosaari, U 1992Parental divorce, adolescence, and transition to young adulthood: A follow-up study.Am J Orthopsychiatry.62421429Google Scholar
  8. Huurre, T, Aro, H, Rahkonen, O 2003Well-being and health behaviour by parental socioeconomic status.A follow-up study of adolescents aged 16 until age 32 years. Soc Psychiatry Psychiatr Epidemiol38249255Google Scholar
  9. Central Statistical Office of Finland. Classification of occupations. Helsinki, Central Statistical Office of Finland, 1975.Google Scholar
  10. Aro, H 1988Parental discord, divorce and adolescent development.Eur Arch Psychiatry Neurol Sci237106111CrossRefGoogle Scholar
  11. Pelkonen, M, Marttunen, M, Aro, H 2003Risk for depression: A 6-year follow-up of Finnish adolescents.J Affect Disord774151CrossRefGoogle Scholar
  12. Beck, AT, Beck, RW 1972Screening depressed patients in family practice.A rapid technic. J. Postgrad Med528185Google Scholar
  13. Kaltiala-Heino, RK, Rimpelä, M, Rantanen, P, Laippala, P 1999Finnish modification of the 13-item Beck Depression Inventory in screening an adolescent population for depressiveness and positive mood.Nord J Psychiatry53451457CrossRefGoogle Scholar
  14. Mattlar C-E, Raitasalo R, Putkonen A-R, et al The prevalence of depression in a random sample of Finns, and the association of depression with various cognitive functions. Abstract of the Eleventh International Meeting of the Epidemiological Association (No. 629). International Association, Helsinki, 1987.Google Scholar
  15. Aro, S 1981Stress, morbidity and health-related behaviour: A five-year follow-up study among mental industry employeesScand J Soc Med9S25Google Scholar
  16. Paronen, O, Pasanen, M, Rantanen, P, Aro, S 1982Test–retest study of psychosomatic symptoms among 14-15 year-old schoolchildren (English summary).Sosiaalilääketieteellinen Aikakauslehti19234239Google Scholar
  17. Rimpelä, M, Rimpelä, A, Pasanen, M 1982Perceived symptoms among 12–18-year-old Finns (English summary).Sosiaalilääketieteellinen Aikakauslehti19219233Google Scholar
  18. Rosenberg, M 1965Society and the adolescent self-image.Princeton University PressPrincetonGoogle Scholar
  19. Diggle P, Heaqerty P, Liang K-Y, Zeger S. Analysis of longitudinal data, 2nd edn. Oxford Press, 2002.Google Scholar
  20. Robins, J, Rotnitzky, A, Zhao, L 1995Analysis of semiparametric regression models repeated outcomes in the presence of missing data.J Am Stat Assoc90106120Google Scholar
  21. Rosenbaum, P, Rubin, D 1983The central role of the propensity score in observational studies for causal effects.Biometrika704155Google Scholar

Copyright information

© Springer 2005

Authors and Affiliations

  1. 1.Department of Mental Health and Alcohol ResearchNational Public Health InstituteHelsinkiFinland

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