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Psychological distress in mid-life: evidence from the 1958 and 1970 British birth cohorts

Published online by Cambridge University Press:  13 October 2016

G. B. Ploubidis*
Department of Social Science, Centre for Longitudinal Studies, UCL – Institute of Education, University College London, London, UK
A. Sullivan
Department of Social Science, Centre for Longitudinal Studies, UCL – Institute of Education, University College London, London, UK
M. Brown
Department of Social Science, Centre for Longitudinal Studies, UCL – Institute of Education, University College London, London, UK
A. Goodman
Department of Social Science, Centre for Longitudinal Studies, UCL – Institute of Education, University College London, London, UK
*Address for correspondence: G. B. Ploubidis, Centre for Longitudinal Studies, Room 212, 55–59 Gordon Square, London WC1H 0NU, UK. (Email:



This paper addresses the levels of psychological distress experienced at age 42 years by men and women born in 1958 and 1970. Comparing these cohorts born 12 years apart, we ask whether psychological distress has increased, and, if so, whether this increase can be explained by differences in their childhood conditions.


Data were utilized from two well-known population-based birth cohorts, the National Child Development Study and the 1970 British Cohort Study. Latent variable models and causal mediation methods were employed.


After establishing the measurement equivalence of psychological distress in the two cohorts we found that men and women born in 1970 reported higher levels of psychological distress compared with those born in 1958. These differences were more pronounced in men (b = 0.314, 95% confidence interval 0.252–0.375), with the magnitude of the effect being twice as strong compared with women (b = 0.147, 95% confidence interval 0.076–0.218). The effect of all hypothesized early-life mediators in explaining these differences was modest.


Our findings have implications for public health policy, indicating a higher average level of psychological distress among a cohort born in 1970 compared with a generation born 12 years earlier. Due to increases in life expectancy, more recently born cohorts are expected to live longer, which implies – if such differences persist – that they are likely to spend more years with mental health-related morbidity compared with earlier-born cohorts.

Original Articles
Copyright © Cambridge University Press 2016 

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Anonymous (2015). Human Mortality Database. University of California: Berkeley, USA and Max Planck Institute for Demographic Research, Germany (; Scholar
Booker, CL, Sacker, A (2012). Psychological well-being and reactions to multiple unemployment events: adaptation or sensitisation? Journal of Epidemiology and Community Health 66, 832838.Google Scholar
Carpenter, J, Kenward, M (2012). Multiple Imputation and its Application. John Wiley & Sons: Chichester.Google Scholar
Cheung, GW, Rensvold, RB (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling 9, 233255.CrossRefGoogle Scholar
Colman, I, Ploubidis, GB, Wadsworth, MEJ, Jones, PB, Croudace, TJ (2007). A longitudinal typology of symptoms of depression and anxiety over the life course. Biological Psychiatry 62, 12651271.Google Scholar
De Stavola, BL, Daniel, RM, Ploubidis, GB, Micali, N (2015). Mediation analysis with intermediate confounding: structural equation modeling viewed through the causal inference lens. American Journal of Epidemiology 181, 6480.CrossRefGoogle ScholarPubMed
Elliott, J, Shepherd, P (2006). Cohort profile: 1970 British Birth Cohort (BCS70). International Journal of Epidemiology 35, 836843.Google Scholar
Ferrari, AJ, Charlson, FJ, Norman, RE, Patten, SB, Freedman, G, Murray, CJL, Vos, T, Whiteford, HA (2013). Burden of depressive disorders by country, sex, age, and year: findings from the Global Burden of Disease Study 2010. PLoS Medicine 10, e1001547.CrossRefGoogle ScholarPubMed
Ferri, E, Bynner, J, Wadsworth, M (2003). Changing Britain, Changing Lives. Institute of Education Press: London.Google Scholar
Friedman, L, Wall, M (2005). Graphical views of suppression and multicollinearity in multiple linear regression. American Statistician 59, 127136.Google Scholar
Fries, JF (1980). Aging, natural death, and the compression of morbidity. New England Journal of Medicine 303, 130135.Google Scholar
Fries, JF, Bruce, B, Chakravarty, E (2011). Compression of morbidity 1980–2011: a focused review of paradigms and progress. Journal of Aging Research 2011, 261702261702.CrossRefGoogle ScholarPubMed
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, 3034.Google Scholar
Gallo, WT, Bradley, EH, Siegel, M, Kasl, SV (2000). Health effects of involuntary job loss among older workers: findings from the health and retirement survey. Journals of Gerontology. Series B, Psychological Sciences and Social Sciences 55, S131S140.Google Scholar
Griffiths, LJ, Cortina-Borja, M, Sera, F, Pouliou, T, Geraci, M, Rich, C, Cole, TJ, Law, C, Joshi, H, Ness, AR, Jebb, SA, Dezateux, C (2013). How active are our children? Findings from the Millennium Cohort Study. BMJ Open 3, e002893.Google Scholar
Gruenberg, EM (1977). Failures of success. Milbank Memorial Fund Quarterly – Health and Society 55, 324.Google Scholar
Hawkes, D, Plewis, I (2006). Modelling non-response in the National Child Development Study. Journal of the Royal Statistical Society: Series A (Statistics in Society) 169, 479491.Google Scholar
Hoynes, H, Miller, DL, Schaller, J (2012). Who suffers during recessions? Journal of Economic Perspectives 26, 2747.Google Scholar
Imai, K, Keele, L, Tingley, D (2010). A general approach to causal mediation analysis. Psychological Methods 15, 309334.Google Scholar
Jorm, AF, Windsor, TD, Dear, KBG, Anstey, KJ, Christensen, H, Rodgers, B (2005). Age group differences in psychological distress: the role of psychosocial risk factors that vary with age. Psychological Medicine 35, 12531263.Google Scholar
Katikireddi, SV, Niedzwiedz, CL, Popham, F (2012). Trends in population mental health before and after the 2008 recession: a repeat cross-sectional analysis of the 1991–2010 Health Surveys of England. BMJ Open 2, e001790.Google Scholar
Keyes, KM, Utz, RL, Robinson, W, Li, G (2010). What is a cohort effect? Comparison of three statistical methods for modeling cohort effects in obesity prevalence in the United States, 1971–2006. Social Science and Medicine 70, 11001108.Google Scholar
Krause, ED, Mendelson, T, Lynch, TR (2003). Childhood emotional invalidation and adult psychological distress: the mediating role of emotional inhibition. Child Abuse and Neglect 27, 199213.CrossRefGoogle ScholarPubMed
Kuder, GF, Richardson, MW (1937). The theory of the estimation of test reliability. Psychometrika 2, 151160.Google Scholar
Lara, C, Fayyad, J, De Graaf, R, Kessler, RC, Aguilar-Gaxiola, S, Angermeyer, M, Demytteneare, K, De Girolamo, G, Haro, JM, Jin, R, Karam, EG, Lepine, J-P, Mora, MEM, Ormel, J, Posada-Villa, J, Sampson, N (2009). Childhood predictors of adult attention-deficit/hyperactivity disorder: results from the World Health Organization World Mental Health Survey Initiative. Biological Psychiatry 65, 4654.Google Scholar
Layard, R (2013). Mental health: the new frontier for labour economics. IZA Journal of Labor Policy 2, 2.CrossRefGoogle Scholar
Leon, DA (2011). Trends in European life expectancy: a salutary view. International Journal of Epidemiology 40, 271277.CrossRefGoogle ScholarPubMed
Little, RJA, Rubin, DB (2002). Statistical Analysis with Missing Data. Wiley: Chichester.Google Scholar
Maassen, GH, Bakker, AB (2001). Suppressor variables in path models – definitions and interpretations. Sociological Methods and Research 30, 241270.Google Scholar
MacKinnon, DP, Krull, JL, Lockwood, CM (2000). Equivalence of the mediation, confounding and suppression effect. Prevention Science: the Official Journal of the Society for Prevention Research 1, 173.Google Scholar
Manton, KG (1982). Changing concepts of morbidity and mortality in the elderly population. Milbank Memorial Fund Quarterly – Health and Society 60, 183244.Google Scholar
McGee, R, Williams, S, Silva, PA (1986). An evaluation of the Malaise Inventory. Journal of Psychosomatic Research 30, 147152.Google Scholar
Meredith, W (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika 58, 525543.CrossRefGoogle Scholar
Miech, R, Power, C, Eaton, WW (2007). Disparities in psychological distress across education and sex: a longitudinal analysis of their persistence within a cohort over 19 years. Annals of Epidemiology 17, 289295.Google Scholar
Mostafa, T, Wiggins, R (2015). The impact of attrition and non-response in birth cohort studies: a need to incorporate missingness strategies. Longitudinal and Life Course Studies 6, 16.Google Scholar
Muthén, B (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika 49, 115132.Google Scholar
Muthén, B, Asparouhov, T (2002). Latent variable analysis with categorical outcomes: multiple-group and growth modeling in Mplus. Mplus Web Notes 4, 122.Google Scholar
Muthén, B, Asparouhov, T (2013). New methods for the study of measurement invariance with many groups. Mplus ( Scholar
Muthén, B, Asparouhov, T (2015). Causal effects in mediation modeling: an introduction with applications to latent variables. Structural Equation Modeling – a Multidisciplinary Journal 22, 1223.Google Scholar
Muthén, LK, Muthén, BO (1998–2015). Mplus User's Guide, 7th edn. Muthén & Muthén, Los Angeles, CA.Google Scholar
Myers, J (2003). Exercise and cardiovascular health. Circulation 107, e2e5.Google Scholar
Olshansky, SJ, Rudberg, MA, Carnes, BA, Cassel, CK, Brody, JA (1991). Trading off longer life for worsening health: the expansion of morbidity hypothesis. Journal of Aging and Health 3, 194216.Google Scholar
Paul, KI, Moser, K (2009). Unemployment impairs mental health: meta-analyses. Journal of Vocational Behavior 74, 264282.Google Scholar
Pearl, J (2001). Direct and indirect effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (ed. Breese, J and Koller, D), pp. 411420. Morgan Kaufmann Publishers Inc.: San Francisco, CA.Google Scholar
Power, C, Elliott, J (2006). Cohort profile: 1958 British Birth Cohort (National Child Development Study). International Journal of Epidemiology 35, 3441.Google Scholar
Power, C, Stansfeld, SA, Matthews, S, Manor, O, Hope, S (2002). Childhood and adulthood risk factors for socio-economic differentials in psychological distress: evidence from the 1958 British Birth Cohort. Social Science and Medicine 55, 19892004.Google Scholar
Prince, MJ, Beekman, ATF, Deeg, DJH, Fuhrer, R, Kivela, SL, Lawlor, BA, Lobo, A, Magnusson, H, Meller, I, Van Oyen, H, Reischies, F, Roelands, M, Skoog, I, Turrina, C, Copeland, JRM (1999). Depression symptoms in late life assessed using the EURO-D scale – effect of age, gender and marital status in 14 European centers. British Journal of Psychiatry 174, 339345.Google Scholar
Rabe-Hesketh, S, Skrondal, A (2008). Classical latent variable models for medical research. Statistical Methods in Medical Research 17, 532.Google Scholar
Robins, JM (2003). Semantics of causal DAG models and the identification of direct and indirect effects. In Highly Structured Stochastic Systems (ed. Green, PJ, Hjort, NL and Richardson, S), pp. 7078. Oxford University Press: Oxford.Google Scholar
Robins, LN, Price, RK (1991). Adult disorders predicted by childhood conduct problems – results from the NIMH Epidemiologic Catchment-Area Project. Psychiatry – Interpersonal and Biological Processes 54, 116132.CrossRefGoogle ScholarPubMed
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, 333341.Google Scholar
Rutter, M (1995). Relationships between mental disorders in childhood and adulthood. Acta Psychiatrica Scandinavica 91, 7385.Google Scholar
Rutter, M, Tizard, J, Whitmore, K (1970). Education, Health and Behaviour. Longman Publishing Group: London.Google Scholar
Sacker, A, Wiggins, R (2002). Age–period–cohort effects on inequalities in psychological distress, 1981–2000. Psychological Medicine 32, 977990.Google Scholar
Sass, D (2011). Testing measurement invariance and comparing latent factor means within a confirmatory factor analysis framework. Journal of Psychoeducational Assessment 29, 347363.Google Scholar
Spiers, N, Bebbington, P, Mcmanus, S, Brugha, TS, Jenkins, R, Meltzer, H (2011). Age and birth cohort differences in the prevalence of common mental disorder in England: National Psychiatric Morbidity Surveys 1993–2007. British Journal of Psychiatry 198, 479484.Google Scholar
Sullivan, A, Brown, M, Bann, D (2015). Guest editorial: Generation X enters middle age. Longitudinal and Life Course Studies 6, 120130.Google Scholar
Ten, Ha ve, TR, Joffe, MM (2012). A review of causal estimation of effects in mediation analyses. Statistical Methods in Medical Research 21, 77107.Google Scholar
Valeri, L, Vanderweele, TJ (2013). Mediation analysis allowing for exposure–mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. Psychological Methods 18, 137150.CrossRefGoogle ScholarPubMed
Wu, Z, Schimmele, CM, Chappell, NL (2012). Aging and late-life depression. Journal of Aging and Health 24, 328.Google Scholar
Yang, Y (2007). Is old age depressing? Growth trajectories and cohort variations in late-life depression. Journal of Health and Social Behavior 48, 1632.CrossRefGoogle ScholarPubMed
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