Gender Patterns of Socioeconomic Differences in Premature Mortality: Follow-up of the Hungarian Epidemiological Panel

  • Mária S. Kopp
  • Árpád Skrabski
  • Krisztina D. László
  • Imre Janszky
Article
First Online:

Abstract

Background

Gender differences in premature mortality rates and in the size of socioeconomic inequalities in mortality vary across countries.

Purpose

We aimed to quantify the gender differences in the association between socioeconomic status (SES) and premature all-cause mortality and to analyse whether psychosocial factors might associate between SES and mortality among men and women separately in the middle-aged Hungarian population.

Method

Men (n = 1130) and women (n = 1529), aged 40–69 years, participants in the Hungarian Epidemiological Panel (2002) were followed up for 3.5 years for total mortality. Cox proportional hazard models were used to evaluate the association between several socioeconomic measures and total death.

Results

During the follow-up, 99 men (8.8%) and 53 women (3.5%) died. The age-adjusted hazard ratios and the Rothman’s synergy indexes showed that each measure of socioeconomic position was more deleterious in men compared with women. When investigating potential explanatory factors for the SES–mortality association, we found that adjustment for severe depression resulted in the most pronounced reduction in the regression coefficients for the association between most socioeconomic factors and male premature death. There was no indication that depression would mediate between SES and mortality in women. Work stress factors, poor lifestyle and low social support also contributed to the explanation of the link between socioeconomic disadvantage and premature death in men.

Conclusion

Middle-aged Hungarian men seem to be considerably more vulnerable to the chronic stress of material disadvantage than women. This effect modification by gender might partly be explained by a stronger connection between low SES and depressive symptoms in men.

Keywords

Hungary Premature mortality Gender differences Socioeconomic status Work Depression 
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References

  1. 1.
    Bobak M, Marmot M. East-West mortality divide and its potential explanations: proposed research agenda. Bmj. 1996;312(7028):421–5.PubMedGoogle Scholar
  2. 2.
    Hardarson T et al. The relationship between educational level and mortality. The Reykjavik study. J Intern Med. 2001;249(6):495–502.CrossRefPubMedGoogle Scholar
  3. 3.
    Marmot MG, Wilkinson R. Social determinants of health. Oxford: Oxford University Press; 1999.Google Scholar
  4. 4.
    Wilkinson RG. Health, civic society in Eastern Europe before 1989. In: Hertzman C, editor. Environmental and non-environmental determinants of the East-West life expectancy gap. Amsterdam: Kluwer; 1996.Google Scholar
  5. 5.
    Marmot M, Wilkinson R. The Social Pattern of Health and disease. In: Blane D, Brunner E, Wilkinson R, editors. Health and Social Organization. Evanston, IL: Routledge; 1996. p. 42–70.Google Scholar
  6. 6.
    Marmot MG, Kogevinas M, Elston MA. Social/economic status and disease. Annu Rev Public Health. 1987;8:111–35.CrossRefPubMedGoogle Scholar
  7. 7.
    Marmot MG et al. Health inequalities among British civil servants: the Whitehall II study. Lancet. 1991;337(8754):1387–93.CrossRefPubMedGoogle Scholar
  8. 8.
    Smith GD et al. Socioeconomic differentials in mortality risk among men screened for the Multiple Risk Factor Intervention Trial: I. White men. Am J Public Health. 1996;86(4):486–96.CrossRefPubMedGoogle Scholar
  9. 9.
    Dennis BH et al. The association of education with coronary heart disease mortality in the USSR Lipid Research Clinics Study. Int J Epidemiol. 1993;22(3):420–7.CrossRefPubMedGoogle Scholar
  10. 10.
    Mackenbach JP et al. Socioeconomic inequalities in mortality among women and among men: an international study. Am J Public Health. 1999;89(12):1800–6.CrossRefPubMedGoogle Scholar
  11. 11.
    Mackenbach JP et al. Socioeconomic inequalities in health in 22 European countries. N Engl J Med. 2008;358(23):2468–81.CrossRefPubMedGoogle Scholar
  12. 12.
    Blane D, Smith GD, Bartley M. Social class differences in years of potential life lost: size, trends, and principal causes. BMJ. 1990;301(6749):429–32.CrossRefPubMedGoogle Scholar
  13. 13.
    Koskinen S, Martelin T. Why are socioeconomic mortality differences smaller among women than among men? Soc Sci Med. 1994;38(10):1385–96.CrossRefPubMedGoogle Scholar
  14. 14.
    Mustard CA, Etches J. Gender differences in socioeconomic inequality in mortality. J Epidemiol Community Health. 2003;57(12):974–80.CrossRefPubMedGoogle Scholar
  15. 15.
    Martikainen P, Blomgren J, Valkonen T. Change in the total and independent effects of education and occupational social class on mortality: analyses of all Finnish men and women in the period 1971–2000. J Epidemiol Community Health. 2007;61(6):499–505.CrossRefPubMedGoogle Scholar
  16. 16.
    McDonough P et al. Gender and the socioeconomic gradient in mortality. J Health Soc Behav. 1999;40(1):17–31.CrossRefPubMedGoogle Scholar
  17. 17.
    Berkman L, Epstein AM. Beyond health care–socioeconomic status and health. N Engl J Med. 2008;358(23):2509–10.CrossRefPubMedGoogle Scholar
  18. 18.
    Pongrácz M, Murinkó L. Household work distribution. In: Changes of Roles. Report about the Situation of Men and Women. Budapest: TÁRKI ZRT; 2009. p. 95–116.Google Scholar
  19. 19.
    Lynch J, Kaplan G. Socioeconomic position. In: Berkman L, Kawachi I, editors. Social epidemiology. New York: Oxford University Press; 2000. p. 13–35.Google Scholar
  20. 20.
    Geyer S et al. Education, income, and occupational class cannot be used interchangeably in social epidemiology. Empirical evidence against a common practice. J Epidemiol Community Health. 2006;60(9):804–10.CrossRefPubMedGoogle Scholar
  21. 21.
    Marmot M. Status syndrome. London: Times Books; 2004.Google Scholar
  22. 22.
    Jonassaint CR, et al. Low Life Course Socioeconomic Status (SES) is Associated with Negative NEO PI-R Personality Patterns. Int J Behav Med. 2009; (in press).Google Scholar
  23. 23.
    Sacker A et al. Comparing health inequality in men and women: prospective study of mortality 1986–96. BMJ. 2000;320(7245):1303–7.CrossRefPubMedGoogle Scholar
  24. 24.
    Hemstrom O. Health inequalities by wage income in Sweden: the role of work environment. Soc Sci Med. 2005;61(3):637–47.CrossRefPubMedGoogle Scholar
  25. 25.
    Kopp MS et al. Low socioeconomic status of the opposite sex is a risk factor for middle aged mortality. J Epidemiol Community Health. 2005;59(8):675–8.CrossRefPubMedGoogle Scholar
  26. 26.
    Kopp MS, Skrabski Á, Lőke J. The Hungarian state of Mind in a Transforming society. In: Spéder Z, editor. Hungary in Flux, Society, Politics and Transformation. Hamburg: Verlag Dr. Reinhold Kramer; 1999. p. 117–34.Google Scholar
  27. 27.
    Kopp MS, Skrabski A, Szedmak S. Socioeconomic factors, severity of depressive symptomatology, and sickness absence rate in the Hungarian population. J Psychosom Res. 1995;39(8):1019–29.CrossRefPubMedGoogle Scholar
  28. 28.
    Kopp MS, Skrabski A, Szedmak S. Psychosocial risk factors, inequality and self-rated morbidity in a changing society. Soc Sci Med. 2000;51(9):1351–61.CrossRefPubMedGoogle Scholar
  29. 29.
    Rethelyi JM, Berghammer R, Kopp MS. Comorbidity of pain-associated disability and depressive symptoms in connection with sociodemographic variables: results from a cross-sectional epidemiological survey in Hungary. Pain. 2001;93(2):115–21.CrossRefPubMedGoogle Scholar
  30. 30.
    Réthelyi J, Kopp MS. Hierarchy disruption: women and men. Behav Brain Sci. 2004;27(2):17–9.CrossRefGoogle Scholar
  31. 31.
    Geyer S, Peter R. Income, occupational position, qualification and health inequalities—competing risks? (comparing indicators of social status). J Epidemiol Community Health. 2000;54(4):299–305.CrossRefPubMedGoogle Scholar
  32. 32.
    Martikainen P, Valkonen T, Moustgaard H. The effects of individual taxable income, household taxable income, and household disposable income on mortality in Finland, 1998–2004. Popul Stud (Camb). 2009;63(2):147–62.CrossRefGoogle Scholar
  33. 33.
    Skrabski A et al. Life meaning: an important correlate of health in the Hungarian population. Int J Behav Med. 2005;12(2):78–85.CrossRefPubMedGoogle Scholar
  34. 34.
    Demographic Yearbook. Budapest: Central Statistical Office; 2006.Google Scholar
  35. 35.
    Rahe RH et al. The stress and coping inventory: an educational and research instrument. Stress Med. 2000;16:199–208.CrossRefGoogle Scholar
  36. 36.
    Kopp M et al. Work stress and mental health in a changing society. Eur J Public Health. 2008;18(3):238–44.CrossRefPubMedGoogle Scholar
  37. 37.
    Caldwell RA, Pearson JL, Chin RJ. Stress-moderating effects: social support in the context of gender and locus of control. Pers Soc Psychol Bull. 1987;13(1):5–17.CrossRefGoogle Scholar
  38. 38.
    Lasa L et al. The use of the Beck Depression Inventory to screen for depression in the general population: a preliminary analysis. J Affect Disord. 2000;57(1–3):261–5.CrossRefPubMedGoogle Scholar
  39. 39.
    Rózsa S, Szádóczky E, Füredi J. Psychometric properties of the Hungarian version of the shortened Beck Depression Inventory. Psychiatr Hung. 2001;16:384–402.Google Scholar
  40. 40.
    Schwarzer R. Measurement of perceived self-efficacy. Psychometric scales for cross-cultural research. Berlin: Freie Universität; 1993.Google Scholar
  41. 41.
    Kopp M et al. Psychosocial determinants of premature cardiovascular mortality differences within Hungary. J Epidemiol Community Health. 2006;60(9):782–8.CrossRefPubMedGoogle Scholar
  42. 42.
    Piccinelli M et al. Efficacy of the alcohol use disorders identification test as a screening tool for hazardous alcohol intake and related disorders in primary care: a validity study. Bmj. 1997;314(7078):420–4.PubMedGoogle Scholar
  43. 43.
    Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51(6):1173–82.CrossRefPubMedGoogle Scholar
  44. 44.
    Greenland S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology. 2003;14(3):300–6.CrossRefPubMedGoogle Scholar
  45. 45.
    Schisterman EF, Cole SR, Platt RW. Overadjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiology. 2009;20(4):488–95.CrossRefPubMedGoogle Scholar
  46. 46.
    Rothman KJ, Greenland S, Lash TL. Modern epidemiology. Philadelphia: Wolters Kluwer/Lippincott Williams&Wilkins; 2008. p. 71–83.Google Scholar
  47. 47.
    Lundberg M et al. A SAS program calculating three measures of interaction with confidence intervals. Epidemiology. 1996;7(6):655–6.PubMedGoogle Scholar
  48. 48.
    Demographic Yearbook. United Nations; 2002.Google Scholar
  49. 49.
    Human Development Report 2007/2008 Available from: www.undp.org.
  50. 50.
    Kopp MS, Rethelyi J. Where psychology meets physiology: chronic stress and premature mortality–the Central-Eastern European health paradox. Brain Res Bull. 2004;62(5):351–67.CrossRefPubMedGoogle Scholar
  51. 51.
    Black D et al. Inequalities in health: the black report. Health divide. London: Penguin; 1992.Google Scholar
  52. 52.
    Stuckler D, King L, McKee M. Mass privatisation and the post-communist mortality crisis: a cross-national analysis. Lancet. 2009;373(9661):399–407.CrossRefPubMedGoogle Scholar
  53. 53.
    Cockerham WC. Health and social change in Russia and Eastern Europe. New York: Routledge; 1999. p. 123–41.Google Scholar
  54. 54.
    Andorka R. The Week Links in Social Integration- System Transformation in Hungary. In: Spéder Z, editor. Hungary in Flux, Society, Politics and Transformation. Hamburg: Kramer; 1999. p. 19–33.Google Scholar
  55. 55.
    Eurobarometer: Health in the European Union. European Commission; 2006.Google Scholar
  56. 56.
    Theorell T, Karasek RA. Current issues relating to psychosocial job strain and cardiovascular disease research. J Occup Health Psychol. 1996;1(1):9–26.CrossRefPubMedGoogle Scholar
  57. 57.
    Hofstede G. Culture’s consequences, comparing values, behaviors, institutions, and organizations across nations. Thousand Oaks: Sage; 2001.Google Scholar
  58. 58.
    Hemstrom O. Alcohol-related deaths contribute to socioeconomic differentials in mortality in Sweden. Eur J Public Health. 2002;12(4):254–62.CrossRefPubMedGoogle Scholar
  59. 59.
    Makela P, Valkonen T, Martelin T. Contribution of deaths related to alcohol use to socioeconomic variation in mortality: register based follow up study. Bmj. 1997;315(7102):211–6.PubMedGoogle Scholar
  60. 60.
    Skrabski A, Kopp M, Kawachi I. Social capital in a changing society: cross sectional associations with middle aged female and male mortality rates. J Epidemiol Community Health. 2003;57(2):114–9.CrossRefPubMedGoogle Scholar
  61. 61.
    Skrabski A, Kopp M, Kawachi I. Social capital and collective efficacy in Hungary: cross sectional associations with middle aged female and male mortality rates. J Epidemiol Community Health. 2004;58(4):340–5.CrossRefPubMedGoogle Scholar
  62. 62.
    Bosma H, Schrijvers C, Mackenbach JP. Socioeconomic inequalities in mortality and importance of perceived control: cohort study. BMJ. 1999;319(7223):1469–70.PubMedGoogle Scholar
  63. 63.
    Lundberg J et al. Adverse health effects of low levels of perceived control in Swedish and Russian community samples. BMC Public Health. 2007;7:314.CrossRefPubMedGoogle Scholar
  64. 64.
    De Vogli R, Chandola T, Marmot MG. Negative aspects of close relationships and heart disease. Arch Intern Med. 2007;167(18):1951–7.CrossRefPubMedGoogle Scholar
  65. 65.
    Weihs KL, Enright TM, Simmens SJ. Close relationships and emotional processing predict decreased mortality in women with breast cancer: preliminary evidence. Psychosom Med. 2008;70(1):117–24.CrossRefPubMedGoogle Scholar
  66. 66.
    Hemstrom Ö. Is marriage dissolution linked to differences in mortality risks for men and women? J Marriage Fam. 1996;58(2):366–78.CrossRefGoogle Scholar
  67. 67.
    Laszlo KD. Psychosocial Factors and Prognosis in Coronary Heart Disease. In: Department of Public Health Sciences. Stockholm: Karolinska Institutet; 2009.Google Scholar
  68. 68.
    Laszlo KD, Janszky I, Ahnve S. Income and recurrent events after a coronary event in women. Eur J Epidemiol. 2008;23(10):669–80.CrossRefPubMedGoogle Scholar
  69. 69.
    Brunner E. Stress and the biology of inequality. BMJ. 1997;314(7092):1472–6.PubMedGoogle Scholar
  70. 70.
    Kristenson M et al. Psychobiological mechanisms of socioeconomic differences in health. Soc Sci Med. 2004;58(8):1511–22.CrossRefPubMedGoogle Scholar
  71. 71.
    Chandola T et al. Health selection in the Whitehall II study, UK. Soc Sci Med. 2003;56(10):2059–72.CrossRefPubMedGoogle Scholar
  72. 72.
    Kopp MS. Stress: the invisible hand in Eastern Europe’s death rates (interview). Science. 2000;288:1732–3.CrossRefGoogle Scholar

Copyright information

© International Society of Behavioral Medicine 2010

Authors and Affiliations

  • Mária S. Kopp
    • 1
  • Árpád Skrabski
    • 2
  • Krisztina D. László
    • 1
    • 3
  • Imre Janszky
    • 3
  1. 1.Institute of Behavioural SciencesSemmelweis UniversityBudapestHungary
  2. 2.Vilmos Apor Catholic CollegeVácHungary
  3. 3.Division of Public Health Epidemiology, Department of Public Health SciencesKarolinska InstituteStockholmSweden

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