WORK STRESS, GENDER AND MIDDLE AGED CARDIOVASCULAR MORTALITY RATES IN A CHANGING SOCIETY

1
WORK STRESS, GENDER AND MIDDLE AGED
CARDIOVASCULAR MORTALITY RATES IN A CHANGING
SOCIETY

• Maria S. Kopp, Árpád Skrabski, Johannes Siegrist
• www.behsci.sote.hu
• WPA Conference
• Cairo, 10-15 September, 2005

2
What can explain the opposite changes in
East-West life expectancy?

• In the 1970s no differences in Austrian and
  Hungarian life expectancy
• Life expectancy in Hungary today
• Male 68.2, female 76.5 years,
• Life expectancy in neighbouring Austria
• Male 75.9- they live 7.7 years longer,
• Female 81.7- they live 5.2 years longer

3
Mid-aged mortality crisis in Central-Eastern-Europ
e

• Since the late 1980s, the mortality rates among
  45-64 year old men in Hungary has risen to higher
  levels than they were in the 1930s, in spite of
  economic development,
• Large regional mid-aged mortality differences in
  Hungarian counties and in the 150 subregions
• Excess cardiovascular mortality in midlife- three
  times higher than the European average

4
Experimental model in the society

• These paradoxical features of premature mortality
  and morbidity in Central-Eastern-European
  countries might be regarded as
• a special experimental model to understand better
  the human consequences of societal changes
• and the protective and risk factors on society
  level

5
Possible role of work stress

• Hungary has witnessed a major change in the
  labour market since 1970
• High degree of job instability
• Increased loss of contol in work
• Second or even third jobs- weekend work
• Decreased social support at work
• Since 1990 unemployment

6
Outcome measures

• Age standardized midlife (45-64 years) male and
  female
• total cardiovascular (CV) (ICD 10,100-199)
• ischemic heart disease (IHD) and
• cerebrovascular mortality rates for 10.000
  persons in the same sex and age were computed for
  each of the 150 Hungarian sub-regions and for the
  20 Hungarian counties for the last available
  years, between 2001 and 2003.

7
Total CV mortality rates in the 45-64 years old
males and females/10.000 (2001-2003) in the 20
counties
8
Total CV mortality rates in the 45-64 years old
males and females/10.000 (2001-2003) in the 150
subregions of Hungary
9
Ischemic heart disease (IHD) mortality rates in
the 45-64 years old males and females/10.000
(2001-2003) in the 20 counties
10
Cerebrovascular mortality rates in the 45-64
years old males and females/10.000 (2001-2003) in
the 20 counties
11
National representative surveys in the Hungarian
population

• The samples represent the Hungarian population
  above age 16 according to gender, age and county
• Hungarostudy 1983 more than 6000 persons
• Hungarostudy 1988 20.902 persons
• Hungarostudy 1995 12.463 persons
• Kopp MS, Skrabski Á, Szedmák S (2000)
  Psychosocial risk factors, inequality and
  self-rated morbidity in a changing society,
  Social Sciences and Medicine 51, 1350-1361.

12
Latest survey Hungarostudy 2002

• 12,643 persons were interviewed in their homes,
  they represented the population above age 18
  according to age and sex and counties
• The refusal rate was 17,7 for the full sample,
  although there were significant differences,
  depending on settlements.

13
Work stress variables

• Control at work
• Social support at work
• Working hours per week days
• and weekend days
• Income as job related reward
• Job security
• Unemployment

14
Socio-economic factors

• Education,
• Income,
• Subjective socioeconomic status
• Acces to car
• Marital status
• Housing environment

• Chicago collective efficacy
• Family environment
• Childhood experiences
• Self-rated socioeconomic changes

15
Mental health indicators in the present study

• Shortened Beck Depression Score
• Hostility score (Cook-Medley, 1954)
• Social support (Caldwell,1987)
• Anomie- inability for long term planning
  Eurobarometer study
• Hopelessness Score (Beck, 2000)
• Juhász Anxiety (Juhász, 1978)

• Vital exhaustion (Appels, 1988)
• Type D Personality (Dennolet, 2000)
• Dysfunctional attitude (Weissman,1979)
• Life events (Rahe, 2002)
• Marital stress

16
Further mental health indicators

• Self-rated health
• WHO Wellbeing (Bech,1996)
• Meaning in life (R.Rahe, 2002)
• Purposes in Life (Crumbaugh, Maholick,1964)
• Self-efficacy score (Schwarzer, 1992)

• Ways of coping (Folkman, Lazarus, 1980)
• Stress and coping (Rahe, 2002)
• Social capital measures
• TCI shortened cooperativeness and sensation
  seeking

17
Health behaviour, lifestyle and other confounding
factors

• Drug consumption
• Physical activity
• Body weight and height- BMI
• Suicidal behaviour
• Womens health
• Ethnic identity
• Religious involvement
• Illness intrusiveness
• Health care related needs
• Self-rated pain
• Sleep complaints

• Smoking history
• Alcohol abuse (CAGE-H)
• Non stop alcohol after beginning
• Morning alcohol
• Self-blame because of alcohol

18
Ecological analyses

• The relative importance of work related
  conditions in comparison to other estabilished
  psychosocial and behavioural risk factors in
  relation to premature CV mortality rates were
  analysed
• Because the mortality rates are available only on
  ecological level,
• the risk factors of mid-aged mortality rates can
  be analysed only on aggregate level

19
Ecological level analysesare based onnational
representative survey data andnational
statistical mortality data

• For 150 subregions of Hungary

20
Significant correlations of total mid-aged CV
mortality rates among men (n150)

• Education -.599
  
• Income -.512
• Unemployment .465
• Social support from friends -.372
• Subjective social status .353
• Depression .352
• Weekend work hours
  .344
• Anomie .340

• Non stop alcohol .288
• Morning alcohol .266
• Hostility .257
• Control at work -.255
• Self-blame because of alcohol .250
• Job security -.220
• Social support at work -.197
• Smoking (cigarettes pro day)
  .188

21
Significant correlations of total mid-aged CV
mortality rates among women

• Education -.527
  
• Income -.402
• Unemployment .378
• Social support from friends -.345
• Depression .331
• Non stop alcohol .313
• Job security -.304
• Subjective social status .303

• Anomie .287
• Hostility .229
• Control at work -.275
• Weekend work hours
  .225
• Morning alcohol .224
• Social support at work -.179
• Smoking (cigarettes pro day)
  .151

22
Work stress variables in relation to total
mid-aged CV mortality rates

• Total male mid-aged CV mortality
• Explained
  variance
• - weekend work hours 11.2
• - social support at work 14.7
• Total female mid-aged CV mortality
• - job security 8.7
• - weekend work hours 10.9

23
Work stress variables in relation to mid-aged
ischemic heart disease mortality rates

• Male mid-aged IHD mortality
• Explained
  variance
• - social support at work 3.9
• - weekend work hours 7.6
• Female mid-aged IHD mortality
• - control at work 10.6

24
Work stress variables in relation to mid-aged
cerebrovascular mortality rates

• Male mid-aged cerebrovascular mortality
• Explained
  variance
• - weekend work hours 11.7
• - control at work 14.4
• Female mid-aged cerebrovascular mortality
• - job security 4.8
• - week day work hours 7.2

25
Gender differences

• Low control at work and low social support at
  work were strongly associated with premature
  cardiovascular mortality rates in both sexes
• although considerable gender differences
• Weekend workload was most closely connected with
  male
• Job insecurity with female CV mortality

26
Other psychosocial risk factors

• Low social support from friends
• Depression
• Anomie
• Hostility were significantly connected with
  premature CV mortality differences,
• These factors explained 18.4 of male a
• And 15.1 of female total CV mortality
  differences

27
Socio-economic and behavioural factors

• Low personal income, low education and non-stop
  drinking explained 31.6 of male premature CV
  mortality differences,
• Low education and non stop drinking explained
  25.3 of female CV mortality differences,
• Low education and income were strongly associated
  with work stress, i.e. low control at work,
  weekend workload, low job security, low social
  support at work and depression

28
Conclusion mediating role of work stress and
psycosocial factors

• The worse socioeconomic situation (low education,
  low income) is linked to higher CV mortality
  rates in Hungary as well,
• however, higher CV mortality rates are connected
  to relatively poor socioeconomic situations
  mainly through the mediation of work related and
  psychosocial risk factors,
• These factors create chronic stress situations,
  which can be measured by depressive
  symptomatology, especially in the low
  socio-economic strata and in the deprived
  regions.
• Kopp MS, Réthelyi J (2004) Where psychology meets
  physiologychronic stress and premature
  mortality- the Central-Eastern-European health
  paradox, Brain Research Bulletin ,62,351-367.

29
Mortality rates of middle aged men and depression
scores in 2002
30
Summary

• In a representative sample of the adult Hungarian
  population (12.530 persons)
• High weekend workload, low social support and low
  control at work account for 14.7 of male
  mid-aged CV mortality differences,
• Job insecurity, weekend workload and low control
  at work account for 10.9 of female mid-aged CV
  mortality differences accross 150 subregions

31
References

• Kopp MS, Réthelyi J (2004) Where psychology meets
  physiologychronic stress and premature
  mortality- the Central-Eastern-European health
  paradox, Brain Research Bulletin ,62,351-367.
• Kopp MS, Skrabski Á, Réthelyi J, Kawachi I, Adler
  N (2004) Self Rated Health, Subjective Social
  Status and Middle- Aged Mortality in a Changing
  Society, Behavioral Medicine,30, 65-70.
• Kopp MS (interview) (2000) Stress The invisible
  Hand in Eastern Europe s Death Rates, Science,
  288, 9.June 2000, 1732-1733.
• Kopp MS, Skrabski Á, Szedmák S (2000)
  Psychosocial risk factors, inequality and
  self-rated morbidity in a changing society,
  Social Sciences and Medicine 51, 1350-1361.
• Skrabski,Á.Kopp MS, Rózsa S, Réthelyi J, Rahe RH
  (2005)Life meaning an important correlate of
  health int he Hungarian population, International
  Journal of Behavioral Medicine
• Kopp MS, Skrabski Á, Kawachi I, Adler NE (2005)
  Low socioeconomic staus of the opposite gender is
  a risk factor for middle aged mortality, J.
  Epidemiology and Community Health

32
Ischemic heart disease (IHD) mortality rates in
the 45-64 years old males and females/10.000
(2001-2003) in the 150 subregions
33
Cerebrovascular mortality rates in the 45-64
years old males and females/10.000 (2001-2003) in
the 150 subregions
34
Mortality rates of middle aged (45-64) men and
women in 2001
35
Possible explanations

• This deterioration cannot be ascribed to
  defficiencies in health care,because
• during these years there was a significant
  decrease in infant and old age mortality and
  improvements in other dimensions of health care.
• Between 1960 and 1989 there was a constant
  increase in the gross domestic product in
  Hungary. Worsening material situation cannot be
  the explanation

36
Growing polarization of the socio-economic
situation between 1960 and 2002

• Until 1960, practically no income inequality,
  there were no mortality differences between
  socio-economic strata.
• Since that time increasing disparities in
  socio-economic conditions have been accompanied
  by a widening socio-economic gradient in
  morbidity and mortality, but much more among men.

37
Aggregate mortality according to low versus high
education(Mackenbach et al, 1999)
38
Mental health indicators in the present study

• Shortened Beck Depression Score
• Hostility score (Cook-Medley, 1954)
• Hopelessness Score (Beck, 2000)
• Juhász Anxiety (Juhász, 1978)

• Vital exhaustion (Appels, 1988)
• Type D Personality (Dennolet, 2000)
• Dysfunctional attitude (Weissman,1979)
• Life events (Rahe, 2002)
• Marital stress

39
Positive mental health indicators

• Self-rated health
• WHO Wellbeing (Bech,1996)
• Meaning in life (R.Rahe, 2002)
• Purposes in Life (Crumbaugh, Maholick,1964)
• Social support outside work (Caldwell,1987)

• Self-efficacy score (Schwarzer, 1992)
• Ways of coping (Folkman, Lazarus, 1980)
• Stress and coping (Rahe, 2002)
• TCI shortened cooperativeness and sensation
  seeking

40
Physical health, health behaviour, lifestyle and
other confounding factors

• Smoking
• Alcohol (AUDIT)
• Drug consumption
• Physical activity
• Body weight and height- BMI
• Suicidal behaviour
• Womens health
• Ethnic identity

• Self-rated Disability
• Self-reported illness history (26 disorders)
• Illness intrusiveness
• Health care related needs
• Self-rated pain
• Sleep complaints

41
Ecological level significant psychosocial risk
factors of mid-aged (45-64 years) mortality in
the 150 subregions

• Partial correlation coefficients controlled for
  age (r)
• Male
  mortality Female mortality
• Unemployment .4817
  .4424
• Anomie3- rapid changes .4549
  .2921
• Anomiesum(1-4) .4411
  .2765
• Gipsy population .4332
  .3994
• Depression (BDI) .4282
  .2927
• Hostilitysum .3641
  .2118
• Distrust (Hostility4) .3562
  .2149
• Competitiveness(Hostility5) .3251
  NS !
• Social distrust .2719
  .1852
• Daily cigarettes .2468
  .2372
• Boredome (no purposes in life) .2086
  .2176
• Hopelessness .1934
  NS

42
Ecological level significant psychosocial
protective factors of mid-aged (45-64 years)
mortality in the 150 subregions

• Partial correlation coefficients controlled for
  age (r)
• Male
  mortality Female mortality
• Education -.6646
  -.4399
• Self-rated health (srh) -.3693
  -.2318
• Secure work -.3099
  -.2777
• Subjective social status -.3089
  -.2835
• Personal income -.3040
  -.2553
• Social support from friends -.2656
  -.2145
• Social support from family -.2543
  -.1529
• Fresh at awakening(WHO4) - .2446
  NS
• WHO Well-being score -.2357
  NS
• Control at work -.2302
  -.2167
• Cheerful, in good spirits(WHO1)-.2022
  NS
• Reliable (Purposes in life) -.1895
  NS

43
Male and female differences

• Socio-economic status explained considerable more
  from variance of middle aged (45-64 years) male
  mortality differences
• The socio-economic deprivation seems to be less
  important for women
• Depression, hostility, competitiveness and anomie
  were in more close connection with male than with
  female mortality differences
• These psychological variables showed more close
  connection with premature mortality than smoking

44
Why are men more susceptible to relative income
inequality?

• 1.Income inequality is much higher among men.
• 2. Men are more susceptible to loss of status
  than women. Animal experiments have shown males
  to be more sensitive than females to loss of
  dominance position, that is loss of position in
  hierarchy. In animal studies social rank is the
  best predictor of quality of life and health
  among males.

45
Cognitive schemas of men

• Anomie, first of all lack of possibility for long
  term planning because of sudden changes has
  increased significantly in the last decades,
• Anomie seems to be a more important risk factor
  for men, than for women
• Subjective evaluation of socio-economic status
  seem to be more closely connected with male
  mid-aged mortality, than with female mortality

46
Gender paradox of subjective social status (SSS)

• Female education and subjective social status
  influenced highly significantly the male
  mid-aged mortality rates
• That is, the subjective evaluation of the social
  status of women was strongly and inversely
  correlated with male mid-aged mortality rates,
  which means that sub-regions where women hold
  more negative appraisal of their social standing
  there is a higher male health deterioration