You are here

Article Text

Article menu

Download PDFPDF

Original article
Workplace
The impact of ill health on exit from paid employment in Europe among older workers
  1. Tilja van den Berg1,
  2. Merel Schuring1,
  3. Mauricio Avendano1,2,
  4. Johan Mackenbach1,
  5. Alex Burdorf1
  1. 1Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
  2. 2Center for Population and Development Studies, Harvard School of Public Health, Boston, Massachusetts, USA
  1. Correspondence to Alex Burdorf, Department of Public Health, Erasmus MC, PO Box 2040, Rotterdam 3000 CA, the Netherlands; a.burdorf{at}erasmusmc.nl

Abstract

Objective To determine the impact of ill health on exit from paid employment in Europe among older workers.

Methods Participants of the Survey on Health and Ageing in Europe (SHARE) in 11 European countries in 2004 and 2006 were selected when 50–63 years old and in paid employment at baseline (n=4611). Data were collected on self-rated health, chronic diseases, mobility limitations, obesity, smoking, alcohol use, physical activity and work characteristics. Participants were classified into employed, retired, unemployed and disabled at the end of the 2-year follow-up. Multinomial logistic regression was used to estimate the effect of different measures of ill health on exit from paid employment.

Results During the 2-year follow-up, 17% of employed workers left paid employment, mainly because of early retirement. Controlling for individual and work related characteristics, poor self-perceived health was strongly associated with exit from paid employment due to retirement, unemployment or disability (ORs from 1.32 to 4.24). Adjustment for working conditions and lifestyle reduced the significant associations between ill health and exit from paid employment by 0–18.7%. Low education, obesity, low job control and effort–reward imbalance were associated with measures of ill health, but also risk factors for exit from paid employment after adjustment for ill health.

Conclusion Poor self-perceived health was strongly associated with exit from paid employment among European workers aged 50–63 years. This study suggests that the influence of ill health on exit from paid employment could be lessened by measures targeting obesity, problematic alcohol use, job control and effort–reward balance.

  • Self-perceived health
  • unemployment
  • early-retirement
  • disability
  • mental health
  • ageing
  • disability
  • retired

https://doi.org/10.1136/oem.2009.051730

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    What this paper adds

    • The current study showed that self-perceived poor health was more strongly associated with exit from paid employment during follow-up compared with other health measures such as ‘chronic diseases’, ‘mobility problems’ and ‘instrumental limitations in daily activities’.

    • Lifestyle factors as well as working conditions explained 0% to 19% of health related exits from paid employment.

    • The population attributable fraction of less than good health for exit from paid employment was largest for disability (61%), followed by unemployment (27%) and retirement (9%).

    Introduction

    In many industrialised countries the population is ageing due to increasing life expectancy and falling birth rates.1 A rather paradoxical development is that, despite increases in life expectancy, the average time people spend in paid employment has decreased in most European countries. Although part of this decrease is explained by prolonged education among younger cohorts, a more important contributor is the higher rate of exit from the labour market at older ages.2 As a consequence, many countries are developing policies to encourage older workers to remain longer in the labour market and delay retirement.3 Clearly, the success of these policies will depend on a better understanding of ageing in the workforce and the particular role of health and work characteristics in continuing work or exiting from the labour market. Recent evidence suggests that work can be good for health, reversing the harmful effects of long-term unemployment and prolonged sickness absence.4 However, the current assumption seems to be that illness is incompatible with being in work.4

    It is obvious that ill health plays a role in exit from the labour market through work disability. Although less consistent, there is also evidence that ill health may cause exit through unemployment and early retirement. 3 5–10 In several European countries it was shown that ill health was a risk factor for transitions between paid employment and various forms of non-employment, including retirement, unemployment and taking care of the household.5 9 The strength of the current study is the exploration of the three pathways simultaneously. Second, four different measures of health were used to study the role of ill health in exit from paid employment.

    In order to minimise the negative effect of ill health on work participation, it is of interest to study factors that explain the relationship between ill health and exit from paid employment and may therefore be important targets for primary preventive interventions in occupational populations. Research on occupational health has shown the negative influence of poor working conditions on workers' health.11–13 Lifestyle factors, such as lack of physical activity, smoking, alcohol use and overweight, are well-established determinants of poor health, and thus, may be important targets of intervention in order to decrease health related exit from paid employment.

    The following research questions were formulated. First, which measures of health are predictive for exit from paid employment? Second, how much of the observed associations between ill health and future exit can be explained by work related factors and lifestyle?

    Methods

    Study population

    The study population consisted of participants of the Survey on Health and Ageing in Europe (SHARE study). SHARE is a longitudinal survey that aims to collect medical, social and economic data on the population aged over 50 years in 11 European Union countries (Sweden, Denmark, the Netherlands, Belgium, Germany, Austria, Switzerland, France, Italy, Spain and Greece).14 15 In the participating SHARE countries, the institutional conditions with respect to sampling were so different that a uniform sampling design for the entire project was not feasible. Different registries at national or local level were used that permitted stratification by age. The sampling designs varied from simple random selection of households to complicated multistage designs.

    The first wave of data was collected by interviews between April and October 2004. The overall household response across the 11 SHARE countries in which data collection took place in 2004 was 57.4%, although substantial differences among countries were observed.15 The available dataset from the first wave of data collection (SHARE Release 2.0) contains information on 28 517 participants, with 12 965 subjects (45%) aged between 50 and 63 years. Individuals aged 63 years and older were excluded from the current study, since it was assumed that workers normally retired when they reached 65 years of age at the end of follow-up. While this assumption certainly has limitations, given the complexity of defining retirement at the individual level and the small proportion of workers above the age of 63 years in the study population (about 2%), it was considered to be the definition that was most comparable across countries. Employment status was unknown for 93 persons, resulting in a study population of 12 872 subjects, of whom 7119 (55%) were in paid employment. After 2 years, 8729 subjects participated again in the questionnaire survey (SHARE Release 1.0), resulting in a response rate of 67%. Complete information on employment status in 2006 was available for 8568 subjects. For the longitudinal analysis of the influence of ill health on exit from the labour market, a cohort of 4611 subjects was available with paid employment in 2004 and complete information on individual and work related characteristics at baseline and work status at follow-up in 2006.

    Labour force participation

    The outcome of this study is work status, which was based on self-reported current economic status that best described each respondent's situation based on four mutually exclusive categories: paid work, retired, unemployed or disabled. According to the SHARE definition, employed individuals were those who had declared they had carried out any kind of formal paid work in the last 4 weeks, including self-employed work in a family business. The unemployed were those who had been laid off from their last job but could not claim normal pension benefits, and had therefore spent some time in unemployment before effectively being retired. Sickness or disability insurance applied to people who exited the labour force because of recognised health problems.14 The category of disabled participants predominantly includes those whose health problems at work were an eligibility criterion for receiving a disability pension. Total exit from the workforce was defined as exit either through early retirement, unemployment or disability.

    Health measurements

    The European version of Self-Perceived Health, a 5-point scale questionnaire with answers ranging from very good to (very) bad, was used to define poor health (less than good). This frequently used questionnaire has been shown to be a good indicator of general physical and mental health.16 17 A second health measure was having at least one of the following chronic diseases diagnosed by a doctor during one's lifetime: heart disease, stroke, diabetes, lung disease, asthma, arthritis or rheumatism, and osteoporosis. Functional limitations, reflecting the ability of individuals to perform normally in society, were characterised with two dichotomous measures of health. The first measure of interest, mobility problems, reflects limitations with mobility, arm or fine motor functions. Mobility problems were defined as one or more affirmative answers on a list of 10 mobility problems, such as walking 100 m and reaching or extending arms above shoulder level. The second measure, instrumental limitations, was positive for subjects with limitations in one or more of the 13 instrumental activities of daily life, such as preparing meals and making phone calls.

    Individual characteristics

    The highest education successfully completed was coded according to the 1997 International Standard Classification of Education (ISCED-97) and categorised into low (pre-primary, primary and lower secondary education), intermediate (upper secondary education) and high (post secondary education). Body mass index (BMI) was calculated by dividing body weight in kilograms by the square of body height in metres. BMI was recoded into normal (<25 kg/m2), overweight (≥25 and <30 kg/m2) or obese (≥30 kg/m2). Marital status was used to categorise individuals into those who were living with a spouse or a partner in the same household (reference category) and those living alone. Smokers were subjects who were currently smoking; all others were categorised as non-smokers. Problematic alcohol use was defined as alcohol consumption of two or more glasses of alcoholic beverage on at least 5 days a week in the last 6 months. Physical activity was measured with single questions on regular participation in moderate and vigorous activities, both on a 4-point scale ranging from ‘more than once a week’ to ‘hardly ever, or never’. Those who reported moderate or vigorous activity less than once a week were considered to lack leisure time physical activity.15

    Work related characteristics

    Work related characteristics were assessed by a short battery of items derived from (i) the Job Content Questionnaire assessing the demand–control balance18 and (ii) the effort–reward imbalance questionnaire.12 All items were on a 4-point scale ranging from 1 ‘strongly agree’ to 4 ‘strongly disagree’. A single item measured high time pressure (‘I'm under constant time pressure due to a heavy work load’). Lack of job control was measured by the sum score of two items (‘I have very little freedom to decide how I do my work’; ‘I have an opportunity to develop new skills’). Country-specific median values were used to define the presence of high time pressure and lack of job control.

    Effort–reward imbalance was measured by two items on ‘effort’ (‘physically demanding’ and ‘time pressure’) and five items on ‘reward’ (‘receive adequate support’, ‘receive recognition’, ‘adequate salary’, ‘job promotion prospects’, ‘job security’). Effort–reward imbalance was defined by the ratio of the sum score of the ‘effort’ items and the sum score of the ‘reward’ items, adjusted for the number of items.19 Effort–reward imbalance was defined as a score within the upper tertile of this ratio per country.19

    A high physical work demand was measured with one item (‘My job is physically demanding’). A country-specific median value was used to define the presence of high physical work demand.

    Statistical analysis

    Logistic regression was used to evaluate cross-sectional associations at baseline between four measures of ill health as dependent variables and individual and work characteristics as independent variables, adjusting for country.

    Risk factors for exit from paid employment during the 2-year follow-up period were evaluated by means of multinomial logistic regression analysis. The study population consisted of subjects with paid employment at baseline and ORs were calculated for the likelihood of transition to early retirement, unemployment or disability during the 2-year follow-up period. The results for homemakers were not shown as this group consisted mainly of women, but subjects who exited paid employment through becoming a home worker remained in the sample. The first step in the analysis was to establish univariate associations between the dependent variable work status, and health measures, sociodemographic factors, lifestyle factors and work characteristics as independent factors, including country as fixed effect. In the second step, multivariate analyses were conducted to model employment status at the end of follow-up as a function of four measures of health. For the initial selection of potential covariates for the multivariate model, univariate associations with a significance level of p<0.05 were considered. For each independent variable measure of health, we calculated ORs for the dependent variable exit from work adjusted for age, gender and education (reference model) and further adjusted for lifestyle factors and work related characteristics separately, and in combination. For each regression model the percentage change in the OR of each pathway of exit was calculated (100×(ORreference model−OR+explanatory factors)/(ORreference model−1).20 One of the main advantages of this method is that it can be used to estimate the direct and indirect contributions of explanatory factors. One limitation is that the percentage change can be similar for different absolute changes in ORs. However, all contributions were calculated relative to the same ORs, which were also presented. Therefore, we believe that this limitation has a minor effect on our results.

    Population attributable fractions (PAF) were calculated for significant determinants of exit from paid employment, using the formula PAF=Pe(OR−1)/(1+PE(OR−1)),21 where Pe represents the prevalence of exposure in the study population.

    All statistical models were based on the number of people with complete data available. The statistical analyses were carried out with SPSS v 15.0.22

    Results

    Approximately 17% of the employed workers reported less than good health (table 1). Interrelations of the four health measures were moderate, with Spearman correlations varying from 0.06 to 0.33. In total, 55% of the subjects with poor health had a chronic disease, 57% mobility problems and 9% instrumental limitations. The chronic diseases with the highest prevalences were depression (17.7%, n=814), arthritis/osteoporosis (12.3%, n=565) and respiratory diseases (5.7%, n=265) (data not shown). About 61% of subjects with a chronic disease perceived their health as being good.

    View this table:
    Table 1

    Individual characteristics, lifestyle factors, health status and work characteristics among 4611 employed persons aged 50–63 years in 11 European countries during the first wave of the Survey on Health and Ageing in Europe (SHARE)

    Table 2 shows that important determinants at baseline for all four health measures were lack of physical activity in leisure time (ORs from 1.24 to 1.87) and effort–reward imbalance at work (ORs from 1.25 to 1.64). A high BMI was also associated with most measures of health.

    View this table:
    Table 2

    Cross-sectional multivariate associations between individual characteristics, lifestyle and work characteristics, and different health outcomes among 4611 employed people aged 50–63 years in 11 European countries, during the first wave of the Survey on Health and Ageing in Europe (SHARE)

    During the 2-year follow-up period, 17% (n=794) of employed workers exited the workforce, primarily because of retirement (11%) (table 3). Considerable differences in the prevalences of exit from paid employment and pathways of exit were found among countries. Table 4 shows that self-perceived poor health was the measure of health most predictive of transition to unemployment (OR 2.49), retirement (OR 1.50) and work disability (OR 5.04). All four health measures were associated with any exit from work (ORs from 1.56 to 2.08). The role of ill health on exit from paid employment was comparable for workers with a full-time or part-time contract (data not shown). All lifestyle factors except smoking were associated with exit from paid employment through retirement (ORs from 1.23 to 1.40). Among work related factors, lack of job control showed the highest increased risks for all three exit pathways (ORs from 1.23 to 2.68). Table 5 shows the observed associations between different measures of ill health and transitions to non-participation, after adjustment for lifestyle factors and work characteristics. Significant ORs between ill health and exit from paid employment decreased by 0% to 10% after adjustment for lifestyle factors, 4% to 9% after adjustment for working conditions, and 4% to 19% after adjustment for lifestyle factors and working conditions simultaneously. Adjustment for lifestyle factors and work related characteristics had a smaller influence on the association between ill health and work disability compared to the other pathways of exit from paid employment. In the fully adjusted models for each of the four health measures, the lifestyle factors obesity and problematic alcohol use remained significant in at least one of the models. Regarding work related characteristics, lack of job control and effort–reward imbalance at work remained significant after full adjustment in at least one of the four models.

    View this table:
    Table 3

    Exit from paid employment among 4611 participants aged 50–63 years in 11 European countries during 2 years of follow-up during the first two waves of the Survey on Health and Ageing in Europe (SHARE)

    View this table:
    Table 4

    Univariate associations between health, individual characteristics, lifestyle and work characteristics, and transitions into unemployment, retirement and disability among 4611 initially employed subjects aged 50–63 years during 2 years of follow-up in the Survey on Health and Ageing in Europe (SHARE)

    View this table:
    Table 5

    Multivariate associations between four different health measures and transition to unemployment, retirement and disability among 4611 initially employed subjects aged 50–63 years in 11 European countries during 2 years of follow-up in the Survey on Health and Ageing in Europe (SHARE)

    The population attributable fractions of less than good self-perceived health for transition into unemployment, retirement and disability were 27%, 9% and 61%, respectively.

    Discussion

    During a 2-year follow-up, 17% of workers employed at baseline left paid employment, primarily because of early retirement. Controlling for individual and work related characteristics, poor self-perceived health was strongly associated with exit from paid employment due to retirement, unemployment or disability (ORs from 1.32 to 4.24). In order of decreasing importance, chronic diseases, mobility problems and instrumental limitations also influenced exit from paid employment, most notably through disability. Significant associations between ill health and exit from paid employment changed from 0% to 19% after adjustment for lifestyle and work characteristics.

    Some limitations must be taken into account in this study. First, the attrition rate between baseline and follow-up was high (68%).23 However, in our analyses among subjects initially employed at baseline, no differences were found between responders and non-responders during follow-up for all health measures at baseline.

    Second, there are large variations between European countries in the association between ill health and various forms of exit from paid employment.5 These variations may reflect differences between countries in institutional arrangements (eg, availability of disability benefit schemes for those with health problems), or other factors (eg, more or less selectivity of unemployment dependent on overall levels of unemployment). All analyses were therefore adjusted for country. Due to small numbers, country-specific or region-specific analyses were not feasible. The analyses stratified for Scandinavian (Sweden, Denmark), Bismarckian (Austria, Belgium, France, Germany, the Netherlands, Switzerland) and Southern European regions (Greece, Italy, Spain) showed that the conclusions drawn from the total population were also valid within the regions. That is, in each region the health measure self-perceived health was the measure most predictive for exit from paid employment, most notably through disability.

    Third, all variables were based on self-reported data, which could have caused reporting bias. The problem with using self-reported health in an empirical analysis of labour force participation is that it may be an endogenous explanatory variable.16 24 25 According to the justification hypothesis, individuals justify their non-participation by claiming that they are have ill health. Subjects with intentions at baseline to leave paid employment in the near future may also have been more prone to report high work demands or a less beneficial effort–reward balance in order to justify their future exit from paid employment.17

    Fourth, the current study used a follow-up period of 2 years and, therefore, had limited discriminatory power and does not afford insight into the long-term effects of poor health on exit from paid employment or the relevant time windows for these effects. A European study showed that poor health had the strongest effects on leaving the workforce in the year before the transition.8 Thus, it is expected that the reported influence of ill health on exit from paid employment is a fair reflection of the effects of ill health on work participation. The influence of ill health on exit from paid employment decreased for older workers, as the decision to continue work after 60 years of age is more influenced by other factors, such as eligibility criteria for early retirement and the labour market.

    Several studies have analysed the effects of health on exit from paid employment in older workers. 3 9 5–7 26–29 The results of this study support the selection hypothesis, whereby people with poor health are more likely to leave paid employment.30 The influence of type of health measure differs by route of exit, but an overall effect on total exit was consistently present for all measures of ill health.

    The relationship between poor health and exit from paid employment may be explained by a mismatch between an individual's capacities and the requirements of the job.2 Functional limitations might therefore be more important than self-perceived poor health for future loss of paid employment. However, the analyses showed that poor self-perceived health was a stronger predictor for pathways of exit than functional limitations, expressed by either mobility problems or instrumental limitations in daily activities. An explanation could be that self-perceived health also includes mental health, whereas functional limitations primarily concern physical health. The high prevalence of depression in the cohort may have contributed to the association between self-perceived health and future exit from paid employment.

    The analyses also showed that having ever being diagnosed with a chronic disease played a less important role in exit from paid employment. This may be explained by the fact that people diagnosed with chronic conditions who remained in paid employment are a selection of the fittest survivors,31 while those who had already left paid employment due to these diseases before baseline were not included in our sample. Analyses on the role of onset of disease during the follow-up period was not feasible as only 12 subjects reported that the onset of their chronic disease had been diagnosed during the follow-up period.

    The direct influence of ill health on exit from paid employment had ORs varying between 1.37 and 5.04. The corresponding population attributable fractions of less than good self-perceived health for transition into unemployment, retirement and disability were 27%, 9% and 61%, respectively. Under the assumption that the observed associations represent a causal process, these associations and population attributable fractions indicate that good health is an important factor in maintaining paid employment. Based on this finding, interventions aimed at discouraging exit from paid employment should aim to prevent or minimise ill health. Given the strong associations at baseline between obesity and lack of leisure time physical activity with several measures of ill health, health promotion interventions should be considered that increase physical activity and support a healthy diet.32 33 The consistent associations at baseline between lack of job control, high physical work demands and effort–reward imbalance with several measures of ill health, outline the importance of improving both working conditions and work organisation.

    We observed that adjustment for lifestyle factors and work related characteristics resulted in reasonable changes in health related exit from paid employment. The change was only important for statistically significant associations because a small difference in OR could otherwise result in a high proportion of change. The influence of lifestyle factors and work characteristics on the impact of ill health on labour force exit highlights the importance of helping workers with health problems continue working, for example by empowering workers with chronic diseases.34

    In the fully adjusted model, obesity, problematic alcohol use, low job control and effort–reward imbalance remained statistically significant for at least one of the pathways of exit in at least one of the models.

    Different studies support the association between unhealthy lifestyles, such as lack of physical activity, obesity and problematic alcohol use, and exit from paid employment. 5 29 35–37 In the fully adjusted multinominal models, problematic alcohol use was consistently associated with entering work disability with ORs varying from 1.84 to 1.88. However, it has been suggested that this may be explained by problems with working times, work output, concentration, occupational safety and cooperation, irrespective of health status.8 In the multivariate model, obesity was associated with becoming unemployed (OR 1.67). This is in agreement with a French study that reported obesity as a risk factor for unemployment after controlling for self-reported health.36

    Smoking was not associated with early exit. Previous studies have shown contradictory results, with significant associations for smoking 8 38–41 as well as non-significant associations with different forms of exit from paid employment.36 42 43

    In the fully adjusted models, lack of job control remained a significant predictor of exit through retirement and disability, whereas effort–reward imbalance predicted unemployment. Several studies have corroborated the observed direct influence of strenuous working conditions on exit from paid employment.7 29 38 44 45In a cross-sectional analysis of the SHARE survey at baseline, a high imbalance between efforts and rewards was also associated with intended early retirement after controlling for poor self-perceived health.19 Hence, preventive measures against problematic alcohol use, obesity, job control and effort–reward imbalance, will help reduce health related early exit from paid employment.

    This study only focused on exit from paid employment, but poor health could have an additional impact in terms of changing jobs and stalled careers. The Health and Retirement Survey27 showed that workers often changed jobs within several years after the onset of health problems. This might also be true for the onset of poor health in earlier phases of their careers (younger workers). Poor health may also have adverse effects on performance at work, as observed regarding the influence of poor health on sickness absence46 and loss of productivity at work.47 48 The duration of employment contracts could also influence the maintenance of paid employment. However, only 7% of the subjects with paid employment had a temporary employment contract, and thus this parameter could not be evaluated in this study.

    The health status of older European workers has a major influence on the likelihood of maintaining paid employment. Self-perceived poor health and, to a lesser extent, having a chronic disease, perceived mobility problems and limitations seem predictive for future work participation. There is consistent evidence that social inequalities in health depend on work related factors as well as lifestyle behaviours.49 50 The results of this study suggest that labour market participation by older workers with ill health may be sustained by interventions that promote a healthier lifestyle and healthier working conditions. As exit from paid employment is often irreversible at an older age, prevention of work loss by improving the worker's health or improving an ill worker's work circumstances and lifestyle should be a key priority. Important entry points for policy could be lifestyle interventions, improvements in job control and effort–reward balance, and social policies to encourage employment among older people with health problems.

    References

    1. ↵
      1. Ilmarinen JE
      . Aging workers. Occup Environ Med 2001;58:546–52.
      OpenUrlFREE Full Text
    2. ↵
      1. Stattin M
      . Retirement on grounds of ill health. Occup Environ Med 2005;62:135–40.
      OpenUrlFREE Full Text
    3. ↵
      1. Cai L,
      2. Kalb G
      . Health status and labour force participation: evidence from Australia. Health Econ 2006;15:241–61.
      OpenUrlCrossRefPubMedWeb of Science
    4. ↵
      1. Black C
      . Working for a healthier tomorrow. Occup Environ Med 2009;66:1–2.
      OpenUrlFREE Full Text
    5. ↵
      1. Alavinia SM,
      2. Burdorf A
      . Unemployment and retirement and ill-health: a cross-sectional analysis across European countries. Int Arch Occup Environ Health 2008;82:39–45.
      OpenUrlCrossRefPubMedWeb of Science
    6. ↵
      1. Bartley M
      . Unemployment and ill health: understanding the relationship. J Epidemiol Community Health 1994;48:333–7.
      OpenUrlAbstract/FREE Full Text
    7. ↵
      1. Karpansalo M,
      2. Manninen P,
      3. Kauhanen J,
      4. et al
      . Perceived health as a predictor of early retirement. Scand J Work Environ Health 2004;30:287–92.
      OpenUrlCrossRefPubMedWeb of Science
    8. ↵
      1. Leino-Arjas P,
      2. Liira J,
      3. Mutanen P,
      4. et al
      . Predictors and consequences of unemployment among construction workers: prospective cohort study. BMJ 1999;319:600–5.
      OpenUrlAbstract/FREE Full Text
    9. ↵
      1. Schuring M,
      2. Burdorf L,
      3. Kunst A,
      4. et al
      . The effects of ill health on entering and maintaining paid employment: evidence in European countries. J Epidemiol Community Health 2007;61:597–604.
      OpenUrlAbstract/FREE Full Text
    10. ↵
      1. Thomas C,
      2. Benzeval M,
      3. Stansfeld SA
      . Employment transitions and mental health: an analysis from the British household panel survey. J Epidemiol Community Health 2005;59:243–9.
      OpenUrlAbstract/FREE Full Text
    11. ↵
      1. Donders NC,
      2. van der Gulden JW,
      3. Furer JW,
      4. et al
      . Work stress and health effects among university personnel. Int Arch Occup Environ Health 2003;76:605–13.
      OpenUrlCrossRefPubMedWeb of Science
    12. ↵
      1. Siegrist J
      . Adverse health effects of high-effort/low-reward conditions. J Occup Health Psychol 1996;1:27–41.
      OpenUrlCrossRefPubMed
    13. ↵
      1. Waddell G,
      2. Burton K,
      3. Aylward M
      . Work and common health problems. J Insur Med 2007;39:109–20.
      OpenUrlPubMed
    14. ↵
      1. Borsch-Supan A,
      2. Jurges H,
      3. Mackenbach J,
      4. et al
      . Health, Ageing and Retirement in Europe. First results from the survey of Health, Ageing and Retirement in Europe. First results from the survey of Health, Ageing and Retirement in Europe. Mannheim: Mannheim Research Institute for the Economic of Ageing (MEA), 2005.
    15. ↵
      1. Borsch-Supan A,
      2. Kartsen H,
      3. Jurges H
      . A new comprehensive and international view on ageing: introducing the ‘Survey of Health, Ageing and Retirement in Europe’. Eur J Ageing 2005;2:245–53.
      OpenUrlCrossRef
    16. ↵
      1. Dwyer DS,
      2. Mitchell OS
      . Health problems as determinants of retirement: are self-rated measures endogenous? J Health Econ 1999;18:173–93.
      OpenUrlCrossRefPubMedWeb of Science
    17. ↵
      1. Idler EL,
      2. Benyamini Y
      . Self-rated health and mortality: a review of twenty-seven community studies. J Health Soc Behav 1997;38:21–37.
      OpenUrlCrossRefPubMedWeb of Science
    18. ↵
      1. Karasek R,
      2. Brisson C,
      3. Kawakami N,
      4. et al
      . The Job Content Questionnaire (JCQ): an instrument for internationally comparative assessments of psychosocial job characteristics. J Occup Health Psychol 1998;3:322–55.
      OpenUrlCrossRefPubMed
    19. ↵
      1. Siegrist J,
      2. Wahrendorf M,
      3. von dem Knesebeck O,
      4. et al
      . Quality of work, well-being, and intended early retirement of older employees: baseline results from the SHARE Study. Eur J Public Health 2007;17:62–8.
      OpenUrlAbstract/FREE Full Text
    20. ↵
      1. van Oort FV,
      2. van Lenthe FJ,
      3. Mackenbach JP
      . Material, psychosocial, and behavioural factors in the explanation of educational inequalities in mortality in The Netherlands. J Epidemiol Community Health 2005;59:214–20.
      OpenUrlAbstract/FREE Full Text
    21. ↵
      1. Hennekens C,
      2. Buring B,
      3. Mayrent S
      . Epidemiology in medicine. Boston: Lippincott Williams&Wilkins, 1987.
    22. ↵
      1. Anonymous
      . Statistical package for social sciences (SPSS). 15th edn. Chicago, IL:SPSS, 2006.
    23. ↵
      1. Borsch-Supan A,
      2. Brugiavini A,
      3. Jürges H,
      4. et al.
      1. Schröder M
      . Attrition. In: Borsch-Supan A, Brugiavini A, Jürges H, et al., eds. Health, ageing and retirement in Europe (2004-2007) Starting the longitudinal dimension. Mannheim: Mannheim Research Institute for the Economics of Aging (MEA), 2008:325–30.
    24. ↵
      1. Bound J
      . Self-reported versus objective measures of health in retirement models. J Hum Resour 1991;26:106–38.
      OpenUrlCrossRefWeb of Science
    25. ↵
      1. Kalwij A,
      2. Vermeulen F
      . Health and labour force participation of older people in Europe: what do objective health indicators add to the analysis? Health Econ 2008;17:619–38.
      OpenUrlCrossRefPubMedWeb of Science
    26. ↵
      1. Baanders AN,
      2. Rijken PM,
      3. Peters L
      . Labour participation of the chronically ill. A profile sketch. Eur J Public Health 2002;12:124–30.
      OpenUrlAbstract/FREE Full Text
    27. ↵
      1. Bound J,
      2. Schoenbaum M,
      3. Stinebrickner TR,
      4. et al
      . The dynamic effects of health on the labor force transitions of older workers. Labour Econ 1999;6:179–202.
      OpenUrlCrossRef
    28. ↵
      1. McDonough P,
      2. Amick BC 3rd.
      . The social context of health selection: a longitudinal study of health and employment. Soc Sci Med 2001;53:135–45.
      OpenUrlCrossRefPubMedWeb of Science
    29. ↵
      1. Friis K,
      2. Ekholm O,
      3. Hundrup YA,
      4. et al
      . Influence of health, lifestyle, working conditions, and sociodemography on early retirement among nurses: The Danish Nurse Cohort Study. Scand J Public Health 2007;35:23–30.
      OpenUrlAbstract/FREE Full Text
    30. ↵
      1. Arrow JO
      . Estimating the influence of health as a risk factor on unemployment: a survival analysis of employment durations for workers surveyed in the German Socio-Economic Panel (1984-1990). Soc Sci Med 1996;42:1651–9.
      OpenUrlCrossRefPubMedWeb of Science
    31. ↵
      1. Detaille SI,
      2. Heerkens YF,
      3. Engels JA,
      4. et al
      . Common prognostic factors of work disability among employees with a chronic somatic disease: a systematic review of cohort studies. Scand J Work Environ Health 2009;35:261–81.
      OpenUrlPubMedWeb of Science
    32. ↵
      1. Dauchet L,
      2. Amouyel P,
      3. Hercberg S,
      4. et al
      . Fruit and vegetable consumption and risk of coronary heart disease: a meta-analysis of cohort studies. J Nutr 2006;136:2588–93.
      OpenUrlAbstract/FREE Full Text
    33. ↵
      1. Matson-Koffman DM,
      2. Brownstein JN,
      3. Neiner JA,
      4. et al
      . A site-specific literature review of policy and environmental interventions that promote physical activity and nutrition for cardiovascular health: what works? Am J Health Promot 2005;19:167–93.
      OpenUrlCrossRefPubMedWeb of Science
    34. ↵
      1. Varekamp I,
      2. Heutink A,
      3. Landman S,
      4. et al
      . Facilitating empowerment in employees with chronic disease: qualitative analysis of the process of change. J Occup Rehabil 2009;19:398–408.
      OpenUrlCrossRefPubMedWeb of Science
    35. ↵
      1. Biering-Sorensen F,
      2. Lund J,
      3. Hoydalsmo OJ,
      4. et al
      . Risk indicators of disability pension. A 15 year follow-up study. Dan Med Bull 1999;46:258–62.
      OpenUrlPubMedWeb of Science
    36. ↵
      1. Jusot F,
      2. Khlat M,
      3. Rochereau T,
      4. et al
      . Job loss from poor health, smoking and obesity: a national prospective survey in France. J Epidemiol Community Health 2008;62:332–7.
      OpenUrlAbstract/FREE Full Text
    37. ↵
      1. Rissanen A,
      2. Heliovaara M,
      3. Knekt P,
      4. et al
      . Risk of disability and mortality due to overweight in a Finnish population. BMJ 1990;301:835–7.
      OpenUrlAbstract/FREE Full Text
    38. ↵
      1. Eriksen W,
      2. Natvig B,
      3. Rutle O,
      4. et al
      . Smoking as a predictor of long-term work disability in physically active and inactive people. Occup Med 1998;48:315–20.
      OpenUrlAbstract/FREE Full Text
    39. ↵
      1. Liira J,
      2. Leino-Arjas P
      . Predictors and consequences of unemployment in construction and forest work during a 5-year follow-up. Scand J Work Environ Health 1999;25:42–9.
      OpenUrlCrossRefPubMedWeb of Science
    40. ↵
      1. Lund T,
      2. Csonka A
      . Risk factors in health, work environment, smoking status, and organizational context for work disability. Am J Ind Med 2003;44:492–501.
      OpenUrlCrossRefPubMedWeb of Science
    41. ↵
      1. Rothenbacher D,
      2. Arndt V,
      3. Fraisse E,
      4. et al
      . Early retirement due to permanent disability in relation to smoking in workers of the construction industry. J Occup Environ Med 1998;40:63–8.
      OpenUrlCrossRefPubMedWeb of Science
    42. ↵
      1. Haahr JP,
      2. Frost P,
      3. Andersen JH
      . Predictors of health related job loss: a two-year follow-up study in a general working population. J Occup Rehabil 2007;17:581–92.
      OpenUrlCrossRefPubMedWeb of Science
    43. ↵
      1. Lund T,
      2. Iversen L,
      3. Poulsen KB
      . Work environment factors, health, lifestyle and marital status as predictors of job change and early retirement in physically heavy occupations. Am J Ind Med 2001;40:161–9.
      OpenUrlCrossRefPubMedWeb of Science
    44. ↵
      1. Blekesaune M,
      2. Solem PE
      . Working conditions and early retirement. A prospective study of retirement behavior. Res Aging 2005;27:3–30.
      OpenUrlAbstract
    45. ↵
      1. Solomon C,
      2. Poole J,
      3. Palmer KT,
      4. et al
      . Health-related job loss: findings from a community-based survey. Occup Environ Med 2007;64:144–9.
      OpenUrlAbstract/FREE Full Text
    46. ↵
      1. Duijts SF,
      2. Kant I,
      3. Swaen GM,
      4. et al
      . A meta-analysis of observational studies identifies predictors of sickness absence. J Clin Epidemiol 2007;60:1105–15.
      OpenUrlCrossRefPubMedWeb of Science
    47. ↵
      1. Meerding WJ,
      2. IJzelenberg W,
      3. Koopmanschap MA,
      4. et al
      . Health problems lead to considerable productivity loss at work among workers with high physical load jobs. J Clin Epidemiol 2005;58:517–23.
      OpenUrlCrossRefPubMedWeb of Science
    48. ↵
      1. Schultz AB,
      2. Edington DW
      . Employee health and presenteeism: a systematic review. J Occup Rehabil 2007;17:547–79.
      OpenUrlCrossRefPubMedWeb of Science
    49. ↵
      1. Vahtera J,
      2. Virtanen P,
      3. Kivimaki M,
      4. et al
      . Workplace as an origin of health inequalities. J Epidemiol Community Health 1999;53:399–407.
      OpenUrlAbstract
    50. ↵
      1. Kuper H,
      2. Marmot M
      . Job strain, job demands, decision latitude, and risk of coronary heart disease within the Whitehall II study. J Epidemiol Community Health 2003;57:147–53.
      OpenUrlAbstract/FREE Full Text

    Footnotes

    • Funding This paper uses data from release 2 of SHARE 2004 and release 1 of SHARE 2006. The SHARE data collection has been primarily funded by the European Commission through the 5th framework programme (project QLK6-CT-2001-00360 in the thematic programme Quality of Life). Additional funding came from the US National Institute on Ageing (U01 AG09740-13S2, P01 AG005842, P01 AG08291, P30 AG12815, Y1-AG-4553-01 and OGHA 04-064). Data collection in Austria (through the Austrian Science Foundation, FWF), Belgium (through the Belgian Science Policy Office) and Switzerland (through BBW/OFES/UFES) was nationally funded. The SHARE data collection in Israel was funded by the US National Institute on Ageing (R21 AG025169), by the German-Israeli Foundation for Scientific Research and Development (G.I.F.) and by the National Insurance Institute of Israel. Further support by the European Commission through the 6th framework program (projects SHARE-I3, RII-CT-2006-062193, and COMPARE, CIT5-CT-2005-028857) is gratefully acknowledged. Mauricio Avendano is supported by a grant from the Netherlands Organisation for Scientific Research (NWO, grant no. 451-07-001) and a Bell Fellowship from the Harvard Center for Population and Development Studies.

    • Competing interests None.

    • Provenance and peer review Not commissioned; externally peer reviewed.