Occupational and behavioural factors in the explanation of social inequalities in
premature and total mortality: a 12.5-year follow-up in the Lorhandicap study
Isabelle Niedhammer,1,2,3,4 Eve Bourgkard,5 Nearkasen Chau,6,7,8 and the Lorhandicap study
1 INSERM, U1018, CESP Centre for research in Epidemiology and Population Health,
Epidemiology of occupational and social determinants of health Team, Villejuif, France
2 Univ Paris-Sud, UMRS 1018, Villejuif, France
3 Université de Versailles St-Quentin, UMRS 1018, Villejuif, France
4 UCD School of Public Health, University College Dublin, Dublin, Ireland
5 Institut National de Recherche et de Sécurité (INRS), WHO Collaborative Centre,
Département Epidémiologie en Entreprise, Vandœuvre-lès-Nancy, France
6 INSERM, U669, Paris, France
7 Univ Paris-Sud, UMRS 669, Paris, France
8 Univ Paris Descartes, UMRS 669, Paris, France
Running title: Social inequalities in mortality
Dr Isabelle Niedhammer
UCD School of Public Health, University College Dublin,
Woodview House, Belfield, Dublin 4, Ireland
Tel: +353 1 716 3467
Fax: +353 1 716 3421
Word count of abstract: 233
Word count: 3504
The respective contribution of occupational and behavioural factors to social disparities in all-
cause mortality has been studied very seldom. The objective of this study was to evaluate the
role of occupational and behavioural factors in explaining social inequalities in premature and
total mortality in the French working population. The study population consisted of a sample
of 2189 and 1929 French working men and women, who responded to a self-administered
questionnaire in mid-1996, and were followed up until the end of 2008. Mortality was derived
from register-based information and linked to the baseline data. Socioeconomic status was
measured using occupation. Occupational factors included biomechanical and physical
exposures, temporary contract, psychological demands, and social support, and behavioural
factors, smoking, alcohol abuse, and body mass index. Significant social differences were
observed for premature and total mortality. Occupational factors reduced the hazard ratios of
mortality for manual workers compared to managers/professionals by 72% and 41%, from
1.88 (95% CI: 1.17-3.01) to 1.25 (95% CI: 0.74-2.12) for premature mortality, and from 1.71
(95% CI: 1.18-2.47) to 1.42 (95% CI: 0.95-2.13) for total mortality. The biggest contributions
were found for biomechanical and physical exposures, and job insecurity. The role of
behavioural factors was very low. Occupational factors played a substantial role in explaining
social disparities in mortality, especially for premature mortality and men. Improving working
conditions amongst the lowest social groups may help to reduce social inequalities in
Key words: occupational groups, mortality, occupational exposures, health behaviours
Social inequalities in health have been reported for a long time. They refer to differences in
morbidity and mortality between social groups, i.e. the lower the social position, the poorer
the health status, and the measures of morbidity and mortality. These inequalities have been
demonstrated for various chronic diseases such as cardiovascular diseases, and general
measures of morbidity and mortality [1-4]. Several indicators may be used to measure social
position or socioeconomic status (SES), education, occupation, and income being the most
widely used of these indicators [5, 6]. Besides the report of social inequalities in health, it
appears crucial to better understand the mechanisms linking social position and health.
Consequently, identifying mediating factors that may contribute to explain social inequalities
in health may be helpful to reduce the exposure to these factors in specific social groups, and
thus to reduce social inequalities in health.
Various theories have been developed to explain the pathways and mechanisms underlying
these inequalities [7-9]. These theories include the materialist explanation, that put the
emphasis on material conditions (access to goods/services, and exposures to material risk
factors in the living and working environment), the psychosocial explanation, that focuses on
psychosocial and stress related influences with a plethora of risk factors such as social support
or sense of control, and the behavioural explanation, that emphasizes the importance of
behavioural risk factors in explaining social inequalities in health. As mediating factors
probably are interrelated, some authors have suggested simplified causal models to
disentangle the direct (independent) effect of mediating factors, and their indirect effect
through other factors [10, 11].
Social inequalities in all-cause mortality have been described extensively. Studies showed
strong and persistent social inequalities in mortality in various countries, such as France ,
and other European countries , but the studies that attempted to explain these inequalities
are still sparse. Most of them focused on behavioural factors, such as smoking, alcohol
consumption, physical activity, body mass index, etc., and biologic factors (fibrinogen,
cholesterol, triglycerides, blood pressure, etc.) as potential mediating factors [14-19]. These
studies, in general, found that these factors, and especially smoking, explained a part (that
may be small in some studies) of social inequalities in mortality, suggesting that a wider range
of factors need to be considered to explain these inequalities.
Occupational factors, that included both material and psychosocial factors, such as physico-
chemical, biomechanical, and psychosocial exposures, are considered as major determinants
of health, and they may be socially graded (the lower the social position, the higher the
exposures). Consequently, they may be pertinent candidates to explain social inequalities in
health, as underlined in a recent commentary . Some studies have already mentioned the
contribution of occupational factors, especially psychosocial work factors, in explaining at
least partly social differences in various measures of morbidity [21-28]. To our knowledge, no
previous study has attempted to evaluate the impact of both occupational and behavioural
factors on social inequalities in all-cause mortality.
The objectives of this study were to analyse the association between SES as measured using
occupation, and two measures of all-cause mortality, premature and total mortality, and to
evaluate the contribution of occupational and behavioural factors in explaining social
differences in mortality among a sample of men and women of the French working
Materials and methods
This study was based on the data from the Lorhandicap survey set up in 1996 in the nord-east
of France. Several studies have already been published using this survey [29-31]. The initial
sample consisted of everyone aged 15 years or more living in 8000 randomly selected
households in the Lorraine region of the north-east of France. Only households with a
telephone were eligible. The investigation was approved by the Commission Nationale de
l’Informatique et des Libertés (CNIL), and written informed consent was obtained from the
respondents. The study protocol included: an application to participate to ascertain the number
of persons in the household, and three self-administered questionnaires with a covering letter
and a pre-paid envelope for the reply, were mailed at 1-month interval. When the number of
individuals was unknown, two questionnaires were sent first, and a complementary one was
sent later. The questionnaire included various sections covering socio-demographic
characteristics, job characteristics, working conditions, health status, and behavioural factors.
If people were retired, they were asked about their main job during working life.
SES was measured using occupational groups. Four occupational categories were considered
following the international classification of occupation (ISCO): professionals/managers,
associate professionals/technicians, service workers/clerks, and manual workers.
Professionals/managers were used as reference category. Occupation was studied as a marker
of SES because it characterises adult SES, is available for all working people, and may reflect
occupational exposures better than education [5, 6].
Occupational factors were assessed by: biomechanical exposure (exposure to vibrations -
manual handling of vibrating tools or vibration from a fixed machine-, manual materials
handling, postural and articular constraints such as standing/walking, awkward posture,
handling objects or tools, working on a production line, or other constraints), physical
exposure (exposure to noise, cold or hot temperatures, or outdoor work), work status
(temporary versus permanent job) as a marker of job insecurity, psychological demands
(exposure to high work pace, or mental load), and social support from colleagues (very
unsatisfied, or unsatisfied versus neither satisfied nor unsatisfied, satisfied, or very satisfied).
Exposure to biomechanical and physical factors, and to psychological demands was defined
by the presence of at least one item. Details on the formulation of the question and items, as
well as the number and percentage of exposed people for each item separately may be found
in the appendix. The factors that were the most prevalent were standing/walking, awkward
posture, and manual materials handling for biomechanical factors, and noise, and hot and cold
temperatures for physical factors. The study of the associations between the 5 occupational
exposures studied (biomechanical and physical exposures, work status, psychological
demands, and social support) showed three significant positive associations (p<0.001)
between biomechanical exposure and physical exposure, between biomechanical exposure
and psychological demands, and between temporary contract and low social support, as well
as two significant negative associations between psychological demands and temporary
contract (p<0.001) and between psychological demands and low social support (p<0.01) i.e.
that people with high levels of psychological demands were less likely to have a temporary
work contract and low levels of social support.
Behavioural factors included: smoking status (smoker, ex-smoker or non-smoker), body mass
index (BMI) in kg/m2, and alcohol abuse measured using the French version of the
Cut/Annoyed/Guilty/Eye-opener (CAGE) questionnaire  and defined by at least two
positive responses to four items: consumption considered excessive by the subject,
consumption considered excessive by people around the subject, subject wishes to reduce
consumption, and consumption on waking.
The cohort was followed up for mortality from 1st July 1996 to 31th December 2008. The
vital status of all subjects was assessed by searching using the national computerised database
listing all deceased subjects in France, contacting the registry offices of the birth places for
people born in France, and the registry office devoted to foreign born French people (Ministry
of Foreign Affairs). Two measures of mortality were considered: death, and premature death
before the age of 70. Premature death focusses on deaths occurring at younger ages, and may
be considered as a useful public health measure providing information on preventable deaths.
In addition, this outcome was retained because, as reported by Krieger et al. , unlike life
expectancy and years of personlives lost, it is easy to understand, easy to compare,
methodologically transparent, and a sensitive indicator of inequities in health.
The associations between SES (i.e. occupation) and occupational and behavioural factors
were tested using the Chi-Square test to determine the significance and direction of these
associations. Cox proportional hazard model, which yields hazard ratios (HRs), was used to
examine the association between SES and mortality. The duration of follow-up for each
subject was calculated for each subject from 1st July 1996 to 31th December 2008, or earlier
in the case of death, or 70th birthday for premature mortality. The associations between
occupational and behavioural factors and mortality were also examined using Cox regression
models. Occupational and behavioural factors that displayed inverse social gradients were
excluded in subsequent analyses. Several models were performed: a basic model (model 1)
measuring the association between SES and mortality after adjustment for age (and sex),
behavioural factors added to model 1 (model 2), occupational factors added to model 1
(model 3), and behavioural and occupational factors added simultaneously to model 1
(model 4). The contribution of behavioural and/or occupational factors to the explanation of
the social differences in mortality was estimated by the change in the HRs for occupational
groups after inclusion of the variable(s) in the model, i.e. explained fraction calculated by the
formula: (HRmodel 1–HRextended model)/(HRmodel 1–1) . Positive % values indicate reductions
in HRs, and negative % values increases in HRs. The contribution was calculated only if the
HR for a given occupational group was significant in model 1. The proportional hazard
assumption was checked based on Schoenfeld residuals for the global model and for each
covariate. Analyses were also done with additional adjustment for chronic disease at baseline,
the results were unchanged. Results are presented for men and women separately, and for the
total sample for Cox regression models. The statistical analyses were performed using
Of the 8000 households included in the sample, mailings to 193 (2%) were lost (due to
addressing error or death). Of the 7807 households contacted, 3460 (44.3%) participated (all
eligible members of the family took part in 86% of those). In total, 6235 subjects filled in the
questionnaire, 19 were of unknown sex or age, leaving 6216 subjects who were similar in age
and sex distribution to the overall population of the north-east of France . During the
follow-up, 143 subjects (2.3%) were lost and excluded. The subjects with unknown smoking
habit or alcohol abuse were excluded (296 subjects, i.e. 4.8%). Only the subjects who had
been working, were alive, and aged 70 years or less at baseline (1st July 1996) were retained
for this study i.e. 4118 subjects, 2189 men and 1929 women. In total, 291 deaths (206 and 85
among men and women) occurred, and 165 deaths before age 70 (115 and 50 among men and
Almost all behavioural and occupational factors displayed strong and significant associations
with SES, except alcohol abuse for men and women, and smoking for women (Table 1). A
trend towards increasing alcohol abuse with lower SES was observed for men. Biomechanical
and physical exposures, temporary contract, and low social support were strongly socially
graded, the lower the occupational group, the higher the exposure. High psychological
demands displayed a significant inverse social gradient, managers/professionals being more
likely to be exposed. Psychological demands were consequently omitted from subsequent
A significant association was found between SES and premature mortality (Table 2), which
was confirmed after adjustment for age and sex (model 1), manual workers being at increased
risk of mortality. This association was observed for men and women separately, although non
significant for women. Male gender and all behavioural and occupational factors displayed
significant crude associations with premature mortality (not all significant for each gender
separately). Adding behavioural factors to model 1 did not change the HRs for manual
workers very much (model 2). Additional analyses (not shown) exploring the separate effects
of each behavioural factor showed that the biggest contribution was found for alcohol abuse
(7%) in the total sample. The inclusion of occupational factors to model 1 led to a substantial
decrease in the HRs for manual workers, by 72% for the total sample, 74% for men, and 61%
for women. The HRs for manual workers were no longer significant after adjustment for
occupational factors. The occupational factors that contributed to decrease the social
difference in premature mortality were in the total sample (not shown): biomechanical
exposure (35%), job insecurity (28%), physical exposure (24%), and social support (14%).
Model 4 that included behavioural and occupational factors simultaneously provided a similar
explanation of social differences in premature mortality than model 3.
Significant social differences for total mortality were observed (Table 3). Manual workers
were at higher risk for mortality, a similar trend was observed for service workers/clerks after
adjustment for age and sex (model 1). Social differences were also found for men and women
separately, manual workers being at significant higher risk of mortality for men. Male gender
and almost all behavioural and occupational factors were found to be predictive factors of
mortality in crude associations. The inclusion of behavioural factors to model 1 did not
modify the HRs for manual workers very much (model 2). Studying each factor separately
showed that the biggest contributions were found for BMI (6%) and alcohol abuse (4%) in the
total sample. Adding occupational factors to model 1 contributed to decrease the social
differences between manual workers and managers/professionals by 41% for the total sample,
44% for men, and 31% for women (model 3). The HRs for manual workers were no longer
significant after adjustment for occupational factors. The contributions of each occupational
factor separately were as follows in the total sample: job insecurity (23%), social support
(11%), biomechanical exposure (10%), and physical exposure (8%). Adding behavioural and
occupational factors simultaneously increased only slightly the explained fractions (model 4)
compared to model 3.
Significant social differences were observed for premature and total mortality in this 12.5-
year follow-up study among the French working population. Manual workers were at
increased risk of total and premature mortality compared to managers/professionals with HRs
reaching almost 2. Occupational factors played a substantial role in explaining social
differences in mortality. Their contributions were 31-74%, and were more pronounced for
men and for premature mortality. The contribution of behavioural factors was very low.
Manual workers were the occupational group that displayed a significant excess of mortality
compared to managers/professionals. Other previous studies showed social disparities in
mortality in France and in other countries, using various SES markers [12, 13]. Our study also
underlined social inequalities in occupational exposures, with the lowest occupational groups,
especially manual workers, being more likely to be exposed to negative working conditions.
Other previous studies reported the accumulation of unfavourable working conditions in the
lowest occupational categories [22, 23, 25-27]. One major exception was psychological
demands, that displayed a strong inverse social gradient, managers/professionals being more
likely to be exposed, something already reported [22, 23, 25, 26].
Although there were a number of studies describing social inequalities in mortality in various
populations, studies that tried to explain these inequalities were less numerous. This study is
one of the seldom studies evaluating the contribution of occupational factors to social
inequalities in all-cause mortality, and suggested that these factors may play a substantial role.
Other studies explored the contribution of occupational factors to social inequalities in several
measures of morbidity. Our study is in agreement with some previous studies underlying the
role of physical and biomechanical exposure [22, 24-26, 28], job insecurity [21, 23], and low
social support [25, 27] in explaining social inequalities in health outcomes such as self-
reported health. Furthermore, the occupational factors, that were the most prevalent, may play
a substantial role in explaining social differences in mortality, i.e. standing/walking, awkward
posture, and manual materials handling among the biomechanical factors, and noise, and hot
and cold temperatures among the physical factors, supporting previous results on the
explanation of social inequalities in morbidity outcomes in France . Our results are also in
agreement with another study showing that the role of occupational factors in explaining
social inequalities in health was not modified very much when behavioural factors were taken
into account . The issue of independent (direct) and indirect (through behavioural factors)
effects of occupational factors is consequently less important in our study as we did not
observe any major role of behavioural factors. Consequently, the contribution of occupational
factors remained almost the same with or without adjusting for behavioural factors.
Behavioural factors did not play an important role in explaining social inequalities in
mortality in our study. Other authors demonstrated that behavioural factors may explain only
a modest proportion of social inequalities in mortality [10, 11, 15]. Several hypotheses may be
assumed to explain this. Behavioural factors were evaluated only at baseline, and as the
follow-up was long, people might change their behaviours, which is likely to lead to
misclassification and dilution of their effects. Behavioural factors were based on self-reported
data, that may lead to an underreporting bias of the most negative health behaviours. For
example, the heaviest drinkers may be underrepresented in our sample, because of both
selection and underreporting bias. Evaluation of alcohol consumption was done using the
CAGE instrument that may be adequate to measure alcohol-related problems, but may neglect
some specific ones that may be more strongly related to SES, such as binge drinking.
Gender differences were also of interest in our study, this was why genders were studied
separately. Women were more likely to be service workers/clerks, and men manual workers.
The prevalence of occupational and behavioural factors was found to be different between
genders. Men were more likely to be exposed to physical exposure, and women to job
insecurity. Men were more likely to be smokers and overweight, and to have alcohol-related
problems. The risk of mortality was also higher for men than for women. These results
confirm the different patterns of occupation and occupational exposures between genders,
related to the strong sexual division of labour, as well as the differences in health behaviours
and mortality between genders. Similar social inequalities in mortality were observed for men
and women, but the contribution of occupational factors was found to be higher in men than
in women. This result is in agreement with other studies . Strong gender differences were
observed for the associations between behavioural factors and mortality; smoking and alcohol
abuse were found to be strong predictors of premature and total mortality for men, but not for
women. Nevertheless, the contribution of behavioural factors was very modest and appeared
to be similar in explaining social inequalities in mortality in both genders.
Limitations of our study may be mentioned. A selection bias may have occurred, as the
response rate was about 44%. However, this response rate is similar to those of other studies
using postal self-administered questionnaires in France . Furthermore, the gender and age
distributions of the initial sample were close to those of the census population. Nevertheless,
previous studies showed that non-respondents may be more likely to have lower SES, poorer
health-related and behavioural factors . Consequently, it is likely that such a bias may
lead to underestimate social inequalities in health. A limitation was related to sample size
especially for women, and led to more uncertainty in the estimation of HRs and explained
fractions for this group. Another limitation was that behavioural and occupational factors
were not based on lifetime exposures. Other authors demonstrated that this may lead to
underestimate the contributions of behavioural and occupational factors to social inequalities
in health [35, 36]. The contribution of these factors may also be underestimated because some
behavioural and occupational factors were not explored, such as diet or physical activity, as
well as chemical/biological exposures, decision latitude at work, reward, or workplace
violence. Thus, inclusion of more mediators might result in different estimates of the
contributions of mediators. Finally, the generalisation of our results to other populations
should be made with caution because of cultural and socioeconomic differences between
Strengths of the study also deserve to be mentioned. The sample was derived from the general
population, making generalisation possible for the population in the nord-east of France.
Sample size allowed us to study men and women separately, which may be crucial in
occupational epidemiology . The study was based on a 12.5-year follow-up, i.e. a rather
long period. Mortality was measured using national database (an exhaustive and independent
source of data). Mortality is also an objective outcome measure, consequently no reporting
bias may be suspected. Occupational groups were used in this study as a marker of social
position, and are a well-known measure of social position in the working population.
Although results may differ somewhat using other measures of social position (such as
education or income) [5, 6], relatively similar conclusions have been provided by others [24,
36]. We performed additional analyses that included the presence of chronic disease at
baseline in our models to make sure that no previous chronic disease may introduce a
confounding effect in our results. These results confirmed the robustness of our findings. We
also performed the analyses for premature mortality before 65 and found similar results, but
statistical power was lower because of a smaller number of premature deaths.
To conclude, occupational factors may play a substantial role in explaining social inequalities
in mortality, especially premature mortality. Preventive actions focusing on these factors and
specific social groups may be useful to reduce social inequalities in mortality. More research
is needed to better understand the role of these factors, over the life course, on social
inequalities in various health outcomes.
Lorhandicap study group: N Chau, F Guillemin, JF Ravaud, J Sanchez, S Guillaume, JP
Michaely, C Otero Sierra, B Legras, A Dazord, M Choquet, L Méjean, N Tubiana-Rufi, JP
Meyer, Y Schléret, and JM Mur.
The authors would like to thank D Saouag, M Weiss, M Depesme-Cuny, and B Phélut for
their help during the survey. The work was granted by the Pôle Européen de Santé.
1. Dalstra JA, Kunst AE, Borrell C, Breeze E, Cambois E, Costa G, et al. Socioeconomic
differences in the prevalence of common chronic diseases: an overview of eight
European countries. Int J Epidemiol 2005; 34(2):316-326.
2. Huisman M, Kunst AE, Bopp M, Borgan JK, Borrell C, Costa G, et al. Educational
inequalities in cause-specific mortality in middle-aged and older men and women in
eight western European populations. Lancet 2005; 365(9458):493-500.
3. Mackenbach JP, Stirbu I, Roskam AJ, Schaap MM, Menvielle G, Leinsalu M, et al.
Socioeconomic inequalities in health in 22 European countries. N Engl J Med 2008;
4. van Lenthe FJ, Gevers E, Joung IM, Bosma H, Mackenbach JP. Material and behavioral
factors in the explanation of educational differences in incidence of acute myocardial
infarction: the Globe study. Ann Epidemiol 2002; 12(8):535-542.
5. Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey SG. Indicators of socioeconomic
position (part 1). J Epidemiol Community Health 2006; 60(1):7-12.
6. Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey SG. Indicators of socioeconomic
position (part 2). J Epidemiol Community Health 2006; 60(2):95-101.
7. Goldman N. Social inequalities in health disentangling the underlying mechanisms. Ann
N Y Acad Sci 2001; 954:118-139.
igitalasset/dh_4121584.pdf: European Commission; 2006.
J-P. Health inequalities: Europe in profile. In.
9. Wilkinson R, Marmot M. Social determinants of health: The solid facts. Second Edition.
In: World Health Organization; 2003.
10. Skalicka V, Van Lenthe F, Bambra C, Krokstad S, Mackenbach J. Material,
psychosocial, behavioural and biomedical factors in the explanation of relative socio-
economic inequalities in mortality: evidence from the HUNT study. Int J Epidemiol
11. van Oort FV, van Lenthe FJ, Mackenbach JP. Material, psychosocial, and behavioural
factors in the explanation of educational inequalities in mortality in The Netherlands. J
Epidemiol Community Health 2005; 59(3):214-220.
12. Saurel-Cubizolles MJ, Chastang JF, Menvielle G, LeClerc A, Luce D. Social
inequalities in mortality by cause among men and women in France. J Epidemiol
Community Health 2009; 63(3):197-202.
13. Mackenbach JP, Bos V, Andersen O, Cardano M, Costa G, Harding S, et al. Widening
socioeconomic inequalities in mortality in six Western European countries. Int J
Epidemiol 2003; 32(5):830-837.
14. Laaksonen M, Talala K, Martelin T, Rahkonen O, Roos E, Helakorpi S, et al. Health
behaviours as explanations for educational level differences in cardiovascular and all-
cause mortality: a follow-up of 60 000 men and women over 23 years. Eur J Public
Health 2008; 18(1):38-43.
15. Lantz PM, House JS, Lepkowski JM, Williams DR, Mero RP, Chen J. Socioeconomic
factors, health behaviors, and mortality: results from a nationally representative
prospective study of US adults. JAMA 1998; 279(21):1703-1708.
16. Lynch JW, Kaplan GA, Cohen RD, Tuomilehto J, Salonen JT. Do cardiovascular risk
factors explain the relation between socioeconomic status, risk of all-cause mortality,
cardiovascular mortality, and acute myocardial infarction? Am J Epidemiol 1996;
17. Pekkanen J, Tuomilehto J, Uutela A, Vartiainen E, Nissinen A. Social class, health
behaviour, and mortality among men and women in eastern Finland. BMJ 1995;
18. Siahpush M, English D, Powles J. The contribution of smoking to socioeconomic
differentials in mortality: results from the Melbourne Collaborative Cohort Study,
Australia. J Epidemiol Community Health 2006; 60(12):1077-1079.
19. Woodward M, Oliphant J, Lowe G, Tunstall-Pedoe H. Contribution of contemporaneous
risk factors to social inequality in coronary heart disease and all causes mortality. Prev
Med 2003; 36(5):561-568.
20. Landsbergis PA. Assessing the contribution of working conditions to socioeconomic
disparities in health: a commentary. Am J Ind Med 2010; 53(2):95-103.
21. Aldabe B, Anderson R, Lyly-Yrjanainen M, Parent-Thirion A, Vermeylen G, Kelleher
CC, et al. Contribution of material, occupational, and psychosocial factors in the
explanation of social inequalities in health in 28 countries in Europe. J Epidemiol
Community Health 2010.
22. Borg V, Kristensen TS. Social class and self-rated health: can the gradient be explained
by differences in life style or work environment? Soc Sci Med 2000; 51(7):1019-1030.
23. Borrell C, Muntaner C, Benach J, Artazcoz L. Social class and self-reported health
status among men and women: what is the role of work organisation, household material
standards and household labour? Soc Sci Med 2004; 58(10):1869-1887.
24. Hemstrom O. Health inequalities by wage income in Sweden: the role of work
environment. Soc Sci Med 2005; 61(3):637-647.
25. Niedhammer I, Chastang JF, David S, Kelleher C. The contribution of occupational
factors to social inequalities in health: findings from the national French SUMER
survey. Soc Sci Med 2008; 67(11):1870-1881.
26. Schrijvers CT, van de Mheen HD, Stronks K, Mackenbach JP. Socioeconomic
inequalities in health in the working population: the contribution of working conditions.
Int J Epidemiol 1998; 27(6):1011-1018.
27. Sekine M, Chandola T, Martikainen P, Marmot M, Kagamimori S. Socioeconomic
inequalities in physical and mental functioning of Japanese civil servants: explanations
from work and family characteristics. Soc Sci Med 2006; 63(2):430-445.
28. Warren JR, Hoonakker P, Carayon P, Brand J. Job characteristics as mediators in SES-
health relationships. Soc Sci Med 2004; 59(7):1367-1378.
29. Chau N, Khlat M. Strong association of physical job demands with functional
limitations among active people: a population-based study in North-eastern France. Int
Arch Occup Environ Health 2009; 82(7):857-866.
15 Download full-text
30. Gauchard GC, Deviterne D, Guillemin F, Sanchez J, Perrin PP, Mur JM, et al.
Prevalence of sensory and cognitive disabilities and falls, and their relationships: a
community-based study. Neuroepidemiology 2006; 26(2):108-118.
31. Loos-Ayav C, Chau N, Riani C, Guillemin F. Functional disability in France and its
relationship with health-related quality of life - a population-based prevalence study.
Clin Exp Rheumatol 2007; 25(5):701-708.
32. Beresford TP, Blow FC, Hill E, Singer K, Lucey MR. Comparison of CAGE
questionnaire and computer-assisted laboratory profiles in screening for covert
alcoholism. Lancet 1990; 336(8713):482-485.
33. Krieger N, Rehkopf DH, Chen JT, Waterman PD, Marcelli E, Kennedy M. The fall and
rise of US inequities in premature mortality: 1960-2002. PLoS Med 2008; 5(2):e46.
34. Goldberg M, Chastang JF, LeClerc A, Zins M, Bonenfant S, Bugel I, et al.
Socioeconomic, demographic, occupational, and health factors associated with
participation in a long-term epidemiologic survey: a prospective study of the French
GAZEL cohort and its target population. Am J Epidemiol 2001; 154(4):373-384.
35. Monden CW. Current or lifetime smoking? Consequences for explaining educational
inequalities in self-reported health. Prev Med 2004; 39(1):19-26.
36. Monden CW. Current and lifetime exposure to working conditions. Do they explain
educational differences in subjective health? Soc Sci Med 2005; 60(11):2465-2476.
37. Niedhammer I, Saurel-Cubizolles MJ, Piciotti M, Bonenfant S. How is sex considered in
recent epidemiological publications on occupational risks? Occup Environ Med 2000;