Socioeconomic indicators in epidemiologic research: A practical example from the LIFEPATH study
Socioeconomic indicators in epidemiologic research: A practical example from the LIFEPATH study
0 1 Epidemiology Unit , ASL TO3, Piedmont Region, Grugliasco, Torino , Italy , 2 Department of Clinical and Biological Science, University of Turin , Turin , Italy , 3 Institute of Social and Preventive Medicine and Department of Psychiatry and Department of Internal Medicine, Lausanne University Hospital , Lausanne , Switzerland , 4 University College London, Department of Epidemiology and Public Health , London , United Kingdom , 5 Clinicum , Faculty of Medicine, University of Helsinki , Helsinki, Finland, 6 The Irish Longitudinal Study on Ageing (TILDA) , Trinity College Dublin, Ireland, 7 Cancer Registry, Department of Prevention, ASP , Ragusa , Italy , 8 Population-based Epidemiological Cohorts Unit, INSERM UMS 11, Villejuif, France, 9 Paris Descartes University , Paris , France , 10 EPIUnit- Institute of Public Health, University of Porto , Porto , Portugal , 11 Cancer Epidemiology Centre, Cancer Council Victoria , Melbourne , Australia , 12 Centre de Recherche en EÂ pideÂmiologie et Sant eÂ des Populations (CESP, Inserm U1018), Universit eÂ Paris-Saclay , UPS, USQ, Gustave Roussy, Villejuif, France, 13 Human Genetics Foundation (HuGeF), Turin , Italy , 14 MRC-PHE Centre for Environment and Health, School of Public Health, Department of Epidemiology and Biostatistics, Imperial College London , London , United Kingdom
1 Editor: Massimo Ciccozzi, National Institute of Health , ITALY
Data Availability Statement: Data cannot be made
freely available because of restrictions imposed by
the Project Steering Board of the LIFEPATH project,
as written in the ethical document provided to the
European Commission. However, aggregate data
are available for other researchers, on request.
Requests should be sent to the PI of the LIFEPATH
Project, Paolo Vineis (e-mail .
Funding: This study is supported by the European Commission (Horizon 2020 grant nÊ 633666) and
Several social indicators have been used in epidemiological research to describe
socioeconomic position (SEP) of people in societies. Among SEP indicators, those more frequently
used are education, occupational class and income. Differences in the incidence of
several health outcomes have been reported consistently, independently from the indicator
employed. Main objectives of the study were to present the socioeconomic classifications of
the social indicators which will be employed throughout the LIFEPATH project and to
compare social gradients in all-cause mortality observed in the participating adult cohorts using
the different SEP indicators.
Information on the available social indicators (education, own and father's occupational class, income) from eleven adult cohorts participating in LIFEPATH was collected and harmonized. Mortality by SEP for each indicator was estimated by Poisson regression on each cohort and then evaluated using a meta-analytical approach.
the Swiss State Secretariat for Education, Research
and Innovation SERI. Silvia Stringhini is supported
by an Ambizione Grant (PZ00P3_167732) from the
Swiss National Science Foundation. The funders
had no role in study design, data collection and
analysis, decision to publish, or preparation of the
Competing interests: The authors have declared that no competing interests exist.
In the meta-analysis, among men mortality was significantly inversely associated with
both occupational class and education, but not with father's occupational class; among
women, the increase in mortality in lower social strata was smaller than among men and,
except for a slight increase in the lowest education category, no significant differences were
Among men, the proposed three-level classifications of occupational class and education
were found to predict differences in mortality which is consistent with previous research.
Results on women suggest that classifying them through their sole SEP, without considering
that of their partners, may imply a misclassification of their social position leading to
attenuation of mortality differences.
Socioeconomic position (SEP) is the general term used to refer to the most common forms of
inequality. These are usually accepted to be income, wealth, status (or prestige) and social
class. These dimensions are correlated in different ways according to the social stratification
mechanisms operating in a determined society. There is a longstanding debate on how to
measure the rank of individuals in a society, which has its roots in social class theory (see for
example: Wright, 2005) [
]. Different measures have been employed in epidemiological research to
assess socioeconomic position (SEP). In many studies where measures of income, status and
class are not available, educational level is frequently used as the social position indicator [
and it tends to be correlated empirically with the other measurements [
Differences in health by occupational class, education and income have been reported
consistently for several health outcomes, including self-reported health, chronic and long-term
health conditions, and mortality [5±18].
In most theoretical models the occupation performed by people is a central element for
attributing them a social position, based on the consideration that in market economies life
chances of individuals are mainly determined by their position in the labour market and in
the occupational division of labour. Two main sociological schools can be distinguished, one
defining SEP in terms of status or prestige attributes [
], the other one through people's
relational power in society [
The conceptualization of social position as represented by prestige or social status derives
from the functionalist tradition, largely based on Durkheim's work in Europe [
Parsons' in the U.S. [
], which both consider society as a ªliving organismº, whose functioning is
provided by the different parts. The social stratification of the different occupational groups
derives from their functions in society: social roles needing higher skills are more highly
remunerated by society in terms of income and social respect [
]. A limitation of the
prestigebased measures is that they are represented by continuous scales, such as SIOPS [
Cambridge Scale [
], SEI [
], which may lack conceptual clarity on the different social strata, in
terms of their number and the cut-offs separating them. In theoretical terms, questions have
been raised in relation to the degree of consensus that is assumed by prestige measures of this
kind, and the absence of any consideration of power or conflict [
2 / 32
The second way of conceptualizing social position derives from Weber's social theory, in
which classes are identified through the level (or probability) of access to economic resources
their members have, defined by Weber as ªlife chancesº [
]. Starting from his work, the
neoWeberian school has developed a social classification based on people's ªlife chanceº in the
labour market and at work [
], according to which people are classified both through their
educational credentials, which mainly determine their success in the labour market, and their
occupation. The Erikson & Goldthorpe (EG) schema, besides distinguishing employers from
employees, also keeping separate, within employers, large from small employers and from
selfemployed workers, categorizes the employees through the nature of their relationship with the
employer, discerning those having a ªservice relationshipº, characterized by higher workers'
skills, knowledge, autonomy, salary, benefits, job security and career prospects, from those
having a ªlabour relationshipº, characterized by exchange of effort, often physical, with salary,
lower wages, higher job insecurity and tighter supervision. The degree of each type of
relationship in a certain occupation determines its position on a seven-class ordinal scale, which
reaches eleven classes in its most disaggregated form [
]. This is the model that has influenced
the way of conceptualizing and measuring socioeconomic position (SEP) in Europe [10,12,28±
31], from which derived the European Socio-economic ClassificationÐESeC, that is our
conceptual reference for classifying occupations in the LIFEPATH project. ESeC was built on the
same principles of the EG classification (1992) and closely resembles it, classifying occupations
in nine ordinal categories based on similarity of resources, in terms of opportunities and `life
chances', including employment relations and conditions (Fig 1). By defining what social class
is, ESeC also defines what is not, allowing to conceptualize separately the position in the
division of labour from that related to education and income.
Education is a stable indicator over time, changing little during adulthood, and in most
nations and social groups it is highly correlated with social class, status and income. Education
also allows classifying all subjects in a society, independently from their participation in the
labour market [
]. Regarding education as a separate phenomenon to socioeconomic
position has many advantages. As well as being a strong predictor of occupation and income in
adulthood, education also reflects childhood and adolescent SEP, being strongly influenced by
material and cultural resources of the family of origin. Therefore, its influence on health could
be attributable to many life course processes that are distinct from adult socioeconomic
position, such as the long-term effects of early life circumstances. Part of the association between
health and education may be due to social selection of those experiencing ill health during
childhood into lower educational groups. Indeed, poor health in early life could both limit
educational attainment and increase the likelihood of morbidity and premature mortality in
adulthood. However, with respect to occupation and income, which can be affected by poor
health during adulthood leading to the possibility of reverse causation, educational level has
the advantage of being unlikely to be influenced by health conditions occurred in adulthood.
A major problem in using educational level as a SEP indicator is that its meaning varies among
different birth cohorts, because of changes in educational systems and in the extent of the
diffusion of educational credentials in the population.
Income can affect health in two main ways: providing material resources for living, such as
those needed to obtain decent housing, clothing and food, or for having access to health care.
It also provides opportunities for the household members to avail of professional services (e.g.
domestic help, child care, household maintenance) and to participate in social activities, such
as cultural events, sports, friends and family gathering, and more in general to exert control
over one's life [
]. Experience of long-term deprivation has been reported as particularly
health-damaging, which supports the role of accumulated financial strain across the life course
on health [
3 / 32
Fig 1. Dimension of work as sources of contractual hazard, forms of employment contract, and location of employee
classes of the schema (adapted from Goldthorpe 2000, p. 223, fig 10.2).
In many studies health status during adulthood has also been associated with early
circumstances of social disadvantage (see the reviews by Galobardes et al. [
]), mostly measured
through father's or mother's occupation or educational level. Disadvantaged socioeconomic
position in childhood appears to have a stronger influence on the occurrence of certain
diseases, such as stroke or stomach cancer, whose determinants would act predominantly during
this period of life, whereas for other disorders the relevant exposures would mainly occur later,
during adulthood, or exposures in both periods would be important for other disorders, as for
coronary heart disease, lung cancer and other respiratory diseases [37±40].
In summary, social gradients in health have been consistently found using occupational
class, educational level, income or household financial resources, or parents' SEP (occupation
or education), with some differences related to the health outcome investigated.
Though epidemiology has extensively investigated chemical, physical and behavioural
risk factors (smoking, diet, alcohol, physical exercise, occupational factors), in several studies
these still explain less than half of the socioeconomic differences in mortality and morbidity
], although strong variations are observed by country, depending on the existing social
gradient in terms of distribution (prevalence) of chemical, physical and behavioural exposures
by social group [
]. What is missing in linking SEP with health is an understanding of the
intermediate mechanisms and pathways that relate less advantaged SEP with deterioration of
organic parameters. For example, research on immune markers in the Whitehall II study has
4 / 32
shown that glucocorticoids and inflammation may in part explain how the body mediates the
effects of social and economic disadvantage thus leading to disease, and this is partly
independent from common/known risk factors [
]. More generally, recent studies have shown
that SEP can influence the global physiological dysregulation across the life-course, measured
using allostatic load, a measure of biological multisystem wastage [46±48].
The study of the biological mechanisms through which SEP influences health, with a
particular focus on healthy ageing, is the main aim of the EU-funded LIFEPATH project, of which
the present study is part.
Aims of this paper are: 1) to present the socioeconomic classifications of occupational class,
education and income which will be employed throughout the EC Horizon 2020 LIFEPATH
project to categorize the socioeconomic position of the population enrolled, as well as the
distribution of these SEP indicators in the different cohorts, and 2) to compare social gradients in
all-cause mortality observed in the adult cohorts using the different SEP indicators, both in the
individual cohorts and overall, through a meta-analytic process.
LIFEPATH is a scientific project funded by the European Community that is devoted to the
investigation of the biological pathways underlying social differences in healthy aging. The
specific objectives of this project, that integrates social science approaches with biology (including
molecular epidemiology) using existing population cohorts and omics measurements
(particularly epigenomics), are to show that healthy ageing is an achievable goal for society, as it is
already experienced by individuals of more advantaged SEP, and to improve the
understanding of the mechanisms through which healthy ageing pathways diverge by SEP, by
investigating life course biological pathways using omic technologies.
For this purpose, a consortium of cohorts was built, including seven child cohorts and
eleven adult cohorts (Fig 2). To merge and analyze together data from the different LIFEPATH
cohorts, information on occupational class, education, father's occupational class and income
had to be harmonized. The harmonization was performed in the initial phases of the project,
in order to avoid post hoc decision making on SEP measures categorization in the different
cohorts, which may give authors the possibility of reformulating their exposure definition
during the analytic process [
Given that SEP indicators relevant for adults differ from those important to characterize
child SEP, in this paper we will focus only on the adult cohorts, whose detailed description is
available in Table 1 and more details can be found in the S1 File. Briefly, the consortium is
composed of one Portuguese cohort (Epiporto [
], participants randomly selected within
Porto dwellers), three French cohorts (Constances , adult subjects randomly selected from
French adults; E3N [
], volunteers from the French National school system; Gazel ,
workers of the French national gas and electricity company), two Italian cohorts (EPIC-Italy [
volunteers from 4 Italian cities; WHIP-retired [
], random sample of workers retired from
private enterprises), two Swiss cohorts (CoLaus/PsycoLaus [
], random sample of Lausanne
inhabitants; Skipogh [
], Swiss volunteer families), one Irish cohort (TILDA [
sample of community dwelling older persons aged 50 years+), one English cohort (Whitehall
], London-based civil servants), and one Australian cohort (MCCS [
], random sample
of Melbourne dwellers), for a total of 518,061 participants.
Each cohort was approved by the appropriate Ethical Committee. Details are provided in
S1 File, together with the cohorts' description. All investigations have been conducted
5 / 32
Fig 2. Lifepath distribution of cohorts.
according to the principles expressed in the Declaration of Helsinki. Written informed consent
has been obtained from the participants.
Educational level. Educational level is available in all LIFEPATH cohorts, except for
WHIP-retired. Educational systems are nation-specific and each cohort collected data in
different ways (most of the cohorts collected information about the level of education achieved
with different depth, and Epiporto about years of school only). Despite that, it was possible to
identify at least three levels that were comparable across countries: 1) primary and lower
secondary school (from 7 to 9 years after the kindergarten with a basic curriculum in languages,
mathematics and other subjects), 2) higher secondary school (around 4±5 years more, high
school diploma level) and 3) tertiary education (any degree after high school, such as BSc,
MSc, and further education). Primary education was not kept separate from low secondary,
because information on the former was available only for few cohorts (E3N, EPIC-Italy and
Gazel). In some countries (Italy, France, and Switzerland), it was also possible to identify a
subgroup of participants that attended a vocational school for professional training (2±3 years
after lower secondary school).
6 / 32
Random sample of
Random sample of French
Volunteers (National school
Volunteers (from Turin,
Varese, Naples, and
Random sample of Porto
Workers of the French
national gas and electricity
Volunteers (from Melbourne
Recruitment N of subjects
(N of death)
Occupation. Occupation was collected in all cohorts, except for the Australian cohort
(MCCS), where information was collected on employment status and not about type of
occupation. A simple dichotomous variable regarding the employment status was created. For a
few cohorts (MCCS, Skipogh, TILDA, Constances) there was also information on different
types of non-employment (retired, housewife, disabled, unemployed).
We decided to harmonize participants' current occupation and participants' last-known
occupation (before retirement, before unemployment, etc.) using two different variables. First,
a dichotomous variable distinguishing between manual and non-manual workers was created.
Then, all occupations were classified using the ESeC (Table 2). Because information about
occupation within LIFEPATH cohorts was collected with different depth, it was not possible
to use the 9-categories detail of ESEC classification and, according to the available data, we
decided to group the ESeC classes into three categories:
· Higher occupations including:
· E-SeC class 1: large employers, higher professionals and managers;
· E-SeC class 2: lower professionals and managers, and higher grade technical and
· E-SeC class 3: higher grade clerical, services and sales workers;
· Intermediate occupations including:
· ESeC class 4: small employers and self-employed outside of agriculture;
7 / 32
Large employers and
Owners and service
3- class schema
Routine and manual
· E-SeC class 5: farmers, self employed in agriculture;
· E-SeC class 6: lower supervisory and technical occupations;
· Routine and manual occupations including:
· ESeC class 7: lower clerical, services, and sales workers;
· E-SeC class 8: skilled workers;
· E-SeC class 9: semi- and unskilled workers.
The three-category variable was judged the most detailed possibility of reclassifying
participants' occupations, in order to avoid exclusion of cohorts or excessive misclassification among
Father's occupation. In order to infer information about socioeconomic position in
childhood, some cohorts collected also information on the main occupation of participants'
fathers. Two cohorts did not collect this information (CoLaus/PsycoLaus and
WHIPretired) and other two used categories of father's occupational class less detailed than those
used to collect participants' occupation (Gazel and Skipogh), although sufficiently enough to
classify subjects according to the same two- and three- occupational classes used for own
Income. Only in three cohorts information about income was collected and, furthermore,
it was done differently: in the TILDA cohort it was collected asking participants their precise
yearly income, in CoLaus/PsycoLaus it was collected using categories, while in the
WHIPretired cohort the exact income amount was available from administrative records. Since
mean amount of salary was different across countries and time period, we decided to
harmonize it using cohort-based quintiles.
Harmonization was performed using SAS 9.2 software and all harmonized databases are
stored at the University of Turin.
8 / 32
Each cohort provided participants' vital status, follow-up time, and eventual mortality date.
In most cohorts vital status was assessed through record linkage with administrative data. In
CoLaus/PsycoLaus it was assessed through active follow-up.
Data were described using absolute frequencies and percentages or means and standard
deviations for categorical or continuous variables, respectively. Correlations among SEP variables
were tested using the Spearman co-graduation coefficient, due to the ordinal behaviour of the
For each harmonized SEP variable, the impact on mortality was tested using a Poisson
model with the Huber estimator of the variance [
], adjusted for age, separately for gender
and cohort. Constances, Skipogh and TILDA cohorts were not included in the analyses on
mortality, the former two because follow-up duration was too short to observe a sufficient
number of deaths, while for TILDA mortality had not been made available to the LIFEPATH
project. Results coming from the different cohorts were pooled through a meta-analytic
process with random effects, using the DerSimonian & Laird weights . Heterogeneity among
cohorts was tested using the Higging heterogeneity index (I2) and the Cochrane's Q test based
upon inverse variance weights.
A 5% level of significance was considered for all tests. All analyses were performed using
Harmonization of the available variables was done for each cohort and a detailed codebook
can be found in the S2, S3 and S4 Files, for education level, subjects' occupation, and fathers'
occupation respectively. In Tables 3 (males) and 4 (females), numbers of participants in
each socioeconomic category are presented separately for each cohort. Results across genders
were similar, although women showed in general slightly higher proportions in lower SEP
Percentages of participants in the different educational categories, not considering those
with missing information, were similar across cohorts (around 50±70% in the lowest category
and the remaining more or less equally distributed in the other two categories). There were
two exceptions: E3N cohort, with a very low percentage of participants in the lowest category
(around 5%) and Constances, with a high percentage of participants in the highest one (more
than 50%). The proportion of subjects in the lowest educational category was also quite low in
Whitehall II, but only among males (30%), whereas among females it was similar to the other
Employment status varied from 100% employed in Gazel (participants were recruited at the
Electricity and Gas Company in France) to 0% employed in WHIP-retired, which includes
only retired people. Whitehall II study was also an occupational cohort, but we set the baseline
at the third wave for reasons of availability of data, and only 95% of participants were still
working at that time. In the remaining cohorts, the proportion of participants not in
employment varied between 30% to 60%, dependently on the different baseline mean age of the
subjects. For the few cohorts for which information on the reason for non-employment was
available (Constances, Skipogh, TILDA, MCCS), non-employed were mainly retired people,
followed by housewives, while only a small proportion was unemployed or disabled.
9 / 32
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Regarding current/last occupation, the proportion of non-manual workers, not considering
those with missing information, was above 50% in almost all cohorts for both genders, except
WHIP-retired (33% among males and 44% among females) and TILDA (42% among males),
with high proportions (above 80%) in Constances, E3N, Skipogh, Gazel and Whitehall II
(males only). These cohorts, except for Skipogh, were also the ones showing a low percentage
of participants in the lowest occupational class (mainly below 30%), while in all other cohorts
it was around 50%, with the highest percentages (around 60%) in Epiporto and WHIP-retired.
In all cohorts for which information on fathers' occupation was available, the occupations
of the participants fell on average in higher occupational classes compared to those of their
fathers, with the exception of Skipogh, although with a high variability among cohorts; the
proportion of subjects having tertiary education was also increased in most cohorts, although less
All correlations among socioeconomic indicators in each cohort were statistically
significant and were in general quite similar across genders, so they are presented without gender
stratification (Table 5). A strong positive correlation between educational level and
occupational class was present for almost all cohorts (range of Spearman coefficients: 0.29±0.79), as
well as a fair positive correlation between participants' education and fathers' occupational
class (range of Spearman coefficients: 0.20±0.39), and a mild positive correlation between
subjects' and fathers' occupational class (range of Spearman coefficients: 0.14±0.36), with the
exception of E3N (Spearman coefficients: -0.08 and 0±02, respectively). In the few cohorts
where it was available, personal income was fairly positively correlated both with subjects'
education and occupational class.
Main results of the meta-analyses on the associations between SEP indicators and mortality
(adjusted for age) are presented in Table 6 and in Figs 3, 4, 5, 6, 7 and 8, for men and women
separately. Detailed results for each cohort are available in the S1 and S2 Tables, for men and
All SEP indicators, except fathers' occupational class, were associated with mortality in
men, while only education and current occupational class showed an association in women.
Statistically significant increased risks of mortality were found in men with high secondary
school (RR = 1.22, I2 = 45.6%) and with primary or lower secondary school (RR = 1.36),
compared to men with tertiary education (Fig 3), although with a significant heterogeneity among
cohorts for the latter risk estimate (I2 = 72.0%). In women, only participants with the lowest
level of education showed a significant increase in risk, compared to the highest one
(RR = 1.15), whereas among those with intermediate education the risk was close to one
(RR = 1.01). A clear trend of increase in risk was also observed among men, but not among
women, when subjects who attended a vocational school were separated by those in the lowest
educational class, in the cohorts for which this information was available.
The strongest effect was observed for occupational class based on current/last job in men,
with an 81% increase in mortality risk, comparing the lowest with the highest class, and a 41%
increase in the intermediate one, both of which were statistically significant (Fig 5). Pooled
risk estimates were quite similar when only the employed population was considered and was
categorized through the current job, or also retired or unemployed subjects who worked in the
past were included, classifying them using the last job held. In contrast, among women both
lower occupational categories displayed a modest significant increase in risk (RR = 1.06 and
RR = 1.14 for the intermediate and the low occupational classes, respectively), which however
lost significance when the population of individuals formerly employed was included in the
14 / 32
¶Education: 1 = primary or lower secondary school, 2 = higher secondary school, 3 = tertiary education;
²Current/last job: 1 = Class 7±9 ESEC (low), 2 = Class 4±6 ESEC (medium), 3 = Class 1±3 ESEC (high);
³Father's job: 1 = Class 7±9 ESEC (low), 2 = Class 4±6 ESEC (medium), 3 = Class 1±3 ESEC (high);
*Income: 1 = 1st quintile of income (lowest), 2 = 2nd quintile; 3 = 3rd quintile; 4 = 4th quintile; 5 = 5th quintile
analyses (Fig 6). A low level of heterogeneity was present among risk estimates across studies
for occupational class in both genders.
When the association between occupational class and mortality was examined keeping
separate low-grade non-manual workers and skilled workers (ESEC classes 7 and 8) from
semiskilled and unskilled workers (ESEC class 9), we found in men a RR = 1.66 (95% CI 1.29±2.02)
for low-grade non-manual workers and skilled workers vs. the highest occupational class. For
semi-skilled and unskilled workers the risk was slightly higher (RR = 1.76, 95% CI: 1.29±2.30),
although with a wide overlap between confidence intervals of the risk estimates (S1 Fig).
15 / 32
Among women, the mortality risk was equal to one for low-grade non-manual workers and
skilled workers, compared to the highest occupational class, while it was slightly increased
for semi-skilled and unskilled workers, but also with great uncertainty of the point estimate
(RR = 1.22, 95% CI: 0.63±1.80).
Null results in the pooled analyses were observed both in men and women for the type of
occupation of subjects' fathers (Figs 7 and 8). Most of the mortality risks were around unity,
except for the Epiporto and EPIC-Italy cohorts. In the first one, in both genders low or
intermediate father's occupational classes were associated with mortality risks around 2±3 times
higher than the highest class, although only the risk estimate for men with fathers in the lowest
16 / 32
Fig 3. Meta-analysis of the association between education level and mortality (Males).
17 / 32
Fig 4. Meta-analysis of the association between education level and mortality (Females).
18 / 32
Fig 5. Meta-analysis of the association between current/last job and mortality (Males).
19 / 32
Fig 6. Meta-analysis of the association between current/last job and mortality (Females).
20 / 32
Fig 7. Meta-analysis of the association between father's job and mortality (Males).
21 / 32
Fig 8. Meta-analysis of the association between father's job and mortality (Females).
22 / 32
class reached statistical significance (RR = 2.36). Risks were also non-significantly elevated
among men in the EPIC-Italy cohort (RR = 1.22 and RR = 1.18 for the lowest and the
intermediate categories, respectively). Even when the category of semi-skilled and unskilled workers
was examined separately from the rest of the lowest occupational class, the results for this
category were in each cohort similar to those obtained using the three-level classification and
the pooled risk of mortality was also around unity in both genders (S2 Fig). However, most
cohorts with information on father's occupation included quite few deaths, with the
consequence of low statistical power and risk estimates characterized by wide confidence intervals.
Moreover, for both genders the results of the meta-analysis were driven by one French
occupational cohort (more than 80% of the weight for Gazel in men and for E3N in women).
Low personal income was associated with an increased risk of mortality, but among men
risk estimates were very different in the two cohorts examined (RR = 3.60 and RR = 1.35 for
the lowest vs. the highest quintile in CoLaus/PsycoLaus and WHIP-retired, respectively),
whereas among females no deaths occurred in the reference category in one study (CoLaus/
PsycoLaus) and in the other one the risk in the lowest quintile was similar to that of males
(RR = 1.33). No meta-analysis was performed on income and mortality, considering that the
meta-RR for men would have been strongly influenced by one study (WHIP-retired), because
of its size.
The objectives of this study were: firstly, to harmonize the SEP classifications on occupational
class, education and income among the cohorts belonging to the LIFEPATH Consortium and
to present their distribution in the different cohorts; and, secondly, to compare social gradients
in overall mortality observed in the available cohorts according to the different SEP indicators.
In spite of differences in recruitment among the cohorts, especially in terms of time, age,
gender composition and type of sample, variability in the distribution of the socioeconomic
indicators in the different study populations was relatively low.
Regarding education, in most cohorts the proportion of subjects in the lowest category was
consistently around or above 50%, with the exception of E3N, where the proportion was very
low because it was composed mainly of teachers, Constance, which started only recently and
includes volunteering subjects younger than in other cohorts, and Whitehall II, which includes
a large proportion of male high-grade employees. Data are more variable across cohorts for
the proportion of subjects with tertiary education, which however seems to reflect mostly
cross-country differences in educational achievements and in school systems.
The distribution of occupational class, based on current/last job, was also quite consistent
across cohorts, except for E3N and Constances, and for Gazel and Whitehall II, where the
proportion of subjects in higher occupational classes was higher, in spite their educational level
was similar to other cohorts; a likely explanation is that these workers were employed in public
or semi-public work organizations where career advancements were more linked to skills
acquired at the workplace rather than to formal education.
Despite differences in the study populations and in the SEP categories employed, their
distribution in the different cohorts was roughly comparable to that observed in other studies
conducted in Europe, including several performed on representative samples of the general
population, both for education [5,63±65] and occupational class [
A shift toward higher occupational classes was noted between fathers' and subjects'
occupations in most cohorts, which seems to reflect an increasing trend of social improvement in
these populations, as well as the decrease of the proportion of people employed in manual jobs
and in agriculture during the last fifty years. As expected, the SEP indicators examined were
23 / 32
quite correlated each other, especially occupational class and education, although with some
differences between cohorts; the lowest correlation was observed for the E3N and the Gazel
cohorts, for the reasons explained above.
The association between SEP and mortality strongly differed by gender, with much higher
and significant associations in men than women, although characterized by variable strength
in the different cohorts.
Regarding education, mortality risks were significantly heterogeneous among men in the
lowest category, so that the increases computed through the meta-analysis cannot be
considered a reliable pooled estimate (36%), whereas no significant heterogeneity was present for the
intermediate category, resulting in a 22% increased risk compared to the most educated. These
figures appear comparable to those produced in other studies on education and mortality
conducted in Europe and U.S. for men, indicating that the predictive validity of our education
classification was relatively good [
]. Among women, the association with
education was instead lower than that observed in most other studies [
], although it
was in the expected direction. Gradients in mortality by educational level were possibly
underestimated by 5±15% because of the aggregation of low secondary and primary school,
assuming that the two groups were in most countries of similar size; in fact, those studies where the
two categories were kept separate have shown in general 10±30% higher mortality in the
primary education category, compared to low secondary one [5; 63±65]. Nonetheless, the
threelevel educational classification adopted has been widely used in other European studies
], also because it allows aggregating subjects who went through different educational
systems with different length of compulsory education.
For occupational class, our mortality results among men are generally in agreement with
those observed in other European countries, considering differences in the categories used in
this, compared to other studies [
]. The choice of having more robust categories,
keeping aggregated ESeC classes 7, 8 and 9 (low grade non-manual, skilled manual and unskilled
manual workers), may have led to an underestimation of the actual occupational social
gradient, likely of magnitude similar to that for education, considering the relative proportions of
these workers' groups and the differences in mortality observed between them in other studies
]. Again, the small and non-significant occupational gradient in mortality
found among women seems in contrast with the higher risks observed in several other studies
investigating mortality [5,65,71±75] or morbidity [
], although in most studies the
gradient was lower than among men and in some others no or only slight increases in risk were
found [70,78±81]. The shallower social gradient observed among women, compared to other
reports in the literature, appears difficult to explain and may be related to specific features of
some of the cohorts examined (e.g. lower participation of ill women belonging to lower social
strata), although risk estimates were quite homogeneous across them.
In the two studies with information on income, participants in the lowest income quintiles
showed a significantly increased risk of mortality, although very different in. Such a difference
appears due to the fact that in the WHIP-retired cohort, workers with the highest salary were
the executive, who represented only 5% of the total population, so the highest quintile included
also other three quarters of white collars with a lower income. These results indicate that use of
quantiles is not appropriate to describe income distribution, as subjects within quantiles may
be too heterogeneous to be pooled together, but it would be preferable to put limits between
categories in correspondence of major discontinuities in income distribution. Reports in the
literature are consistent in showing an association with income, but with a wide variability in
the strength of the association observed [
]. Our conclusion is that available data on
income and mortality were too scarce to conduct a meaningful examination of their predictive
24 / 32
Unexpectedly, no association was found between father's occupational class and mortality,
except for the lowest category in Epiporto. This appears in contrast with consistent reports of
increased mortality among subjects with childhood disadvantage, measured through a variety
of social indicators [
], although with moderate increases in risk, generally not
exceeding 50%. The lack of an association in our study cannot be explained by the choice of
aggregating ESEC classes 7, 8 and 9 together, because even keeping separate subjects whose
father was in the lowest ESEC class, their pooled risk of mortality was still around one,
compared to people whose father was in the highest class. The null results of this meta-analysis are
possibly due to the specificity of the two French cohorts, both occupational-based, which drive
the results because of their large weight. Unfortunately, we could not investigate the
association with other SEP measures in childhood, because of data unavailability.
In general, results on the relationship between our classification of occupational class and
mortality showed a more consistent relationship across studies than the other social measures.
They are also in agreement with the conclusions of an article on morbidity and occupational
class, measured through the EG scheme, by the European Network on Social Inequalities [
Another source of support to our occupational class definition comes from the work by Evans
and Mills (2000) [
], who examined the criterion validity of the EG scheme on British survey
data from 1996, using eight indicators of the employment relationships and applying to them
latent class analysis; these authors also found three latent classes, which corresponded to
service, intermediate and labor contract occupations, supporting this way a good criterion
validity of a three-category classification.
Among the strengths of the study, the data employed derive from many studies
participating in a large consortium, which together includes a population of over 500,000 subjects. The
harmonization of the socioeconomic indicators available in the different cohorts and the large
size of the study allowed us to assess with a large degree of statistical power differences in
mortality by SEP according to the different indicators. Moreover, the availability of such a large
amount of harmonized data combined with that of many biological measurements at different
times during the lifecourse, will provide a unique opportunity to try identifying mechanisms
and pathways leading from low SEP to unhealthy ageing.
Regarding limitations, as said in the discussion with respect to both education and
occupational class, the wide categories used have likely produced a certain degree of heterogeneity
within the categories, whose consequence would be an underestimation of the true social
gradient in mortality.
However, from results of our own analyses and those in the literature, such an
underestimation appears quite small and counterbalanced by the advantage of having robust social
categories, each with large numbers of events, as well as avoiding the misclassification bias
potentially present in the effort of assigning subjects to more detailed and extended categories.
The heterogeneous nature of the cohorts may also be a limitation, particularly the presence of
both population-based studies and occupational studies in the analysis on occupational class.
In mitigation, it could be argued that, this provided greater statistical power to evaluate the
relation between SEP and mortality. The choice of using a measure of occupational class based
on employment relations, rather than prestige, may have also led to an underestimation of the
true social gradient in mortality, as suggested by the results of two British studies [
could be because prestige-based indicators are more closely linked to household income
availability, education, or lifestyle than those constructed on employment relations. A measure
based on employment relations may also be an inferior indicator of social and economic
conditions in women in nations where they have a weaker attachment to the labour market.
In conclusion, among men the proposed three-level classifications of occupational class and
education appear to not differ substantially from more detailed classifications in discriminating
25 / 32
between main social strata, and in predicting differences in mortality between them, which
however seem to be slightly underestimated compared to other studies. In contrast, among
women mortality was only moderately increased in the lowest categories of education and
current occupational class, possibly because classifying them through their sole occupation,
without taking into account the SEP of their partners or of their household, may imply a
misclassification of their social position leading to attenuation of differences in health outcomes.
The lack of association with father's occupational class was unexpected: possibly attributable to
a ªFrench cohort effectº, due to the occupational nature of the Gazel and E3N cohorts, which
drive strongly the results, although the higher risks found in Portugal and Italy suggest that this
dimension may be more relevant for Southern Europe.
S1 File. Cohorts description.
S2 File. Harmonization of EDUCATION.
S3 File. Harmonization of CURRENT/LAST JOB.
S4 File. Harmonization of FATHER'S JOB.
S1 Table. Association between socioeconomic variables and mortality separated by cohort.
S2 Table. Association between socioeconomic variables and mortality separated by cohort.
S1 Fig. Meta-analysis of the association between current/last job and mortality separating
skilled and semi- and unskilled workers.
S2 Fig. Meta-analysis of the association between fathers' job and mortality separating
skilled and semi- and unskilled workers.
The members of the LIFEPATH Consortium are (alphabetic order): Harri Alenius (Finnish
Institute of Occupational Health, Systems Toxicology Unit, Helsinki, Finland), Mauricio
Avendano (Department of Global Health and Social Medicine, King's College London, UK),
Henrique Barros (EPIUnit- Institute of Public Health, University of Porto, Porto, Portugal),
Murielle Bochud (Institute of Social and Preventive Medicine and Department of Psychiatry
and Department of Internal Medicine, Lausanne University Hospital, Lausanne, Switzerland),
Cristian Carmeli (Institute of Social and Preventive Medicine and Department of Psychiatry
and Department of Internal Medicine, Lausanne University Hospital, Lausanne, Switzerland),
Luca Carra (ZADIG srl, Milano, Italy), Raphaele CastagneÂ (Department of Epidemiology and
Biostatistics, School of Public Health, Imperial College London, UK), Marc Chadeau-Hyam
26 / 32
(Department of Epidemiology and Biostatistics, School of Public Health, Imperial College
London, UK), FrancËoise Clavel-Chapelon (Center for Research in Epidemiology and
Population Health, INSERM U1018, Villejuif, France), Giuseppe Costa (Epidemiology Unit, ASL
TO3, Piedmont Region, Grugliasco (TO), Italy; Department of Clinical and Biological Science,
University of Turin, Italy), Emilie Courtin (London School of Economics and Political Science,
LSE Health and Social Care, Department of Social Policy, Houghton Street, London, UK;
King's College London, Department of Social Science, Health and Medicine, London, UK),
Cyrille Delpierre (INSERM, UMR1027, Toulouse, France; UniversiteÂ Toulouse III
Paul-Sabatier, UMR1027, Toulouse, France), Angelo d'Errico (Epidemiology Unit, ASL TO3, Piedmont
Region, Grugliasco (TO), Italy), Pierre-Antoine DugueÂ (Cancer Council Victoria, Victoria,
Australia; The University of Melbourne, Victoria, Australia), Paul Elliott (Department of
Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK), Silvia
Fraga (EPIUnit±Institute of Public Health, University of Porto, Portugal), ValeÂrie Gares
(Inserm, UMR 1027, Toulouse, France; UniversiteÂ Toulouse III, Toulouse, France; NHMRC
Clinical Trials Centre, The University of Sydney, Australia), Graham Giles (Cancer
Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia), Marcel Goldberg
(Populationbased Epidemiological Cohorts Unit, INSERM UMS 11, Villejuif, France; Paris Descartes
University, Paris, France), Dario Greco (Institute of Biotechnology, University of Helsinki,
Finland), Allison Hodge (Centre for Epidemiology and Biostatistics, Melbourne School of
Population & Global Health, The University of Melbourne, Australia), Michelle Kelly Irving
(INSERM, UMR1027, Toulouse,France), Piia Karisola (Unit of Systems Toxicology, Finnish
Institute of Occupational Health, Helsinki, Finland), Mika KivimaÈki (University College
London, Department of Epidemiology and Public Health, London, United Kingdom; Clinicum,
Faculty of Medicine, University of Helsinki, Helsinki, Finland), Vittorio Krogh (Epidemiology
and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy),
Thierry Lang (INSERM, UMR1027, Toulouse, France; UniversiteÂ Toulouse III Paul-Sabatier,
UMR1027, Toulouse, France), Richard Layte (Department of Sociology, Trinity College
Dublin, Ireland), Benoit Lepage (INSERM, UMR1027, Toulouse,France), Johan Mackenbach
(Department of Public Health, Erasmus University Medical Center, Rotterdam, Netherlands),
Michael Marmot (University College London, Department of Epidemiology and Public
Health, London, UK), Cathal McCrory (The Irish Longitudinal Study on Ageing (TILDA),
Trinity College Dublin, Ireland), Roger Milne (Cancer Epidemiology Centre, Cancer Council
Victoria, Melbourne, Australia), Peter Muennig (Columbia University New York, USA),
Wilma Nusselder (Public Health Erasmus MC, University Medical Center Rotterdam, the
Netherlands), Salvatore Panico (Department of Clinical and Experimental Medicine,
University of Naples Federico II, Naples, Italy), Dusan Petrovic (Institute of Social and Preventive
Medicine, Lausanne University Hospital, Lausanne, Switzerland), Silvia Polidoro (Human
Genetics Foundation, Turin, Italy), Martin Preisig (Department of Psychiatry, Lausanne
University Hospital, Lausanne, Switzerland), Olli Raitakari (Research Centre of Applied and
Preventive Cardiovascular Medicine, University of Turku, Finland), Ana Isabel Ribeiro
(EPIUnit±Institute of Public Health, University of Porto, Portugal), Fulvio Ricceri (Epidemiology
Unit, ASL TO3, Piedmont Region, Grugliasco (TO), Italy; Department of Clinical and
Biological Science, University of Turin, Italy), Oliver Robinson (Department of Epidemiology and
Biostatistics, Imperial College London, London UK), Jose Rubio Valverde (Centro Nacional
de BiotecnologÂõa, CSIC. c/Darwin, Madrid, Spain.), Carlotta Sacerdote (Unit of Cancer
Epidemiology, University of Turin and Città della Salute e della Scienza Hospital, Turin, Italy),
Roberto Satolli (ZADIG srl, Milano, Italy), Gianluca Severi (CESP, Inserm U1018, UniversiteÂ
Paris-Saclay, UPS, USQ, Gustave Roussy, Villejuif, France; Human Genetics Foundation,
Turin, Italy), Martin J Shipley (Department of Epidemiology and Public Health, University
27 / 32
College London, UK), Silvia Stringhini (Institute of Social and Preventive Medicine and
Department of Psychiatry and Department of Internal Medicine, Lausanne University
Hospital, Lausanne, Switzerland), Rosario Tumino (Cancer Registry, Department of Prevention,
ASP, Ragusa, Italy), Paolo Vineis (MRC-PHE Centre for Environment and Health, School of
Public Health, Department of Epidemiology and Biostatistics, Imperial College London,
London UK), Peter Vollenweider (Institute of Social and Preventive Medicine and Departments
of Psychiatry and Internal Medicine, Lausanne University Hospital, Lausanne, Switzerland),
and Marie Zins (Population-based Epidemiological Cohorts Unit, INSERM UMS 11, Villejuif,
France; Paris Descartes University, Paris, France).
Lead author: Paolo Vineis (MRC-PHE Centre for Environment and Health, School of
Public Health, Department of Epidemiology and Biostatistics, Imperial College London, London
E-mail address: .uk
Conceptualization: P. Vineis AD FR SS GC.
Data curation: AD FR SS CC.
Formal analysis: FR CC.
Funding acquisition: P. Vineis SS.
Investigation: P. Vineis SS MK CM.
Methodology: AD FR SS MK.
GS GC P. Vineis.
Supervision: GC P. Vineis.
Writing ± original draft: AD FR GC.
RT MG MZ HB GG GS GC P. Vineis.
Resources: AD FR SS CC MK M. Bartley CM M. Bochud P. Vollenweider RT MG MZ HB GG
Writing ± review & editing: AD FR SS CC MK M. Bartley CM M. Bochud P. Vollenweider
28 / 32
29 / 32
30 / 32
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