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ORIGINAL INVESTIGATION
The relationship between health and partnership history
in adulthood: insights through retrospective information
from Europeans aged 50 and over
Jordi Guma
`•Antonio D. Ca
´mara •Rocı
´o Trevin
˜o
Springer-Verlag Berlin Heidelberg 2014
Abstract The association between health and partnership
status is a growing concern within the social sciences.
Some partnership situations exhibit positive effects on
health, while partnership breakdowns display negative
impacts. However, case studies show that these associa-
tions may change with age, due to potential sources of
heterogeneity within a population. The current analysis
explored this association over the adult life course (ages
30–64) of Europeans aged 50 years and older based on
retrospective information on health and partnership from
SHARELIFE (N=23,535 after data screening). The data
allowed us to control for socio-demographic covariates as
well as for individual infirmity, measured by childhood
health. We also considered contextual survival selection
effects by comparing 13 European countries for which pre-
adult mortality levels largely differed among the cohorts
involved (1907–1958). Discrete-time hazard analyses
examined the risk of suffering from a major episode of
poor health (self-reported) in adulthood as a function of
partnership history, using two approaches: a pooled model
and country-specific models. The results revealed no dif-
ferences between those who lived with a partner (first
union) and single individuals in terms of the retrospective
hazards of poor health. We hypothesize that this result
stems from the cumulative effect of survival selection on
individuals in advanced ages according to partnership sta-
tus. The results also partially point to the plausibility of a
contextual survival selection, which should be confirmed
by further research based on additional health indicators.
Keywords Partnership status Health Biographical
information Survival Selection SHARE
Introduction
For the most part, previous research on the relationship
between partnership status and health has shown that living
with a partner is associated with better health outcomes
when measured in terms of mortality (Waite 1995; Val-
konen et al. 2004; Martikainen et al. 2005) or morbidity
(Joung et al. 1994; Liu and Umberson 2008; Hughes and
Waite 2009). Furthermore, some of these studies have
uncovered that partnership could determine mortality dif-
ferentials to a larger extent than socioeconomic status in
some circumstances (Martikainen et al. 2005).
Three main mechanisms behind partnership have been
emphasized as contributing to explain its health benefits:
(1) the reduction of risky behaviors and unhealthy habits
(Lillard and Waite 1995; Duncan et al. 2006), (2) the
development of a support network that can buffer the effect
of poor health episodes (Waite 1995), and (3) the increase
in material well-being associated with economies of scale
resulting from the addition of resources of both partners
and the possibility of task specialization (Lillard and Panis
1996). These processes do not influence males and females
identically. First, the benefits appear to be greater and
manifest more quickly among males. That is to say, men’s
health improves shortly after the beginning of a union,
whereas its effects on women’s health occur more
This study is part of Jordi Guma
`’s PhD within the Doctoral Program
in Demography from the Universitat Auto
`noma de Barcelona. A
previous version of the paper was developed as a Master’s Thesis
within the European Doctoral School of Demography.
Responsible Editor: H. Litwin.
J. Guma
`(&)A. D. Ca
´mara R. Trevin
˜o
Centre d’Estudis Demogra
`fics, Barcelona, Spain
e-mail: jguma@ced.uab.es
123
Eur J Ageing
DOI 10.1007/s10433-014-0316-x
gradually. Secondly, women appear to benefit principally
from the increase in material well-being, whereas males
mainly benefit from the cessation or moderation of risky
behaviors and unhealthy habits (Waite 1995; Lillard and
Panis 1996; Duncan et al. 2006). In contrast, other part-
nership situations, as well as the transitions between them,
have been associated with negative effects on health, as
exemplified by the increases in depressive symptoms and/
or chronic morbidity among men and women who either
experienced a separation or divorce or became widows
(Simon 2002; Wade and Pevalin 2004; Hughes and Waite
2009). In addition, those engaged in subsequent unions
after an episode of separation did not completely recover
the original advantage they once had in terms of health
(Hughes and Waite 2009).
However, the health benefits of partnership among
mature and older adults have been questioned (i.e., benefits
of partnership with respect to singleness seem to moderate,
disappear, or even reverse at these ages) (Goldman et al.
1995; Regidor et al. 2001; Bardage et al. 2005). Some
studies explain this result as the effect of survival selection;
a major source of heterogeneity within populations, which
causes unexpected pattern of a given phenomenon (e.g.,
health and mortality) as a function of age (Vaupel et al.
1979; Vaupel and Yashin 1985). Survival selection may
originate on contextual and/or individual levels. Contex-
tually, it is associated with the living conditions of the
population, which strongly determine mortality levels
throughout the life course and particularly in childhood.
For instance, current populations that were exposed to hard
living conditions and high-mortality levels at pre-adult
ages are more likely to be selected in terms of survival.
Accordingly, these populations might exhibit better or less
disadvantaged than expected health outcomes at old ages
than populations with better living conditions and lower
mortality levels. At the individual level, survival selection
may be determined by a number of behavioral, socioeco-
nomic, and/or genetic factors. For instance, if singleness is
associated with risky health behaviors, then it is expected
that singles suffer from higher mortality levels and thus
become a more selected segment of the population when
reaching old age.
This work aims to explore the association between
health and partnership in adulthood, comparing individuals
who entered into their first union (either cohabitation or
marriage) and those who remained single at mature ages.
Biographical information on health status and partnership
status (a time-varying variable) provided by Europeans
aged 50 and over in the SHARELIFE survey is utilized for
this purpose. All 13 countries included in this survey
(Austria, Belgium, the Czech Republic, Denmark, France,
Germany, Greece, Italy, the Netherlands, Poland, Sweden,
Spain, and Switzerland) were analyzed both jointly and
specifically. These countries represent different mortality
backgrounds associated with diverse contexts of living
conditions for the cohorts that are analyzed in this study
(1907–1958), which indirectly permit us to approach the
influence of survival selection effects between these pop-
ulations. Another potential source of heterogeneity at the
individual level (i.e., individuals’ health trajectories) is
partly controlled by utilizing their health status in
childhood.
Differences between these populations were also
noticeable with regard to the intensity and the timing of
some key aspects of the second demographic transition
(SDT) that took place from Northwestern to Southeastern
regions, mainly during the second half of the twentieth
century. SDT proposes that changes in a number of
demographic-related behaviors (e.g., the postponement of
childbearing and marriage, the diversification of family
forms, and the increase of divorce and cohabitation rates)
are associated not only with socioeconomic variables, but
also with large-scale changes in values (Lesthaeghe 1995).
The spread of these values differs across countries and is
related to both cultural and legislative aspects (for instance,
the official approval of divorce; Gonza
´lez and Viitanen
2009), which are important to understand the cross-national
partnership-related differentials observed among Europe-
ans aged 50 and older surveyed by SHARELIFE.
Methods
Data
Microdata from the Survey of Health, Ageing and Retire-
ment in Europe (SHARE) were utilized. SHARE is a cross-
national panel survey conducted on individuals (intervie-
wees and their partners) aged 50 and over (Bo
¨rsch-Supan
et al. 2005; Schro
¨der 2011). The third wave of SHARE
(named SHARELIFE) was held between the autumn of
2008 and the summer of 2009, and it included retrospective
questions that permit a life-cycle approach to certain issues
such as health and partnership trajectories. The initial
sample size of interviewees aged 50?and their partners
was slightly reduced after discarding proxies (indirect
informants) and excluding missing cases of any of the
variables involved in the analysis and individuals who
experienced an event of poor health before turning 30.
Valid cases amounted to 89 % overall, ranging from 84 %
(Poland and France) to 94 % (Italy). The distribution of
cases according to age group and sex prior to and after the
data screening remained largely unchanged. The retro-
spective analysis of health and partnership spanned work-
ing ages (30–64) to avoid the potential biases related to
changes in socioeconomic status after retirement
Eur J Ageing
123
(Demakakos et al. 2008). The lower age boundary in this
analysis (age 30) responds to the aim of avoiding the effect
of age to mediate the relationship between partnership
status and health (i.e., younger individuals are more likely
to be single as well as to be less likely to experience poor
health episodes). At the age of 30, 82.5 % of individuals
were already in a partnership status other than single, and
this percentage rises slightly to 90.1 % at the age of 64.
Individuals younger than 64 at the time of the interview
were right-censored in the analysis.
Study variables
The health outcome (SHARELIFE item GL009) represents
whether the individual experienced ‘‘a distinct period
during which your health was poor compared to the rest of
your life’’ over adulthood (ages 30–64) and in the case of
an affirmative answer, when this period occurred. The
prevalence of this event was 33.5 % among men and
37.1 % among women. Germany displayed the highest
prevalence (47.2 % among men and 46.8 % among
women) and Greece the lowest prevalence (17.0 % among
men and 20.2 % among women). Overall, in approximately
74 % of the cases, this event occurred between the ages of
30 and 64.
As with other self-reported health indicators, this item
captures both the objective and subjective dimensions of
health, which maintain a solid association for the most part
(Idler 1993). Therefore, it approaches the health status of
individuals in a more comprehensive manner (WHO 1946)
than more specific health indicators (i.e., whether the
individual suffers or has suffered from a certain disease
1
).
This indicator also shows a significant relationship with
other subjective health indicators such as the current self-
perceived health status at the time of the survey (62 % of
those who declared a poor health status experienced the
event of poor health, whereas the incidence of the event
among those who declared a good health status was 28 %;
the v
2
statistic was significant when both indicators were
cross-tabulated). That said, it must be acknowledged that
what constitutes a ‘‘distinct period’’ of poor health may
differ from individual to individual as well as from country
to country.
Partnership history for each interviewee between the
ages of 30 and 64 (or alternatively until the age at time of
the interview) was re-constructed from retrospective
questions that recorded the occurrence of unions, separa-
tions, and widowhood. Technically, this information was
stored in a time-varying variable that was categorized as
follows:
•Single (no previous union)
•First union (living with a partner in a first union)
•Interruption of the relationship (not living with a
partner after having experienced separation or divorce,
independent of the rank of the union)
•Widowhood
•Second or higher rank union (living with a partner in a
second or higher rank union).
The time-varying approach to partnership status as
practiced in this study prevents the artifact of the attenu-
ated effects of marital status on morbidity and mortality, as
Rendall et al. proposed in 2011. The first unions at the time
of the interview were clearly prevalent in all the countries
under analysis (overall, 75 % of cases among men and
60 % among women), but significant differences between
countries were observed for the other partnership situa-
tions. Southern European countries (Spain, Italy, and
Greece) together with Poland showed the lowest percent-
age of breakdowns (below 10 % for both sexes), whereas
Nordic countries (Denmark and Sweden) showed the
highest percentages for these situations (above 20 % in
both sexes). As expected, a lower percentage of widowers
(6.3 %) with respect to widows (21.9 %) was also found.
With regard to this, the main difference between countries
was found among widows ranging from 15.6 % (the
Netherlands) to 29.7 % (Austria).
Age at the time of the interview was collapsed into four
groups: 50–59, 60–69, 70–79, and 80 and over. This vari-
able controlled for two possible sources of bias in the time
of the occurrence of the event of interest, namely (1) a
choice set bias in that longer life increases the number of
potential episodes of poor health and consequently forced
the individual to prioritize health to a greater extent than
younger individuals; and (2) a recall bias in that longer life
may worsen the ability to remember and report past events
precisely. For instance, it might be the case that older
individuals prioritized more recent episodes, thus resulting
in an artifact of postponement of poor health episodes.
Childhood health was included to control for individual
infirmity in our analyses through the question, ‘‘Would you
say that your health during your childhood was in general
excellent, very good, good, fair or poor and/or not
1
Among other problems, a more specific set of items in this survey
(HS054) likely underestimates the periods or episodes of poor health
with respect to the indicator that was chosen for this work. For
example, the prevalence of a period of poor health is 21 % using
HS054, whereas the value is 40.4 % when GL009 is used. This
necessarily has to do with the different wording and different
conceptual nature of both items. Moreover, any period of poor health
in HS054 can be embodied by several illnesses, which provokes a
problem of ambiguity in the use of specific illnesses as health
indicators. Therefore, we believe that GL009 is a better technical
choice for our purposes, and its figures are more trustworthy taking
into account that SHARELIFE focuses on the population aged 50 and
over. Nevertheless, specific comments in the discussion section are
devoted to deal with the implications of this choice in our results.
Eur J Ageing
123
constant?’’ These answers were grouped into two catego-
ries: good health (excellent, very good, and good) and poor
health (fair, poor, or ever poor).
Working status asked retrospectively about the changes
in a person’s labor situation throughout adulthood, which
was also treated as a time-varying variable in our analyses
(working/not working).
Education (the highest educational level attained by the
interviewee) has two purposes in the analyses: (1) to
approach the socioeconomic status of individuals together
with their working status, and (2) to approach differences
in health risk behaviors (higher education is associated
with healthier behaviors) (Marmot 2005). Educational
levels across countries were harmonized using the Inter-
national Standard Classification of Education (ISCED
1997) by UNESCO, and the resulting variable grouped
some categories to obtain more robust cross-tabulations for
all countries: (1) First level of education includes no
studies completed and primary studies or first stage of basic
education (Levels 0 and 1 of ISCED); (2) Second level of
education: Second stage of basic education and secondary
education (Levels 2 and 3 of ISCED); and (3) Third level
of education: Post-secondary (non-tertiary) and all possible
levels of tertiary education (Levels 4-6 of ISCED).
Sex: men/women
Age (and age squared) in our models was a time-varying
variable that ranged from the age of 30 (first observation
for each individual) to the age of 64 (maximum) or,
alternatively, the age at time of the interview. Each indi-
vidual was observed as many times as the years passed
until the event (a distinct period of poor health) occurred or
until the age of 64 in the absence of an event. Age squared
was included in the models to better fit with age effects
beyond a linear function of age.
To ease the interpretation of our results, the analyzed
countries were grouped according to their infant mortality
rates during a decade of the inter-war period (1927–1938)
for which data are available for all countries (Chesnais
1986):
1 High mortality (thus high levels of potential survival
selection effect) includes countries with infant mortal-
ity rates above 100 per thousand at the beginning of the
period and mostly remained so over the period. These
countries are Poland, the Czech Republic,
2
Spain,
Italy, and Greece.
3
2 Intermediate mortality (rates between 75 and 100 per
thousand): Austria, Belgium, France, Germany, and
Denmark
3 Low mortality (rates systematically below 75 per
thousand): Sweden, Switzerland, and the Netherlands.
Accordingly, this group represents the lowest level of
potential survival selection at a contextual level
Analysis
First, a descriptive analysis displays the percentage of in-
terviewees that experienced the event of poor health across
the range of ages studied (30–64). In this analysis, the
countries are sorted according to the mortality levels
defined above.
Second, survival analysis was utilized to measure the
risk of suffering from a distinct episode of poor health
(poor health hereafter) as a function of the partnership
status and controlling for the above-mentioned variables.
This analysis was performed through discrete-time hazard
models because the variables involved in the analysis
were interval censored (i.e., the exact time at which
every event occurred within 1 year was unknown).
Control variables were entered into the models as (1)
time-varying covariates (partnership status, working sta-
tus, and age) and (2) time-constant covariates (sex,
childhood health, age at the time of the interview, and
education).
Two different model specifications are presented and
discussed. First, a pooled model was run to capture the net
differences in the hazard of poor health among countries as
well as to test the hypothesis of the attenuation or reversion
of a first-union advantage at mature and older ages. Sec-
ond, country-specific models explored the existence of
patterns of partnership-related determinants of poor health
hazard across countries (for instance, whether the direction
and the significance of first-union effects on the hazard of
poor health differ between countries).
The coefficients displayed report the relative difference
with respect to the reference group, whereby positive val-
ues indicate a higher hazard than the reference category
and vice versa. Only statistically significant coefficients
(significance \0.05) are commented and discussed in
detail.
Results
Table 1displays that women exhibit a higher prevalence of
experiencing the event of poor health during the 30–64 age
range, particularly in Switzerland, Sweden, and Spain
(11.6, 9.6, and 6.9 % points of difference, respectively; the
2
The series depicted here are based on mortality data from the
former Czechoslovakia.
3
We included this country in the high-mortality group based on its
values from 1931 and onwards because Greek civil registration started
relatively late in 1925, and it was not fully enforced until 1931.
Eur J Ageing
123
total difference in the whole sample is 3.6). There are three
exceptions to this pattern: Austria, Germany, and the
Netherlands.
In addition, younger groups at the time of the interview
(ages 50–59 and 60–69) display a higher prevalence, which
for the most part peaks at ages 60–69, whereas it bottoms
out among individuals aged 80 and over. Likewise, the
prevalence of poor health is invariably higher among
individuals that reported poor or unstable health during
their childhoods. Finally, by contrast, education does not
make any significant difference in most of the countries
analyzed, once other variables are controlled for.
Table 2displays the hazard coefficients (of poor health)
whereby country-specific differences are controlled for in a
pooled model. In this specification, the Netherlands was
taken as the reference country because it exhibited the
lowest infant mortality rate during the 1927–1938 period.
4
Eight of the twelve countries show a higher (and sta-
tistically significant) hazard of poor health compared to the
Netherlands. All four countries with lower hazards (the
Czech Republic, Greece, Italy, and Spain) belong to the
high infant mortality group, whereas only one of the five
countries so categorized (Poland) shows a relatively higher
hazard of poor health with respect to the Netherlands.
Looking at partnership status categories, when country-
specific differences were controlled for, none showed a
lower hazard of poor health than singleness. Indeed, two of
them (second union and separation-divorce) displayed
significantly higher hazards.
Poor or unstable health at pre-adult ages clearly
increases the risk of suffering from poor health during
adulthood. In addition, most of the socio-demographic
covariates included in the analysis displayed significant
effects. The hazard of poor health increases significantly
with age, although the assumption of linearity is refuted by
the significant coefficient of the age squared, whereas the
age at the time of the interview displays the opposite (and
expected) effect, in that being interviewed at older ages
likely reduces the probability of prioritizing a single dis-
tinct event of poor health within the range of ages 30–64.
Those who did not work between ages 30–64 show a higher
risk of experiencing poor health. Education does not dis-
play any significant effect when country-specific differ-
ences were controlled for, and no significant differences
between the hazards experienced by men and women are
found.
The results from country-specific models (Table 3)
support the most substantial part of those provided in the
pooled model: there is no evidence of health advantages
associated with partnership (first unions) throughout
adulthood in these European countries among the individ-
uals who reached the age of 50. In reality, Spain and France
display significant differences (lower hazards) in favor of
single individuals. Regarding other partnership situations,
living in a second or higher rank union as well as a sepa-
ration or divorce show a similar pattern across countries:
positive coefficients that are significant in few cases
4
The interaction between country and partnership situations was
tested without obtaining significant results.
Table 1 Prevalence of a poor health event within the range of ages 30–64 by sex, age at the time of the interview, childhood health, education,
and country
Sex Age at the interview Childhood health Education Total
Men
(%)
Women
(%)
50–59
(%)
60–69
(%)
70–79
(%)
80 and
over (%)
Good
(%)
Poor or
variable (%)
First
level (%)
Second
level (%)
Third
level (%)
Czech
Republic
29.8 31.5 32.3 33.7 26.1 19.1 29.9 44.1 30.9 30.8 30.7 30.8
Greece 17.0 20.2 14.5 22.4 22.2 14.7 18.7 42.9 22.6 14.4 17.4 18.8
Italy 29.5 31.3 30.6 32.7 28.9 21.8 29.6 45.0 30.4 30.4 31.3 30.5
Poland 38.3 44.2 39.0 50.7 35.2 26.8 40.6 55.0 41.5 42.2 40.5 41.7
Spain 32.3 39.2 34.9 43.3 33.2 23.1 34.3 53.0 38.0 34.7 27.4 36.1
Austria 43.9 40.7 43.7 45.7 37.3 33.3 41.5 46.0 44.4 40.0 44.8 42.0
Belgium 34.2 38.2 38.7 41.1 33.3 21.5 35.1 52.5 33.5 36.8 38.1 36.4
Denmark 35.4 40.1 40.3 42.3 34.0 19.5 36.9 52.4 36.4 37.8 38.7 37.9
France 41.2 43.9 46.6 43.7 40.7 31.5 41.6 53.7 41.6 44.8 40.8 42.7
Germany 47.2 46.8 47.8 49.9 42.7 39.6 45.1 61.2 36.4 47.6 45.9 47.0
Netherlands 32.9 32.0 38.6 34.6 23.3 17.4 31.7 38.9 34.7 31.0 35.0 32.4
Sweden 35.6 45.2 45.6 44.4 36.0 27.8 39.4 61.4 40.0 42.7 39.9 40.9
Switzerland 31.8 43.4 39.9 41.6 33.2 33.0 36.4 57.0 37.1 38.5 38.5 38.3
Total 33.5 37.1 36.3 39.3 32.2 24.3 34.2 51.7 34.2 35.7 36.6 35.5
Eur J Ageing
123
(France and the Netherlands for second unions and Spain,
France, Germany, and Sweden in the case of separation or
divorce). Widowhood displays the most heterogeneous
effect across countries, with significant results only in
Spain.
Age, age squared, age at the time of the interview,
childhood health, and working history show homogenous
effects within almost all of the countries analyzed, and
these effects are in accordance with those described in the
pooled model. Finally, this model specification helps
understand the tenuous and erratic effect of sex and edu-
cational level uncovered by the pooled model. Sex is sig-
nificant in five countries (Greece, Germany, Netherlands,
Sweden, and Switzerland), but while women exhibit a
higher hazard of poor health in Greece, Sweden, and
Switzerland, the opposite occurs in Germany and the
Netherlands. Education only shows an effect (lower hazard
for secondary education) in Greece and the Netherlands.
Discussion
This study analyzed the hazard of experiencing poor health
(self-reported) over adulthood as a function of changes in
partnership history and a number of individual socio-
demographic characteristics in thirteen European countries.
The countries represent different paces and pathways into
the SDT, and they also embody different contexts of living
conditions among the cohorts analyzed (population aged
50?and born between 1907 and 1958). The latter might
imply different levels of potential health- and mortality-
driven selection effects.
Our results demonstrate that in this segment of the
European population, living in a first union throughout
adulthood (i.e., over ages 30–64) is not associated with a
meaningful advantage compared to those who remained
single, at least when health is measured by a comprehen-
sive indicator, such as the one utilized here. This result
supplements and enhances previous partial evidence from
case studies based on either cross-sectional or longitudinal
analyses, which made use of different health indicators. For
instance, in the urban Spanish region of Madrid, Regidor
et al. (2001) found higher survivorship rates among singles
(not cohabitating) aged 65?with respect to those living
with a partner. Goldman et al. (1995) reached similar
conclusions about mortality and disability from a short-
term longitudinal study (1984–1990) conducted with a
sample of Americans aged 70 and over. Bardage et al.
(2005) also found no difference in terms of self-rated
health among married and not married individuals aged
65–89 in study comparing Sweden, the Netherlands, and
Spain. A number of these studies hypothesized that the
absence of the (expected) protective effect of partnership
on health does not necessarily reflect the net effect of
partnership biography on health but rather some degree of
selection among individuals that have reached older adult
ages. This selection may work on two levels: individually
and contextually.
On the one hand, single individuals suffer from a higher
mortality risk (Waite 1995; Martikainen et al. 2005; Val-
konen et al. 2004; Joutsenniemi 2007), and as a conse-
quence, those who survive until mature and older ages may
Table 2 Coefficients of poor health hazards between ages 30 and 64
Coefficient significance
Country (Ref: Netherlands)
Czech Rep. 0.063
Greece -0.547***
Italy -0.092
Poland 0.329***
Spain 0.080
Austria 0.290***
Belgium 0.187**
Denmark 0.290***
France 0.324***
Germany 0.479***
Sweden 0.398***
Switzerland 0.252***
Partnership biography (Ref: Single)
First Union 0.119
Second Union 0.318***
Separation or divorce 0.387***
Widowhood 0.169
Childhood health (Ref: Good)
Poor or variable 0.405***
Working status (Ref: No working)
Working -0.479***
Age at the survey (Ref: 50–59)
60–69 -0.358***
70–79 -0.868***
80 and over -1.472***
Educational Level (Ref: First Level)
Second Level -0.049
Third Level 0.010
Age 0.194***
Age Squared -0.001***
Sex (Ref: Men)
Women -0.015
Constant -9.698***
Pseudo R-Square 0.045
N23535
Pooled countries specification
\0.001***; \0.01**; \0.05*; \0.1
Eur J Ageing
123
Table 3 Coefficients of country-specific poor health hazards between ages 30 and 64
Czech Rep. Greece Italy Poland Spain Austria Belgium Denmark France Germany Netherlands Sweden Switzerland
Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign. Coef. Sign.
Partnership biography (Ref: Single)
First Union -0.089 0.205 0.189 0.051 0.597** 0.221 -0.242 -0.206 0.413* 0.192 0.023 0.176 0.034
Second or higher
Union
0.292 0.433 0.465 0.159 0.740
0.654
-0.086 -0.047 0.592** 0.443
0.590** 0.263 0.033
Separation or divorce 0.094 0.315 0.696
-0.158 0.806* 0.559 -0.029 0.058 0.690** 0.617* 0.160 0.588* 0.234
Widowhood -0.074 0.056 0.394 0.172 0.931** 0.377 -0.419 0.121 0.404 0.316 0.435 -0.335 -0.305
Childhood health (Ref: Good)
Poor or variable 0.483** 0.815 0.319* 0.357* 0.445** -0.074 0.499*** 0.533** 0.322** 0.396** 0.230
0.532** 0.622***
Working status (Ref: No working)
Working -1.242*** 0.004 -0.303** -0.987*** -0.681*** -0.271 -0.553*** -0.824*** -0.350*** -0.481*** -0.399** -0.648*** -0.048
Age at the survey (Ref: 50–59)
60–69 -0.423*** -0.062 -0.339** 0.017 -0.409** -0.284 -0.417*** -0.415*** -0.538*** -0.431*** -0.505*** -0.393*** -0.271
70–79 -0.928*** -0.377** -0.718*** -0.673*** -1.169*** -0.839*** -0.845*** -0.992*** -1.038*** -0.881*** -1.117*** -0.833*** -0.864***
80 and over -1.429*** -0.991*** -1.224*** -1.148*** -1.728*** -1.547*** -1.589*** -1.777*** -1.635*** -1.317*** -1.835*** -1.672*** -0.926***
Educational Level (Ref: First Level)
Second Level 0.052 -0.422** 0.041 0.131 -0.133 -0.147 0.064 0.083 0.039 0.238 -0.296* 0.005 0.030
Third Level 0.096 -0.106 0.176 0.126 -0.340
0.051 0.190
0.134 -0.104 0.268 0.184 -0.047 0.143
Age 0.553*** 0.269*** 0.087* 0.419*** 0.161** 0.156* 0.140*** 0.243*** 0.144** 0.097* 0.199** 0.248*** 0.018
Age Squared -0.005*** -0.002** -0.001 -0.004*** -0.001
-0.001 -0.001* -0.002*** -0.001* -0.001 -0.001* -0.002*** 0.000
Sex (Ref: Men)
Women -0.121 0.253* -0.093 -0.025 -0.223 -0.155 -0.038 0.049 0.072 -0.196* -0.219* 0.270** 0.469***
Constant -16.828*** -13.546*** -7.657*** -14.125*** -9.430*** -8.148*** -7.582*** -10.044*** -8.471*** -7.152*** -8.797*** -10.882*** -5.681***
Pseudo R-Square 0.061 0.050 0.028 0.065 0.065 0.032 0.036 0.052 0.043 0.040 0.039 0.054 0.026
N1678 2645 2319 1601 1777 708 2532 1913 2037 1652 1911 1684 1078
\0.001***; \0.01**; \0.05*; \0.1
Eur J Ageing
123
represent a more select segment of the population. This
point was supported through independent hazard models
for each age group (not shown; available upon request) in
which the higher the age group (at the time of the inter-
view), the lower the risk of poor health of single individ-
uals in comparison with those who were in a first union.
Explanations other than the survival selection effect could
be proposed, but they are not very plausible in our opinion
(e.g., if there are mechanisms whereby married individuals
report the distinct period of poor health early more often
than singles). In addition, some authors have shown that
health influences the probability of individuals to enter into
a union (Joung et al. 1998; Brockmann and Klein 2004).
The retrospective data from SHARELIFE are insufficient
to fully consider reverse causality between partnership
status and health even though the interaction between
childhood health and partnership status was tested in this
analysis without having obtained significant results.
On the other hand, our results also point to some degree
of mortality-related selection at the country level because
four of the five countries categorized as high-mortality
countries during the first half of the twentieth century
(Greece, Italy, the Czech Republic, and Spain) show no
significant effect or even lower hazards of poor health with
respect to the low-mortality reference (the Netherlands),
once individual-level variables are controlled for. How-
ever, we acknowledge that this is an intuitive interpreta-
tion. This hypothesis is not supported by the hazard
coefficients based on specific illnesses reported in
SHARELIFE (item HS054; not shown and available from
authors upon request).
All types of data, whether cross-sectional, longitudinal,
or retrospective, the latter being the case of this study, have
proven to be influenced by survival selection when mature
and older ages are analyzed. As a consequence, the true
effect of partnership status on health among this specific
subpopulation cannot be precisely measured and the likely
survival selection should be of concern in all cases when
the relationship between any socio-demographic factor and
health is addressed. Only longitudinal data associated with
long-term follow-up can adequately measure the actual
magnitude of that selection effect. Although we applied a
survival analysis throughout adulthood (ages 30–64), in
this study, the selection of individuals with a better health
profile cannot be avoided, creating a potential effect on the
results. This effect is likely to be larger among those seg-
ments of the population that are, in principle, more exposed
to health-related disadvantages and for longer periods,
which seems to be the case of single individuals. By con-
trast, other partnership situations such as divorce or wid-
owhood are less likely to be affected by the survival
selection, or its effect is less intense due to a later start or
the temporary nature associated with those situations.
However, the influence of these situations on health found
in results must be taken with some caution due to the few
individuals included in these categories (i.e., separation or
divorce and second and subsequent unions).
Separation or divorce displays a negative effect on
health over adulthood, and the same is observed among
second or higher ranks of unions, thus confirming findings
from previous research (Joung et al. 1998; Hughes and
Waite 2009). The end of a relationship is stressful enough
in itself to have negative consequences on an individual’s
health. In fact, poor health status has been shown to be one
of the contributing factors for separation (Joung et al.
1998). In addition, it may imply a worsening of economic
status (separation or divorce usually reduces the purchasing
power of both partners; Andress et al. 2006), which toge-
ther with the expected consequences of aging may derive a
disadvantaged position within the marriage market before
an occasional new union. That is, these individuals become
less attractive and less competitive as a function of the
factors previously described.
Widowhood is usually associated with increasing
socioeconomic-related vulnerability, but in the current
study, only Spain shows a significant health disadvantage
associated with widowhood. In our opinion, several factors
might contribute to buffering the potential effect of wid-
owhood on the hazard of poor health: (1) the range of ages
is retrospectively analyzed (30–64 years) that reduces the
probabilities of widowhood, and (2) the set of provisions
from the welfare state that mitigates the negative effect of
this episode. Using SHARELIFE data, Biro
´(2013) pointed
out the complexity of the relationship between widowhood,
economic restrictions, and health with regard to different
welfare policies across European countries (i.e., the char-
acteristics of pension systems, the degree of female par-
ticipation in the labor market, and the development of
private annuity).
Although individuals’ working status shows a significant
effect on the hazard of poor health, it is not possible to
hypothesize about the underlying mechanisms because
poor health itself is a determinant of working status (De
Lange et al. 2005). The control exerted in our models
through the information on childhood health status does not
solve this problem because no direct causal association
between health at childhood and working status during
adulthood can be ascertained through our data. A similar
reasoning applies to the educational level (Ross and Wu
1996), although in this case, no significant effect is
observed in our results.
Finally, it is important to note that no significant dif-
ferences in the risk of poor health are observed between
men and women in eight of the thirteen countries analyzed
once the remaining socio-demographic variables are con-
trolled for. In the five countries, where sex differences are
Eur J Ageing
123
statistically significant, they do not point univocally: in
Greece, Sweden, and Switzerland, there is a lower hazard
of poor health among men, whereas in Germany and the
Netherlands, the opposite is observed. This finding is
interesting to us because it would invite a supplement and
revision to the so-called ‘‘sex health-survival paradox’’
(Oksuzyan et al. 2010) on the basis of retrospective data.
Acknowledgments This study uses data from SHARELIFE release
1, as of November 24, 2010. The collection of SHARE data has been
primarily funded by the European Commission through the fifth
framework programme (project QLK6-CT-2001- 00360 in the the-
matic programme Quality of Life), through the sixth framework
programme (projects SHARE-I3, RII-CT- 2006-062193, COMPARE,
CIT5-CT-2005-028857, and SHARELIFE, CIT4-CT-2006-028812)
and through the seventh framework programme (SHARE-PREP,
211909 and SHARE-LEAP, 227822). Additional funding from the
U.S. National Institute on Aging (U01 AG09740-13S2, P01
AG005842, P01 AG08291, P30 AG12815, Y1-AG-4553-01 and
OGHA 04-064, IAG BSR06-11, R21 AG025169) as well as from
various national sources is gratefully acknowledged.
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