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Original Contribution
Social Participation and Depression in Old Age: A Fixed-Effects Analysis in 10
European Countries
Simone Croezen, Mauricio Avendano, Alex Burdorf, and Frank J. van Lenthe*
*Correspondence to Dr. Frank J. van Lenthe, Department of Public Health, Erasmus MC, University Medical Center, P.O. Box 2040,
3000 CA Rotterdam, the Netherlands (e-mail: f.vanlenthe@erasmusmc.nl).
Initially submitted May 13, 2014; accepted for publication January 16, 2015.
We examined whether changes in different forms of social participation were associated with changes in depres-
sive symptoms in older Europeans. We used lagged individual fixed-effects models based on data from 9,068 per-
sons aged ≥50 years in wave 1 (2004/2005), wave 2 (2006/2007), and wave 4 (2010/2011) of the Survey of Health,
Ageing and Retirement in Europe (SHARE). After we controlled for a wide set of confounders, increased participa-
tion in religious organizations predicted a decline in depressive symptoms (EURO-D Scale; possible range, 0–12)
4 years later (β=−0.190 units, 95% confidence interval: −0.365, −0.016), while participation in political/community
organizations was associated with an increase in depressive symptoms (β= 0.222 units, 95% confidence interval:
0.018, 0.428). There were no significant differences between European regions in these associations. Our findings
suggest that social participation is associated with depressive symptoms, but the direction and strength of the
association depend on the type of social activity. Participation in religious organizations may offer mental health
benefits beyond those offered by other forms of social participation.
aging; depression; Europe; fixed-effects models; social participation
Abbreviations: CI, confidence interval; SHARE, Survey of Health, Ageing and Retirement in Europe.
The recent Global Burden of Disease Study ranked major
depressive disorders as a leading cause of disability (1,2). In
a study comparing 10 countries in Northern, Southern, and
Western Europe, Castro-Costa et al. (3) reported that the prev-
alence of clinically significant depressive symptoms in older
adults ranged from 18% in Denmark to 37% in Spain. De-
spite the high burden of depression in old age, there is limited
understanding of its potential causes and of interventions that
may help in preventing depression among older persons.
Lower social participation and less social interaction in old
age are each associated with higher levels of depressive symp-
toms (4–15). Social interaction provides people with a sense of
belonging and social identity, together with opportunities for
participation in activities and projects (16). With some excep-
tions (17), several studies have found that active participation
in religious or church activities, clubs, and political groups
and volunteering are associated with better mental health and
reduced levels of depressive symptoms (6,8,11,13–15).
However, the causal impact of social participation on depres-
sion has not been well established. Associations may reflect
confounding by unmeasured characteristics or reverse causal-
ity from depression to social participation. One source of con-
founding comes from permanent personal characteristics that
differ between individuals and that may be associated with
both depressive symptoms and social participation, such as
personality traits, socioeconomic status, childhood conditions,
or intellectual ability (18). For example, persons with certain
psychological or personality traits may be more likely to en-
gage in social participation and may also exhibit lower levels
of depression, which could result in a spurious association be-
tween social participation and depression.
Fixed-effects models have been advocated as a useful ap-
proach for controlling for the impact of these permanent char-
acteristics (19–22). Fixed-effects estimators, sometimes called
“within-person”estimators, control for unobserved individ-
ual heterogeneity that may be correlated with the explanatory
168 Am J Epidemiol. 2015;182(2):168–176
American Journal of Epidemiology
© The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of
Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Vol. 182, No. 2
DOI: 10.1093/aje/kwv015
Advance Access publication:
May 29, 2015
variable. They exploit the longitudinal nature of the data by as-
sessing the association between changes in the explanatory
variable and changes in the outcome variable within individu-
als, thus controlling for permanent characteristics that vary
across individuals. This is in contrast to the more commonly
applied random-effects or “between-person”estimators, which
combine variation between individuals as well as within individ-
uals for estimation. While confounding by unmeasured time-
varying characteristics is also a potential concern in fixed-effects
models, they can provide additional insights into the potential
causal association between social participation and depres-
sion by controlling for individual heterogeneity.
Earlier studies linking social participation to depressive
symptoms focused primarily on single populations or coun-
tries (5,6,13,23–25). Levels of both depressive symptoms
and social participation vary considerably across countries,
possibly due to cross-national variations in the availability
of state-provided support and services, family and social
structures, or policies that promote or discourage social par-
ticipation and mental well-being (3,26,27). A potential hy-
pothesis is that the social significance of different forms of
social participation is context-dependent, such that the men-
tal health benefits of social participation vary across countries
or regions. For example, in Southern European countries with
stronger family networks, voluntary work may be less rele-
vant to health than in Northern European countries, where
family support roles have been replaced by formal care and
the social benefits of voluntary work may be larger (28).
Building upon earlier research (29), we examined how
changes in different forms of social participation predict
changes in levels of depressive symptoms in older persons
using fixed-effects models. In addition, we explored whether
the association between various forms of social participation
and depressive symptoms differs across regions of Europe.
METHODS
Study design
Data for this study were drawn from the Survey of Health,
Ageing and Retirement in Europe (SHARE) (30). In SHARE,
information on health, social networks, and economic fac-
tors was collected from adults aged 50 years or older using
computer-assisted personal interviews. During the first wave
of the study (2004/2005), 31,115 participants from 12 coun-
tries were included. The total household response rate was
62%, varying from 38.8% in Switzerland to 81.0% in France.
We included respondents who entered SHARE during wave
1 (2004/2005) and were followed up in wave 2 (2006/2007)
and wave 4 (2010/2011) (n= 10,706). Data from wave 3
(2008/2009) were excluded, because depressive symptoms
were not assessed in wave 3. Ten countries contributed to
all 3 waves of the longitudinal sample: Austria, Belgium,
Denmark, France, Germany, Italy, Spain, Sweden, Switzerland,
and the Netherlands.
Social participation
In each wave of SHARE, respondents were asked whether
they had engaged in the following activities during the last
month: 1) voluntary or charity work; 2) educational or train-
ing courses; 3) sports, social clubs, or other kinds of club
activities; 4) participation in religious organizations; and
5) participation in political or community organizations.
For each activity, an additional question was asked about
the frequency of participation, using 4 response options: “al-
most daily,”“almost every week,”“almost every month,”and
“less often.”In wave 4, the recall period for participation
in social activities was altered to refer to the last 12 months.
To maintain consistency in the recall period, our analysis
focused on changes in social participation between waves 1
and 2 only.
Depressive symptoms
Depressive symptoms were assessed in all 3 waves of
the study and were measured by means of the EURO-D
Scale (31). The EURO-D consists of 12 items: depression,
pessimism, death wishes, guilt, sleep, interest, irritability, ap-
petite, fatigue, concentration, enjoyment, and tearfulness.
Each item is scored 0 (symptom not present) or 1 (symptom
present), and item scores are summed (0–12). Previous stud-
ies have demonstrated the validity of this measure against a
variety of criteria for clinically significant depression, with
an optimal cutoff point of 4 or above (31,32).
Background variables
Educational level was based on the highest educational
degree obtained. National levels were reclassified according
to the 1997 International Standard Classification of Educa-
tion into 3 categories: lower education (classifications 0–2),
medium education (classifications 3–4), and higher educa-
tion (classifications 5–6) (33). Countries were classified
into 3 geographical regions: Northern Europe (Sweden and
Denmark), Southern Europe (Italy and Spain), and Western
Europe (Austria, Belgium, France, Germany, Switzerland,
and the Netherlands). Marital status was defined as 1) married;
2) divorced, separated or unmarried; or 3) widowed. House-
hold size was categorized as 1, 2, 3, or ≥4 persons. Concerning
employment status, respondents were classified as either
1) employed, including self-employment; 2) unemployed, in-
cluding permanently sick or disabled persons and homemak-
ers; or 3) retired. The variable “financial difficulties,”which
measured the extent to which respondents were able to make
ends meet on their income, included 4 response options rang-
ing from “with great difficulty”to “easily.”Self-rated health
was measured using a 5-point scale with 5 response options:
“excellent,”“very good,”“good,”“fair,”and “poor.”Long-
term illness was assessed as a self-reported long-term health
problem, illness, disability, or infirmity. Respondents’levels
of functioning and disability were assessed by means of the
Global Activity Limitation Index, Activities of Daily Living,
and Instrumental Activities of Daily Living (34,35). Scores
for each index of activity limitations were dichotomized on the
basis of whether respondents had limitations in performing 1
or more activities. The presence of a physician-diagnosed
disease was assessed for heart attack, high blood pressure
or hypertension, stroke, diabetes or high blood sugar, and
chronic lung disease.
Social Participation and Depression in Old Age 169
Am J Epidemiol. 2015;182(2):168–176
Statistical analysis
We applied fixed-effects models (19–21)toassesswhether
within-person changes in social participation were associated
with within-person changes in depressive symptoms. Fixed ef-
fects control for potential time-invariant confounders that vary
across individuals, such as sex, family background, preexisting
health, and levels of depression. In essence, fixed-effects mod-
els use each individual as his or her own control, by comparing
an individual’s depression score when exposed to a given level
of social participation with that same individual’sdepression
score when he or she is exposed to a different level of social
participation. Assuming that intraindividual changes in expo-
sure are uncorrelated with changes in other variables, the differ-
ence in depression scores between these 2 periods is an estimate
of the association between social participation and depressive
symptoms for that individual. Averaging these differences across
all persons in the sample yields an estimate of the “average
treatment effect,”which controls for all stable individual char-
acteristics. Although it does not control for time-varying factors
Table 1. Weighted General Characteristics of Selected Respondents (Participants in Waves 1, 2, and 4) Aged 50
Years or Older at Baseline, by Geographical Region, Survey of Health, Ageing and Retirement in Europe, 2004/2005
Total (n= 9,068) Geographical Region, %
No. %
Western
Europe
(n= 5,459)
Northern
Europe
(n= 1,673)
Southern
Europe
(n= 1,936)
Age, years
a
9,068 62.9 (8.8) 62.9 (8.9) 62.7 (9.3) 63.1 (8.7)
Male sex 9,068 44.9 44.1 45.9 45.8
Educational level 8,998
Lower 50.6 32.4 34.8 78.6
Medium 30.8 41.1 34.0 15.6
Higher 18.6 26.4 31.2 5.9
Marital status 9,067
Married 70.1 68.1 64.1 73.9
Divorced, separated, or unmarried 14.4 16.5 21.8 10.4
Widowed 15.5 15.5 14.1 15.7
Household size (no. of persons) 9,068
1 21.5 24.7 30.8 15.7
2 50.9 57.1 57.6 41.0
3 14.8 10.4 6.7 22.1
≥4 12.9 7.8 4.9 21.2
Employment status 9,068
Employed 29.1 32.4 45.3 22.2
Unemployed 22.6 18.5 7.1 30.5
Retired 48.3 49.1 47.6 47.3
Financial difficulties 6,460 41.1 27.0 19.8 63.6
Less than very good self-rated health 9,068 74.0 73.0 45.8 79.1
Long-term illness 9,067 50.9 50.0 52.2 52.0
Activity limitations
GALI 9,068 39.8 40.2 38.9 39.3
ADL 9,066 7.2 7.1 5.8 7.4
IADL 9,066 10.4 9.7 9.5 11.5
Physician-diagnosed disease 9,065
Heart attack 10.3 11.2 9.8 9.2
Hypertension 33.0 32.0 29.1 35.0
Stroke 2.6 2.9 2.9 2.1
Diabetes 9.6 9.1 6.7 10.7
Lung disease 5.5 4.7 3.2 6.9
Abbreviations: ADL, Activities of Daily Living; GALI, Global Activity Limitation Index; IADL, Instrumental Activities of
Daily Living.
a
Expressed as mean (standard deviation).
170 Croezen et al.
Am J Epidemiol. 2015;182(2):168–176
such as employment and marital status, these variables can be
handled conventionally by incorporating them into the regres-
sion model. Fixed-effect models have 2 requirements. First, the
dependent variable must be measured for each individual in a
comparable fashion using a similar metric at 2 or more points
in time. Second, the exposure variable of interest must change
across these 2 occasions for at least a fraction of the sample (36).
Specification of our basic model was as follows:
EURO-Dit ¼μtþβ1social participationit þβ2xit
þ∝iþεit;ð1Þ
where EURO-D
it
indicates EURO-D score for individual iat
time t, social participation
it
is a vector of indicator variables
for social participation, x
it
is a vector of supplementary con-
trol regressors, and ε
it
is the error term. μ
t
accounts for time
effects that are constant across individuals, while ∝
i
controls
for time-invariant individual characteristics.
To minimize the potential impact of reverse causality, we
implemented fixed-effects models that used lagged (by 4
years) social participation and examined whether changes in
social participation indicators between waves 1 and 2 were
associated with changes in depressive symptoms between
waves 2 and 4. In the Web material, we also show results from
contemporaneous models that examined the association
between changes in social participation between waves 1 and
2 and changes in depressive symptoms during the same peri-
od (see Web Table 1 and Web Figure 1, available at http://aje.
oxfordjournals.org/).
In addition to the fixed-effects models, we implemented
a series of random-effects models. We followed standard ap-
proaches and conducted a Hausman specification test (37),
which tests the null hypothesis that estimates from the fixed-
effects model are not different from estimates from the random-
effects model. Our results yielded a significant Hausman test
result (P< 0.0001), which indicated that at conventional levels
of significance, the assumption of no correlation between ex-
planatory variables and individual characteristics was violated
in the random-effects model. We report estimates from random-
effects models in Web Table 2.
To calculate population-descriptive statistics, we used ap-
propriate weights to account for the sampling design, non-
response, and attrition. Weights were calibrated against the
national population by age group and sex, as well as for
mortality between waves. The analytical sample was limited
to respondents with valid weights for the balanced panel
(n= 9,491). Respondents were dropped if information was
missing for depressive symptoms at wave 2 or 4 (n=363)
or for social participation at wave 1 or 2 (n= 132); this re-
sulted in an analytical sample of 9,068 persons.
Table 2. Weighted Prevalence (%) of the Frequency of Social Participation Among Selected Respondents
(Participants in Waves 1 and 2) Aged 50 Years or Older (n= 9,068)
a
, by Geographical Region, Survey of Health,
Ageing and Retirement in Europe, 2004/2005–2006/2007
Type of Activity and Frequency,
times/week
Study Wave and Geographical Region
Wave 1 (2004/2005) Wave 2 (2006/2007)
Western
Europe
Northern
Europe
Southern
Europe
Western
Europe
Northern
Europe
Southern
Europe
Voluntary/charity work
0 81.6 78.0 92.9 80.7 74.5 91.8
<1 6.3 9.3 2.6 6.9 10.0 2.4
≥1 12.2 12.7 4.5 12.4 15.5 5.8
Education/training
0 91.8 85.5 98.5 91.6 83.0 97.4
<1 4.9 9.5 0.7 4.4 8.8 0.6
≥1 3.4 5.1 0.8 4.0 8.2 2.0
Sports/social clubs
0 73.5 67.4 92.5 72.1 62.8 89.9
<1 7.8 6.8 1.9 7.2 4.8 2.0
≥1 18.7 25.7 5.7 20.7 32.4 8.2
Religious organizations
0 89.3 93.7 91.4 88.4 87.8 90.3
<1 4.1 1.8 2.9 4.3 5.9 2.1
≥1 6.6 4.5 5.7 7.3 6.3 7.6
Political/community organizations
0 94.1 94.4 96.9 94.1 94.1 98.2
<1 4.2 3.1 1.6 3.8 3.8 0.9
≥1 1.8 2.5 1.6 2.1 2.1 0.9
a
Sample size varied by 0–3 missing values, according to the type of activity.
Social Participation and Depression in Old Age 171
Am J Epidemiol. 2015;182(2):168–176
We followed a stepwise approach in the construction of
the fixed-effects models, starting with a basic model that con-
trolled for age and time (wave) only. Models additionally
incorporated controls for time-varying marital status, house-
hold size, employment status, financial difficulties, self-rated
health, long-term illness, activity limitations, and self-reports
of major disease diagnoses (heart attack, high blood pressure/
hypertension, stroke, diabetes/high blood sugar, and chronic
lung disease). We did not apply weights in regression models,
because when sampling probabilities vary only on the basis of
explanatory variables, weighting is unnecessary for consis-
tency and potentially harmful for precision (38). Nonetheless,
we report estimates from weighted regression analyses in
Web Table 3. Because of the low efficiency in the fixed-effects
models, estimates from weighted models were very impre-
cise; therefore, we decided to emphasize unweighted results.
We applied robust standard errors to account for nonindepen-
dence clustering at the individual level. All analyses were car-
ried out using Stata statistical software, release 13 (StataCorp
LP, College Station, Texas).
RESULTS
The mean age at baseline was 63 years (Table 1). Fewer than
half of respondents were male (44.9%), and about half had a
lower level of education (50.6%). Educational attainment var-
ied across European regions; the highest share of persons with
lower education lived in Southern Europe (78.6%). Almost
half of the study population was retired (48.3%), and 41.1%
reported having difficulties making ends meet. Over 50% re-
ported having a long-term illness; a physician’sdiagnosisof
hypertension was the condition reported most often (33.0%),
followed by heart attack (10.3%) and diabetes (9.6%).
Levels of social participation varied markedly across regions
(Table 2). Respondents from the Southern European countries
reported the least participation. This difference was most pro-
nounced for participation in sports, social clubs, or other kinds
of club activities (7.5% in Southern Europe, 26.5% in Western
Europe, 32.6% in Northern Europe). Although the prevalence
increased slightly for several measures, social participation
was very similar across the 2 waves for all regions and mea-
sures. Therewas great variation in the prevalence of depressive
symptoms across regions, as well as over time (Figure 1). In
wave 1, 26.0% of the respondents had a depressive symptom
score of ≥4 points, the cutoff indicative of clinical depression
symptomatology, but levels varied from 15.5% in Northern
Europe to 34.6% in Southern Europe. There was a small de-
cline in the prevalence of depressive symptoms between waves
1 and 2, whereas an increase in depressive symptoms was ob-
served between waves 2 and 4. Within types of social partici-
pation, the lowest baseline prevalence of depressive symptoms
was found for participation in political activities (18.0%) and
0
5
10
15
20
25
30
35
40
Western Europe
Prevalence, %
Geographical Region
Northern Europe Southern Europe
Figure 1. Weighted estimates of the prevalence (%) of ≥4 depres-
sive symptoms among respondents aged 50 years or older, by
geographical region, in waves 1 (n= 9,027), 2 (n= 9,068), and 4 (n=
9,068) of the Survey of Health, Ageing and Retirement in Europe,
2004–2011. White columns represent wave 1 (2004/2005), gray col-
umns represent wave 2 (2006/2007), and black columns represent
wave 4 (2010/2011). T-shaped bars, standard errors.
Table 3. Four-Year-Lagged Associations Between Changes in Social Participation and Changes in Depressive
Symptom Score Among Selected Respondents (Participants in Waves 1, 2, and 4) Aged 50 Years or Older, Surveyof
Health, Ageing and Retirement in Europe, 2004/2005–2010/2011
Type of Activity Model 1
a
(n= 9,068) Model 2
b
(n= 7,385)
βRobust 95% CI βRobust 95% CI
Voluntary/charity work 0.085 −0.022, 0.193 0.020 −0.112, 0.152
Education/training 0.023 −0.096, 0.141 0.041 −0.101, 0.183
Sports/social clubs 0.097 0.004, 0.190 0.081 −0.036, 0.199
Religious organizations −0.145 −0.281, −0.010 −0.190 −0.365, −0.016
Political/community organizations 0.111 −0.051, 0.273 0.222 0.018, 0.428
Abbreviation: CI, confidence interval.
a
Results were adjusted for social participation (mutually adjusted), age, and time.
b
Results were adjusted for social participation (mutually adjusted), age, time, household size, marital status,
employment status, financial difficulties, self-rated health, long-term illness, activity limitations, and physician-
diagnosed diseases (heart attack, high blood pressure or hypertension, stroke, diabetes or high blood sugar, and
chronic lung disease).
172 Croezen et al.
Am J Epidemiol. 2015;182(2):168–176
the highest for participation in religious activities (23.2%)
(data not shown). For all types of activities, the prevalence of
depressive symptoms was highest among persons who were
not active.
In models that assessed the contemporaneous association
between changes in social participation and depressive symp-
toms between waves 1 and 2 and controlled for confounders,
participation in sports, social clubs, or other kinds of clubs
and participation in political or community organizations pre-
dicted a decline in depressive symptoms (for sports/social
clubs, β=−0.102, 95% confidence interval (CI): −0.186,
−0.019; for political/community organizations, β=−0.170,
95% CI: −0.319, −0.022) (Web Table 1 and Web Figure 1).
However, many of these associations did not hold in lagged
fixed-effects models. As shown in Table 3, only increased
participation in religious organizations was associated with a
decline in depressive symptoms 4 years later, even after con-
trolling for all confounders (β=−0.190, 95% CI: −0.365,
−0.016). In addition, increased participation in political/
community organizations was associated with higher de-
pressive symptom scores (β= 0.222, 95% CI: 0.018, 0.428).
To explore whether there were differences in the associa-
tion between social participation and depressive symptoms
across Europe, we carried out stratified analysis by geograph-
ical region (Figure 2). There was no evidence of significant or
systematic differences between European regions in these
associations, although this was partly due to wide confidence
intervals in each region.
DISCUSSION
Our findings suggest that social participation is associated
with levels of depressive symptoms; however, the strength and
direction of the association depend on the type of activity.
Participation in religious activities was the only form of so-
cial engagement associated with a decline in depressive
symptoms 4 years later. Participation in a political or commu-
nity organization was instead associated with an increase in
depressive symptoms. Thus, the mechanisms linking social
participation to mental health in old age may differ for differ-
ent activities.
Our results offer mixed support for the previously observed
association between social participation and depressive symp-
toms (5,6,11,13–15,23–25,39). We did not find significant
associations for participation in voluntary or charity work or
participation in educational or training courses. This finding
seems to be in contrast with results from previous research
(40,41). In models that adjusted only for age and time, we did
find contemporaneous associations between volunteering and
depressive symptoms. However, these associations were not
robust to control for time-varying confounding, and these ac-
tivities did not predict changes in depressive symptoms 4
years later. Similarly, changes in participation in sports, social
clubs, and other club activities were associated with a contem-
poraneous decline in depressive symptoms but did not predict
changes in depressive symptoms 4 years later. A possible
explanation is that short-term benefits arising from these
forms of social participation diminish over time or that they
reflect the impact of depression on the likelihood of partic-
ipating in social activities.
Earlier research found that religiously active persons have
better mental health than the religiously inactive (24,42).
Our findings suggest that this association might reflect a causal
association. Participation in religious organizations may protect
mental health through several pathways, including influencing
lifestyle, enhancing social support networks, and offering a
mechanism for coping with stress (24,42). For example, reli-
gion has been shown to serve as a coping mechanism during a
–0.5 0.0 0.5 1.0 1.5
Voluntary or charity work
Education or training
Sports or social clubs
Religious organizations
Political or community organizations
Type of Social Participation
–1.0
b (95% CI)
Figure 2. Four-year-lagged associations (βcoefficients) between changes in social participation and changes in depressive symptom scores
among selected respondents (participants in waves 1, 2, and 4) aged 50 years or older (n= 7,385), by geographical region, Survey of Health, Ageing
and Retirement in Europe, 2004–2011. White columns represent Northern Europe, gray columns represent Western Europe, and black columns
represent Southern Europe. T-shaped bars, robust 95% confidence intervals (CIs).
Social Participation and Depression in Old Age 173
Am J Epidemiol. 2015;182(2):168–176
period of illness in late life (43,44).Throughparticipationin
religious activities, people may also become more attached to
their communities, which prevents social isolation, a predictor
of old-age depression. Spirituality has also been proposed as an
important promoter of mental health, but this construct is not
well defined, and its relationship with depression is not well un-
derstood (24). By contrast, people may not accrue the same so-
cial support, lifestyle, and coping benefits from participating in
sports, social clubs, or other kinds of clubs, which may explain
why these forms of social participation did not predict levels
of depressive symptoms 4 years later. Although we expected
stronger associations between social participation and depres-
sive symptoms in Northern and Western European countries,
the lack of regional differences in the associations across Eu-
rope supports the findings of Di Gessa and Grundy (17).
We found that participation in a political or community
organization was associated with an increase in depressive
symptoms 4 years later. Insights from the effort-reward bal-
ance theory may provide a partial explanation. Participation
in political or community organizations could be beneficial
for health when reciprocity is expected (45), which may partly
explain the positive association in contemporaneous models.
Respondents may experience a higher sense of reward when
starting participation in a political or community organiza-
tion. In the long run, however, the balance may shift towards
higher effort and lower reward, which may trigger depressive
symptoms. Another potential explanation for contemporane-
ous associations is reverse causality—that is, that depressed
persons may be less likely to participate in political or com-
munity organizations. Lagged models are less susceptible to
reverse causality, as they relate current changes in social par-
ticipation to subsequent changes in depressive symptoms. In
our study, however, there was relatively little change in partic-
ipation in a political or community organization, so fixed ef-
fects may not be the best method for assessing the impact of
this particular form of social participation.
Some limitations of our study should be considered. Changes
in social participation may be correlated with changes in other
variables associated with depressive symptoms. For example,
older persons may increase or initiate participation in religious
activities after the birth of a grandchild, the death of a child or
sibling, or the onset of illness. The influence of several of these
variables on our estimates is difficult to anticipate, however, as
several of them might increase rather than decrease levels
of depression, leading to underestimation of the association
between participation in religious activities and depression.
Another concern is reverse causation. Although we found
that participation in a religious organization was associated
with decreased depression scores overa 4-year period, we can-
not completely rule out the possibility that this association may
have been due to the impact of depression on social participa-
tion. However, sensitivity analysis that excluded respondents
who had 4 or more depressive symptoms at baseline confirmed
our finding for participation in religious organizations, dimin-
ishing concerns about reverse causation (β=−0.306; 95% CI:
−0.481, −0.131). Next, as with other longitudinal studies,
SHARE suffered from attrition resulting from both mortality
and nonresponse. This may have led to sample selection bias,
potentially compromising internal validity (30). Earlier sub-
studies from the SHARE project showed that although health
and living arrangements at baseline predicted initial survey
participation and panel retention, there were no systematic
differences in response and attrition rates according to key
characteristics such as sex, age, and employment status (46,
47). While there is no fully satisfactory way to address this,
we incorporated these and other time-varying factors into our
models and focused our interpretation on these models. Fi-
nally, a limitation of fixed-effects models is that estimation
is based only on the small fraction of people who change
their exposure during the follow-up period. For example, be-
tween waves 1 and 2, only 6.8% of the sample changed their
participation in political/community organizations, which re-
sulted in large standard errors. Changes were more common
for participation in voluntary/charity work (15.0%), educa-
tion/training (11.9%), sports/social clubs (20.1%), and reli-
gious activities (10.6%).
In conclusion, our findings suggest that increased social
participation is associated with depressive symptoms. How-
ever, the strength and sometimes direction of the association
varies by social activity. We found that increased participa-
tion in religious activities was associated with subsequent de-
clines in depressive symptoms, suggesting the possibility of a
causal association. Our results highlight the importance of
distinguishing between different types of social participation
to understand how social engagement influences mental
health and well-being. Further research is required to identify
the specific mechanisms that explain the association between
participation in religious activities and depressive symptoms.
If the association is proven to be causal, however, our results
suggest that policies encouraging orenabling older persons to
maintain their affiliations with religious communities (e.g.,
by facilitating their attendance at religious events via public
transport) may result in reduced levels of depressive symp-
toms among older persons.
ACKNOWLEDGMENTS
Author affiliations: Department of Public Health, Erasmus
MC, University Medical Center, Rotterdam, the Netherlands
(Simone Croezen, Mauricio Avendano, Alex Burdorf, Frank
J. van Lenthe); and LSE Health, LondonSchoolof Economics
and Political Science, London, United Kingdom (Mauricio
Avendano ).
M.A. was supported by a Starting Researcher grant from
the European Research Council (ERC grant 263684), by the
National Institute on Aging, US National Institutes of Health
(awards R01AG040248 and R01AG037398), and by the
McArthur Foundation Research Network on Ageing. Addi-
tional funding was obtained from the National Institute on
Aging (grants U01 AG09740-13S2, P01 AG005842, P01
AG08291, P30 AG12815, R21 AG025169, Y1-AG-4553-
01, IAG BSR06-11, and OGHA 04-064) and the German
Ministry of Education and Research, as well as various na-
tional sources (see www.share-project.org for a full list of
funding institutions).
This study used data from SHARE wave 4, release 1.1.1
(March 28, 2013; Digital Object Identifier (DOI): 10.6103/
SHARE.w4.111) and SHARE waves 1 and 2, release 2.6.0
174 Croezen et al.
Am J Epidemiol. 2015;182(2):168–176
(November 29, 2013; DOIs: 10.6103/SHARE.w1.260
and 10.6103/SHARE.w2.260) or SHARELIFE release 1
(November 24, 2010; DOI: 10.6103/SHARE.w3.100). Data
collection in SHARE has been funded primarily by the Euro-
pean Commission through the Fifth Framework Programme
(project QLK6-CT-2001-00360 in the “Quality of Life”pro-
gram), the Sixth Framework Programme (projects SHARE-I3
(grant RII-CT-2006-062193), COMPARE (grant CIT5-CT-
2005-028857), and SHARELIFE (grant CIT4-CT-2006-
028812)), and the Seventh Framework Programme (projects
SHARE-PREP (grant 211909), SHARE-LEAP (grant
227822), and SHARE M4 (grant 261982)).
Conflict of interest: none declared.
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