Ethical Issues and Dilemmas in Caring for Children and Adolescents
Depressive Symptoms in Children and Adolescents with Chronic Physical Illness: An Updated Meta-Analysis
Martin Pinquart, PHD, and Yuhui Shen, CAND PSYCH
Department of Psychology, Philipps University
All correspondence concerning this article should be addressed to Martin Pinquart, PHD, Department of
Psychology, Philipps University, D-35032 Marburg, Germany. E-mail: pinquart@staff.uni-marburg.de
Received July 8, 2010; revisions received and accepted October 20, 2010
Objective To integrate results of available studies that compared levels of depressive symptoms of children
and adolescents with chronic physical illness to healthy peers or test norms. Methods Random-effects
meta-analysis was computed with 340 studies and 450 subsamples. Results Children and adolescents
with chronic illness have, on average, higher levels of depressive symptoms than their healthy peers (d¼ .19
SD units). Differences are strongest for chronic fatigue syndrome (d¼ .94), fibromyalgia (d¼ .59), cleft lip
and palate (d¼ .54), migraine/tension head ache (d¼ .51), and epilepsy (d¼ .39). Larger effect sizes were
found in studies with higher proportion of girls, with a healthy control group, from developing countries,
published before 1990, and that used parent rating or clinician ratings rather than child
ratings. Conclusions Pediatricians and others working with children with chronic illnesses should screen
children with chronic physical illness for symptoms of psychological distress and make appropriate referrals
for mental health services, when needed.
Key words chronic illness; depression; meta-analysis; psychological functioning; psychological health.
In the United States, the number of children and adoles-
cents with chronic health conditions has increased dramat-
ically in the past four decades (Perrin, Bloom, &
Gortmaker, 2007). Although results from epidemiological
studies differ considerably, an overview of articles found
that, on average, 15% of children and adolescents have a
chronic health condition (van der Lee, Mokkink,
Grootenhuis, Heymans, & Offringa, 2007).
Chronic illness is a risk factor for psychological prob-
lems, such as depressive symptoms (e.g., Bennett, 1994).
For example, the presence of physical symptoms, such as
pain and fatigue, combined with the need for disease man-
agement regimes, are likely to interfere with many aspects
of daily life, such as regular school attendance and main-
taining peer relations, and may cause frustration (e.g.,
Suris, Michaud, & Viner, 2004). Children with chronic
illness may feel different from his peers and experience
peer rejection, which may have detrimental effects on
their self-concept (e.g., Sandstrom & Schanberg, 2004).
In addition, chronic illnesses may foster inappropriate
parental attitudes and behaviors, ranging from overprotec-
tion to rejection, which may impair psychological
well-being (e.g., Holmbeck et al., 2002). In some cases,
poor prognosis may cause feelings of helplessness and
hopelessness. Finally, side effects of treatments may
cause psychological distress (e.g., Miller et al., 2008).
A meta-analysis by Bennett (1994) on 60 statistical
effects from 46 studies found that children and adolescents
with chronic medical problems have elevated levels of de-
pressive symptoms, but differences with test norms or
healthy control groups were small (mean d¼ .27 SD
units). Because (a) the number of studies has increased
considerably since this meta-analysis, (b) the effects of
chronic illness on depressive symptoms may have changed
over time, and (c) the previous meta-analysis could not test
for moderating effects of many study characteristics, the
Journal of Pediatric Psychology 36(4) pp. 375–384, 2011 doi:10.1093/jpepsy/jsq104
Advance Access publication November 18, 2010 Journal of Pediatric Psychology vol. 36 no. 4 � The Author 2010. Published by Oxford University Press on behalf of the Society of Pediatric Psychology.
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goal of the present study was to provide an updated
meta-analysis on the association between chronic physical
illness and depressive symptoms in children and
adolescents.
Depressive symptoms have to be distinguished from a
depressive disorder, such as major depression. Rating
scales assess depressive symptoms as a continuous vari-
able, and scores above defined cutoffs on valid scales
would imply a depressive disorder. However, depression
diagnosis is based on a clinical interview, and a number
of symptoms have to be present during a specified time
period (Emslie & Mayers, 1999). Because about 90% of the
available studies with chronically ill children used depres-
sion rating scales rather than clinical diagnoses, the present
meta-analysis focuses on depressive symptoms.
Research Questions
In the first research question we ask whether children and
adolescents with chronic physical illnesses have elevated
levels of depressive symptoms, and whether this would
differ between illnesses. Some authors have argued that
the nature of the child’s disorder is not important in de-
termining its psychological consequences, because chil-
dren with chronic physical disorders face common life
experiences and problems based on generic dimensions
of their conditions, rather than on idiosyncratic character-
istics of any specific disease entity (e.g., Stein & Jessop,
1982). Other authors have suggested that certain illness
characteristics or parameters may be more related to de-
pressive symptoms, such as neurologically related illnesses
(e.g., epilepsy; Plioplys, 2003), characteristics of illnesses
that have social implications (e.g., cosmetic effects, e.g.,
cleft lip; De Sousa, Devare, & Ghanshani, 2009), and
chronic pain (Eccleston, Crombez, Scotford, Clinch, &
Connell, 2004). Bennett (1994) reported moderate effect
sizes for asthma (d¼ .54) and sickle cell disease (d¼ .48),
a small effect size for diabetes (d¼ .22), and no elevated
levels of depressive symptoms in young people with cancer
(d¼ .00) and cystic fibrosis (d¼–.04). However, due to
the small number of studies per type of illness
(N¼4–13), effect sizes were not tested to see if they dif-
fered significantly from zero or if illnesses differed from one
another.
In the second research question we analyze whether
the effect sizes would differ by other study characteristics.
Age
On the one hand, depressive symptoms are more common
in adolescence than in childhood, and adolescents may be
confronted with more illness-related stressors than chil-
dren (e.g., when chronic illnesses hinder the development
of peer groups and intimate relationships; Suris et al.,
2004). On the other hand, adolescents might also have
better coping abilities (e.g., because of higher cognitive
abilities; Skinner & Zimmer-Gembeck, 2007). Thus, aver-
age age differences in the association between chronic ill-
ness and depressive symptoms are probably small. Bennett
(1994) reported that age was generally unrelated to depres-
sive symptoms, but he did not provide results from a
statistical test of age differences.
Gender
On average, female adolescents are more likely than males
to react to stressors with depressive symptoms (Piccinelli
& Wilkinson, 2000), which could lead to stronger effects
of chronic illness on depressive symptoms. Bennett (1994)
reported that the results of individual studies were incon-
sistent but he did not formally test for gender differences.
Race/Ethnicity
Because it is less clear whether race/ethnicity would mod-
erate the size of between-group differences, we did not
state a hypothesis.
Country
Because young people from industrialized, developed
countries may have better access to health care than their
peers from developing countries, we expected finding
lower between-group differences in depressive symptoms
in developed countries.
Year of Publication
Progress in the treatment of many diseases (e.g., Bleyer,
2002) and the development of services for young people
with chronic illness may lead to lower between-group
differences in more recent studies.
Rater and Assessment Methods
Bennett (1994) found higher between-group differences in
parent-rated depressive symptoms (d¼0.58) than in
self-rated depressive symptoms (d¼0.02), which may
either indicate that young patients tend to underreport
their psychological symptoms or that parents underesti-
mate their children’s ability to adapt to their illness.
Differences between raters were also expected to lead to
higher levels of depressive symptoms in young people with
chronic illnesses in studies that used parent ratings as a
measure of depressive symptoms (e.g., the Affective
Problems scale of the Child Behavior Checklist (CBCL);
376 Pinquart and Shen
Achenbach, Dumenci, & Rescorla, 2003) than in studies
that used self-reports of the child.
Duration of Illness
A longer duration of the disease gives more time for
psychological adaptation, but may also lead to an accumu-
lation of negative illness-related consequences, such as the
effect of repeated school absence on grades. Thus, we did
not state a specific hypothesis.
Study Quality
Associations of chronic illness with depressive symptoms
may be stronger in clinical convenience samples than in
representative community-based samples, because clinical
samples may overrepresent highly distressed young people
seeking treatment for their chronic disease. Similarly, the
size of between-group differences in depressive symptoms
may vary between studies that used groups matched on
sociodemographic variables and studies that did not con-
trol for these between-group differences, because the lack
of control for demographic variables may cause unsyste-
matic bias rather than a general overestimation or under-
estimation of between-group differences in depressive
symptoms.
Target of Comparison
Finally, between-group differences may be larger in studies
that compared children with chronic illnesses to healthy
peers than in studies that compared depressive symptoms
of chronically ill children to test norms, because the norm
population probably includes some children with chronic
illnesses. In fact, Bennett (1994) found such a difference
(d¼0.67 vs. d¼0.02).
Methods Sample
Studies were identified from the literature through elec-
tronic databases [PSYCINFO, MEDLINE, Google Scholar,
PSNYDEX (an electronic data base of psychological litera-
ture from German-speaking countries)—search terms:
(chronic illness or disability or aids or arthritis or asthma
or cancer or cleft or chronic fatigue syndrome or cystic
fibrosis or diabetes or fibromyalgia or hemophilia or hear-
ing impairment or HIV or epilepsy or inflammatory bowel
disease or migraine or rheumatism or sickle cell or spina
bifida or visual impairment) and (children or adolescents
or adolescence) and (depression or depressive or mental
health or psychological health)], and cross-referencing.
Criteria for inclusion of studies in the present
meta-analysis were:
(a) the studies have been published before September,
2010,
(b) they compared the levels of depressive symptoms
or the frequency of depression diagnoses between
children and adolescents with chronic physical ill-
ness and their healthy peers or test norms, or they
provided sufficient information for a comparison
with established normative data (e.g., by reporting
standardized T-scores),
(c) mean age of participants �18 years, and
(d) standardized between-group differences in depres-
sive symptoms were reported or could be
computed.
Documentation of physician diagnosis within each study
was not a requirement, because of the need to include
broad-based survey studies for which medical documenta-
tion might not be available. However, studies were excluded
if they focused on young people with chronic illnesses that
have been referred to psychological services due to depres-
sive symptoms, or if sufficient information for computing
effect sizes was not reported. In order to include studies
from different regions around the world, we also did not
limit the included studies to those written in English.
Available unpublished studies were also included.
Approximately 25% of the total number of studies
surveyed were eliminated, mainly because they did not
assess depressive symptoms or depression diagnosis
(14%), provided insufficient information about the effect
sizes (4%), did not exclusively focus on children and ado-
lescents with chronic physical illnesses (3%), had an aver-
age age of participants >18 years (1%), duplicated results
of previously published studies (1%), or were not available
via interlibrary loan (1%). After the exclusion of such stud-
ies, we were able to include 340 studies in the
meta-analysis that provided results for 450 subsamples.
The studies included are listed in the Appendix S1 (see
the Supplementary Data).
We entered the number of patients and control group
members, mean age, percentage of girls and of members of
ethnic minorities, the country of data collection, year of
publication, type of illness, duration of illness, the sam-
pling procedure (1¼probability samples, 0¼convenience
samples), the use of a control group (0¼yes, 1¼compar-
ison with test norms), equivalence of patients and control
group (1¼yes, 2¼not tested, 3¼no), the rater of depres-
sive symptoms (1¼child, 2¼parent, 3¼ teacher, 4¼cli-
nician), the measurement of the variables, and the
standardized size of between-group differences in
Depression and Chronic Illness 377
depressive symptoms. If between-group differences were
provided for several subgroups within the same publication
(e.g., for different illnesses), we entered them separately in
our analysis instead of entering the global association.
If data from more than one rater were collected, we entered
the effect sizes separately because we were interested in
whether the effect size would vary by the source of infor-
mation. However, in order to avoid a disproportional
weight of these studies, we adjusted the weights of the
individual effect sizes so that the sum of the weights of
the effect sizes was equal to the weight of the study if
only one effect size had been reported (Lipsey & Wilson,
2001). Based on one third of the coded studies, a mean
inter-rater reliability of 93% (range 86–100%) was
established.
Measures
Depressive symptoms were most often assessed with the
Child Depression Inventory (CDI; Kovacs, 1992; 203 sam-
ples), structured clinical interviews (46 samples), the Beck
Depression Inventory/Beck Youth Inventory (Beck, Beck,
& Jolly, 2001; 41 samples), the Behavior Assessment
System for Children (Reynolds & Kamphaus, 2004;
39 samples), the Affective Problems scale of the CBCL
(13 samples), and the depression scale of the Minnesota
Multiphasic Personality Inventory (MMPI) (Tellegen et al.,
2003; 10 samples).
Information from the World Bank (2010) was used for
coding countries as developed or developing/threshold
countries.
Statistical Integration of the Findings
Calculations for the meta-analysis were performed in six
steps, using random-effects models and the method of mo-
ments (for computations, see Lipsey & Wilson, 2001).
1. We computed effect sizes d for each study as the
difference in depressive symptoms between the
sample with chronic illness and the control
sample divided by the pooled SD. If the authors
provided only test scores for children and adoles-
cents with chronic illness, we used the norms
from the test manual for comparison. However,
because Twenge and Nolen-Hoeksema (2002) pro-
vided norms for the CDI based on a much larger
sample than the original manual (Kovacs, 1992),
we used the norms by Twenge and
Nolen-Hoeksema (2002). Outliers that were more
than two SD from the mean of the effect sizes
were recoded to the value at two SD (Lipsey &
Wilson, 2001).
2. Effect size estimates were adjusted for bias due to
overestimation of the population effect size in
small samples.
3. Weighted mean effect sizes and 95% confidence
intervals (95% CIs) were computed. The signifi-
cance of the mean was tested by dividing the
weighted mean effect size by the SE of the mean.
To interpret the practical significance of the re-
sults, we used the Binomial Effect Size Display
(BESD; Rosenthal & Rubin, 1982) and Cohen’s
criteria (Cohen, 1988). According to Cohen, differ-
ences of d� .8 are interpreted as large, of
d¼ .50–.79 as medium, and of d¼ .20–.49 as
small.
4. For testing whether the results may be influenced
by publication bias (a trend for nonsignificant re-
sults not being published), we used the ‘‘trim and
fill’’ algorithm (Duval & Tweedie, 2000), which
estimates an adjusted effect size in the presence of
publication bias.
5. Homogeneity of effect sizes was computed by use
of the Q statistic.
6. In order to test the influence of moderator vari-
ables, we used an analogue of analysis of variance
and weighted ordinary least squares regression
analyses.
Results
Data from 33,047 children and adolescents with chronic
illnesses were included. The largest subgroups had asthma
(N¼9,274), diabetes (N¼4,058), cancer (N¼3,400), mi-
graine or tension-type head ache (N¼2,300), and epilepsy
(N¼2,096). The participants had a mean age of 12.6 years
(SD¼2.6 years); 50.2% of them were girls and 32.5% were
members of ethnic minorities.
On average, children and adolescents with chronic
physical illnesses had higher levels of depressive symptoms
than their healthy peers—a small to very small effect
(Table I). According to the BESD, 54.8% of children with
chronic illnesses and 45.2% of their healthy peers would
show depressive symptoms above the median. The
trim-and-fill algorithm did not find any evidence for a
file-drawer problem, and the original effect size remained
unchanged after applying this procedure.
We computed separate effects in cases where at least
five studies were available for a particular chronic disease.
Separate effect sizes could be computed for 16 illnesses.
Strongest between-group differences were found for
chronic fatigue syndrome, fibromyalgia, migraine/tension
378 Pinquart and Shen
Table I. Differences in Depression between Children with and without Chronic Illness: Univariate Analysis of Moderator Variables
Total difference
k d 95% CI Z Qw 450 .19 0.15 to 0.23 9.28*** 577.72***
Kind of illness QB(15,433)¼114.31***
Arthritis/rheumatism 24 �.08 �0.26 to 0.10 �0.89 27.25
Asthma 56 .12 0.01 to 0.23 2.21* 66.52
Cancer 62 �.07 �0.18 to 0.04 �1.21 53.65
Chronic fatigue syndrome 14 .94 0.67 to 1.213 6.90*** 13.62
Chronic migraine/tension-type head ache 22 .51 0.32 to 0.70 5.25*** 15.32
Cleft lip and palate 7 .54 0.21 to 0.86 3.23** 11.78
Cystic fibrosis 15 �.05 �0.27 to 0.18 �0.41 6.96
Diabetes 57 .09 �0.02 to 0.20 1.55 65.51
Epilepsy 32 .39 0.24 to 0.54 5.09*** 35.86
Fibromyalgia 10 .59 0.30 to 0.87 4.06*** 10.50
HIV infection/AIDS 9 �.02 �0.30 to 0.26 �0.15 2.61
Heart disease 5 .23 �0.15 to 0.62 1.19 3.90
Inflammatory bowel disease 14 .18 �0.05 to 0.42 1.52 22.24
Sensory impairment 9 .31 0.03 to 0.58 2.17* 5.48
Sickle cell disease 24 .04 �0.14 to 0.22 0.40 19.94
Spina bifida 9 .30 0.03 to 0.58 2.14* 2.84
Other illnesses/mixed samples 83 .34 0.25 to 0.44 7.00*** 97.00
Mean age QB(1,443)¼0.67
�12 years 175 .16 0.09 to 0.24 4.45*** 196.70
>12 years 270 .20 0.15 to 0.26 6.89*** 260.66
Percentage of girls QB(2,392)¼8.97*
<33.3% 48 .16 0.03 to 0.30 2.33* 46.66
33.3–66.6% 290 .15 0.10 to 0.21 5.47*** 282.44
>66.6% 57 .36 0.24 to 0.49 5.65*** 75.98
Percentage of members of ethnic minorities QB(1,175)¼2.63
<Mean 83 .14 0.03 to 0.24 2.55** 104.90*
>Mean 94 .19 0.07 to 0.32 3.63*** 65.09
Country QB(1,449)¼8.12*
Developing/threshold countries 41 .40 0.25 to 0.54 5.33*** 32.30
Developed countries 410 .17 0.13 to 0.22 7.20*** 431.40
Year of publication QB(2,448)¼9.81**
<1990 55 .29 0.16 to 0.42 4.29*** 55.84
1990–1999 128 .08 �0.00 to 0.17 1.86 138.37
2000–2010 268 .23 0.17 to 0.29 7.75*** 268.26
Rater of depressive symptoms QB(3,444)¼38.32***
Child/adolescent 336 .12 0.07 to 0.17 4.67*** 381.33*
Parent 60 .50 0.38 to 0.62 8.24*** 44.79
Teacher 7 .15 �0.19 to 0.49 0.89 3.33
Clinician 47 .34 0.21 to 0.48 5.05*** 34.50
Assessment of depression QB(7,439)¼67.18***
Beck Depression Inventory/Beck Youth Inventory 41 .24 0.10 to 0.38 3.34*** 43.22
Behavior Assessment System for Children 39 .17 0.03 to 0.31 2.38* 23.61
CBCL: Affective problems 13 .69 0.45 to 0.94 5.52*** 13.37
Child Depression Inventory 203 .03 �0.04 to 0.09 0.83 228.03
Depression scale of the MMPI 10 .60 0.28 to 0.92 3.74*** 4.90
Revised Child Anxiety and Depression Scale 6 .06 �0.30 to 0.41 0.31 4.07
Structured clinical interview 46 .33 0.20 to 0.46 5.11*** 36.37
Other measures 89 .36 0.26 to 0.45 7.40*** 105.25
(continued)
Depression and Chronic Illness 379
type headache, epilepsy, and spina bifida. However, no
significant between-group differences were found for arthri-
tis/rheumatism, cancer, cystic fibrosis, diabetes, heart dis-
eases, HIV infection/AIDS, inflammatory bowel disease,
and sickle cell disease. Health status explained between
0% (sickle cell disease) and 18.1% (chronic fatigue syn-
drome) of the variance of depressive symptoms.
According to the BESD, 71.3% of children with chronic
fatigue syndrome show depressive symptoms above the
median, as compared to 28.7% in healthy controls. In ad-
dition, 64.1% of children with fibromyalgia show depres-
sive symptoms above the median, but only 35.9% of their
healthy peers. As indicated by the non-overlap of the 95%
CIs, between-group differences were stronger for chronic
fatigue syndrome than for arthritis, asthma, cancer, cystic
fibrosis, diabetes, epilepsy, heart disease, HIV infection,
inflammatory bowel disease, sensory impairment, sickle
cell disease, and spina bifida. Differences were also stron-
ger for fibromyalgia and migraine/tension-type head ache
than for arthritis, asthma, cancer, cystic fibrosis, diabetes,
HIV infection, and sickle cell disease. In addition, differ-
ences were stronger for epilepsy than for arthritis, cancer,
cystic fibrosis, diabetes, and sickle cell disease.
Furthermore, differences were stronger for cleft lip and
palate than for arthritis, cancer, cystic fibrosis, and
diabetes.
The size of between-group differences did not vary by
age and race/ethnic minority status. We also checked
whether the results would differ between studies that in-
cluded some young adults and studies that exclusively fo-
cused on adolescents, and found no significant differences
[Q(1,256)¼0.21, NS]. However, larger between-group dif-
ferences were found in samples with higher percentages of
girls. Larger differences were also found in studies from
developing countries than from developed countries and
for studies published before 1990 than in studies that were
published in the 1990s. In line with our expectations,
between-group differences were stronger in studies that
used parent ratings of depressive symptoms than in studies
that used child reports. In addition, differences were stron-
ger when clinician-ratings rather than child ratings were
used. Regarding measures used, between-group differences
were stronger in studies that used the Affective Problems
scale of the CBCL than in other studies. Between-group
differences were also stronger when using the Beck
Depression Inventory/Beck Youth Inventory, the depres-
sion scale of the MMPI, or structured clinical interviews
than when using the CDI.
Because the lack of significant effect size on the CDI
may indicate that this measure might not be sensitive for
depressive symptoms of young people with chronic ill-
nesses, we also checked whether the results would be con-
sistent in studies that compared children with chronic
illness to test norms and to healthy control groups.
Children with chronic physical illness reported higher
CDI scores than healthy members of the control group
(d¼ .29, 95% CIs .19–.39, Z¼5.69, p < .001). However,
the reverse was true when comparing them to test norms
(d¼–.20, CI –0.26 to –0.14, Z¼–5.84, p < .001). These
results indicate that the CDI norms reported by Twenge
and Nolen-Hoeksema (2002) may overestimate the preva-
lence of depressive symptoms in general populations, and
researchers should collect data from a healthy control
group. We also checked whether our results would
change if we use the test norms by Kovacs (1992) but
our results remained unchanged.
Table I. Continued
Total difference
k d 95% CI Z Qw 450 .19 0.15 to 0.23 9.28*** 577.72***
Duration of illness QB(1,179)¼0.97
<Median (4.7 years) 91 .21 0.11 to 0.32 3.97*** 99.55
>Median 90 .14 0.03 to 0.24 2.57* 84.86
Representativeness of the sample QB(1,449)¼8.04*
Convenience sample (clinical sample) 412 .17 0.13 to 0.22 7.19*** 426.11
Random sample/community sample 39 .39 0.25 to 0.54 5.33*** 38.38
Basis of comparison QB(2,446)¼45.39***
Control group 241 .33 0.27 to 0.39 10.89*** 236.95
Test norms 207 .03 �0.03 to 0.10 1.00 223.25
Equivalence of patients and control group QB(2,234)¼0.13
No 31 .33 0.17 to 0.49 4.09*** 25.63
Yes 109 .32 0.23 to 0.40 7.04*** 122.14
Not tested 97 .34 0.25 to 0.43 7.22*** 94.60
Note. k¼number of studies; d¼effect size; Z¼ test for significance of d. 95% CI¼ lower and upper limits of 95% confidence interval; Qw/Qb¼ test for homogeneity of
effect sizes within (w) and between (b) groups. *p < .05, **p < .01, ***p < .001.
380 Pinquart and Shen
Illness duration did not moderate the size of
between-group differences, but this information was avail-
able only for about one third of the included studies.
Similarly, the equivalence of patient group and control
group had no significant moderating effect. However,
effect sizes varied by the representativeness of the sample
and by target of comparison. Stronger between-group dif-
ferences were found in studies with representative samples
than in those with convenience samples, and in studies
that compared young people with chronic illnesses against
a healthy control group.
Finally, because the moderator variables may not be
independent from each other, we checked whether the ob-
served bivariate moderator effects would persist in multi-
variate analysis. We included study characteristics with
significant univariate moderating effects. Because of the
large number of types of illnesses that were compared,
we could not include this variable in the multivariate anal-
ysis. As shown in Table II, the effects of gender, year of
publication, rater, CDI, country, and target of comparison
remained significant in multivariate analysis. However, the
effect of representativeness of the sample was no longer
significant in multivariate analysis.
Discussion
The present meta-analysis shows that young people with
chronic physical illnesses have, on average, higher levels of
depressive symptoms than their healthy peers. However,
this difference varies by the kind of illness, country,
gender, rater of depressive symptoms, method of assessing
depressive symptoms, year of publication, and target of
comparison. This study goes beyond previous
meta-analysis and narrative reviews by testing whether
the levels of depressive symptoms differ between types of
illness and whether the effect size is influenced by a large
number of study characteristics.
When comparing different kinds of illness, we found
that the effect sizes were quite divergent and impressive for
some of these illnesses. Depressive symptoms were highest
in chronic fatigue syndrome, diseases characterized by
chronic pain (fibromyalgia, migraine/tension-type head-
ache), cleft lip and palate, and epilepsy diseases that
were not analyzed in the previous meta-analysis.
There has been a debate about whether the visibility
and social consequences of a disease, restrictions of posi-
tive activities, brain dysfunction associated with some
kinds of illnesses, side effects of treatments, or specific
symptoms of illnesses, such as pain, have the strongest
effect on psychological health. Although these factors are
difficult to compare across illnesses and patient samples,
our results indicate that the strongest effect sizes are found
if more than one of these factors occur simultaneously.
Chronic fatigue syndrome is associated with tiredness
and restrictions of positive activities, alterations in brain
physiology and associated cognitive difficulties, and often
with headaches and muscle aches (Afari & Buchwald,
2003). In addition, symptom overlap of chronic fatigue
syndrome and depression may play a role because the
CDI (Kovacs, 1992) contains items on fatigue and de-
creased school performance.
Fibromyalgia showed the second highest effect size,
and is characterized by chronic widespread pain as well
as associated restrictions of positive activities. In addition,
neurophysiological changes and cognitive dysfunction are
found, as indicated by impaired concentration and
memory problems (e.g., Glass, 2006). Similarly, migraine
and tension-type headache are a significant detriment to
daily functioning and productivity (Roth-Isigkeit et al.,
2006).
Whereas the co-occurrence of more than one factor
could be suggested for explaining the elevated depressive
symptoms in four out of five chronic illnesses with the
highest effect sizes (chronic fatigue syndrome,
Table II. Multivariate Test for Moderating Effects (weighted multiple linear regression analysis)
Variable B b Z p
Percentage girls (1¼66% and higher, 0¼others) .16 .15 3.50 .001
Publication before 1990 (1¼yes, 0¼no) .13 .10 2.31 .03
Developed county (1¼yes, 0¼no) �.09 �.11 �2.50 .02
Child rating (1¼yes, 0¼no) �.14 �.13 �2.61 .01
CDI (1¼yes, 0¼no) �.18 �.18 �3.83 .001
Comparison with test norm (1¼yes, 0¼no) �.23 �.23 �5.23 .001
Representativeness of the sample .05 .03 0.74 .46
Constant 1.17 7.20 .001
R2 .21
N 436
Note. B (b)¼ (un-)standardized regression coefficient. R2¼explained variance.
Depression and Chronic Illness 381
fibromyalgia, migraine/tension-type headache, epilepsy),
there seems to be only one main explanation for elevated
levels of depressive symptoms in young people with cleft
lip and palate. Their symptoms probably reflect concerns
about appearance and negative social consequences (such
as being teased or rejected because of visible deformities
and speech abnormalities; e.g., De Sousa et al., 2009). In
fact, many adolescents with congenital and acquired facial
differences report stigma experiences, such as being teased
about how their face looks (Strauss et al., 2007).
In addition to explanations for above average effect
sizes, is has to be explained why young people with arthri-
tis, cancer, cystic fibrosis, diabetes, HIV infection, and
sickle cell disease did not show higher levels of depressive
symptoms than their healthy peers. The lack of elevated
average levels of depressive symptoms in some kinds of
illness may be based on the fact that many young patients
experience few or even no symptoms of their disease. For
example, many participants of studies on HIV infection
and AIDS were HIV positive without experiencing symp-
toms of AIDS. Similarly, many children and adolescents
with sickle cell disease are free of painful episodes and
other severe symptoms for longer time intervals (Telfer et
al., 2007). In addition, many children with juvenile arthri-
tis experience prolonged periods of low levels of disease
activity or even complete remission due to the develop-
ment of new therapeutic agents (Ravelli & Martini, 2006).
The lack of elevated levels of depressive symptoms in
patients with cancer and cystic fibrosis replicates previous
findings (Bennett, 1994). Because both kinds of illness are
life-threatening, patients may respond with denial in order
to protect their psychological well-being. Nonetheless,
Phipps, Steele, Hall, and Leigh (2001) did not find differ-
ences in the levels of defensiveness of cancer patients and
children with other chronic illnesses. Another explanation
may be that a larger number of studies focused on cancer
survivors who have already successfully completed their
therapy and may, therefore, no longer show elevated
levels of distress. In fact, Jorngarden, Mattsson, and von
Essen (2007) reported that adolescent cancer patients had
higher levels of depressive symptoms than healthy peers 6
months after being diagnosed but lower levels at the
18-month follow-up. Unfortunately, we could not include
the time since completion of therapy in our meta-analysis,
because too few studies provided this information.
Because the moderator effects of gender, country, year
of publication, and target of comparison were in line with
our expectations, they do not need not be discussed here.
In addition to comparisons of child ratings and parent
ratings (Bennett, 1994), we added an analysis of clinician
ratings and teacher ratings. Because the effect sizes were
lower for child ratings than for parent and clinician ratings,
our results may support the suggestion that children with
chronic illness underreport depressive symptoms or that
parents and clinicians overreport depressive symptoms.
However, because different measures were used for child,
parent, and clinician ratings, we cannot rule out the pos-
sibility that the different effect sizes were caused by
different methods of assessment.
Because the univariate effect of the representativeness
of the sample was lost in multivariate analysis, we conclude
that this effect was based on a confounding variable (the
use of control groups rather than test norms in
community-based studies with representative samples).
Limitations and Conclusions
Some limitations of the present meta-analysis need to be
mentioned. First, too few studies were available for sepa-
rately analyzing depressive symptoms in some kinds of
chronic illness, such as renal failure or congenital heart
disease. Second, we analyzed cross-sectional data that did
not allow for causal interpretation. The observed associa-
tions between chronic illness and depressive symptoms
may indicate that chronic illness is a risk factor for depres-
sive symptoms, but that depression may also affect the
course of chronic illness (e.g., Helgeland, Sandvik,
Mathiesen, & Kristensen, 2010), for example as mediated
by a delay of seeking medical help and low compliance
with medical procedures. In addition, third variables,
such as living in poverty, may increase the risk for both
chronic illness and depressive symptoms. Third, we could
not test for some moderators, such as time since last treat-
ment (because this information was rarely reported) or se-
verity of the disease (which would be difficult to compare
across different kinds of illness). Fourth, we could not an-
alyze the processes that link chronic illness with depressive
symptoms, such as metabolic changes, changes in activity
patterns, or social stigmatization. Fifth, we focused on de-
pressive symptoms rather than on depression diagnosis
because too few studies were available that provided com-
parative data on the frequency of clinical depression.
Finally, we assessed only one outcome variable. Effects
on other variables (such as externalizing problem behav-
iors) have to be analyzed in future meta-analyses.
Nonetheless, some important conclusions can be
drawn from the present meta-analysis. First, the small
average differences between the levels of depressive symp-
toms in children with and without chronic illness indicate
that many young people with chronic physical illnesses are
well-adapted and resilient. Although average differences
382 Pinquart and Shen
between depressive symptoms of children with and with-
out chronic physical illnesses are small to very small in a
statistical sense, most effect sizes are practically meaningful
when using Cohen’s criteria for interpreting effect sizes or
the BESD. Second, we conclude from the comparisons of
levels of depressive symptoms across different kinds of ill-
nesses that there are not only common effects of chronic
illnesses (which would cause similar levels of depressive
symptoms irrespective of the kind of chronic illness), but
also illness-specific effects.
Third, we conclude that children with chronic fatigue
syndrome, fibromyalgia, migraine or tension-type head-
ache, cleft lip and palate, and epilepsy are at highest risk
for developing depressive symptoms. Thus, pediatricians
and others who work with these children should be
aware of symptoms of psychological distress and make ap-
propriate referrals for mental health services when needed.
Fourth, because children and adolescents reported lower
levels of depressive symptoms than their parents and cli-
nicians, we recommend not to exclusively rely on child
ratings. We also recommend using healthy peers as control
group when working with the CDI.
With regard to future research, more research is
needed that specifies the conditions under which children
with chronic illnesses show elevated levels of psychological
distress and that provides empirically supported explana-
tions as to why some kinds of illness seem not to cause
elevated levels of depressive symptoms. Similarly, more
longitudinal studies are needed that analyze the extent to
which chronic illness affects the course of depressive symp-
toms and depressive symptoms affect the course of the
chronic illness. Furthermore, more studies are recom-
mended on depressive symptoms in those chronic illnesses
that could not be compared in our meta-analysis.
Supplementary Data
Supplementary data can be found at: http://www.jpepsy.
oxfordjournals.org/.
Conflicts of interest: None declared.
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