Factors Influencing Risk of Premature Mortality in Community Cases of Depression: A Meta-Analytic Review

Abstract

Background. Depressive disorders are associated with substantial risk of premature mortality. A number of factors may contribute
to reported risk estimates, making it difficult to determine actual risk of excess mortality in community cases of depression. The
aim of this study is to conduct a systematic review and meta-analysis of excess mortality in population-based studies of clinically
defined depression. Methods. Population-based studies reporting all-cause mortality associated with a clinically defined depressive
disorder were included in the systematic review. Estimates of relative risk for excess mortality in population-representative cases
of clinical depressive disorders were extracted. A meta-analysis was conducted using Stata to pool estimates of excess mortality
and identify sources of heterogeneity within the data. Results. Twenty-one studies reporting risk of excess mortality in clinical
depression were identified. A significantly higher risk of mortality was found for major depression (RR 1.92 95% CI 1.65–2.23),
but no significant difference was found for dysthymia (RR 1.37 95% CI 0.93–2.00). Relative risk of excess mortality was not
significantly different following the adjustment of reported risk estimates. Conclusion. A mortality gradient was identified with
increasing severity of clinical depression. Recognition of depressive symptoms in general practice and appropriate referral for
evidence-based treatment may help improve outcomes, particularly in patients with comorbid physical disorders.

Full text

Hindawi Publishing Corporation
Epidemiology Research International
Volume 2011, Article ID 832945, 12 pages
doi:10.1155/2011/832945
Review Article
Factors Influencing Risk of Premature Mortality in
Community Cases of Depression: A Meta-Analytic Review
Amanda J. Baxter,1, 2 Andrew Page,1and Harvey A. Whiteford1, 2
1School of Population Health, The University of Queensland, QLD 4006, Australia
2Policy and Evaluation Group, Queensland Centre for Mental Health Research, QLD 4074, Australia
Correspondence should be addressed to Amanda J. Baxter, amanda baxter@qcmhr.uq.edu.au
Received 14 December 2010; Revised 15 February 2011; Accepted 15 March 2011
Academic Editor: Susana Sans Menendez
Copyright © 2011 Amanda J. Baxter et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
Background. Depressive disorders are associated with substantial risk of premature mortality. A number of factors may contribute
to reported risk estimates, making it difficult to determine actual risk of excess mortality in community cases of depression. The
aim of this study is to conduct a systematic review and meta-analysis of excess mortality in population-based studies of clinically
defined depression. Methods. Population-based studies reporting all-cause mortality associated with a clinically defined depressive
disorder were included in the systematic review. Estimates of relative risk for excess mortality in population-representative cases
of clinical depressive disorders were extracted. A meta-analysis was conducted using Stata to pool estimates of excess mortality
and identify sources of heterogeneity within the data. Results. Twenty-one studies reporting risk of excess mortality in clinical
depression were identified. A significantly higher risk of mortality was found for major depression (RR 1.92 95% CI 1.65–2.23),
but no significant difference was found for dysthymia (RR 1.37 95% CI 0.93–2.00). Relative risk of excess mortality was not
significantly different following the adjustment of reported risk estimates. Conclusion. A mortality gradient was identified with
increasing severity of clinical depression. Recognition of depressive symptoms in general practice and appropriate referral for
evidence-based treatment may help improve outcomes, particularly in patients with comorbid physical disorders.
1. Introduction
Depressive disorders make a substantial contribution to
the global burden of disease [1–3]. Their contribution to
disease burden is largely attributed to the high prevalence
of, and disability caused by, depression [1]. Contribution
to disease burden of premature mortality in individuals
with depression is less well studied, with the exception of
suicide and more recently coronary heart disease. Depressive
disorders are a well-recognized risk factor for suicide [4], and
increased treatment of depression has been associated with a
decrease in suicide rates [5,6].
Increased risk of excess all-cause mortality has previously
been shown in psychiatric inpatients [7–9]. Excess mortality
in psychiatric inpatients has been associated with conditions
such as gastrointestinal infection and respiratory disease, and
previously attributed to conditions within hospitals or asy-
lums [10], although the introduction of modern psychiatric
treatments and shorter duration of stay in hospitals has
improved mortality outcomes for individuals hospitalized
with depression [11].
Deinstitutionalization of individuals with chronic mental
disorders has been continuing over the past 50 years [12],
with only a small proportion of those with depression
now hospitalized, and then for short periods. Due to
both increased reliance on community care [13]andlow
treatment-seeking rates for depression [14], those hospi-
talised are more likely to be presenting with severe symp-
tomatology and not representative of depressive disorder in
the community. Despite improvements in the treatment of
depression, a growing body of literature suggests that persons
with depressive disorder in the community still experience
excess mortality compared to those without depression.
Two pathways have been proposed leading to increased
mortality in depression. The first is increased tendency
for adverse health behaviours [15]. Depression has been

2Epidemiology Research International
associated with greater likelihood of smoking [16,17],
alcohol, and drug abuse [18–20] and more sedentary
lifestyles [21]. In those with chronic diseases, depression is
associated with noncompliance with medical treatment [22–
24] and worse health outcomes including increased deaths
[25]. The WHO World Health Survey (WHS) collected
data on mental disorders and a range of physical disorders
in 60 countries [26]. Respondents with depression and
one or more comorbid physical disorders had the worst
overall health states of all the disease states, including either
combined physical disorders or depression alone.
Thesecondpathwayisbasedonabodyofevidence
suggesting a biological progression [27], including the
dysfunction of inflammatory response [28]. Depression has
been described as an independent risk factor for both
coronary heart disease [29]andanumberofcancers[27]. It
has also been independently associated with increased levels
of inflammatory markers [30,31]. While this finding is not
always consistent, it may be that methodological differences
between studies are having a confounding effect on the
relationship. For example, one study where an association
was not found reported depression as identified through the
General Health Questionnaire (GHQ) [32]. This measure is
likely to reflect subthreshold depressive symptomatology, as
well as clinical depression. This highlights the importance
of a consistent definition of depression when looking at
associations with adverse health outcomes. Inflammation has
also been implicated in the pathogenesis of a range of chronic
diseases including diabetes [33], atherosclerosis, and related
high mortality diseases such as coronary artery disease [34].
Thus depression is thought to be a contributing factor to the
development of these diseases and also associated with an
increased risk of mortality in those with comorbid physical
disease.
Previous systematic reviews have shown increased risk
of mortality in people suffering from depression [35–40],
especially in males and in those with severe depression [35].
However, previous estimates of mortality combined clinical
and community samples [35,37] and nonclinical definitions
of depression [36,38].
Three previous meta-analyses focusing on all-cause
mortality in community-based studies of depression [36,
38,40] found higher mortality in those with depressive
disorders compared to those without. Pooled effect sizes
ranged from 1.56 to 1.81 [36,38,40]. In two of these analyses
[36,38], depression was variably defined, based on both
clinically defined depression (i.e., meeting internationally
recognized diagnostic criteria such as DSM [41]orICD
[42]) and subclinical depression or depressive symptoms
ascertained through symptom scales such as the General
Health Survey or CES-D. If severity is a mediating factor, the
inclusion of subclinical cases of depression may result in an
underestimate of excess mortality for depression.
Synthesis of the current evidence linking clinical depres-
sion with premature death, along with the identification of
potential modifiers, may be relevant in informing public
health policy, and clinical practice aimed at reducing mortal-
ity. The aim of this paper and meta-analysis is to examine the
risk of premature mortality in clinically defined depression
and identify factors which may influence reported mortality
estimates.
2. Methods
2.1. Data sources. A systematic search was conducted to
identify papers reporting mortality for population-based
studies of depressive disorders. The methodology follows
the recommendations by the Meta-analysis of Observational
Studies in Epidemiology (MOOSE) Group [43]. A broad
search string was developed with the assistance of a research
librarian to search electronic databases (Medline, Embase,
Psychinfo, Scopus and Google Scholar). Broad search terms
(mortality∗death∗fatality∗)and(mental
∗psychiatric∗)
were employed as well as key words for specific mental
disorders, including depression, depending on database
requirements. Searches were limited to human participants
and individual-level analytic studies (either cohort or case-
control studies). No limitations were set on language of
publication. Article titles were scanned for relevance, and
abstracts of potential papers were read to identify duplicates,
further reduced the list according to predetermined criteria.
Prospective cohort or case-control studies were sought
reporting excess all-cause mortality. Studies were excluded if
they were not observational and analytic (e.g.; case studies
or treatment trials), did not report relative associations
between exposure and outcome (or provided insufficent data
to calculate effect size) or did not contain primary data (such
as review articles). If multiple papers were identified from a
single study, the most recent or relevant article was included.
The full text of all potentially relevant papers was reviewed.
Citations from identified primary data papers, reviews, and
monographs were examined to locate additional sources of
data.
Depressive disorders were defined as those disorders
meeting ICD or DSM diagnostic criteria (including where
survey tools map to these criteria) for depressive disorders.
Only studies based on samples from the general population
were included in analyses. Studies based on occupational
groups (e.g., veterans) or conducted in clinical settings, or
inpatient populations, were excluded to reduce the effects
of potential confounders that may be associated with both
occurrence of depression and increased risk of mortality, and
in the case of inpatient samples, where cases were more likely
to be severe [44].
A summary of the studies meeting the review’s inclusion
criteria is shown in Ta b l e 2 . Studies meeting inclusion
criteria included samples identified with depressive disorders
between 1952 and 2001 in Western Europe, North America,
Australia, and Africa.
2.2. Data Abstraction. Data extracted from papers included
study design, sample ascertainment, diagnostic instrument,
geographic location, adjustment for confounding, and loss to
followup. Design factors rather than aggregate quality scores
are perhaps more important in interpreting heterogeneity
across studies [43], hence a range of variables reflecting study
methodology and reporting were also abstracted. Other

Epidemiology Research International 3
variables included sample descriptors (e.g., age, gender,
characteristics) and measurement parameters (e.g., type of
estimate, period of follow-up, error).
Adjusted estimates of relative risk and confidence inter-
vals were extracted where reported. Estimates of relative risk
included odds ratios, hazard rate ratios, and standardized
mortality ratios. Numbers of exposed (depressed) and
nonexposed (controls) were extracted, as well as numbers
of deaths in each group. Where an adjusted effect size was
not reported, or an adjusted effect size was reported without
uncertainty, crude relative risk and confidence intervals were
calculated using numbers of exposed and nonexposed and
relative numbers of deaths in each group.
2.3. Statistical Methods. Meta-analyses were conducted to
estimate pooled relative risk for all-cause mortality. Analyses
we re car ried o ut on S TATA-I C 1 0 sof twa re. D u e to t h e hig h
level of heterogeneity we used the metan function which
specifies a random-effects model using the DerSimonian and
Laird method [45]. I2statistics were calculated to determine
variation attributable to heterogeneity with a value of 0%
indicating no observable heterogeneity between studies and
larger values indicating increasing heterogeneity [46,47].
Egger’s regression test for small study effects was conducted
using the metabias function.
A stepwise metaregression was carried out to explore the
degree to which covariates explained the degree of between-
study variability [48]. A number of covariates identified
through univariate analysis comprised the original regression
model. These included follow-up period, gender, age range
and definition of depression (major depression, dysthymia
or unspecified depression). The meta-regression was carried
out i n STATA usi ng th e metareg command which reported
an adjusted R2statistic. Covariates were then excluded one at
a time until the model reflected the greatest between-study
variability.
3. Results
Risk statistics for all-cause mortality were identified for
twenty studies (see Figure 1). Data were reported for 153,965
participants with 51% from Western Europe, 48% from
North America, and <1% each from Australasia and Africa.
After reviewing the articles and excluding duplicated sam-
ples, twenty studies from twenty-one papers provided an
estimate of risk for all-cause mortality in community samples
[49–69]. The estimates were based on an estimated total of
13,090 deaths with a median follow-up period of 4.4 years
(range 1–17 years).
Excess mortality was significantly higher in those with
clinically defined depression compared to those without
depression (RR 1.67, 95% CI 1.48–1.90). In addition, a
dose-response effect was observed for pooled estimates when
depressive disorders were stratified by severity (Figure 2).
Relative risk for premature mortality in major depressive
disorder was highest with a RR of 1.92 (95% CI 1.65–2.23)
compared to studies reporting unspecified depression (RR
1.46 95% CI 1.22–1.76) and dysthymia (RR 1.37 95% CI
0.93–2.00). Heterogeneity between studies was reduced when
the definition was narrowed to include only major depressive
disorder (I2=38.2%P=.066).
Males with depression had a 78% greater risk of dying
prematurely compared to controls and females with depres-
sion were at 63% greater risk (see Ta b le 1). Ad hoc sensitivity
analyses were conducted to explore the relationship between
risk of excess mortality and various covariates such as
age range, study follow-up period, definition of depression
(MDD, unspecified depression, and dysthymia), and type of
estimate (RR, OR, SMR, HRR).
Seven studies reported risk of excess mortality for adults
in a broad age range [49,52,62–65,68] and thirteen reported
on only older adults (60 years and over) [50,51,53–61,66,
67,69]. No studies were identified that reported risk for
children/adolescents or that stratified risk by age. Tabl e 1
shows that risk of excess mortality was slightly higher for
samples comprising all adults compared to those only in
the older age group (RR 1.85 and 1.59, resp.). A higher risk
of excess mortality was also observed in studies that had a
follow-up period of less than five years (RR 1.91) compared
to those with follow-up periods of five years or greater (RR
1.41).
Approximately one quarter of studies identified reported
risk estimates adjusted for age and/or sex and slightly
fewer reported estimates adjusted for additional factors such
as demographic (marital status, education, or household
income), behavioral and health risk factors (smoking, alco-
hol consumption, or presence of other chronic disease). No
substantial differences were found between the pooled effect
sizes for studies that adjusted for age and/or sex (RR 1.85),
those adjusted for additional risk factors (RR 1.77), and those
that reported unadjusted effect size (RR 1.59).
A stepwise metaregression was carried out to identify a
model explaining the greatest proportion of between-study
variance. The final model, explaining 80.4% of between study
variability, included follow-up period, gender, and definition
of depression. Residual variation due to heterogeneity (I2res)
was reduced from 69.1% to 17.2%.
A funnel plot was generated with a fitted regression line
from the standard regression (Egger) test for presence of
asymmetry (Figure 3). The plot of risk for excess mortality
is skewed and asymmetric with evidence of smaller studies
showing associations that differ systematically from larger
studies (Egger’s test P<.02). It is possible that small studies
showing little risk for excess mortality remain unpublished.
Alternatively small studies may overestimate risk compared
to larger studies.
4. Discussion
The present study found significantly higher risk of excess
mortality in community cases of depression compared to
those without depression. Previous analyses have reported
effect sizes ranging between 1.56 and 1.81 [36,38,40]. The
present review highlights the increased risk of premature
mortality with greater severity of symptoms. While major
depression was associated with almost twice the risk of

4Epidemiology Research International
Tab le 1: Pooled relative risk of excess all-cause mortality in community cases of depressive disorders.
Studies#
Depressive
disorder
(deaths/Total)
No depressive disorder
(deaths/Total) RR 95% CI I2∗
Overall 21 1,167/6,687 11,650/151,721 1.67 1.48–1.90 69.10%
Gender#
Males and females 16 982/5,620 9,291/90,765 1.66 1.42–1.94 75.10%
Males 5 82/377 1,263/28,669 1.78 1.27–2.51 61.60%
Females 5 103/690 1,096/32,287 1.63 1.32–2.02 14.10%
Disorder type#
Major depression 12 345/2,284 5,986/79,615 1.92 1.65–2.23 38.20%
Unspecified depressive disorders 8 738/4,183 4,277/67,797 1.46 1.22–1.76 70.00%
Dysthymic disorder 2 84/220 1,387/4,309 1.37 0.93–2.00 76.60%
Age range
Adults (all ages) 8 479/4,411 6,656/130,033 1.85 1.46–2.35 73.10%
Older adults (60+) 13 688/2,276 4,994/21,688 1.59 1.37–1.83 60.50%
Follow–up period
Less than 5 years 13 651/5,631 6,651/134,016 1.91 1.70–2.14 15.90%
5 years or more 8 516/1,326 5,089/17,705 1.41 1.23–1.62 55.30%
Adjustment factors
Unadjusted 10 462/1,151 5,115/17,996 1.59 1.25–2.01 57.50%
Age and/or sex 6 509/3,913 4,768/74,453 1.85 1.60–2.15 28.90%
Age and/or sex and other factors 5 196/1,623 1,767/59,272 1.77 1.32–2.38 61.70%
RR: relative risk; CI: confidence intervals.
∗I2represents as a percentage of the variation attributable to heterogeneity between studies.
#number of studies do not add up to 21 as 5 studies reported estimates for males and females, and 1 study reported estimates for both major depression and
dysthymia.
premature mortality, no significant risk was found in
association with dysthymia. Inclusion criteria for this meta-
analysis were restricted to diagnostic instruments with high
specificity for clinically significant depression. Subthreshold
disorders are likely to be associated with lower risk of excess
mortality. Inclusion of subthreshold depression or depressive
symptoms may bias pooled estimates toward the null.
The current review found a pooled estimate for excess
mortality higher than that reported by Harris and Barra-
clough [35] for major depression (SMR =1.36). However,
due to data availability at that time, the earlier review
included papers published prior to DSM diagnostic criteria,
with diagnoses such as melancholia, unipolar depression,
primary depressive illness, and late onset primary clear-cut
depression. As this review has demonstrated, definition of
risk factor is an important source of variability. Greater
consistency of definition for clinical depression provided
a more homogenous representation of mortality risk in
depressed cases according to modern DSM/ICD diagnostic
criteria.
The present study found slightly higher risk in studies of
all ages compared to older adult samples, and the study with
the youngest age group (15–49 years) reported the highest
risk of excess mortality (RR 3.55 95% CI 1.97–6.39). One
other review has examined mortality by age group and found
increased risk for adult samples over age 40, compared to
all adults (adults ≥18 years) or older adults (≥65 years)
[36]. The nonlinear trend by age reported in this review
may reflect the heterogeneity of age groupings within the
data available. One hypothesis for the reduced risk in older
age groups is that it reflects survivor bias within depressed
samples. It is possible that people with depressive disorders
are less likely to survive into older age and hence the risk of
excess mortality is reduced in this age group.
Several possible explanations have been advanced for
the higher mortality risk associated with depression. First,
depression and physical disorders co-occur and frequently
complicate each other [39]. Co-occurring disorders may
mask the presence of depression, and depression in com-
bination with physical disorders results in poorer health
outcomes [26,39]. Data from the recent World Mental
Health Survey show that individuals are more likely to seek
treatment for physical disorders than for mental disorders
[14]. Other studies have found that even though individ-
uals may not seek treatment specifically for their mental
disorders, they often seek treatment for coexisting physical
health problems [72]. Although 5.4% of a patient sample
attending a General Practice [GP] sought treatment for
mental disorders, prevalence of clinical or subclinical criteria
for a mental disorder within the GP attending population
was over 40% [72]. Individuals may be accessing health
care but are not reporting symptoms of mental disorder,
or medical professionals are not recognizing symptoms in
persons with coexisting physical disorders. It is likely that

Epidemiology Research International 5
Tab le 2: Studies reporting risk of all-cause excess mortality in community cases of depressive disorder.
Source Disorder Country Study
period Sample Survey
Follow-
up
(years)
Gender Estimate
type Effect Size 95% CI Factors
adjusted for
Murphy et
al. [64]MDD USA 1952–1952 Gen pop
18+ yrs Structured iv 17 M, F Unadj RR 1.84, 2.13 0.9–3.73,
1.01–4.51
Davidson
et al. [59]Unspecified
depression UK 1982–1986 Gen pop
65+ yrs GSM–CATEGO 3 M&F Unadj RR 1.74 1.03–2.94
Jorm et al.
[54]MDD Australia 1982–1983 Gen pop
70+ yrs GMS + MMSE 5 M&F Unadj RR 1.5 1.06–2.11
Aromaa et
al. [68]Unspecified
depression Finland 1978–1981 Gen pop
40–64 PSE–CATEGO 6.6 M&F Unadj RR 2 1.35–2.96
Bruce et al.
[65]MDD
Dysthymia USA 1980–1980 Gen pop
40+ yrs DIS (DSM3) 9 M, F Unadj RR 1.16, 0.97 0.86–1.57,
0.74–1.29
Kouzis et
al. [62]MDD USA 1980–1980 Gen pop
18+ yrs DIS 1 M&F OR 2.6 1.1–6.0
Age, sex,
marital
status,
household
income
Snowdon
and Lane
[53]
Unspecified
depression Australia 1985–1987 Gen pop
65+ yrs
Clinical iv
(DSM3R) 2 M&F Unadj RR 2.23 0.8–6.2
Engedal
[60]Unspecified
depression Norway 1984–1987 Gen pop
75+ yrs
Clinical iv
(DSM3R) 3 M&F OR 1.9 1.0–3.6 Age
Henderson
et al. [55]Unspecified
depression Australia 1990–1994 Gen pop
70+ yrs CIE 3.6 M&F Unadj RR 1.26 0.69–2.32
Pulska et
al. [58]
Unspecified
depression Finland 1984–1985 Gen pop
65+ yrs
Clinical iv
(DSM3) 5.9 M, F RR 1.21 0.94–2.06,
0.85–1.69
Age, sex,
marital
status, low
education,
smoking
Zheng et
al. [63]MDD USA 1989–1989 Gen pop
(white) 25+
Self-reported
(71% males and
79% females
report diagnosis
by physician)
2.5 M,F HRR 3.1,1.7 2.0–4.9,
0.9–3.1
Age,
education,
marital status
&BMI
Pulska et
al. [66,67]MDD Finland 1984-1985 Gen pop
65+ yrs
Clinical iv
(DSM3) 5.9 M, F RR 1.88, 2.06 1.11–3.19,
1.25–3.39 Unadj RR

6Epidemiology Research International
Tab le 2: Continued.
Source Disorder Country Study
period Sample Survey
Follow-
up
(years)
Gender Estimate
type Effect Size 95% CI Factors
adjusted for
Pulska et
al. [66,67]Dysthymia Finland 1989-1990 Gen pop
65+ yrs
Clinical iv
(DSM3) 6 M, F RR 1.52, 1.77 1.04–2.21,
1.30–2.40 Unadj RR
Penninx et
al. [61]MDD Netherlands 1992–1997 Gen pop
55–85 DIS 4.2 M&F RR 2.32 1.38–3.89 Age, sex
Vinkers et
al. [69]MDD Netherlands 1997–1999 Gen pop
85+ yrs
GDS–15 (≥4)
MMSE (>18) 3.2 M&F RR 2.07 1.35–3.17
Sex, smoking,
alcohol, and
other chronic
diseases
Adamson
et al. [50]MDD England 1996–1998 Gen pop
75+ GDS–15 3 M&F HR 1.79 1.5–2.13 Age
Bergdahl
et al. [56]MDD Sweden 2000–2002 Gen pop
85+
Clinical iv
(DSM4) 1 M&F Unadj RR 2.15 1.16–3.97
Gallo et al.
[51]MDD USA 2001–2001
Primary
Care 60+
yrs
SCAN 2 M&F OR 1.78 1.06–2.99
Age, sex,
marital
status,
education,
and smoking
Mogga et
al. [52]MDD Ethiopia 1998–2001 Gen pop
15–49 CIDI 3 M&F SMR 3.55 1.97–6.39 Age, sex
Mykletun
et al. [49]Unspecified
depression Norway 1995–1997 Gen pop
19+ yrs HADS 4.4 M&F OR 1.68 1.46–1.92 Age, sex
Schoevers
et al. [57]Unspecified
depression Netherlands 1990–2000 Gen pop
65–84 GMS–AGECAT 10 M&F Unadj RR 1.18 1.08–1.28
MDD: major depressive disorder;
RR: Relative risk; Unadj RR: Unadjusted relative risk; OR: Odds ratio; SMR: Standardised mortality ratio; HRR: Hazard Risk Ratio;
M&F: person; M: Males; F: Females.

Epidemiology Research International 7
Records identified through database searching
(n=11.452)
Additional records identified
through manual search
(n=77)
Records after duplicates removed
(n=11.309)
Full-text articles assessed for eligibility
(n=554)
Studies included in quantitative analysis
(n=20)
Records excluded after abstract/title search (pre-
morbid sample, clinical trial, inpatient sample)
(n=3.025)
1Not community cases =study featured clinical samples or members of a treatment group only
2Risk estimate not reported =reported number of exposed who died but not the denominator therefore not allowing mortalit
y
rate to be calculated, or did not report deaths in controls
1Not clinically defined depression =study used a scale not validated against dsm or icd criteria therefore reports on depressive
symptomatology only
Not community cases (86)1, sample not
representative (162), all =cause mortality
not reported (76)2, risk estimate not reported
(118), not clinically defined depression (90)3,
study overlap (2)
Figure 1: Flowchart showing results of systematic review.
individuals with multiple health problems are not receiving
appropriate treatment for a comorbid mental disorder
[39]. Underdiagnosis is a concern as depression has been
associated with poorer health outcomes including higher
fatality rates in those with coronary heart disease [73,74],
cancer [75], and stroke [71].
Persons suffering depression are also more likely to
neglect their health and show poor adherence to prescribed
medication regimens [15,22–24]. The causal direction of
these relationships is a focus of current research. Possible
associations include depression as a result of lifestyle factors,
depression leading to lifestyle factors or both depression
and lifestyle factors resulting from other independent factors
[76]. Improvement of diagnosis and treatment of comorbid
physical and mental health problems in primary care may
reduce mortality in these groups.
A shorter followup period was associated with increased
risk of excess mortality compared to longer follow-up
periods. Two of the studies with shorter follow-up period
included exposure measures of period prevalence such past-
year [52]andlifetime[63] rather than current preva-
lence. This finding is unexpected as period prevalent cases,
which include those without the current disorder, would
presumably be less likely to neglect their health and have
better adherence to medication at the time of the study
baseline [15] which may be expected to show lower relative
mortality. Possible recall bias in measures of period and
lifetime prevalence may result in misclassification and reduce
relative differences in mortality if nondifferential. However,
insufficient data were available to look at the association
between exposure measure and effect size. The effect of
exposure measure deserves further exploration, particularly
the possible interaction with follow-up period and age group.
The main strength of this study relates to the inclusion
criteria which ensured relatively consistent diagnosis of
depressive disorder, limited to population-based studies.
Studies were included where estimates for clinically defined
depression (depression meeting DSM or ICD diagnos-
tic criteria) were reported while broader mental disorder
categories of affective disorders and mood disorder were
excluded. Dimensional measures of symptomatology and
psychological distress were also excluded. Inclusion of sub-
clinical samples may reduce the risk of excess mortality as
severity of depressive symptoms is related to higher rates

8Epidemiology Research International
RR = relative risk; CI = confidence interval
I-squared
represents as a percentage the variation attributable to heterogeneity between studies
Snowdon et al., 1995
Pulska et al., 1998
Pulska et al., 1997
Pulska et al., 1998
Zheng et al., 1997
Mogga et al., 2006
Depression
Engedal, 1996
Mykletun et al., 2009
Dysthymia
Aromaa et al., 1994
Murphy et al., 1987
Davidson et al., 1988
Schoevers et al., 2009
Murphy et al., 1987
Zheng et al., 1997
Bergdahl et al., 2005
Bruce et al.,1994
Penninx et al., 1999
Pulska et al., 1998
Vinkers et al., 2004
Bruce et al.,1994
Kouzis et al., 1995
Gallo et al., 2005
Pulska et al., 1997
Adamson et al., 2005
Jorm et al., 1991
Pulska et al., 1998
Major Depression
Henderson et al., 1997
person
male
female
female
male
person
person
person
person
male
person
person
female
female
person
person
person
female
person
person
person
person
male
person
person
male
person
1.67 (1.48, 1.90)
2.23 (0.80, 6.20)
1.52 (1.04, 2.21)
1.20 (0.85, 1.69)
1.77 (1.30, 2.40)
3.10 (2.00, 4.90)
1.37 (0.93, 2.00)
3.55 (1.97, 6.39)
1.90 (1.00, 3.60)
1.68 (1.46, 1.92)
2.00 (1.35, 2.96)
1.84 (0.90, 3.73)
1.74 (1.03, 2.94)
1.18 (1.08, 1.28)
1.46 (1.22, 1.76)
1.92 (1.65, 2.23)
2.13 (1.01, 4.51)
1.70 (0.90, 3.10)
2.15 (1.16, 3.97)
0.97 (0.74, 1.29)
2.32 (1.38, 3.89)
2.06 (1.25, 3.39)
2.07 (1.35, 3.17)
1.16 (0.86, 1.57)
2.60 (1.10, 6.00)
1.78 (1.06, 2.99)
1.20 (0.94, 2.06)
1.79 (1.50, 2.13)
1.50 (1.06, 2.11)
1.88 (1.11, 3.19)
1.26 (0.69, 2.32)
100.00
1.21
4.31
4.60
4.96
3.69
14.50
2.73
2.45
6.56
4.16
Weight
2.13
3.13
6.88
35.76
49.73
1.97
2.56
2.59
5.23
3.17
3.30
3.86
4.98
1.64
3.17
4.15
6.24
4.61
3.09
2.61
1.46
(%)Weight
0.5 1 5 10
Overall (I-squared =69.1%, P=0)
Subtotal (I-squared =76.6%, P=.014)
Subtotal (I-squared =70.0%, P=.001)
Subtotal (I-squared =38.2%, P=.066)
Gender
Source RR (95% CI)
Figure 2: Forest plot showing included studies and pooled relative risks of excess all-cause mortality in community cases of depressive
disorders, by diagnostic type.
of suicide and self-harm [77]. Whilst acknowledging that
mental ill health is a continuum rather than dichotomous (as
conceptualized by modern diagnostic standards), it would be
inaccurate to compare outcomes for different categories of
risk, for example, the inclusion of studies where the “at risk”
group comprised major depression, minor depression, and
subthreshold depression, compared to studies where the “at
risk” group included only major depression [26,72].
A limitation is the low number of studies focusing
on children or adolescents with depression. It may be
that the inclusion of studies featuring child and adolescent
samples would affect the pooled estimate. More cohort
studies involving young people suffering mental disorders are
needed to gain a true picture of the long-term outcome of
depression across the lifespan.
The only information identified for children and adoles-
cents with depression was on clinical or inpatient samples,
or where the focus was on traits and behaviors rather than
clinically significant mental disorders [70,78–84]. The lack
of data for long-term followup of community adolescent
cases of depression may be due, at least in part, to lack of
epidemiological studies which screen for mental disorders
in young people. While many countries have carried out
regional or national level epidemiologic surveys of mental
health in adults, few similar surveys have been conducted
for young people. Those studies that have done so have
not yet reported long-term outcomes such as mortality
[85,86]. Considering the consistency of the relationship
between early mortality and depression, and the link between
depression and serious physical disorders, should be given to

Epidemiology Research International 9
0
0.2
0.4
0.6
0.8
Standard error of log RR
−
10 1 2
Log relative risk
Funnel plot with pseudo 95% confidence limits
Figure 3: Funnel plot, using data from 21 studies of excess all-
cause mortality in cases of clinical depression, with log relative risk
displayed on the horizontal axis.
the identification of children and adolescents with depression
as early as possible not only to address their mental health but
also so that physical health can be monitored.
Analysis of heterogeneity in this review found a high
proportion of between-study variance was attributable to
follow-up period, gender, and type of depression. Further
research is required in order to compare and contrast the
risk of premature mortality in mental disorders, other than
depression. Similar analyses are needed across the spectrum
of mental disorders. If conducted along with similar spec-
ifications to this study, the results could be compared to
identify those disorders which present the greatest risk of
premature death.
Our findings support the importance of identification
and treatment of depression in primary care, including
where depression is comorbid with physical disorders. Most
patients with depression are treated in primary care, and here
it should be possible to adequately identify and treat comor-
bid physical disorders. Where depression is being treated in
a mental health service, it is important for clinicians to be
vigilant regarding the physical health status of the patient and
intervene to minimize lifestyle disease risk factors and have
emerging physical disease treated early and effectively.
Appendix
See Table 2.
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