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Anxiety and Depression as Bidirectional Risk Factors for One Another:
A Meta-Analysis of Longitudinal Studies
Nicholas C. Jacobson and Michelle G. Newman
The Pennsylvania State University
Not only do anxiety and depression diagnoses tend to co-occur, but their symptoms are highly correlated.
Although a plethora of research has examined longitudinal associations between anxiety and depression,
these data have not yet been effectively synthesized. To address this need, the current study undertook
a systematic review and meta-analysis of 66 studies involving 88,336 persons examining the prospective
relationship between anxiety and depression at both symptom and disorder levels. Using mixed-effect
models, results suggested that all types of anxiety symptoms predicted later depressive symptoms (r⫽
.34), and all types of depressive symptoms predicted later anxiety symptoms (r⫽.31). Although anxiety
symptoms more strongly predicted depressive symptoms than vice versa, the difference in effect size for
this analysis was very small and likely not clinically meaningful. Additionally, all types of diagnosed
anxiety disorders predicted all types of later depressive disorders (OR ⫽2.77), and all depressive
disorders predicted later anxiety disorders (OR ⫽2.73). Most anxiety and depressive disorders predicted
each other with similar degrees of strength, but depressive disorders more strongly predicted social
anxiety disorder (OR ⫽6.05) and specific phobia (OR ⫽2.93) than vice versa. Contrary to conclusions
of prior reviews, our findings suggest that depressive disorders may be prodromes for social and specific
phobia, whereas other anxiety and depressive disorders are bidirectional risk factors for one another.
Public Significance Statement
This meta-analysis suggests that anxiety and depression symptoms bidirectionally prospectively
predict one another with moderate and similar strength. These prospective bidirectional relationships
are stronger over shorter time periods and weaken over longer time periods. Most anxiety and
depressive disorders significantly bidirectionally prospectively predicted one another with strong
nonsignificantly different strength. However, depressive disorders more strongly predicted social and
specific phobia than vice versa, suggesting that depressive disorders may be prodromes for social and
specific phobia.
Keywords: anxiety, depression, mood, risk factor, prodrome
Supplemental materials: http://dx.doi.org/10.1037/bul0000111.supp
Approximately one fifth of the population suffers from an
anxiety or depressive disorder in any given year based on a recent
meta-analysis evaluating global prevalence rates (Steel et al.,
2014). Moreover, within their lifetimes, 49% to 81% of persons
with a depressive disorder have met diagnostic criteria for an
anxiety disorder, and 47% to 88% of those with an anxiety disorder
have met criteria for a depressive disorder (de Graaf, Bijl, Spijker,
Beekman, & Vollebergh, 2003;Hek et al., 2011;Lamers et al.,
2011;Regier, Rae, Narrow, Kaelber, & Schatzberg, 1998). Com-
pared with pure diagnoses, developing a depressive disorder after
an anxiety disorder is associated with lower quality social rela-
tionships (Clancy, Noyes, Hoenk, & Slymen, 1978), greater de-
pression severity (Breier, Charney, & Heninger, 1984;Clancy et
al., 1978;Kessler et al., 1998;Lesser et al., 1988), chronicity
(Geller, Chestnut, Miller, Price, & Yates, 1985), interference (Kes-
sler, Stang, Wittchen, Stein, & Walters, 1999), impairment (Kes-
sler et al., 1999), recurrence (Kessler et al., 1999), and persistence
(Kessler et al., 2008;Lesser et al., 1988). Given the impact of such
sequential comorbidity, it is important to better understand the
longitudinal relationship between anxious and depressive disor-
ders.
With the advent of factor analysis (a statistical technique aimed
at reducing the number of constructs), cross-sectional studies be-
gan to find that anxiety and depression both loaded onto a single
higher order factor (e.g., onto a “social adaptability” factor; Fiske,
1949). This led to the suggestion that anxiety and depression were
part of the same underlying construct. Although these models may
collapse longitudinal relationships between these constructs into a
single factor (Allport & Odbert, 1936), such cross-sectional factor
This article was published Online First August 14, 2017.
Nicholas C. Jacobson, Department of Psychology, The Pennsylvania
State University; Michelle G. Newman, Department of Psychology and
Psychiatry, The Pennsylvania State University.
Correspondence concerning this article should be addressed to Nicholas
C. Jacobson, Department of Psychology, The Pennsylvania State Univer-
sity, 378 Moore Building, University Park, PA 16802-3103. E-mail:
njacobson@psu.edu
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Psychological Bulletin © 2017 American Psychological Association
2017, Vol. 143, No. 11, 1155–1200 0033-2909/17/$12.00 http://dx.doi.org/10.1037/bul0000111
1155
analytic findings have been largely emphasized and continue to
represent a major theoretical conceptualization of anxiety and
depression to date (e.g., see discussions of neuroticism, negative
affect, and hierarchical models of anxiety and depression; Clark &
Watson, 1991;McCrae & John, 1992;Mineka, Watson, & Clark,
1998;Watson, 2005,2009). For example, the tripartite model
suggests that although anxiety is uniquely characterized by high
arousal and depression is uniquely characterized by low positive
affect, anxiety and depression are both components of negative
affect (Brown, Chorpita, & Barlow, 1998;Clark & Watson, 1991).
Subsequent studies examined the validity of combining anxiety
and depression into the same construct compared to modeling
anxiety and depression as separate constructs.
1
Some research has
supported hierarchical models (Brown, Chorpita, & Barlow, 1998;
Chorpita, 2002;Henry & Crawford, 2005;Norton, Cosco, Doyle,
Done, & Sacker, 2013). Other research has found that such hier-
archical models did not fit the data well or did not fit it as well as
models specifying anxiety and depression as distinct constructs
(Boschen & Oei, 2006;Buckby, Cotton, Cosgrave, Killackey, &
Yung, 2008;Clara, Cox, & Enns, 2001;Crawford & Henry, 2003;
den Hollander-Gijsman, de Beurs, van der Wee, van Rood, &
Zitman, 2010;Jacobson, 2016;Lovibond & Lovibond, 1995;Ol-
lendick, Seligman, Goza, Byrd, & Singh, 2003;Yufik & Simms,
2010). Although there is somewhat mixed support for whether
anxiety and depression load onto a higher-order factor within
cross-sectional data, this literature does tend to show that there
appears to be high associations between anxiety and depression.
There have been several early clinical observations and theories
about the longitudinal relationship between anxiety and depres-
sion. For example, Darwin recounted how, following a major
stressor, anxiety produced a state of helplessness followed by
depression (Darwin, 1873). This laid the foundation for a later
model that perceived helplessness is likely to develop into a state
of hopelessness because of the expectation of powerlessness to
solve internal, stable, and global deficits. The model further posits
that hopelessness leads to depressive symptoms as a result of
despair and perceived loss (Alloy, Kelly, Mineka, & Clements,
1990). Likewise, Freud observed that high anxiety led to higher
generalized inhibition, later producing depression (Freud, 1936/
1961). Such a view is consistent with findings that reactivity to
anxiety via avoidance might lead to depression (Jacobson & New-
man, 2014). Similarly, Jung (1916) described a patient who, fol-
lowing a state of great fear, began to punish herself, developing
feelings of guilt and leading to a state of depression. This is similar
to later views that depression often results from a demoralization
reaction to broad impairments caused by anxiety (Belzer & Sch-
neier, 2004;Cloninger, Martin, Guze, & Clayton, 1990;Denollet,
Strik, Lousberg, & Honig, 2006;Frank, 1974). Klein (1948) the-
orized that anxiety often leads to depression via aggression turned
inward toward the self. Internalized aggression has been more
recently linked to generalized anxiety disorder, posttraumatic
stress disorder, and major depression (Deschênes, Dugas, Fraca-
lanza, & Koerner, 2012;Riley, Treiber, & Woods, 1989). At the
same time, others have theorized that depression results in later
anxiety. Schmideberg (1935) noted how depressive guilt, through
internalized aggression, transforms into anxiety over time. Beard
(1969) described how depression leads to the later development of
anxiety as the depression forces one to confront one’s fear of
imminent death. Fear of death has been shown to uniquely predict
later anxiety, and not later depression or demoralization (Vehling
et al., 2011).
One of the leading overarching theories, the prodromal theory
(Clark & Watson, 1991), encompasses all of the unidirectional
models described above. Consistent with others (Kessler et al.,
2008;Perugi et al., 1998), we define a prodrome as a prior
manifestation of a construct preceding and predicting its onset with
greater strength than the construct predicts its prior manifestation
(Perez-Edgar & Guyer, 2014). Also, a prodrome includes both a
prior manifestation contributing to the first onset as well as later
symptom elevation contributing to relapse (e.g., Bauer et al., 2006;
Birchwood & Spencer, 2001;Herz & Melville, 1980;Lauriello,
Horan, & Bustillo, 2002;Malla & Norman, 1994;Morriss, 2004;
Ryu, Song, Ha, Ha, & Cho, 2012). Importantly, the prodromal
model suggests that anxiety is a unidirectional predictor of later
depression. Because a prodromal theory of anxiety and depression
requires a stronger unidirectional relationship of one construct
predicting another, this theory would not be supported if anxiety
and depression both significantly predicted one another at later
times and with similar magnitudes. In this case, they would be
bidirectional risk factors for one another.
Differentiating between whether a prodromal or a bidirectional
risk factor relationship better captures the longitudinal relationship
between anxiety and depression is important. Prodromal theories
would suggest that anxiety and depression are essentially one
underlying construct (i.e., part of the same underlying disease
process; Kuerbis, Hagman, & Morgenstern, 2013;Larson, Walker,
& Compton, 2010). In contrast, if anxiety and depression are
bidirectional risk factors for one another, this would suggest either
(a) anxiety and depression arise from a single disease process
predicting aspects of itself over time, or (b) anxiety and depression
have distinctions from one another and can be meaningfully inter-
related over time.
Examining prodromal and bidirectional theories also has impor-
tant implications for practical prevention efforts. If anxiety were a
prodrome for depression, it would suggest that prevention efforts
should attempt to monitor and treat early manifestations of anxiety,
rather than waiting for depression to develop. Moreover, efforts to
prevent early manifestations of anxiety might effectively also
prevent manifestations of depression. In contrast, if anxiety and
depression were bidirectional risk factors for one another, primary
prevention strategies should target both anxiety and depression,
whichever one comes first or both, as either alone would increase
the risk of the other disorder occurring at a later time.
1
The current review evaluates factor analyses based on research that
reports practical fit indices, which are essential to determining whether
models examining hierarchical structure of affect fit the data well, based on
cutoff values associated with simulation studies (Hu & Bentler, 1998).
Note that requiring these fit indices resulted in the exclusion of some
papers that claimed to show support for hierarchical models of anxiety and
depression (Krueger, 1999;Slade & Watson, 2006;Watson, 2005), and
some that claimed to support specificity between anxiety and depression
(Antony, Bieling, Cox, Enns, & Swinson, 1998;Bedford, Lukic, Aller-
hand, & Deary, 2011;Brown, Chorpita, Korotitsch, & Barlow, 1997). This
also includes papers that have claimed to find support for hierarchical
models (e.g., Caspi et al., 2014), but fit indices presented within the results
section suggested that these models actually provided poor fit to the data
(Hu & Bentler, 1998).
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1156 JACOBSON AND NEWMAN
Many individual studies have tested whether this relationship
was unidirectional or bidirectional. Some concluded that anxiety
was a unidirectional risk factor for depression (i.e., prodrome; e.g.,
Hettema, Kuhn, Prescott, & Kendler, 2006;Merikangas et al.,
2003;Rice, van den Bree, & Thapar, 2004), whereas others con-
cluded that depression also predicted later anxiety (i.e., bidirec-
tional risk factors; e.g., Fichter, Quadflieg, Fischer, & Kohlboeck,
2010;Silberg, Rutter, & Eaves, 2001). Thus, findings have been
mixed in this regard.
Seven qualitative nonsystematic review papers to date have
synthesized such evidence, and each review concluded that anxiety
was a prodrome for later depression (Alloy et al., 1990;Andover,
Izzo, & Kelly, 2011;Fava & Tossani, 2007;Horn & Wuyek, 2010;
Moras & Barlow, 1992;Schleider, Krause, & Gillham, 2014;
Wittchen, Beesdo, Bittner, & Goodwin, 2003). Perhaps because of
entrenched beliefs about anxiety unidirectionally predicting later
depression, no review has evaluated the evidence of depression
predicting later anxiety. As a test of the prodromal theory requires
an examination of the bidirectional relationship between anxiety
and depression, these reviews were fundamentally unable to test
this theory.
Additionally, prior reviews of this literature only included a
small subset of the studies that exist currently. For example, one of
the most cited review papers is from 1990 (Alloy et al., 1990), but
most studies on this topic have been published within the past two
decades. Other review papers only included five (Moras & Barlow,
1992), six (Fava & Tossani, 2007;Wittchen, Beesdo, et al., 2003),
11 (Horn & Wuyek, 2010), 12 (Schleider et al., 2014), and 16
(Andover et al., 2011) studies examining the naturalistic longitu-
dinal relationship between anxiety and depression.
Given that review studies have been qualitative and nonsystem-
atic, have only examined the unidirectional relationship between
anxiety and depression, have not included the majority of existing
longitudinal studies, and individual studies have arrived at contra-
dictory conclusions, a quantitative review was needed to synthe-
size the large number of findings and to examine study differences
that may have impacted outcomes. Thus, we sought to quantita-
tively synthesize the prospective research on associations between
anxiety and depression.
The current meta-analysis included studies that measured both
symptoms and disorders of anxiety and depression. Symptom mea-
sures were included because anxiety and depression have been shown
to fall along a continuum (Kollman, Brown, Liverant, & Hofmann,
2006;Olatunji, Broman-Fulks, Bergman, Green, & Zlomke, 2010;
Ruscio, Borkovec, & Ruscio, 2001;Ruscio & Ruscio, 2000,2002;
Ruscio, Ruscio, & Keane, 2004), and dimensional measures tend to
have greater power (Bjelland et al., 2009). Clinical diagnoses were
examined separately as they may be more directly meaningful to
clinicians and better reflect substantial distress and impairment
(Helzer, van den Brink, & Guth, 2006;Kamphuis & Noordhof,
2009). By including both symptoms and disorders, we attempted to
capitalize on the strengths of each.
Given differing conclusions reached by prior studies (i.e., some
studies suggesting unidirectionality, other studies suggesting bidi-
rectionality; Fichter et al., 2010;Hettema et al., 2006;Merikangas
et al., 2003; e.g., Rice et al., 2004;Silberg, Rutter, & Eaves, 2001),
another major goal of this meta-analysis was to examine factors
that moderated the prospective strength of anxiety and depression
predicting one another. In particular, we considered demographic
characteristics (i.e., gender, age, race, targeted population) to de-
termine whether the strength of associations was influenced by the
section of the population being studied, and, consequently,
whether these models were specific to certain demographic pop-
ulations or more widely applicable. Given their unique func-
tional relationships (Nandi, Beard, & Galea, 2009), we also
examined whether separate anxiety and depression constructs
(e.g., worry vs. panic, generalized anxiety vs. obsessive–
compulsive disorder) impacted the different associations. To de-
termine the longevity of these associations, we examined whether
lag time between measurements moderated the associations.
Lastly, we examined other assessment features, such as type of
measurement (e.g., self-report, independent observer report), recall
period (e.g., six months, two years ago), and the diagnostic and
statistical manual version.
Thus, this systematic meta-analysis analyzed prospective studies
to determine (a) the strength of anxiety symptoms predicting later
depressive symptoms, (b) the strength of depressive symptoms
predicting later anxiety symptoms, (c) the strength of anxiety
symptoms predicting later depressive symptoms compared to the
strength of depressive symptoms predicting later anxiety symp-
toms, (d) the strength of anxiety disorders predicting later depres-
sive disorders, (e) the strength of depressive disorders predicting
later anxiety disorders, and (f) the strength of anxiety disorders
predicting later depressive disorders compared to the strength of
depressive disorders predicting later anxiety disorders. In addition,
we examined moderators to each of these relationships.
Method
Obtaining and Selecting Studies
We searched four databases: PsycINFO, MEDLINE, Google,
and Google Scholar. PsycINFO and MEDLINE are common da-
tabases for meta-analyses within psychology and were determined
to be appropriate and useful sources (Rothstein, Sutton, & Boren-
stein, 2006). Additionally, Google Scholar was used to find articles
typically thought to be outside the domain of psychology itself, but
that contained data relevant to the current study. Moreover, Google
was used to obtain both unpublished and published materials. In an
attempt to be inclusive of unpublished material, we also incorpo-
rated unpublished doctoral theses. The following keyword search
terms were used: anxi
ⴱ
, posttraumatic stress, posttraumatic stress,
phobia, obsessive–compulsive, depress
ⴱ
, dysphor
ⴱ
, dysthym
ⴱ
,
temporal
ⴱ
, anteced
ⴱ
, sequen
ⴱ
, chronol
ⴱ
, predat
ⴱ
, antedat
ⴱ
, and
preced
ⴱ
(
ⴱ
denotes a wildcard search). Note that all combinations
of anxiety, depression, and temporal were used to create separate
searches. Thus, we conducted a total of 105 searches within each
database. Articles were selected based on their abstracts, and
articles that mentioned a potential time relationship between anx-
iety and depression were selected. Using these search terms, we
searched through 16,098 abstracts (5,763 from PsycINFO; 2,487
for Medline; 3,372 for Google Scholar; and 4,476 for Google).
After obtaining these articles, we read each article and examined
the reference section of each article in an attempt to find other
related articles (see Figure 1). In addition, we used Google Schol-
ar’s “cited by” feature and examined all citations of the articles we
obtained. Study searches continued until May, 2015.
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1157
ANXIETY AND DEPRESSION META-ANALYSIS
To qualify for the meta-analysis, the articles needed to meet the
following inclusion requirements: (a) the study measured anxiety
or anxiety disorders, (b) the study measured depression or depres-
sive disorders, (c) the studies investigated the relationship between
anxiety and depression over time (i.e., there was a time difference
between the measurement of anxiety and depression either via
prospective design or retrospective report; did not examine con-
current time periods),
2
(d) the study reported an effect size (or
variables that could be converted to an effect size) of the relation-
ship between anxiety and depression, (e) the study did not include
a treatment component or any manipulation by researchers to
affect the naturalistic course of anxiety or depression, and (f) the
study was a primary research article (i.e., not a review article). See
supplemental materials for a list of all obtained articles that were
excluded and the reasons for their exclusion. This resulted in 77
studies (66 prospective studies used in all analyses and 11 retro-
spective studies used only in sensitivity analyses; 74 published; 3
unpublished [2 unpublished doctoral dissertations; and 1 unpub-
lished manuscript]) being selected (see Figure 1).
Coding
Raters. A team of eight persons coded the articles. Two or
more raters coded all articles to establish interrater reliability. All
conflicts between raters were resolved by all raters who had coded
the article. See results section for interrater reliability.
Study characteristics. First, the research team coded sample
size. Second, the team coded the targeted population, including (a)
general population (i.e., not a clinical sample or did not measure
clinical diagnoses), (b) clinical population (i.e., sampled from
inpatients or outpatient services), (c) persons at risk for a specific
disorder (i.e., subthreshold but elevated symptoms), (d) low risk
sample (i.e., a sample with low levels of symptoms and also did
not meet criteria for a given disorder), (e) pregnant women, (f)
mixed population (i.e., one or more other categories included), or
(g) other population. The team also coded mean age, percentage of
females, and percentage of Caucasians (based on an overview of
the studies, it did not appear that examining the continuum of other
racial groups would be possible due to too few articles examining
them). We also coded the percentage of the sample with anxious
and depressive disorders at baseline.
3
In relation to anxiety symptoms, we coded the measurement
focus, including (a) physiological aspects of anxiety (e.g., anxious
arousal from the Mood and Anxiety Symptom Questionnaire), (b)
cognitive aspects of anxiety (e.g., Brief Measure of Worry Sever-
ity; Penn State Worry Questionnaire), (c) non-disorder-specific
anxiety (e.g., Spielberger State–Trait Anxiety Inventory; Anxiety
Scale from the Depression Anxiety Stress Scale; Spence Chil-
dren’s Anxiety Scale), (d) generalized anxiety disorder symptoms
(e.g., Generalized Anxiety Disorder Questionnaire—IV), (e) post-
traumatic stress disorder symptoms (e.g., Child Posttraumatic
Stress Disorder Checklist; Child PTSD Symptom Scale; Posttrau-
2
Note that only prospective designs were used for the analyses, but we
included retrospective methods to determine if these reports had substan-
tially different effect sizes.
3
We decided to utilize percentage of those with an anxiety or depressive
disorder at baseline for several reasons. Although this measure reflects
presence versus absence of clinical levels of psychopathology for a single
individual, for an entire sample, using percentage with a given diagnosis,
represents the degree of clinically significant anxiety and/or depression
within that sample. This metric was important in determining whether
samples with higher rates of an anxiety or depressive disorder had unique
functional relationships between anxiety and later depression, compared
with samples without any clinical diagnoses or lower rates.
355 articles obtained from searching
PsycINFO, Medline, Google
Scholar, and Google
231 articles obtained from article
references
175 articles obtained from Google
Scholar’s “cited by” feature
Obtained a total of 761 articles
77 articles met inclusion
criteria
684 did not meet inclusion criteria
17 did not measure anxiety
21 did not measure depression
266 did not investigate the relationship
between anxiety and depression over time
151 did not report an effect size of the
relationship between anxiety and depression
28 included a treatment component or a
manipulation by researchers
201 were not a primary research article
(i.e. review article)
77 total articles were included
in the meta-analysis:
• 66 prospective studies
were used in all meta-
analyses
• 11 retrospective studies
were only used in analyses
comparing effect sizes
between retrospective and
prospective studies.
Figure 1. This flow diagram depicts the source in which articles were selected, which met inclusion criteria,
and which met exclusion criteria.
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1158 JACOBSON AND NEWMAN
matic Stress Disorder Checklist), (f) panic disorder symptoms
(e.g., Structured Psychopathological Interview and Rating of the
Social Consequences of Psychic Disturbances for Epidemiology),
(g) social anxiety disorder symptoms (e.g., Liebowitz Social Anx-
iety Scale, The Social Anxiety Scale for Adolescents [SAS-A]),
(h) specific phobia symptoms (e.g., Structured Psychopathological
Interview and Rating of the Social Consequences of Psychic
Disturbances for Epidemiology), or (i) other.
4
For depressive
symptoms, coding included (a) general depression (e.g., Beck
Depression Inventory, Center for Epidemiologic Studies Depres-
sion Scale), or (b) other.
5
With respect to the diagnosis, we coded whether the study
measured (a) mixed anxiety disorders, (b) obsessive–compulsive
disorder (OCD), (c) generalized anxiety disorder (GAD), (d) post-
traumatic stress disorder (PTSD), (e) panic disorder, (f) social
phobia, (g) specific phobia, (h) mixed depressive disorders, (i)
major depressive disorder (MDD), (j) dysthymia, or (k) other (i.e.,
overanxious anxiety disorder, separation anxiety disorder). Like-
wise, when diagnoses were included, we coded the diagnostic
system used (i.e., Diagnostic and Statistical Manual of Mental
Disorders, third edition [DSM–III], Diagnostic and Statistical
Manual of Mental Disorders, third edition revised [DSM–III–R],
Diagnostic and Statistical Manual of Mental Disorders, fourth
edition [DSM-IV]; American Psychiatric Association, 1980,1987,
2000). For both anxiety and depressive disorders, we also coded
the time reference period during which participants were asked to
rate their anxiety or depressive symptoms (i.e., “Over the past
year, have you had a period of at least 6 months when you
experienced uncontrollable and excessive worry most of the day
nearly every day?”), using the following categories (1) less than 1
year, (2) 1 to 2 years, and (3) greater than 2 years. Note the time
reference period should not be considered a proxy for chronicity,
as these periods measured whether a person met diagnostic criteria
at any point in time within this interval.
Furthermore, we coded type of measurement, including (a) self-
report, (b) parent-report, (c) teacher-report, or (d) independent-
observer report. For each effect size, we noted days between mea-
surements to determine lag time (when time was reported in ranges,
the average was taken, e.g., 4–5 weeks later would be coded as 31.5
days). Moreover, we coded whether each effect size was based on a
prospective or retrospective design. Although primary study analyses
used only prospective designs, retrospective designs were not an
exclusionary criterion in determining whether prospective versus ret-
rospective designs influenced the effect size. Lastly, we coded
whether the study was published or unpublished. See Tables 1–4 for
summaries of the studies included in the meta-analysis. See Table 5
for a summary of demographics.
Odds ratio. As recommended by Haddock, Rindskopf, and
Shadish (1998) and similar to Liu, Huang, and Wang (2014), odds
ratios were used to determine effect sizes for categorical measures
of anxiety disorders predicting depressive disorders and vice versa.
In this case, an odds ratio represented increased odds of being
diagnosed with a depressive disorder when participants had a prior
anxiety disorder, compared to those without a prior anxiety disor-
der (or vice versa for depressive disorders predicting anxiety
disorders). An odds ratio greater than 1 indicated increased like-
lihood of being diagnosed with a depressive disorder when one had
a prior anxiety disorder, whereas an odds ratio less than 1 indicated
decreased likelihood of being diagnosed with a depressive disorder
when individuals had a prior anxiety disorder, compared with
those without a prior anxiety disorder. Odds ratios have an asym-
metrical distribution, and, as such, were converted into log odds
ratios for analyses (Lipsey & Wilson, 2001). When a study con-
trolled for baseline levels of either anxiety or depression, it was
coded as a moderator variable. After results were obtained, data
was converted back into odds ratios for interpretation.
Correlation coefficient. For predictions of temporal prece-
dence of continuous measures of anxiety and depression, we
used correlation coefficients and partial correlation coefficients
to determine the primary effect size. When a study controlled
for baseline levels of either anxiety or depressive symptoms,
baseline levels were coded as a moderator variable. When
baseline correlation coefficients were not reported, standard-
ized and unstandardized regression coefficients were converted
to correlation coefficients using a t-statistic estimator as rec-
ommended by Wolf (1986), using the following formula r⫽
兹t2
t2⫹N⫺1.Because correlation coefficients have a skewed
distribution (Silver & Dunlap, 1987), Fisher’s r-to-ztransfor-
mation was used to calculate primary effect sizes, using the
following equation: zr⫽1
2ln
共
1⫹r
1⫺r
兲
(Fisher, 1921). Following
primary analyses, zs were converted back to correlations as the
final output.
Planned Analyses
Prior to primary analyses, agreement between raters was com-
pared using Krippendorff’s alpha with both nominal and ratio data.
Krippendorff’s alpha is optimal as it allows for incomplete data
(important because all raters did not code every article, and most
articles were coded by two or three raters; Hayes & Krippendorff,
2007;Krippendorff, 2004,2012). Interrater reliability was con-
ducted using the Rpackage irr (Gamer, Lemon, Fellows, & Singh,
2007).
Effect size meta-analyses used the Rpackage Metafor (Viech-
tbauer, 2010). All analyses used mixed-effect models, nesting each
effect size within a study as a random effect. Mixed-effect models
allow multiple measures of the same type to be utilized within the
same study. This method was important as studies frequently
included multiple effect sizes, such as when studies included the
same measures being administered repeatedly over multiple time
periods. This method also allows one to include all effect sizes for
multiple publications using the same dataset. When this was the
case, each study used different parts of the data (i.e., different time
periods, different diagnostic groups, etc.). Similar to traditional
multilevel models outside of the meta-analytic framework, random
effects were used to account for lack of independence between
effect size estimates (with the primary difference being the nesting
of effect sizes within studies; see for Kalaian & Raudenbush, 1996
more information). Similar to Karelaia and Hogarth (2008) and
Petersen and Hyde (2010), prior to analyzing the primary results,
statistical tests of publication bias were examined, including Begg
4
We also intended to code other constructs (i.e., obsessive–compulsive
symptoms), but there were no qualified studies that actually examined the
longitudinal relationship between anxiety and depression using these con-
structs.
5
We hoped to include other depressive measures, such as dysthymia, but
no studies measured these symptoms.
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1159
ANXIETY AND DEPRESSION META-ANALYSIS
Table 1
Summaries of Anxiety Symptoms Predicting Depressive Symptoms
Study N
Time apart in
measurements rAnxiety construct Depression construct Sample summary
Adams and Boscarino (2011) 1,681 1 year .17 panic symptoms mixed depression Prospective and retrospective sample
of adults living in New York City
on 9/11/2001.
2 years .03 panic symptoms mixed depression
Alipour, Lamyian, and Hajizadeh
(2012)
140 4.6 months .35 mixed anxiety mixed depression Prospective Iranian women 28–30
weeks pregnant6.2 months .32 mixed anxiety mixed depression
Austin, Tully, and Parker (2007) 575 5 months .07 mixed anxiety mixed depression Prospective: women in their third
trimester of pregnancy5 months .10 cognitive anxiety mixed depression
Borelli and Prinstein (2006) 478 11 months .46 social phobia mixed depression Prospective: adolescents in grades
6–8
Bromberger and Matthews
(1996) 460 3 years .37 mixed anxiety mixed depression Prospective: middle-aged women
Cole et al. (1998) 330 6 months .62 mixed anxiety mixed depression Prospective: adolescents in grades
3–61 years .57 mixed anxiety mixed depression
1.5 years .49 mixed anxiety mixed depression
2 years .41 mixed anxiety mixed depression
2.5 years .29 mixed anxiety mixed depression
Fairbrother and Woody (2007) 127 1 month .19 anxiety sensitivity mixed depression Prospective: women in their third
trimester of pregnancy
Gazelle and Ladd (2003) 388 5 years .40 anxious solitude mixed depression Prospective: kindergarteners
Grant et al. (2007) 102 1 year .28 social phobia mixed depression Prospective sample of
undergraduates
Grant, McMahon, et al. (2008) 100 7.5 months .23 mixed anxiety mixed depression Prospective: pregnant women at risk
for anxiety or depression.
Jacobson and Newman (2014) 6,504 13 years .19 mixed anxiety depression Prospective sample of nationally
representative adolescents
Jakobsen, Horwood, and
Fergusson (2012)
948 15 years .09 mixed anxiety mixed depression Prospective: New Zealand birth
cohort
Leigh and Milgrom (2008) 367 1.5 months .44 mixed anxiety mixed depression Prospective: pregnant women
5 months .13 mixed anxiety mixed depression
Mazza et al. (2009) 938 7 years .18 mixed anxiety mixed depression Prospective: children in grades 1–2
Orcutt (2006) 182 9 months .35 mixed anxiety mixed depression Prospective: female undergraduates
Rholes et al. (2011) 387 7.5 months–2
years
.24 attachment anxiety mixed depression Prospective: pregnant parents
Schindel-Allon et al. (2010) 147 2 weeks .65 PTSD symptoms mixed depression Prospective: Israeli adults two weeks
after single-event traumas2 months .68 PTSD symptoms mixed depression
2.5 months .56 PTSD symptoms mixed depression
Sears and Armstrong (1998) 131 2 years .17 mixed anxiety mixed depression Prospective: adolescents from Nova
Scotia.34
Silberg, Rutter, and Eaves (2001) 1,511 3 years .11 specific phobia mixed depression Prospective: monozygotic and
dizygotic twins3 years .24 OAD mixed depression
3 years .22 separation anxiety mixed depression
Skouteris et al. (2009) 220 3 months .51 mixed anxiety mixed depression Prospective: pregnant women
4 months .42 mixed anxiety mixed depression
7 months .44 mixed anxiety mixed depression
Snyder et al. (2009) 267 6 months .17 mixed anxiety mixed depression Prospective: children at age 5
1 year .14 mixed anxiety mixed depression
Starr and Davila (2012b) 55 28 days .40 mixed anxiety mixed depression Prospective: persons with GAD
28 days .41 physiological anxiety mixed depression
Starr and Davila (2012a) 55 1 day .38 mixed anxiety mixed depression Prospective: persons with GAD
2 days .56 mixed anxiety mixed depression
3 days .56 mixed anxiety mixed depression
4 days .41 mixed anxiety mixed depression
1.5 days .48 cognitive anxiety mixed depression
Starr et al. (2014) 815 9 years .10 GAD mixed depression Prospective: children with depressed
mothers9 years .07 social phobia mixed depression
Weems et al. (2007) 52 1.8 years .31 mixed anxiety, GAD mixed depression Prospective: adolescents before
hurricane Katrina1.8 years .18 GAD symptoms mixed depression
1.8 years .09 PTSD symptoms mixed depression
Wetherell et al. (2001) 1,391 3 years .48 mixed anxiety mixed depression Prospective: elderly Swedish twins
6 years .45 mixed anxiety mixed depression
Ying et al. (2012) 200 6 months .58 PTSD symptoms mixed depression Prospective sample of adolescent
survivors of the Wenchuan
earthquake in China
1 year .42 PTSD symptoms mixed depression
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1160 JACOBSON AND NEWMAN
and Mazumdar’s (1994) test and Egger, Davey Smith, Schneider,
and Minder’s (1997) regression method. After examining publica-
tion bias, all results were tested for total effect (i.e., pooled effect
size). Following the test of total effect, a Qtest was used to
determine whether there was substantial heterogeneity between
studies (Cochran, 1954). This test of heterogeneity was impor-
tant because, if studies were too homogeneous, it would make
examination of moderation across studies impossible. Follow-
ing a significant Q-test to determine whether there were sys-
tematic differences between effect sizes of different studies,
moderators were tested using metaregression. Post hoc tests
were provided directly within metaregression estimates,
wherein the referenced intercept value was changed such that
each value estimated the simple slope and difference in slopes.
Table 1 (continued)
Study N
Time apart in
measurements rAnxiety construct Depression construct Sample summary
Zaers et al. (2008) 47 7 months .69 mixed anxiety mixed depression Prospective sample of pregnant
women
Zavos et al. (2012) 2,603 2 years .39 mixed anxiety mixed depression Prospective sample of adolescent
twins2 years .33 anxiety sensitivity mixed depression
Note. GAD ⫽generalized anxiety disorder; OCD ⫽obsessive compulsive disorder; PTSD ⫽posttraumatic stress disorder; OAD ⫽overanxious disorder.
Table 2
Summaries of Depression Symptoms Predicting Anxiety Symptoms
Study N
Time apart in
measurements rAnxiety construct Depression construct Sample summary
Aune and Stiles (2009) 1,439 1 year .01 social anxiety mixed depression adolescents
Borelli and Prinstein (2006) 478 11 months .37 social anxiety mixed depression adolescents
Britt et al. (2007) 1,685 6 months .39 PTSD symptoms mixed depression US soldiers on a peacekeeping
mission to Kosovo
Britton (2008) 296 1 month .21 mixed anxiety mixed depression postnatal mothers
Cole et al. (1998) 330 6 months .64 mixed anxiety mixed depression adolescents in grades 3–6
1 year .60 mixed anxiety mixed depression
1.5 years .52 mixed anxiety mixed depression
2 years .42 mixed anxiety mixed depression
2.5 years .34 mixed anxiety mixed depression
Grant et al. (2007) 102 1 year .26 social anxiety mixed depression undergraduates
Grant, McMahon, et al.
(2008)
100 7.5 months .16 mixed anxiety mixed depression pregnant women at risk for later
anxiety or depression.
Guglietti et al. (2010) 75 1 month .25 PTSD symptoms mixed depression women with ovarian cancer
Orcutt (2006) 182 9 months .48 mixed anxiety mixed depression female undergraduates
Schindel-Allon et al. (2010) 147 2 weeks .77 PTSD symptoms mixed depression Israeli adult victims two weeks after
single-event traumas
1.8 months .78 PTSD symptoms mixed depression
2.3 months .70 PTSD symptoms mixed depression
Sears and Armstrong (1998) 131 2 years .32 mixed anxiety mixed depression adolescents from Nova Scotia
Silberg, Rutter, and Eaves
(2001)
1,511 3 years .01 specific phobia mixed depression monozygotic and dizygotic twins
3 years .11 overanxious disorder mixed depression
3 years .05 separation anxiety mixed depression
Skouteris et al. (2009) 220 3 months .28 mixed anxiety mixed depression pregnant women
4 months .64 mixed anxiety mixed depression
6 months .38 mixed anxiety mixed depression
Snyder et al. (2009) 267 6 months ⫺.15 mixed anxiety mixed depression children at age 5
1 year ⫺.06 mixed anxiety mixed depression
Whitehead et al. (2006) 135 3 months .43 PTSD symptoms mixed depression persons with acute coronary
syndromes
9 months .29 PTSD symptoms mixed depression
Wikman (2009) 452 6 months .66 PTSD symptoms mixed depression patients with acute coronary
syndrome
1 year .67 PTSD symptoms mixed depression
3 years .68 PTSD symptoms mixed depression
Ying et al. (2012) 200 6 months .59 PTSD symptoms mixed depression adolescent earthquake survivors in
China
1 year .51 PTSD symptoms mixed depression
Zavos et al. (2012) 2,603 2 years .42 mixed anxiety mixed depression adolescent twins
2 years .35 anxiety sensitivity mixed depression
Note. GAD ⫽generalized anxiety disorder; OCD ⫽obsessive compulsive disorder; PTSD ⫽posttraumatic stress disorder; OAD ⫽overanxious disorder;
PDD ⫽premenstrual dysphoric disorder.
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1161
ANXIETY AND DEPRESSION META-ANALYSIS
Table 3
Summaries of Anxiety Disorders Predicting Depressive Disorders
Study N
Time apart in
measurements OR Anxiety construct Depression construct Sample summary
Alipour et al. (2012) 140 2 months 3.11 mixed anxiety mixed depression Prospective: women 28–30 weeks
pregnant in Iran3.5 months 3.25 mixed anxiety mixed depression
4.3 months 3.27 mixed anxiety mixed depression
5.8 months 2.98 mixed anxiety mixed depression
Alloy et al. (2006) 347 1.5 months 2.27 mixed anxiety major depression Prospective: undergraduates with
high or low risk for depression
based on cognitive styles
1.5 months 1.76 mixed anxiety minor depression
1.5 months 2.32 mixed anxiety hopeless depression
Andrade et al. (2003) 37,000 Retrospective 81.60 GAD major depression Retrospective: 10 community
samples across 10 countries
worldwide
Retrospective 52.70 OCD major depression
Retrospective 27.03 panic disorder major depression
Retrospective 9.40 PTSD major depression
Retrospective 11.90 social phobia major depression
Retrospective 10.70 specific phobia major depression
Austin et al. (2007) 575 5 months 3.11 mixed anxiety mixed depression Prospective: women during their
third trimester of pregnancy
Biederman et al. (2007) 303 5 years 3.20 separation anxiety mixed depression Prospective: children of parents
with panic disorder, major
depression, or neither
Bittner et al. (2004);Friis
et al. (2002);Stein
et al. (2001)
2,548 4 years 3.5 social phobia mixed depression Prospective: adolescents and
young adults4 years 1.73 mixed anxiety major depression
4 years 3.90 GAD major depression
4 years 2.74 panic disorder major depression
4 years 1.80 specific phobia major depression
4 years 2.30 social phobia major depression
Bittner et al. (2007);
Copeland et al. (2009);
Costello et al. (2003)
1,420 1 year 3.0 mixed anxiety mixed depression Prospective: children and
adolescents4 years 1.2 GAD mixed depression
4 years 2.3 social phobia mixed depression
4 years 6.7 OAD mixed depression
4 years 2.9 separation anxiety mixed depression
5.5 years 2.4 OAD mixed depression
5.5 years 7.4 GAD mixed depression
Breslau and Davis (1992) 861 1.2 years 2.14 mixed anxiety mixed depression Prospective: persons with a
history of migraines
Coelho et al. (2011) 246 5 months 5.25 mixed anxiety major depression Prospective: women during their
second trimester of pregnancy5 months 5.74 GAD major depression
5 months 1.71 social phobia major depression
7 months 4.57 mixed anxiety major depression
7 months 5.19 GAD major depression
7 months 2.24 social phobia major depression
1.2 years 29.53 mixed anxiety major depression
1.2 years 7.8 GAD major depression
1.2 years 4.33 social phobia major depression
1.6 years 2.72 mixed anxiety major depression
1.6 years 6.58 GAD major depression
1.6 years 1.66 social phobia major depression
2.4 years 8.42 mixed anxiety major depression
2.4 years 6.11 GAD major depression
2.4 years 2.92 social phobia major depression
Fichter et al. (2010) 835 25 years 5.99 mixed anxiety major depression Prospective: persons from a rural
Bavarian community25 years 3.84 mixed anxiety dysthymia
Gallerani (2008) 240 6 years later 1.12 mixed anxiety mixed depression Prospective: adolescents
Goodwin (2002) 15,703 1 year later 5.60 OCD major depression Prospective nationally
representative sample1 year later 3.74 panic disorder major depression
1 year later 2.30 specific phobia major depression
Goodwin et al. (2004);
Jakobsen et al. (2012)
1,053 3 years 9.25 panic disorder major depression Prospective: young adults
8 years 1.18 mixed anxiety major depression
Grant, McMahon, et al.
(2008)
100 7.5 months 5.52 mixed anxiety mixed depression Prospective: pregnant women at
risk for later anxiety or
depression.
Heron et al. (2004) 8,323 4 months 2.1 mixed anxiety major depression Prospective: pregnant women
7 months 1.73 mixed anxiety major depression
9 months 2.17 mixed anxiety major depression
1 year 1.72 mixed anxiety major depression
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1162 JACOBSON AND NEWMAN
All categorical moderation post hoc statistics are provided in
tables below, and only significant simple slopes and the differ-
ences in slopes are discussed in text. Qtests were also used to
determine whether categorical variables explained significant
degrees of variance, and a Ztest was performed to examine
continuous moderators. Transformations of time variables were
used to model nonlinear trajectories of time (including square,
cube, log, and square root), and transformation with the best fit
to the data was reported in analyses below. For analyses of
symptoms and disorders, percentage of the sample with anxiety
disorders at baseline and percentage of the sample with depres-
sive disorders at baseline were examining using slopes of the
moderator. However, correlation coefficients of the minimum
and maximum value of this moderator were shown for illustra-
tive purposes (not using formal statistical tests).
The strength of effect sizes of anxiety symptoms predicting later
depressive symptoms was compared against the strength of depres-
sive symptoms predicting later anxiety symptoms; and the strength of
effect sizes of anxiety disorders predicting depressive disorders was
compared to the strength of depressive disorders predicting later
anxiety disorders. These direct comparisons were made by modeling
both bidirectional effects within one multilevel model and examining
a dummy variable as a moderator to determine directionality (Mac-
Callum, Kim, Malarkey, & Kiecolt-Glaser, 1997).
Table 3 (continued)
Study N
Time apart in
measurements OR Anxiety construct Depression construct Sample summary
Karsten et al. (2011) 1,167 2 years 2.47 mixed anxiety mixed depression Prospective: adults in the
Netherlands
Kessler et al. (1999);
Kessler et al. (2008);
Parker and Hadzi-
Pavlovic (2001)
8,098 unknown 1.40 mixed anxiety major depression Retrospective nationally
representative sampleunknown 1.49 mixed anxiety dysthymia
unknown 3.5 GAD major depression
unknown 2.5 social phobia major depression
unknown 3.1 social phobia dysthymia
Liabsuetrakul et al.
(2007) 400 2 months 1.1 mixed anxiety major depression Prospective: pregnant women
Mauri et al. (2010) 1,066 7 months 4.21 GAD major depression Prospective: pregnant women
7 months 6.88 OCD major depression
7 months 6.71 panic disorder major depression
7 months 5.86 PTSD major depression
7 months 5.09 social phobia major depression
7 months 1.05 specific phobia major depression
1.5 years 4.21 GAD major depression
1.5 years 4.42 OCD major depression
1.5 years 3.45 panic disorder major depression
1.5 years 16.50 PTSD major depression
1.5 years 3.25 social phobia major depression
1.5 years .82 specific phobia major depression
Perkonigg et al. (2004) 1,251 3.5 years 2.6 mixed anxiety PDD Prospective: adolescents and
young women3.5 years 2.5 PTSD PDD
Pine et al. (1998);Pine
et al. (2001)
776 2.5 years 3.10 specific phobia major depression Prospective: adolescents
7 years .89 specific phobia major depression
7 years 1.20 social phobia major depression
7 years 2.73 fearful spells major depression
7 years 2.23 OAD major depression
7 years .69 separation anxiety major depression
9 years 1.64 specific phobia major depression
9 years 1.31 social phobia major depression
9 years 1.59 fearful spells major depression
9 years 2.92 OAD major depression
9 years 1.32 separation anxiety major depression
Prenoveau et al. (2013) 296 3 months - 2
years
1.79 GAD major depression Prospective: postnatal mothers
Reinherz et al. (1989);
Reinherz et al. (1993)
378 3 years 5.20 mixed anxiety major depression Prospective: adolescents
6 years 2.18 mixed anxiety mixed depression
9 years 1.94 mixed anxiety major depression
Ruscio et al. (2005);
Ruscio et al. (2007)
5,692 unknown 4.66 GAD major depression Retrospective nationally
representative sampleunknown 6.12 GAD dysthymia
Söderquist et al. (2009) 1,224 7 months 4.3 mixed anxiety mixed depression Prospective: pregnant women
Sutter-Dallay et al.
(2004) 497 2 months 2.6 mixed anxiety major depression Prospective: pregnant women
Trumpf et al. (2010) 1,538 1.5 years 1.56 specific phobia major depression Prospective: German adolescent
girls
Wittchen, Perkonigg,
et al. (2003)
1,091 1 year 8.7 mixed anxiety PDD Prospective: young women
1 year .2 mixed anxiety subthreshold PDD
Note. GAD ⫽generalized anxiety disorder; OCD ⫽obsessive compulsive disorder; PTSD ⫽posttraumatic stress disorder; RBA ⫽recurrent brief
anxiety; RBD ⫽recurrent brief depression; OAD ⫽overanxious disorder; PDD ⫽premenstrual dysphoric disorder.
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1163
ANXIETY AND DEPRESSION META-ANALYSIS
Results
Interrater Reliability
Interrater reliability was high for all types of study information.
Mean Krippendorff’s alpha was 0.871 (range 0.722–1.000). Thus,
all measures had at least adequate reliability with most items
demonstrating excellent agreement (Krippendorff, 2012).
Anxiety Symptoms Predicting Depressive Symptoms
The number of participants in studies of anxiety symptoms
predicting depressive symptoms was 21,201. There was no evi-
dence of publication bias based on the Begg test (⫽0.077, p⫽
.213) or the Egger regression method (Z⫽1.498, p⫽.134).
Anxiety symptoms significantly predicted depressive symptoms
(Z⫽8.8017, p⬍.001, see Table 6). There was also substantial
heterogeneity between studies, allowing moderation analyses to be
conducted.
As length of time between measurements increased, effect sizes
of anxiety symptoms predicting depressive symptoms showed
degradation (see Figure 2; see Table 7 for a list of all omnibus
moderation effects). When anxiety symptoms predicted depressive
symptoms 1 month later, the average correlation was 0.609 CI
[0.498, 0.720], 1 year later it was 0.326 CI [0.214, 0.438], and 3
years later it was 0.201 (CI ⫽0.089–0.312). The percentage of the
sample with anxiety disorders at baseline significantly positively
moderated the effect. In samples in which 0% of respondents were
diagnosed with baseline anxiety disorders, the correlation was
0.310 CI [0.136, 0.465], and in samples with 100% of respondents
diagnosed with anxiety disorders at baseline, the correlation was
0.585 CI [0.452, 0.693]. Likewise, percentage of the sample with
depressive disorders at baseline significantly positively moderated
the effect. In samples with 0% of respondents diagnosed with
Table 4
Summaries of Depressive Disorders Predicting Anxiety Disorders
Study N
Time apart in
measurements OR Anxiety construct Depression construct Sample summary
Acarturk et al. (2009) 7,076 1 year 7.31 social phobia major depression Prospective: Netherlands nationally
representative sample1 year 10.98 social phobia dysthymia
Alloy et al. (2006) 347 1.5 months 1.23 mixed anxiety mixed depression Prospective: college students with
high or low risk for depression
Breslau et al. (2008) 990 4 years 2.38 PTSD mixed depression Prospective: young adults
Britton (2008) 296 1 month 4.15 mixed anxiety mixed depression Prospective: postnatal mothers
Fergusson and Woodward
(2002) 1,265 3.5 years 3.90 mixed anxiety major depression Prospective: adolescents
Fichter et al. (2010) 835 25 years 2.59 mixed anxiety mixed depression Prospective: Bavarian persons from
a rural community25 years .81 GAD mixed depression
25 years 2.25 OCD mixed depression
25 years 6.12 panic disorder mixed depression
25 years 2.66 specific phobia mixed depression
Gallerani (2008) 240 6 years later .59 mixed anxiety mixed depression Prospective sample of adolescents
Grant, McMahon, et al.
(2008)
100 7.5 months 3.39 mixed anxiety mixed depression Prospective: pregnant women at
risk for later anxiety or
depression.
Hapke et al. (2006) 4,075 unknown 5.21 PTSD mixed depression Retrospective: German adults
Karsten et al. (2011) 1,167 2 years 2.81 mixed anxiety mixed depression Prospective: adults in the
Netherlands
Kessler et al. (1998);Kessler
et al. (2008)
8,098 10 years 1.2 GAD major depression Prospective and Retrospective
nationally representative sampleunknown 2.7 GAD major depression
unknown 1.7 panic disorder major depression
Kessler et al. (2002) 20,189 unknown 3.1 GAD major depression Retrospective nationally
representative sampleunknown 2.7 GAD dysthymia
Neufeld et al. (1999) 3,481 13.5 years 2.8 mixed anxiety mixed depression Prospective: adults in Baltimore
Pine et al. (1998) 716 7 years 10.87 GAD major depression Prospective: adolescents
7 years 2.05 social phobia major depression
7 years 3.36 specific phobia major depression
9 years 3.22 GAD major depression
9 years 1.73 social phobia major depression
9 years 2.79 specific phobia major depression
Prenoveau et al. (2013) 296 3 months–2
years
3.61 GAD major depression Prospective postnatal mothers
Skodol et al. (1996) 2,741 unknown 1.7 PTSD mixed depression Retrospective: Israeli men exposed
to combat
Söderquist et al. (2009) 1,224 3–7 months 16.3 PTSD mixed depression Prospective: pregnant women
Sourander et al. (2005) 2,712 12.5 years 1.4 mixed anxiety mixed depression Prospective sample of Finnish boys
Vaananen et al. (2011) 1,843 2 years 6.60 social phobia mixed depression Prospective sample of 9th graders
Note. GAD ⫽generalized anxiety disorder; OCD ⫽obsessive compulsive disorder; PTSD ⫽posttraumatic stress disorder; OAD ⫽overanxious disorder;
PDD ⫽premenstrual dysphoric disorder.
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1164 JACOBSON AND NEWMAN
depressive disorders at baseline, the correlation was 0.295 CI
[0.200, 0.383], and in samples with 100% of respondents diag-
nosed with depressive disorders at baseline, the correlation was
0.607 CI [0.539, 0.666]. No other moderators were significant.
Depressive Symptoms Predicting Anxiety Symptoms
In studies examining depressive symptoms predicting anxiety
symptoms, the number of participants was 7,750. There was no
evidence of publication bias based on the Begg test (⫽0.014,
p⫽.900) or Egger regression method (Z⫽0.332, p⫽.740). The
average correlation between depressive symptoms and later anxi-
ety symptoms was significantly positive (Z⫽5.407, p⬍.001).
Additionally, there was significant heterogeneity in effect sizes
across studies.
In terms of moderation, as length of time between assessments
increased, the effect of depressive symptoms on subsequent anx-
iety symptoms decreased showing log-linear decay. When depres-
sive symptoms predicted anxiety symptoms 1 month later, the
average correlation was 0.609 CI [0.498, 0.721], 1 year later it was
0.255 CI [0.086, 0.424], and 3 years later it was 0.073 CI [⫺0.096,
0.241]. See Figure 2 for a graphical depiction of this result.
The percentage of the sample with anxiety disorders at baseline
significantly positively moderated the effect. In samples in which 7%
of respondents were diagnosed with anxiety disorders at baseline, the
correlation coefficient was 0.213 CI [⫺0.096, 0.523], and in samples
in which 100% of respondents were diagnosed with anxiety disorders
at baseline, the correlation coefficient was 0.939 (CI [0.629–1.000]).
Likewise, percentage of the sample with depressive disorders at
baseline significantly positively moderated the effect. In samples in
which 7% of respondents were diagnosed with depressive disorders at
baseline, the correlation coefficient was 0.088 CI [⫺0.580, 0.234],
and in samples in which 66% of respondents were diagnosed with
depressive disorders at baseline, the correlation coefficient was 0.662
CI [0.516, 0.808].
Likewise, type of anxiety symptom being predicted was a sig-
nificant moderator (see Table 8). Depressive symptoms only sig-
nificantly predicted non-disorder-specific anxiety (SE ⫽0.095,
Z⫽3.540, p⬍.001) and PTSD symptoms (SE ⫽0.104, Z⫽
5.189, p⬍.001). Moreover, depressive symptoms predicted PTSD
symptoms more strongly than specific phobia symptoms (Z⫽
2.135, p⫽.037).
6
Also, higher percentages of Caucasians were
associated with lower effect sizes. Additionally, studies that did
not control for baseline had significantly larger effect sizes than
studies that did. No other moderators were significant.
Strength of Directionality of Anxious and
Depressive Symptoms
Next, we tested the strength of directionality of anxiety symp-
toms predicting depressive symptoms compared to depressive
symptoms predicting anxiety symptoms. Anxiety symptoms more
strongly predicted later depressive symptoms, than depressive
symptoms predicted later anxiety symptoms (⫽0.028, SE ⫽
0.012, Z⫽2.267, p⫽.023) across all anxiety constructs.
Anxiety Disorders Predicting Depressive Disorders
The total number of participants in the studies obtained for
anxiety disorders predicting depressive disorders was 38,902.
There was no evidence of publication bias based on the Begg test
(⫽0.134, p⫽.086) or Egger regression method (Z⫽1.498, p⫽
.134). Anxiety disorders significantly positively predicted depres-
sive disorders (Z⫽8.384, p⬍.001; see Table 6). Those with an
anxiety disorder had a 154.8% greater probability of being diag-
nosed with a depressive disorder at the later assessment compared
to those without a prior anxiety disorder. The heterogeneity test
was significant, suggesting that there were between-study differ-
ences, allowing for the examination of moderation.
In the examination of moderators, anxiety disorder diagnosis also
had a significant impact on anxiety disorders predicting later depres-
sive disorders. Mixed anxiety disorder samples (SE ⫽0.130, Z⫽
6.011, p⬍.001), OCD (SE ⫽0.155, Z⫽10.469, p⬍.001), GAD
(SE ⫽0.202, Z⫽6.565, p⬍.001), PTSD (SE ⫽0.471, Z⫽2.776,
p⫽.006), panic disorder (SE ⫽0.151, Z⫽7.997, p⬍.001), social
phobia (SE ⫽0.170, Z⫽5.175, p⬍.001), specific phobia (SE ⫽
0.150, Z⫽4.820, p⬍.001), and “other” anxiety disorder (e.g.,
overanxious anxiety disorder, separation anxiety disorder; SE ⫽
0.215, Z⫽4.811, p⬍.001) significantly predicted later depression.
Thus, all anxiety disorders significantly predicted later depressive
disorders.
In comparing the different anxiety disorders, OCD was signifi-
cantly stronger in predicting depressive disorders than mixed anxiety
6
We were unable to examine depressive symptoms predicting OCD,
GAD, and panic symptoms because no studies reported effect sizes using
these symptoms.
Table 5
Study Demographic at Baseline
Demographic variable
Anxiety disorders
predicting depressive
disorders
Depressive disorders
predicting anxiety
disorders
Anxiety symptoms
predicting depression
symptoms
Depression symptoms
predicting anxiety
symptoms
M(SD) Range M(SD) Range M(SD) Range M(SD) Range
Percentage female 68.13 (27.44) 0–100 60.54 (21.47) 0–100 67.34 (23.41) 0–100 60.62 (24.05) 8–100
Percentage White 88.43 (3.83) 79–95 85.76 (6.34) 74–93 65.70 (14.92) 43–100 74.47 (12.49) 59–100
Mean age 23.41 (8.50) 6–38 24.73 (9.72) 12–38 21.36 (13.27) 5–61 18.58 (14.21) 5–61
Percentage anxiety
disorders 18.82 (14.85) 7–61 19.36 (16.17) 7–61 25.00 (22.55) 0–98 38.09 (35.47) 15–100
Percentage depressive
disorders 11.62 (6.29) 5–18 16.01 (9.03) 5–34 26.52 (30.00) 0–99 38.74 (27.12) 7–82
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1165
ANXIETY AND DEPRESSION META-ANALYSIS
disorder samples (Z⫽5.237, p⬍.001), panic disorder (Z⫽10.253,
p⬍.001), social phobia (Z⫽4.205, p⬍.001), specific phobia (Z⫽
20.989, p⬍.001), and other anxiety disorders (Z⫽2.774, p⫽.006).
Likewise, GAD was a significantly stronger predictor of depressive
disorders than mixed anxiety disorders (Z⫽2.706, p⫽.007), social
phobia (Z⫽2.207, p⫽.027), and specific phobia (Z⫽2.796, p⫽
.005). The effect of panic disorder was also stronger than mixed
anxiety disorder samples (Z⫽2.726, p⫽.006) and specific phobia
(Z⫽17.455, p⬍.001). See Table 9 for a summary.
The length of time of the depressive disorders reference period at
outcome significantly moderated the relationship. Anxiety disorders
significantly predicted depressive disorders across depressive disorder
reference periods of less than one year (⫽4.951, CI [3.077, 7.967],
SE ⫽0.243, Z⫽6.591, p⬍.001, k⫽3), one to two years (⫽
2.685, CI [1.933, 3.728], SE ⫽0.167, Z⫽5.897, p⬍.001, k⫽6),
and greater than two years (⫽2.104, CI [1.547, 2.861], SE ⫽0.157,
Z⫽4.743, p⬍.001, k⫽8). Nevertheless, studies that measured
depressive disorders across less than one year had significantly larger
effect sizes than studies that measured depression over one-two years
(⌬ ⫽ 2.267, SE ⫽0.295, Z⫽2.076, p⫽.038) or greater than two
years (⌬ ⫽ 2.847, SE ⫽0.289, Z⫽2.961, p⫽.003). There were
no significant differences between depressive disorders measured
across one to two years compared with those with greater than two
years (⌬ ⫽ 0.581, SE ⫽0.225, Z⫽1.084, p⫽.278). No other
moderators were significant.
Note that all results described above only included prospective
studies. Nevertheless, a sensitivity analysis to determine whether
effects differed between retrospective and prospective studies
found that retrospective studies tended to have marginally signif-
icantly larger effect sizes than prospective studies.
Depressive Disorders Predicting Anxiety Disorders
In regard to depressive disorders predicting anxiety disorders,
there were a total of 30,686 participants in the studies obtained.
There was no evidence of publication bias based on the Begg test
(⫽0.005, p⫽.984) or the Egger regression method
(Z⫽⫺0.057, p⫽.955). Depressive disorders significantly pre-
dicted later anxiety disorders (Z⫽5.119, p⬍.001, see Table 6).
Those with a depressive disorder had a 200.1% greater probability
of being diagnosed with an anxiety disorder compared with those
without a prior depressive disorder. There was also significant
heterogeneity in effect sizes for these studies, allowing for the
examination of moderation.
Such moderation showed that type of anxiety measure im-
pacted the relationship. Depressive disorders significantly pre-
dicted both self-reports (⫽6.903, CI [2.959, 16.105], SE ⫽
0.432, Z⫽4.470, p⬍.001, k⫽3) and independent observer
reports (⫽2.431, CI [1.600, 3.701], SE ⫽0.215, Z⫽4.141,
p⬍.001, k⫽12) of anxiety disorders. However, self-report
Table 6
Summary of Effect Sizes for the Relationship Between Anxiety and Depression
Relationship kN
Weighted mean
effect size CI Homogeneity (Q)
Anxiety symptoms predicting depressive symptoms 29 21,201 r⫽.338 [.267–.406] 1429.618
ⴱ
Depressive symptoms predicting anxiety symptoms 17 7,705 r⫽.346 [.226–.455] 1561.380
ⴱ
Anxiety disorders predicting depressive disorders 38 38,902 OR ⫽2.548 [2.007–3.233] 2599.660
ⴱ
Depressive disorders predicting anxiety disorders 17 30,686 OR ⫽3.001 [1.970–4.570] 114.720
ⴱ
Note.N⫽88,336.
ⴱ
p⬍.05.
Figure 2. This figure depicts the relationship between anxiety symptoms predicting depressive symptoms over
time, and depressive symptoms predicting anxiety symptoms over time. The black lines represent the correlation
coefficients of anxiety symptoms on depressive symptoms, and the gray lines represent the correlation of
depressive symptoms on anxiety symptoms. The solid lines represent the estimates, and the dashed lines
represent the lower and upper confidence intervals.
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1166 JACOBSON AND NEWMAN
Table 7
Summary of Moderation Analyses
Moderator kN Statistics
Anxiety symptoms predicting depressive symptoms
Length of time between measurements 25 16,859 ⫽⫺.114, SE ⫽.014, Z⫽⫺7.930, p⬍.001
ⴱ
Percentage of the sample with anxiety disorders at baseline 8 2,225 ⫽.004, SE ⫽.002, Z⫽2.290, p⫽.022
ⴱ
Percentage of the sample with depressive disorders at baseline 8 2,720 ⫽.004, SE ⫽.001, Z⫽3.447, p⫽.001
ⴱ
Type of anxiety construct 24 16,472 Q(6) ⫽5.875, p⫽.437
Type of depression construct N/A N/A N/A
Type of anxiety measure 25 16,859 Q(3) ⫽4.932, p⫽.177
Type of depression measure 25 16,859 Q(3) ⫽5.903, p⫽.116
Gender 23 16,425 ⫽⫺.002, SE ⫽.002, Z⫽⫺.121, p⫽.903
Age 21 13,455 ⫽.001, SE ⫽.003, Z⫽.270, p⫽.787
Percentage of Whites 11 9,842 ⫽⫺.003, SE ⫽.003, Z⫽⫺.881, p⫽.378
Targeted population 24 16,472 Q(6) ⫽8.888, p⫽.180
Controlling for baseline 25 16,859 ⫽⫺.049, SE ⫽.036, Z⫽⫺1.362, p⫽.173
Unpublished studies N/A N/A N/A
Prospective versus retrospective N/A N/A N/A
Depressive symptoms predicting anxiety symptoms
Length of time between measurements 15 7,052 ⫽⫺.166, SE ⫽.021, Z⫽⫺8.148, p⬍.001
ⴱ
Percentage of the sample with anxiety disorders at baseline 6 2,168 ⫽.008, SE ⫽.003, Z⫽2.269, p⫽.023
ⴱ
Percentage of the sample with depressive disorders at baseline 6 2,389 ⫽.008, SE ⫽.002, Z⫽4.623, p⬍.001
ⴱ
Type of anxiety construct 15 7,052 Q(4) ⫽10.991, p⫽.027
ⴱ
Type of depression construct N/A N/A N/A
Type of anxiety measure 15 7,052 Q(3) ⫽4.322, p⫽.229
Type of depression measure 15 7,052 Q(3) ⫽2.294, p⫽.402
Gender 15 7,052 ⫽⫺.003, SE ⫽.002, Z⫽⫺1.254, p⫽.210
Age 15 7,052 ⫽.003, SE ⫽.004, Z⫽.663, p⫽.507
Percentage of Whites 11 6,418 ⫽⫺.009, SE ⫽.004, Z⫽⫺2.690, p⫽.007
ⴱ
Targeted population 15 7,052 Q(5) ⫽10.586, p⫽.060
Controlling for baseline 15 7,052 ⫽.247, SE ⫽.061, Z⫽4.121, p⬍.001
ⴱ
Unpublished studies N/A N/A N/A
Prospective versus retrospective N/A N/A N/A
Anxiety disorders predicting depressive disorders
Length of time between measurements 19 30,122 ⫽.109, SE ⫽.077, Z ⫽.428, p⫽.440
Percentage of the sample with anxiety disorders at baseline 7 20,365 ⫽⫺.022, SE ⫽.005, Z⫽⫺1.134, p⫽.257
Percentage of the sample with depressive disorders at baseline 5 3,278 ⫽⫺.068, SE ⫽.143, Z⫽⫺.277, p⫽.782
Type of anxiety construct 19 30,122 Q(7) ⫽542.388, p⬍.001
Type of depression construct 19 30,122 Q(3) ⫽1.861, p⫽.602
Type of anxiety measure 19 30,122 Q(3) ⫽4.470, p⫽.251
Type of depression measure 19 30,122 Q(3) ⫽4.010, p⫽.379
Gender 14 9,258 ⫽⫺.043, SE ⫽.005, Z⫽⫺1.841, p⫽.066
Age 12 7,310 ⫽.030, SE ⫽.016, Z⫽1.492, p⫽.136
Percentage of Whites 7 2,873 ⫽.003, SE ⫽.037, Z⫽1.111, p⫽.267
Targeted population 16 27,509 Q(4) ⫽2.522, p⫽.641
Controlling for baseline 19 30,122 ⫽⫺.144, SE ⫽.272, Z⫽⫺.205, p⫽.838
Published versus unpublished studies 19 30,122 ⫽⫺1.540, SE ⫽.657, Z⫽⫺1.295, p⫽.195
Anxiety disorder reference period 16 28,979 Q(2) ⫽.057, p⫽.972
Depressive disorder reference period 15 28,683 Q(2) ⫽8.999, p⫽.011
ⴱ
DSM version 19 30,122 Q(3) ⫽.424, p⫽.935
Prospective versus retrospective 31 107,830 ⫽1.085, SE ⫽.317, Z⫽1.747, p⫽.081
Depressive disorders predicting anxiety disorders
Length of time between measurements 15 28,034 ⫽⫺.428, SE ⫽.134, Z⫽⫺1.030, p⫽.303
Percentage of the sample with anxiety disorders at baseline 6 14,457 ⫽.012, SE ⫽.011, Z⫽.505, p⫽.616
Percentage of the sample with depressive disorders at baseline 6 4,398 ⫽⫺.057, SE ⫽.013, Z⫽⫺.838, p⫽.402
Type of anxiety construct 15 28,034 Q(6) ⫽3.040, p⫽.804
Type of depression construct 15 28,034 Q(2) ⫽1.498, p⫽.473
Type of anxiety measure 15 28,034 Q(1) ⫽4.679, p⫽.031
ⴱ
Type of depression measure 15 28,034 Q(1) ⫽.704, p⫽.401
Gender 12 22,414 ⫽.013, SE ⫽.010, Z⫽.672, p⫽.502
Age 9 5,975 ⫽.038, SE ⫽.034, Z⫽1.265, p⫽.206
Percentage of Whites 6 2,749 ⫽.002, SE ⫽.054, Z⫽.809, p⫽.419
Targeted population 13 25,895 Q(3) ⫽2.324, p⫽.508
Controlling for baseline 15 28,034 ⫽⫺.213, SE ⫽.286, Z⫽⫺.248, p⫽.804
Published versus unpublished studies 15 28,034 ⫽⫺1.993, SE ⫽.961, Z⫽⫺1.612, p⫽.107
(table continues)
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1167
ANXIETY AND DEPRESSION META-ANALYSIS
measures had significantly larger effects compared to indepen-
dent observers (⌬⫽4.472, SE ⫽0.483, Z⫽2.163, p⫽.031).
The length of the anxiety disorders reference period at outcome had
a significant impact on the strength of depressive disorders predicting
later anxiety disorders. Depressive disorders significantly predicted
anxiety disorders when the reference time period was less than one
year (⫽2.960, CI [1.352, 6.481], SE ⫽0.400, Z⫽2.714, p⫽.007,
k⫽4) and one to two years (⫽5.604, CI [3.032, 10.355], SE ⫽
0.313, Z⫽5.500, p⬍.001, k⫽5), but not over two years (⫽
1.653, CI [0.882, 3.100], SE ⫽0.321, Z⫽1.568, p⫽.117, k⫽5).
Among the different anxiety disorder reference periods, measure-
ments across one to two years in time had significantly larger effect
sizes than studies over two years (⌬ ⫽ 3.950, SE ⫽0.448, Z⫽
2.722, p⫽.007). However, there were no significant differences
between reference periods of less than one year compared to reference
periods over two years (⌬ ⫽ 1.306, SE ⫽0.513, Z⫽1.136, p⫽
.256), or between reference periods of less than one year with refer-
ence periods from one to two years (⌬ ⫽ 2.644, SE ⫽0.508, Z⫽
1.256, p⫽.209).
Similarly, the length of the depressive disorder reference period
also significantly impacted the effect of depressive disorders on later
anxiety disorders. A depressive disorder reference period of less than
one year (⫽2.865, CI [1.182, 6.947], SE ⫽0.452, Z⫽2.329, p⫽
.020, k⫽3) or from one to two years (⫽5.617, CI [2.984, 10.576],
SE ⫽0.323, Z⫽5.347, p⬍.001, k⫽6) significantly predicted later
anxiety disorders. However, when reference periods were longer than
two years, depressive disorders did not significantly predict later
anxiety disorders (⫽1.651, CI [0.865, 3.152], SE ⫽0.330, Z⫽
1.519, p⫽.129, k⫽7). Likewise, studies with reference periods from
one to two years had significantly larger effect sizes than those with
measurement periods longer than two years (⌬⫽3.967, SE ⫽0.462,
Z⫽2.653, p⫽.008), but there were no significant differences
between reference periods of less than one year with reference periods
longer than two years (⌬ ⫽ 1.214, SE ⫽0.560, Z⫽0.985, p⫽
.325), or between reference periods of less than one year with refer-
ence periods from one to two years (⌬ ⫽ 2.752, SE ⫽0.555, Z⫽
1.212, p⫽.225). No other moderators were significant.
Similar to anxiety disorders predicting depressive disorders, the
above analyses only included prospective studies. Nevertheless,
sensitivity analyses suggested that retrospective studies had sig-
nificantly larger effect sizes than prospective studies.
Strength of Directionality of Anxiety and
Depressive Disorders
Next, we tested the strength of the directionality of anxiety
disorders predicting depressive disorders compared to depressive
disorders predicting anxiety disorders. The results showed that
depressive disorders predicted social phobia significantly more
strongly than social phobia predicted depressive disorders. Like-
wise, the results showed that depressive disorders predicted spe-
cific phobia significantly more strongly than specific phobia pre-
dicted depressive disorders. No other anxiety disorder significantly
Table 7 (continued)
Moderator kN Statistics
Anxiety disorder reference period 14 27,044 Q(2) ⫽7.419, p⫽.025
ⴱ
Depressive disorder reference period 13 26,944 Q(2) ⫽7.058, p⫽.029
ⴱ
DSM version 15 28,034 Q(3) ⫽2.719, p⫽.257
Prospective versus retrospective 19 57,751 ⫽1.786, SE ⫽.177, Z⫽2.838, p⫽.005
ⴱ
Note. This table presents all omnibus moderation effects (i.e. Qtests for categorical outcomes, and regression coefficients for continuous moderation).
ⴱ
p⬍.05.
Table 8
Comparing Strength of Predicting Different Types of Anxiety Symptoms From Depression Symptoms
Type of anxiety
symptoms r(CI)
Comparisons between anxiety symptoms at outcome
Anxiety as a whole
(k⫽6, N⫽15,216)
⌬ (SE)
Posttraumatic stress
symptoms
(k⫽5, N⫽2,694)
⌬ (SE)
Social phobia
symptoms
(k⫽3, N⫽2,019)
⌬ (SE)
Specific phobia
symptoms
(k⫽1, N⫽1,511)
⌬ (SE)
Other anxiety
symptoms
(k⫽1, N⫽1,511)
⌬ (SE)
Non–disorder-specific
anxiety .324 [.149, .479]
ⴱ
Posttraumatic stress
symptoms .496 [.326, .634]
ⴱ
.20 (.14)
Social phobia
symptoms .215 [⫺.049, .448] ⫺.11 (.16) ⫺.32 (.17)
Specific phobia
symptoms .010 [⫺.409, .425] ⫺.32 (.24) ⫺.49 (.24)
ⴱ
⫺.21 (.26)
Other anxiety
symptoms .080 [⫺.347, .480] ⫺.25 (.25) ⫺.43 (.24) ⫺.14 (.26) .07 (.03)
Total .346 [.226, .455]
ⴱ
Note.k⫽15, N⫽7,750. The “total” reflects the cumulative effects of depressive symptoms predicting anxiety symptoms between studies. Note that no
studies reported effect sizes of obsessive-compulsive, generalized anxiety, and panic symptoms being predicted by depressive symptoms.
ⴱ
p⬍.05.
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1168 JACOBSON AND NEWMAN
predicted depressive disorders more strongly than depressive dis-
orders predicted later anxiety disorders (see Table 10).
7
Discussion
We examined the longitudinal relationship between anxiety and
depression using 66 prospective studies with a total sample size of
88,336. Results showed that across a wide variety of symptoms
and disorders, anxiety and depression had bidirectional prospective
relationships with one another. Unlike prior reviews (Alloy et al.,
1990;Andover et al., 2011;Fava & Tossani, 2007;Horn &
Wuyek, 2010;Moras & Barlow, 1992;Schleider et al., 2014;
Wittchen, Beesdo, et al., 2003), these findings do not support the
theory that anxiety disorders constitute a prodrome for depressive
disorders. Concluding that anxiety is a prodrome requires that (a)
anxiety disorders precede and predict the onset of later depressive
disorders, and (b) anxiety disorders predict later depressive disor-
ders with greater strength than depressive disorders predict later
anxiety disorders. No anxiety disorders evidenced superior direc-
tional predictive power than depressive disorders predicting later
anxiety disorders. Further, the strength of depressive disorders
predicting social and specific phobia was greater than vice versa,
in stark contrast to prior reviews (Alloy et al., 1990;Andover et al.,
2011;Fava & Tossani, 2007;Horn & Wuyek, 2010;Moras &
Barlow, 1992;Schleider et al., 2014;Wittchen, Beesdo, et al.,
2003). This may mean that depressive disorders are prodromes for
both social and specific phobia, however, more research should be
conducted before drawing any definitive conclusions.
As opposed to the bidirectionality of anxiety and depressive
disorders, anxiety symptoms predicted later depressive symptoms
more strongly than vice versa. Although there were significant
differences in effect sizes of this analysis, the magnitude of the
difference may not be clinically meaningful (difference in esti-
mated rs⫽.03 lower for depressive symptoms predicting anxiety
symptoms). Nevertheless, contrasting symptom versus disorder
findings may suggest that the effect of depressive symptoms on
later anxiety symptoms is intensified at the disorder level (perhaps
due to a substantial decrease in behavioral activation in depressive
disorders).
There were other notable differences between disorder and
symptom level results. In particular, depressive disorders signifi-
cantly predicted later social and specific phobia diagnoses, but
depressive symptoms did not show the same pattern. The eligible
Table 9
Comparing Strength of Different Types Anxiety Disorders Predicting Depressive Disorders
Disorder Odds ratio (CI)
Comparisons between anxiety disorders predicting depressive disorders
Anxiety disorder
(k⫽11, N⫽
20,223)
⌬ (SE)
Obsessive-compulsive
disorder (k⫽2, N⫽
16,769)
⌬ (SE)
Generalized anxiety
disorder (k⫽5,
N⫽5,576)
⌬ (SE)
Posttraumatic stress
disorder (k⫽2,
N⫽2,317)
⌬ (SE)
Panic disorder
(k⫽4, N⫽
20,370)
⌬ (SE)
Social phobia
(k⫽5, N⫽
5,280)
⌬ (SE)
Specific phobia
(k⫽5, N⫽
20,855)
⌬ (SE)
Other disorders
(k⫽3, N⫽
2,499)
⌬ (SE)
Mixed anxiety
disorders 2.183 [1.693, 2.816]
ⴱ
Obsessive-compulsive
disorder 5.039 [3.723, 6.821]
ⴱ
2.86 (.16)
ⴱ
Generalized anxiety
disorder 3.773 [2.538, 5.608]
ⴱ
1.59 (.20)
ⴱ
⫺1.27 (.22)
Posttraumatic stress
disorder 3.693 [1.468, 9.289]
ⴱ
1.51 (.47) ⫺1.35 (.48) ⫺.08 (.50)
Panic disorder 3.342 [2.487, 4.492]
ⴱ
1.16 (.16)
ⴱ
⫺1.70 (.04)
ⴱ
⫺.43 (.22) ⫺.35 (.48)
Social phobia 2.407 [1.726, 3.356]
ⴱ
.22 (.16) ⫺2.63 (.18)
ⴱ
⫺1.37 (.20)
ⴱ
⫺1.29 (.49) ⫺.94 (.17)
Specific phobia 2.062 [1.537, 2.768]
ⴱ
⫺.12 (.16) ⫺2.98 (.04)
ⴱ
⫺1.71 (.22)
ⴱ
⫺1.63 (.48) ⫺1.28 (.03)
ⴱ
⫺.34 (.17)
Other disorders 3.406 [2.364, 4.908]
ⴱ
.63 (.22) ⫺2.23 (.20)
ⴱ
⫺.96 (.25) ⫺.88 (.51) ⫺.53 (.21) .41 (.23) .75 (.21)
Total 2.548 [2.007, 3.233]
ⴱ
Note.k⫽19, N⫽38,902. The “total” reflects the cumulative effects of anxiety disorders predicting depressed disorders between studies, whereas mixed anxiety disorders reflect the cumulative effects
of anxiety disorders predicting depressed disorders within studies.
ⴱ
p⬍.05.
7
As a sensitivity analysis, to account for effect sizes that could have been
influenced by the failure to control for baseline levels of the outcome measure, all
analyses were repeated using a dummy-coded variable indicating whether studies
controlled for baseline levels of the outcome of interest (i.e., controlling for
baseline depressive symptoms when examining anxiety symptoms predicting
depressive symptoms). With one exception, the significance of all results remained
consistent with the present findings. The one exception was that percentage of
baseline anxiety disorders no longer significantly moderated the relationship of
depressive symptoms predicting later anxiety symptoms (⫽0.007, SE ⫽0.004,
Z⫽1.818, p⫽.069). Note that even though this effect became marginally
significant with this control in place, it still led to large effect sizes for estimated
differences. Samples with 7% of respondents who were diagnosed with an anxiety
disorder had an estimated correlation coefficient of 0.122 CI [⫺.310, 0.554], and
samples with 100% of respondents meeting criteria for an anxiety disorder had an
estimated correlation of 0.782 CI [0.351, 1.000]. Thus, although marginally sig-
nificant, the effect appears to remain large and may still be clinically significant.
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1169
ANXIETY AND DEPRESSION META-ANALYSIS
studies that examined depressed mood predicting social and spe-
cific phobia symptoms had lower means and standard deviations of
baseline depressive symptoms (Borelli & Prinstein, 2006;Grant,
McMahon, & Austin, 2008;Silberg, Rutter, & Eaves, 2001) com-
pared with comparable validation samples (Angold & Costello,
1995;Boyce, Stubbs, & Todd, 1993;Finch, Saylor, & Edwards,
1985;Green, 1998;Simonoff et al., 1997). Thus, in these studies,
there may have been insufficient variability and elevation in de-
pressive symptoms to observe their impact on later social and
specific phobia symptoms. At the same time, there were no rep-
resentative studies available to examine whether depressive symp-
toms predicted OCD, GAD, or panic disorder symptoms. Taken
together, this may be why the only anxiety disorder symptoms
significantly predicted by depressed mood were PTSD and non-
disorder-specific anxiety symptoms.
In an attempt to explain the findings of depressive disorders
predicting social and specific phobia more strongly than most
anxiety disorders, consider that this may be attributable to lack of
behavioral activation in those with depressive disorders (Kasch,
Rottenberg, Arnow, & Gotlib, 2002). Such diminished activity
may lead to decreased exposure to new situations and experiences
(Kasch et al., 2002) and increased fear and avoidance cycles (File
& Hyde, 1978). There is also evidence that social and specific
phobia are defined by discrete fears to a greater extent than other
anxiety disorders (American Psychiatric Association, 2013). Thus,
reduced activity leading to more discrete fears and increased
avoidance may explain the unique relationship between depressive
disorders and later social and specific phobia.
Another explanation for the relationship between depressive
disorders and later social phobia may be that it is driven by the
irritating and consequently negative interpersonal impact of de-
pressive disorders (Coyne, 1976). Compared with those without
depression, persons with depression are more likely to be evalu-
ated negatively and rejected by people close to them (Burchill &
Stiles, 1988). They also experience lower satisfaction in romantic
relationships (Weissman, 1987) than nondepressed controls, sug-
gesting that they may generate stressful interpersonal life events
(Daley et al., 1997). Additionally, interpersonal rejection predicted
later social anxiety symptoms (Siegel, La Greca, & Harrison,
2009). Thus, future research should examine behavioral inhibition
and social rejection as potential mediators between depressive
disorders and later social and specific phobia.
An alternative potential link between depressive disorders and later
social phobia could be the influence of depressive disorders on self-
esteem (Sowislo & Orth, 2013) and cognitions of worthlessness
(Beck, Brown, Steer, Eidelson, & Riskind, 1987). Low self-esteem
and worthless cognitions could directly increase depressed individu-
als’ expectations of being rejected (Kang, Downey, Iida, & Rodri-
guez, 2009) and consequently promote interpersonal avoidance. Thus,
further research should examine low self-esteem, worthless cogni-
tions, and expectations of rejection as mediators between depressive
disorders and later social phobia.
Depressive disorders leading to specific phobia may be ex-
plained by evolutionary mechanisms, which have emphasized the
role of life history in activating both disorders to improve one’s
survival and chance of reproduction (Del Giudice, 2014;Jacobson,
2016). For example, according to evolutionary models of specific
phobia (Coelho & Purkis, 2009), objects related to evolutionary
threat can be more rapidly tied to fear learning compared to
evolutionarily neutral stimuli (Ohman, Eriksson, & Olofsson,
1975). In parallel, depression has been theorized to be an evolu-
tionarily adaptive coping response (Allen & Badcock, 2003). Thus,
perhaps in response to substantial early environmental threats
(Allen & Badcock, 2003), depression leads to a strategy uniquely
tied to increasing one’s likelihood of survival, wherein one de-
creases one’s behavioral activation and subsequently avoids po-
tentially threatening cues (Lieb, Isensee, Höfler, Pfister, &
Wittchen, 2002).
Importantly, those with OCD had larger odds of being diag-
nosed with a subsequent depressive disorder compared to those
with most other anxiety disorders (including mixed anxiety disor-
ders, panic disorder, social phobia, specific phobia, and other
anxiety disorders [e.g., overanxious anxiety disorder, separation
anxiety disorder]). This may imply that OCD has unique ties to
later depressive disorders. Such ties may be explained partially by
OCD’s strong relationship with shame and perfectionism (Fergus,
Valentiner, McGrath, & Jencius, 2010), relative to other anxiety
disorders. For example, OCD may be associated with thoughts of
harming one’s children, which may bring about feelings of shame
(Abramowitz, Moore, Carmin, Wiegartz, & Purdon, 2001), and
shame has strong ties to depression (Kim, Thibodeau, & Jor-
gensen, 2011). Shame also cross-sectionally mediated the relation-
ship between OCD and depressive symptoms (Weingarden &
Renshaw, 2014). Likewise, OCD is characterized by high levels of
perfectionism (Coles, Frost, Heimberg, & Rhéaume, 2003), and
perfectionistic dysfunctional attitudes are central to the diagnosis
of depression (Mongrain & Blackburn, 2005).
Table 10
Comparisons Between the Strength of Anxiety Disorders Predicting Depressive Disorders Against the Strength of Depressive
Disorders Predicting Anxiety Disorders
Anxiety disorder k
Odds ratio (CI) of anxiety disorder
predicting depressive disorder Odds ratio (CI) of depressive disorder
predicting anxiety disorder ⌬ (SE)
Generalized anxiety disorder 14 2.582 [1.811, 5.232]
ⴱ
1.876 [1.259, 2.796]
ⴱ
⫺.71 (.18)
Obsessive-compulsive disorder 3 5.599 [5.214, 6.014]
ⴱ
2.255 [.203, 25.030] ⫺3.34 (1.23)
Panic disorder 5 4.597 [3.026, 6.982]
ⴱ
6.126 [1.168, 32.119]
ⴱ
1.53 (.87)
Posttraumatic stress disorder 4 4.651 [.755, 28.627] 5.745 [1.118, 29.540]
ⴱ
1.09 (1.25)
Social phobia 7 2.606 [1.709, 3.973]
ⴱ
6.045 [3.387, 10.789]
ⴱ
3.44 (.35)
ⴱ
Specific phobia 6 1.719 [1.314, 2.248]
ⴱ
2.934 [1.791, 4.807]
ⴱ
1.22 (.27)
ⴱ
Total 24 2.770 [2.101, 3.653]
ⴱ
2.725 [2.030, 3.657]
ⴱ
⫺.05 (.11)
ⴱ
p⬍.05.
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1170 JACOBSON AND NEWMAN
A growing body of literature has examined potential mecha-
nisms of anxiety leading to later depression. Avoidance mediated
the relationship between anxiety and later depressive symptoms
across a decade (Jacobson & Newman, 2014), perhaps related to
diminished exposure to positive events. These pathways also ap-
plied to those who met diagnostic criteria for anxiety and depres-
sive disorders (Jacobson & Newman, 2014). Additionally, percep-
tions of close and group relationships mediated anxiety and later
depressive symptoms and anxiety and later depressive disorders,
such that high anxiety symptoms predicted poorer perceptions of
close and group relationships which in turn led to higher depres-
sive symptoms (Jacobson & Newman, 2016). Similarly, sociability
and interpersonal oversensitivity was a mediator of earlier anxiety
disorders and later depressive disorders (Starr, Hammen, Con-
nolly, & Brennan, 2014). Thus, although more research is needed,
preliminary findings suggest that avoidance and interpersonal re-
lationships mediate the relationship between earlier anxiety and
later depression, and this applies to both symptoms and disorders.
Results regarding percentage of the sample with anxiety and
depressive disorders at baseline as a moderator were particularly
striking and may have important implications. Specifically, the
percentage of the sample with anxiety or depressive disorders at
baseline moderated the relationship between anxiety and depres-
sive symptoms predicting one another. At the same time, this
moderation was not observed with anxiety or depressive disorders
predicting one another. Although the former pattern became mar-
ginally significant after controlling for baseline symptoms, this
analysis still had strong effect sizes and therefore may still be
clinically important. Perhaps anxiety and depressive disorders
have unique impacts on the relationship between anxiety and
depressive symptoms. This impact could be attributable to the
presence of the disorders creating broad (as opposed to transient or
specific) levels of avoidance behaviors and low behavioral activa-
tion. It is also possible that reaching the level or chronicity of
symptoms required for a disorder diagnosis, majorly increases the
likelihood of higher symptoms of the other disorder later.
There was also a striking difference between symptoms and
disorders in impact of the time lag between measurements. The
effect of anxiety symptoms on later depressive symptoms and
depressive symptoms on later anxiety symptoms was depleted as
lag time increased whereas time lag did not deplete the impact of
disorders on one another. There are several potential methodolog-
ical explanations for this difference. First, the minimum amount of
time between symptom measures was 1 day whereas the minimum
amount of time between disorder measures was 45 days. There-
fore, symptom measures allowed for the examination of much
smaller time lags when moods could affect one another. Second,
disorder measures build a certain level of chronicity (did you feel
xnearly every day for xtime period) into their assessments, which
makes them potentially less susceptible than symptoms to vari-
ability of their effects over time. Also, because symptoms did not
require participants to meet a specific threshold and reflected the
full continuum, they were likely more sensitive to detecting
smaller changes over time compared to disorders.
It is also possible that the relationship between anxiety and depres-
sive symptoms is best characterized by short-term fluctuations of
symptoms bidirectionally predicting one another. Indeed, there is
some preliminary support that anxiety and depression symptoms may
be maintained by across time periods as short as hours (Jacobson &
Newman, 2012). Thus, anxiety and depressive disorders may be much
more robust predictors/risk factors for one another. Once people reach
a threshold in anxiety and depressive symptoms they may be contin-
ually at risk for the other respective disorder, but, until that point, the
relationship between anxiety and depression may degrade over time.
It is also important to consider “third variables” that could
explain the bidirectional relationship between anxiety and depres-
sion. One such variable is the common higher-order factor of
neuroticism. Substantial evidence has accrued that neuroticism
levels are high across anxiety and depressive disorders (see Kotov,
Gamez, Schmidt, & Watson, 2010 for a meta-analysis). Addition-
ally, based on cross-sectional research, some have tended to em-
phasize neuroticism as a joint risk factor for both anxiety and
depression (Manee & Majreh, 2016;Merino, Senra, & Ferreiro,
2016;Muris, Roelofs, Rassin, Franken, & Mayer, 2005;Roelofs,
Huibers, Peeters, & Arntz, 2008). Lastly, neuroticism accounts for
a large portion of prospective change patterns of anxiety and
depression (Brown, 2007), and neuroticism interacts with chronic
stress to predict change in depressive symptoms (Brown & Ro-
sellini, 2011). Thus, this—on its face—suggests that neuroticism
may confound and explain the current findings.
Nevertheless, suggesting that neuroticism confounds the current
findings is problematic. Most principally, this argument is tauto-
logical as neuroticism itself combines face-valid anxiety, depres-
sion, and anger items (Costa & McCrae, 1992). Thus, using a
combined measure of anxiety and depression items to predict
anxiety and depression across time as separate constructs ignores
the containment of anxiety and depression symptoms within mea-
sures of neuroticism.
A second problem of attempting to explain the current findings
using neuroticism is that it assumes that the construct of neuroti-
cism can be validly applied to within-person changes in anxiety
and depression, disregarding a pervasive and widespread measure-
ment issue. In particular, neuroticism was formed based on the
between-person structure of correlations of negative emotionality
(i.e., anxiety, depression, and anger) given to persons on one
measurement occasion (Eysenck, 1947). Many would assume that
constructs examined between persons also readily define the factor
structure of the same items within persons across time. This
presumes that the process is ergodic (a strict mathematical assump-
tion that rarely holds in psychological research, for a review see
Molenaar, 2004). However, when one analyzes neuroticism items
within the same people across time (using a method known as
dynamic factor analysis), the factor structure of these items does
not well-define the within-person variance (Borkenau & Osten-
dorf, 1998;Molenaar & Campbell, 2009). Thus, the between-
person factor structure of neuroticism does not reflect a fundamen-
tal building block of within-person variation of negative mood,
suggesting that neuroticism, itself, may not be applicable to
changes within persons.
In addition to tautological and psychometric issues, attempting
to explain the current findings using a measure that lumps anxiety
and depression items into the same construct disregards substantial
experimental research that anxious and depressive moods can be
induced independently of one another (Cryder, Lerner, Gross, &
Dahl, 2008;Ferrer, Grenen, & Taber, 2015;Gray, Ishii, & Am-
bady, 2011;Innes-Ker, 2015;Kreibig, Wilhelm, Roth, & Gross,
2007;Nabi, 2002). Such experimental findings provide strong
evidence that these moods do not represent the same construct.
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1171
ANXIETY AND DEPRESSION META-ANALYSIS
Thus, these issues may suggest that neuroticism does not represent
a “third variable” that confounds the relationship between anxiety
and depression.
Nevertheless, aspects of neuroticism that do not overlap directly
with anxiety and depression symptoms may be important to ex-
plaining their comorbidity. In particular, neuroticism is often mea-
sured by items such as “moody,” “unstable,” and “stable” (reverse
coded; John & Srivastava, 1999). Similarly, recent models have
suggested that stress reactivity is a central feature of neuroticism
(Barlow, Ellard, Sauer-Zavala, Bullis, & Carl, 2014), which has
been supported by research examining the contribution of neurot-
icism to reactivity to stressors in daily life (Bolger & Schilling,
1991). Such unstable and varying moods may be a byproduct of
previous anxiety and depression (Roberts & Gotlib, 1997). Thus,
future research should examine whether neuroticism’s facet of
stress reactivity helps to inform the longitudinal relationship be-
tween anxiety and depression at both symptom and disorder levels.
Another potential third variable not accounted for in the current
meta-analysis is stress. Notably, stress can vary independently of
anxiety and depression, and consequently it may not represent a
confound. This has been supported by studies controlling for the
influence of stressful life events or perceived stress, which have
found (a) anxiety symptoms significantly predicted later depres-
sive symptoms (Jacobson, Lord, & Newman, 2017), (b) depressive
symptoms significantly predicted later anxiety symptoms (Britton,
2008;Jacobson et al., 2017), (c) anxiety disorders significantly
predicted later depressive disorders (Friis, Wittchen, Pfister, &
Lieb, 2002); and (d) depressive disorders significantly predicted
later social phobia and mixed anxiety disorders (Acarturk et al.,
2009;Britton, 2008). Nevertheless, stress may be important to
examine in future studies, as it has been associated frequently with
anxiety and depression (e.g., Andrews & Wilding, 2004;Paykel,
2003).
The current findings have important clinical implications. With
regard to prevention efforts, as the effect of anxiety and depressive
symptoms tends to degrade over time, but anxiety and depressive
disorders appear to predict one another across decades, prevention
strategies should be particularly focused on early identification of
subthreshold elevation to prevent one from meeting criteria for an
anxiety or depressive disorder. The current findings also may have
implications about symptom change within psychotherapy. As anxi-
ety and depressive disorders are mostly bidirectional risk factors for
one another, this may suggest that transdiagnostic treatments aimed at
either anxiety or depressive disorders would likely effectively impact
both disorders (Ellard, Fairholme, Boisseau, Farchione, & Barlow,
2010). Nevertheless, exceptions to this may occur within domains of
depressive disorders and subsequent social and specific phobia. For
these disorders, symptom reduction may be more effective if depres-
sion is treated prior to social or specific phobia, given that depressive
disorders may predict greater likelihood of development and/or resur-
gence than vice versa. However, it is also possible that transdiagnostic
treatments would be just as effective in this regard.
The current meta-analysis had several strengths. For instance,
we examined differential impacts of anxiety and depressive disor-
ders on one another at both symptom and disorder levels, and
results were largely consistent with one another. There was also
substantial heterogeneity between studies, including sample ages,
populations, and time lag between measurements. As age, race,
and gender did not moderate the findings, relationships between
anxiety and depression may be generalizable across many different
groups.
Although there were many strengths, there were also limitations.
Most importantly, all studies included had no experimental manipu-
lation, and consequently this research is unable to address the causal
relationship between anxiety and depression. Nonetheless, the present
data are important because they may be among the strongest form of
evidence ethically possible, as experimentally examining the causal
relationship between anxiety and depressive disorders would require
experimenters to give participants an anxiety or depressive disorder.
At the same time, the present research establishes the strength and
temporal precedence between anxiety and depression. However, it
does not eliminate the possibility of a co-occurring confounding
variable, and, as such, use of the term “risk factor” over “cause” is
more appropriate. Also, only about half of the studies we included
controlled for baseline depression or anxiety symptoms. Given that
these symptoms are known to co-occur concurrently, we attempted to
offset this problem by rerunning all analyses controlling for baseline
symptoms and disorders. We were also unable to test directly first
onset versus recurrent prodromal relationships and controlling for
baseline symptoms did not offset this limitation. Nevertheless, be-
cause age did not moderate the findings and we included studies
within theorized developmental periods of anxiety and depressive
diagnoses, this may suggest that there are not differential relationships
between anxiety and depression for first onset compared with recur-
rent anxiety and depression. Further, because only one study exam-
ined bidirectional longitudinal relationships between anxiety and de-
pression for both symptoms and disorders, we were unable to rule out
the possibility that disorder versus symptom findings may have been
influenced by methodological differences across studies. Addition-
ally, most included studies examined Western cultures (only five
studies used non-North American/non-European country samples),
and, as such, future research should determine if these findings would
be applicable to other countries. Moreover, we only examined longi-
tudinal associations but not mechanisms between anxiety and depres-
sion. Little research has examined such longitudinal mechanisms
(Jacobson, Lord, & Newman, 2017;Jacobson & Newman, 2014,
2016;Starr et al., 2014), and no research has examined mechanisms
of depression on later anxiety. Thus, more research is needed to
explore potential mediators of these relationships.
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Received May 23, 2016
Revision received May 1, 2017
Accepted May 4, 2017 䡲
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