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32 The APA Publishing Textbook of Mood Disorders, Second Edition
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this respect, as they account for more than 40% of all years lived with disability due
to these disorders (Whiteford et al. 2013). Other epidemiological studies have found
that mood disorders contribute to an even greater burden than the role-related bur
dens examined in the GBD studies (Alonso et al. 2013). But why are mood disorders
so burdensome from a societal perspective? We review the major factors contributing
to an answer in this chapter.
We will not review the epidemiological data on mood disorders among children or
among the elderly in this chapter, because significant methodological and conceptual
problems exist in estimating the prevalence and burden of mood disorders in both of
these populations. These issues are discussed elsewhere (Arean et al. 2017; Powell et
al. 2017; Richards and Bearden 2017; see also Chapter 37, “Pediatric Mood Disorders,”
and Chapter 38, “Geriatric Mood Disorders,” in this volume). We also will not discuss
epidemiological risk factors for mood disorders, which are reviewed elsewhere (John
son et al. 2017; Monroe and Cummins 2017).
Epidemiology of Mood Disorders
Prevalence
The many forms of mood disorders are typically divided into two groups: 1) unipolar
depressive disorders, which include various forms of major depressive disorder and
dysthymia (the latter term is used in DSM-IV and DSM-IV-TR [American Psychiatric
Association 1994, 2000], whereas persistent depressive disorder is the term used in
DSM-5 [American Psychiatric Association 2013]), and 2) bipolar and related disorders, including bipolar I and bipolar II disorders as well as cyclothymia. The epidemiological evidence presented in this chapter is almost exclusively based on DSM-IV
criteria due to the lack of available epidemiological data on DSM-5 disorders.
Depressive disorders of various degrees of severity are by far the most frequent
mood disorders. An estimated 4.4%–5.0% of the world’s population experience an epi
sode of major depressive disorder (MDD) in any given 12-month period (Ferrari et al.
2013; World Health Organization 2018), although there is substantial variation by
country and region. Perhaps the most accurate 12-month MDD prevalence estimate for
adults in the United States before the increase caused by the coronavirus disease 2019
(COVID-19) pandemic was 7.7% (Kessler et al. 2012b). Similar estimates have been re
ported in European Union countries (Wittchen et al. 2011). Much less information exists on lifetime MDD prevalence, with the most comprehensive data coming from the
WHO’s World Mental Health (WMH) Survey Initiative (Bromet et al. 2018), which
conducted coordinated adult community epidemiological surveys in 28 countries. In
those surveys, retrospectively reported lifetime MDD prevalence rates averaged
10.6% across countries, with an interquartile range (IQR) of 6%–14%. It is likely that
these retrospective self-reports underestimate true lifetime MDD prevalence (Moffitt
et al. 2010).
Bipolar I disorder (which requires the occurrence of a full manic episode) and bipolar II disorder (which requires the occurrence of a hypomanic episode or a major
depressive episode [MDE] in a person with a lifetime history of the other type of episode) are less prevalent than MDD. For example, the WMH surveys estimated that
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the 12-month prevalence of broad bipolar spectrum disorders was 1.2%, with estimates for bipolar I disorder, bipolar II disorder, and subthreshold bipolar disorder
(i.e., either a hypomanic episode by a person with no lifetime history of MDE or a subthreshold hypomanic episode by a person with a lifetime history of MDE) of 0.4%,
0.3%, and 0.8%, respectively (Merikangas et al. 2011). Across countries, retrospectively reported lifetime broad bipolar spectrum disorder prevalence was 1.9% in the
WMH surveys, although this estimate varied widely across surveys (IQRs of 0.6–2.5;
Kessler et al. 2018). Aggregate lifetime prevalence estimates were 0.6% for bipolar I
disorder, 0.4% for bipolar II disorder, and 1.4% for subthreshold bipolar disorder
(Merikangas et al. 2011). As with MDD, these retrospective lifetime prevalence esti
mates likely underestimate true lifetime prevalence (Moffitt et al. 2010).
Point prevalence estimates of dysthymia (i.e., chronic minor depression with a duration of more than 2 years that has never met diagnostic criteria for MDD) average
about 1.5% in the epidemiological studies in which such estimates have been assessed
(Charlson et al. 2013). Reliable estimates for cyclothymic disorder—as part of the bi
polar spectrum—are not available.
Available nationally representative data from adults in the United States suggest
that about 30% of MDD cases in the population at a point in time are severe, 50%
moderate, and 20% mild using conventional clinical severity thresholds (Kessler et al.
2005). The great majority of bipolar disorder cases at a point in time, in comparison,
are estimated to be serious (82%) and the remainder moderate. Among adolescents,
nationally representative U.S. data suggest that about one-third of mood disorder
cases are severe and the remainder are moderate or mild (Kessler et al. 2012a).
In addition to the prevalence of mood disorders, another useful metric for describing the magnitude of the burden of mood disorders is the lifetime morbid risk. The latter
is an estimate of the proportion of individuals in the population who will ever experi
ence a mood disorder in their lifetime. This estimate is considerably higher than the
proportion of the population that has ever experienced a mood disorder to date be
cause of the relatively high proportion of all mood disorders, especially MDD, with
first onsets in middle or old age (Kessler et al. 2012b).
Beyond the established DSM diagnoses, there is also substantial interest in clinically
significant mood syndromes that do not meet established diagnostic criteria (Angst and
Merikangas 1997; Ayuso-Mateos et al. 2010). Although no broad agreement exists on
the best way to define these syndromes, prevalence estimates of “minor depression”
(i.e., when individuals experience fewer symptoms or a shorter duration of symptoms,
typically less than 2 weeks) and of “recurrent brief depression” (i.e., when individuals
experience depressive episodes that last only a few days at a time but recur on a peri
odic basis over 1 or more years) have been as high as 17% (Rodríguez et al. 2012; Vandeleur et al. 2017). Epidemiological studies of these syndromes have only assessed
current prevalence, and therefore no research is available on their lifetime prevalence.
However, we know from available research that the prevalence of minor depression,
unlike MDD, is high among children (Wesselhoeft et al. 2013) and adolescents (Carrellas et al. 2017). We also know that minor depressive syndromes can be associated with
substantial distress and impairment, indicating that they are clinically significant
(Ayuso-Mateos et al. 2010; Merikangas et al. 1994). Furthermore, individuals with
subthreshold depressive syndromes have an elevated risk for subsequently developing MDD (Angst and Merikangas 1997; Forsell 2007) and bipolar disorder, which sug-
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gests that people with subthreshold depressive disorders might particularly benefit
from early intervention (Angst and Merikangas 1997; Judd et al. 2002).
A syndrome referred to as mixed anxiety-depression disorder (MADD) has also
been studied as a cross-sectional diagnosis of clinical interest. ICD-10 defined MADD
as the co-occurrence of subsyndromal depression with subsyndromal anxiety disorder
(World Health Organization 1992). Prevalence estimates for MADD, which require
that the person never meet full criteria for either MDD or an anxiety disorder, are
highly variable due to different definitions and study designs (Batelaan et al. 2012). A
key question, however, is whether it is important to distinguish between MADD and
minor depression. The small amount of research that has investigated this issue suggests that this distinction is not important, because the persistence-progression and
severity are comparable in the two syndromes (Spijker et al. 2010). This is a different
matter from the new ICD-11 category of threshold anxious depression (which requires
full criteria for MDD and a reduced duration requirement for anxiety symptoms from
the several months required for a diagnosis of generalized anxiety disorder to the
same 2 weeks as MDD), as the evidence is clear that this subtype of MDD is much
more impairing than non-anxious MDD (Ziebold et al. 2019).
Course
Prospective evidence from both clinical and community epidemiological studies
shows that MDD tends to be an intermittent recurrent disorder over the life course
(Coryell et al. 2009; Klein et al. 2008; Ten Have et al. 2019), with a variable number,
type, and duration of episodes. However, there is great heterogeneity in the natural
course of MDD. Persistence and length of episodes are lower in the general popula
tion than among individuals who seek treatment, with the latter often exhibiting only
partial remission between episodes (Coryell et al. 2009; Klein et al. 2008; Ten Have et
al. 2019). Most depressive episodes remit within 1 year. However, a large minority of
individuals in community samples (Eaton et al. 2008; Hardeveld et al. 2013; Mattisson
et al. 2007; Ten Have et al. 2018) and a majority in clinical samples (Conradi et al. 2017;
Gopinath et al. 2007; Hardeveld et al. 2013; Holma et al. 2008; Mueller et al. 1999; Pa
terniti et al. 2017; Ramana et al. 1995; Riihimäki et al. 2014) experience a recurrence of
MDD after an initial remission of symptoms. Moreover, chronic episodes, character
ized by symptoms persisting for at least 2 consecutive years, are relatively common
(Comijs et al. 2015; Judd et al. 1998; Penninx et al. 2013; Rhebergen et al. 2010; Ten
Have et al. 2018; Verduijn et al. 2017). Especially if individuals with MDD are followed up over longer periods of time, recurrent and chronic episodes are more often
the rule than the exception (Verduijn et al. 2017).
The course of bipolar disorder is also very heterogeneous but is typically characterized by distinct periods of mania, hypomania, depression, and mixed mood episodes
of variable duration and severity interspersed with periods of euthymia (i.e., a relatively stable mood state) (Alvarez Ariza et al. 2009; Judd et al. 2008; Miller et al. 2004;
Paykel et al. 2006). Manic/hypomanic, depressive, and mixed periods can switch rapidly from one pole to the other (i.e., have great polarity) or can be separated by periods
of subsyndromal symptoms rather than euthymia. The polarity, frequency, duration,
and intensity of manic and depressive periods (which may include episodes of psy-
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chosis) are highly variable, both within and between individuals (Alvarez Ariza et al.
2009; Judd et al. 2008; Miller et al. 2004; Paykel et al. 2006).
Clinical studies in people with bipolar disorder show that depressive episodes typically have first onsets earlier than manic or hypomanic episodes; that these depressive
episodes are typically more frequent than mania/hypomania or mixed episodes; that
mixed episodes typically have the longest durations, followed by depressive episodes;
and that depressive and mixed episodes are associated with more impairment than
manic/hypomanic episodes (Saunders and Goodwin 2010). In addition, rapid cycling
between manic and depressive periods in clinical samples has been associated with
earlier age at onset and greater disease burden (Bauer et al. 1994). Individuals with
rapid cycling are likely to experience greater distress and disturbance in everyday
functioning (Bauer et al. 1994; Coryell et al. 2003) and therefore may be more likely to
seek treatment than individuals without rapid cycling (Demyttenaere et al. 2004). Es
timates of the relative prevalence and correlates of rapid cycling in clinical samples
(Oepen et al. 2004) thus may provide a biased portrait of patterns in the population.
True prevalence and correlates of rapid cycling are unknown in the general population because it is difficult to date the onset and offset of manic and depressive episodes
or to delineate clear periods of partial or full remission in community samples. How
ever, some relevant information has been reported on frequent mood episodes, defined
as self-reports of four or more separate major depressive episodes or mania/hypoma
nia episodes within a year, as a proxy measure for rapid cycling, in the WMH surveys
(Lee et al. 2010; Nierenberg et al. 2010). About 40% of WMH survey respondents with
12-month bipolar disorder and close to one-third of those with lifetime bipolar disor
der reported a history of frequent mood episodes. These respondents reported an earlier age at onset, higher persistence, more severe depressive symptoms, greater
impairment associated with depressive symptoms, more days out-of-role associated
with mania/hypomania, more anxiety disorders, and an increased likelihood of using health services in comparison with respondents without frequent mood episodes.
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Age at Onset
Recently, the literature on age at onset of MDD (Yalin and Young 2019) and of bipolar
disorder (Dagani et al. 2019) was reviewed comprehensively. Age at onset is an im
portant but often neglected aspect of the epidemiology of mood disorders. Early age
at onset is associated with delays in initial help-seeking and increased disorder per
sistence and severity (de Girolamo et al. 2019). For MDD, the median age at onset was
found to be in the mid-20s. This is later than the median age at onset for several other
psychiatric disorders that are highly comorbid with MDD (Kessler et al. 2007b, 2011),
which means that MDD is typically the (temporally) secondary disorder in commonly occurring multivariate disorder profiles. This relatively late age at onset also
means that when comparing estimates of lifetime disorder burden, adjustments must
be made for between-disorder differences in future onsets, because relative estimated
lifetime prevalence (i.e., the proportion of the population who have experienced the
disorder to date) will be lower than relative estimated lifetime morbid risk (i.e., the
projected proportion of the population who will experience the disorder at some time
in their life, as estimated using actuarial methods with life tables [Kessler et al. 2007c,
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2012b]) for disorders with later-age-at-onset distributions. The situation is similar for
bipolar disorder, in which the median age at onset—that is, in the mid-20s—was esti
mated to be similar to that of MDD.
Comorbidities With Other Mental Disorders and
the Higher-Order Structure of Comorbidity
The majority of people with a history of a mood disorder also meet lifetime criteria
for at least one other mental disorder (Kessler and Ustün 2008; Merikangas et al.
2011). This comorbidity—confirmed by prospective and retrospective longitudinal
data (Beesdo et al. 2010; Beesdo-Baum et al. 2015)—is particularly strong for MDD
with anxiety and substance use disorders, as observed in both community-based epidemiological studies (Hasin et al. 2018; Wanders et al. 2016) and clinical studies with
patients from both primary care (Aillon et al. 2014; Kotiaho et al. 2019) and specialty
care settings (Lamers et al. 2011). Community-based epidemiological studies have
similarly observed that bipolar disorder is highly comorbid with anxiety disorders
(particularly panic attacks) as well as with behavior and substance use disorders
(Johnson et al. 2000; Lewinsohn et al. 1995; Merikangas et al. 2011).
There has been some controversy over how best to approach the complexity of comorbid disorders among patients and community cases. In time-pressured clinical
settings, assigning a broad principal diagnosis rather than multiple single diagnoses
may be most clinically useful, but doing so neglects the fact that information about
comorbidity is often of prognostic value. Although a number of clinical and method
ological concerns have been expressed (Wittchen et al. 2009), numerous studies have
suggested that latent predispositions to two broad classes of internalizing and exter
nalizing disorders account for most of the bivariate associations between hierarchyfree pairs of mood, anxiety, behavior, and substance use disorders (de Jonge et al.
2018; Eaton et al. 2012; Krueger and Markon 2006; Lahey et al. 2008). The internalizing
disorders have been further divided into fear disorders (e.g., panic, phobia) and dis
tress disorders (e.g., major depressive episodes, generalized anxiety disorder, posttraumatic stress disorder). A stable structure of this sort has been documented crossnationally (de Jonge et al. 2018). Researchers have also investigated clustering of
symptoms in ways that define syndromes that are more consistent with the actual
structure of psychopathology than those in the existing DSM and ICD classifications,
although this work is still preliminary (Kotov et al. 2018; Krueger et al. 2018).
An important line of investigation involving the evolving structure of comorbidity
has examined the temporal progression across lifetime comorbid mental disorders
and considered whether risk factors for individual disorders are more accurately conceptualized as risk factors for the latent dimensions underlying these disorders.
Kramer et al. (2008), in an early study of this sort, found that significant gender differ
ences in MDD became statistically insignificant when latent internalizing and externalizing dimensions were controlled for statistically (Kramer et al. 2008). A broader
analysis of a similar sort, based on cross-national self-reported data using retrospective
age-at-onset reports in cross-sectional community epidemiological surveys to mimic
temporal progression, found that essentially all temporally primary lifetime mood,
anxiety, behavior, and substance use disorders were significantly associated with subsequent secondary mental disorders (Kessler et al. 2011). Within-domain (i.e., inter-
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nalizing or externalizing) associations were for the most part stronger than betweendomain associations. Also, virtually all time-lagged associations between pairs of dis
orders were explained by a model that assumed the existence of mediating latent internalizing and externalizing variables. Importantly, MDD and bipolar disorder were
not found in this analysis to be more important than other internalizing disorders in
defining these latent variables. In line with these studies, Wittchen et al. (2014) pro
posed “symptom progression models” for diagnosing and treating mental disorders,
similar to approaches used in somatic medicine for hypertension, cardiovascular dis
ease, and diabetes.
Applying the same basic logic as in the comorbidity study by Kessler et al. (2011),
a more recent cohort study, using information about age at first treatment of common
mental disorders from health registries for the entire population of Denmark, found
that all temporally primary mental disorders were associated with elevated risk of subsequent onset of all temporally secondary mental disorders (Plana-Ripoll et al. 2019).
Decays in these associations were found as a function of number of years since onset
of the primary disorders. Early-onset (i.e., diagnosis before age 20 years) mood disorders were associated with an especially high absolute risk of subsequent neurotic disorders (ICD-10 codes F40–F48 [Neurotic, stress-related, and somatoform disorders;
includes phobic, anxiety, obsessive-compulsive, and dissociative disorders]) over the
next 5 years among both men (30.6%) and women (38.4%). Another noteworthy result
was that the time-lagged associations of temporally primary mood disorders with
subsequent secondary other mental disorders were consistently higher than the timelagged associations of the other mental disorders with subsequent onset of temporally secondary mood disorders.
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Disease Burden of Mood Disorders
Evidence has accumulated that mood disorders, including MDD (Broadhead et al.
1990; Coryell et al. 1993; Greenberg et al. 2015; Kessler et al. 2006a, 2007a; Rohde et al.
1990; Tweed 1993; Wells et al. 1989; Zeiss and Lewinsohn 1988), bipolar disorder
(Calabrese et al. 2003; Coryell et al. 1993; Dion et al. 1988; Kessler et al. 2006b; Lish et
al. 1994; MacQueen et al. 2001), and dysthymia (Cassano et al. 1990; Ferrari et al. 2016;
Hays et al. 1995; Hellerstein et al. 2010; Klein et al. 1988, 2008), impose substantial so
cietal burdens. The 2017 GBD Study estimated, for example, that MDD was the third
leading cause of years lived with disability (YLD) among women and the fifth leading
cause of YLD among men out of 354 types of disease and injury considered (James et
al. 2018). Although bipolar disorder is relatively rare, the early onset, severity, and chronicity of bipolar disorder in comparison with most serious chronic physical disorders
make it a substantially debilitating illness (Ferrari et al. 2016). The 2013 GBD Study estimated that bipolar disorder explained 1.3% of the total YLD, ranking it the sixteenth
leading cause of YLD (Ferrari et al. 2016). Although not examined in the GBD Study,
even highly prevalent subsyndromal levels of affective symptomatology have been
found in other studies to be associated with significant reductions in role functioning
(Backenstrass et al. 2006; da Silva Lima and de Almeida Fleck 2007; Judd et al. 1994,
1996, 2012; Rapaport and Judd 1998). Mood disorders may impair several aspects of
life, including educational attainment, marriage, parental functioning, working ability,
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and physical health, as discussed below, all of which may contribute to the high burden
and economic costs associated with mood disorders (Alonso et al. 2013).
Education
Numerous studies have demonstrated that early-onset mental disorders are associated with the premature termination of education (Breslau et al. 2008, 2011b, 2013;
Kessler et al. 1995; Lee et al. 2009; McLeod and Kaiser 2004; Porche et al. 2011; Vaughn
et al. 2011). The WMH surveys found that in comparison with peers without mood
disorders, individuals with childhood-onset mood disorders in high-income countries had twice the odds of not completing primary school and 1.5 times the odds of
not completing secondary education (Breslau et al. 2013). Other community-based
epidemiological surveys have also found that early-onset mood disorders were asso
ciated with lower odds of graduating from high school (Breslau et al. 2008, 2011b; Lee
et al. 2009; Porche et al. 2011) and higher odds of being disengaged while in school
(Vaughn et al. 2011).
Marital Timing, Stability, and Quality
Associations between pre-existing mental disorders and subsequent marriage have
been examined in a number of studies (Breslau et al. 2011a; Forthofer et al. 1996;
Whisman et al. 2007). These studies consistently show that early-onset mental disorders confer a lower probability of ever marrying (Breslau et al. 2011a; Forthofer et al.
1996), with these associations largely the same for men and women and across coun
tries. Mood disorders are among the most important pre-existing mental disorders in
these respects. Premarital history of mental disorders also predicts divorce (Butter
worth and Rodgers 2008; Kessler et al. 1998), with MDD and bipolar disorder among
the most important mental disorders in this regard (Breslau et al. 2011a). Again, asso
ciations are quite similar for men and women across countries.
MDD and bipolar disorder are also related to marital dissatisfaction (Whisman 1999)
and distress (Whisman 2007). Using nationally representative data from a U.S. epide
miological survey, one study found that bipolar disorder was the strongest predictor
of marital distress out of 11 mental disorders considered (Whisman 2007). Longitudi
nal studies show that the association of depressive symptoms with marital quality is
bidirectional (Mamun et al. 2009; Whisman and Uebelacker 2009), but with a stronger
time-lagged association of marital discord predicting depressive symptoms than vice
versa (Proulx et al. 2007). The relatively fewer studies that considered the effects of
clinical depression or bipolar disorder on marital functioning consistently docu
mented significant negative associations (Coyne et al. 2002; Grover et al. 2017; Kronmüller et al. 2011; Pearson et al. 2010).
Although many studies have examined the presumed mental health consequences
of relationship violence (Afifi et al. 2009; Kim et al. 2008; Renner 2009), some research
suggests that marital violence is partly a consequence of preexisting mental disorders
(Kessler et al. 2001; Lorber and O’Leary 2004; O’Leary et al. 2008; Riggs et al. 2000). A
WMH survey found that whereas bipolar disorder and depression were not individually associated with marital violence, premarital externalizing disorders (i.e., disruptive
behaviour disorders [ADHD, oppositional defiant disorder, conduct disorder], inter-
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mittent explosive disorder, and substance use disorders) among men were significantly associated with marital violence perpetration (Miller et al. 2011). This study also
found that premarital internalizing disorders (i.e., mood disorders, anxiety disorders,
and PTSD) among women were significantly associated with being in a violent rela
tionship. The causal mechanisms involved are unclear but warrant further investigation.
Parental Functioning and Offspring Health
It is well established that depression runs in families (Lieb et al. 2002) and that parental depression is associated with negative outcomes for offspring during infancy, prepubescence, adolescence, and adulthood (Gelaye and Koenen 2018; Goodman et al.
2011; Tronick and Reck 2009; Weissman et al. 2016). Findings include associations of
parental depression with low offspring birth weight, poor school performance, phys
ical health problems, depression, anxiety, substance abuse, and suicidal behavior.
These associations are stronger when parents experience persistent depression, live
in poverty, or both (Netsi et al. 2018; Santavirta et al. 2018; Stein et al. 2014; Weissman
2018). Research suggests that, like MDD, parental bipolar disorder is associated with
worse psychosocial functioning in children (Bella et al. 2011; Henin et al. 2005; Hirsh
feld-Becker et al. 2006; Maciejewski et al. 2018). Children with parents with bipolar
disorder have been found to have cognitive deficits (de la Serna et al. 2016; Diwadkar
et al. 2011; Klimes-Dougan et al. 2006), difficult temperaments (Bruder-Costello et al.
2007; Duffy et al. 2007; Sanches et al. 2014), worse coping skills (Jones et al. 2006; Nijjar
et al. 2014; Silk et al. 2006), and an elevated risk of developing mental disorders (Birmaher et al. 2009; DelBello and Geller 2001; Henin et al. 2005; Lapalme et al. 1997), including mood, anxiety, and behavior disorders.
Both observational studies (Garber et al. 2011; Pilowsky et al. 2008; Weissman et al.
2006) and randomized trials (Swartz et al. 2016; Weissman et al. 2015) have found that
children’s psychosocial functioning may improve when their mother’s depressive
symptoms remit and maternal depression is treated. In one 12-week randomized clin
ical trial (Weissman et al. 2015), mothers with depression were randomly assigned to
receive treatment with either escitalopram (a selective serotonin reuptake inhibitor),
bupropion (a norepinephrine and dopamine reuptake inhibitor), or the combination
of the two medications. This study found that depressive symptoms improved over
the 12 weeks across all treatment groups. However, a reduction in depressive symptoms in mothers was significantly associated with improvement in their child’s depressive symptoms only among the escitalopram monotherapy group. The authors
hypothesized that this association may have been due in part to the fact that mothers
in the escitalopram monotherapy group showed improvements in self-reported parental functioning, whereas mothers in the other treatment groups did not. A number
of studies have documented significant associations of both maternal (Lovejoy et al.
2000) and paternal (Wilson and Durbin 2010) depression with negative parenting behaviors. In another study that randomly assigned mothers with depression to nine
sessions of either brief interpersonal psychotherapy for mothers or brief supportive
psychotherapy, an improvement in the mothers’ depressive symptoms was associated with improved psychosocial functioning in their children (Swartz et al. 2016).
This effect was not different between treatment groups. However, other clinical trials
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that included younger children have not observed an association between an improvement in maternal depressive symptoms and child psychosocial outcomes
(Coiro et al. 2012; Verduyn et al. 2003).
Physical Morbidity
Mood disorders are associated with myriad chronic physical disorders, including
arthritis, asthma, cancer, cardiovascular disease, diabetes, hypertension, cognitive
impairment, and a variety of chronic pain conditions (Buist-Bouwman et al. 2005;
Derogatis et al. 1983; Ferro 2016; Forty et al. 2014; McWilliams et al. 2003; Ortega et al.
2006; Scott et al. 2007; Smith et al. 2013). These associations may be due to causal ef
fects of mental disorders on physical health, causal effects of physical disorders on
mental disorders, or shared antecedents. Spurious associations between mental and
physical disorders may also occur when the same set of symptoms is counted twice
to arrive at both psychiatric and physical diagnoses (Dowrick et al. 2005). This sort of
confounding, however, would not likely explain time-lagged associations (Penninx et
al. 2013) that are also observed.
In a study using WMH survey data from 10 countries, Scott et al. (2011) examined
time-lagged associations of early-onset (before age 21 years) mental disorders (includ
ing MDD, but not bipolar disorder) with the subsequent onset of a range of adult-onset
chronic physical health conditions. Early-onset MDD was associated with heart dis
ease, asthma, osteoarthritis, chronic spinal pain, and frequent or severe headaches, but
not with diabetes mellitus. Associations of early-onset mental disorders with these
conditions remained statistically significant, although the magnitudes of association
were slightly attenuated, after further adjusting for childhood adversities, a potentially
strong confounding variable in these associations. A recent nationwide study from
Denmark also observed an increased risk for onset of a range of somatic disorders
among persons with mood disorders (Momen et al. 2020). The association between the
presence of a mental disorder and an increased risk of subsequent medical conditions
was present for all mental disorders examined, however, suggesting that the association was not specific to mood disorders.
Several plausible mechanisms have been proposed to explain time-lagged associations of mood disorders with subsequent physical conditions. One possible pathway
is through poor health behaviors. Individuals with mood disorders are more likely to
smoke, be sedentary, drink heavily, use drugs, and be less adherent to medication or
treatment regimens, all of which may contribute to the development of physical conditions across the life course (Davidson et al. 2001; Scott et al. 2006). Another possible
pathway is through dysregulation of endocrinological, immune, or stress response systems, which are associated with both mental disorders and chronic disease development (Gibney and Drexhage 2013; Goodyer 2007; Juster et al. 2010; Pawelec et al. 2014;
Watson and Mackin 2006). In line with these mechanisms, MDD has been linked to an
accelerated cellular biological aging process (Han et al. 2018; Verhoeven et al. 2014).
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Comparative Impairments
The comparative associations of diverse diseases with various aspects of role functioning have been examined in community surveys. Results typically show that musculoskeletal disorders and mood disorders are associated with the highest levels of
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disability at the individual level among all commonly occurring disorders. For example, a study of 15 national surveys in the WMH Survey Initiative compared disorderspecific self-reported role impairment scores among people who experienced each of
10 chronic physical disorders and 10 mental disorders in the year before being inter
viewed (Ormel et al. 2008). MDD and bipolar disorder were the mental disorders
most often rated “severely impairing.” Even when including such severe conditions
as cancer, diabetes, and heart disease, none of the physical disorders considered had
impairment levels as high as those associated with MDD or bipolar disorder. Comparable results were observed when analyses were limited to subsamples of cases in
treatment and when comparisons were restricted to respondents who had both dis
orders in a pair (e.g., respondents who had both a mood disorder and cancer or both
a mood disorder and heart disease).
Individuals with mood disorders also report experiencing a high number of days
out of role. For example, in the WMH surveys, 62,971 respondents across 24 countries
reported on a wide range of common physical and mental disorders as well as days
out of role, defined as being totally unable to work or carry out normal activities be
cause of problems with physical health, mental health, or use of alcohol or drugs, in
the 30 days before the interview (Alonso et al. 2011). Individuals with MDD and bi
polar disorder experienced, on average, 34.4 and 41.2 days out of role per year, respectively. In comparison, the average number of days out of role for physical disorders
was 24.5 days per year. MDD was associated with 5.1% of all days out of role, which
was the fourth-highest population attributable risk proportion of all the disorders
considered (exceeded only by headache/migraine, other chronic pain conditions, and
cardiovascular disorders) and by far the largest among the mental disorders. This
large population attributable risk proportion is due to the comparatively high prevalence and strong individual-level effect of MDD on days out of role (Collins et al.
2005; Munce et al. 2007; Wang et al. 2003). The population attributable risk proportion
for bipolar disorder, in comparison, was 1.4%.
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Economic Costs
Estimates of the economic burden attributable to mood disorders are staggering. For
example, Greenberg et al. (2015) estimated that the annual cost of adults in the United
States with MDD was $210.5 billion in 2010. Approximately 47% of this cost was attributable to direct treatment costs, 5% to suicide-related costs, and the remaining 48%
to workplace costs. Of the total costs, only 38% were due to MDD itself, as opposed
to comorbid conditions. This analysis also found that every dollar spent on MDD direct costs (including both medical and pharmaceutical services directly related to
MDD treatment) was associated with an additional $1.90 in MDD-related indirect
costs (e.g., suicide-related, workplace) and another $4.70 in direct and workplace comorbidity costs. In a parallel study on the costs of bipolar I disorder in the United
States, Cloutier et al. (2018) estimated a total annual cost of $81,559 per individual
with bipolar I disorder. They estimated that the excess cost, defined as the difference
between costs incurred by individuals with bipolar I disorder and individuals in the
general population, was $48,333 per individual with bipolar I disorder. The largest
contributors to bipolar I disorder costs were caregiving costs, direct health care costs,
and unemployment costs. Another study estimated that in 2010 in Europe, 33 million
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