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1 Comorbidity: What Is It and Why Is It Important? 17
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2
Models of Comorbidity
Rhonda Brown and Einar Thorsteinsson
2.1 Computational and Clinical Models
of Concurrent Symptom Development
Several authors have suggested that unravelling the causes of comorbidity
ranks among the top clinical priorities [1, 2]. However, most existing the-
ories of disease comorbidity provide only a broad overvie w of the manner
in which comorbidity is likely to arise. Logically, there are several broad
hypotheses that can be advanced to explain the existence of comorbidities,
including that: (i) a causal relationship exists between the coexisting dis-
orders; (b) a common factor(s) increases the likelihood that both disorders
will occur; and/or (c) the relationship is spurious [3]. However, the broad
overarching theories have, for the most part, not been tested empirically.
R. Brown (
B
)
Australian National University, Canberra, ACT, Australia
e-mail: rhonda.brown@anu.edu.au
E. Thorsteinsson
University of New England, Armidale, NSW, Australia
e-mail: ethorste@une.edu.au
© The Author(s) 2020
R. Brown and E. Thorsteinsson (eds.), Comorbidity,
https://doi.org/10.1007/978-3-030-32545-9_2
23
24 R. Brown and E. Thorsteinsson
Further, few comorbidity theories are comprehensive yet specific enough
to help guide the exploration of relationships between specific comorbid
disorders and the likely role played by intervening factors. As a result,
we know little about the extent to which comorbidity is relevant to our
understanding of single disorders and conditions; the mechanisms which
likely underpin the development of comorbidities; and the nature of the
interactions between specific risk factors and comorbid disorders.
Nevertheless, in some instances, there is clarity as to the likely tem-
poral evolution of the clinical co-occurrences (e.g. mood disorders and
insomnia) [4]. However, bidirectional relationships are ubiquitous in the
scientific literature, and they have been documented in regards to most of
the disorders and symptoms covered in this book. For example, anxiety
and depression can predict the later onset and/or worsening of impaired
sleep, but, additionally, sleep problems can predict the later onset or wors-
ening of anxiety and depressed mood [4]. Crucially, we do not yet fully
understand the nature of these so-called bidirectional relationships. In
particular, noting the existence of a bidirectional relationship between
anxiety and depression over time tells us nothing about how the disorders
are functionally related to each other, including whether (or not) they are
causally related.One possibility, as discussedin Chapter 6,isthatimpaired
sleep and low mood are causally related to each other; such that sleep dis-
turbance can impair a person’s mood and low mood can worsen sleep, but
it is unclear exactly how this occurs and whether the mechanism/s is the
same (or different) in each case.
In contrast, it might be the case that the relationship is spurious.For
instance, anxietyand depression symptomstend to overlap witheach other
(e.g. impaired sleep is characteristic of both states) [5]; and the Diagnos-
tic and Statistical Manual of Mental Disorders-5 (DSM-5) criteria for
the disorders includes a similar range of symptoms (e.g. impaired sleep,
gut problems) [5]. Further, the scales and clinical interviews that exam-
ine these states tend to ask about a similar range of somatic and affective
symptoms. Thus, the observed relationship between anxiety and depres-
sion may at least in part be a by-product of these overlapping symptoms
and/or the double-counting of the symptoms, although this is unlikely to
fully explain the observed relationships [6, 7]. Finally, it is possible that a
common factor (e.g. stressfullife-events) can explainthe tendency of people
2 Models of Comorbidity 25
to experience comorbid anxiety and depression; for example, stress may
result in rumination at night which interferes with sleep and, in turn, this
may contribute to anxiety and/or depressed mood [8]. However, it is not
exactly clear how anxiety and depression comorbidity develops, despite
the plethora of research on the topic, and partly this is due to the lack of
complex real-world models of anxiety and depression co-causation.
Outside the context of anxiety and depression comorbidity, even less is
known about the roots of comorbidity, even in the case of disorders that
are highly comorbid with each other (e.g. obesity, insomnia). Relatively
few studies in the scientific literature have specifically sought to address
this comorbidity, and few clinical theories have been developed to explain
the phenomenon. Thus, there is a crucial need to develop a theoretical
model/s that can parsimoniously explain the presence and development
of the comorbidities.
Nevertheless, some specific (but limited) theories of comorbidity do
exist which seek to explain the mechanisms by which specific disorders
can co-occur. For example, anxious and/or depressive rumination [9]and
cognitive and somatic arousal (i.e. stress-diathesis model of insomnia)[10]
are posited to interfere with the onset of sleep in some people (e.g. insom-
niacs), and, over time, this may contribute to shorter sleep duration and
cumulative tiredness,if the hyper-arousal orru mination persists over time.
Additionally,late night-eatinghas beenposited to interferewith sleeponset
and shorten sleep duration, and as a result, people with night-eating syn-
drome are likely to develop sleep problems and be overweight/obese, rela-
tive tounaffected individuals [11,12]. However, as discussed in Chapter3,
it is unclear exactly how overweight/obese people will develop comorbid
sleep problems, for example whether the weight or sleep problem develops
first and the exact mechanism/s by which the late night-eating impacts on
sleep and weight.
As detailed in Chapters 3, 4,and7, overweight/obese people are at an
increased risk of developing a range of other comorbid disorders and con-
current symptoms including stress, anxiety, depression, binge-eating dis-
order, diabetes mellitus type-II, and sleep-disordered breathing, relative to
unaffected individuals, which mightalso contributeto morbidityand mor-
tality in affected individuals. Thus, it is apparent that overweight/obesity
is highly comorbid with many different disorders, although we know little
26 R. Brown and E. Thorsteinsson
about the specific mechanisms underpinning some of the comorbidities.
As a result, if an overly specific mechanism (e.g. night-eating syndrome
diagnosis) is examined for its utility in explaining the sleep and weight
problems in overweight/obese individuals, we may learn little about the
extent to which other concurrences (e.g. depression) or related risk factors
(e.g. physical inactivity) will interact together to contribute to the prob-
lems. Clearly, there is a crucial need to employ a more holistic theoretical
approach to the problem of disease comorbidity, which specifies the likely
roles played by multiple different intervening variables.
Currently, there are only two theories that have sought to explain the
presence and development of comorbidity in regards to multiple disorders
and symptoms.The first is a computational model that seeks to explain the
co-occurrence of multiple psychiatric symptoms. Schmittmann and col-
leagues [13] have used a network approach to conceptualise psychological
attributes (e.g. anxiety) as ‘networks of directly related observables’; that
is, a single construct (e.g. anxiety) might be conceptualised as a network of
related variables (e.g. somatic arousal, cognitive arousal, fear cognitions),
which overlaps with other constructs (e.g. somatic arousal, depression).
In another paper, the authors [1] sought to examine the network’s struc-
ture and its dynamics in an effort to replicate the empirical evidence of
psychiatric symptom comorbidity, using simulation methods to evaluate
the plausibility of the network’s properties. This work has resulted in the
network model of psychiatric symptoms that was derived from the analysis
of overlapping symptoms in DSM-4.
In their computational model, individual DSM-listed symptoms were
represented as individual nodes. In the analysis, nearly one-half of the
entire DSM-4 network of psychiatric symptoms (N = 208 symptoms,
across 69 disorders) was shown to be connected in the so-called giant
component. In the computational models, the distances between the
nodes for major depressive episode (MD) and generalised anxiety dis-
order (GAD) were reported to be consistent with the published comor-
bidity rates for the disorders, and model simulations of MD and GAD
reproduced the published population statistics (e.g. prevalence statistics)
for the two disorders [1].