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CHAPTER 4
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Rating Scales
and Structured
Diagnostic Interviews
for Mood Disorders
David V. Sheehan, M.D., M.B.A.
“If something exists, it can be measured,” according to
Edward L. Thorndike. If something cannot be measured,
it is very difficult to study.
If you want to use rating scales and related assessment instruments, you
should first be clear on your goals. The most common goals are these:
1. You are following someone else’s (“just do it”) directive.
2. You have a genuine interest in integrating more targeted and precise measurement
into your clinical practice for assessment and follow-up monitoring.
3. You need to choose rating scales to address specific questions in your planned research studies.
The solutions to these goals are different.
The author thanks Janet Williams and Mark Zimmerman for contributions to the descriptions
of their scales in the manuscript.
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• Solution to Goal 1: Someone in your management thinks there is value in measurement-based care. They should be commended. Some managers are genuinely
interested in improving the quality of care in their systems. They may even intend
to do so in the best way possible. They may hire expert consultants to advise in
these choices. For this group there is hope.
Others may have more mercenary reasons; for example, they have been told by
a payor that they will not be reimbursed unless they assess treatment outcomes.
Alternately, they may need to justify why they should be the prime beneficiary in
a competitive bid for services. Unless the evaluators of such a process are sophisticated, this leads to a scramble to meet this need in the cheapest, easiest way possible, without serious regard for quality. They delegate responsibility for the selection of outcome measures to juniors in their systems “to make it happen.” This in
turn leads to the uninitiated frantically trolling search engines in a quest for quick,
cheap, easy solutions. I confess to little sympathy with this latter group. If following another’s directive is your only goal, you can skip the rest of this chapter and
instead use Google or Bing or social media. Good luck.
• Solution to Goal 2: If you have this goal, I can give you some useful guidance. You
should choose assessment instruments that are simple, brief, easy to use, and well
tried, and that meet your own specific goals for assessment. In general, instruments
that are self-rated are better choices. In fact, several scales designed as clinicianrated scales can often be self-rated by patients in a clinic. These include the Montgomery-Åsberg Depression Rating Scale (MADRS) (Montgomery and Åsberg
1979) and the Hamilton Depression Rating Scale (HAM-D) (Hamilton 1960, 1967).
Clinicians rarely spend time completing these rating scales at the first visit and
then at each follow-up visit in the clinic; the best initial intentions will soon fall
apart under visit time pressure.
Have the patient input this information in the waiting room or on a di gital plat-
form just before the visit. Front office staff need to be trained to routinely provide
these scales to patients on arrival. Clinicians should explain the need and value of
such scales and train patients to use them properly. The scales are completed be
fore the face-to-face clinician-patient visit begins. This practice helps eliminate the
often-heard clinician complaint “I don’t have enough time to implement assess
ment tools,” and provides the clinician with much fresh, precise information at the
start of each visit. The clinician can then check the data for accuracy and focus on
already reported information. In addition to saving time, this procedure also pro
vides some medicolegal protection because the clinician has a record of patientprovided information at each visit, in addition to the clinician’s progress notes,
which could be deemed as either misleading or self-serving.
Some patients who fear that a human rater is judging them are often more honest with a self-report form on paper or a digital platform. The paper or digital platform is judgment neutral, particularly for assessments of suicidality and substance
use.
• Solution to Goal 3: Design your research studies to answer your research question(s) in a clear, precise, and very focused manner. All too often researchers try to
address too many unresolved questions in one study. Accordingly, they adopt the
“let’s throw the kitchen sink” clutter of scales into the study, in the hope that
“something will stick.” The added clutter, however, distracts from the main focus.
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It is always better to spend more time at each research visit on the measure that
addresses the primary question. Purge the clutter that detracts from the time avail
able to properly address the central question. The more research experience that
you accumulate, the more you grasp the importance of this.
Too little time and attention are given to selecting the scale that could most accurately address the research question. Priority is often given to choosing an older
scale on which there are many “validation” studies or publications, when a newer
revised and improved version of the same scale or another scale with fewer asso
ciated publications would be a better choice. This lack of attention and lack of
thoughtful selection is not restricted to those who design such research studies or
to those who make these choices for clinical settings. Often the choice is driven by
pushback from study sponsors or clinicians, who have their own biases, or from
those who adjudicate the merits of such research protocols or clinical choices, who
are so focused on “validation” that they miss the forest for the trees. More on this
later in the section on validation.
I have seen many examples of sponsors choosing wrong versions of scales they
found on the internet (or at any rate not the proper version approved by the scale au
thor). Then the administration of the scale is not properly implemented, leading to
failures in studies that would have otherwise succeeded. This is a particular problem
with the accurate digital implementation of scales and the failure to follow author
guidance.
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A Simple Model for Measurement-Based
Care Assessments
The following is a simple formula to apply in selecting assessment instruments in
clinical and research settings for any psychiatric disorder. It has emerged from de
cades of experience in designing clinical research studies.
1. Confirm and document the diagnosis using a structured diagnostic interview.
2. Measure and track severity using dimensional rating scales.
3. Use one or more dimensional symptom scales to measure the severity of the cen-
tral cluster(s) of the primary disorder’s symptoms of interest. This may be the
cluster of depressive symptoms in major depressive disorder (MDD) or the mania
symptoms in bipolar disorder, or both.
4. Use a dimensional scale to measure the level of functional impairment associated
with the disorder.
5. Use a dimensional scale to measure the seriousness of suicidality associated with
the disorder.
6. Use a dimensional scale to measure the global improvement in the disorder over
time in response to treatment.
In addition, you can choose whatever you need or deem necessary. However, these
additional scales are usually not central, unless they are included to very specifically
measure a target not otherwise captured.
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Role of Structured Diagnostic Interviews
As a first step, you need to confirm your diagnosis accurately. This cannot be done
using a rating scale. No, you did not misread the last sentence. Let me repeat it, be
cause there is so much misunderstanding around this point. You CANNOT confirm
a psychiatric diagnosis using a rating scale.
Attempting to make a diagnosis using a rating scale that assesses the symptoms of
one syndrome, without gathering similar information on other potential psychiatric
diagnoses, is a mistake and potentially dangerous. This practice is regrettably ram
pant and has even been recommended by some august bodies. The 9-item Patient
Health Questionnaire (PHQ-9) (Spitzer et al. 1999), for example, cannot and should
not be used to confirm a diagnosis of MDD. It should only be used as a first-step
screening instrument. Using it for confirmation of a diagnosis would be like using a
mammogram to confirm a diagnosis of breast cancer and then proceeding directly to
doing a mastectomy. However, all too often primary care physicians who find an ele
vated PHQ-9 score after a brief discussion with the patient immediately start an antidepressant. Some of these patients have bipolar disorder; they often fail to improve
from any of a series of antidepressants, have rapid cycling induction, and become
more suicidal. PHQ-9 scores can be elevated in patients with various psychiatric and
medical conditions. To confirm and accurately document the diagnosis, you need to
do a structured (or, more accurately, semistructured) diagnostic interview that maps
directly to diagnostic criteria from DSM-5 (American Psychiatric Association 2013) or
ICD-11 (World Health Organization 2019).
-
-
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The Difference Between Structured
and Semistructured Diagnostic Interviews
In a semistructured diagnostic interview, the interviewer begins by following the
scripted wording in the questions provided, but then is allowed to ask follow-up
questions. In addition, the interviewer makes the final call on how to most accurately
code (score) the response, based on clinical judgment, experience, and training. Because the semistructured diagnostic interview gives the interviewer more latitude, it
is more suitable for clinically experienced and trained interviewers. In contrast,
strictly structured diagnostic interviews give the interviewer no latitude and must be
strictly adhered to. Fully structured diagnostic interviews are used only in rare situations, such as in epidemiology studies when only lay interviewers with limited
training are available. For the great majority of situations in health care settings, the
semistructured interview is the preferred choice.
The Difference Between a Structured Diagnostic
Interview and a Rating Scale
A structured diagnostic interview uses very carefully worded questions that map
closely to the DSM or ICD diagnostic criteria being probed. The response options are
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binary (yes/no). The algorithms that navigate through criteria rules are usually integrated into the sequence of questions. A patient’s symptoms may meet criteria for one
or more diagnostic categories (in which case these separate disorders are considered
comorbid diagnoses). Each structured interview handles the navigation and the algo
rithms in a different and more or less efficient way. Some structured diagnostic interviews are shorter to implement than others. Each covers a different number of
diagnoses from DSM-IV (American Psychiatric Association 1994), DSM-5, or ICD-10
(World Health Organization 1992). Some have several different versions (for different
sets of disorders) to accommodate different clinical settings and different research
populations. For this purpose, some make customized variants available to accom
modate different needs in the most time-efficient manner for each setting and study.
For example, the standard (time-efficient) Mini International Neuropsychiatric Inter
view (MINI) (Lecrubier et al. 1997; Sheehan 2020; Sheehan et al. 1997, 1998) has an abbreviated psychotic disorders module to rule out all psychotic disorders of all kinds
from most outpatient studies. However, for schizophrenia studies when it is neces
sary to identify and precisely differentiate all of the psychotic disorders from each
other, the shorter psychotic disorders module of the MINI should be swapped out for
a more expanded psychotic disorders module. This latter version is called the MINI
for Psychotic Disorders (Amorim et al. 1998).
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Examples of Semistructured Diagnostic Interviews
The principal semistructured diagnostic interviews used for adults are the MINI, the
Structured Clinical Interview for DSM-5 (SCID-5) (First 2015), and the Composite In
ternational Diagnostic Interview (CIDI) for ICD-10 (World Health Organization 1990)
or CIDI 3.0 for DSM-IV (Haro et al. 2006). There is also a SCID-5-PD for the evaluation
of personality disorders (First et al. 2016). The principal semistructured diagnostic interviews for children and adolescents are the Mini International Neuropsychiatric Interview for Children and Adolescents (MINI-KID) (Sheehan et al. 2010) and the
Kiddie Schedule for Affective Disorders and Schizophrenia (Kiddie SADS, K-SADS
PL-DSM-5) (Kaufman et al. 2016).
Both the MINI and the SCID (and their several variants) are organized into separate disorder- or episode-specific modules. Both cover the most common disorders
seen in clinical and research settings. The CIDI does not map exactly to DSM-5.
Variants of the SCID include the Clinician Version (SCID-CV) (First et al. 2015b)
and the Clinical Trials version (SCID-CT) (First et al. 2015a).
Variants of the MINI include the MINI for Psychotic Disorders (Amorim et al. 1998),
which has an enhanced psychotic disorders module to disaggregate all the psychotic
disorder subtypes from each other; the MINI KID (Sheehan et al. 2010); the MINI KID
for Psychotic Disorders Studies (Sheehan 2016a); the MINI Screen (Sheehan 2016b),
which has all the screening questions on one page (two sides) for use in primary care
settings; and the MINI Tracking (Sheehan 2016c), which provides dimensional (0–4
Likert scale) response options for all the key symptoms of each disorder in the MINI
and can be used as a collection of symptom severity outcome measures.
Earlier versions of the MINI, the SCID, and the CIDI have been validated against
each other (Lecrubier et al. 1997; Sheehan et al. 1997, 1998). Additionally, an earlier
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version of the MINI KID was validated against an earlier version of the K-SADS (Sheehan et al. 2010). Typically, the decision to choose one or another of these interviews is
driven by practical matters such as brevity, simplicity of use, and ease of navigation
through the questions. Some take several hours to administer correctly, whereas oth
ers can be done in 15–20 minutes on average, and the final information yield may not
be substantially different. Your best option is to get evaluation copies of each, test them
out on real patients, and make a selection based on the needs of your setting.
Depression Rating Scales
Adult Depression Rating Scales
Hamilton Depression Rating Scale
The HAM-D (Hamilton 1960, 1967) is a 17-item clinician-administered questionnaire
designed to assess the severity of depression in research and clinical practice. Over
the last 50 years, many variants have evolved, including versions with 6, 21, 24, 27,
and 31 items—the HAM-D6, HAM-D21, HAM-D24, HAM-D27, and HAM-D31 (Williams 2001).
The HAM-D6, pioneered by Per Bech and his team, was found to capture the items
most sensitive in assessing the severity of depression (Bech et al. 1975, 1981, 2009).
Maier and Philipp (1985) reported similar results, using a six-item version that has
five items in common with the version proposed by Bech and colleagues. Maier and
Philipp reported that their 6-item HAM-D was as sensitive as the 17-, 21-, and 24-item
versions. One study found that the 6-, 17-, 21-, and 24-item versions correlated
strongly with each other at baseline and at study endpoint (O’Sullivan et al. 1997).
Other authors reported that the shorter version of the scale assesses depression severity with comparable sensitivity to the longer versions (Williams 2001).
In a systematic review of the clinimetric properties of the HAM-D6 in comparison
to the HAM-D17 and the MADRS, Timmerby et al. (2017) identified 51 articles that
met their inclusion criteria from a universe of 681 unique search records on the HAMD6 in the PubMed, PsycInfo, and EMBASE databases. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (Moher
et al. 2009). The authors concluded that the HAM-D6 was “superior to both HAM-D17
and MADRS in terms of scalability (each item contains unique information regarding
syndrome severity), transferability (scalability is constant over time and irrespective of
sex, age, and depressive subtypes), and responsiveness (sensitivity to change in severity during treatment)” (Timmerby et al. 2017, p. 141).
The longer 24-item version assesses helplessness, hopelessness, pessimism, and
worthlessness (Williams 1988). The 27- and 31-item versions accommodate what are
referred to as symptoms of “atypical depression” (Williams 1988). These include
items such as increased appetite, hypersomnia, feelings of heaviness in the arms and
legs, and sensitivity to rejection or criticism. It is not unusual for authors to fail to
identify which “version” of the HAM-D they used in a study.
Hamilton’s original 17-item scale yields a maximum total score of 52; nine of the
items are scored 0–4, and eight items are scored 0–2. Hamilton did not assign these
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different dimensional ratings based on differential weighting. He thought that the
eight items were more difficult to anchor dimensionally and elected to use fewer re
sponse options for these items. The result is that the nine items contribute more to the
total score than the other eight (Hamilton 1967). In addition, there are three insomnia
items, two anxiety items, and two somatic items. The result is that these seven items
contribute disproportionately to the total score. These considerations and others have
led some reviewers to conclude that the instrument is psychometrically and concep
tually flawed (Bagby et al. 2004).
There was a significant period of time during which it was reported that benzodi-
azepines, notably alprazolam, had “significant antidepressant effects” (Warner et al.
1988), although they most definitely do not. These reports were based on misinterpre
tation of the HAM-D total score. Benzodiazepines do impact the insomnia, anxiety,
and somatic items of the HAM-D (and therefore the total score), but they have little
or no concurrent effect on the depressed mood or suicide items. To assert that they
have antidepressant effects based on the total is a mistake (Sheehan et al. 1980).
Some HAM-D items lack interval constancy between response options. For example, sexual symptoms can be rated as absent, mild, and severe, but not moderate.
There is a similar lack of interval constancy across the suicide response options. The
suicide item lacks sensitivity in detecting treatment effects.
Zimmerman et al. (2013b) provided an empirical basis for a severity of depression
classification using the 17-item HAM-D. In a study of 627 outpatients, the authors rec
ommended the following score ranges for each of the major severity categories: no
depression (0–7); mild depression (8–16); moderate depression (17–23); and severe
depression (≥24).
Reynolds and Kobak (1995) developed a self-report version of the HAM-D called
the Hamilton Depression Inventory (HDI). This measure consists of a 23-item full
form, a 17-item form, and a 9-item short form. The 17-item HDI corresponds in content to the HAM-D17. Substantial psychometric data support its value and use in outpatients.
Despite its wide use, the HAM-D is not optimally designed to be easily learned or
reliably administered. Indeed, clinicians often develop their own idiosyncratic interpre
tations of items. As a result, reliability is compromised. Williams’s (1988) Structured
Interview Guide for the Hamilton Depression Rating Scale (SIGH-D) was developed
to improve item reliability and facilitate rater training so that inexperienced clinicians
are not left to devise their own questions to assess each item. The SIGH-D provides
the rater with helpful parenthetical qualifications and semistructured follow-up
questions for individual items. This guide encourages more standard administration
and has been shown to result in improved levels of interrater agreement for most of
the HAM-D17 items (Williams 1988).
Given the growing frequency of scale administration by telephone, video, and virtual conferencing, several studies have examined the accuracy of clinical assessments
obtained from face-to-face, in-person administration compared with telephone and
video administration, yielding results that favor the continued use of remote administration in training and research (Amarendran et al. 2011; Hubley et al. 2016; Kobak
et al. 2008a; Shore et al. 2007). The COVID-19 pandemic has increased the need to improve the consistency, accuracy, and comparability of scale administration between
face-to-face and remote video and virtual conferencing. This goal can be accom-
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