ORIGINAL RESEARCH
published: 29 November 2021
doi: 10.3389/fpsyt.2021.775762

Poor Separation of Clinical Symptom
Profiles by DSM-5 Disorder Criteria
Jennifer Jane Newson*, Vladyslav Pastukh and Tara C. Thiagarajan
Sapien Labs, Arlington, VA, United States

Edited by:
M. Zachary Rosenthal,
Duke University, United States
Reviewed by:
Gerald Young,York University, Canada
Matthew Southward,
University of Kentucky, United States
Aaron Reuben,
Duke University, United States*Correspondence:
Jennifer Jane Newson
jennifer@sapienlabs.org
Specialty section:
This article was submitted to
Psychopathology,
a section of the journal
Frontiers in Psychiatry
Received: 14 September 2021
Accepted: 05 November 2021
Published: 29 November 2021
Citation:
Newson JJ, Pastukh V and
Thiagarajan TC (2021) Poor
Separation of Clinical Symptom
Profiles by DSM-5 Disorder Criteria.
Front. Psychiatry 12:775762.
doi: 10.3389/fpsyt.2021.775762

Frontiers in Psychiatry | www.frontiersin.org

Assessment of mental illness typically relies on a disorder classification system that is
considered to be at odds with the vast disorder comorbidity and symptom heterogeneitythat exists within and across patients. Patients with the same disorder diagnosis exhibit
diverse symptom profiles and comorbidities creating numerous clinical and researchchallenges. Here we provide a quantitative analysis of the symptom heterogeneity anddisorder comorbidity across a sample of 107,349 adult individuals (aged 18–85 years)
from 8 English-speaking countries. Data were acquired using the Mental Health Quotient,an anonymous, online, self-report tool that comprehensively evaluates symptom profiles
across 10 common mental health disorders. Dissimilarity of symptom profiles within andbetween disorders was then computed. We found a continuum of symptom prevalence
rather than a clear separation of normal and disordered. While 58.7% of those with
5 or more clinically significant symptoms did not map to the diagnostic criteria of
any of the 10 DSM-5 disorders studied, those with symptom profiles that mapped toat least one disorder had, on average, 20 clinically significant symptoms. Within thisgroup, the heterogeneity of symptom profiles was almost as high within a disorderlabel as between 2 disorder labels and not separable from randomly selected groups
of individuals with at least one of any of the 10 disorders. Overall, these results quantify
the scale of misalignment between clinical symptom profiles and DSM-5 disorder labelsand demonstrate that DSM-5 disorder criteria do not separate individuals from random
when the complete mental health symptom profile of an individual is considered. Greater
emphasis on empirical, disorder agnostic approaches to symptom profiling would help
overcome existing challenges with heterogeneity and comorbidity, aiding clinical andresearch outcomes.
Keywords: diagnosis, symptom profiles, DSM-5, comorbidity, heterogeneity, depression, ADHD, autism spectrum
disorder (ASD)

INTRODUCTION
The mental health of our society is a problem of growing concern. In 2017, 792 million people
lived with a mental health disorder globally (1), while depression is the leading cause of disability as
measured by Years Lived with Disability (YLDs) and a major contributor to the global burdenof disease (2, 3). As people grapple with the consequences of the Covid-19 pandemic, thenumber reporting challenges with their mental health has further increased (4–7), emphasizing
the importance of improving our understanding of mental illness to enable better outcomes.
In the absence of an understanding of underlying etiologies and biology of mental healthchallenges, the classification systems of DSM-5 (8) and ICD-11 (9) evolved to define mental

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health disorders by symptom criteria whereby specific groupingsof symptoms are each assigned “disorder” labels. This approachpresupposes that (i) the specific groupings of symptoms are goodat separating individuals based on their symptom profile, suchthat individuals with a particular diagnosis have similar symptom
presentations and (ii) that these symptom-based diagnostic
groups each share a common underlying etiology. However, alarge literature now highlights major misalignments between
these disorder classifications and the symptomatic experience of
patients. Firstly, the criteria-based approach to diagnosis, where
one must have a subset of symptoms out of a larger group, means
there are many ways to be diagnosed with the same disorder.
For example, by some estimates there are 636,120 different
possible symptom combinations that can lead to a diagnosis
of PTSD (10) and 227 different possible ways to be diagnosed
with depression (11), indicating considerable heterogeneity insymptom profiles within disorders. The observed heterogeneity
in symptom profiles within disorders (12–19) has also led
to various new definitions of disorder subtypes (20, 21).
Secondly, there are many possible ways that patients can becomorbid across DSM-5 disorders (22), with studies showing that
individuals commonly meet the criteria for multiple disorders(23–31) and that evolution of disorders across a lifetime is apervasive phenomenon (28, 32–34). This misalignment is further
exacerbated by an array of mental health assessment tools that areheterogeneous and overlapping, creating a system of diagnosis
and evaluation that is poorly standardized and introducing
further ambiguity (35, 36).
As a result, there has been considerable discussion overthe validity of the DSM-5 classification approach (37–46). In
addition, numerous studies have reported on the consequencesof this misalignment between disorder classifications and patient
symptom profiles. First, by focusing on a specific subset ofsymptoms for the definition of a disorder, this classification
system precludes an understanding of the true range anddiversity of symptom profiles present across clinical populations
as the objective is typically to narrow down to a single
disorder diagnosis, rather than embrace individual differences(47) or symptom complexity (48–50). However, the nuancesof an individual’s symptom profile hold important informationthat can guide clinical decision-making (51–58). Second, it
results in inaccurate, not otherwise specified (NOS) or mixed
diagnoses (59–63) where those with symptom profiles that area poor fit for the clinical criteria, may have to embark on along struggle to find effective treatment (64). Third, from a
research perspective, studies aiming to develop new therapies
and medications for mental health disorders typically selectparticipants based on a diagnosis, whereas this group may besubstantially heterogeneous in terms of their symptom profiles,
and therefore outcomes (19, 21). To try to overcome some ofthese challenges, alternative transdiagnostic frameworks suchas the Research Domain Criteria (RDoC) from the National
Institute of Mental Health (NIMH) (65, 66) and the HierarchicalTaxonomy of Psychopathology (HiTOP) (67–69) have been
proposed. These offer alternative ways to evaluate symptom
profiles, either in terms of specific transdiagnostic constructs
and subconstructs (RDoC), or dimensions at multiple levels ofFrontiers in Psychiatry | www.frontiersin.org

hierarchy (HiTOP), where the peak of the hierarchy can bedenoted as a single overall factor of psychopathology, or p-factor(70, 71).
However, despite these various criticisms, the disorderclassification system laid out by the DSM-5 is ingrained in
psychiatric decision-making, social policy and popular discourse,
and continues to dominate the field of mental health. One
reason for the continued debate, is that there has not been aclear empirical assessment of how well the disorder classification
system separates individuals into groups based on symptom
criteria. For example, although there may be many combinationsof symptoms that deliver a diagnosis of any individual disorder(10, 11), the overall symptom profiles of individuals with one
diagnosis may nonetheless be substantially different relative toindividuals in another disorder group. If so, the classification
system would then serve to broadly discriminate between groups,
which has a first order utility for differential determination oftreatment pathways.
Here we assessed quantitatively the degree to which thesymptom profiles of individuals associated with one disorderclassification, as determined by the DSM-5 symptom criteria,
were distinct from those of another, and how much they differedfrom a random selection of individuals with any disorder. To
do so, we used a self-report mental well-being assessment tool,
the Mental Health Quotient (MHQ) (72) that comprehensively
covers mental health symptoms pertaining to 10 common
DSM-5 disorders in a manner that is easy to administer, andon completion provides individuals with an aggregate score(quotient) along with a detailed report with feedback andrecommendations for help seeking and self-care.
The MHQ was developed through a systematic analysis of
the question content from 126 commonly used mental healthquestionnaires and interviews which typically conform to criteria
laid down in the DSM (35). The 126 assessments included
commonly used diagnostic scales and assessments of depression,anxiety, bipolar disorder, attention-deficit/hyperactivity disorder(ADHD), posttraumatic stress disorder (PTSD), obsessive
compulsive disorder (OCD), addiction, schizophrenia, eating
disorder, and autism spectrum disorder (ASD), as well as
cross-disorder tools. Forty-three identified symptom categories
were then combined with additional elements from RDoC
to develop a questionnaire that comprehensively assessed acomplete profile of 47 mental elements using a 9-point life impact
rating scale [for details on the question format see (72) and
Supplementary Figures 1A,B]. In this study, we used data from107,349 respondents across 8 English-speaking countries (73)
who completed the MHQ between April 2020 and June 2021.
The 47 MHQ elements were mapped to the diagnostic criteria, as
outlined in DSM-5, for the 10 mental health disorders on which
the MHQ was based. We then evaluated the heterogeneity ofsymptom profiles of those within DSM-5 disorder groups relative
to between disorder groups and random groupings of individuals
who met the criteria for any one of the 10 disorders.
We show here that, contrary to expectation, the heterogeneityof the symptom profiles across individuals selected randomly
across any disorder group, was no different than acrossindividuals selected from within any specific disorder group.

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Furthermore, symptom profiles were as heterogeneous within
a disorder group as they were between disorder groups. This
challenges the fundamental premise on which the DSM-5 has
been developed and has significant implications from both aclinical and research perspective.

TABLE 1 | Demographic breakdown of the sample by age, gender, and country.
%

18–24 years

24,14922.50%

25–34 years

12,74011.87%

35–44 years

9,927

9.25%

45–54 years

15,31414.27%

55–64 years

20,40219.01%

65–74 years

17,741

16.53%

75–84 years

6,337

5.90%

85+ years

739

0.69%

Males

43,31340.35%

Females

62,19557.94%

Non-binary/third gender

1,016

0.95%

Prefer not to say

825

0.77%

United States

27,57125.68%

United Kingdom

16,11515.01%

Australia

7,413

6.91%

Canada

8,012

7.46%

India

28,28526.35%New Zealand

3,866

3.60%

Singapore

1,639

1.53%

South Africa

7,804

7.27%

Other countries

6,644

6.19%

Age group

MATERIALS AND METHODS
Data Acquisition
The data sample was taken from the Mental Health Million openaccess database which acquires data by offering the MHQ online
and free of charge in multiple languages (72, 73). Respondents
took this 15–20-min assessment anonymously online for the
purpose of receiving a mental well-being score (the mental
health quotient) and personalized report on completion, basedon their responses (see below and Supplementary Figure 1C).
The MHQ was publicized primarily through social media and
Google Ads targeting a broad cross section of adults aged 18 andabove, where self-selecting respondents may have had a specificmental health interest or concern. Outreach directed participants
to the MHQ website (https://sapienlabs.org/mhq/) to complete
the assessment. No financial compensation was provided. The
Mental Health Million Project is a public interest project thattracks the evolving mental well-being of the global population,and its social determinants, and is governed by an academic
advisory committee. The project has ethics approval from the
Health Media Lab Institutional Review Board (HML IRB), an
independent IRB that provides assurance for the protection ofhuman subjects in international social & behavioral research(OHRP Institutional Review Board #00001211, Federal Wide
Assurance #00001102, IORG #0000850).
For this study, we utilized responses to the English versionof the MHQ between April 2020 and June 2021 which included
107,349 respondents predominantly from the United States,
Canada, United Kingdom, Australia, New Zealand, South Africa,
Singapore and India, with the majority of respondents living inIndia (26.35%) United States (25.68%), and smaller proportionsof respondents living in Singapore (1.53%) and New Zealand(3.60%). Across all countries, 40.35% of the sample were male;
57.94% female; 0.95% reported as non-binary/third gender and
0.77% preferred not to say. The sample covered all age brackets
but had larger samples in the younger (18–24; 22.50%) andolder (55–64; 19.01%) age groups relative to their proportion in
the population. These differences likely reflect the self-selectednature of the sample (see Limitations section in the Discussion).
The demographic breakdown (including age, gender, country,
employment status, and education) of the sample is shown in
Table 1 and Supplementary Table 1.

GenderCountryquestions across 126 different mental health questionnaires andinterviews spanning 10 major mental health disorders as wellas cross-disorder assessments. These included questionnaires fordepression [e.g., Patient Health Questionnaire, PHQ-9 (74)],
anxiety [e.g., Generalized Anxiety Disorder Assessment, GAD7 (75)], bipolar disorder [e.g., Mood Disorder Questionnaire,
MDQ (76)], ADHD [e.g., Conners Adult ADHD Rating
Scale, CAARS (77)], PTSD [e.g., Clinician-Administered PTSD
Scale for DSM-5, CAPS-5 (78, 79)], OCD [e.g., Yale Brown
Obsessive Compulsive Symptom Scale & ChecklistTM , Y-BOCS(80, 81)], addiction [e.g., Addiction Severity Index, ASI-5 (82)],
schizophrenia [e.g., Brief Psychiatric Rating Scale, BPRS (83, 84)],
eating disorder [e.g., Eating Disorder Inventory, EDI-3 (85)], andASD [e.g., Autism Diagnostic Interview Revised, ADI-R (86, 87)],
as well as cross-disorder tools [e.g., Structured Clinical Interview
for DSM-5, SCID-5-CV (88); see (72) for more details and a fulllist of the 126 assessment tools]. These disorders were selectedbased on their inclusion in the DSM-5 clinical interview (SCIDCV) (88). In addition, ASD and eating disorder were included
due to both their prevalence and their broad public and scientificinterest. A total of 10,154 questions were coded and consolidated
into a set of 43 symptom categories. The resultant items werethen reviewed in the context of other transdiagnostic frameworks
including RDoC constructs and subconstructs put forward by
the NIMH (65), and a few additions (e.g., selective attention,
coordination) were made to ensure that the list of items reflected

MHQ Assessment
The MHQ is a transdiagnostic assessment that comprehensively
covers all possible symptoms across 10 major mental healthdisorders as well as elements derived from RDoC constructs
and subconstructs. The development and structure of the MHQ
are described in previously published papers (35, 72). Briefly,
the list of MHQ elements or items was determined based on acomprehensive coding of mental health symptoms assessed in

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N

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unrealistic responses (e.g., those who stated they had not eaten
for 48+ hours or who stated they had slept for 30+ hours) were
excluded. These responses might be poorly considered by the
respondent or reflect circumstances where thinking was impaired
and therefore invalid. Altogether, this resulted in the exclusion of
6.9% of respondents, with 107,349 respondents available for the
final analysis.

components within this non-DSM framework. The resulting
categories were then reorganized into a set of 47 elements that
describe mental health and mental well-being (Figure 3 andSupplementary Table 2).
Within the MHQ, each of these 47 elements were rated byrespondents using a 9-point life impact rating scale reflecting
the impact of a particular mental aspect on one’s ability
to function (Supplementary Figures 1A,B). This scale wasdesigned depending on whether the item exists on a spectrum
from positive to negative (spectrum items such as memory) or
as varying degrees of problem severity (problem items such as
suicidal thoughts). For spectrum items, 1 on the 9-point scalereferred to “Is a real challenge and impacts my ability to function,”
9 referred to “It is a real asset to my life and my performance,”
and 5 referred to “Sometimes I wish it was better, but it’s ok.”
For problem items, 1 on the 9-point scale referred to “Nevercauses me any problems,” 9 referred to “Has a constant and severe
impact on my ability to function,” and 5 referred to “Sometimes
causes me difficulties or distress but I can manage.” Respondents
made rating responses based on their current perception ofthemselves rather than a specific time frame. However, the ratingon the 9-point life impact scale has been shown to have a goodcorrelation against more common metrics of symptom frequency
and severity, and ratings across elements are highly reliable fromsample to sample (89).
Data across 30 descriptors relating to the demographic, life
experience, lifestyle and situational profile of the individual
were also collected. Demographic descriptors relating to the age,gender, geographical location, employment status, and educationof the individual were obtained prior to the collection ofmental well-being ratings, while life experience (e.g., life traumas,
Covid-19 impacts, medical conditions, life satisfaction, substanceuse), lifestyle (sleep, exercise and socializing), and help seeking(including reasons for/ not) were obtained after the collection ofmental well-being ratings [see (73) for a complete list].
On completion, respondents received their MHQ (acomposite mental well-being score that places them along aspectrum from distressed to thriving), along with a personal
report that provided recommendations for help-seeking andself-care. Provision of a personal report aimed to ensure greaterinterest of the respondent to answer questions thoughtfully
and accurately. An extract of an example MHQ reportdetailing the MHQ score and sub-scores is presented in
Supplementary Figure 1C; see (72, 73) for further information
on how these scores were calculated. The MHQ score has
substantial criterion validity, relating systematically to bothproductivity and clinical burden (89).

Symptom Criteria
For each of the 47 MHQ items, responses were determined
to be clinically significant symptoms if they met a particularthreshold of impact on the individual’s ability to function(hereafter referred to as “clinical symptom(s)” or “symptom(s)”).
For problem items which represented a unidimensional scaleof symptom severity from 1 to 9, the threshold selection for aclinical symptom was ≥8. For spectrum items where elements ofmental function could be either a negative symptom or a positive
asset (e.g., memory) and the 1–9 scale ranged from negative
symptom (1–4) to positive asset (6–9), the threshold selection fora clinical symptom was a rating of ≤1. Testing of the 9-point
scale for one problem element (Feelings of Sadness, Distress or
Hopelessness) demonstrated that a selection of 8 corresponded
to an average symptom frequency of 5 days per week (89). This
threshold thus corresponds to the DSM-5 criteria of experiencing
the symptom “nearly every day” for depression. However, wenote that the correspondence between frequency and life impact
rating may differ from item to item (see Limitations section of
the Discussion).

Computing Dissimilarity of Symptom
ProfilesSymptom dissimilarity between each pair of individuals wascalculated as the percentage of symptoms that differed (i.e., where
one person had the symptom and the other did not) out of
the total number of possible symptoms (i.e., 47 MHQ items
representing 47 possible symptoms; Supplementary Table 2).
Figure 1A shows the symptom profile determined for one
person whereby each of the 47 MHQ elements (representing
a comprehensive set of possible symptoms) are coded as 1
if it was a symptom (i.e., the rating selected was above thethreshold where it was considered a symptom, generally in thispaper ≥8 for problem items and ≤1 for spectrum items), and0 if it was not a symptom (i.e., did not meet the threshold).
Symptom dissimilarity between two people was calculated as
the sum of the absolute difference between the symptom matrix
for each person as a fraction of the total number of symptoms(Figure 1B). Restated:

Exclusion Criteria

N
X
Symptom dissimilarity = (Abs(Sp1 − Sp2))/N

The following exclusion criteria were applied to the data. First,
those respondents who took under 7 min (an indication that thequestions were not actually read) or over 1 h to complete theassessment (suggesting that the individual was not focused on
the assessment), were excluded. Secondly, individuals who foundthe assessment hard to understand (i.e., responded no to thequestion, “Did you find this assessment easy to understand?”)were excluded. Third, respondents who made unusual or

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MHQ=1

where Sp1 is the symptom profile of person 1, Sp2 is thesymptom profile of person 2 and N is the total number of possible
symptoms (here 47).
For example, out of 47 possible symptoms, if each person had
20 symptoms which did not overlap at all they would have a4

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FIGURE 1 | Calculation of the symptom dissimilarity between two people. (A) Symptom profile for one person (person 1 in B). Each of the 47 MHQ elements(representing a comprehensive set of possible mental symptoms) are coded as 1 if it is a symptom (i.e., the rating selected is above the threshold where it isconsidered a symptom, generally in this paper ≥8 for problem items and ≤1 for spectrum items), and 0 if it is not a symptom (i.e., does not meet the threshold). (B)Symptom profile for two people. Symptom dissimilarity is calculated as the sum of the absolute difference between the symptom matrix for each person/total numberof symptoms.

dissimilarity of 40/47, or 85%. If they each had just 10 symptoms
which did not overlap, they would have a dissimilarity of 20/47, or43%. Conversely, if all 10 or 20 symptoms perfectly overlapped,
they would have a dissimilarity of 0/47 or 0%. This measure thus
reflects not only which symptoms the two people had in common,but also the common lack of symptoms.

Hopelessness or poor Outlook & Optimism AND/OR poor
Drive & Motivation or poor Curiosity, Interest & Enthusiasm,
AND must have least 5 of (1) Feelings of Sadness, Distress or
Hopelessness or poor Outlook & Optimism; (2) poor Drive &Motivation or poor Curiosity, Interest & Enthusiasm; (3) poor
Appetite Regulation; (4) Confusion or slowed thinking; (5) low
Energy Level; (6) low Self-image or low Self-worth & Confidence orGuilt & Blame; (7) poor Decision-making & Risk-taking or poor
Focus & Concentration or poor Selective Attention; (8) Suicidal
thoughts or intentions (Figure 3 and Table 2).
Although this MHQ diagnostic criteria mapping does notmean that the individual would be diagnosed with that disorderin the context of a clinical interview, it indicates that their patternof clinical symptoms broadly aligned with the diagnostic criteria
for that disorder. However, we note the caveat that for bipolar
disorder, symptoms denoted extreme versions of positive assets(e.g., grandiosity and decreased need for sleep) that were notfully articulated within the MHQ, while for OCD the MHQ items
were broader (e.g., obsessive thoughts were incorporated within ageneral item reflecting Strange, unwanted and obsessive thoughts;
see section Limitations). Furthermore, specific criteria relating
to symptom timing were not included, as this is not includedin the MHQ which assesses the individual’s current perceptionof themselves.

Mapping of the MHQ to DSM-5 Criteria
MHQ items were first mapped to the symptoms describedwithin the criteria of each of the 10 DSM-5 disorders, basedon the closest semantic match (Figure 3 and Table 2). We notethat some disorder criteria included multiple symptoms andtherefore mapped to more than one MHQ item. For example,
one criterion for depression includes “Diminished ability to thinkor concentrate, or indecisiveness,” which mapped to MHQ itemsof Focus and concentration, Selective attention, and Decisionmaking & risk-taking. As MHQ questions were formulated from
DSM-based assessment tools, all symptoms described in the
DSM-5 criteria for these 10 disorders had an MHQ match. The
specific criteria rules of the DSM-5 were then applied to arrive at
diagnostic rules using the MHQ for each of the 10 disorders (seeTable 2).
For example, within the DSM-5, a positive diagnosis fordepression requires that the individual has experienced atleast 5 out of a list of 8 different criteria with one of them
being a depressed mood or loss of interest or pleasure. These
criteria, when applied to the MHQ elements, required thatthe symptom profile (MHQ elements meeting the symptom
threshold) included at least Feelings of Sadness, Distress or

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Within Disorder Analysis
To determine the heterogeneity of symptom profiles within
a disorder group (i.e., all those who mapped to a particulardisorder as described in section Mapping of the MHQ to DSM-5

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TABLE 2 | Rules for mapping MHQ items to DSM-5 diagnostic criteria.

TABLE 2 | Continued

Depression

Must have at least ONE of: (1) Drive & Motivation OR Curiosity,
Interest & Enthusiasm; (2) Feelings of Sadness, Distress or
Hopelessness OR Outlook & optimism
AND
Must have at least FIVE of: (1) Drive & Motivation OR Curiosity,
Interest & Enthusiasm; (2) Feelings of Sadness, Distress or
Hopelessness OR Outlook & Optimism; (3) Appetite Regulation; (4)Confusion or slowed thinking; (5) Energy Level; (6) Self-worth &Confidence OR Self-image OR Guilt & Blame; (7) Focus &Concentration OR Selective Attention OR Decision-making &Risk-taking; (8) Suicidal thoughts or intentions.

Addiction

Must have Addictions
AND
Must have at least two of (1) Decision-making & Risk-taking; (2)Emotional Control (3) Avoidance & Withdrawal (4) Relationships
with others; (5) Self-control & Impulsivity

ADHD

Must have at least FOUR of (1) Focus & Concentration; (2)Selective Attention; (3) Drive & Motivation; (4) Planning andOrganization; (5) Memory
OR
Must have Restlessness & Hyperactivity AND Self-control &Impulsivity

AnxietyMust have Fear & Anxiety AND Stability & Calmness OR
Emotional control
AND
Must have at least THREE of (1) Restlessness & Hyperactivity; (2)Energy Level; (3) Focus & Concentration OR Selective Attention;(4) Anger & Irritability; (5) Experience of Pain (6) Sleep Quality; (7)Avoidance & Withdrawal

ASD

Must have at least THREE of: (1) Social Interactions andCooperation; (2) Relationships with others; (3) Repetitive orcompulsive actions; (4) Adaptability to Change; (5) Sensory
sensitivity; (6) Selective Attention OR Focus & Concentration OR
Curiosity, Interest & Enthusiasm; (7) Empathy

BipolarMust have: Mood swings
AND
Must have at least ONE of: (1) Drive & Motivation OR Curiosity,
Interest & Enthusiasm; (2) Feelings of Sadness, Distress or
Hopelessness OR Outlook & optimism
AND
Must have at least FIVE of: (1) Drive & Motivation OR Curiosity,
Interest & Enthusiasm; (2) Feelings of Sadness, Distress or
Hopelessness OR Outlook & Optimism; (3) Appetite Regulation; (4)Confusion or slowed thinking; (5) Energy Level; (6) Self-worth &Confidence OR Self-image OR Guilt & Blame; (7) Focus &Concentration OR Selective Attention OR Decision-making &Risk-taking; (8) Suicidal thoughts or intentions.

PTSD

OCD

Each item needed to be above the rating threshold of ≥8 for problem items or ≤1 forspectrum items to be considered a clinically significant symptom.

Criteria), we computed the symptom dissimilarity (as defined in
section Computing Dissimilarity of Symptom Profiles) between
all pairs of individuals within the group and determined theaverage symptom dissimilarity and the standard deviation (SD)of the dissimilarity between all pairs. We similarly computed
the dissimilarity of symptom profiles of each individual within
a disorder group to each individual in a random demographicallymatched group, computing statistics on these distributions as
described in section Statistics. We note that 10 iterations ofrandom groupings (each N = 3,333) yielded similar results(average symptom dissimilarity across all pairs of individuals ±SD was 40.5 ± 11.3% and ranged from 39.5 to 41.2%) indicatingthat any comparison would provide a similar result.

Must have a life trauma (or prefer not to say) response on the<life_trauma> question (i.e., any response except “I HAVE NOTEXPERIENCED ANY TRAUMA”)
AND
Must have at least ONE of: (1) Traumatic Flashbacks (2)Nightmares (3) Unwanted, Strange or Obsessive thoughts
AND
Must have Avoidance & withdrawal
AND
Must have at least TWO of: (1) Memory (2) Self-worth &Confidence OR Self-image OR Outlook & Optimism; (3) Guilt &Blame; (4) Feelings of Sadness, Distress & Hopelessness; (5)Curiosity, Interest & Enthusiasm OR Drive & Motivation; (6)Relationships with others.
AND
Must have at least TWO of:(1) Aggression toward others OR Anger& Irritability; (2) Decision-making & Risk-taking; (3) Fear & Anxiety;(4) Focus & Concentration OR Selective Attention; (5) Sleep Quality

Between Disorder Analysis
We next compared the symptom profiles of individuals between
two disorder groups. To do so, we first computed the symptom
dissimilarity between each individual from one disorder groupand each individual from another disorder group. We did so
by including all individuals in each group (but excluding selfcomparisons), and also by removing those individuals who
were part of both disorder groups (i.e., would be considered“comorbid”). This computation was performed across the wholesample, and separately across different country, age and genderdemographic groups.

Must have Unwanted, Strange or Obsessive Thoughts AND
Repetitive or compulsive actions AND Fear & Anxiety
ANDMust have at least ONE of: (1) Stability & Calmness; (2)Self-control & Impulsivity; (3) Emotional Control

Statistics
To determine whether individuals within a disorder group were
statistically more homogenous than a randomly selected group ofindividuals with at least one disorder, we computed the p-valueof the difference in the distribution of dissimilarities for eachdisorder group compared to a randomly selected group using a
two tailed t-test.
To test the hypothesis that each disorder group represented
sufficient symptom homogeneity such that it represented acluster that could be differentiated from across the disorder space,
we used the Hopkins Statistic (H) as described in (90). This

Schizophrenia Must have Unwanted, Strange or Obsessive Thoughts
AND Hallucinations
AND
Must have at least ONE of (1) Speech & Language; (2) Repetitive
or compulsive actions (3) Drive & Motivation OR Relationships withothers OR Social Interactions and Cooperation OR Curiosity,
Interest & Enthusiasm
Eating disorder Must have Appetite Regulation AND Fear & Anxiety AND
Self-image(Continued)

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Coordination and Selective attention were rarer, with rates <2%.
When the threshold was shifted one point (gray bars in 2B;≥7 for problem items; ≤2 for spectrum items), the prevalenceof different symptoms increased across all elements, but to agreater or lesser degree across elements. We note that while there
were significant differences in symptom prevalence between
age and gender groups, the exponential structure of symptom
prevalence was the same. While not shown here, differences
between age and gender groups have been shown with asubset of this data previously (91). Given these differences, ouranalysis looked at the whole sample as well as across different
demographic groups.

statistic compares the distance (i.e., symptom dissimilarity) ofelements from each disorder group to the nearest neighbor within
the disorder group and to the nearest neighbor in the randomly
selected group as follows:
n
P

H=

n
P

di

i=1

di +

i=1

n
P

ri

i=1

Where d is the nearest neighbor distance within the disordergroup and r is the nearest neighbor distance to the random group.
If the total nearest neighbor distance between symptom
profiles within the disorder group is, on average, the same as
the distance to the nearest neighbor from the random group, H
should be about 0.5, implying that the disorder group does not
represent a cluster that can be differentiated from random. On
the other hand, if the disorder groups each represent relatively
compact and isolated clusters, H should be larger than 0.5 andalmost equal to 1.0 for very well-defined clusters.

Mapping of Clinical Symptoms to theDSM-5 Criteria
Figure 3 (left) shows the mapping of elements or items of the
MHQ to each of 10 mental health disorders, based on the DSM-5
diagnostic criteria. Only 12 out of 47 MHQ items that mapped
to DSM-5 disorder criteria were unique to one “disorder”
label. However, the specific criteria for each disorder differed(see Table 2). Applying these rules to the clinical symptomsof each individual (as determined in Symptom Criteria sectionin the Methods), the prevalence for each of these disorders
ranged between 1.6% (schizophrenia) to 6.9% (PTSD) (Figure 3,
right). These fall within the broad ranges of prevalence acrossepidemiological studies. For example, the prevalence of ADHD
within the current adult sample (2.4%) closely matched theestimated global prevalence of ADHD of 2.8% (92). Similarly,
the prevalence of depression within this study (5.8%), lay within
the range of prevalence estimated by some studies (3, 93,94), although is lower than more recent estimates from othersources (95, 96). However, prevalence estimates vary substantiallydepending on the assessment tool used, the geographical location,
and also the timing in relation to the Covid-19 pandemic (7, 96–
103). For example, the epidemiological estimates for PTSD varied
between 7 and 53.8% in one recent meta-analysis (7).

RESULTS
Prevalence and Profile of ClinicalSymptoms
The prevalence of clinical symptoms in the sample wasdependent on the threshold used to determine clinicalsignificance. While the DSM-5 typically specifies that a mental
aspect should be considered a symptom if it occurs “nearly
every day,” “persistently,” or “more days than not,” in reality,
thresholding is determined using a wide variety of criteria acrossdiagnostic screening tools, from severity and frequency to timing
and duration of symptoms (35). Here the criterion used wasseverity of impact on one’s ability to function. At the stringent
threshold criterion used to define a clinical symptom throughout
this paper (see Symptom Criteria section in the Methods), 40.1%
of all respondents across the sample had no clinical symptoms,
while 25.9% had more than 5 clinical symptoms (Figure 2A).
However, when the threshold was shifted one point (dotted line
in 2A; ≥7 for problem items; ≤2 for spectrum items), it resulted
in only 24.0% of all respondents having no clinical symptoms
while 44.4% had more than 5. The fraction of the sample withsuccessively larger numbers of clinical symptoms decreased ina manner that was best fit by an exponential function. Thus,
purely from the perspective of number of clinical symptoms,
there was no specific distinction where one might draw the line
between normal and disordered. Changing the threshold todefine a clinical symptom less stringently in terms of severity
decreased the fraction of people with no clinical symptoms but
did not change the shape of this curve (Figure 2A dotted line).
Within this distribution, some clinical symptoms were moreprevalent than others (Figure 2B; black bars). Four clinicalsymptoms were reported by >20% of the sample including
Unwanted, strange or obsessive thoughts, Feelings of sadness,
distress or hopelessness, Fear and anxiety and Avoidance andwithdrawal. Others such as problems with Sensory sensitivity,

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DSM-Diagnostic Comorbidity Profiles
Overall, 12.3% of all respondents mapped to one or moredisorders (Figure 4A), within which 62.7% mapped to morethan one disorder (Figure 4A, inset) in line with findingsthat comorbidity is the norm (33, 34, 71). Figure 4B showscomorbidity for the 10 disorders by rows (numbers in
Supplementary Table 3). For example, row 1 represents thefraction of individuals whose symptom profiles aligned withthe criteria for depression and who also aligned with thecriteria for each of the other disorders (shown across thecolumns). Here 25.7 and 37.3% of those with depression
also met the criteria for ADHD and ASD, respectively.
Conversely, 62.6 and 68.1% of individuals who mapped to
ADHD and ASD criteria, respectively, also aligned with
criteria for depression. Note from prevalence estimates in
Figure 3, while 5.8% of individuals met the criteria for
depression, only 2.4 and 3.2% met the criteria for ADHD and
ASD, respectively.

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FIGURE 2 | Symptom prevalence in the sample. (A) Prevalence of number of clinical mental health symptoms in the sample as defined by a threshold of severity in
the MHQ (≥8 for problem items and ≤1 for spectrum items; black line). Dotted line denotes the prevalence when the threshold is shifted by one point (≥7 for problem(Continued)

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FIGURE 2 | items and ≤2 for spectrum items). The percentage with each higher number of clinical symptoms decreases exponentially. (B) The proportion of
respondents who reported severe problems with each of the 47 elements of mental well-being included in the MHQ, as defined by a threshold of severity in the MHQ(≥8 for problem items and ≤1 for spectrum items; black bars). Prevalence of specific symptoms across the sample ranged from 24.4 to 1.1%. Gray bars denote theproportion when the threshold is shifted by one point (≥7 for problem items and ≤2 for spectrum items).

FIGURE 3 | Mapping of MHQ symptoms to DSM-diagnostic criteria. MHQ items corresponding to each DSM-5 defined disorder (left; red squares) and associatedprevalence of each disorder following application of diagnostic rules (right). Full mapping and rules shown in Table 2.

FIGURE 4 | Prevalence and comorbidity among DSM-disorders. (A) Percentage of individuals by number of disorders to which they map. Inset shows the mapping of
the 12.3% with one or more disorder. (B) Percentage of individuals with each disorder who also map to each other disorder (numbers in Supplementary Table 3).
(C) Distribution of number of clinical symptoms across individuals who do not map to any disorder criteria (black line) and those who map to at least one (red line).

Symptom Prevalence Beyond
DSM-Disorder Criteria

any one disorder (Figure 4C; red curve). On the other hand,
58.7% of respondents with 5 or more clinical symptoms did not
map to any of the 10 disorder criteria (Figure 4C, black curve).

The minimum number of clinical symptoms required for adiagnosis of each of these 10 disorders, as per the DSM-5, ranges
from 3 to 6. For instance, depression requires a minimum of 5
symptoms across 5 criteria out of a possible 18 symptoms and8 criteria. Nonetheless, among those who mapped to at least
one disorder, the average number of clinical symptoms was 20(median 15), substantially higher than the minimum criteria for

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Symptom Heterogeneity Within Disorders
If a disorder group had significantly lower symptom dissimilarity,i.e., more homogeneous symptom profiles, compared to arandomly selected group, this would indicate that the DSM5 diagnostic criteria for that disorder was good at separating

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randomly selection individuals from across any of the 10 disordergroups. We hypothesized that overall, the symptom profiles ofindividuals within each DSM-5 disorder group would be moreindividuals by a particular profile. We therefore first evaluated theheterogeneity of symptom profiles within each DSM-5 disordergroup to see how they compared to the heterogeneity of groups of

FIGURE 5 | Heterogeneity of symptom profiles within and between disorders. (A) Distribution of symptom profile dissimilarity across all individuals who map to criteria
for PTSD (gray), ASD (black) and a randomly selected groups of individuals from the pool of individuals with at least one of the 10 disorders (red). (B) Distribution of thedissimilarity of symptom profiles of all individuals mapping to the diagnostic criteria for addiction (black) and the dissimilarity of symptom profiles between those withaddiction and those with ADHD excluding comorbid individuals. (C) Symptom dissimilarity within and between each pair of disorders ranges from 37.9% (within
PTSD) to 45.6% (between ASD and Schizophrenia). (D) Symptom dissimilarity as in (C) but with comorbid individuals excluded ranged from 37.8 to 45.7%.

TABLE 3 | Dissimilarity of symptom profiles within each disorder group and the comparison against symptom profiles within groups of randomly selected individuals fromany disorder group.
Dissimilarity within disordergroup (average ± SD)

Dissimilarity between disorder group andrandom group (average ± SD)

T-testH-statistic

Depression

41.3 ± 9.8%

41.9 ± 10.2%

P < 0.00000

0.51

Anxiety

40.1 ± 10.0%

41.5 ± 10.6%

P = 0.00091

0.50

Bipolar40.4 ± 9.4%

42.1 ± 10.6%

P < 0.00000

0.50

OCD

38.7 ± 10.7%

42.1 ± 11.4%

P < 0.00000

0.50

Schizophrenia

38.8 ± 12.7%

42.7 ± 11.8%

P < 0.00000

0.50

PTSD

37.9 ± 10.7%

40.0 ± 11.4%

P < 0.00000

0.50

Eating disorder38.8 ± 11.4%

40.9 ± 11.4%

P < 0.00000

0.49

Addiction

40.5 ± 10.9%

41.9 ± 11.0%

P < 0.00000

0.50

ADHD

43.4 ± 11.9%

44.1 ± 11.7%

P < 0.00000

0.52

ASD

44.9 ± 10.1%

44.6 ± 10.4%

P < 0.00000

0.53

All random groups

40.5 ± 11.3%

N/a

N/a

N/a

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homogeneous than a randomly selected group and therefore
reasonably well-separated.
First, within each disorder, the heterogeneity (average
dissimilarity ± SD) ranged from 37.9 ± 10.7 for PTSD (i.e.,the most consistent) to 44.9 ± 10.1 for ASD (the mostheterogeneous), while the mean heterogeneity for randomly
selected groups was 40.5 ± 11.3% (Figure 5A, diagonal of
Figure 5C and Table 3). Only PTSD (which had the highest
prevalence in the sample, see Figure 3) had more than 5% lower
average dissimilarity (6.4%) and therefore greater homogeneity
than the average of the random groups, while ADHD and ASD
had more than 5% higher average dissimilarity and therefore
greater heterogeneity than the average of the random groups (7.2
and 10.9%, respectively; Figures 5A,C and Table 3). While thedifferences in dissimilarity values were small, even very small
differences were statistically significant by t-test (all p < 0.0009),
since the sample size was large.
Given that the difference in heterogeneity from the random
groups was significant, although in both positive and negative
directions (i.e., more and less heterogeneous), we also lookedat the Hopkins Statistic, a measure of how well disordergroups could be separated. The Hopkins Statistic measures how
much the distance (dissimilarity) between nearest neighbors ofelements in the disorder group differs from the distance to thenearest neighbor in the random group such that a value of
0.5 would indicate that there was no difference. On the other
hand, separable groups where the elements have closer neighbors
within their group relative to a random group would typicallyhave a Hopkins Statistic value >0.7 (see section Methods). Herethe Hopkins statistic ranged from 0.49 to 0.53 across all 10
disorders indicating that they were not distinguishable from arandom sample and therefore did not represent a valid cluster.
We similarly ran this analysis for different demographic
segments in order to determine whether there was any difference
for a particular demographic (e.g., age, gender, country) suchthat the DSM-5 criteria better separated individuals in one
particular group. However, the results were similar irrespectiveof demographic (Supplementary Table 4).

TABLE 4 | Dissimilarity of symptom profiles between each disorder group, withand without comorbid individuals removed.

Symptom Dissimilarity Between Disorders
As randomly selected groups would typically have a composition
that reflected the prevalence of disorders and their comorbidities,
we next hypothesized that the symptom profiles between
disorders might be significantly different from one anotherwhen comorbid individuals were removed. Significantly, between
disorders, dissimilarity was not much higher than the within
disorder dissimilarity, ranging from 39.8% (between Anxiety
and PTSD) to 45.6% (between Schizophrenia and ASD)
(Figure 5C and Table 4). In addition, contrary to expectation,
excluding all comorbid individuals from the comparison
decreased heterogeneity, although not substantially, as comorbid
individuals tended to have a larger number of symptoms overall,and therefore greater symptom dissimilarity (Figure 5D and
Table 4). Only in 18% of comparisons did heterogeneity increase.
Overall, in either direction the maximal difference was 5.7%.
Figure 5B shows an example of the within disorder heterogeneity

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Dissimilarity
between disorder
group(average ± SD)

Dissimilarity between
disorder group; comorbid
individuals removed(average ± SD)

Depression & Anxiety

41.6 ± 9.6%

40.2 ± 9.0%

Depression & Bipolar

41.4 ± 9.7%

N/a

Depression & OCD

42.3 ± 10.0%

41.2 ± 9.2%

Depression &Schizophrenia

43.6 ± 10.6%

42.8 ± 10.2%

Depression & PTSD

40.7 ± 10.0%

39.4 ± 9.9%

Depression & Eating
Disorder40.9 ± 10.3%

39.4 ± 9.7%

Depression & Addiction

42.7 ± 9.9%

41.9 ± 9.6%

Depression & ADHD

44.0 ± 10.1%

41.5 ± 8.6%

Depression & ASD

44.3 ± 9.6%

42.4 ± 8.3%

Anxiety & Bipolar

41.0 ± 9.5%

40.3 ± 8.9%

Anxiety & OCD

40.4 ± 10.2%

39.3 ± 9.4%

Anxiety & Schizophrenia

42.3 ± 10.7%

41.7 ± 10.2%

Anxiety & PTSD

39.8 ± 10.2%

38.3 ± 9.8%

Anxiety & Eating Disorder

41.0 ± 10.2%

39.6 ± 9.2%

Anxiety & Addiction

41.7 ± 10.2%

41.9 ± 9.9%

Anxiety & ADHD

42.9 ± 10.6%

42.5 ± 10.0%

Anxiety & ASD

43.9 ± 9.7%

43.4 ± 8.9%

Bipolar & OCD

41.1 ± 9.7%

40.8 ± 9.1%

Bipolar & Schizophrenia

42.5 ± 10.3%

42.5 ± 10.0%

Bipolar & PTSD

40.5 ± 9.9%

39.5 ± 9.4%

Bipolar & Eating Disorder

41.0 ± 10.0%

41.5 ± 9.5%

Bipolar & Addiction

42.3 ± 9.9%42.5 ± 9.5%

Bipolar & ADHD

43.3 ± 10.0%

42.8 ± 9.2%

Bipolar & ASD

43.9 ± 9.5%

43.6 ± 8.7%

OCD & Schizophrenia

40.7 ± 11.2%

40.4 ± 10.6%

OCD & PTSD

39.9 ± 10.7%

37.8 ± 10.1%

OCD & Eating Disorder41.6 ± 10.5%

41.4 ± 9.4%

OCD & Addiction

41.5 ± 10.7%

42.1 ± 10.1%

OCD & ADHD

42.7 ± 11.2%

43.5 ± 10.3%

OCD & ASD

43.8 ± 10.3%

44.3 ± 9.7%

Schizophrenia & PTSD

40.9 ± 11.4%

39.3 ± 10.8%

Schizophrenia & Eating
Disorder43.4 ± 10.9%

42.9 ± 9.9%

Schizophrenia & Addiction

42.9 ± 11.2%

42.6 ± 10.5%

Schizophrenia & ADHD

44.9 ± 11.8%

44.7 ± 11.1%

Schizophrenia & ASD

45.6 ± 10.9%

45.7 ± 10.5%

PTSD & Eating Disorder40.1 ± 10.6%

38.4 ± 10.0%

PTSD & Addiction

40.7 ± 10.5%

39.8 ± 10.3%

PTSD & ADHD

43.0 ± 11.0%

41.7 ± 10.7%

PTSD & ASD

43.7 ± 10.0%

43.4 ± 9.9%

Eating Disorder &Addiction

42.1 ± 10.7%

41.9 ± 10.1%

Eating Disorder & ADHD

43.5 ± 10.8%

42.7 ± 9.7%

Eating Disorder & ASD

44.0 ± 10.1%

43.3 ± 9.0%

Addiction & ADHD

43.9 ± 11.2%

44.1 ± 10.5%

Addiction & ASD

44.7 ± 10.3%

44.6 ± 9.8%

ADHD & ASD

45.3 ± 10.4%

43.5 ± 8.6%

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Psychiatric Symptom Profiles and DSM-5

DSM-5 disorder diagnosis. However, changing the thresholdof clinical significance within the MHQ rating framework by
one severity point changed clinical prevalence to 25.8%. Whileprevalence estimates in this data, based on point estimates andusing the most stringent threshold criteria, matched up withsome epidemiological prevalence estimates (3, 92–94), studies
have wide ranges that depend on the tool used, the thresholds
considered, as well as geography and time period (7, 96–103).
For example, a recent meta-analysis found that point prevalenceof depression ranged from 3.1 to 87.3% across 48 studies
and 7 populations (97). Notwithstanding the heterogeneity ofdisorders, this highlights that there is no absolute epidemiologicalestimate but rather that each estimate must be qualified by
the thresholds used, as well as other factors. Thus, changes
in prevalence across time periods cannot be performed using
different tools. It also raises the question of who decides theappropriate threshold, or boundary, between a disorder andthe normal variation of human existence (104–106). This has
implications for numerous societal aspects, such as the thresholdat which a person warrants medical attention as well as debate
over financial and resource allocation.

for addiction compared to the heterogeneity between individuals
with addiction and ADHD after removing those individuals
who were comorbid for both. This demonstrates the very small
difference in dissimilarity among those with addiction comparedto the dissimilarity between those who had addiction and those
who had ADHD (but not both).
Thus, contrary to expectation, the DSM-5 criteria by which
disorders are defined does not separate individuals based on
their overall symptom profile and is closely equivalent to arandom drawing of individuals. As a demonstration of theheterogeneity within disorders, we show the clinical symptom
profile of 2 individuals who met the diagnostic criteria for
depression (Figure 6). If these individuals were only consideredon the basis of a depression screening, their dramatically different
symptom profiles would be missed.

DISCUSSION
The heterogeneity of symptom profiles within disorders, andcomorbidity across disorders, are known to present a significant
challenge to the effective diagnosis, treatment and research ofmental illness (20, 23, 31). Here we quantify the degree ofheterogeneity of comprehensive symptom profiles of 107,349
individuals within and across DSM-diagnostic criteria. While
58.7% of those with 5 or more clinically significant symptoms
did not map to the diagnostic criteria of any of the 10 DSM5 disorders, those with symptom profiles that mapped to atleast one disorder reported, on average, 20 clinically significant
symptoms. The heterogeneity of symptom profiles was almost
as high within a disorder group (average dissimilarity of 40.5%
across all disorders) as between 2 disorder groups (41.8% on
average with comorbid individuals removed) and no individual
disorder group was separable from randomly selected groups ofindividuals with at least one disorder, with the Hopkins statistic
ranging between 0.49 and 0.53. Overall, the DSM-5 disorder
criteria failed to separate individuals by symptom profiles any
better than random assignment. Thus, rather than representing amethod of separating groups based on symptoms, disorder labels
primarily serve to emphasize a particular subset of symptoms
without consideration of the entire symptom profile. Given thatthe symptom criteria of the DSM-5 do not specifically reflect any
known underlying biology or etiology, this calls into question
their utility as a diagnostic system and likely plays a greaterrole in hindering than helping progress in the understanding
and treatment of mental health challenges. We discuss here theimplications of these results for how we consider mental healthepidemiology, clinical diagnosis, treatment, and research.

Implications for Clinical Diagnosis andResearch
These results highlight the quantitative extent of the challengeof utilizing DSM-5 based criteria for diagnosis and treatment
selection, where symptoms of those who map to DSM-5 criteria
are as heterogeneous within disorder labels as between andcannot be differentiated from random. Given this heterogeneity,
it is no surprise that only 39% of clinicians report often or always
referring to written DSM criteria during an initial evaluation
and approximately a third often or always use not otherwise
specified (NOS) categories, primarily due to the patient notmeeting criteria for a specific category (107). These results also
illustrate the challenges of selecting patients or participants intoa research study based on their meeting criteria for a disorderlabel. When participants are recruited into a trial based on these
diagnostic criteria, they are as symptomatically heterogeneous
a pool as if one were to simply recruit randomly among thosewith mental health distress from any of these 10 disorders. This
likely contributes substantially to the challenge of obtaining clear
results in clinical trials where outcomes are rarely unequivocal
and typically successful for a low proportion of patients (15).
In addition, efficacy of a treatment with respect to the subsetof symptoms emphasized by a DSM-5 based disorder label may
not reflect overall efficacy in treating mental well-being, as othersymptoms that are not considered in a particular diagnosis may
fail to improve or even get worse.

Estimating Prevalence and Mental Health
Epidemiology

Limitations
It is important that we acknowledge some limitations of thisdata and study. First, symptom profiles are obtained through aself-report questionnaire. While the assessment has now been
extensively tested for reliability and validity (89), this would
nonetheless limit the inclusion of individuals who are so impairedthat they may be unable to take an online survey, or unable tounderstand the questions sufficiently to answer them.

Across the sample, symptom prevalence decreased systematicallywith an exponential decay. Thus, drawing the line between how
many severe symptoms are normal vs. clinical is essentially anarbitrary judgement. Across the sample, using the more stringent
threshold of negative life impact to define clinical significance,
12.3% had symptom profiles that aligned with at least one

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FIGURE 6 | Symptom profile of 2 individuals who map to DSM-criteria for depression. Comparison of symptom profiles of 2 individuals mapping to diagnostic criteria
for depression is an example of the within disorder symptom heterogeneity. Higher numbers toward the outside of the circle represent a greater problem.

items. Nonetheless, while one would expect that most thresholds
would not be far off the mark, this highlights the challenges
of a threshold-based approach which plagues most, if not all,assessment tools.
Finally, the presence of broad or imperfect matches for certain
symptoms pertaining to OCD and bipolar disorder could haveaffected the accuracy of the mapping and the specific values ofprevalence and overlap as derived from this data. While this must
be verified in future studies, the inaccurate mapping of one or
two symptoms is not likely to impact the overall finding—that
overall symptom profiles are as heterogeneous within disordergroups as between and not separable as a group from randomly
selected individuals with any disorder. This is because the DSM5 typically considers only between 3 and 6 symptoms for adiagnosis whereas individuals present with 20 symptoms on
average (Figure 4C). Therefore, while more accurate mapping
may improve the symptom overlap by one or two symptoms, thiswould not move the needle substantially since the 15–17 othersymptoms that the diagnosis did not consider would contribute
far more to the heterogeneity.
Nonetheless, it is important to compare the MHQ criteria
mapping for diagnostic determination to more commonly usedassessments, for example mapping against the PHQ-9 (74) and
the GAD-7 (75) to determine the alignment between MHQ

Second, the sample consisted of self-selected individuals
whose symptom profiles may differ from those who chose not
to take an assessment. However, this concern is mitigated by thelarge sample size, broad demographic representation and similar
results obtained across demographics. Even if the results only
apply to those who would present for mental health assessment
this is nonetheless important as they represent the population
more likely to be seeking help.
Third, the sample was acquired during the Covid-19
pandemic during which symptom profiles may differ from prepandemic times. Should this be the case, it would highlight thechallenge of a hard and fast diagnostic system that does notconsider the evolution of mental health challenges over time.
Fourth, the criteria mapping process was limited by theabsence of specific information pertaining to symptom frequency
within the MHQ for each item. The threshold of 8 wasdetermined as an appropriate selection for at least one problem
item (Feelings of sadness, distress or hopelessness), equivalent
on average to experiencing the symptom 5 days per week inalignment with the DSM-5 requirement of experiencing thesymptom “nearly every day” for depression. However, it ispossible that the correspondence between frequency and life
impact rating may differ from item to item and so otherthreshold values may have been more appropriate for other

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criteria for depression and anxiety, and the scores from thesetwo questionnaires, respectively. Furthermore, validation of theseresults with an alternative questionnaire where questions arephrased differently but still cover all symptoms comprehensively
would also be important.

psychopathology spectra (e.g., internalizing and externalizing).
We note that the MHQ is a symptom profiling tool, rather
than a diagnostic framework, and thus would be envisaged asa transdiagnostic or disorder agnostic assessment that could be
used to support insights arising from network studies, or thoseusing frameworks such as HiTOP and RDoC. Ultimately, various
approaches proposed should be tested to determine which
provides the closest correspondence to clinical trial response
criteria and insight in relation to physiological parameters.

Future Directions
Despite the many challenges of the DSM-5, there is no
denying that it is deeply embedded in the fabric of clinical,
economic and social decision-making. Indeed 64% of clinicians
often or always use the diagnostic codes for administrative
or billing purposes and 55% find it very or extremely usefulfor communicating clinical diagnoses with colleagues andother healthcare professionals (107). However, given the broadreaching negative implications of a disorder classification systemthat is indistinguishable from random groupings, it is exceedingly
important to identify and develop alternative approaches.
From the symptom perspective, we advocate for an approachthat is rooted in empirical understanding of symptoms clusters.
A stepwise change toward disorder agnostic phenotypes ofsymptom profiles would have multiple benefits. First, it would
allow clinicians to obtain a complete picture of patient
symptoms so that they could make more informed treatment
decisions based on the whole patient experience leading tomore streamlined or personalized treatment pathways. Second,
it would further support the application of transdiagnostic
frameworks such as RDoC (65). Third, it would aid thesearch for underlying etiologies, and the identification of social
determinants, by allowing phenotypic testing of the efficacy
of medications and interventions. In the quest to constructempirical phenotypes of symptoms, different mechanisms of
constructing and comparing symptom profiles should be
explored. For example, dissimilarity is likely amplified bythresholding, and comparisons that look across the scale ofimpact may yield a clearer picture.
Finally, it would also be important to utilize large-scale dataof comprehensive symptom profiles, such as we have used
here, to understand the relative stability of symptoms within
individuals and the relationships between symptoms. Multiple
approaches have already been proposed in this direction. For
example, network approaches (49, 50) have been utilized to
identify which symptoms are having the greatest impact insustaining other symptoms, introducing possibilities for specifictargeting of symptoms that will likely have the greatest impact
on clinical outcomes (56–58). Another approach, HiTOP (67–69), proposes a hierarchical framework that combines individual
symptoms into components or traits, assembling them into
empirically derived syndromes, and finally grouping them into

DATA AVAILABILITY STATEMENT
The datasets presented in this study can be found in online
repositories. The names of the repository/repositories andaccession number(s) can be found at: https://sapienlabs.org/
mhm-data-access-request/.

ETHICS STATEMENT
The studies involving human participants were reviewed
and approved by Health Media Lab Institutional Review
Board. Participation was anonymous and involved online
informed consent.

AUTHOR CONTRIBUTIONSJN and TT conceptualized, designed, led the study,
interpreted the data, drafted the manuscript, and madecritical revisions. JN and VP performed the analysis. JN
created the figures. JN, TT, and VP approved the final version
and agreed to be accountable for all aspects of the work.
All authors contributed to the article and approved thesubmitted version.

FUNDING
This work was supported by Sapien Labs using
private unrestricted donor funding. Sapien Labs was a501(c)(3) not for profit research organization foundedin 2016 with a mission to understand and enable thehuman mind.

SUPPLEMENTARY MATERIALThe Supplementary Material for this article can be foundonline at: https://www.frontiersin.org/articles/10.3389/fpsyt.
2021.775762/full#supplementary-material

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1. Ritchie H, Roser M. Published Online at OurWorldInData.org. Available
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Conflict of Interest: TT received a grant award from the National Instituteof Mental Health (NIMH) to develop a commercial version of the MHQ toolreferenced herein.
The remaining authors declare that the research was conducted in the absence ofany commercial or financial relationships that could be construed as a potential
conflict of interest.
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