The assessment and diagnosis of mental health disorders is ambiguous, inconsistent and negatively biased frustrating efforts to identify underlying etiologies and appropriate intervention. New tools are essential to enable research progress.
Across the domains of clinical practice and research, the assessment and diagnosis of mental health disorders is carried out using questionnaires and interviews that ask about the presence, severity, frequency and duration of a broad range of psychiatric symptoms. The symptoms asked about in these assessment tools are based on well established classification systems, such as the DSM-5 or ICD-11, where predefined patterns of symptom criteria have been grouped together and designated as specific mental health disorders. However, there are a number of major concerns with this approach to psychiatric diagnosis (e.g. ). Here we mention three challenges which are particularly relevant to the assessment of these life-disabling disorders.
Challenge 1: Mental health disorders are ambiguously defined.
Although the use of a clinical classification system where each disorder is aligned with a theoretical constellation of symptoms has strong clinical utility, it does not currently take into account the underlying biology which may or may not align with these theoretical disorder demarcations. This means that, unlike the case of most physiological disorders where symptoms are used as a guide to diagnose an underlying etiology (such as fever being a symptom of underlying viral or bacterial infection), the diagnosis of a mental health disorder is based purely on the assessment of symptom sets and not on any underlying physiology or causal factor. This makes it more difficult to identify possible pharmacological treatment targets and creates a disconnect between clinical insights and neuroscientific understanding.
See related post What are Mental Disorders?
What’s more, it’s well established that a patient’s symptom experience doesn’t usually fit neatly into any one disorder category and comorbidity is the norm rather than the exception [2,3]. This reflects the fact that the boundaries between mental health disorders are blurry, and questions the relevance of having “disorder-specific” assessment tools which could result in a poor characterization of a patient’s experience and therefore suboptimal treatment outcomes.
Challenge 2: Mental health assessment tools are inconsistent
Beyond this ambiguity, a second challenge is that existing mental health assessment tools, despite being broadly based on set symptom criteria, are highly heterogeneous. For example, our recent analysis of commonly used mental health screening assessments revealed considerable inconsistency in symptom assessment across different tools focusing on the same disorder, and substantial overlap between disorders (See ADHD: Symptom Similarity Across Diagnostic Tools; The Ambiguous Symptoms of Autism; Quantitative Similarity of Depression Tools; also see [4,5]).
Consequently, two assessments that target the same clinical group, but which use different tools to assess symptom experience, may deliver different results simply because they are assessing a different set of symptoms. Moreover, we also found that none of the cross-disorder assessment tools that we looked at covered the complete breadth of mental health symptoms. (See A Disorder Agnostic Approach to Mental Health Symptoms) Together, this creates considerable ambiguity, bias and inconsistency in mental health determination and really confuses the development of effective interventions.
Challenge 3: Mental health assessments are negatively biased
According to The World Health Organization (WHO) mental health is “a state of wellbeing in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community”. In keeping with this definition any assessment of mental health should ideally reflect not just negative symptoms, but also the presence of mental assets and abilities.
However, the medical heritage of mental health assessment means that existing tools don’t readily assess wellbeing but instead focus on negative symptoms and dysfunction. Furthermore, symptoms are often assessed in a binary manner, asking simply about their presence or absence, rather than on a spectrum or scale. This one-sided perspective presents a challenge to understanding the borders between “normal” mental health and clinical disorder, especially as many “symptoms” such as sadness, anxiety and risk-taking also fall within the spectrum of normal mental functioning. Understanding this distinction is necessary not only to prevent false positives in diagnosis, a label that can be unduly associated with stigma, but also to ensure that people receive appropriate treatment.
Addressing these challenges
Addressing these challenges requires, as a first step, a more standardized, disorder agnostic mental health profiling that encompasses the spectrum of both symptoms and mental assets and abilities. Taking these requirements into consideration, we have developed an online assessment tool called the Mental Health Quotient (MHQ). This new tool isn’t tied to a disorder-based classification system but instead takes a disorder-agnostic approach to include all semantically distinct symptom categories across all disorders to provide a complete and standardized profile, and considers both symptoms and the spectrum of assets and abilities (See Introducing the Mental Health Quotient (MHQ)).
The tool has multiple research and clinical applications but one of our aims is to use it to obtain a global assessment of population mental health and wellbeing to uncover different risk and protective factors. In doing so, we hope to be able to develop a map of the mental health status of our planet and to support the development of interventions and policies that can improve the lives and wellbeing of all people, and not just of those with disorder or dysfunction. Stay tuned for more on this exciting new initiative in the near future.
 Maj, M. (2005). ‘Psychiatric comorbidity’: an artefact of current diagnostic systems? British Journal of Psychiatry 186(3), 182-184. doi: 10.1192/bjp.186.3.182.
 Plana-Ripoll, O., Pedersen, C.B., Holtz, Y., Benros, M.E., Dalsgaard, S., de Jonge, P., et al. (2019). Exploring Comorbidity Within Mental Disorders Among a Danish National Population. JAMA Psychiatry 76(3), 259-270. doi: 10.1001/jamapsychiatry.2018.3658.
 Fried, E.I. (2017). The 52 symptoms of major depression: Lack of content overlap among seven common depression scales. Journal of Affective Disorders 208, 191-197. doi: 10.1016/j.jad.2016.10.019
 Allsopp, K., Read, J., Corcoran, R., and Kinderman, P. (2019). Heterogeneity in psychiatric diagnostic classification. Psychiatry Research 279, 15-22. doi: 10.1016/j.psychres.2019.07.005.