eMind Blog

June 14, 2022

Mental Health Index for ESG: Issues for Consideration

Philip T Ninan, M.D.


Dr. Ninan is CEO and Co-Founder of eMind Science, and Affiliate Professor of Psychiatry and Behavioral Medicine, Brody School of Medicine, East Carolina University.

© eMind Science Corp.

Corporate market value is driven by human capital, and mental health is at the core of human capital. An argument is made to include a metric for mental health under the social arm of environment, social and governance (ESG). Several issues are worthy of consideration in a mental health metric. This paper contributes to a broad dialogue on the need for and challenges inherent in this effort.

Mental health is an amorphous and ill-defined term that is need of clarification and consensus.

The Mind Conundrum: The reader is advised to avoid this paragraph if it makes them dizzy, and simply move to the next paragraph. A discussion of mental health compels agreement, or at a minimum appreciation, of what constitutes the mind. The mind is not equivalent to the brain. The mind is what the brain does and is what each of us is consciously aware of. Yet consciousness continues to be a mystery. The mind is a private internal subjective experience constructed by perception, emotions, thoughts, and behavior. Additional influences are attention, memories, relationships, culture, a higher order self, and a search for one's (spiritual) essence. Psychologically, we are captive to the observer perspective, which is largely self-centric and constrained within the system - a reflection of what is termed projective geometry. Yet neuroscience tells us the psychological self is a construction, and observations from outside the projected self, paints a different and more multiplex and comprehensive picture. These complexities reflect the fundamental mystery of the nature of life. Oversimplification of what constitutes the mind is the basis of errors in approaching mental health.

One can truly know only one's own internal experiences of the mind. We can project what another's mental experiences are based on one's own mind, surmise motivation, interpret emotional expressions, what is spoken, overt behaviors, etc. Such complexities raise significant hurdles for ESG relevant metrics.

Terms in Mental Health: is a broad term with multiple dimensions subsumed under the term, leading to understandable confusion. There are several terms that overlap with mental health, including mental wellness, mental illness, and mental disability. There is a need to harmonize the definition of such terms and parse their distinguishing characteristics.

There is a distinction between mental wellness and mental illness. According to the World Health Organization, mental wellness is defined as “a state of well-being 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.” Thus, wellness is not just the absence of illness, but includes affirmative mental faculties and functioning.

The U.S. government distinguishes mental wellness from mental health defining the latter  as including "our emotional, psychological, and social well-being. It affects how we think, feel, and act. It also helps determine how we handle stress, relate to others, and make choices. Mental health is important at every stage of life, from childhood and adolescence through adulthood." (www.mentalhealth.gov). The loss of mental health can be defined categorically or dimensionally.

The categorical approach is taken in mental illness where labels such as Major Depression, distinguish individuals who meet a threshold of criteria, from those who do not. Such a diagnostic decision is the domain of trained mental health professionals.

Mental Illnesses labels are largely used by professionals and healthcare systems. These are diagnostic labels defined in manuals such as the DSM-5 (published by the American Psychiatric Association) that states the criteria for some 250+ different categories. Such diagnostic labels are useful in classifying individuals who share common symptoms, that can guide treatments, codify for reimbursement by insurance companies, and be the basis for approval of treatments by regulatory bodies such as the FDA.

The limitation of such diagnostic labels is they are simply a compilation of symptoms that experts agree are components of the disorder. As a case, the criterion for Major Depression requires a minimum of 5 of potentially 9 symptoms, present for a duration and associated with distress and/or dysfunction. There is potentially 200+ combinations of these 9 symptoms that can lead to meeting the MDD label. Two individuals can both be labeled having MDD and share only one symptom.

Additionally, in much of medicine, a diagnostic label is supported by an understanding of the underlying physiological processes (pathophysiology) that permit causal explorations (etiologies). Treatments are commonly aimed, not just at the symptoms, but at the pathophysiology and etiology. Thus, for example, in coronary heart disease, the pathophysiology is plaques that restrict the flow of blood in coronary vessels. The etiologies of plaques are inflammation, high LDL cholesterol, etc. Treatment can reduce cholesterol (e.g., statins) and inflammation, or surgical use of shunts to bypass the obstruction. Symptomatic treatment would be pain medications to reduce the perception of pain.

In mental illness however, we are devoid of a full understanding of pathophysiology or etiology. Mental health labels are stuck at the symptom level, and hence treatments may be symptomatic, and fail to address pathophysiology or etiology.

There are accepted, validated rating scales that measure a mental illness such as Major Depression. These measures, used professionally in healthcare, are of limited value to the ESG space as the basis of the mental categorical/diagnostic approach is hardly fundamental. Thus, basing ESG metrics on assessment of diagnostic labels is of questionable value.

The dimensional approach, subsumed under the term mental ill-health,  defines key mental components and scores them on continuous dimensions, enabling individuals to be on a spectrum from health to illness. The dimensional approach emphasizes that individuals are not static in their mental experiences and permits dynamic tracking to document the flowing nature and lability of mental experiences. The dilemma is that experts from different mental health disciplines are yet to achieve consensus on the appropriate dimensions. Nevertheless, the dimensional approach is more appropriate for ESG metrics.

The goal is wide corporate acceptance which requires demonstration that such dimensional mental measures add value to financial metrics. This will initially be from an explanatory point of view, then correlational and finally causal to financial metrics.  What is needed in the beginning, is the gathering of data and exploration of such value. The ultimate goal is predictive value, which will require big data amenable to  machine learning algorithms that demonstrate quantifiable, relevant, higher order dimensions of the mind.

Measurement: Metrology is the science of measurement. Measurement, or metrics is the basis of advances in science and medicine. It is grounded in quantity and numbers, with equivalent intervals, consistency, uniformity and precision. These characteristics permit calibration, standardization and verification. A new age in metrology has dawned with digitization of information and its influence on computation. Qualitative phenomena require transformation (reduction) to quantities, with the inherent introduction of errors that add noise, reducing precision.

ESG requires quantitative assessments that are valid and generally agreed upon. Given the multiplex nature of both wellness and mental ill-health, no single assessment tool can capture all the underlying components.  Hence the concept of an index - where data from diverse sources can be compiled into a composite score. The construction of such indexes requires transparency (clear, consistent, reliable, and comparable), freedom from conflicts of interests, and reasonable effort/costs associated with the gathering of such data.

Traditional measures that go into mental health indexes currently arise from two sources:

a) Surveys of perceptions on organization efforts to support the well-being of the workforce. These are the basis of programmatic corporate initiatives addressing mental wellness.

b) Measures of mental symptoms of the workforce. These are typically addressed privately at an individual, employee level. One could extrapolate the proportion of the workforce meeting criteria for mental illnesses as defined by certain diagnoses. Such aggregate information may be available through health insurance data or obtained as estimates from surveys of mental symptoms. Additionally corporate employee assistance programs (EAPs) may provide aggregate data on those reaching out for assistance.

This paper addresses one aspect of these issues for illustrative purposes - the challenges of measurement of mental experiences, that are in the domain of mental ill-health. With sufficient information, diagnoses relevant to workforce mental health can be derived from such mental ill-health data. For the maintenance of individual privacy, these are available only at an aggregate level. The aggregate data of employee mental ill-health are relevant for mental wellness.

Towards a Mental Health Index: A fundamental challenge in measuring mental ill-health is the basis of such measures are subjective. Measurement in the subjective sphere is, by definition, different from objective assessment. In objective measures, there are potentially different approaches that can independently validate a measure. In mental ill-health, there are no objective measures or 'biomarkers' that are established as independent validators. Hence for ESG measures of mental ill-health to be accepted by the wider corporate world, it is critical the 'signal-to-noise' ratio be dominated by the signal. What approaches can be taken to reduce noise and enhance the signal? There are four measurement levels defined in the world of numbers, the domain of statistics, that address this issue.

The first level of measurement is nominal - essentially naming something. Examples of names include terms such as depression, anxiety, stress, burnout, etc. However, there are no universally accepted definitions for these terms that provide both a description of the thing that is named and a boundary to the concept named. The relationship between such terms is unclear. Terms used  are often binary, meaning the potential answers are either yes or no. These terms describe an internal experience that is personal and private. Comparisons are based on their descriptions, not the actual experience itself. Their framing is fluid - what is the reference standard? Compared to what? The past worst experience of the individual or the imagined worst? The expectation of what the individual should experience or what an 'average' person should experience?

External manifestations of the internal experience can be masked for a variety of reasons, including stigma and/or social pressures to moderate their expression. The best that can be achieved is consistency in their reporting. The repeat reliability of a measure is taken as a proxy for validity. As these experiences flow over time, tracking an item may support validity as it reflects flow rather than a cross-sectional viewpoint.

The second level of measurement is ordinal - an approach that provides descriptors, such as mild, moderate, severe, etc. These are ecologically valuable, but the problem when quantified, is that the separations between such descriptors are not necessarily equal and linear. Thus, the difference between mild and moderate may not be the same as the difference between moderate and severe. The statistical approach to such data is non-parametric, which elevates the risk of false negatives.

The third level of measurement is interval, typically using numbers. Here the difference between say, 2 and 3 is the same as the difference between 6 and 7. This permits statistical assumptions for analysis of data along the lines of objective measures. The limitation in the interval approach is it fails to  incorporate zero, or the difference between the absence and the low-level presence of an item.

The final level is that of ratios, which does permit the presence of zero, and thus incorporates nominal, ordinal and interval components. Ratios are important because the relationship between dimensions can be contrasted. This is of value in the subjective sphere as the current mental state has a global influence on what is reported. Canceling this global influence permits exploring the relationships between specific subjective elements.  

Subjective measures that consider all four levels should be a requirement for  mental ill-health metrics to be used in ESG. This opens the data to exploration using machine learning techniques. After all, the mind is the result of deep learning from neural networks in the brain. Emergent patterns in such data are more likely true than spurious from 'noise'. Welcome to the world of  predictive analytics for mental ill-health!  

ESG and Financial Metrics: Ultimately, any ESG measure of mental health must  relate to financial metrics. Excessive divergence in measures implies the signal is lost in the background noise. An acceptable mental health index should also be available for disassembly to examine key performance indicators (KPI) that potentially move the needle.

Conclusion: The uncertainties and challenges of today's world supports the addition of corporate non-financial metrics for sustainability into the future. Mental health of the workforce that includes concepts of mental wellness and mental ill-health, are fundamental to human capital. If mental health metrics are to be included in non-financial measures, there are challenges that need addressing, so optimal value is demonstrated.

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