Mental health care policies and practices failing to meet demands is placing an immense economic and social burden on the planet, costing trillions each year, where the incremental indirect costs of untreated depression in terms of lost productivity is the biggest offender.
The latest solution, Augmented Mental Health with advanced mental health monitoring and therapeutic intervention, has the capacity to yield unmatched, cost-effective improvements for the currently extortionate direct and indirect costs of untreated depression that cripples lives and communities world over.
Untreated Depression Prevails...and it Costs!
Whether the condition is untreated or ineffectively managed, long-lasting moderate to severe depression can be a serious disability causing health condition, with great suffering, and poor functioning at work, at school, or at home in the family. At worst, depression leads to suicide, where up to 15% of clinically depressed individuals die by suicide.
As described at the World Bank Group (WBG) and World Health Organization (WHO) conference on the global mental health crisis:
“mental disorders account for 30 percent of the non-fatal disease burden worldwide and 10 percent of overall disease burden, including death and disability, and that the global cost—estimated to be approximately $2.5 trillion in 2010—is expected to rise to $6 trillion by 2030.”
Yet even in the highest income countries, most episodes of depression and mental illness go untreated. In the US for example, more than half of adults with depression received no past year treatment, and more than half of the $210.5 billion economic burden was lost at the workplace in sick days and poorer productivity.
The average age of onset of depression is when people are in the workforce, between 20-40 years old, where the productivity and health costs of untreated depression far outweigh the costs of treating it.
The average full-time US employee with untreated depression can cost an organization $1000s annually, mainly in indirect costs in terms of lost productivity: requiring the use of more sick absence days (i.e., absenteeism) and what is consistently proving to be the bigger problem, longer periods of working while negatively affected by depression symptoms (i.e., presenteeism). Major depressive disorder (MDD), for example, translates into over 8 hours per week of lost productivity and 20> absent days per year on average (compared to 5 days for those without MDD).
As Untreated Depression Worsens So Does the Magnitude of Productivity Loss
This year, groundbreaking research revealed that if unchecked, untreated depression may even run a degenerative course, leading to damaged brain tissue—narrowing the gap between neurodegenerative disorders and depression. As might be expected, the incremental mental and physical health deterioration that coincides with the progression of untreated depression is interlinked with an incremental decrease in productivity and increased disability.
Research has identified a linear relationship between depression symptom severity and productivity loss, where even minor subclinical levels of depression symptoms are associated with decrements in work function. Simply having a low sense of well-being or general health (mental or physical) without a clinical mental disorder can result in substantial productivity losses.
Generally, co-morbid depression that is associated with many medical conditions and injuries tends to impacts the course of recovery, length, and severity of disability, return to work, and degree of productivity loss. Moreover, with injured workers (workplace injuries included) and workers with common pain conditions (like back pain), the development and progression of depression symptoms is common and, if untreated, impedes returning to work, resulting in longer work disability.
Untreated Depression Risks Becoming UNTREATABLE—The Costliest Type of Depression
A major concern in failing to recognize and treat emerging depression, is progression from an untreated depressive disorder to treatment-resistant depression, incurring much higher mental health insurance claims, as well as costs to health and productivity.
Health claim data from the OptumHealth Care Solutions, Inc database (July 2009–March 2015) on over 19.1 million privately insured individuals covered by 84 self-insured Fortune 500 companies in the US found that annual health care costs (direct and indirect) were roughly double per year ($17,261) for employees with treatment-resistant depression compared with those with non-treatment-resistant depression ($9,790).
Early Intervention and Access to Care = Core Strategy for Improved Productivity
The earlier in its development that untreated depression is identified, the better outcomes there are in terms of minimizing productivity loss (and concurrently, healthcare costs). On the other hand, the later in the time course of depression that individuals get treatment, the greater the productivity loss and costs.
For example, in an Australian study reporting that the average full-time employee with untreated depression costs an organization $9,665 per year (in direct and indirect mental health costs), even implementing educational early intervention seminars achieved a five-fold return on investment by increasing productivity through facilitating early recognition and encouraging help-seeking behavior.
The take-home message: is that the most effective AND cost-effective strategies work optimally (in terms of reducing depression symptoms and enhancing productivity) the earlier in depression progression the employee is treated—early intervention is the prerogative.
Augmented Mental Health Programs = Earliest Possible Intervention and Access to Care
Employers, governments, healthcare providers, and insurance companies around the globe are already using mobile health (mHealth) apps for mental health for large-scale improvements in mental health and the associated generation of savings.
Although even basic mHealth apps have clear benefits as stand-alone tools, the time when someone realizes that something is wrong with their mental health and wants to use tools in the app is when depression is progressing and symptoms are already causing serious problems.
Using novel algorithms, technology can now be used to detect signals indicative of emotional and mental states in real-time, allowing automated recognition of worsening mental health and risk of depressive episodes. With augmented mental health, partnering objective mental health monitoring with mHealth apps to permit access to mental health care professionals and automated interventions as soon as the problems emerge could largely make untreated depression a thing of the past.
Fresh on the market, it is not feasible to wait for lifetime data to validate cost-effectiveness of the next generation of smart mHealth for mental health, meaning that high quality sensitivity analyses of economic evaluations are eagerly anticipated. It will be exciting to see the extent that augmented mental health solutions can further offset the costs of depression in upcoming economic evaluations as the real-time results roll in.
Australian study: Hilton, M. (2004) Assessing the financial return on investment of good management strategies and the WORC Project. The University of Queensland. As presented by Beyondblue.
Amos, T., Tandon, N., Lefebvre, P., Pilon, D., Kamstra, R., Pivneva, I., & Greenberg, P. (2018). Direct and Indirect Cost Burden and Change of Employment Status in Treatment-Resistant Depression. The Journal Of Clinical Psychiatry, 79(2), 24-32. doi: 10.4088/jcp.17m11725
Beck, A., Crain, L., Solberg, L., Unützer, J., Glasgow, R., Maciosek, M., & Whitebird, R. (2014). Does Severity of Depression Predict Magnitude of Productivity Loss?. The American Journal Of Managed Care.
Cancelliere, C., Donovan, J., Stochkendahl, M., Biscardi, M., Ammendolia, C., Myburgh, C., & Cassidy, J. (2016). Factors affecting return to work after injury or illness: best evidence synthesis of systematic reviews. Chiropractic & Manual Therapies, 24(1). doi: 10.1186/s12998-016-0113-z
Carnide, N., Franche, R., Hogg-Johnson, S., Côté, P., Breslin, F., & Severin, C. et al. (2015). Course of Depressive Symptoms Following a Workplace Injury: A 12-Month Follow-Up Update. Journal Of Occupational Rehabilitation, 26(2), 204-215. doi: 10.1007/s10926-015-9604-3
Gonda, X., Fountoulakis, K., Kaprinis, G., & Rihmer, Z. (2007). Prediction and prevention of suicide in patients with unipolar depression and anxiety. Annals Of General Psychiatry, 6(1), 23. doi: 10.1186/1744-859x-6-23
Greenberg, P., Fournier, A., Sisitsky, T., Pike, C., & Kessler, R. (2015). The Economic Burden of Adults With Major Depressive Disorder in the United States (2005 and 2010). The Journal Of Clinical Psychiatry, 155-162. doi: 10.4088/jcp.14m09298
Iribarren, S., Cato, K., Falzon, L., & Stone, P. (2017). What is the economic evidence for mHealth? A systematic review of economic evaluations of mHealth solutions. PLOS ONE, 12(2), e0170581. doi: 10.1371/journal.pone.0170581
Luoma, J., Martin, C., & Pearson, J. (2002). Contact With Mental Health and Primary Care Providers Before Suicide: A Review of the Evidence. American Journal Of Psychiatry, 159(6), 909-916. doi: 10.1176/appi.ajp.159.6.909
Marquez, P., & Saxena, S. (2016). Making Mental Health a Global Priority. Cerebrum, eCollection 2016 Jul-Aug, 10-16.
Nguyen, T., & Davis, K. (2018). The State of Mental Health in America 2017. Alexandria: Mental Health America.
Post, R. (2015). Heading off depressive illness evolution and progression to treatment resistance. Dialogues In Clinical Neuroscience, 17(2), 105-109.
Setiawan, E., Attwells, S., Wilson, A., Mizrahi, R., Rusjan, P., & Miler, L. et al. (2018). Association of translocator protein total distribution volume with duration of untreated major depressive disorder: a cross-sectional study. The Lancet Psychiatry, 5(4), 339-347. doi: 10.1016/s2215-0366(18)30048-8
Stewart, W., Ricci, J., Chee, E., Hahn, S., & Morganstein, D. (2003). Cost of Lost Productive Work Time Among US Workers With Depression. JAMA, 289(23), 3135-3144. doi: 10.1001/jama.289.23.3135
The 5 Biggest Threats to Mental Health
#CureStigma: Is That Even Possible?
Using AI Chatbots for Mental Health Care — An Expert Opinion
An Interview With Stop, Breathe & Think
Zero Suicide? New Scientific Tools to Turn Fantasy into Reality
Smart Mental Health Insurance—The Key to Insurtech Industry Dominations?
Can New Mental Health Tech Enhance the Therapeutic Relationship?
Data Privacy & Security in the Era of Augmented Mental Health
The Death of Psychotherapy As We Know It
Mental Health Insurance Crisis: Can mHealth Help?
Plug the Employee Productivity Drain with Augmented Mental Health
A Global Access Solution to the Trillion Dollar Mental Health Crisis