The burden of anxiety and depressive disorders
Living with anxiety and depressive disorder comes at a great cost to the individual and to our society.
In recent years, concerns about mental disorders have increased rapidly. Anxiety is one of the most common mental health disorders, currently affecting ~278 million people worldwide (Kessler, 2007; Kessler & Greenberg, 2002; Kessler, Petukhova, Sampson, & Wittchen, 2012; Ritchie & Roser, 2018). Anxiety disorders are ranked sixth most disabling among disorders generally and are linked to 7% of suicide deaths (Baxter, Vos, Scott, Ferrari, & Whiteford, 2014). Similarly, depression is also a prevalent mental health issue in today’s society, with around ~280 million people affected worldwide (World Health Organization, 2021). Further, recent studies have also shown that COVID-19 survivors are at an increased risk of developing neurological and psychiatric problems including anxiety and mood disorders (Taquet et al., 2021), which would only further add to the burden of mental health issues. Nearly, 50% of the individuals that die of suicide have previously had depression. Further, individuals with depression are 25 times more likely to die by suicide than individuals in the general population (Centre for suicide prevention, 2014).
Living with anxiety and depressive disorder comes at a great cost to the individual and to our society. The disabilities manifested due to the presence of anxiety and depressive disorders can directly impact an individual’s well-being by influencing an individuals mood, self-esteem, self-confidence, appetite, sleep pattern, and behaviour. These issues can further impact an individual’s interpersonal relationships which could lead to social withdrawal and lower-levels of adaptive coping (Kinderman et al., 2018), which would also contribute towards a worsening individual well-being. Often times, poor diagnosis or misdiagnosis of mental health disorders (Vermani et al., 2011) could lead to the development of inadequate treatment plans that costs the individuals their time and money. Additionally, the individual’s burden of anxiety and depressive disorder extends to lost production at work which then affects the efficiency of the overall economy (Wade, 2012).
Effective assessment and treatment of anxiety and depressive disorders can significantly help lessen the burden of these disorders for the individual and for the society at large. However, current methods of assessment relies heavily on the subjective clinician’s judgement that relies on the criteria of the The Diagnostic and Statistical Manual of Mental Disorders, 5th ed. (DSM-5; American Psychiatric Association, 2013) and/or the International Statistical Classification of Diseases, 10th ed. (ICD-10; World Health Organisation, 2010). As a result, the assessment process can be time-consuming and inaccessible for a lot of the population. Therefore, there is a clear need for an objective method of assessment to aid diagnosis. Digital markers could potentially provide a solution by using machine learning and behavioural data from smartphones and other wearables to predict mental health issues objectively. The ubiquitous nature of smartphones could be used by mental health service providers to passively collect various behavioural data to determine user well-being. Passively data collection minimises the time constraints and inaccessibility issue as the user would not have to actively engage in any activity nor would they have to interact with a clinician. Digital markers could be used as an early-detection assessment method. An improved assessment could then be used to improve treatment and intervention strategy for the individual.
References
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders : DSM-5. American Psychiatric Association.
Center for Suicide Prevention (2014). Depression and Suicide Prevention from https://www.suicideinfo.ca/resource/depression-suicide-prevention/
Baxter, A. J., Vos, T., Scott, K. M., Ferrari, A. J., & Whiteford, H. A. (2014). The global burden of anxiety disorders in 2010. Psychological Medicine, 44(11), 2363-2374. https://doi.org/10.1017/S0033291713003243
Kessler, R. C., & Greenberg, P. E. (2002). The Economic Burden of Anxiety and Stress Disorders. Journal of Neuropsychopharmacology: The fifth generation of progress(67), 981-992.
Kessler, R. C., Petukhova, M., Sampson, N. A., & Wittchen, H. U. (2012). Twelvemonth and lifetime prevalence and lifetime morbid risk of anxiety and mood disorders in the United States. International Journal of Methods in Psychiatric Research, 21(3), 169-184. https://doi.org/doi:10.1002/mpr.1359
Kinderman, P., Tai, S., Pontin, E., Schwannauer, M., Jarman, I., & Lisboa, P. (2015). Causal and mediating factors for anxiety, depression and well-being. British Journal of Psychiatry, 206(6), 456-460. doi:10.1192/bjp.bp.114.147553
Ritchie, H., & Roser, M. (2018). Mental Health. Our World in Data.
Taquet, M., Geddes, J. R., Husain, M., Luciano, S., & Harrison, P. J. (2021). 6-month neurological and psychiatric outcomes in 236 379 survivors of COVID-19: a retrospective cohort study using electronic health records. The Lancet Psychiatry, 8(5), 416-427. https://doi.org/10.1016/s2215-0366(21)00084-5
Vermani, M., Marcus, M., & Katzman, M. A. (2011). Rates of detection of mood and anxiety disorders in primary care: a descriptive, cross-sectional study. The primary care companion to CNS disorders, 13(2). Wade, G. A. (2012). The economic burden of anxiety and depression. Medicographia, 34(3), 300-306.
World Health Organisation. (2021). Depression from https://www.who.int/news-room/fact-sheets/detail/depression World Health Organisation. (2010). International Statistical Classification of Diseases and Related Health Problems 10th Revision from http://apps.who.int/classifications/icd10/browse/2010/en.