MQ Ambassador Dr Esther Beierl is a data scientist, trial statistician and psychometrician in psychology and mental health research (currently at the University of Cambridge, previously at the University of Oxford), yoga teacher and personal trainer. She also has personal experience of mental illness. On the occasion of Research Recognition Day, Esther explains why data science is essential for mental health research.
Individual needs
75% of mental health problems begin before adulthood, and 50% of all mental health problems that occur throughout life begin before the age of 14 (MQ Mental Health Research, 2017). Mine also began almost 30 years ago.
As a child, I had unique needs (and still do), which carry advantages and challenges for myself and others. My nervous system works differently than most people. I am highly intelligent and extremely sensitive, particularly with respect to auditory stimuli, but also olfactory, tactile, and visual. I am also highly susceptible to social dynamics and very empathetic. I absorb and process much more information and faster than others, which is both a blessing and a curse. I get overstimulated and overwhelmed sooner than others, so I need more downtime in peace and quiet to recharge.
The invalidation and disregard of my specific needs contributed to my mental health issues in the first place and also made it nearly impossible for me to access appropriate treatment at the time.
My vision is that no child, adolescent or young person goes through the suffering that I experienced.
As a data scientist at the forefront of psychological and mental health research, I know what is possible now and I am confident we can do better.
I see great potential in the development of data-driven therapies in these times of technological innovation. The prospect of personalizing therapies to suit individual needs using data science promises to improve treatment outcomes.
What can data science achieve that wasn’t possible before?
More than 90% of the world’s data has been generated in the last two years and data will continue to grow exponentially.
The amount of data related to human nature and mental illness has increased, and it is now easier to access large data sets. We can use multidimensional approaches to collect a wide range of information (such as working with online and offline behavioral data, self-reports and expert ratings, cognitive and emotional measures, text narratives, physiological and biological data, etc.).
Meanwhile, statistical approaches to working with these large data sets are being developed and improved.
By leveraging large data sets and sophisticated statistical tools, researchers can ask more complex research questions and more accurately capture the complexity of mental illness in their studies. Compared to traditional approaches and building on previous research based on theories and population averages, a data science-based research approach can complement and improve current clinical practice.
Why is it essential to achieve this? Why is it important?
I firmly believe that data science-based research can significantly improve our understanding, prediction, treatment and prevention of mental illness.
Understanding in detail the interaction of risk factors and predictors with their impact on mental illness can improve diagnostic accuracy. This improved understanding also allows us to make more accurate predictions about how these factors influence an individual’s mental health outcome, as well as how they may exacerbate or perpetuate a mental health problem. In addition, gaining more knowledge about risk factors and predictors offers research opportunities aimed at preventing mental illness. Further research on specific patterns in individuals who have not responded to traditional treatment approaches (such as I) or in subgroups (e.g., people from minority groups) presents opportunities to adapt existing treatment compounds or develop entirely new therapies that could benefit people like us.
I would even argue that data science applied to mental health research can ultimately contribute to a more just healthcare system and society.
How is this achieved?
Some examples of research that illustrate how mental health research based on complex computational approaches works and how its results can be practically used by the healthcare system include my own research project or the MQ-funded research conducted by Zac Cohen and Rob DeRubeis.
During my postdoctoral research at the University of Oxford, I developed a data science algorithm to predict the onset of post-traumatic stress disorder (PTSD) one month after a traumatic event using predictors and risk factors assessed shortly after the trauma in a prospective study of assault or road traffic accident survivors (Beierl et al. 2024). Our research can be used to identify individuals at risk of PTSD.
Zac Cohen and Rob DeRubeis’ Stratified Medicine Approaches for Treatment Selection (SMART) mental health prediction tournament used data from the NHS Talking Therapies programme (Clark, 2018) to predict whether people would benefit more from low- or high-intensity treatment and how effective the algorithms would be at identifying appropriate therapies.
The MQ Mental Health Data Science Group continues to drive research and policy (McIntosh et al., 2016; Russ et al., 2019).
What are the challenges?
Despite the potential of data science for precision medicine, contextual, statistical, technical, and clinical/practical challenges need to be addressed. Contextual challenges include social, financial, and ethical considerations. Statistical challenges relate to model evaluation (e.g., which statistical model best represents the data?), interpretation and drawing sensible conclusions from these complex algorithms, generalization to real-world settings, and clinical utility. Clinical and practical challenges apply to the needs and concerns of physicians (e.g., how can these algorithms best support their clinical practice).
If you want to go deeper into the topic, I recommend reading our article (Deisenhofer, Barkham, Beierl, et al., 2023). We have analyzed and addressed those challenges in depth in our framework “Implementing Precision Medicine.”
My vision may become a reality, but we have not achieved it yet.
Finally, with the invaluable support of exceptionally open and competent healthcare professionals, I was able to formulate my own personalized treatment plan tailored to my needs. I am grateful to be where I am, but I continue to live with several chronic diagnoses, which lead to significant limitations in my daily life.
In this era of data science, the focus should be on funding cutting-edge research based on quantitative data and including lived experience in qualitative data to specifically prevent and address individual trajectories of mental illness like mine. Otherwise, selecting an appropriate therapy becomes a cumbersome process of trial and error.
My goal is to contribute to my vision through mental health advocacy, reshaping personal experiences, and conducting further research.
You can find Esther on social media at the following profiles: X: @EBeierl, Instagram: @estherbeierl, Substack: @estherbeierl
References
Beierl, E.T., Böllinghaus, I., Clark, D.M., Glucksman, E., & Ehlers, A. (2024). Data science for mental health: developing a predictive algorithm to identify people at risk of PTSD 1 month after trauma, within hours or days after trauma [Manuscript in preparation]Department of Experimental Psychology, University of Oxford, UK.
Clark, DM (2018). Harnessing the massive public benefits of evidence-based psychological therapies: the IAPT program. Annual Journal of Clinical Psychology, 14159-183, https://doi.org/10.1146/annurev-clinpsy-050817-084833
Deisenhofer, Alaska, Barkham, M., Beierl, E.T., Schwartz, B., Aafjes-van Doorn, K., Beevers, C.G., Berwian, I.M., Blackwell, SE, Bockting, CL, Brakemeier, E.L., Brown, G., Buckman, J.E.J. , Castonguay, LG, Cusack, CE, Dalgleish, T., de Jong, K., Delgadillo, J., DeRubeis, RJ, Driessen, E., Ehrenreich-May, J., Fisher, AJ, Fried, EI, Fritz , J., Furukawa, TA, Gillan, CM, Gómez Penedo, JM, Hitchcock, PF, Hofmann, SG, Hollon, SD, Jacobson, NC, Karlin, DR, Lee, CT, Levinson, CA, Lorenzo-Luaces, L ., McDanal, R., Moggia, D., Ng, MY, Norris, LA, Patel, V., Piccirillo, ML, Pilling, S., Rubel, JA, Salazar-de-Pablo, G., Schleider, JL, Schnurr, PP, Schueller, SM, Siegle, GJ, Saunders, R., Uher, R. , Watkins, E., Webb, C.A., Wiltsey Stirman, S., Wynants, L., Youn, S.J., Zilcha-Mano, S., Lutz, W., & Cohen, Z.D. (2024). Implementation of precision methods in personalization Psychological therapies: Barriers and possible paths forward. Behavior Research and Therapy, 172(9), 104443. doi: https://doi.org/10.1016/j.brat.2023.104443
McIntosh, A. M, Stewart, R., John, A., Smith, D. J., Davis, K., Sudlow, C., Corvin, A., Nicodemus, K., Kingdon, D., Hassan, L., Hotopf, M., Lawrie, S. M., Russ, T., C., Geddes, J. R., Wolpert, M., Wölbert, E., Porteous, D. J. and the MQ Data Science Group (2016). Data science for mental health: a UK perspective on a global challenge. The Lancet Psychiatry, July 3, 2011.(10), 993-998. https://doi.org/10.1016/S2215-0366(16)30089-X
MQ Mental Health Research (2017). MQ Manifesto for Young Peoples Mental Health. https://www.mqmentalhealth.org/wp-content/uploads/MQManifestoforyoungpeoplesmentalhealth2017.pdf
Mental health research MQ (undated). Stratified Medicine Approaches to the Selection of Treatments (SMART) Mental Health Prediction Tournament. https://www.mqmentalhealth.org/research/the-stratified-medicine-approaches-for-treatment-selection-smart-mental-health-prediction-tournament/
Russ, T. C., Wölbert, E., Davis, K. A.S., Hafferty, J. D., Ibrahim, Z., Inkster, B., John, A., Lee, W., Maxwell, M., McIntosh, A., Stewart, R., & the MQ Data Science Group (2019). How data science can advance mental health research. Nature Human Behavior, 3, 24-32. https://doi.org/10.1038/s41562-018-0470-9
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