Predicting major mental illness: ethical and practical considerations

Abstract

SummaryAn increasing body of genetic and imaging research shows that it is becoming possible to forecast the onset of major psychiatric disorders such as depression and schizophrenia before people become ill with ever improving accuracy. Practical issues such as the optimal combination of clinical and biological variables are being addressed, but the application of predictive algorithms to individuals or in routine clinical settings have yet to be tested. The development of predictive methods in mental health comes with substantial ethical questions, including whether people wish to know their level of risk, as well as individual and societal attitudes to the potential adverse effects of data sharing, early diagnosis and treatment, which so far have been largely ignored. Preliminary data suggests that at least some people think predictive research is valuable and would take part in such studies, and some would welcome knowing the results. Future initiatives should systematically assess opinions and attitudes in conjunction with scientific and technical advances.Declaration of interestIn the past 3 years, S.M.L. has received personal fees from Otsuaka, Sunovion and Janssen, and research grant support from Janssen and Lundbeck. A.M.M. has received research support from the Sackler Trust, Eli Lilly and Janssen. S.M.L. is part of the PSYSCAN consortium.

Ethical dilemmas posed by mobile health and machine learning in psychiatry research

“…Sharing information from an uninterpretable model may adversely affect a patient's conceptualizations of their own illness, cause confusion and prompt concerns about transparency. 37,38 So far, research suggests there is no clear consensus among patients on whether they would want to know this kind of information about themselves, 37 which leaves psychiatrists to balance the potential utility of a machine learning model's predictions against the risk of liability and the patient's reactions. 39 Furthermore, when a model is not interpretable, a clinician's ability to be cognizant of possible fairness issues could be limited.…”

BJPsych Open fifth anniversary editorial: history, accomplishments, trajectory and passion

“…case analysis with literature review 14 • Trends in female authorship in research papers on eating disorders: 20-year bibliometric study 15 • The prevalence and treatment outcomes of antineuronal antibody-positive patients admitted with first episode of psychosis 16 • Mother and baby units matter: improved outcomes for both 17 • Suicide attempts and non-suicidal self-harm: national prevalence study of young adults 18 • Association between suicidal ideation and suicide: meta-analyses of odds ratios, sensitivity, specificity and positive predictive value 19 • Predicting major mental illness: ethical and practical considerations 20 • Indirect costs of depression and other mental and behavioural disorders for Australia from 2015 to 2030 21 • Social gradients in the receipt of medication for attention-deficit hyperactivity disorder in children and young people in Sheffield 22 • Long-term subjective memory after electroconvulsive therapy 23 The breadth of published articles ranges from treatment efficacy to adverse effects, from nonadherence to social gradients in prescriptions, from forensic psychiatry to mental health law, from ethics to global mental health, from history of terminology to guidelines, from digital mental health to determination of value and healthcare economics, from neuropsychiatry to perinatal psychiatry, from stigma to quality of life…and the list goes on.…”

Embracing the positive: an examination of how well resilience factors at age 14 can predict distress at age 17

“…adverse life-events), or biological indicators (e.g. inflammatory markers, cortisol, metabolic syndrome, brain-derived neurotrophic factor, white and grey matter, and heart rate variables) [5][6][7]9 . Thus, previous studies primarily examined predictors that are relatively static (e.g.…”