We are delighted to read the publication of a new dementia risk score for prediction of dementia up to 14 years. We congratulate the authors for incorporating almost all modifiable factors identified by the 2020 Lancet Commission on dementia prevention, management and care. Since the publication of the European Brain Health Guidelines earlier this year, memory clinic professionals in the UK have been desperately looking for a home grown tool. We hope to see an online training for using this clinical tool in the near future.
We congratulate Oxford colleagues for publishing the UK Biobank Dementia Risk Score (UKBDRS) for predicting dementia up to 14 years in individuals aged 50-73 years. For NHS clinicians aspiring to enhance existing memory clinics by adding Brain Health Services, the UKBDRS is an extremely valuable clinical tool. We are in the process of piloting a basic Brain Health clinic in Hertfordshire where we will use UKBDRS for risk assessment and profiling. The authors claim to provide access to an online spreadsheet for risk calculation, but it failed to open. an accessible online link or a mobile app may help busy clinicians use this risk score more effectively.
We congratulate Oxford colleagues for publishing the UK Biobank Dementia Risk Score (UKBDRS) for predicting dementia up to 14 years in individuals aged 50-73 years. For NHS clinicians aspiring to enhance existing memory clinics by adding Brain Health Services, the UKBDRS is an extremely valuable clinical tool. We are in the process of piloting a basic Brain Health clinic in Hertfordshire where we will use UKBDRS for risk assessment and profiling. The authors claim to provide access to an online spreadsheet for risk calculation, but it failed to open. an accessible online link or a mobile app may help busy clinicians use this risk score more effectively.
Recent systematic reviews, clinical guidelines, and suicide prevention strategies suggest we should abandon the endeavour of using risk assessment to predict suicide and instead focus on clinical need. (1, 2) Does the recent carefully conducted paper by Fazel and colleagues mean that this advice should be overturned? We think not.
At the turn of the century several of us were involved in developing risk tools for self-harm. Nearly two decades later, colleagues have used larger samples, novel methodology, and painstaking analysis to produce OxSATs. Of course, interpretation of how good tools are depends on multiple diagnostic accuracy statistics and clinical context. However, one accepted measure of overall performance is the ‘area under the curve’ (AUC).
What is striking is that the AUC for predicting suicide in the 6 months after self-harm was nearly identical for OxSATs and the first generation scales (0.75 vs 0.71). (3, 4) What is perhaps even more striking is the very different interpretation of the findings. The authors of the recent study suggest OxSATs accurately predicts the risk of suicide whereas Steeg et al. concluded the opposite and suggested scales should not be used to determine treatment.
What should we make of this discrepancy? In the end it perhaps comes down to what different researchers mean by ‘accurate’. A commonly used measure which may reflect real world utility is the positive predictive value (PPV) - of those rated as at...
Recent systematic reviews, clinical guidelines, and suicide prevention strategies suggest we should abandon the endeavour of using risk assessment to predict suicide and instead focus on clinical need. (1, 2) Does the recent carefully conducted paper by Fazel and colleagues mean that this advice should be overturned? We think not.
At the turn of the century several of us were involved in developing risk tools for self-harm. Nearly two decades later, colleagues have used larger samples, novel methodology, and painstaking analysis to produce OxSATs. Of course, interpretation of how good tools are depends on multiple diagnostic accuracy statistics and clinical context. However, one accepted measure of overall performance is the ‘area under the curve’ (AUC).
What is striking is that the AUC for predicting suicide in the 6 months after self-harm was nearly identical for OxSATs and the first generation scales (0.75 vs 0.71). (3, 4) What is perhaps even more striking is the very different interpretation of the findings. The authors of the recent study suggest OxSATs accurately predicts the risk of suicide whereas Steeg et al. concluded the opposite and suggested scales should not be used to determine treatment.
What should we make of this discrepancy? In the end it perhaps comes down to what different researchers mean by ‘accurate’. A commonly used measure which may reflect real world utility is the positive predictive value (PPV) - of those rated as at high risk, how many go on to have the outcome of interest. In supplemental table 8 of the OxSATS paper the PPV for suicide within six months is 2%. In other words of 100 people rated as at high risk only 2 are truly at high risk. We acknowledge that the PPV is not the only the only way to judge a tool’s performance but we would argue that it is one that matters to clinicians.
Where should we go from here? Listening to those with lived experience is essential. Service users report that risk scales may detract from therapeutic engagement and lead to exclusion and iatrogenic harms. (5) For many, stigma and poor experiences of care are the more pressing concerns.(6) In the end, there are probably no short-cuts to careful assessment and no easy way to identify those who go on to have adverse outcomes. However, we can agree with Fazel and colleagues on the bigger issue of making high quality, evidence-based interventions available and accessible.
1. National Institute for Health and Care Excellence. Self-harm: assessment, management and preventing recurrence . NICE Guideline., 2022.
2. Department of Health and Social Care. Suicide prevention in England: 5-year cross-sector strategy. https://www.gov.uk/government/publications/suicide-prevention-strategy-f..., 2023.
3. Fazel S, Vazquez-Montes MDLA, Molero Y, et al. Risk of death by suicide following self-harm presentations to healthcare: development and validation of a multivariable clinical prediction rule (OxSATS). BMJ Mental Health 2023;26(1) doi: 10.1136/bmjment-2023-300673
4. Steeg S, Quinlivan L, Nowland R, et al. Accuracy of risk scales for predicting repeat self-harm and suicide: a multicentre, population-level cohort study using routine clinical data. BMC Psychiatry 2018;18
5. Graney J, Hunt IM, Quinlivan L, et al. Suicide risk assessment in UK mental health services: a national mixed-methods study. Lancet Psychiatry 2020;7(12):1046-53. doi: 10.1016/s2215-0366(20)30381-3
6. Quinlivan LM, Gorman L, Littlewood DL, et al. 'Relieved to be seen'-patient and carer experiences of psychosocial assessment in the emergency department following self-harm: qualitative analysis of 102 free-text survey responses. BMJ Open 2021;11(5):e044434. doi: 10.1136/bmjopen-2020-044434 [published Online First: 2021/05/25]
I read this paper with great interest. The finding that 29% of somatic deaths were alcohol-related warrants further investigation, especially since, as the authors state, alcohol contributes to other somatic causes of death (e.g., cancer, CVD) that, in this methodology, were not classified as alcohol-related.
I would encourage the authors to refrain from using terms such as “alcohol abuse” as was used in this publication. While this term is ubiquitous in the alcohol literature, it perpetuates stigma toward individuals who have alcohol use disorder and/or drink alcohol at high-risk levels. Indeed, evidence has demonstrated that using words like “substance abuser” or “abuse” can lead to feelings that those who use alcohol and/or other drugs are to blame for their situation (1). Alternative terms such as “those drinking at high-risk levels” would be preferred (2).
In our meta-analysis, we synthesised evidence on risk factors for suicide based on psychological autopsy studies [1]. We included data from 37 case-control studies and examined associations for 40 risk factors in 12,734 adults. Novel aspects are the inclusion of a wide range of risk factors across four domains – sociodemographic, family history, clinical, and life events – and quantitative methods to examine sources of heterogeneity.
In their response, Soper and Large question one interpretation to the findings (rather than methods, analyses, or reporting) stating that consideration of risk factors and risk assessment has limited clinical utility. We think that this is a misreading of the evidence.
First, assessing the risk of suicide and linking assessment to preventative measures is a central component of clinical care. We suggest that prediction models can assist in stratifying an individual’s suicide risk. One advantage of empirically derived prediction models over subjective clinical judgment is that they attempt to incorporate the relative strength of multiple risk factors and their interactions. In addition, subjective clinical judgement tends to be optimistic with an over-reliance on recent events [2]. Furthermore, risk assessment tools can improve consistency within and between clinical services. They can also raise the ceiling of expertise, particularly where high staff turnover and variations in training experience exist, and anchor decision-maki...
In our meta-analysis, we synthesised evidence on risk factors for suicide based on psychological autopsy studies [1]. We included data from 37 case-control studies and examined associations for 40 risk factors in 12,734 adults. Novel aspects are the inclusion of a wide range of risk factors across four domains – sociodemographic, family history, clinical, and life events – and quantitative methods to examine sources of heterogeneity.
In their response, Soper and Large question one interpretation to the findings (rather than methods, analyses, or reporting) stating that consideration of risk factors and risk assessment has limited clinical utility. We think that this is a misreading of the evidence.
First, assessing the risk of suicide and linking assessment to preventative measures is a central component of clinical care. We suggest that prediction models can assist in stratifying an individual’s suicide risk. One advantage of empirically derived prediction models over subjective clinical judgment is that they attempt to incorporate the relative strength of multiple risk factors and their interactions. In addition, subjective clinical judgement tends to be optimistic with an over-reliance on recent events [2]. Furthermore, risk assessment tools can improve consistency within and between clinical services. They can also raise the ceiling of expertise, particularly where high staff turnover and variations in training experience exist, and anchor decision-making in evidence.
Second, research has identified limitations in current approaches [3], which commonly draw on tools and checklists developed for assessing depression and suicidal ideation rather than for predicting future risk of suicide [4]. Thus, discounting the potential value of risk assessment on the basis of tools designed for other purposes is unfounded. Focusing on single factors in isolation is another misreading – prediction models incorporate multiple factors. For example, in a longitudinal UK study [5], of 20,230 women aged 15–24 who self-harmed, 14 died by suicide in the subsequent year (incidence rate 111/100,000). In men aged 55 and older, 2766 self-harmed and 29 died by suicide (incidence rate 1874/100,000). This is a >15-fold difference based on two factors. Incorporating others, for which our meta-analysis provides an empirical basis [1], will improve assessment.
Another problem with risk assessment critiques is that they do not compare prediction models with current approaches (eg, unstructured clinical approaches) nor do they consider a full range of performance measures, including negative predictive value (NPV) and calibration [6]. NPV is potentially important as identifying true negatives can preserve resources by screening out persons who do not need further assessment and treatment [7]. Calibration, whether a tool predicts a risk level that is close to the observed risk, is also a key metric.
More recently, prognostic models to predict suicidal behaviour in high-risk groups such as individuals with psychiatric disorders have been developed with good discrimination and calibration [8]. Similarly, the Oxford Mental Illness and Suicide (OxMIS) tool [9] showed good performance in predicting suicide in people with severe mental illness (schizophrenia-spectrum disorders and bipolar disorder). Risk scores from OxMIS or other tools can be used to assist clinical decision-making. The Oxford Suicide after Self-harm (OxSATS) tool is another recent prediction model developed with high-quality methods [10].
Using risk prediction tools in combination with clinical judgment can improve care by identifying those at higher risk earlier and lead to more targeted management, which could be cost saving [11] and improve allocation of limited clinical resources. Decision curve analyses will help inform appropriate cut-offs for clinical use [12].
Our meta-analysis [1] was not about the potential role of risk assessment. Rather, by outlining the range and magnitude of individual risk factors, it underscores the rationale for combining multiple predictors when assessing risk.
References
1. Favril L, Yu R, Uyar A, Sharpe M, Fazel S. Risk factors for suicide in adults: systematic review and meta-analysis of psychological autopsy studies. Evid Based Ment Health 2022; 25: 148-55.
2. Pease JL, Forster JE, Davidson CL, et al. How Veterans Health Administration Suicide Prevention Coordinators assess suicide risk. Clin Psychol Psychother 2017; 24: 401-10.
3. Wolf A, Fazel, S. Overstating the lack of evidence on suicide risk assessment. Br J Psychiatry 2017; 210: 369.
4. Fazel S, Runeson B. Suicide. N Engl J Med 2020; 382: 266-74.
5. Geulayov G, Casey D, Bale L, et al. Suicide following presentation to hospital for non-fatal self-harm in the Multicentre Study of Self-harm: a long-term follow-up study. Lancet Psychiatry 2019; 6: 1021-30.
6. Whiting D, Fazel S. How accurate are suicide risk prediction models? Asking the right questions for clinical practice. Evid Based Ment Health 2019; 22: 125-8.
7. Bolton JM, Gunnell D, Turecki G. Suicide risk assessment and intervention in people with mental illness.
BMJ 2015; 351: h4978.
8. Chen Q, Zhang-James YL, Barnett EJ, et al. Predicting suicide attempt or suicide death following a visit to psychiatric specialty care: a machine learning study using Swedish national registry data. PLoS Med 2020;
17: e1003416.
9. Fazel S, Wolf A, Larsson H, Mallett S, Fanshawe TR. The prediction of suicide in severe mental illness: development and validation of a clinical prediction rule (OxMIS). Transl Psychiatry 2019; 9: 98.
10. Fazel S, Vazquez-Montes M, Molero Y, et al. Risk of death by suicide following self-harm presentations to healthcare: development and validation of a multivariable clinical prediction rule. BMJ Ment Health; in press.
11. Botchway S, Tsiachristas A, Pollard J, Fazel S. Cost-effectiveness of implementing a suicide prediction tool (OxMIS) in severe mental illness: economic modeling study. Eur Psychiatry 2022; 66: e6.
12. Fitzgerald M, Saville BR, Lewis RJ. Decision curve analysis. JAMA 2015; 313: 409-10.
It is regrettable that BMJ Mental Health marks its transition from the Journal Evidence-based Mental Health with the publication of a paper that could, at best, be judged evidence-informed than evidence-based. The authors of the O’Driscoll et al (2023) paper make no acknowledgements of possible publication bias. But they work either for the NHS trusts or IAPT. Further NHS Trusts operate the IAPT services. They make no critical appraisal of their usage of IAPT’s chosen metric of recovery. There is no acknowledgement of works that cast serious doubts on the Services claimed 50% recovery rate, Capobianco et al (2023), Scott (2018).
The O’Driscoll et al (2023) paper claims that CBT may be preferred to counselling for clients who have anxiety symptoms comorbid with depression. But the conclusions are built on sand in that:
a) there can be no certainty that the subjects studied were depressed as there was no ‘gold standard’ diagnostic interview conducted. Instead reliance was placed on a psychometric test, PHQ-9
b) there can be no certainty about comorbidity because of the absence of a diagnostic interview
c) no fidelity checks were carried out to establish whether therapists were conducting CBT or counselling. Reliance was instead placed on therapists claims.
d) no blind-raters were used to assess outcome
e) there can be no certainty that the observed changes would not have happened anyway because of the absence of a credible attention co...
It is regrettable that BMJ Mental Health marks its transition from the Journal Evidence-based Mental Health with the publication of a paper that could, at best, be judged evidence-informed than evidence-based. The authors of the O’Driscoll et al (2023) paper make no acknowledgements of possible publication bias. But they work either for the NHS trusts or IAPT. Further NHS Trusts operate the IAPT services. They make no critical appraisal of their usage of IAPT’s chosen metric of recovery. There is no acknowledgement of works that cast serious doubts on the Services claimed 50% recovery rate, Capobianco et al (2023), Scott (2018).
The O’Driscoll et al (2023) paper claims that CBT may be preferred to counselling for clients who have anxiety symptoms comorbid with depression. But the conclusions are built on sand in that:
a) there can be no certainty that the subjects studied were depressed as there was no ‘gold standard’ diagnostic interview conducted. Instead reliance was placed on a psychometric test, PHQ-9
b) there can be no certainty about comorbidity because of the absence of a diagnostic interview
c) no fidelity checks were carried out to establish whether therapists were conducting CBT or counselling. Reliance was instead placed on therapists claims.
d) no blind-raters were used to assess outcome
e) there can be no certainty that the observed changes would not have happened anyway because of the absence of a credible attention control condition
f) there can be no certainty that the observed changes were clinically meaningful or that changes endured. A 6 point improvement on in the CBT group and a 5 point improvement in the counselling for depression group.
g) the study was restricted to patients who attended 5 or more treatment sessions, but these are unrepresentative of IAPT clients with only half of clients having 2 or more treatment sessions (defined by IAPT as ‘treatment’). The mean number of IAPT treatment sessions is 7 but the mean number of treatment sessions in the O’Driscoll et al (2023) was 10 in counselling for depression and 11 in CBT. Further the third of IAPT clients who undergo low intensity intervention alone were excluded. Generalisation from this study is fraught with difficulties
Does the emergence of BMJ Mental Health signal the demise of evidence-based mental health? I hope not.
Capobianco, L., Verbist, I., Heal, C., Huey, D., & Wells, A. (2023). Improving access to psychological therapies: Analysis of effects associated with remote provision during COVID-19. The British journal of clinical psychology, 62(1), 312–324. https://doi.org/10.1111/bjc.12410
O'Driscoll C, Buckman JEJ, Saunders R, et al Symptom-specific effects of counselling for depression compared to cognitive–behavioural therapy BMJ Ment Health 2023;26:e300621.
Scott M. J. (2018). Improving Access to Psychological Therapies (IAPT) - The Need for Radical Reform. Journal of health psychology, 23(9), 1136–1147. https://doi.org/10.1177/1359105318755264
In a recent article Favril and associates report a systematic review and meta-analysis of risk factors for suicide derived from psychological autopsy studies that compared community samples of suicide decedents to living or deceased controls. 1 They found a range of risk factors that were, in retrospect, strongly statistically associated with suicide, including the presence of mental disorder (Odds Ratio (OR) = 13.1), depression (OR = 11.0), previous psychiatric treatment (OR = 10.1), previous self-harm (OR = 10.1), and previous suicide attempt (OR = 8.5). While acknowledging methodological weaknesses intrinsic to psychological autopsy studies, the authors maintain a position that “Identifying factors associated with suicide can improve risk stratification and help target interventions for high-risk groups” (p. 1). We consider this conclusion to be premature and fear the article will perpetuate a misplaced confidence in these risk factors as a basis for suicide risk assessment and clinical decision-making.
Three problems deserve attention. First, more methodologically sound longitudinal studies show much weaker prospective associations between risk factors and suicide. For example, in 2017 Franklin and associates published a survey of 50 years of longitudinal research into factors associated with suicidal thoughts and behaviours, including suicide.2 The top five risk factors for suicide in the Franklin meta-analysis were previous psychiatric hospitalisation (OR = 3...
In a recent article Favril and associates report a systematic review and meta-analysis of risk factors for suicide derived from psychological autopsy studies that compared community samples of suicide decedents to living or deceased controls. 1 They found a range of risk factors that were, in retrospect, strongly statistically associated with suicide, including the presence of mental disorder (Odds Ratio (OR) = 13.1), depression (OR = 11.0), previous psychiatric treatment (OR = 10.1), previous self-harm (OR = 10.1), and previous suicide attempt (OR = 8.5). While acknowledging methodological weaknesses intrinsic to psychological autopsy studies, the authors maintain a position that “Identifying factors associated with suicide can improve risk stratification and help target interventions for high-risk groups” (p. 1). We consider this conclusion to be premature and fear the article will perpetuate a misplaced confidence in these risk factors as a basis for suicide risk assessment and clinical decision-making.
Three problems deserve attention. First, more methodologically sound longitudinal studies show much weaker prospective associations between risk factors and suicide. For example, in 2017 Franklin and associates published a survey of 50 years of longitudinal research into factors associated with suicidal thoughts and behaviours, including suicide.2 The top five risk factors for suicide in the Franklin meta-analysis were previous psychiatric hospitalisation (OR = 3.57), previous suicide attempt (OR = 2.24), suicidal ideation (OR = 2.22), lower socioeconomic status (OR = 2.20) and life events (OR = 2.18). In contrast to the conclusions of Favril and associates, the authors commented, “No broad category or subcategory [of suicide risk factor] predicted far above chance levels” (p. 187).
Second, even if the higher odds ratios derived by psychological autopsy were taken at face value, they do not justify the authors’ credence in the utility of suicide risk factors in clinical practice. For example, their assessed OR of 11 for depression, when applied to a base rate of suicide in the order of 10 fatalities per 100,000 per annum in the community samples, points to suicide remaining a highly unlikely outcome for depressed people, even in the long term.
Third, the authors’ construal of suicide as the “result of a cumulation of multiple risk factors” (p. 7) is contradicted by evidence of the marked weakness suicide risk models based on multiple risk factors. 3.4 Belsher and associates concluded that the accuracy of suicide prediction models for “predicting a future event is near 0” 3. Corke and associates found that suicide risk models using many input variables are no less inaccurate than those using as few as two.4 In this light, suicide emerges as a largely stochastic event, opaque to prediction even in principle (Soper, Malo Ocejo, & Large, 2022).
A widespread and enduring over-confidence in the utility of suicide risk assessment, whether based on risk factors or by other means, may have the effect of magnifying the perceived risks of suicide, an exaggeration that serves neither the patient nor their clinician. In contrast, we have argued that a brighter future awaits mental healthcare if suicide’s rarity and non-predictability are accepted, acknowledged, and empathically communicated to those in distress. 5
References
1. Favril, L., Yu, R., Uyar, A., Sharpe, M., & Fazel, S. (2022). Risk factors for suicide in adults: systematic review and meta-analysis of psychological autopsy studies. Evidence Based Mental Health, ebmental-2022-300549. doi:10.1136/ebmental-2022-300549
2. Franklin, J. C., Ribeiro, J. D., Fox, K. R., Bentley, K. H., Kleiman, E. M., Huang, X., Nock, M. K. (2017). Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research. Psychological Bulletin, 143(2), 187-232.
3. Belsher, B. E., Smolenski, D. J., Pruitt, L. D., Bush, N. E., Beech, E. H., Workman, D. E., Skopp, N. A. (2019). Prediction models for suicide attempts and deaths: A systematic review and simulation. JAMA Psychiatry, 76(6), 642-651
4. Corke, M., Mullin, K., Angel-Scott, H., Xia, S., & Large, M. M. (2021). Meta-analysis of the strength of exploratory suicide prediction models; from clinicians to computers. BJPsych open, 7(e26), 1-11.
5. Soper, C. A., Malo Ocejo, P., & Large, M. M. (2022). On the randomness of suicide: An evolutionary, clinical call to transcend suicide risk assessment. In R. Abed & P. St John-Smith (Eds.), Evolutionary Psychiatry: Evolutionary Perspectives on Mental Health (pp. 134-152). Cambridge, UK: Cambridge University Press and Royal College of Psychiatrists.
We applaud the article by Dr Neale (1) which highlights the importance of simulation training to teach communication skills in psychiatry. However, there was no reference to the role of simulation training in teaching medical students skills in addictive medicine.
As a result of an increase in alcohol related harm in Israel over the last 20 years (2) and recommendations (3) for controlled and replicable studies in undergraduate medical education in alcohol and substance abuse, we studied the impact of a short term intervention on the knowledge of psychiatric aspects of alcohol amongst 4th and 5th year medical students (4). The intervention consisted of a powerpoint lecture on alcohol related harm to small groups of students, followed immediately by an active member of an alcoholics anonymous group wherever possible relating his story to the material in the lecture. After 2 weeks the same group of students participated in a structured simulation of a family doctor interviewing a female adolescent because of a concern that she suffered from alcohol related harm.
The students who did not participate directly in the simulation were asked to provide constructive feedback to the student who simulated the primary care physician along the lines of motivational interviewing (Engaging the patient, Focussing on the goals of the meeting, Evoking "change talk" by the patient as a way of introducing behavioural change and Closure of the meeting while maintaining...
We applaud the article by Dr Neale (1) which highlights the importance of simulation training to teach communication skills in psychiatry. However, there was no reference to the role of simulation training in teaching medical students skills in addictive medicine.
As a result of an increase in alcohol related harm in Israel over the last 20 years (2) and recommendations (3) for controlled and replicable studies in undergraduate medical education in alcohol and substance abuse, we studied the impact of a short term intervention on the knowledge of psychiatric aspects of alcohol amongst 4th and 5th year medical students (4). The intervention consisted of a powerpoint lecture on alcohol related harm to small groups of students, followed immediately by an active member of an alcoholics anonymous group wherever possible relating his story to the material in the lecture. After 2 weeks the same group of students participated in a structured simulation of a family doctor interviewing a female adolescent because of a concern that she suffered from alcohol related harm.
The students who did not participate directly in the simulation were asked to provide constructive feedback to the student who simulated the primary care physician along the lines of motivational interviewing (Engaging the patient, Focussing on the goals of the meeting, Evoking "change talk" by the patient as a way of introducing behavioural change and Closure of the meeting while maintaining the momentum for change) (5).
The learning objectives of the simulation were:
1. To practise talking to a young person about her/his alcohol use using the Alcohol Use Disorder Identification Test/AUDIT (6).
2. To practise motivational interviewing.
3. To help the students understand that alcohol related harm is not limited to the chronic homeless alcoholic stereotype.
We were able to demonstrate a significant increase in knowledge of psychiatric aspects of alcohol amongst the 5th year students who participated in the intervention 12 months earlier compared to a control group of 5th year medical students who did not participate in the intervention. It is unclear to what extent the simulation contributed to the change or the lecture or a combination of the two educational modalities.
As a result of reduced time allocated to psychiatric undergraduate teaching, we are now studying the impact of a simulated brief intervention by the primary physician for alcohol related harm alone amongst 5th year students. We are including clinical associations of alcohol and substance abuse such as childhood adversive events, post traumatic stress disorder and self harming behaviour self which have been described in the literature.
We are asking the students to rate whether the simulation has improved their willingness to take an alcohol informed history. The results are still preliminary but appear promising.
References:
1.Neale J. What is the evidence for the use of simulationtraining to teach communication skills in psychiatry?Evid Based Mental Health 2019;22:23-25.
2.Horowitz T, Brosh T, Bar-Hamburger R. Patterns of drug and alcohol abuse among youthfromtheFormerSovietUnion2011;availableat:http://www.antidrugs.gov.il/pages/1697.aspx
3.Kothari D, Gourevitch MN, Lee JD et al. Undergraduate medical education in substance abuse: a review of the quality of the literature. Acad Med 2011;86:98-112.
4. Jaworowski S, Raveh-Brawer D, Haber P et al. Preliminary results of a controlled educational intervention on alcohol related harm among medical students with a 12 month follow up. Isr J Psychiatry 2018;55:37-39.
5. Miller WR, Rollnick S. Motivational interviewing: helping people change 3rd Edition APA PsycNet.
6. Saunders JB, Aasland OG, Babor TF et al. Development of the alcohol use disorders identification test (AUDIT): WHO Collaborative Projecton early detectionon persons with harmful alcohol consumption II. Addiction 1993; 88: 791-804.
The review by Havard and colleagues [1] does not take into account
the fact that brief advice, either oral or written, is good enough to
bring about behavioral change. To have such a group as the control group
is self-defeating. In fact, in an emergency department (ED) setting, where
both emotions and tension run high, it would be futile to try and attempt
other time-consuming interventions such as motivational interviewi...
The review by Havard and colleagues [1] does not take into account
the fact that brief advice, either oral or written, is good enough to
bring about behavioral change. To have such a group as the control group
is self-defeating. In fact, in an emergency department (ED) setting, where
both emotions and tension run high, it would be futile to try and attempt
other time-consuming interventions such as motivational interviewing and
pharmacological treatment. An ideal form of treatment is therefore the
Brief advice. As the commentary [2] observes, brief interventions are
sufficient to reduce the negative health consequences, such as alcohol
related injuries in this group. I believe that such patients may also be
amenable to further behavioral changes in order to prevent development of
alcohol dependence by proper identification of "at-risk" individuals. This
can be done by enquiring for craving, tolerance and loss of control [3]
which can prove beneficial to these problem and pre-dependent drinkers
[4].
References
1)Havard A, Shakeshaft A, Sanson-Fisher R. Systematic review and meta
-analyses of strategies targeting alcohol problems in emergency
departments: interventions reduce alcohol-related injuries. Addiction
2008;103:368–76.
2)Crawford MJ. Review: Screening and intervention for alcohol misuse
in emergency rooms reduces alcohol-related injuries. Evid. Based Ment.
Health 2008;11;88
3)Manjunatha N, Saddichha S, Sinha BNP et al. Chronology of alcohol
dependence: Implications in prevention.Ind J Com Med 2008; 33: 228-32.
We are delighted to read the publication of a new dementia risk score for prediction of dementia up to 14 years. We congratulate the authors for incorporating almost all modifiable factors identified by the 2020 Lancet Commission on dementia prevention, management and care. Since the publication of the European Brain Health Guidelines earlier this year, memory clinic professionals in the UK have been desperately looking for a home grown tool. We hope to see an online training for using this clinical tool in the near future.
We congratulate Oxford colleagues for publishing the UK Biobank Dementia Risk Score (UKBDRS) for predicting dementia up to 14 years in individuals aged 50-73 years. For NHS clinicians aspiring to enhance existing memory clinics by adding Brain Health Services, the UKBDRS is an extremely valuable clinical tool. We are in the process of piloting a basic Brain Health clinic in Hertfordshire where we will use UKBDRS for risk assessment and profiling. The authors claim to provide access to an online spreadsheet for risk calculation, but it failed to open. an accessible online link or a mobile app may help busy clinicians use this risk score more effectively.
We congratulate Oxford colleagues for publishing the UK Biobank Dementia Risk Score (UKBDRS) for predicting dementia up to 14 years in individuals aged 50-73 years. For NHS clinicians aspiring to enhance existing memory clinics by adding Brain Health Services, the UKBDRS is an extremely valuable clinical tool. We are in the process of piloting a basic Brain Health clinic in Hertfordshire where we will use UKBDRS for risk assessment and profiling. The authors claim to provide access to an online spreadsheet for risk calculation, but it failed to open. an accessible online link or a mobile app may help busy clinicians use this risk score more effectively.
Recent systematic reviews, clinical guidelines, and suicide prevention strategies suggest we should abandon the endeavour of using risk assessment to predict suicide and instead focus on clinical need. (1, 2) Does the recent carefully conducted paper by Fazel and colleagues mean that this advice should be overturned? We think not.
At the turn of the century several of us were involved in developing risk tools for self-harm. Nearly two decades later, colleagues have used larger samples, novel methodology, and painstaking analysis to produce OxSATs. Of course, interpretation of how good tools are depends on multiple diagnostic accuracy statistics and clinical context. However, one accepted measure of overall performance is the ‘area under the curve’ (AUC).
What is striking is that the AUC for predicting suicide in the 6 months after self-harm was nearly identical for OxSATs and the first generation scales (0.75 vs 0.71). (3, 4) What is perhaps even more striking is the very different interpretation of the findings. The authors of the recent study suggest OxSATs accurately predicts the risk of suicide whereas Steeg et al. concluded the opposite and suggested scales should not be used to determine treatment.
What should we make of this discrepancy? In the end it perhaps comes down to what different researchers mean by ‘accurate’. A commonly used measure which may reflect real world utility is the positive predictive value (PPV) - of those rated as at...
Show MoreI read this paper with great interest. The finding that 29% of somatic deaths were alcohol-related warrants further investigation, especially since, as the authors state, alcohol contributes to other somatic causes of death (e.g., cancer, CVD) that, in this methodology, were not classified as alcohol-related.
I would encourage the authors to refrain from using terms such as “alcohol abuse” as was used in this publication. While this term is ubiquitous in the alcohol literature, it perpetuates stigma toward individuals who have alcohol use disorder and/or drink alcohol at high-risk levels. Indeed, evidence has demonstrated that using words like “substance abuser” or “abuse” can lead to feelings that those who use alcohol and/or other drugs are to blame for their situation (1). Alternative terms such as “those drinking at high-risk levels” would be preferred (2).
References
(1) https://www.sciencedirect.com/science/article/pii/S0955395909001546?via%...
(2) https://journals.sagepub.com/doi/10.1177/17579139221093163?icid=int.sj-f...
In our meta-analysis, we synthesised evidence on risk factors for suicide based on psychological autopsy studies [1]. We included data from 37 case-control studies and examined associations for 40 risk factors in 12,734 adults. Novel aspects are the inclusion of a wide range of risk factors across four domains – sociodemographic, family history, clinical, and life events – and quantitative methods to examine sources of heterogeneity.
In their response, Soper and Large question one interpretation to the findings (rather than methods, analyses, or reporting) stating that consideration of risk factors and risk assessment has limited clinical utility. We think that this is a misreading of the evidence.
First, assessing the risk of suicide and linking assessment to preventative measures is a central component of clinical care. We suggest that prediction models can assist in stratifying an individual’s suicide risk. One advantage of empirically derived prediction models over subjective clinical judgment is that they attempt to incorporate the relative strength of multiple risk factors and their interactions. In addition, subjective clinical judgement tends to be optimistic with an over-reliance on recent events [2]. Furthermore, risk assessment tools can improve consistency within and between clinical services. They can also raise the ceiling of expertise, particularly where high staff turnover and variations in training experience exist, and anchor decision-maki...
Show MoreIt is regrettable that BMJ Mental Health marks its transition from the Journal Evidence-based Mental Health with the publication of a paper that could, at best, be judged evidence-informed than evidence-based. The authors of the O’Driscoll et al (2023) paper make no acknowledgements of possible publication bias. But they work either for the NHS trusts or IAPT. Further NHS Trusts operate the IAPT services. They make no critical appraisal of their usage of IAPT’s chosen metric of recovery. There is no acknowledgement of works that cast serious doubts on the Services claimed 50% recovery rate, Capobianco et al (2023), Scott (2018).
Show MoreThe O’Driscoll et al (2023) paper claims that CBT may be preferred to counselling for clients who have anxiety symptoms comorbid with depression. But the conclusions are built on sand in that:
a) there can be no certainty that the subjects studied were depressed as there was no ‘gold standard’ diagnostic interview conducted. Instead reliance was placed on a psychometric test, PHQ-9
b) there can be no certainty about comorbidity because of the absence of a diagnostic interview
c) no fidelity checks were carried out to establish whether therapists were conducting CBT or counselling. Reliance was instead placed on therapists claims.
d) no blind-raters were used to assess outcome
e) there can be no certainty that the observed changes would not have happened anyway because of the absence of a credible attention co...
In a recent article Favril and associates report a systematic review and meta-analysis of risk factors for suicide derived from psychological autopsy studies that compared community samples of suicide decedents to living or deceased controls. 1 They found a range of risk factors that were, in retrospect, strongly statistically associated with suicide, including the presence of mental disorder (Odds Ratio (OR) = 13.1), depression (OR = 11.0), previous psychiatric treatment (OR = 10.1), previous self-harm (OR = 10.1), and previous suicide attempt (OR = 8.5). While acknowledging methodological weaknesses intrinsic to psychological autopsy studies, the authors maintain a position that “Identifying factors associated with suicide can improve risk stratification and help target interventions for high-risk groups” (p. 1). We consider this conclusion to be premature and fear the article will perpetuate a misplaced confidence in these risk factors as a basis for suicide risk assessment and clinical decision-making.
Three problems deserve attention. First, more methodologically sound longitudinal studies show much weaker prospective associations between risk factors and suicide. For example, in 2017 Franklin and associates published a survey of 50 years of longitudinal research into factors associated with suicidal thoughts and behaviours, including suicide.2 The top five risk factors for suicide in the Franklin meta-analysis were previous psychiatric hospitalisation (OR = 3...
Show MoreWe applaud the article by Dr Neale (1) which highlights the importance of simulation training to teach communication skills in psychiatry. However, there was no reference to the role of simulation training in teaching medical students skills in addictive medicine.
Show MoreAs a result of an increase in alcohol related harm in Israel over the last 20 years (2) and recommendations (3) for controlled and replicable studies in undergraduate medical education in alcohol and substance abuse, we studied the impact of a short term intervention on the knowledge of psychiatric aspects of alcohol amongst 4th and 5th year medical students (4). The intervention consisted of a powerpoint lecture on alcohol related harm to small groups of students, followed immediately by an active member of an alcoholics anonymous group wherever possible relating his story to the material in the lecture. After 2 weeks the same group of students participated in a structured simulation of a family doctor interviewing a female adolescent because of a concern that she suffered from alcohol related harm.
The students who did not participate directly in the simulation were asked to provide constructive feedback to the student who simulated the primary care physician along the lines of motivational interviewing (Engaging the patient, Focussing on the goals of the meeting, Evoking "change talk" by the patient as a way of introducing behavioural change and Closure of the meeting while maintaining...
The review by Havard and colleagues [1] does not take into account the fact that brief advice, either oral or written, is good enough to bring about behavioral change. To have such a group as the control group is self-defeating. In fact, in an emergency department (ED) setting, where both emotions and tension run high, it would be futile to try and attempt other time-consuming interventions such as motivational interviewi...
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