Leveraging data science to enhance suicide prevention research: a literature review

Reappraisal Mitigates, While COVID-19 Burnout Exacerbates the Impact of Depressive Symptoms on Suicidal Ideation Among Chinese College Students

Background

Suicide is a global public health issue. This study examined the role of COVID-19 burnout and reappraisal in suicidal ideation caused by depressive symptoms among Chinese college students.

Methods

587 students (261 boys, M age = 19.53, SD = 1.42) were assessed using the Short Depression-Happiness Scale (SDHS), Positive and Negative Suicide Ideation (PANSI) inventory, and Emotion Regulation Scale (ERS).

Results

Correlation analysis indicated depressive symptoms were negatively correlated with reappraisal and positively correlated with suicidal ideation and COVID-19 burnout. Reappraisal was negatively correlated with suicidal ideation and COVID-19 burnout. The moderated mediation model showed COVID-19 burnout enhanced the direct effect of depressive symptoms on suicidal ideation and indirectly enhanced this effect by weakening the protective role of reappraisal.

Conclusion

These finding show that reappraisal acts as a protective factor against suicidal ideation in individuals with depressive symptoms, while COVID-19 burnout exacerbates this effect by weakening reappraisal's protective role.

Challenges in the evaluation of suicide prevention measures and quality of suicide data in Germany, Austria, and Switzerland: findings from qualitative expert interviews

BACKGROUND

Suicide prevention requires diverse, integrated, and evidence-based measures. Comprehensive evaluation of interventions and reliable suicide data are crucial for guiding policy-making and advancing suicide prevention efforts. This study aimed to analyze current issues and gaps in the evaluation of suicide prevention measures and the quality of suicide data in Germany, Austria, and Switzerland to derive specific recommendations for improvement.

METHODS

Online, semi-structured interviews were conducted with 36 experts in suicide prevention from Germany, Austria, and Switzerland, covering insights from policy, science, and practice. The interviews took place between September 2022 and February 2023, were audio-recorded, transcribed verbatim, and analyzed using the Framework method.

RESULTS

While solid evidence supports the effectiveness of some suicide prevention interventions, experts indicated that the evaluation of many other measures is weak. Conducting effectiveness studies in suicide prevention presents a range of methodological and practical challenges, including recruitment difficulties, choosing adequate outcome criteria, ethical considerations, and trade-offs in allocating resources to evaluation efforts. Many interviewees rated the quality of national suicide statistics in Germany, Austria, and Switzerland as comparatively high. However, they noted limitations in the scope, timeliness, and reliability of these data, prompting some regions to implement their own suicide monitoring systems. None of the three countries has national routine data on suicide attempts.

CONCLUSION

While some challenges in evaluating suicide prevention measures are inevitable, others can potentially be mitigated. Evaluations could be enhanced by combining traditional and innovative research designs, including intermediate outcomes and factors concerning the implementation process, and employing participatory and transdisciplinary research to engage different stakeholders. Reliable suicide data are essential for identifying trends, supporting research, and designing targeted prevention measures. To improve the quality of suicide data, a standardized monitoring approach, including uniform definitions, trained professionals, and cross-sector agreement on leadership and financing, should be pursued. This study provides actionable recommendations and highlights existing good practice approaches, thereby supporting decision-makers and providing guidance for advancing suicide prevention on a broader scale.

Dynamic suicide topic modelling: Deriving population-specific, psychosocial and time-sensitive suicide risk variables from Electronic Health Record psychotherapy notes

In the machine learning subfield of natural language processing, a topic model is a type of unsupervised method that is used to uncover abstract topics within a corpus of text. Dynamic topic modelling (DTM) is used for capturing change in these topics over time. The study deploys DTM on corpus of electronic health record psychotherapy notes. This retrospective study examines whether DTM helps distinguish closely matched patients that did and did not die by suicide. Cohort consists of United States Department of Veterans Affairs (VA) patients diagnosed with Posttraumatic Stress Disorder (PTSD) between 2004 and 2013. Each case (those who died by suicide during the year following diagnosis) was matched with five controls (those who remained alive) that shared psychotherapists and had similar suicide risk based on VA's suicide prediction algorithm. Cohort was restricted to patients who received psychotherapy for 9+ months after initial PTSD diagnoses (cases = 77; controls = 362). For cases, psychotherapy notes from diagnosis until death were examined. For controls, psychotherapy notes from diagnosis until matched case's death date were examined. A Python-based DTM algorithm was utilized. Derived topics identified population-specific themes, including PTSD, psychotherapy, medication, communication and relationships. Control topics changed significantly more over time than case topics. Topic differences highlighted engagement, expressivity and therapeutic alliance. This study strengthens groundwork for deriving population-specific, psychosocial and time-sensitive suicide risk variables.

Leveraging unstructured electronic medical record notes to derive population-specific suicide risk models

Electronic medical record (EMR)-based suicide risk prediction methods typically rely on analysis of structured variables such as demographics, visit history, and prescription data. Leveraging unstructured EMR notes may improve predictive accuracy by allowing access to nuanced clinical information. We utilized natural language processing (NLP) to analyze a large EMR note corpus to develop a data-driven suicide risk prediction model. We developed a matched case-control sample of U.S. Department of Veterans Affairs (VA) patients in 2015 and 2016. We randomly matched each case (all patients that died by suicide in that interval, n = 5029) with five controls (patients that remained alive). We processed note corpus using NLP methods and applied machine-learning classification algorithms to output. We calculated area under the curve (AUC) and risk tiers to determine predictive accuracy. NLP-derived models demonstrated strong predictive accuracy. Patients that scored within top 10% of risk model accounted for up to 29% of suicide decedents. NLP-derived model compares positively to other leading prediction methods. Our approach is highly implementable, only requiring access to text data and open-source software. Additional studies should evaluate ensemble models incorporating NLP-derived information alongside more typical structured variables.