Stability of clustering of lifestyle risk factors in the Dutch adult population and the association with mental health

BACKGROUND

Lifestyle factors often co-occur in clusters. This study examines whether clusters of lifestyle risk factors, such as smoking, alcohol use, physical inactivity, poor diet, sexual risk behaviour, cannabis and other drug use, change over time in a representative sample of Dutch adults. Additionally, the association between mental health and self-reported depression of lifestyle clusters was examined.

METHODS

Each year cross-sectional data of approximately 7500 individuals of 18 years and older from the annual Dutch Health Survey of 2014-2019 were used. Clusters were determined by a two-step cluster analysis. Furthermore, regression analyses determined the association between clusters of lifestyle risk factors and mental health.

RESULTS

Results show six clusters composed of one, multiple or no lifestyle risk factors. The clusters remained relatively stable over time: in some clusters, the number of people slightly changed between 2014 and 2019. More specifically, clusters that increased in size were the cluster with no lifestyle risk factors and the cluster with multiple lifestyle risk factors. Furthermore, results show that clusters with none to a few lifestyle risk factors were associated with better mental health and a lower prevalence of self-reported depression compared with clusters with multiple lifestyle risk factors.

CONCLUSIONS

The clustering of lifestyle risk factors remained stable over time. People with multiple lifestyle risk factors had poorer mental health than those without risk factors. These findings may emphasize the need for intervention strategies targeting this subgroup with multiple lifestyle risk factors.

[Suicidal behavior in emergency care: 10 tips for an active, empathetic and preventative approach]

Patients with suicidal behaviour are frequently seen in A&E departments. Providing care for this patient group can be challenging and medical staff have often not received specialized training to improve their knowledge and skills. An empathetic approach combined with brief interventions such as safety planning, collaborating with carers and partnership with local organizations can help A&E professionals to optimize care for these patients and contribute to the prevention of future suicide attempts.

A Proof of Concept Study on Individual Trends in Suicidal Ideation: An Ecological Momentary Assessment Study of 5 Patients Over Three Months

Background

Suicidal ideation (SI) is a significant and long-lasting mental health problem, with a third of individuals still experiencing SI after two years. To date, most Ecological Momentary Assessment (EMA) studies of SI have assessed its day-to-day course over one to four consecutive weeks and found no consistent trends in average SI severity over time.

Aim

The current proof of concept study assessed daily fluctuations of SI over a time span of 3 to 6 months to explore whether individual trends in SI severity could be detected, and if so, if the trajectory of changes were gradual or sudden. The secondary aim was to explore whether changes in SI severity could be detected at an early stage.

Method

Five adult outpatients with depression and SI used an EMA app on their smartphone in addition to their regular treatment for 3 to 6 months, where SI was assessed 3 times a day. To detect trends in SI for each patient, three models were tested: a null model, a gradual change model and a sudden change model. To detect changes in SI before a new plateau was reached, Early Warning Signals and Exponentially Weighted Moving Average control charts were used.

Results

In each patient, average SI severity had a unique trajectory of sudden and/or gradual changes. Additionally, in some patients, increases in both sudden and gradual SI could be detected at an early stage.

Conclusions

The study presents a first indication of unique individual trends in SI severity over a 3 to 6 months period. Though replication in a larger sample is needed to test how well results generalize, a first proof-of-concept is provided that both sudden and gradual changes in SI severity may be detectable at an early stage using the dynamics of time-series data.

The role of anxiety and depression in suicidal thoughts for autistic and non-autistic people: A theory-driven network analysis

BACKGROUND

Autistic adults experience more frequent suicidal thoughts and mental health difficulties than non-autistic adults, but research has yet to explain how these experiences are connected. This study explored how anxiety and depression contribute to suicidal thoughts according to the Interpersonal Theory of Suicide for autistic and non-autistic adults.

METHODS

Participants (autistic adults n = 463, 61% female; non-autistic n = 342, 64% female) completed online measures of anxiety, depression, thwarted belonging, and perceived burdensomeness. Network analysis explored whether: (i) being autistic is a risk marker for suicide; and (ii) pathways to suicidal thoughts are consistent for autistic and non-autistic adults.

RESULTS

Being autistic connected closely with feeling like an outsider, anxiety, and movement, which connected to suicidal thoughts through somatic experiences, low mood, and burdensomeness. Networks were largely consistent for autistic and non-autistic people, but connections from mood symptoms to somatic and thwarted belonging experiences were absent for autistic adults.

CONCLUSION

Autistic people experience more life stressors than non-autistic people leading to reduced coping, low mood, and suicidal thoughts. Promoting belonging, reducing anxiety, and understanding the role of movement could inform suicide prevention for autistic people. Research should accurately capture autistic lived experience when modeling suicide to ensure suicide prevention meets autistic needs.

Comparison of depression care provided in general practice in Norway and the Netherlands: registry-based cohort study (The Norwegian GP-DEP study)

BACKGROUND

Depression is highly prevalent in general practice, and organisation of primary health care probably affects the provision of depression care. General practitioners (GPs) in Norway and the Netherlands fulfil comparable roles. However, primary care teams with a mental health nurse (MHN) supplementing the GP have been established in the Netherlands, but not yet in Norway. In order to explore how the organisation of primary mental care affects care delivery, we aimed to examine the provision of GP depression care across the two countries.

METHODS

Registry-based cohort study comprising new depression episodes in patients aged ≥ 18 years, 2011-2015. The Norwegian sample was drawn from the entire population (national health registries); 297,409 episodes. A representative Dutch sample (Nivel Primary Care Database) was included; 27,362 episodes. Outcomes were follow-up consultation(s) with GP, with GP and/or MHN, and antidepressant prescriptions during 12 months from the start of the depression episode. Differences between countries were estimated using negative binomial and Cox regression models, adjusted for patient gender, age and comorbidity.

RESULTS

Patients in the Netherlands compared to Norway were less likely to receive GP follow-up consultations, IRR (incidence rate ratio) = 0.73 (95% confidence interval (CI) 0.71-0.74). Differences were greatest among patients aged 18-39 years (adj IRR = 0.64, 0.63-0.66) and 40-59 years (adj IRR = 0.71, 0.69-0.73). When comparing follow-up consultations in GP practices, including MHN consultations in the Netherlands, no cross-national differences were found (IRR = 1.00, 0.98-1.01). But in age-stratified analyses, Dutch patients 60 years and older were more likely to be followed up than their Norwegian counterparts (adj IRR = 1.21, 1.16-1.26). Patients in the Netherlands compared to Norway were more likely to receive antidepressant drugs, adj HR (hazard ratio) = 1.32 (1.30-1.34).

CONCLUSIONS

The observed differences indicate that the organisation of primary mental health care affects the provision of follow-up consultations in Norway and the Netherlands. Clinical studies are needed to explore the impact of team-based care and GP-based care on the quality of depression care and patient outcomes.

How chronic are depressive and anxiety disorders? 9-year general population study using narrow and broad course outcomes

BACKGROUND

Existing studies on disease course usually apply relatively short follow-up periods and narrow definitions of disease course resulting in too optimistic views on disease prognosis. This study explores the relevance of using a longer and broader (cross-disorder) perspective.

METHODS

Respondents with a 12-month disorder at baseline and available at 3-, 6- and 9-year follow-up were selected (major depressive disorder, MDD: n = 208; anxiety disorder: n = 220) from a general population study (N = 6646). DSM-IV disorders were assessed with the Composite International Diagnostic Interview. Disease course was described using a short and narrow perspective (i.e., 3-year follow-up, and considering presence of the index disorder only) and a long and broad perspective (9-year follow-up, and considering presence of any mood, anxiety or substance use disorder as outcome).

RESULTS

The recovery rates of both MDD and anxiety disorder reduced by half when the perspective switched from short and narrow (MDD: 74.0 %; anxiety disorder: 79.5 %) to long and broad (35.6 % and 40.0 % respectively). At 9-year follow-up, the rates of a persistent disorder (a disorder at each follow-up assessment) tripled when the perspective switched from narrow to broad (MDD: from 4.8 % to 13.9 %; anxiety disorder: from 4.5 % to 15.5 %).

LIMITATIONS

The findings are not generalizable to the most severe depressed and anxious patients.

CONCLUSIONS

Most people with MDD or anxiety disorder in the general population have a rather favourable prognosis when a narrow perspective is applied, but an unfavourable prognosis when a long-term and broad perspective is applied. Consequently, MDD and anxiety disorder should not merely be perceived as episodic disorders, and require longer-term disease monitoring and management.

The feasibility of using smartphone apps as treatment components for depressed suicidal outpatients

Mental health smartphone apps could increase the safety and self-management of patients at risk of suicide, but it is still unclear whether it is feasible to integrate such apps into routine mental healthcare. This study reports on the feasibility of using a safety planning app (BackUp) and a self-monitoring app (mEMA) as components of the routine treatment of depressed outpatients with suicidal ideation. Clinicians were trained in working with both of the apps, and they invited their eligible patients with suicidal ideation for study participation. Patients used the apps for 3 months and discussed these with their clinician during treatment. Patients completed assessments at baseline (T0), 4 weeks (T1) and post-test (T2, 12 weeks after baseline). Both patients and clinicians also participated in telephone interviews. Feasibility was assessed in terms of usability (score > 70 on System Usability Scale, SUS), acceptability (score > 20 on Client Satisfaction Questionnaire-8, CSQ-8), and uptake (sufficient rates of component completion and app usage in treatment). The sample included 17 adult outpatients (52.9% male, age range 20-50 years) diagnosed with a depressive disorder and suicidal ideation at baseline. BackUp was rated by patients at above the cut-off scores for usability (SUS mean score at T1 75.63 and at T2 77.71) and acceptability (CSQ-8 mean score at T1 23.42 and at T2 23.50). mEMA was similarly rated (SUS mean score at T1 75.83 and at T2 76.25; CSQ-8 mean score at T1 23.92 and at T2 22.75). Telephone interviews with patients and clinicians confirmed the usability and acceptability. The uptake criteria were not met. Our findings suggest that mobile safety planning and mobile self-monitoring can be considered acceptable and usable as treatment components for depressed suicidal outpatients, but the integration of apps into routine treatment needs to be further explored.

Examining the Interrelationships Among Suicide Cognitions, Suicidal Ideation, and Theoretically Derived Protective Factors

INTRODUCTION

Several protective factors for mitigating suicidal ideation (SI) such as positive affect, reasons for living, purpose in life, meaning in life, gratitude, grit, optimism, social support, and hope have been identified and received empirical support. However, few studies have examined the interrelationships of these protective factors and the identification of protective factors most closely linked to lower levels of SI may be useful for both theory-building initiatives and improvement of suicide-specific interventions. Network analysis offers an approach for testing the relation among these constructs, SI, and suicide risk factors.

METHODS

A sample N = 557 undergraduate students oversampled for lifetime SI completed a cross-sectional, online survey. The data was used to estimate an undirected, cross-sectional network of the aforementioned protective factors.

RESULTS

The resulting inferred network implicates strong negative influence of suicide cognitions, but not recent SI, and the strong positive influence of presence of meaning in life, trait hope, and low negative affect.

CONCLUSIONS

Implications for dimensionality of SI versus suicide cognitions, targeting presence of meaning in life, trait hope, and negative affect in treatment, and cross-cultural variations in reasons for living are discussed. The study is limited by the cross-sectional and convenience sampling methodology.HighlightsProtective factors may have less direct influence on suicidal ideationSuicide cognitions and the suicidal mode may be of phenomenological importancePresence of meaning and trait hope may be primary targets for suicide interventions.

The interplay between psychopathological symptoms: transdiagnostic cross-lagged panel network model

BACKGROUND

Recent paradigm shifts suggest that psychopathology manifests through dynamic interactions between individual symptoms.

AIMS

To investigate the longitudinal relationships between symptoms in a transdiagnostic sample of patients with psychiatric disorders.

METHOD

A two-wave, cross-lagged panel network model of 15 nodes representing symptoms of depression, (social) anxiety and attenuated psychotic symptoms was estimated, using baseline and 1-year follow-up data of 222 individuals with psychiatric disorders. Centrality indices were calculated to determine important predictors and outcomes.

RESULTS

Our results demonstrated that the strongest relationships in the network were between (a) more suicidal ideation predicting more negative self-view, and (b) autoregressive relationships of social anxiety symptoms positively reinforcing themselves. Negative self-view was the most predictable node in the network as it had the highest 'in-expected influence' centrality, and may be an important transdiagnostic outcome symptom.

CONCLUSIONS

The results give insight into longitudinal interactions between symptoms, which interact in ways that do not adhere to broader diagnostic categories. Our results suggest that self-view can also be a transdiagnostic outcome of psychopathology rather than just a predictor, as is normally posited, and may especially have an important relationship with suicidal ideation. Overall, our study demonstrates the dynamic complexity of psychopathology, and further supports the importance of investigating symptom interactions of different psychopathological dimensions over time and across disorders.

Internet-delivered interventions for personality disorders - A scoping review

BACKGROUND

Personality disorders (PDs) form a substantial part of the mental health disease burden. Effective therapies to treat PDs exist, but they are time-consuming, costly, and difficult to scale up. Delivery through the internet could facilitate the scalability of effective treatment methods.

OBJECTIVE

This review summarizes existing evidence on internet-delivered psychotherapy for personality disorders.

METHODS

Because few randomized controlled trials (RCTs) have been carried out, we conducted a scoping review. We performed a systematic literature search in PubMed, Embase, MEDLINE, CINAHL, PsycInfo, and Cochrane. Studies were selected if they conveyed research findings on internet-delivered PD interventions.

RESULTS

Eleven studies were included. The majority (n = 8) focused specifically on borderline personality disorder (BPD) and the other three on PD in general. The most frequently used form of intervention (n = 7) was the addition of a mobile app to a conventional evidence-based face-to-face treatment such as dialectical behavioral therapy (DBT). Most interventions (n = 8) were still in the development and piloting phase; only two RCTs were found. Usability and patient satisfaction were moderate to high in all studies. Three studies demonstrated significant decreases in borderline personality disorder symptoms.The majority of the studies found were pilot or feasibility studies, most involving mobile apps offered in addition to face-to-face treatment. The add-ons were rated feasible, acceptable, and useful by patients. Reported challenges involved technical difficulties such as programming errors and bugs. Only 45% of the included studies reported on changes in PD symptoms, all showing reduction of symptoms and absence of adverse effects.

CONCLUSIONS

This scoping review found that internet interventions for PD are still under-researched, although initial outcomes show promise. The outcomes also encourage future research in terms of developing internet interventions as an add-on to existing treatments, as well as working toward the creation and testing of more encompassing internet-delivered treatments for PD.

Translating promise into practice: a review of machine learning in suicide research and prevention

In ever more pressured health-care systems, technological solutions offering scalability of care and better resource targeting are appealing. Research on machine learning as a technique for identifying individuals at risk of suicidal ideation, suicide attempts, and death has grown rapidly. This research often places great emphasis on the promise of machine learning for preventing suicide, but overlooks the practical, clinical implementation issues that might preclude delivering on such a promise. In this Review, we synthesise the broad empirical and review literature on electronic health record-based machine learning in suicide research, and focus on matters of crucial importance for implementation of machine learning in clinical practice. The challenge of preventing statistically rare outcomes is well known; progress requires tackling data quality, transparency, and ethical issues. In the future, machine learning models might be explored as methods to enable targeting of interventions to specific individuals depending upon their level of need-ie, for precision medicine. Primarily, however, the promise of machine learning for suicide prevention is limited by the scarcity of high-quality scalable interventions available to individuals identified by machine learning as being at risk of suicide.

The inter-connections between self-harm and aggressive behaviours: A general network analysis study of dual harm

Dual harm is the co-occurrence of self-harm and aggression during an individual's lifetime. This behaviour is especially prevalent within criminal justice and forensic settings. The forms of aggression that should be included in the definition of dual harm have not yet been established. This study aimed to use network analysis to inform an evidence-based definition of dual harm by assessing the relationship between self-harm and different forms of aggressive behaviour in young people (N = 3,579). We used data from the Avon Longitudinal Study of Parents and Children (ALSPAC). Results revealed low correlations between the variables, leading to sparse network models with weak connections. We found that when separated into their distinct forms, aggressive acts and self-harm are only weakly correlated. Our work provides preliminary evidence to assist in understanding and managing dual harm within clinical and forensic settings and informs recommendations for future research.

Identification of Latent Alcohol Use Groups and Transitions over Time Using a 9-Year Follow-Up Study in the Adult General Population

INTRODUCTION

Studies investigating latent alcohol use groups and transitions of these groups over time are scarce, while such knowledge could facilitate efficient use of screening and preventive interventions for groups with a high risk of problematic alcohol use. Therefore, the present study examines the characteristics, transitions, and long-term stability of adult alcohol use groups and explores some of the possible predictors of the transitions.

METHODS

Data were used from the baseline, 3-, 6-, and 9-year follow-up waves of the Netherlands Mental Health Survey and Incidence Study-2 (NEMESIS-2), a representative study of Dutch adults aged 18-64 at baseline (N = 6,646; number of data points: 20,574). Alcohol consumption, alcohol use disorder (AUD), and mental disorders were assessed with the Composite International Diagnostic Interview 3.0. Latent Markov Modelling was used to identify latent groups based on high average alcohol consumption (HAAC) and AUD and to determine transition patterns of people between groups over time (stayers vs. movers).

RESULTS

The best fitting model resulted in four latent groups: one nonproblematic group (91%): no HAAC, no AUD; and three problematic alcohol use groups (9%): HAAC, no AUD (5%); no HAAC, often AUD (3%); and HAAC and AUD (1%). HAAC, no AUD was associated with a high mean age (55 years) and low educational level (41%), and no HAAC, often AUD with high proportions of males (78%) and people with high educational level (46%). Eighty-seven percent of all respondents - mostly people with no HAAC, no AUD - stayed in their original group during the whole 9-year period. Among movers, people in a problematic alcohol use group (HAAC and/or AUD) mostly transitioned to another problematic alcohol use group and not to the nonproblematic alcohol use group (no HAAC, no AUD). Explorative analyses suggested that lack of physical activity possibly plays a role in transitions both from and to problematic alcohol use groups over time.

CONCLUSION

The detection of three problematic alcohol use groups - with transitions mostly between the different problematic alcohol use groups and not to the group without alcohol problems - points to the need to explicitly address both alcohol consumption and alcohol-related problems (AUD criteria) in screening measures and interventions in order not to miss and to adequately treat all problematic alcohol users. Moreover, explorative findings suggest that prevention measures should also include physical activity.

Safety planning-type interventions for suicide prevention: meta-analysis

BACKGROUND

Safety planning-type interventions (SPTIs) for patients at risk of suicide are often used in clinical practice, but it is unclear whether these interventions are effective.

AIMS

This article reports on a meta-analysis of studies that have evaluated the effectiveness of SPTIs in reducing suicidal behaviour and ideation.

METHOD

We searched Medline, EMBASE, PsycINFO, Web of Science and Scopus from their inception to 9 December 2019, for studies that compared an SPTI with a control condition and had suicidal behaviour or ideation as outcomes. Two researchers independently extracted the data. To assess suicidal behaviour, we used a random-effects model of relative risk based on a pooled measure of suicidal behaviour. For suicidal ideation, we calculated effect sizes with Hedges' g. The study was registered at PROSPERO (registration number CRD42020129185).

RESULTS

Of 1816 unique abstracts screened, 6 studies with 3536 participants were eligible for analysis. The relative risk of suicidal behaviour among patients who received an SPTI compared with control was 0.570 (95% CI 0.408-0.795, P = 0.001; number needed to treat, 16). No significant effect was found for suicidal ideation.

CONCLUSIONS

To our knowledge, this is the first study to report a meta-analysis on SPTIs for suicide prevention. Results support the use of SPTIs to help preventing suicidal behaviour and the inclusion of SPTIs in clinical guidelines for suicide prevention. We found no evidence for an effect of SPTIs on suicidal ideation, and other interventions may be needed for this purpose.

Who didn't consult the doctor? Understanding sociodemographic factors in relation to health care uptake before suicide

OBJECTIVE

This study aimed to establish differences between suicide decedents and a reference population across various health care settings.

METHODS

This population-wide registration study combined death statistics, sociodemographic data and health care data from Statistics Netherlands. From 2010 to 2016, 12,015 suicide cases and a random reference group of 132,504 were included and assigned to one of the three health care settings; mental health (MH) care, primary care or no care. Logistic regression analyses were performed to determine differences in suicide risk factors across settings.

RESULTS

In the 1-2 year period before suicide, 52% of the suicide decedents received MH care, 41% received GP care only and 7% received neither. Although sociodemographic factors showed significant differences across settings, the suicide risk profiles were not profoundly distinctive. A decreasing trend in suicide risk across health care settings became apparent for male gender, income level and being in a one-person or one-parent household, whereas for other factors (middle and older age, non-Western migration background, couples without children and people living in more sparsely populated areas), risk of suicide increased when health care setting became more specialized.

LIMITATIONS

Because of the data structure, 18 months of suicide decedents' health care use were compared with two years health care use of the reference group, which likely led to an underestimation of the reported differences.

CONCLUSION

Although there are differences between suicide decedents and a reference group across health care settings, these are not sufficiently distinctive to advocate for a setting-specific approach to suicide prevention.

[Machine learning and suicide prevention: is an algorithm the solution?]

Suicide is inherently difficult to predict. Epidemiological research identified many general risk factors such as a depression, but these predictors have limited predictive power. Machine learning offers a set of tools that can combine hundreds of predictors resulting in the most optimal prediction. It might therefore offer a powerful way to predict inherently complex behaviour such as suicide. In a recent study, state of the art ML algorithms where applied to a large Swedish dataset of 126.205 patients treated in psychiatry containing over 400 potential risk factors. Although the presented results such as an area under the curve if 88% sounds promising, many questions on for example the cost of a false negative remain unanswered. In our comment, we critically discuss the presented findings, and bring up some unanswered questions.

A network perspective on suicidal behavior: Understanding suicidality as a complex system

BACKGROUND

Suicidal behavior is the result of complex interactions between many different factors that change over time. A network perspective may improve our understanding of these complex dynamics. Within the network perspective, psychopathology is considered to be a consequence of symptoms that directly interact with one another in a network structure. To view suicidal behavior as the result of such a complex system is a good starting point to facilitate moving away from traditional linear thinking.

OBJECTIVE

To review the existing paradigms and theories and their application to suicidal behavior.

METHODS

In the first part of this paper, we introduce the relevant concepts within network analysis such as network density and centrality. Where possible, we refer to studies that have applied these concepts within the field of suicide prevention. In the second part, we move one step further, by understanding the network perspective as an initial step toward complex system theory. The latter is a branch of science that models interacting variables in order to understand the dynamics of complex systems, such as tipping points and hysteresis.

RESULTS

Few studies have applied network analysis to study suicidal behavior. The studies that do highlight the complexity of suicidality. Complexity science offers potential useful concepts such as alternative stable states and resilience to study psychopathology and suicidal behavior, as demonstrated within the field of depression. To date, one innovative study has applied concepts from complexity science to better understand suicidal behavior. Complexity science and its application to human behavior are in its infancy, and it requires more collaboration between complexity scientists and behavioral scientists.

CONCLUSIONS

Clinicians and scientists are increasingly conceptualizing suicidal behavior as the result of the complex interaction between many different biological, social, and psychological risk and protective factors. Novel statistical techniques such as network analysis can help the field to better understand this complexity. The application of concepts from complexity science to the field of psychopathology and suicide research offers exciting and promising possibilities for our understanding and prevention of suicide.

Comprehensive database and individual patient data meta-analysis of randomised controlled trials on psychotherapies reducing suicidal thoughts and behaviour: study protocol

INTRODUCTION

Psychotherapy may reduce suicidal thoughts and behaviour, but its effectiveness is not well examined. Furthermore, conventional meta-analyses are unable to test possible effects of moderators affecting this relationship. This protocol outlines the building of a comprehensive database of the literature in this research field. In addition, we will conduct an individual patient data meta-analysis (IPD-MA) to establish the effectiveness of psychotherapy in reducing suicidality, and to examine which factors moderate the efficacy of these interventions.

METHODS AND ANALYSIS

To build a comprehensive database, randomised controlled trials examining the effect of any psychotherapy targeting any psychiatric disorder on suicidal thoughts or behaviour will be identified by running a systematic search in PubMed, Embase, PsycINFO, Web of Science, Scopus and The Cochrane Central Register of Controlled Trials from data inception to 12 August 2019. For the IPD-MA, we will focus on adult outpatients with suicidal ideation or behaviour. In addition, as a comparison group we will focus on a control group (waiting-list, care as usual or placebo). A 1-stage IPD-MA will be used to determine the effectiveness of psychotherapy on suicidal ideation, suicide attempts and/or suicide deaths, and to investigate potential patient-related and intervention-related moderators. Subgroup and sensitivity analyses will be conducted to test the robustness of the findings. Additionally, a conventional MA will be conducted to determine the differences between studies that provided IPD and those that did not. IPD-MA may determine the effectiveness of psychotherapy in reducing suicidality and provide insights into the moderating factors influencing the efficacy of psychotherapy. Answering these questions will inform mental healthcare practitioners about optimal treatments for different groups of individuals with suicidal ideation and/or behaviour and consequently help to reduce suicide risk.

ETHICS AND DISSEMINATION

An ethical approval is not required for this study. The results will be published in a peer-review journal.

PROSPERO REGISTRATION NUMBER

CRD42020140573.

Applying machine learning on health record data from general practitioners to predict suicidality

BACKGROUND

Suicidal behaviour is difficult to detect in the general practice. Machine learning (ML) algorithms using routinely collected data might support General Practitioners (GPs) in the detection of suicidal behaviour. In this paper, we applied machine learning techniques to support GPs recognizing suicidal behaviour in primary care patients using routinely collected general practice data.

METHODS

This case-control study used data from a national representative primary care database including over 1.5 million patients (Nivel Primary Care Database). Patients with a suicide (attempt) in 2017 were selected as cases (N = 574) and an at risk control group (N = 207,308) was selected from patients with psychological vulnerability but without a suicide attempt in 2017. RandomForest was trained on a small subsample of the data (training set), and evaluated on unseen data (test set).

RESULTS

Almost two-third (65%) of the cases visited their GP within the last 30 days before the suicide (attempt). RandomForest showed a positive predictive value (PPV) of 0.05 (0.04-0.06), with a sensitivity of 0.39 (0.32-0.47) and area under the curve (AUC) of 0.85 (0.81-0.88). Almost all controls were accurately labeled as controls (specificity = 0.98 (0.97-0.98)). Among a sample of 650 at-risk primary care patients, the algorithm would label 20 patients as high-risk. Of those, one would be an actual case and additionally, one case would be missed.

CONCLUSION

In this study, we applied machine learning to predict suicidal behaviour using general practice data. Our results showed that these techniques can be used as a complementary step in the identification and stratification of patients at risk of suicidal behaviour. The results are encouraging and provide a first step to use automated screening directly in clinical practice. Additional data from different social domains, such as employment and education, might improve accuracy.

[Suicidality in adults]

Suicide rates appear to be influenced by the economy. In the Netherlands, fluctuation in these rates in the last 10 years was mainly seen in men. The COVID-19 pandemic is likely to have a negative impact on this fluctuation. There are no reliable predictors of suicidality yet, but there are developments that could lead to better predictions. Examples include 'big data' and 'machine learning' in combination with a network approach in which psychopathology is seen as a complex dynamic system. Better differentiation of suicidality can help clinical practice to offer tailored treatment and prevention. Suicidality is a multifactorial and complex phenomenon that goes beyond mental healthcare. In recent years, various prevention strategies and lines of research have been launched that focus on this complexity, making suicidality easier to treat and understand. The 113 foundation for suicide prevention has an important coordinating and facilitating role in this.

Predicting future suicidal behaviour in young adults, with different machine learning techniques: A population-based longitudinal study

BACKGROUND

The predictive accuracy of suicidal behaviour has not improved over the last decades. We aimed to explore the potential of machine learning to predict future suicidal behaviour using population-based longitudinal data.

METHOD

Baseline risk data assessed within the Scottish wellbeing study, in which 3508 young adults (18-34 years) completed a battery of psychological measures, were used to predict both suicide ideation and suicide attempts at one-year follow-up. The performance of the following algorithms was compared: regular logistic regression, K-nearest neighbors, classification tree, random forests, gradient boosting and support vector machine.

RESULTS

At one year follow up, 2428 respondents (71%) finished the second assessment. 336 respondents (14%) reported suicide ideation between baseline and follow up, and 50 (2%) reported a suicide attempt. All performance metrics were highly similar across methods. The random forest algorithm was the best algorithm to predict suicide ideation (AUC 0.83, PPV 0.52, BA 0.74) and the gradient boosting to predict suicide attempt (AUC 0.80, PPV 0.10, BA 0.69).

LIMITATIONS

The number of respondents with suicidal behaviour at follow up was small. We only had data on psychological risk factors, limiting the potential of the more complex machine learning algorithms to outperform regular logistical regression.

CONCLUSIONS

When applied to population-based longitudinal data containing multiple psychological measurements, machine learning techniques did not significantly improve the predictive accuracy of suicidal behaviour. Adding more detailed data on for example employment, education or previous health care uptake, might result in better performance of machine learning over regular logistical regression.

E-Learning to Improve Suicide Prevention Practice Skills Among Undergraduate Psychology Students: Randomized Controlled Trial

BACKGROUND

Despite increasing evidence of the effectiveness of digital learning solutions in higher vocational education, including the training of allied health professionals, the impact of Web-based training on the development of practical skills in psychiatry and psychology, in general, and in suicide prevention, specifically, remains largely understudied.

OBJECTIVE

This study aimed to determine the effectiveness of an electronic learning (e-learning) module on the adherence to suicide prevention guidelines, knowledge of practical skills, and provider's confidence to have a conversation about suicidal behavior with undergraduate psychology students.

METHODS

The e-learning module, comprising video recordings of therapist-patient interactions, was designed with the aim of transferring knowledge about suicide prevention guideline recommendations. The program's effects on guideline adherence, self-evaluated knowledge, and provider's confidence were assessed using online questionnaires before the program (baseline and at 1 month [T1] and 3 months after baseline). The eligible third- and fourth-year undergraduate psychology students were randomly allocated to the e-learning (n=211) or to a waitlist control condition (n=187), with access to the intervention after T1.

RESULTS

Overall, the students evaluated e-learning in a fairly positive manner. The intention-to-treat analysis showed that the students in the intervention condition (n=211) reported higher levels of self-evaluated knowledge, provider's confidence, and guideline adherence than those in the waitlist control condition (n=187) after receiving the e-learning module (all P values<.001). When comparing the scores at the 1- and 3-month follow-up, after both groups had received access to the e-learning module, the completers-only analysis showed that the levels of knowledge, guideline adherence, and confidence remained constant (all P values>.05) within the intervention group, whereas a significant improvement was observed in the waitlist control group (all P values<.05).

CONCLUSIONS

An e-learning intervention on suicide prevention could be an effective first step toward improved knowledge of clinical skills. The learning outcomes of a stand-alone module were found to be similar to those of a training that combined e-learning with a face-to-face training, with the advantages of flexibility and low costs.

Modelling suicide ideation from beep to beep: Application of network analysis to ecological momentary assessment data

•EMA data were analyzed using network analysis (e.g., temporal networks).•Suicidal ideation at t was predicted by itself and perceived burdensomeness at t - 1.•Suicidal ideation at t - 1 predicted perceived burdensomeness, depression etc. at t.•At the same beep, suicidal ideation was related to all variables in the network.•Patients with higher average suicidal ideation had higher average hopelessness.

The longitudinal association between lifetime mental disorders and first onset or recurrent suicide ideation

BACKGROUND

Although the cross-sectional association between mental disorders and suicide ideation is well studied, less is known about the prospective association. In this paper, we estimated among those without 12-month suicide ideation at baseline, the association between a wide variety of common mental disorders at baseline and suicide ideation within the 6-year follow-up period, after controlling for history of other mental disorders and demographic variables.

METHODS

Data were used from the Netherlands Mental Health Survey and Incidence Study-2 (NEMESIS-2), a prospective representative adult cohort study with baseline (n = 6646) with a 6-year follow-up period. Lifetime mental disorders were assessed at baseline with the Composite International Diagnostic Interview 3.0. Within the longitudinal design, participants with first time or recurrent suicide ideation were defined follows: having no suicide ideation in the 12 months before the baseline assessment, and reporting to have had seriously thought about suicide between baseline and the 6-year follow-up period. Multiple logistical regression was used to estimate the longitudinal association between suicide ideation and a specific mental disorder while controlling for comorbidity and baseline variables. To account for the prevalence of a disorder in the population, for each disorder, the population attributable risk proportion (PARP) was calculated.

RESULTS

2.9% (n = 132) of the participants that did not report suicide ideation in the past 12 months at baseline reported suicide ideation at follow-up. Of these 132 cases, 81 (61%) experienced suicide ideation for the first time in their lives and could be viewed as first onset cases. 51 (39%) reported recurrent suicide ideation. After controlling for comorbidity, the only two disorders that were significantly related to suicide ideation at follow-up were lifetime major depressive disorder (MDD) and generalized anxiety disorder (GAD). PARP for MDD was 47.8 and 16.6% for GAD.

CONCLUSIONS

After controlling for all other mental disorders, a lifetime history of MDD and GAD were related to suicide ideation at follow-up. For clinical practice, this indicates that patients with a history of MDD or GAD stay vulnerable for suicide ideation, even though they did not report suicide ideation in the past year.

Toward an Objective Assessment of Implementation Processes for Innovations in Health Care: Psychometric Evaluation of the Normalization Measure Development (NoMAD) Questionnaire Among Mental Health Care Professionals

Background

Successfully implementing eMental health (eMH) interventions in routine mental health care constitutes a major challenge. Reliable instruments to assess implementation progress are essential. The Normalization MeAsure Development (NoMAD) study developed a brief self-report questionnaire that could be helpful in measuring implementation progress. Based on the Normalization Process Theory, this instrument focuses on 4 generative mechanisms involved in implementation processes: coherence, cognitive participation, collective action, and reflexive monitoring.

Objective

The aim of this study was to translate the NoMAD questionnaire to Dutch and to confirm the factor structure in Dutch mental health care settings.

Methods

Dutch mental health care professionals involved in eMH implementation were invited to complete the translated NoMAD questionnaire. Confirmatory factor analysis (CFA) was conducted to verify interpretability of scale scores for 3 models: (1) the theoretical 4-factor structure, (2) a unidimensional model, and (3) a hierarchical model. Potential improvements were explored, and correlated scale scores with 3 control questions were used to assess convergent validity.

Results

A total of 262 professionals from mental health care settings in the Netherlands completed the questionnaire (female: 81.7%; mean age: 45 [SD=11]). The internal consistency of the 20-item questionnaire was acceptable (.62≤alpha≤.85). The theorized 4-factor model fitted the data slightly better in the CFA than the hierarchical model (Comparative Fit Index=0.90, Tucker Lewis Index=0.88, Root Mean Square Error of Approximation=0.10, Standardized Root Mean Square Residual=0.12, χ²₂=22.5, P≤.05). However, the difference is small and possibly not outweighing the practical relevance of a total score and subscale scores combined in one hierarchical model. One item was identified as weak (λ_CA.2=0.10). A moderate-to-strong convergent validity with 3 control questions was found for the Collective Participation scale (.47≤r≤.54, P≤.05).

Conclusions

NoMAD’s theoretical factor structure was confirmed in Dutch mental health settings to acceptable standards but with room for improvement. The hierarchical model might prove useful in increasing the practical utility of the NoMAD questionnaire by combining a total score with information on the 4 generative mechanisms. Future research should assess the predictive value and responsiveness over time and elucidate the conceptual interpretability of NoMAD in eMH implementation practices.

What proportion of initially prescribed antidepressants is still being prescribed chronically after 5 years in general practice? A longitudinal cohort analysis

OBJECTIVES

Antidepressant prescribing almost doubled in the Netherlands between 1996 and 2012, which could be accounted for by longer continuation after the first prescription. This might be problematic given a growing concern of large-scale antidepressant dependence. We aimed to assess the extent and determinants of chronic antidepressant prescribing among patient aged 18 years and older. We hypothesise a relatively large prevalence of chronic (>2 years) prescription.

DESIGN

A longitudinal observational study based on routinely registered prescription data from general practice.

SETTING

189 general practices in the Netherlands.

PARTICIPANTS

326 025 patients with valid prescription data for all 5 years of the study.

OUTCOME MEASURES

Primary outcome measure: the number of patients (N) receiving at least four antidepressant prescriptions in 2011, as well as during each of the four subsequent years. Secondary outcome measure: the above, but specified for selective serotonin reuptake inhibitors and for tricyclic antidepressants.

RESULTS

Antidepressants were prescribed to almost 7% of our 326 025 participants each year. They were prescribed for depression (38%), anxiety (17%), other psychological disorders (20%) and non-psychological indications (25%). Antidepressants were prescribed in all 5 years to the 42% of the population who had at least four prescriptions dispensed in 2011. Chronic prescribing was higher among women than men, for those aged 45-64 years than for those aged >65 years and for those treated for depression or anxiety than for non-psychological indications (eg, neuropathic pain). Chronic prescribing also varied markedly among general practices.

CONCLUSION

Chronic antidepressant use is common for depression and for anxiety and non-psychological diagnoses. Once antidepressants have been prescribed, general practitioners and other prescribers should be aware of the risks associated with long-term use and should provide annual monitoring of the continued need for therapy.

The association between motives, perceived problems and current thoughts of self-harm following an episode of self-harm. A network analysis

BACKGROUND

A history of self-harm is a major risk factor for suicide. Some patients are more likely than others to repeat suicidal behaviour after an episode of self-harm. Insight in the relation between current thoughts of self-harm, motives for the self-harm episode and perceived problems may improve prevention strategies. Network analysis allows to investigate the co-occurence of these factors and their association with each other.

METHODS

Ising model based networks are estimated on data collected between 2007-2015 within the Multicentre Study of Self-harm in Flanders. Patients were interviewed within 24 hours after hospitalization by a trained professional on their motives for the episode of self-harm and their perceived problems. Additionally, they were asked whether they had current thoughts of self-harm. Network analyses are used to determine which motives and problems are uniquely related to current thoughts of self-harm, and which are most central in the network.

RESULTS

Data were used of 6068 patients (2279 males and 3789 females). Four internal motives (wish to die, lost control, escape from situation, situation was unbearable), one external motive (show somebody how hopeless I was) and four perceived problems (psychiatric, loneliness, trauma, rejection) are directly related to current thoughts of self-harm. Of all motives and problems, the motive a wish to die is most strongly related to current thoughts of self-harm. However, external motives are more central in the network when compared to internal motives and perceived problems.

LIMITATIONS

Data most probably refer to a selected group of self-harm patients as many individuals who self-harm do not come to the attention of hospital services. Patients might be reluctant to tell professionals they had current thoughts of self-harm.

CONCLUSIONS

Many internal motives and problems are directly related to current thoughts of self-harm, but external motives are more central in the network. The clinically most important motive (wish to die) does not play a central role in the network.

Smartphone-based safety planning and self-monitoring for suicidal patients: Rationale and study protocol of the CASPAR (Continuous Assessment for Suicide Prevention And Research) study

BACKGROUND

It remains difficult to predict and prevent suicidal behaviour, despite growing understanding of the aetiology of suicidality. Clinical guidelines recommend that health care professionals develop a safety plan in collaboration with their high-risk patients, to lower the imminent risk of suicidal behaviour. Mobile health applications provide new opportunities for safety planning, and enable daily self-monitoring of suicide-related symptoms that may enhance safety planning. This paper presents the rationale and protocol of the Continuous Assessment for Suicide Prevention And Research (CASPAR) study. The aim of the study is two-fold: to evaluate the feasibility of mobile safety planning and daily mobile self-monitoring in routine care treatment for suicidal patients, and to conduct fundamental research on suicidal processes.

METHODS

The study is an adaptive single cohort design among 80 adult outpatients or day-care patients, with the main diagnosis of major depressive disorder or dysthymia, who have an increased risk for suicidal behaviours. There are three measurement points, at baseline, at 1 and 3 months after baseline. Patients are instructed to use their mobile safety plan when necessary and monitor their suicidal symptoms daily. Both these apps will be used in treatment with their clinician.

CONCLUSION

The results from this study will provide insight into the feasibility of mobile safety planning and self-monitoring in treatment of suicidal patients. Furthermore, knowledge of the suicidal process will be enhanced, especially regarding the transition from suicidal ideation to behaviour.The study protocol is currently under revision for medical ethics approval by the medical ethics board of the Vrije Universiteit Medical centre Amsterdam (METc number 2017.512/NL62795.029.17).

Involving Patients and Families in the Analysis of Suicides, Suicide Attempts, and Other Sentinel Events in Mental Healthcare: A Qualitative Study in The Netherlands

Involving patients and families in mental healthcare is becoming more commonplace, but little is known about how they are involved in the aftermath of serious adverse events related to quality of care (sentinel events, including suicides). This study explores the role patients and families have in formal processes after sentinel events in Dutch mental healthcare. We analyzed the existing policies of 15 healthcare organizations and spoke with 35 stakeholders including patients, families, their counselors, the national regulator, and professionals. Respondents argue that involving patients and families is valuable to help deal with the event emotionally, provide additional information, and prevent escalation. Results indicate that involving patients and families is only described in sentinel event policies to a limited extent. In practice, involvement consists mostly of providing aftercare and sharing information about the event by providers. Complexities such as privacy concerns and involuntary admissions are said to hinder involvement. Respondents also emphasize that involvement should not be obligatory and stress the need for patients and families to be involved throughout the process of treatment. There is no one-size-fits-all strategy for involving patients and families after sentinel events. The first step seems to be early involvement during treatment process itself.

Natural environments and suicide mortality in the Netherlands: a cross-sectional, ecological study

BACKGROUND

Natural outdoor environments, such as green spaces (ie, grass, forests, or parks), blue spaces (ie, visible bodies of fresh or salt water), and coastal proximity, have been increasingly shown to promote mental health. However, little is known about how and the extent to which these natural environments are associated with suicide mortality. Our aim was to investigate whether the availability of green space and blue space within people's living environments and living next to the coast are protective against suicide mortality.

METHODS

In this cross-sectional, ecological study, we analysed officially confirmed deaths by suicide between 2005 and 2014 per municipality in the Netherlands. We calculated indexes to measure the proportion of green space and blue space per municipality and the coastal proximity of each municipality using a geographical information system. We fitted Bayesian hierarchical Poisson regressions to assess associations between suicide risk, green space, blue space, and coastal proximity, adjusted for risk and protective factors.

FINDINGS

Municipalities with a large proportion of green space (relative risk 0·879, 95% credibility interval 0·779-0·991) or a moderate proportion of green space (0·919, 0·846-0·998) showed a reduced suicide risk compared with municipalities with less green space. Green space did not differ according to urbanicity in relation to suicide. Neither blue space nor coastal proximity was associated with suicide risk. The geographical variation in the residual relative suicide risk was substantial and the south of the Netherlands was at high risk.

INTERPRETATION

Our findings support the notion that exposure to natural environments, particularly to greenery, might have a role in reducing suicide mortality. If confirmed by future studies on an individual level, the consideration of environmental exposures might enrich suicide prevention programmes.

FUNDING

European Research Council (grant agreement number 714993).

Defeat and entrapment: more than meets the eye? Applying network analysis to estimate dimensions of highly correlated constructs

BACKGROUND

Defeat and entrapment have been shown to be of central relevance to the development of different disorders. However, it remains unclear whether they represent two distinct constructs or one overall latent variable. One reason for the unclarity is that traditional factor analytic techniques have trouble estimating the right number of clusters in highly correlated data. In this study, we applied a novel approach based on network analysis that can deal with correlated data to establish whether defeat and entrapment are best thought of as one or multiple constructs.

METHODS

Explanatory graph analysis was used to estimate the number of dimensions within the 32 items that make up the defeat and entrapment scales in two samples: an online community sample of 480 participants, and a clinical sample of 147 inpatients admitted to a psychiatric hospital after a suicidal attempt or severe suicidal crisis. Confirmatory Factor analysis (CFA) was used to test whether the proposed structure fits the data.

RESULTS

In both samples, bootstrapped exploratory graph analysis suggested that the defeat and entrapment items belonged to different dimensions. Within the entrapment items, two separate dimensions were detected, labelled internal and external entrapment. Defeat appeared to be multifaceted only in the online sample. When comparing the CFA outcomes of the one, two, three and four factor models, the one factor model was preferred.

CONCLUSIONS

Defeat and entrapment can be viewed as distinct, yet, highly associated constructs. Thus, although replication is needed, results are in line with theories differentiating between these two constructs.

Active Involvement of End Users When Developing Web-Based Mental Health Interventions

BACKGROUND

Although many web-based mental health interventions are being released, the actual uptake by end users is limited. The marginal level of engagement of end users when developing these interventions is recognized as an important cause for uptake problems. In this paper, we offer our perceptive on how to improve user engagement. By doing so, we aim to stimulate a discourse on user involvement within the field of online mental health interventions.

METHODS

We shortly describe three different methods (the expert-driven method, intervention mapping, and scrum) that were currently used to develop web-based health interventions. We will focus to what extent the end user was involved in the developmental phase, and what the additional challenges were. In the final paragraph, lessons learned are summarized, and recommendations provided.

RESULTS

Every method seems to have its trade-off: if end users are highly involved, availability of end users and means become problematic. If end users are less actively involved, the product may be less appropriate for the end user. Other challenges to consider are the funding of the more active role of technological companies, and the time it takes to process the results of shorter development cycles.

CONCLUSION

Thinking about user-centered design and carefully planning, the involvement of end users should become standard in the field of web-based (mental) health. When deciding on the level of user involvement, one should balance the need for input from users with the availability of resources such as time and funding.

Maximizing the Impact of e-Therapy and Serious Gaming: Time for a Paradigm Shift

Internet interventions for mental health, including serious games, online programs, and apps, hold promise for increasing access to evidence-based treatments and prevention. Many such interventions have been shown to be effective and acceptable in trials; however, uptake and adherence outside of trials is seldom reported, and where it is, adherence at least, generally appears to be underwhelming. In response, an international Collaboration On Maximizing the impact of E-Therapy and Serious Gaming (COMETS) was formed. In this perspectives' paper, we call for a paradigm shift to increase the impact of internet interventions toward the ultimate goal of improved population mental health. We propose four pillars for change: (1) increased focus on user-centered approaches, including both user-centered design of programs and greater individualization within programs, with the latter perhaps utilizing increased modularization; (2) Increased emphasis on engagement utilizing processes such as gaming, gamification, telepresence, and persuasive technology; (3) Increased collaboration in program development, testing, and data sharing, across both sectors and regions, in order to achieve higher quality, more sustainable outcomes with greater reach; and (4) Rapid testing and implementation, including the measurement of reach, engagement, and effectiveness, and timely implementation. We suggest it is time for researchers, clinicians, developers, and end-users to collaborate on these aspects in order to maximize the impact of e-therapies and serious gaming.