Demographic risk factors for suicide among youths in the Netherlands

In 2000 to 2016 the highest number of suicides among Dutch youths under 20 in any given year was 58 in 2013. In 2017 this number increased to 81 youth suicides. To get more insight in what types of youths died by suicide, particularly in recent years (2013-2017) we looked at micro-data of Statistics Netherlands and counted suicides among youths till 23, split out along gender, age, regions, immigration background and place in household and compared this to the general population of youths in the Netherlands. We also compared the demographics of young suicide victims to those of suicide victims among the population as a whole. We found higher suicide rates among male youths, older youths, those of Dutch descent and youths living alone. These differences were generally smaller than in the population as a whole. There were also substantial geographical differences between provinces and healthcare regions. The method of suicide is different in youth compared to the population as a whole: relatively more youth suicides by jumping or lying in front of a moving object and relatively less youth suicides by autointoxication or drowning, whereas the most frequent method of suicide among both groups is hanging or suffocation

Detecting changes in help seeker conversations on a suicide prevention helpline during the COVID− 19 pandemic: in-depth analysis using encoder representations from transformers

Background: Preventatives measures to combat the spread of COVID− 19 have introduced social isolation, loneliness and financial stress. This study aims to identify whether the COVID-19 pandemic is related to changes in suicide-related problems for help seekers on a suicide prevention helpline. Methods: A retrospective cohort study was conducted using chat data from a suicide prevention helpline in the Netherlands. The natural language processing method BERTopic was used to detect common topics in messages from December 1, 2019 until June 1, 2020 (N = 8589). Relative topic occurrence was compared before and during the lock down starting on March 23, 2020. The observed changes in topic usage were likewise analyzed for male and female, younger and older help seekers and help seekers living alone. Results: The topic of the COVID-19 pandemic saw an 808% increase in relative occurrence after the lockdown. Furthermore, the results show that help seeker increased mention of thanking the counsellor (+ 15%), and male and young help seekers were grateful for the conversation (+ 45% and + 32% respectively). Coping methods such as watching TV (− 21%) or listening to music (− 15%) saw a decreased mention. Plans for suicide (− 9%) and plans for suicide at a specific location (− 15%) also saw a decreased mention. However, plans for suicide were mentioned more frequently by help seekers over 30 years old (+ 11%) or who live alone and (+ 52%). Furthermore, male help seekers talked about contact with emergency care (+ 43%) and panic and anxiety (+ 24%) more often. Negative emotions (+ 22%) and lack of self-confidence (+ 15%) were mentioned more often by help seekers under 30, and help seekers over 30 saw an increased mention of substance abuse (+ 9%). Conclusion: While mentions of distraction, social interaction and plans for suicide decreased, expressions of gratefulness for the helpline increased, highlighting the importance of contact to help seekers during the lockdown. Help seekers under 30, male or who live alone, showed changes that negatively related to suicidality and should be monitored closely

Content-based recommender support system for counselors in a suicide prevention chat helpline: Design and evaluation study

Background: The working environment of a suicide prevention helpline requires high emotional and cognitive awareness from chat counselors. A shared opinion among counselors is that as a chat conversation becomes more difficult, it takes more effort and a longer amount of time to compose a response, which, in turn, can lead to writer's block. Objective: This study evaluates and then designs supportive technology to determine if a support system that provides inspiration can help counselors resolve writer's block when they encounter difficult situations in chats with help-seekers. Methods: A content-based recommender system with sentence embedding was used to search a chat corpus for similar chat situations. The system showed a counselor the most similar parts of former chat conversations so that the counselor would be able to use approaches previously taken by their colleagues as inspiration. In a within-subject experiment, counselors' chat replies when confronted with a difficult situation were analyzed to determine if experts could see a noticeable difference in chat replies that were obtained in 3 conditions: (1) with the help of the support system, (2) with written advice from a senior counselor, or (3) when receiving no help. In addition, the system's utility and usability were measured, and the validity of the algorithm was examined. Results: A total of 24 counselors used a prototype of the support system; the results showed that, by reading chat replies, experts were able to significantly predict if counselors had received help from the support system or from a senior counselor (P=.004). Counselors scored the information they received from a senior counselor (M=1.46, SD 1.91) as significantly more helpful than the information received from the support system or when no help was given at all (M=-0.21, SD 2.26). Finally, compared with randomly selected former chat conversations, counselors rated the ones identified by the content-based recommendation system as significantly more similar to their current chats (β=.30, P<.001). Conclusions: Support given to counselors influenced how they responded in difficult conversations. However, the higher utility scores given for the advice from senior counselors seem to indicate that specific actionable instructions are preferred. We expect that these findings will be beneficial for developing a system that can use similar chat situations to generate advice in a descriptive style, hence helping counselors through writer's block

A call center model for online mental health support

Helplines for mental healthcare differ from other call centers in various aspects. Many agents are volunteers, the conversations are often more complex and emotional, and many helplines use a triage system. In this paper, we first propose a call center model that includes the specifics of online mental health helplines, including features such as a triage system for chats and service times consisting of a warm-up, conversation, and wrap-up cool-down periods. The model is validated using a trace-driven simulation based on real-life (anonymous) data provided by 113 Suicide Prevention. The results show that the model can simulate the waiting-time performance of the helpline accurately. Second, we focus on forecasting the number of chats and telephone calls. Our results show that (S)ARIMA models trained on historical data perform better than other models in the case of short-term forecasting (five weeks or less ahead), while using linear regression works best for long-term forecasts (longer than five weeks)

Team players against headache: multidisciplinary treatment of primary headaches and medication overuse headache

Multidisciplinary approaches are gaining acceptance in headache treatment. However, there is a lack of scientific data about the efficacy of various strategies and their combinations offered by physiotherapists, physicians, psychologists and headache nurses. Therefore, an international platform for more intense collaboration between these professions and between headache centers is needed. Our aims were to establish closer collaboration and an interchange of knowledge between headache care providers and different disciplines. A scientific session focusing on multidisciplinary headache management was organised at The European Headache and Migraine Trust International Congress (EHMTIC) 2010 in Nice. A summary of the contributions and the discussion is presented. It was concluded that effective multidisciplinary headache treatment can reduce headache frequency and burden of disease, as well as the risk for medication overuse headache. The significant value of physiotherapy, education in headache schools, and implementation of strategies of cognitive behavioural therapy was highlighted and the way paved for future studies and international collaboration

Topic modeling for conversations for mental health helplines with utterance embedding

Conversations with topics that are locally contextual often produces incoherent topic modeling results using standard methods. Splitting a conversation into its individual utterances makes it possible to avoid this problem. However, with increased data sparsity, different methods need to be considered. Baseline bag-of-word topic modeling methods for regular and short-text, as well as topic modeling methods using transformer-based sentence embeddings were implemented. These models were evaluated on topic coherence and word embedding similarity. Each method was trained using single utterances, segments of the conversation, and on the full conversation. The results showed that utterance-level and segment-level data combined with sentence embedding methods performs better compared to other non-sentence embedding methods or conversation-level data. Among the sentence embedding methods, clustering using HDBScan showed the best performance. We suspect that ignoring noisy utterances is the reason for better topic coherence and a relatively large improvement in topic word similarity

Predictors of need for help with weight loss among overweight and obese men and women in the Netherlands: a cross-sectional study

Abstract Background Need for help is perceived as an important first step towards weight related health-care use among overweight and obese individuals and several studies have reported gender as an important predisposing characteristic of need for help. Therefore, the goal of the current study is to gain insight into factors that might determine need for help for weight loss in men and women. Methods In the current study, data from the Dutch cross-sectional survey Health Monitor 2012 was used. Overweight and obese men (N = 2218) and women (N = 2002) aged 19–64 years were selected for the current study. Potential predictors of need for help were age, ethnicity, marital status, educational level, perceived health, weight status, comorbidities, physical activity level, and income. Multiple logistic regression analyses were conducted separately among men and women to establish prediction models of need for help for weight loss. Results The mean age of the adult women in this study population was 47.7 years and 68% was medium educated, whereas the mean age of men was 49.0 years and 63.0% was medium educated. Of the respondents, 24.9% indicated they either felt a need for help for weight loss, 6.4% already received help and 68.7% felt no need for help. Women were more likely to indicate a need for help than men (OR = 2.17). Among both genders, need for help was significantly predicted by obesity (ORmen = 3.80, ORwomen = 2.20) and “poor” perceived health (ORmen = 2.14, ORwomen = 1.94). Besides, “unmarried” (ORmen = 1.57) and suffering from comorbidities (ORmen = 1.26) predicted need for help among men. Whereas among women, need for help was predicted by younger age (i.e. 19–34 years (ORwomen = 2.07) and 35–49 years (ORwomen = 1.35)). Conclusion The current study revealed specific predictors of need for help for weight loss for men and women. Among men, the strongest predictors were obesity and poor perceived health, whereas among women need for help was most strongly predicted by obesity and young age. Insight into these specific predictors enables health professionals to reach overweight individuals with a need for help for weight loss by connecting their need to available support

Data and analysis for the publication: Content-Based Recommender Support System for Counselors in a Suicide Prevention Chat Helpline: Design and Evaluation Study

This dataset contains all data and analysis scripts to the research conducted for the JMIR paper: "Content-based Recommender Support System for Counselors in a Suicide Prevention Chat Helpline: Design and Evaluation Study

Automated behavioral coding to enhance the effectiveness of motivational interviewing in a chat-based suicide prevention helpline: Secondary analysis of a clinical trial

Background: With the rise of computer science and artificial intelligence, analyzing large data sets promises enormous potential in gaining insights for developing and improving evidence-based health interventions. One such intervention is the counseling strategy motivational interviewing (MI), which has been found effective in improving a wide range of health-related behaviors. Despite the simplicity of its principles, MI can be a challenging skill to learn and requires expertise to apply effectively. Objective: This study aims to investigate the performance of artificial intelligence models in classifying MI behavior and explore the feasibility of using these models in online helplines for mental health as an automated support tool for counselors in clinical practice. Methods: We used a coded data set of 253 MI counseling chat sessions from the 113 Suicide Prevention helpline. With 23,982 messages coded with the MI Sequential Code for Observing Process Exchanges codebook, we trained and evaluated 4 machine learning models and 1 deep learning model to classify client- and counselor MI behavior based on language use. Results: The deep learning model BERTje outperformed all machine learning models, accurately predicting counselor behavior (accuracy=0.72, area under the curve [AUC]=0.95, Cohen κ=0.69). It differentiated MI congruent and incongruent counselor behavior (AUC=0.92, κ=0.65) and evocative and nonevocative language (AUC=0.92, κ=0.66). For client behavior, the model achieved an accuracy of 0.70 (AUC=0.89, κ=0.55). The model’s interpretable predictions discerned client change talk and sustain talk, counselor affirmations, and reflection types, facilitating valuable counselor feedback. Conclusions: The results of this study demonstrate that artificial intelligence techniques can accurately classify MI behavior, indicating their potential as a valuable tool for enhancing MI proficiency in online helplines for mental health. Provided that the data set size is sufficiently large with enough training samples for each behavioral code, these methods can be trained and applied to other domains and languages, offering a scalable and cost-effective way to evaluate MI adherence, accelerate behavioral coding, and provide therapists with personalized, quick, and objective feedback

Performance modeling for call centers providing online mental health support

Helplines for mental healthcare differ from other call centers in various aspects. Many agents are volunteers, the conversations are often more complex and emotional, and many helplines use a triage system. In this paper, we first propose a call center model that includes the specifics of online mental health helplines, including features such as a triage system for chats and service times consisting of a warm-up, conversation, and wrap-up cool-down periods. The model is validated using a trace-driven simulation based on real-life (anonymous) data provided by 113 Suicide Prevention. The results show that the model can simulate the waiting-time performance of the helpline accurately. Second, we focus on forecasting the number of chats and telephone calls. Our results show that (S)ARIMA models trained on historical data perform better than other models in the case of short-term forecasting (five weeks or less ahead), while using linear regression works best for long-term forecasts (longer than five weeks)