Forecasting call and chat volumes at online helplines for mental health.

BACKGROUND: Each year, many help seekers in need contact health helplines for mental support. It is crucial that they receive support immediately, and that waiting times are minimal. In order to minimize delay, helplines must have adequate staffing levels, especially during peak hours. This has raised the need for means to predict the call and chat volumes ahead of time accurately. Motivated by this, in this paper, we analyze real-life data to develop models for accurately forecasting call volumes, for both phone and chat conversations for online mental health support. METHODS: This research was conducted on real call and chat data (adequately anonymized) provided by 113 Suicide Prevention (Over ons | 113 Zelfmoordpreventie) (throughout referred to as '113'), the online helpline for suicide prevention in the Netherlands. Chat and phone call data were analyzed to better understand the important factors that influence the call arrival process. These factors were then used as input to several Machine Learning (ML) models to forecast the number of call and chat arrivals. Next to that, senior counselors of the helpline completed a web-based questionnaire after each shift to assess their perception of the workload. RESULTS: This study has led to several remarkable and key insights. First, the most important factors that determine the call volumes for the helpline are the trend, and weekly and daily cyclic patterns (cycles), while monthly and yearly cycles were found to be non-significant predictors for the number of phone and chat conversations. Second, media events that were included in this study only have limited-and only short-term-impact on the call volumes. Third, so-called (S)ARIMA models are shown to lead to the most accurate prediction in the case of short-term forecasting, while simple linear models work best for long-term forecasting. Fourth, questionnaires filled in by senior counselors show that the experienced workload is mainly correlated to the number of chat conversations compared to phone calls. CONCLUSION: (S)ARIMA models can best be used to forecast the number of daily chats and phone calls with a MAPE of less than 10 in short-term forecasting. These models perform better than other models showing that the number of arrivals depends on historical data. These forecasts can be used as support for planning the number of counselors needed. Furthermore, the questionnaire data show that the workload experienced by senior counselors is more dependent on the number of chat arrivals and less on the number of available agents, showing the value of insight into the arrival process of conversations.

Identifying populations at ultra-high risk of suicide using a novel machine learning method.

BACKGROUND: Targeted interventions for suicide prevention rely on adequate identification of groups at elevated risk. Several risk factors for suicide are known, but little is known about the interactions between risk factors. Interactions between risk factors may aid in detecting more specific sub-populations at higher risk. METHODS: Here, we use a novel machine learning heuristic to detect sub-populations at ultra high-risk for suicide based on interacting risk factors. The data-driven and hypothesis-free model is applied to investigate data covering the entire population of the Netherlands. FINDINGS: We found three sub-populations with extremely high suicide rates (i.e. >50 suicides per 100,000 person years, compared to 12/100,000 in the general population), namely: (1) people on unfit for work benefits that were never married, (2) males on unfit for work benefits, and (3) those aged 55-69 who live alone, were never married and have a relatively low household income. Additionally, we found two sub-populations where the rate was higher than expected based on individual risk factors alone: widowed males, and people aged 25-39 with a low level of education. INTERPRETATION: Our model is effective at finding ultra-high risk groups which can be targeted using sub-population level interventions. Additionally, it is effective at identifying high-risk groups that would not be considered risk groups based on conventional risk factor analysis.

Predicting academic success of autistic students in higher education.

LAYMEN SUMMARY: What is already known about the topic?Autistic youths increasingly enter universities. We know from existing research that autistic students are at risk of dropping out or studying delays. Using machine learning and historical information of students, researchers can predict the academic success of bachelor students. However, we know little about what kind of information can predict whether autistic students will succeed in their studies and how accurate these predictions will be.What does this article add?In this research, we developed predictive models for the academic success of 101 autistic bachelor students. We compared these models to 2,465 students with other health conditions and 25,077 students without health conditions. The research showed that the academic success of autistic students was predictable. Moreover, these predictions were more precise than predictions of the success of students without autism.For the success of the first bachelor year, concerns with aptitude and study choice were the most important predictors. Participation in pre-education and delays at the beginning of autistic students' studies were the most influential predictors for second-year success and delays in the second and final year of their bachelor's program. In addition, academic performance in high school was the strongest predictor for degree completion in 3 years.Implications for practice, research, or policyThese insights can enable universities to develop tailored support for autistic students. Using early warning signals from administrative data, institutions can lower dropout risk and increase degree completion for autistic students.

Reduced Waiting Times by Preference-Based Allocation of Patients to Nursing Homes.

OBJECTIVES: The long waiting times for nursing homes can be reduced by applying advanced waiting-line management. In this article, we implement a preference-based allocation model for older adults to nursing homes, evaluate the performance in a simulation setting for 2 case studies, and discuss the implementation in practice. DESIGN: Simulation study. SETTING AND PARTICIPANTS: Older adults requiring somatic nursing home care, from an urban region (Rotterdam) and a rural region (Twente) in the Netherlands. METHODS: Data about nursing homes and capacities for the 2 case studies were identified. A set of preference profiles was defined with aims regarding waiting time preferences and flexibility. Guidelines for implementation of the model in practice were obtained by addressing the tasks of all stakeholders. Thereafter, the simulation was run to compare the current practice with the allocation model based on specified outcome measures about waiting times and preferences. RESULTS: We found that the allocation model decreased the waiting times in both case studies. Compared with the current practice policy, the allocation model reduced the waiting times until placement by at least a factor of 2 (from 166 to 80 days in Rotterdam and 178 to 82 days in Twente). Moreover, more of the older adults ended up in their preferred nursing home and the aims of the distinct preference profiles were satisfied. CONCLUSIONS AND IMPLICATIONS: The results show that the allocation model outperforms commonly used waiting-line policies for nursing homes, while meeting individual preferences to a larger extent. Moreover, the model is easy to implement and of a generic nature and can, therefore, be extended to other settings as well (eg, to allocate older adults to home care or daycare). Finally, this research shows the potential of mathematical models in the care domain for older adults to face the increasing need for cost-effective solutions.

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.

On the relation between COVID-19, mobility, and the stock market.

The Covid-19 pandemic has brought forth a major landscape shock in the mobility sector. Due to its recentness, researchers have just started studying and understanding the implications of this crisis on mobility. We contribute by combining mobility data from various sources to bring a novel angle to understanding mobility patterns during Covid-19. The goal is to expose relations between mobility and Covid-19 variables and understand them by using our data. This is crucial information for governments to understand and address the underlying root causes of the impact.

Identifying socio-demographic risk factors for suicide using data on an individual level.

BACKGROUND: Suicide is a complex issue. Due to the relative rarity of the event, studies into risk factors are regularly limited by sample size or biased samples. The aims of the study were to find risk factors for suicide that are robust to intercorrelation, and which were based on a large and unbiased sample. METHODS: Using a training set of 5854 suicides and 596,416 control cases, we fit a logistic regression model and then evaluate the performance on a test set of 1425 suicides and 594,893 control cases. The data used was micro-data of Statistics Netherlands (CBS) with data on each inhabitant of the Netherlands. RESULTS: Taking the effect of possible correlating risk factors into account, those with a higher risk for suicide are men, middle-aged people, people with low income, those living alone, the unemployed, and those with mental or physical health problems. People with a lower risk are the highly educated, those with a non-western immigration background, and those living with a partner. CONCLUSION: We confirmed previously known risk factors such as male gender, middle-age, and low income and found that they are risk factors that are robust to intercorrelation. We found that debt and urbanicity were mostly insignificant and found that the regional differences found in raw frequencies are mostly explained away after correction of correlating risk factors, indicating that these differences were primarily caused due to the differences in the demographic makeup of the regions. We found an AUC of 0.77, which is high for a model predicting suicide death and comparable to the performance of deep learning models but with the benefit of remaining explainable.

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.