Data Linkage Strategies to Advance Youth Suicide Prevention: A Systematic Review for a National Institutes of Health Pathways to Prevention Workshop

Thirteen years progress: a scoping review of suicide prevention initiatives in post-secondary settings

Objective: Describe the literature on suicide prevention initiatives (SPIs) in post-secondary settings internationally since 2010. Methods: A scoping review and bibliographic search were conducted across MEDLINE, PsycINFO, CINAHL, and Scopus. Two reviewers independently screened articles and extracted data in consultation with a community social work team. Included articles described SPIs in post-secondary settings. Secondary sources and articles unavailable in English were excluded. Results: After identifying 931 citations, 76 articles representing five continents were included. Included articles spanned 2010 to 2023. Most articles represented North America, used quantitative methods, and focused on gatekeepers. SPIs in post-secondary settings were characterized by standardized and commercially available training programs and a wide variety of 'in-house' initiatives unique to campus communities. Conclusions: The quantity of research on SPIs in post-secondary settings has not kept pace with escalating mental health issues and the extent to which suicide is an urgent public health issue for young adults.

Preventing Youth Suicide: Potential "Crossover Effects" of Existing School-Based Programs

Notable increases in youth mental health problems combined with strains on the already stretched mental health workforce raise concerns that there will be an ensuing increase in youth suicide thoughts, behaviors, and even deaths. Schools are recognized as crucial settings for youth mental health support and suicide prevention activities, yet schools also face staff shortages and ever-increasing responsibilities for student well-being. Evidence is emerging that prevention programs originally designed to improve problem-solving skills and social-emotional functioning in youth have demonstrated downstream, "crossover effects," that is, unanticipated benefits, on youth suicidal behavior. Relatively little research on crossover effects has been conducted within school settings, despite the strong potential for commonly administered programs to have an impact on later suicide risk. We review key suicide risk factors and their proposed mechanisms of action; we also discuss factors that may protect against suicide risk. We then identify upstream prevention programs targeting the same factors and mechanisms; these programs may hold promise for downstream, crossover effects on youth suicide risk. This paper is intended to provide a framework to help researchers, practitioners, and policymakers as they consider how to prevent youth suicide using existing school-based resources. Rigorous investigation of upstream prevention programs is urgently needed to determine ideal approaches schools and communities can deploy to prevent youth suicide.

Research using population-based administration data integrated with longitudinal data in child protection settings: A systematic review

INTRODUCTION

Over the past decade there has been a marked growth in the use of linked population administrative data for child protection research. This is the first systematic review of studies to report on research design and statistical methods used where population-based administrative data is integrated with longitudinal data in child protection settings.

METHODS

The systematic review was conducted according to Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) statement. The electronic databases Medline (Ovid), PsycINFO, Embase, ERIC, and CINAHL were systematically searched in November 2019 to identify all the relevant studies. The protocol for this review was registered and published with Open Science Framework (Registration DOI: 10.17605/OSF.IO/96PX8).

RESULTS

The review identified 30 studies reporting on child maltreatment, mental health, drug and alcohol abuse and education. The quality of almost all studies was strong, however the studies rated poorly on the reporting of data linkage methods. The statistical analysis methods described failed to take into account mediating factors which may have an indirect effect on the outcomes of interest and there was lack of utilisation of multi-level analysis.

CONCLUSION

We recommend reporting of data linkage processes through following recommended and standardised data linkage processes, which can be achieved through greater co-ordination among data providers and researchers.

Advancing Research in Child Suicide: A Call to Action

OBJECTIVE

To highlight the problem of child suicide, summarize what is known and not known about the problem in the empirical literature, and provide recommendations with ethical considerations for future research and practice.

METHOD

The development of this paper was informed by a meeting of national experts on the topic hosted by the National Institute of Mental Health, as well as by a review of the empirical literature.

RESULTS

We know something about demographic characteristics that are related to higher child suicide rates, but beyond that we know relatively little about risk factors, prevention, and intervention for suicide risk in children <12 years. It is important for child suicide researchers and practitioners to pay particular attention to ethical issues that may be likely to arise in doing this type of work.

CONCLUSION

Much more research is needed on child suicide in the areas of measurement, prevention, and intervention in order to advance the field and provide practitioners with the tools that they critically need.

Discovering the Unclassified Suicide Cases Among Undetermined Drug Overdose Deaths Using Machine Learning Techniques

OBJECTIVE

The Centers for Disease Control and Prevention (CDC) monitor accidental and intentional deaths to answer questions that are critical for the development of effective prevention and resource allocation. CDC's National Violent Death Reporting System (NVDRS) is a major innovation in surveillance linking individual-level data from multiple sources. However, suicide underreporting is common, particularly from drug overdose deaths. This study sought to assess machine learning (ML) techniques in quantifying drug overdose suicide underreporting rates.

METHODS

Clinical, sociodemographic, toxicological, and proximal stressor data on overdose decedents (n = 2,665) were extracted from Utah's NVDRS from 2012 to 2015. The existing well-determined cases were used to train and test our ML models. We assessed and compared multiple machine learning methods including Logistic Regression, Random Forest Classifier, Support Vector Machines, and Artificial Neural Networks. We applied a majority voting methodology to classify undetermined drug overdose deaths.

RESULTS

Overdose suicide rates were estimated to be underreported by 33% across all years, increasing yearly from 29% in 2012 to 37% in 2015. The overall test accuracies for all models ranged from 92.3% to 94.6%.

CONCLUSIONS

This research identifies a cost-effective, replicable, and expandable ML-based methodology to estimate the true rates of suicide which may be partially masked during the opioid epidemic.

Explainable statistical learning in public health for policy development: the case of real-world suicide data

BACKGROUND

In recent years, the availability of publicly available data related to public health has significantly increased. These data have substantial potential to develop public health policy; however, this requires meaningful and insightful analysis. Our aim is to demonstrate how data analysis techniques can be used to address the issues of data reduction, prediction and explanation using online available public health data, in order to provide a sound basis for informing public health policy.

METHODS

Observational suicide prevention data were analysed from an existing online United Kingdom national public health database. Multi-collinearity analysis and principal-component analysis were used to reduce correlated data, followed by regression analyses for prediction and explanation of suicide.

RESULTS

Multi-collinearity analysis was effective in reducing the indicator set of predictors by 30% and principal component analysis further reduced the set by 86%. Regression for prediction identified four significant indicator predictors of suicide behaviour (emergency hospital admissions for intentional self-harm, children leaving care, statutory homelessness and self-reported well-being/low happiness) and two main component predictors (relatedness dysfunction, and behavioural problems and mental illness). Regression for explanation identified significant moderation of a well-being predictor (low happiness) of suicide behaviour by a social factor (living alone), thereby supporting existing theory and providing insight beyond the results of regression for prediction. Two independent predictors capturing relatedness needs in social care service delivery were also identified.

CONCLUSIONS

We demonstrate the effectiveness of regression techniques in the analysis of online public health data. Regression analysis for prediction and explanation can both be appropriate for public health data analysis for a better understanding of public health outcomes. It is therefore essential to clarify the aim of the analysis (prediction accuracy or theory development) as a basis for choosing the most appropriate model. We apply these techniques to the analysis of suicide data; however, we argue that the analysis presented in this study should be applied to datasets across public health in order to improve the quality of health policy recommendations.

The study of effect moderation in youth suicide-prevention studies

PURPOSE

Suicide is now the second leading cause of death among persons between the ages of adolescents and emerging adults and rates have increased despite more funding and broader implementation of youth suicide-prevention programs. A systematic review was conducted focusing on identifying youth suicide-prevention studies within the United States. This paper reports on the methods utilized for understanding possible moderators of suicide-prevention program outcomes.

METHODS

We searched six databases from 1990 through August 2017 to identify studies of suicide-preventive interventions among those under age 26 years. Two independent team members screened search results and sequentially extracted information related to statistical methods of moderation analyses.

RESULTS

69 articles were included in the systematic review of which only 17 (24.6%) explored treatment effect heterogeneity using moderation analysis. The most commonly used analytic tool was regression with an interaction term. The moderators studied included demographic characteristics such as gender and ethnicity as well as individual characteristics such as traumatic stress exposure and multiple prior suicide attempts.

CONCLUSIONS

With a greater emphasis from the federal government and funding agencies on precision prevention, understanding which prevention programs work for specific subgroups is essential. Only a small percentage of the reviewed articles assessed moderation effects. This is a substantial research gap driven by sample size or other limitations which have impeded the identification of intervention effect heterogeneity.

Les soins médicaux de jeunes hommes et de jeunes femmes qui décèdent par suicide

OBJECTIVE

Prior year medical care was compared among youth dying by suicide to their peers. Effect modification of these associations by age or place of residency (rural versus larger community sizes) was examined in a large, medically insured population.

METHOD

This population-based case control study used data from the Office of the Chief Coroner in Ontario, Canada, linked to health care administrative data to examine associations between medical care for mental health or other reasons (versus no medical care) and suicide. Decedents ( n = 1203 males and n = 454 females) were youth (aged 10 to 25 years) who died by suicide in Ontario between April 2003 and March 2014, inclusive. Peers of the same ages were frequency matched to decedents on sex and place of residency. Logistic regression was used to calculate odds ratios and 95% confidence intervals and to test effect modification.

RESULTS

Associations with mental health care were stronger in decedents than peers with a gradation of care (i.e., outpatient only, emergency department [ED], inpatient care) in both sexes. However, these associations were weaker among youth living in rural communities. Furthermore, older males (aged 18 to 25 years) were less likely than younger males (aged 10 to 17 years) to access the ED (ambulatory care only). This decrease was observed in rural and larger communities alongside no increase in medical care for other reasons.

CONCLUSIONS

Geographical and age-related barriers to mental health care exist for youth who die by suicide. Preventive efforts can address these barriers, intervening early and integrating services, including the ED.

Evaluating Integrated Mental Health Care Programs for Children and Youth

Evaluations of integrated care programs share many characteristics of evaluations of other complex health system interventions. However, evaluating integrated care for child and adolescent mental health poses special challenges that stem from the broad range of social, emotional, and developmental problems that need to be addressed; the need to integrate care for other family members; and the lack of evidence-based interventions already adapted for primary care settings. Integrated care programs for children's mental health need to adapt and learn on the fly, so that evaluations may best be viewed through the lens of continuous quality improvement rather than evaluations of fixed programs.