Strombotne, KierstenTenso, Kertu2024-08-282024https://hdl.handle.net/2144/49191Suicide is the tenth leading cause of death among the general U.S. population and the second leading cause of death among those under the age of 45. Veterans are at a particularly high risk, representing 13.7% of the suicides among adult Americans in 2019, despite accounting for only 7% of the total population. The high rates have led the Veterans Health Administration (VHA) and others to develop predictive tools to help identify at-risk patients and facilitate targeting for suicide prevention. Suicide prediction is a notoriously complex challenge. Current theories suggest that suicidal behavior is a result of complex interactions between psychological, clinical, biological, social, and environmental factors. Despite decades of effort in suicide research, predictive abilities for suicide have remained at near-chance levels for the past 50 years. As a result, many organizations, including the VHA, have shifted to advanced statistical methods, such as machine learning, based predictive models. These models have improved suicide prevention efforts by leveraging individual-level electronic health records to detect patterns and identify individuals at highest risk of suicide, thereby enabling the delivery of additional mental health resources to those flagged by the model. Although researchers have made important advances in recent years, we have limited knowledge about how facility-level factors, such as variables related to access and capacity, may affect suicide-related events and aid suicide prediction and prevention. Understanding these factors is important because research demonstrates that clinic operations factors can have direct effects on suicide outcomes and are more easily changed by policymakers and facility managers relative to biological or social factors. The overarching aim of this dissertation was to improve risk prediction and suicide outcomes in the Veteran population by investigating the impact of clinic operations characteristics on suicide outcomes through the use of two methodologies: machine learning and causal inference. Its three specific aims were the following: (1) to investigate the performance of risk-prediction models after adding facility-level predictors of suicide-risk to commonly used machine learning algorithms, (2) and to explore the potential bias in machine learning based suicide risk prediction by stratifying the models by age, sex, race, and ethnicity. The third aim (3) used an instrumental variables approach to explore the causal relationship between virtual care utilization and individual-level suicide related events. Findings from Aim 1 were mixed and showed that adding facility attributes to suicide risk prediction models, specifically logistic regression and elastic net models, could accurately identify a larger number of individuals at greatest risk of suicide, depending on the specification of the model. The analysis from Aim 2 uncovered notable differences in the sensitivity of these models within various subgroups, with enhanced benefits observed for Black, Non-Hispanic, male, and younger populations. Aim 3 results highlighted that a rise in the proportion of virtual mental health visits compared to all visits significantly reduces suicide-related incidents, suggesting that the implementation of virtual mental health services could lower the incidence of suicide outcomes. The findings of this dissertation underscore the value of integrating clinic characteristics into suicide prevention efforts, offering a nuanced approach to improving predictive accuracy and mitigating biases in machine-learning models through the incorporation of facility-level factors. Furthermore, the application of causal inference methods provides critical policy-relevant insights, helping to answer fundamental 'why' questions that underpin suicide-related outcomes. Overall, these findings advocate for a broadening of perspectives from individual-level factors to include facility-level predictors, thereby enhancing the scope and effectiveness of suicide prevention efforts.en-USPublic healthCausal inferenceMachine learningMental healthSuicide predictionVeteransThesis/Dissertation2024-08-280000-0001-8361-8292