Suicide Screening, Risk Assessment, and Lethal Means Counseling During Zero Suicide Implementation.

OBJECTIVE: The authors measured implementation of Zero Suicide (ZS) clinical practices that support identification of suicide risk and risk mitigation, including screening, risk assessment, and lethal means counseling, across mental health specialty and primary care settings. METHODS: Six health care systems in California, Colorado, Michigan, Oregon, and Washington participated. The sample included members ages ≥13 years from 2010 to 2019 (N=7,820,524 patients). The proportions of patients with suicidal ideation screening, suicide risk assessment, and lethal means counseling were estimated. RESULTS: In 2019, patients were screened for suicidal ideation in 27.1% (range 5.0%-85.0%) of mental health visits and 2.5% (range 0.1%-35.0%) of primary care visits among a racially and ethnically diverse sample (44.9% White, 27.2% Hispanic, 13.4% Asian, and 7.7% Black). More patients screened positive for suicidal ideation in the mental health setting (10.2%) than in the primary care setting (3.8%). Of the patients screening positive for suicidal ideation in the mental health setting, 76.8% received a risk assessment, and 82.4% of those identified as being at high risk received lethal means counseling, compared with 43.2% and 82.4%, respectively, in primary care. CONCLUSIONS: Six health systems that implemented ZS showed a high level of variation in the proportions of patients receiving suicide screening and risk assessment and lethal means counseling. Two opportunities emerged for further study to increase frequency of these practices: expanding screening beyond patients with regular health care visits and implementing risk assessment with lethal means counseling in the primary care setting directly after a positive suicidal ideation screening.

Adolescents Who Do Not Endorse Risk via the Patient Health Questionnaire Before Self-Harm or Suicide.

Importance: Given that the Patient Health Questionnaire (PHQ) item 9 is commonly used to screen for risk of self-harm and suicide, it is important that clinicians recognize circumstances when at-risk adolescents may go undetected. Objective: To understand characteristics of adolescents with a history of depression who do not endorse the PHQ item 9 before a near-term intentional self-harm event or suicide. Design, Setting, and Participants: This was a retrospective cohort study design using electronic health record and claims data from January 2009 through September 2017. Settings included primary care and mental health specialty clinics across 7 integrated US health care systems. Included in the study were adolescents aged 13 to 17 years with history of depression who completed the PHQ item 9 within 30 or 90 days before self-harm or suicide. Study data were analyzed September 2022 to April 2023. Exposures: Demographic, diagnostic, treatment, and health care utilization characteristics. Main Outcome(s) and Measure(s): Responded "not at all" (score = 0) to PHQ item 9 regarding thoughts of death or self-harm within 30 or 90 days before self-harm or suicide. Results: The study included 691 adolescents (mean [SD] age, 15.3 [1.3] years; 541 female [78.3%]) in the 30-day cohort and 1024 adolescents (mean [SD] age, 15.3 [1.3] years; 791 female [77.2%]) in the 90-day cohort. A total of 197 of 691 adolescents (29%) and 330 of 1024 adolescents (32%), respectively, scored 0 before self-harm or suicide on the PHQ item 9 in the 30- and 90-day cohorts. Adolescents seen in primary care (odds ratio [OR], 1.5; 95% CI, 1.0-2.1; P = .03) and older adolescents (OR, 1.2; 95% CI, 1.0-1.3; P = .02) had increased odds of scoring 0 within 90 days of a self-harm event or suicide, and adolescents with a history of inpatient hospitalization and a mental health diagnosis had twice the odds (OR, 2.0; 95% CI, 1.3-3.0; P = .001) of scoring 0 within 30 days. Conversely, adolescents with diagnoses of eating disorders were significantly less likely to score 0 on item 9 (OR, 0.4; 95% CI, 0.2-0.8; P = .007) within 90 days. Conclusions and Relevance: Study results suggest that older age, history of an inpatient mental health encounter, or being screened in primary care were associated with at-risk adolescents being less likely to endorse having thoughts of death and self-harm on the PHQ item 9 before a self-harm event or suicide death. As use of the PHQ becomes more widespread in practice, additional research is needed for understanding reasons why many at-risk adolescents do not endorse thoughts of death and self-harm.

Reducing Firearm Access for Suicide Prevention: Implementation Evaluation of the Web-Based "Lock to Live" Decision Aid in Routine Health Care Encounters.

Background: "Lock to Live" (L2L) is a novel web-based decision aid for helping people at risk of suicide reduce access to firearms. Researchers have demonstrated that L2L is feasible to use and acceptable to patients, but little is known about how to implement L2L during web-based mental health care and in-person contact with clinicians. Objective: The goal of this project was to support the implementation and evaluation of L2L during routine primary care and mental health specialty web-based and in-person encounters. Methods: The L2L implementation and evaluation took place at Kaiser Permanente Washington (KPWA)-a large, regional, nonprofit health care system. Three dimensions from the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) model-Reach, Adoption, and Implementation-were selected to inform and evaluate the implementation of L2L at KPWA (January 1, 2020, to December 31, 2021). Electronic health record (EHR) data were used to purposefully recruit adult patients, including firearm owners and patients reporting suicidality, to participate in semistructured interviews. Interview themes were used to facilitate L2L implementation and inform subsequent semistructured interviews with clinicians responsible for suicide risk mitigation. Audio-recorded interviews were conducted via the web, transcribed, and coded, using a rapid qualitative inquiry approach. A descriptive analysis of EHR data was performed to summarize L2L reach and adoption among patients identified at high risk of suicide. Results: The initial implementation consisted of updates for clinicians to add a URL and QR code referencing L2L to the safety planning EHR templates. Recommendations about introducing L2L were subsequently derived from the thematic analysis of semistructured interviews with patients (n=36), which included (1) "have an open conversation," (2) "validate their situation," (3) "share what to expect," (4) "make it accessible and memorable," and (5) "walk through the tool." Clinicians' interviews (n=30) showed a strong preference to have L2L included by default in the EHR-based safety planning template (in contrast to adding it manually). During the 2-year observation period, 2739 patients reported prior-month suicide attempt planning or intent and had a documented safety plan during the study period, including 745 (27.2%) who also received L2L. Over four 6-month subperiods of the observation period, L2L adoption rates increased substantially from 2% to 29% among primary care clinicians and from <1% to 48% among mental health clinicians. Conclusions: Understanding the value of L2L from users' perspectives was essential for facilitating implementation and increasing patient reach and clinician adoption. Incorporating L2L into the existing system-level, EHR-based safety plan template reduced the effort to use L2L and was likely the most impactful implementation strategy. As rising suicide rates galvanize the urgency of prevention, the findings from this project, including L2L implementation tools and strategies, will support efforts to promote safety for suicide prevention in health care nationwide.

A Randomized Control Trial of a Digital Health Tool for Safer Firearm and Medication Storage for Patients with Suicide Risk.

Most patients with suicide risk do not receive recommendations to reduce access to lethal means due to a variety of barriers (e.g., lack of provider time, training). Determine if highly efficient population-based EHR messaging to visit the Lock to Live (L2L) decision aid impacts patient-reported storage behaviors. Randomized trial. Integrated health care system serving Denver, CO. Served by primary care or mental health specialty clinic in the 75-99.5th risk percentile on a suicide attempt or death prediction model. Lock to Live (L2L) is a web-based decision aid that incorporates patients' values into recommendations for safe storage of lethal means, including firearms and medications. Anonymous survey that determined readiness to change: pre-contemplative (do not believe in safe storage), contemplative (believe in safe storage but not doing it), preparation (planning storage changes) or action (safely storing). There were 21,131 patients randomized over a 6-month period with a 27% survey response rate. Many (44%) had access to a firearm, but most of these (81%) did not use any safe firearm storage behaviors. Intervention patients were more likely to be categorized as preparation or action compared to controls for firearm storage (OR = 1.30 (1.07-1.58)). When examining action alone, there were no group differences. There were no statistically significant differences for any medication storage behaviors. Selection bias in those who responded to survey. Efficiently sending an EHR invitation message to visit L2L encouraged patients with suicide risk to consider safer firearm storage practices, but a stronger intervention is needed to change storage behaviors. Future studies should evaluate whether combining EHR messaging with provider nudges (e.g., brief clinician counseling) changes storage behavior.ClinicalTrials.gov: NCT05288517.

Predicting Outcomes of Antidepressant Treatment in Community Practice Settings.

OBJECTIVE: The authors examined whether machine-learning models could be used to analyze data from electronic health records (EHRs) to predict patients' responses to antidepressant medications. METHODS: EHR data from a Washington State health system identified patients ages ≥13 years who started an antidepressant medication in 2016 in a community practice setting and had a baseline Patient Health Questionnaire-9 (PHQ-9) score of ≥10 and at least one PHQ-9 score recorded 14-180 days later. Potential predictors of a response to antidepressants were extracted from the EHR and included demographic characteristics, psychiatric and substance use diagnoses, past psychiatric medication use, mental health service use, and past PHQ-9 scores. Random-forest and penalized regression analyses were used to build models predicting follow-up PHQ-9 score and a favorable treatment response (≥50% improvement in score). RESULTS: Among 2,469 patients starting antidepressant medication treatment, the mean±SD baseline PHQ-9 score was 17.3±4.5, and the mean lowest follow-up score was 9.2±5.9. Outcome data were available for 72% of the patients. About 48% of the patients had a favorable treatment response. The best-fitting random-forest models yielded a correlation between predicted and observed follow-up scores of 0.38 (95% CI=0.32-0.45) and an area under the receiver operating characteristic curve for a favorable response of 0.57 (95% CI=0.52-0.61). Results were similar for penalized regression models and for models predicting last PHQ-9 score during follow-up. CONCLUSIONS: Prediction models using EHR data were not accurate enough to inform recommendations for or against starting antidepressant medication. Personalization of depression treatment should instead rely on systematic assessment of early outcomes.

Minimal Effect of Messaging on Engagement in a Digital Anxiety Intervention.

This study evaluated the effectiveness of different recruitment messages for encouraging enrollment in a digital mental health intervention (DMHI) for anxiety among 1,600 anxious patients in a large healthcare system. Patients were randomly assigned to receive a standard message, or one of five messages designed to encourage enrollment: Three messages offered varying financial incentives, one message offered coaching, and one message provided consumer testimonials. Patients could then click a link in the message to visit the DMHI website, enroll, and start the first session. We examined the effects of message features and message length (short vs. long) on rates of site clicks, enrollment, and starting the first session. We also tested whether demographic and clinical factors derived from patients' electronic health records were associated with rates of enrollment and starting the first session to understand the characteristics of patients most likely to use DMHIs in this setting. Across messages, 19.4% of patients clicked a link to visit the DMHI website, but none of the messages were significantly associated with rates of site clicks, enrollment, or starting the first session. Females (vs. males) had a greater probability of enrollment. No other demographic or clinical variables were significantly associated with enrollment or starting the first session. Findings provide guidance for resource allocation decisions in larger scale DMHI implementations in healthcare settings.

Factors associated with suicide mortality among reproductive age women: a case-control study.

OBJECTIVE: Examine demographic, psychosocial, pregnancy-related, and healthcare utilisation factors associated with suicide mortality among reproductive age women. METHODS: Data from nine health care systems in the Mental Health Research Network were included. A case-control study design was used in which 290 reproductive age women who died by suicide (cases) from 2000 to 2015 were matched with 2,900 reproductive age women from the same healthcare system who did not die by suicide (controls). Conditional logistic regression was used to analyse associations between patient characteristics and suicide. RESULTS: Women of reproductive age who died by suicide were more likely to have mental health (aOR = 7.08, 95% CI: 5.17, 9.71) or substance use disorders (aOR = 3.16, 95% CI: 2.19, 4.56) and to have visited the emergency department in the year prior to index date (aOR = 3.47, 95% CI: 2.50, 4.80). Non-Hispanic White women (aOR = 0.70, 95% CI: 0.51, 0.97) and perinatal (pregnant or postpartum) women were less likely to have died by suicide (aOR = 0.27, 95% CI: 0.13, 0.58). CONCLUSIONS: Reproductive age women with mental health and/or substance use disorders, prior emergency department encounters, or who are of racial or ethnic minority status were at increased risk of suicide mortality and may benefit from routine screening and monitoring. Future research should further examine the relationship between pregnancy-related factors and suicide mortality.

Complex modeling with detailed temporal predictors does not improve health records-based suicide risk prediction.

Suicide risk prediction models can identify individuals for targeted intervention. Discussions of transparency, explainability, and transportability in machine learning presume complex prediction models with many variables outperform simpler models. We compared random forest, artificial neural network, and ensemble models with 1500 temporally defined predictors to logistic regression models. Data from 25,800,888 mental health visits made by 3,081,420 individuals in 7 health systems were used to train and evaluate suicidal behavior prediction models. Model performance was compared across several measures. All models performed well (area under the receiver operating curve [AUC]: 0.794-0.858). Ensemble models performed best, but improvements over a regression model with 100 predictors were minimal (AUC improvements: 0.006-0.020). Results are consistent across performance metrics and subgroups defined by race, ethnicity, and sex. Our results suggest simpler parametric models, which are easier to implement as part of routine clinical practice, perform comparably to more complex machine learning methods.

Are People Who Die by Intentional Medication Poisoning Dispensed Those Medications in the Year Prior to Death?

OBJECTIVE: The rate of suicidal poisoning in the United States has increased substantially over the past 20 years. Understanding whether prescription medications used for self-poisoning were recently dispensed would help inform suicide prevention efforts. Alternatively, medications for self-poisoning could have been formerly dispensed or collected from friends, family, or illicit sources. METHODS: Among those who died by intentional opioid and psychotropic poisonings, we conducted a descriptive study to determine what proportion had a recently filled prescription that could have been the means of suicide. Subjects were all people who died by intentional poisoning across nine health-care systems within the NIH-funded Mental Health Research Network. RESULTS: Among the 3,300 people who died by suicide, 700 died by any poisoning and 194 died by intentional opioid or psychotropic/hypnotic medication poisoning. Among those who died by intentional opioid poisoning 73% were dispensed an opioid in the year prior. Among those who died by intentional psychotropic/hypnotic poisoning, 83% were dispensed any psychotropic and 61% were dispensed a hypnotic in prior year. Most people were continuously dispensed the same medications used in their intentional poisonings in the year prior to death. CONCLUSIONS: Our results indicate that most medications used in suicidal overdose were likely recently dispensed. Therefore, future suicide prevention studies and prevention resources should focus on medication safety interventions such as lethal-means counseling for medication access, limiting quantities dispensed, opioid antagonists, and blister packs. HIGHLIGHTSUnderstanding whether medications used for self-poisoning were recently dispensed or formerly/never dispensed would help inform future studies and suicide prevention efforts.We found that most people who died by intentional poisoning with opioids or psychotropic/hypnotic medications received frequent dispensings of the medication used for self-poisoning in the year prior to death.Future suicide prevention studies and efforts should focus on medication safety interventions such as lethal-means counseling for medication access, limiting quantities dispensed, opioid antagonists, and blister packs.

Patient and Clinician Perspectives of a Standardized Question About Firearm Access to Support Suicide Prevention: A Qualitative Study.

Importance: US residents report broad access to firearms, which are the most common means of suicide death in the US. Standardized firearm access questions during routine health care encounters are uncommon despite potential benefits for suicide prevention. Objective: To explore patient and clinician experiences with a standard question about firearm access on a self-administered mental health questionnaire routinely used prior to primary care and mental health specialty encounters. Design, Setting, and Participants: Qualitative semistructured interviews were conducted from November 18, 2019, to October 8, 2020, at Kaiser Permanente Washington, a large integrated care delivery system and insurance provider. Electronic health record data identified adult patients with a documented mental health diagnosis who had received a standard question about firearm access ("Do you have access to guns? yes/no") within the prior 2 weeks. A stratified sampling distribution selected 30% who answered "yes," 30% who answered "no," and 40% who left the question blank. Two groups of clinicians responsible for safety planning with patients at risk of suicide were also sampled: (1) licensed clinical social workers (LICSWs) in primary and urgent care settings and (2) consulting nurses (RNs). Main Outcomes and Measures: Participants completed semistructured telephone interviews, which were recorded and transcribed. Directive (deductive) and conventional (inductive) content analyses were used to apply knowledge from prior research and describe new information. Thematic analysis was used to organize key content, and triangulation was used to describe the intersections between patient and clinician perspectives. Results: Thirty-six patients were interviewed (of 76 sampled; mean [SD] age, 47.3 [17.9] years; 19 [53%] were male; 27 [75%] were White; 3 [8%] were Black; and 1 [3%] was Latinx or Hispanic. Sixteen participants had reported firearm access and 15 had reported thoughts of self-harm on the questionnaire used for sampling. Thirty clinicians were interviewed (of 51 sampled) (mean [SD] age, 44.3 [12.1] years; 24 [80%] were female; 18 [60%] were White; 5 [17%] were Asian or Pacific Islander; and 4 [13%] were Latinx or Hispanic) including 25 LICSWs and 5 RNs. Key organizing themes included perceived value of standardized questions about firearm access, challenges of asking and answering, and considerations for practice improvement. Clinician interview themes largely converged and/or complemented patient interviews. Conclusions and Relevance: In this qualitative study using semistructured interviews with patients and clinicians, a standardized question about firearm access was found to encourage dialogue about firearm access. Respondents underscored the importance of nonjudgmental acknowledgment of patients' reasons for firearm access as key to patient-centered practice improvement.

Equitable implementation of S.A.F.E. Firearm: A multi-method pilot study.

Attention to health equity is critical in the implementation of firearm safety efforts. We present our operationalization of equity-oriented recommendations in preparation for launch of a hybrid effectiveness-implementation trial focused on firearm safety promotion in pediatric primary care as a universal suicide prevention strategy. In Step 1 of our process, pre-trial engagement with clinican partners and literature review alerted us that delivery of a firearm safety program may vary by patients' medical complexity, race, and ethnicity. In Step 2, we selected the Health Equity Implementation Framework to inform our understanding of contextual determinants (i.e., barriers and facilitators). In Step 3, we leveraged an implementation pilot across 5 pediatric primary care clinics in 2 health system sites to study signals of inequities. Eligible well-child visits for 694 patients and 47 clinicians were included. Our results suggested that medical complexity was not associated with program delivery. We did see potential signals of inequities by race and ethnicity but must interpret with caution. Though we did not initially plan to examine differences by sex assigned at birth, we discovered that clinicians may be more likely to deliver the program to parents of male than female patients. Seven qualitative interviews with clinicians provided additional context. In Step 4, we interrogated equity considerations (e.g., why and how do these inequities exist). In Step 5, we will develop a plan to probe potential inequities related to race, ethnicity, and sex in the fully powered trial. Our process highlights that prospective, rigorous, exploratory work is vital for equity-informed implementation trials.

Opioid-related variables did not improve suicide risk prediction models in samples with mental health diagnoses.

Background: Suicide risk prediction models derived from machine learning of electronic health records and insurance claims are an innovation in suicide prevention. Some models do not include opioid-related variables despite the relationship between opioids and suicide. This study evaluated whether inclusion of opioid-related variables improved suicide risk prediction models developed by the Mental Health Research Network. Methods: Approximately 630 opioid-related variables and interactions terms were introduced into existing suicide prediction models run in datasets of patient visits in mental health care (n = 27,755,401 visits) or primary care when a mental health diagnosis was given (n = 19,340,461 visits). Training and validation datasets were created. LASSO regression with 10-fold validation identified variables to be added to the models. Results: The new models predicting suicide attempts and suicide deaths in the mental health specialty visit sample performed as well as the existing models (new C-statistic for attempts model = 0.855, CI: 0.853-0.857 versus original C-statistic = 0.851, CI 0.848-0.853; death model = 0.868, CI: 0.856-0.879 versus 0.861, CI 0.848-0.875). The new model for suicide death in the primary care sample improved (0.855, CI: 0.837-0.874 versus 0.833, CI 0.813-0.853) while performance of the new model for suicide attempt in that sample degraded (0.843, CI: 0.839-0.847 versus 0.853, CI 0.849-0.857). Limitations: Analyses did not include patients without recent care, data did not include illicit opioid use or unrecognized opioid use disorder. Conclusions: Among patients with mental health diagnoses, inclusion of opioid-related variables did not improve prediction of suicide risk beyond mental health predictors.

"Lock to Live" for firearm and medication safety: Feasibility and acceptability of a suicide prevention tool in a learning healthcare system.

Objective: Few patients with suicide risk are counseled on lethal means safety by health providers. This study tested the feasibility of different delivery methods for Lock to Live (L2L), a web-based decision aid of safe storage options for firearms and medications. Methods: Patients reporting suicide ideation on the PHQ9 depression screener during outpatient health visits were included. Invitation messages to visit L2L were sent via combinations of email, text, Electronic Health Record (EHR) message, mailed letter, or provider referral, followed by a survey about storage behavior and acceptability. Provider interviews evaluated logistical considerations and acceptability. Results: The population-based method reached 2,729 patients and the best method (EHR message plus 2 email reminders) had 11% uptake (L2L visitation rate). Provider referral had small reach (14 patients) and 100% uptake (all visited). Provider interviews identified several strategies to promote uptake including: EHR reminders, provider training, quality metrics with accountability, a clearly communicated lethal means screening/counseling policy, and strong organizational leadership support. Conclusion: Despite the low uptake for population-based (11%), far more patients with suicide risk were engaged in the L2L tool through population-based outreach than provider-referral over the same time frame.

A Critical Review of Text Mining Applications for Suicide Research.

Purpose of Review: Applying text mining to suicide research holds a great deal of promise. In this manuscript, literature from 2019 to 2021 is critically reviewed for text mining projects that use electronic health records, social media data, and death records. Recent Findings: Text mining has helped identify risk factors for suicide in general and specific populations (e.g., older adults), has been combined with structured variables in EHRs to predict suicide risk, and has been used to track trends in social media suicidal discourse following population level events (e.g., COVID-19, celebrity suicides). Summary: Future research should utilize text mining along with data linkage methods to capture more complete information on risk factors and outcomes across data sources (e.g., combining death records and EHRs), evaluate effectiveness of NLP-based intervention programs that use suicide risk prediction, establish standards for reporting accuracy of text mining programs to enable comparison across studies, and incorporate implementation science to understand feasibility, acceptability, and technical considerations.

Machine Learning Prediction of Suicide Risk Does Not Identify Patients Without Traditional Risk Factors.

Objective: To determine whether predictions of suicide risk from machine learning models identify unexpected patients or patients without medical record documentation of traditional risk factors.Methods: The study sample included 27,091,382 outpatient mental health (MH) specialty or general medical visits with a MH diagnosis for patients aged 11 years or older from January 1, 2009, to September 30, 2017. We used predicted risk scores of suicide attempt and suicide death, separately, within 90 days of visits to classify visits into risk score percentile strata. For each stratum, we calculated counts and percentages of visits with traditional risk factors, including prior self-harm diagnoses and emergency department visits or hospitalizations with MH diagnoses, in the last 3, 12, and 60 months.Results: Risk-factor percentages increased with predicted risk scores. Among MH specialty visits, 66%, 88%, and 99% of visits with suicide attempt risk scores in the top 3 strata (respectively, 90th-95th, 95th-98th, and ≥ 98th percentiles) and 60%, 77%, and 93% of visits with suicide risk scores in the top 3 strata represented patients who had at least one traditional risk factor documented in the prior 12 months. Among general medical visits, 52%, 66%, and 90% of visits with suicide attempt risk scores in the top 3 strata and 45%, 66%, and 79% of visits with suicide risk scores in the top 3 strata represented patients who had a history of traditional risk factors in the last 12 months.Conclusions: Suicide risk alerts based on these machine learning models coincide with patients traditionally thought of as high-risk at their high-risk visits.

Accuracy of ICD-10-CM encounter diagnoses from health records for identifying self-harm events.

OBJECTIVE: Assess the accuracy of ICD-10-CM coding of self-harm injuries and poisonings to identify self-harm events. MATERIALS AND METHODS: In 7 integrated health systems, records data identified patients reporting frequent suicidal ideation. Records then identified subsequent ICD-10-CM injury and poisoning codes indicating self-harm as well as selected codes in 3 categories where uncoded self-harm events might be found: injuries and poisonings coded as undetermined intent, those coded accidental, and injuries with no coding of intent. For injury and poisoning encounters with diagnoses in those 4 groups, relevant clinical text was extracted from records and assessed by a blinded panel regarding documentation of self-harm intent. RESULTS: Diagnostic codes selected for review include all codes for self-harm, 43 codes for undetermined intent, 26 codes for accidental intent, and 46 codes for injuries without coding of intent. Clinical text was available for review for 285 events originally coded as self-harm, 85 coded as undetermined intent, 302 coded as accidents, and 438 injury events with no coding of intent. Blinded review of full-text clinical records found documentation of self-harm intent in 254 (89.1%) of those originally coded as self-harm, 24 (28.2%) of those coded as undetermined, 24 (7.9%) of those coded as accidental, and 48 (11.0%) of those without coding of intent. CONCLUSIONS: Among patients at high risk, nearly 90% of injuries and poisonings with ICD-10-CM coding of self-harm have documentation of self-harm intent. Reliance on ICD-10-CM coding of intent to identify self-harm would fail to include a small proportion of true self-harm events.

Detecting and distinguishing indicators of risk for suicide using clinical records.

Health systems are essential for suicide risk detection. Most efforts target people with mental health (MH) diagnoses, but this only represents half of the people who die by suicide. This study seeks to discover and validate health indicators of suicide death among those with, and without, MH diagnoses. This case-control study used statistical modeling with health record data on diagnoses, procedures, and encounters. The study included 3,195 individuals who died by suicide from 2000 to 2015 and 249,092 randomly selected matched controls, who were age 18+ and affiliated with nine Mental Health Research Network affiliated health systems. Of the 202 indicators studied, 170 (84%) were associated with suicide in the discovery cohort, with 148 (86%) of those in the validation cohort. Malignant cancer diagnoses were risk factors for suicide in those without MH diagnoses, and multiple individual psychiatric-related indicators were unique to the MH subgroup. Protective effects across MH-stratified models included diagnoses of benign neoplasms, respiratory infections, and utilization of reproductive services. MH-stratified latent class models validated five subgroups with distinct patterns of indicators in both those with and without MH. The highest risk groups were characterized via high utilization with multiple healthcare concerns in both groups. The lowest risk groups were characterized as predominantly young, female, and high utilizers of preventive services. Healthcare data include many indicators of suicide risk for those with and without MH diagnoses, which may be used to support the identification and understanding of risk as well as targeting of prevention in health systems.