Importance Suicide is a major public health concern, and as most individuals have contact with health care practitioners before suicide, health systems are essential for suicide prevention. The Zero Suicide (ZS) model is the recommended approach for suicide prevention in health systems, but more evidence is needed to support its widespread adoption. Objective To examine suicide attempt rates associated with implementation of the ZS model in outpatient mental health care within 6 US health systems. Design, Setting, and Participants This quality improvement study with an interrupted time series design used data collected from January 2012 through December 2019, from patients aged 13 years or older who received mental health care at outpatient mental health specialty settings within 6 US health systems located in 5 states: California, Oregon, Washington, Colorado, and Michigan. Analyses were conducted from January through December 2024. Exposure The ZS model was implemented in 4 health systems at different points during the observation period (2012-2019) and compared with health systems that implemented the model before the observation period (postimplementation). Implementation included suicide risk screening, assessment, brief intervention (safety plan, means safety protocol), and behavioral health treatment. Main Outcomes and Measures The primary outcome was a measure of standardized monthly suicide attempt rates captured using health system records and government mortality records. Suicide death rates were also measured as a secondary outcome. Results There was a median of 309 107 (range, 55 354-451 837) unique patients per month. In 2017, there were 317 939 eligible individuals (63.2% female). Baseline suicide attempt rates were at least 30 to 40 per 100 000 individuals at each implementation site and decreased to less than 30 per 100 000 individuals at 3 sites by 2019. Decreases in suicide attempt rates were observed at 3 intervention health systems after site-specific implementation: health systems A and B had decreases of 0.7 per 100 000 individuals per month and C, 0.1 per 100 000 individuals per month. System D evidenced a similar suicide attempt rate after implementation (before implementation: median rate: 35.0 [range, 11.0-50.3] per 100 000 patients per month; after implementation: median rate: 34.3 [range, 18.5-42.0] per 100 000 patients per month). The 2 postimplementation health systems maintained low or declining suicide attempt rates throughout the observation period. The rate at system Y decreased by 0.3 per 100 000 individuals per month across the observation period. The rate at system Z began at 11 per 100 000 individuals per month and declined by 0.03 per 100 000 individuals per month during the observation period. Two systems evidenced reductions in the suicide death rate after implementation: system B declined by 0.2 per 100 000 individuals per month and system C by 0.1 per 100 000 individuals per month. Conclusions and Relevance In this quality improvement study, ZS model implementation was associated with a reduction in suicide attempt rates among patients accessing outpatient mental health care at most study sites, which supports widespread efforts to implement the ZS model in these settings within US health systems.

OBJECTIVE:Compare risk of intentional self-harm and overdose after visits for opioid use disorder (OUD) followed by starting vs. not starting buprenorphine. METHODS:Records from four health systems identified visits during 1/1/2012-12/31/2019 by health system members aged 13 or older with OUD diagnosis and no recent OUD medication. Following a target-trial emulation approach, visits followed by buprenorphine dispensing within 7 days were matched to unexposed visits. Analyses compared risk of diagnosed self-harm injury or poisoning (primary outcome) as well as opioid-involved poisoning and any injury or poisoning (secondary outcomes) within 90 days. RESULTS:Among 183,809 visits by 30,955 patients, 15,508 (8.4 %) had buprenorphine dispensing within 7 days, and 2260 (1.2 %) had self-harm diagnosis within 90 days. Average duration of buprenorphine treatment before interruption was 44.3 days (SD 32.1). In primary intention-to-treat analyses using logistic regression and adjusting for baseline risk of self-harm, starting buprenorphine was not associated with significant difference in self-harm (odds ratio [OR] 1.01, 95 % CI 0.81-1.24) or opioid-involved poisoning (OR 1.09, 95 % CI 0.86-1.38). In secondary as-treated analyses censoring outcomes after treatment change, buprenorphine initiation was associated with no significant difference in hazard of self-harm (Hazard Ratio [HR] 0.74, 95 % CI 0.53-1.02) and with significantly lower hazard of opioid-involved poisoning (HR 0.63, 95 % CI 0.43-0.94). CONCLUSIONS:Among people with OUD, starting buprenorphine was not followed by lower risk of self-harm, likely reflecting frequent discontinuation and high risk of self-harm or overdose shortly after discontinuation. These findings reinforce the need to improve treatment continuity among those starting buprenorphine.

While depression and anxiety increased with the COVID-19 pandemic, mental health (MH) care access plummeted. This accelerated the uptake of virtual visits, but the degree to which these supplanted in-person visits is unknown. This study aims to assess in-person and virtual MH visits prior to and during the pandemic. Visits from HealthPartners (Minnesota, Wisconsin), Henry Ford (Michigan) and Kaiser Washington (Washington, Oregon) from 2018 to 2022 were stratified by site and study period in this observational cohort study. Segmented linear regression analysis identified changes in the trend over time by detecting optimal breakpoints. A total of 1333,966 patients received MH care. Average monthly MH service utilization was 11% higher from September 2020 to December 2022 compared to calendar year 2019, driven by more patients seeking care. At their peak in mid-2020, virtual visits accounted for 25.6% of visits compared to 1.8% pre-pandemic. MH care utilization increased by the end of 2022 compared to pre-pandemic levels, driven by more people seeking care and supported in part by an increase in virtual visits.

OBJECTIVE:This study aimed to evaluate screening strategies for identifying risk for self-harm among adolescents making outpatient health care visits. METHODS:Health system records were used to identify a prospective cohort of adolescents completing the Patient Health Questionnaire-9 (PHQ-9) at outpatient visits between October 1, 2015, and March 15, 2020, and a retrospective cohort of adolescents experiencing self-harm events (ascertained from health records and state mortality data) during the same period. Self-harm risk scores were computed from health records. Analyses of the prospective sample examined the sensitivity and positive predictive value (PPV) of questionnaires and risk scores, separately and in combination. Analyses of the retrospective sample examined the proportion of self-harm events that could have been detected by different screening strategies. RESULTS:The prospective sample (N=8,929) included 43,548 questionnaires, with 1,045 questionnaires followed by a self-harm event within 180 days. A score of ≥2 on PHQ-9 item 9 had a sensitivity of 0.37 and a PPV of 0.09 for self-harm within 180 days of a mental health specialty visit, with similar results for primary care visits. In the retrospective sample, 89% of adolescents made a mental health specialty visit or a primary care visit with a recorded psychiatric diagnosis in the 180 days before a self-harm event. CONCLUSIONS:Responses to PHQ-9 item 9 and risk scores computed from health records accurately identified adolescents needing additional assessment for risk for self-harm. Over 80% of adolescents experiencing self-harm could have been identified by screening during an outpatient health care visit.

IntroductionInformation about causes of injury is key for injury prevention efforts. Historically, cause-of-injury coding in clinical practice has been incomplete due to the need for extra diagnosis codes in the International Classification of Diseases-Ninth Revision-Clinical Modification (ICD-9-CM) coding. The transition to ICD-10-CM and increased use of clinical support software for diagnosis coding is expected to improve completeness of cause-of-injury coding. This paper assesses the recording of external cause-of-injury codes specifically for those diagnoses where an additional code is still required.MethodsWe used electronic health record and claims data from 10 health systems from October 2015 to December 2021 to identify all inpatient and emergency encounters with a primary diagnosis of injury. The proportion of encounters that also included a valid external cause-of-injury code is presented.ResultsMost health systems had high rates of cause-of-injury coding: over 85% in emergency departments and over 75% in inpatient encounters with primary injury diagnoses. However, several sites had lower rates in both settings. State mandates were associated with consistently high external cause recording.ConclusionsCompleteness of cause-of-injury coding improved since the adoption of ICD-10-CM coding and increased slightly over the study period at most sites. However, significant variation remained, and completeness of cause-of-injury coding in any diagnosis data used for injury prevention planning should be empirically determined.

Objective –:Evaluate how often encounter diagnoses of self-harm soon after an emergency department visit for self-harm represent new self-harm events. Methods –:Electronic health records (EHR) and insurance claims data from a large integrated health system identified emergency department encounters for injury or poisoning coded as self-harm and then selected those with another encounter diagnosis of self-harm occurring within 91 days. Review of clinical text for these pairs of encounters examined whether the subsequent self-harm diagnosis represented a distinct new self-harm event vs. a repeat. Results –:Of 121 pairs of encounters with relevant clinical text available for review, records indicated a distinct repeat self-harm event for 50 (41%, 95% CI 33%-49%). The proportion confirmed as distinct new events ranged from 3% (95% CI 0%-10%) for self-harm diagnoses the following day to 50% (95% CI 19%-81%) for diagnoses 2 to 7 days later to 100% (95% CI 92%-100%) for diagnoses 8 to 91 days later. The proportion confirmed as distinct, new events did not vary by healthcare setting of recording for the subsequent diagnosis (inpatient, emergency department, other outpatient) or similarity of injury or poisoning type between the two events. Conclusions –:Health systems, researchers, and public health agencies using insurance claims or EHR diagnoses to identify early recurrence of self-harm should be cautious regarding diagnoses that appear to represent early repetition of self-harm. Self-harm diagnoses the day after an emergency department self-harm visit rarely represent a distinct new event, while diagnoses recorded more than a week later usually do.

OBJECTIVE:To compare alternative Difference-in-Differences (DID) methods for evaluating the effect of risk-stratified interventions, or interventions targeting at-risk groups, on binary outcomes. STUDY SETTING AND DESIGN:In simulations, we compared operating characteristics of recycled prediction estimators for common average treatment effect on the treated (ATT) estimands across three DID models: the traditional two groups and two periods model, a risk score adjusted model, and a model adjusting for risk score and its interactions with risk group and period. We compared DID ATT estimates to randomized evaluation estimates of a risk-stratified intervention implemented at Kaiser Permanente Washington (KPWA), delivering additional text-message reminders to reduce missed clinic visits. DATA SOURCES AND ANALYTIC SAMPLE:Our study included 588,503 KPWA visits, with 285,814 (49%) visits pre-evaluation (05/01/2018-10/30/2018) and 302,689 (51%) visits during the evaluation (02/01/2019-09/30/2019). Pre-evaluation, 120,350 visits were classified as high-risk. During the evaluation, 125,076 visits were labeled as high-risk, with 62,557 (50%) randomized to the intervention. We generated data in simulations based on this setting. PRINCIPAL FINDINGS:In simulations, the traditional DID and risk score adjusted models had smaller bias and standard errors, and better coverage probabilities. DID estimates closest to randomized evaluation estimates (-0.007, 95% CI [-0.010, -0.004]) were from the traditional DID model assuming the identity link (-0.008, 95% CI [-0.011, -0.005]) or the risk adjusted model with any link (-0.006, 95% CI [-0.008, -0.003] identity; -0.007, 95% CI [-0.011, -0.003] logit; -0.007, 95% CI [-0.012, -0.003] log) for the ATT on the absolute difference scale (usual DID ATT estimand), and the risk score adjusted model with log or logit links for all other estimands. CONCLUSIONS:Compared with randomized evaluation results, the traditional DID model is appropriate for the ATT on the absolute difference scale, while the risk score adjusted model with log or logit links is appropriate for all ATT estimands considered.

Collaborative care is a multicomponent intervention for patients with chronic disease in primary care. Previous meta-analyses have proven the effectiveness of collaborative care for depression; however, individual participant data (IPD) are needed to identify which components of the intervention are the principal drivers of this effect. To assess which components of collaborative care are the biggest drivers of its effectiveness in reducing symptoms of depression in primary care. Data were obtained from MEDLINE, Embase, Cochrane Library, PubMed, and PsycInfo as well as references of relevant systematic reviews. Searches were conducted in December 2023, and eligible data were collected until March 14, 2024. Two reviewers assessed for eligibility. Randomized clinical trials comparing the effect of collaborative care and usual care among adult patients with depression in primary care were included. The study was conducted according to the IPD guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline. IPD were collected for demographic characteristics and depression outcomes measured at baseline and follow-ups from the authors of all eligible trials. Using IPD, linear mixed models with random nested effects were calculated. Continuous measure of depression severity was assessed via validated self-report instruments at 4 to 6 months and was standardized using the instrument’s cutoff value for mild depression. A total of 35 datasets with 38 comparisons were analyzed (N = 20 046 participants [57.3% of all eligible, with minimal differences in baseline characteristics compared with nonretrieved data]; 13 709 [68.4%] female; mean [SD] age, 50.8 [16.5] years). A significant interaction effect with the largest effect size was found between the depression outcome and the collaborative care component therapeutic treatment strategy (−0.07; P < .001). This indicates that this component, including its key elements manual-based psychotherapy and family involvement, was the most effective component of the intervention. Significant interactions were found for all other components, but with smaller effect sizes. Components of collaborative care most associated with improved effectiveness in reducing depressive symptoms were identified. To optimize treatment effectiveness and resource allocation, a therapeutic treatment strategy, such as manual-based psychotherapy or family integration, may be prioritized when implementing a collaborative care intervention.

OBJECTIVE:The authors sought to examine influenza and COVID-19 vaccine uptake among individuals diagnosed as having psychiatric disorders compared with those without such diagnoses and to examine variations in vaccine uptake by sociodemographic and clinical characteristics. METHODS:The study was conducted in the Kaiser Permanente Georgia, Washington, and Southern California health care systems. Individuals with psychiatric conditions had at least one diagnosis of any psychiatric disorder during a 12-month study period; individuals in the control group had no psychiatric disorder diagnoses during this period, and the two groups were matched on age and sex. Bivariate analyses were conducted with Pearson chi-square tests; multivariate analyses were used to calculate the odds of receiving an influenza vaccine (N=1,307,202 individuals) or COVID-19 vaccine (N=1,380,894 individuals) and were controlled for selected covariates. RESULTS:After controlling for relevant confounders, the authors found that having a diagnosis of any psychiatric illness was associated with significantly increased odds of receiving an influenza vaccine (OR=1.18; 95% CI=1.17-1.19, p<0.001), compared with no diagnosis of a psychiatric disorder. Having any psychiatric illness was associated with decreased odds of receiving a COVID-19 vaccine (OR=0.97; 95% CI=0.96-0.98, p<0.001), after the analysis was controlled for the same covariates. CONCLUSIONS:The findings provide evidence that people with mental health conditions were more likely to receive an influenza vaccine but were less likely to receive a COVID-19 vaccine, compared with individuals without such conditions. However, the vaccination rates observed for individuals with and without diagnosed psychiatric conditions were below national benchmarks, suggesting room for improving vaccine uptake in both patient populations.

Rollout designs, which include stepped wedge designs, are defined by staggered implementation of new or alternative programs or services. Critiques of stepped wedge and other rollout designs have raised concerns regarding the confounding of true implementation or program effects with unrelated, global changes in service delivery, with some recommending they only be used when traditional parallel-group designs are not practicable. However, rollout designs may sometimes be more suitable than traditional parallel group designs for ethical, scientific, or practical reasons. As investigators involved in several recent rollout trials, we define and provide rationale for and examples of stepped wedge and the larger class of rollout designs, in which all participating units receive a new program or service implementation. Staged implementation in a rollout design may be necessary when denying, rather than delaying, implementation of a known effective service is ethically unacceptable. Scientifically, stepped wedge has increased statistical power relative to an equivalent parallel group design, and some rollout designs have the capability to compare different phases of implementation and sustainment. A rollout design may be practically necessary either because of limited resources and other logistical challenges or community requirements that no site serve as a control. Examples of completed and ongoing rollout trials illustrate how these ethical, scientific, and practical considerations influenced trial designs. Stepped wedge and other rollout trial designs may be well suited to evaluation of implementation strategies or policy changes. In implementation trials, rollout designs may be necessary for practical reasons, may be required for ethical reasons, and may be preferred for scientific reasons. We summarize when such rollout designs have advantages and drawbacks.

BACKGROUND:Randomized rollout trial designs, including stepped wedge designs, are commonly used to examine how well an evidence-based intervention or package is being implemented in community or healthcare settings. The multitude of implementation research questions and specific hypotheses suggest the need for diverse randomized rollout implementation trial designs, assignment principles and procedureds, and statistical modeling. METHODS:We separate key research questions and identify mixed effect models for randomized implementation rollout trials involving 1) a single implementation strategy that tests how this strategy varies over time and/or resources that are allocated, 2) comparison of two distinct implementation strategies, and 3) three distinct strategies or components tested in a single trial. Appropriate rollout designs, optimal assignment methods, and other design and analysis considerations are discussed for trials of up to three distinct implementation strategies. RESULTS:To examine improvement in implementation outcomes we present a Fixed-Length Staggered Rollout Trial Design to examine how well a sustainment period continues to produce outcomes, The Rollout Implementation Optimization (ROIO) methodology illustrates testing for quality improvement. For comparing an existing to new strategy, we focus on a Stepped Wedge design, and for comparing two new strategies we describe a Head-to-Head Rollout trial design. To test for synergy between two components, we introduce a Head-to-Head Rollout trial design, and for testing an existing strategy to a new one followed by a sustainment period, we recommend using a Three-Phase Sequential Rollout Implementation trial design. Modeling choices are described, including options for specifying random effects that capture variations in site and clustering. We discuss comparisons of superiority versus non-inferiority testing and multiple contrasts. To support uses of these six designs and analyses, we provide computational code. CONCLUSIONS:The large class of randomized rollout implementation trial designs provides rich opportunities to address research questions posed by implementation scientists. Balance in assigning sites to cohorts is important before random assignment to time of transition to a new implementation occurs. Specific hypotheses are tested with mixed effects models where fixed effects include comparisons of implementation conditions and random effects that account for variation in sites and clustering.

OBJECTIVE:The COVID-19 pandemic caused disruptions in in-person mental health (MH) care and a rapid uptake of virtual MH care, but there is little research on the impacts of this on patients' ability to continue their MH medications. This study used population-level data to examine the impact of the pandemic on MH medication nonadherence. METHODS:This retrospective study used electronic health record and claims data to identify 149,977 patients with MH diagnoses at three U.S. health systems who filled at least one MH medication in the 9 months before and after 3/14/2020. The primary outcome was nonadherence to MH medications (i.e., a disruption in coverage ≥ 25%) during the pandemic. RESULTS:Pre-pandemic, 39% of patients had MH medication nonadherence, while 35% had nonadherence during the pandemic. Nonadherence during the pandemic improved for nearly all patient subgroups, with the exception of Black patients, for whom MH medication nonadherence increased from 47% to 49%. Asian, Black, and Hispanic patients were less adherent to MH medications during the pandemic than White patients, and patients with lower education or income were less adherent than patients with higher education or income. Non-rural patients were less adherent to MH medication than rural patients. CONCLUSIONS:Adherence to MH medications improved during the pandemic for all subgroups except Black patients. Despite these improvements, disparities in MH medication adherence persisted for Asian, Black, and Hispanic patients and for patients with lower education or income, suggesting these populations may need additional outreach and support.

OBJECTIVE:This study investigated ICD-10-CM codes for adverse social determinants of health (SDoH) across 12 U.S. health systems by using data from multiple health care encounter types for diverse patients covered by multiple payers. METHODS:The authors described documentation of 11 SDoH ICD-10-CM code categories (e.g., educational problems or social environmental problems) between 2016 and 2021; assessed changes over time by using chi-square tests for trend in proportions; compared documentation in 2021 by gender, age, race-ethnicity, and site with chi-square tests; and compared all patients' mental health outcomes in 2021 with those of patients with documented SDoH ICD-10-CM codes by using exact binomial tests and one-proportion z tests. RESULTS:Documentation of any SDoH ICD-10-CM code significantly increased, from 1.7% of patients in 2016 to 2.7% in 2021, as did that for all SDoH categories except educational problems. Documentation was often more prevalent among female patients and those of other or unknown gender than among male patients and among American Indian or Alaska Native, Black or African American, and Hispanic individuals than among those belonging to other race-ethnicity categories. More educational problems were documented for younger patients, and more social environmental problems were documented for older patients. Psychiatric diagnoses and emergency department visits and hospitalizations related to mental health were more common among patients with documented SDoH codes. CONCLUSIONS:SDoH ICD-10-CM code documentation was infrequent and differed by population subgroup. Differences may reflect documentation practices or true SDoH prevalence variation. Standardized SDoH documentation methods are needed in health care settings.

The distinguishing characteristics of pragmatic clinical trials merits special attention when developing a monitoring plan. Pragmatic clinical trials are large in scope; participants are often identified from records or routinely collected data; investigators typically have less control over treatments or interventions; outcome data are often extracted from health records; and study activities are commingled with usual health care. We use lessons from The NIH Pragmatic Trials Collaboratory, which supports the conduct of 32 pragmatic clinical trials, to illustrate some of the challenges and solutions. Challenges include the complexity, quality, and timing of a real-world data pipeline; interventions that are embedded in clinical workflows; and the potential for incidental findings. We recommend regular, rigorous data quality checks, ongoing monitoring of adherence to interventions, and including someone who is knowledgeable about pragmatic clinical trials and novel research designs in the development of Data and Safety Monitoring Plans and Data and Safety Monitoring Boards. Close monitoring by study leaders, independent monitors or and Data and Safety Monitoring Boards is critical for a successful study that produces meaningful results. These experts must also decide about what evidence requires action and/or modification of the protocol and what information and thresholds would lead to a decision to pivot or terminate the trial.

Importance Collaborative care is a multicomponent intervention for patients with chronic disease in primary care. Previous meta-analyses have proven the effectiveness of collaborative care for depression; however, individual participant data (IPD) are needed to identify which components of the intervention are the principal drivers of this effect. Objective To assess which components of collaborative care are the biggest drivers of its effectiveness in reducing symptoms of depression in primary care. Data Sources Data were obtained from MEDLINE, Embase, Cochrane Library, PubMed, and PsycInfo as well as references of relevant systematic reviews. Searches were conducted in December 2023, and eligible data were collected until March 14, 2024. Study Selection Two reviewers assessed for eligibility. Randomized clinical trials comparing the effect of collaborative care and usual care among adult patients with depression in primary care were included. Data Extraction and Synthesis The study was conducted according to the IPD guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline. IPD were collected for demographic characteristics and depression outcomes measured at baseline and follow-ups from the authors of all eligible trials. Using IPD, linear mixed models with random nested effects were calculated. Main Outcomes and Measures Continuous measure of depression severity was assessed via validated self-report instruments at 4 to 6 months and was standardized using the instrument's cutoff value for mild depression. Results A total of 35 datasets with 38 comparisons were analyzed (N = 20 046 participants [57.3% of all eligible, with minimal differences in baseline characteristics compared with nonretrieved data]; 13 709 [68.4%] female; mean [SD] age, 50.8 [16.5] years). A significant interaction effect with the largest effect size was found between the depression outcome and the collaborative care component therapeutic treatment strategy (-0.07; P < .001). This indicates that this component, including its key elements manual-based psychotherapy and family involvement, was the most effective component of the intervention. Significant interactions were found for all other components, but with smaller effect sizes. Conclusions and Relevance Components of collaborative care most associated with improved effectiveness in reducing depressive symptoms were identified. To optimize treatment effectiveness and resource allocation, a therapeutic treatment strategy, such as manual-based psychotherapy or family integration, may be prioritized when implementing a collaborative care intervention.

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.

Despite the high correlation between anxiety and depression, little remains known about the course of each condition when presenting concurrently. This study aimed to identify longitudinal patterns during antidepressant treatment in patients with depression and anxiety, and evaluate related factors associated with these patterns. By analyzing longitudinal self-report Patient Health Questionnaire-9 (PHQ-9) and General Anxiety Disorder-7 (GAD-7) scores that tracked courses of depression and anxiety over a three-month window among the 577 adult participants, six depression and six anxiety trajectory subgroups were computationally derived using group-based trajectory modeling. Three depression subgroups showed symptom improvement, while three showed nonresponses. Similar patterns were observed in the six anxiety subgroups. Multinomial regression was used to associate patient characteristics with trajectory subgroup membership. Compared to patients in the remission group, factors associated with depressive symptom nonresponse included older age and lower depression severity.

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.