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.

Transitions in health insurance among continuously insured patients with schizophrenia.

Changes in health insurance coverage may disrupt access to and continuity of care, even for those who remain insured. Continuity of care is especially important in schizophrenia, which requires ongoing medical and pharmaceutical treatment. However, little is known about continuity of insurance coverage among those with schizophrenia. The objective was to examine the probability of insurance transitions for individuals with schizophrenia who were continuously insured and whether this varied across insurance types. The Massachusetts All-Payer Claims Database identified individuals with schizophrenia aged 18-64 who were continuously insured during a two-year period between 2014 and 2018. A logistic regression estimated the association of having an insurance transition - defined as having a change in insurance type - with insurance type at the start of the period, adjusting for age, sex, ZIP code in the lowest quartile of median income, and ZIP code with concentrated poverty. Overall, 15.1% had at least one insurance transition across a 24-month period. Insurance transitions were most frequent among those with plans from the Marketplace. In regression adjusted results, individuals covered by the traditional Medicaid program were 20.2 percentage points [pp] (95% confidence interval [CI]: 24.6 pp, 15.9 pp) less likely to have an insurance transition than those who were insured by a Marketplace plan. Insurance transitions among individuals with schizophrenia were common, with more than one in six people having at least one transition in insurance type during a two-year period. Given that even continuously insured individuals with schizophrenia commonly experience insurance transitions, attention to insurance transitions as a barrier to care access and continuity is warranted.

Predicting suicide death after emergency department visits with mental health or self-harm diagnoses.

OBJECTIVE: Use health records data to predict suicide death following emergency department visits. METHODS: Electronic health records and insurance claims from seven health systems were used to: identify emergency department visits with mental health or self-harm diagnoses by members aged 11 or older; extract approximately 2500 potential predictors including demographic, historical, and baseline clinical characteristics; and ascertain subsequent deaths by self-harm. Logistic regression with lasso and random forest models predicted self-harm death over 90 days after each visit. RESULTS: Records identified 2,069,170 eligible visits, 899 followed by suicide death within 90 days. The best-fitting logistic regression with lasso model yielded an area under the receiver operating curve of 0.823 (95% CI 0.810-0.836). Visits above the 95th percentile of predicted risk included 34.8% (95% CI 31.1-38.7) of subsequent suicide deaths and had a 0.303% (95% CI 0.261-0.346) suicide death rate over the following 90 days. Model performance was similar across subgroups defined by age, sex, race, and ethnicity. CONCLUSIONS: Machine learning models using coded data from health records have moderate performance in predicting suicide death following emergency department visits for mental health or self-harm diagnosis and could be used to identify patients needing more systematic follow-up.

Psychotherapy Disruption Before and After the Transition to Virtual Mental Health Care Induced by the COVID-19 Pandemic.

OBJECTIVE: This study aimed to examine population-level disruption in psychotherapy before and after the rapid shift to virtual mental health care induced by the onset of the COVID-19 pandemic in the United States. METHODS: This retrospective study used electronic health record and insurance claims data from three U.S. health systems. The sample included 110,089 patients with mental health conditions who were members of the health systems' affiliated health plans and attended at least two psychotherapy visits from June 14, 2019, through December 15, 2020. Data were subdivided into two 9-month periods (before vs. after COVID-19 onset, defined in this study as March 14, 2020). Psychotherapy visits were measured via health records and categorized as in person or virtual. Disruption was defined as a gap of >45 days between visits. RESULTS: Visits in the preonset period were almost exclusively in person (97%), whereas over half of visits in the postonset period were virtual (52%). Approximately 35% of psychotherapy visits were followed by a disruption in the preonset period, compared with 18% in the postonset period. Disruption continued to be less common (adjusted OR=0.45) during the postonset period after adjustment for visit, mental health, and sociodemographic factors. The magnitude of the difference in disruption between periods was homogeneous across sociodemographic characteristics but heterogeneous across psychiatric diagnoses. CONCLUSIONS: This study found fewer population-level disruptions in psychotherapy receipt after rapid transition to virtual mental health care following COVID-19 onset. These data support the continued availability of virtual psychotherapy.

Stability of Suicide Risk Prediction Models During Changes in Health Care Delivery.

OBJECTIVE: The authors aimed to use health records data to examine how the accuracy of statistical models predicting self-harm or suicide changed between 2015 and 2019, as health systems implemented suicide prevention programs. METHODS: Data from four large health systems were used to identify specialty mental health visits by patients ages ≥11 years, assess 311 potential predictors of self-harm (including demographic characteristics, historical risk factors, and index visit characteristics), and ascertain fatal or nonfatal self-harm events over 90 days after each visit. New prediction models were developed with logistic regression with LASSO (least absolute shrinkage and selection operator) in random samples of visits (65%) from each calendar year and were validated in the remaining portion of the sample (35%). RESULTS: A model developed for visits from 2009 to mid-2015 showed similar classification performance and calibration accuracy in a new sample of about 13.1 million visits from late 2015 to 2019. Area under the receiver operating characteristic curve (AUC) ranged from 0.840 to 0.849 in the new sample, compared with 0.851 in the original sample. New models developed for each year for 2015-2019 had classification performance (AUC range 0.790-0.853), sensitivity, and positive predictive value similar to those of the previously developed model. Models selected similar predictors from 2015 to 2019, except for more frequent selection of depression questionnaire data in later years, when questionnaires were more frequently recorded. CONCLUSIONS: A self-harm prediction model developed with 2009-2015 visit data performed similarly when applied to 2015-2019 visits. New models did not yield superior performance or identify different predictors.

Racial-Ethnic Differences in Receipt of Past-Year Health Care Services Among Suicide Decedents: A Case-Control Study.

OBJECTIVE: Suicide remains an urgent public health crisis. Although some sociodemographic characteristics are associated with greater suicide risk in the general population, it is unclear whether individuals utilizing health care in the United States have similar suicide incidence patterns. The authors examined whether race-ethnicity is associated with suicide death among patients seeking health care and investigated health care utilization patterns. METHODS: Data were collected from electronic health records and government mortality records for patients seeking health care across nine health care systems in the United States. Patients who died by suicide (N=1,935) were matched with patients in a control group (N=19,350) within each health care system. RESULTS: Patients who died by suicide were significantly more likely to be White, older, male, living in low-education areas, living in rural areas, or diagnosed as having mental health conditions or were significantly less likely to have commercial insurance (p<0.05). Among most racial-ethnic groups, those who died by suicide had a higher number of past-year mental health, primary care, and total health care visits; for American Indian/Alaska Native patients, the number of health care visits tended to be lower among suicide decedents. CONCLUSIONS: These findings suggest that higher past-year health care utilization was associated with increased likelihood of suicide death across several racial-ethnic groups. This observation underscores the need for identifying and managing suicide risk in health care settings, including outside of mental health visits, among most racial-ethnic groups.

Predicting risk of suicidal behavior from insurance claims data vs. linked data from insurance claims and electronic health records.

PURPOSE: Observational studies assessing effects of medical products on suicidal behavior often rely on health record data to account for pre-existing risk. We assess whether high-dimensional models predicting suicide risk using data derived from insurance claims and electronic health records (EHRs) are superior to models using data from insurance claims alone. METHODS: Data were from seven large health systems identified outpatient mental health visits by patients aged 11 or older between 1/1/2009 and 9/30/2017. Data for the 5 years prior to each visit identified potential predictors of suicidal behavior typically available from insurance claims (e.g., mental health diagnoses, procedure codes, medication dispensings) and additional potential predictors available from EHRs (self-reported race and ethnicity, responses to Patient Health Questionnaire or PHQ-9 depression questionnaires). Nonfatal self-harm events following each visit were identified from insurance claims data and fatal self-harm events were identified by linkage to state mortality records. Random forest models predicting nonfatal or fatal self-harm over 90 days following each visit were developed in a 70% random sample of visits and validated in a held-out sample of 30%. Performance of models using linked claims and EHR data was compared to models using claims data only. RESULTS: Among 15 845 047 encounters by 1 574 612 patients, 99 098 (0.6%) were followed by a self-harm event within 90 days. Overall classification performance did not differ between the best-fitting model using all data (area under the receiver operating curve or AUC = 0.846, 95% CI 0.839-0.854) and the best-fitting model limited to data available from insurance claims (AUC = 0.846, 95% CI 0.838-0.853). Competing models showed similar classification performance across a range of cut-points and similar calibration performance across a range of risk strata. Results were similar when the sample was limited to health systems and time periods where PHQ-9 depression questionnaires were recorded more frequently. CONCLUSION: Investigators using health record data to account for pre-existing risk in observational studies of suicidal behavior need not limit that research to databases including linked EHR data.

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.

The rapid shift to virtual mental health care: Examining psychotherapy disruption by rurality status.

BACKGROUND: Given the low usage of virtual health care prior to the COVID-19 pandemic, it was unclear whether those living in rural locations would benefit from increased availability of virtual mental health care. The rapid transition to virtual services during the COVID-19 pandemic allowed for a unique opportunity to examine how the transition to virtual mental health care impacted psychotherapy disruption (i.e., 45+ days between appointments) among individuals living in rural locations compared with those living in nonrural locations. METHODS: Electronic health record and insurance claims data were collected from three health care systems in the United States including rurality status and psychotherapy disruption. Psychotherapy disruption was measured before and after the COVID-19 pandemic onset. RESULTS: Both the nonrural and rural cohorts had significant decreases in the rates of psychotherapy disruption from pre- to post-COVID-19 onset (32.5-16.0% and 44.7-24.8%, respectively, p < 0.001). The nonrural cohort had a greater reduction of in-person visits compared with the rural cohort (96.6-45.0 vs. 98.0-66.2%, respectively, p < 0.001). Among the rural cohort, those who were younger and those with lower education had greater reductions in psychotherapy disruption rates from pre- to post-COVID-19 onset. Several mental health disorders were associated with experiencing psychotherapy disruption. CONCLUSIONS: Though the rapid transition to virtual mental health care decreased the rate of psychotherapy disruption for those living in rural locations, the reduction was less compared with nonrural locations. Other strategies are needed to improve psychotherapy disruption, especially among rural locations (i.e., telephone visits).

Beyond clinical outcomes: Case control study of the role of race in disruptive life events for people with serious mental illness.

OBJECTIVE: To understand how race and serious mental illness (SMI) interact for disruptive life events defined as financial (bankruptcy and judgement filings), and non-financial (arrests). METHODS: Patients were adults with schizophrenia (SCZ; N = 16,159) or bipolar I disorder (BPI; N = 30,008) matched 1:1 to patients without SMI (non-SMI) from health systems in Michigan and Southern California during 1/1/2007 through 12/31/2018. The main exposure was self-reported race, and the outcome was disruptive life events aggregated by Transunion. We hypothesized that Black patients with SCZ or BPI would be the most likely to experience a disruptive life event when compared to Black patients without SMI, and all White or Asian patients regardless of mental illness. RESULTS: Black patients with SCZ had the least likelihood (37% lower) and Asian patients with BPI had the greatest likelihood (2.25 times higher) of experiencing a financial disruptive life event among all patients in the study. There was no interaction of race with either SCZ or BPI for experiencing an arrest. The findings did not support our hypotheses for patients with SCZ and partially supported them for patients with BPI. CONCLUSIONS: Clinical initiatives to assess social determinants of health should consider a focus on Asian patients with BPI.

Association Between Adverse Social Determinants of Health and Suicide Death.

OBJECTIVE: The aim of this study was to identify adverse social determinants of health (SDoH) International Statistical Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code prevalence among individuals who died by suicide and to examine associations between documented adverse SDoH and suicide. RESEARCH DESIGN: A case-control study using linked medical record, insurance claim, and mortality data from 2000 to 2015 obtained from 9 Mental Health Research Network-affiliated health systems. We included 3330 individuals who died by suicide and 333,000 randomly selected controls matched on index year and health system location. All individuals in the study (cases and controls) had at least 10 months of enrollment before the study index date. The index date for the study for each case and their matched controls was the suicide date for that given case. RESULTS: Adverse SDoH documentation was low; only 6.6% of cases had ≥1 documented adverse SDoH in the year before suicide. Any documented SDoH and several specific adverse SDoH categories were more frequent among cases than controls. Any documented adverse SDoH was associated with higher suicide odds [adjusted odds ratio (aOR)=2.76; 95% CI: 2.38-3.20], as was family alcoholism/drug addiction (aOR=18.23; 95% CI: 8.54-38.92), being an abuse victim/perpetrator (aOR=2.53; 95% CI: 1.99-3.21), other primary support group problems (aOR=1.91; 95% CI: 1.32-2.75), employment/occupational maladjustment problems (aOR=8.83; 95% CI: 5.62-13.87), housing/economic problems (aOR: 6.41; 95% CI: 4.47-9.19), legal problems (aOR=27.30; 95% CI: 12.35-60.33), and other psychosocial problems (aOR=2.58; 95% CI: 1.98-3.36). CONCLUSIONS: Although documented SDoH prevalence was low, several adverse SDoH were associated with increased suicide odds, supporting calls to increase SDoH documentation in medical records. This will improve understanding of SDoH prevalence and assist in identification and intervention among individuals at high suicide risk.

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.

Assessing the differential item functioning of PHQ-9 items for diverse racial and ethnic adults with mental health and/or substance use disorder diagnoses: A retrospective cohort study.

BACKGROUND: Improving health equity in depression care and suicide screening requires that measures like the Patient Health Questionnaire 9 (PHQ-9) function similarly for diverse racial and ethnic groups. We evaluated PHQ-9 differential item functioning (DIF) between racial/ethnic groups in a retrospective cohort study of secondary electronic health record (EHR) data from eight healthcare systems. METHODS: The population (n = 755,156) included patients aged 18-64 with mental health and/or substance use disorder (SUD) diagnoses who had a PHQ-9 with no missing item data in the EHR for primary care or mental health visits between 1/1/2009-9/30/2017. We drew two random samples of 1000 from the following racial/ethnic groups originally recorded in EHRs (n = 14,000): Hispanic, and non-Hispanic White, Black, Asian, American Indian/Alaska Native, Native Hawaiian/Other Pacific Islander, multiracial. We assessed DIF using iterative hybrid ordinal logistic regression and item response theory with p < 0.01 and 1000 Monte Carlo simulations, where change in model R2 > 0.01 represented non-negligible (e.g., clinically meaningful) DIF. RESULTS: All PHQ-9 items displayed statistically significant, but negligible (e.g., clinically unmeaningful) DIF between compared groups. The negligible DIF varied between random samples, although six items showed negligible DIF between the same comparison groups in both random samples. LIMITATIONS: Our findings may not generalize to disaggregated racial/ethnic groups or persons without mental health and/or SUD diagnoses. CONCLUSIONS: We found the PHQ-9 had clinically unmeaningful cross-cultural DIF for adult patients with mental health and/or SUD diagnoses. Future research could disaggregate race/ethnicity to discern if within-group identification impacts PHQ-9 DIF.

Diagnosed Posttraumatic Stress Disorder and Other Trauma-Associated Stress Disorders and Risk for Suicide Mortality.

OBJECTIVE: Strong evidence exists for posttraumatic stress disorder (PTSD) as a risk factor for suicidal thoughts and behaviors across diverse populations. However, few empirical studies have examined PTSD and other trauma-associated stress disorders as risk factors for suicide mortality among health system populations. This study aimed to assess trauma-associated stress diagnoses as risk factors for suicide mortality in a U.S. health system population. METHODS: This case-control, matched-design study examined individuals who died by suicide between 2000 and 2015 and had received care from nine U.S. health systems affiliated with the Mental Health Research Network (N=3,330). Individuals who died by suicide were matched with individuals from the general health system population (N=333,000): 120 individuals with PTSD who died by suicide were matched with 1,592 control group members, 84 with acute reaction to stress were matched with 2,218 control individuals, and 331 with other stress reactions were matched with 8,174 control individuals. RESULTS: After analyses were adjusted for age and sex, individuals with any trauma-associated stress condition were more likely to have died by suicide. Risk was highest among individuals with PTSD (adjusted OR [AOR]=10.10, 95% CI=8.31-12.27), followed by those with other stress reactions (AOR=5.38, 95% CI=4.78-6.06) and those with acute reaction to stress (AOR=4.49, 95% CI=3.58-5.62). Patterns of risk remained the same when the analyses were adjusted for any comorbid psychiatric condition. CONCLUSIONS: All trauma-associated stress disorders are risk factors for suicide mortality, highlighting the importance of health system suicide prevention protocols that consider the full spectrum of traumatic stress diagnoses.

Assessment of Disruptive Life Events for Individuals Diagnosed With Schizophrenia or Bipolar I Disorder Using Data From a Consumer Credit Reporting Agency.

Importance: There is a dearth of population-level data on major disruptive life events (defined here as arrests by a legal authority, address changes, bankruptcy, lien, and judgment filings) for patients with bipolar I disorder (BPI) or schizophrenia, which has limited studies on mental health and treatment outcomes. Objective: To conduct a population-level study on disruptive life events by using publicly available data on disruptive life events, aggregated by a consumer credit reporting agency in conjunction with electronic health record (EHR) data. Design, Setting, and Participants: This study used EHR data from 2 large, integrated health care systems, Kaiser Permanente Southern California and Henry Ford Health. Cohorts of patients diagnosed from 2007 to 2019 with BPI or schizophrenia were matched 1:1 by age at analysis, age at diagnosis (if applicable), sex, race and ethnicity, and Medicaid status to (1) an active comparison group with diagnoses of major depressive disorder (MDD) and (2) a general health (GH) cohort without diagnoses of BPI, schizophrenia, or MDD. Patients with diagnoses of BPI or schizophrenia and their respective comparison cohorts were matched to public records data aggregated by a consumer credit reporting agency (98% match rate). Analysis took place between November 2020 and December 2022. Main Outcomes and Measures: The differences in the occurrence of disruptive life events among patients with BPI or schizophrenia and their comparison groups. Results: Of 46 167 patients, 30 008 (65%) had BPI (mean [SD] age, 42.6 [14.2] years) and 16 159 (35%) had schizophrenia (mean [SD], 41.4 [15.1] years). The majoriy of patients were White (30 167 [65%]). In addition, 18 500 patients with BPI (62%) and 6552 patients with schizophrenia (41%) were female. Patients with BPI were more likely to change addresses than patients in either comparison cohort (with the incidence ratio being as high as 1.25 [95% CI, 1.23-1.28]) when compared with GH cohort. Patients with BPI were also more likely to experience any of the financial disruptive life events with odds ratio ranging from 1.15 [95% CI, 1.07-1.24] to 1.50 [95% CI, 1.42-1.58]). The largest differences in disruptive life events were seen in arrests of patients with either BPI or schizophrenia compared with GH peers (3.27 [95% CI, 2.84-3.78] and 3.04 [95% CI, 2.57-3.59], respectively). Patients with schizophrenia had fewer address changes and were less likely to experience a financial event than their matched comparison cohorts. Conclusions and Relevance: This study demonstrated that data aggregated by a consumer credit reporting agency can support population-level studies on disruptive life events among patients with BPI or schizophrenia.