Trends in cigarette smoking and the risk of incident cardiovascular disease among Asian American, Pacific Islander, and multiracial populations.

Background: Cardiovascular disease (CVD) is the leading cause of death in the United States, and rates of CVD incidence vary widely by race and ethnicity. Cigarette smoking is associated with increased risk of CVD. The purpose of the study was: 1) to examine smoking prevalence over time across Asian and Pacific Islander (API) and multi-race API subgroups; 2) to determine whether the CVD risk associated with smoking differed among these subgroups. Methods: We identified patients belonging to 7 single race/ethnicity groups, 4 multi-race/ethnicity groups, and a non-Hispanic White (NHW) comparison group at two large health systems in Hawaii and California. We estimated annual smoking prevalence from 2011 through 2018 by group and gender. We examined incidence of CVD events by smoking status and race/ethnicity, and computed hazard ratios for CVD events by age, gender, race/ethnicity, census block median household income, census block college degree, and study site using Cox regression. Results: Of the 12 groups studied, the Asian Indian and Chinese American groups had the lowest smoking prevalence, and the Asian + Pacific Islander multiracial group had the highest smoking prevalence. The prevalence of smoking decreased from 2011 to 2018 for all groups. Multi-race/ethnicity groups had higher risk of CVD than the NHW group. There was no significant interaction between race/ethnicity and smoking in models predicting CVD, but the association between race/ethnicity and CVD incidence was attenuated after adjusting for smoking status. Conclusions: There is considerable heterogeneity in smoking prevalence and the risk of CVD among API subgroups.

Prevalence of cardiovascular disease among Asian, Pacific Islander and multi-race populations in Hawai'i and California.

BACKGROUND: Cardiovascular disease (CVD) remains the leading cause of death in the US. CVD incidence is influenced by many demographic, clinical, cultural, and psychosocial factors, including race and ethnicity. Despite recent research, there remain limitations on understanding CVD health among Asians and Pacific Islanders (APIs), particularly some subgroups and multi-racial populations. Combining diverse API populations into one study group and difficulties in defining API subpopulations and multi-race individuals have hampered efforts to identify and address health disparities in these growing populations. METHODS: The study cohort was comprised of all adult patients at Kaiser Permanente Hawai'i and Palo Alto Medical Foundation in California during 2014-2018 (n = 684,363). EHR-recorded ICD-9 and ICD-10 diagnosis codes were used to indicate coronary heart disease (CHD), stroke, peripheral vascular disease (PVD), and overall CVD. Self-reported race and ethnicity data were used to construct 12 mutually exclusive single and multi-race groups, and a Non-Hispanic White (NHW) comparison group. Logistic regression models were used to derive prevalence estimates, odds ratios, and confidence intervals for the 12 race/ethnicity groups. RESULTS: The prevalence of CHD and PVD varied 4-fold and stroke and overall CVD prevalence varied 3-fold across API subpopulations. Among Asians, the Filipino subgroup had the highest prevalence of all three CVD conditions and overall CVD. Chinese people had the lowest prevalence of CHD, PVD and overall CVD. In comparison to Native Hawaiians, Other Pacific Islanders had significantly higher prevalence of CHD. For the multi-race groups that included Native Hawaiians and Other Pacific Islanders, the prevalence of overall CVD was significantly higher than that for either single-race Native Hawaiians or Other Pacific Islanders. The multi-race Asian + White group had significantly higher overall CVD prevalence than both the NHW group and the highest Asian subgroup (Filipinos). CONCLUSIONS: Study findings revealed significant differences in overall CVD, CHD, stroke, and PVD among API subgroups. In addition to elevated risk among Filipino, Native Hawaiian, and Other Pacific Islander groups, the study identified particularly elevated risk among multi-race API groups. Differences in disease prevalence are likely mirrored in other cardiometabolic conditions, supporting the need to disaggregate API subgroups in health research.

Predicting adolescent alcohol and other drug problems using electronic health records data.

IMPORTANCE: Alcohol and other drug (AOD) use problems may cause significant burden on affected adolescents and their families, yet treatment providers often do not identify these problems early enough. OBJECTIVE: To develop, and internally and externally validate a multivariable prediction model of adolescent AOD problems using child- and maternal-level predictors before age 12, and child-level predictors between ages 12 to 18, as recorded in the electronic health records (EHR). DESIGN: A retrospective cohort study conducted time-to-event analyses using Cox proportional hazards models. SETTING AND PARTICIPANTS: 41,172 children born between 1997 and 2000 at four health plans (Kaiser Permanente Hawaii, KPHI; Kaiser Permanente Northern California, KPNC; Geisinger Clinic, GC; and Henry Ford Health System, HFHS) who had continuous membership since birth and linkable maternal records in the health plan. OUTCOMES: AOD use problems between ages 12 to 18, defined as either: 1) having a contact with the AOD treatment program or 2) receiving a non-tobacco AOD diagnosis in an inpatient or outpatient encounter. EXPOSURES: Candidate predictor variables include demographics, socioeconomic status, and clinical diagnoses of the children and the mothers. RESULTS: Overall, 1400 (3.4%) adolescents had an AOD disorder between ages 12 to 18; the median follow-up time post-age 12 was 5.3 years. The research team developed two final prediction models: a "baseline" model of 10 child-level and 7 maternal-level predictors before age 12, and a more comprehensive "time-varying" model, which incorporated child risk factors after age 12 as time-varying covariates in addition to the baseline model predictors. Model performance evaluation showed good discrimination performance of the models, with the concordance index improved for the time-varying model, especially for prediction of AOD events in late adolescence. CONCLUSIONS AND RELEVANCE: This study identified a number of child and maternal characteristics and diagnoses routinely available in EHR data as predictive of risk for developing AOD problems in adolescence. Further, we found that risk of developing problems varies significantly by the timing and persistence of the risk factors. Findings may have potential clinical implications for prevention and identification of adolescent AOD problems, but more research is needed, especially across additional health systems.

Health Service Utilization Among Children and Adolescents with Posttraumatic Stress Disorder: A Case-Control Study.

OBJECTIVE: Trauma exposure is widely prevalent, with more than 60% of adolescents having experienced at least 1 traumatic event and a third of those at high risk to develop posttraumatic stress disorder (PTSD). Data are scarce and out of date on the services children and adolescents with PTSD receive, impeding efforts to improve care and outcomes. This study examines health service use for a large and diverse sample of children and adolescents with and without a diagnosis of PTSD. METHOD: Using a matched case-control study, we gathered information from 4 large health care systems participating in the Mental Health Research Network. Data from each site's electronic medical records on diagnoses, health care encounters, and demographics were analyzed. Nine hundred fifty-five 4- to 18-year-olds with a diagnosis of PTSD were identified and matched on a 1:5 ratio to 4770 controls. We compared cases with controls on frequency of service use in outpatient primary care, medical specialty care, acute care, and mental health care. We also assessed psychotropic medication use. RESULTS: Children and adolescents diagnosed with PTSD used nearly all physical and mental health service categories at a higher rate than controls. However, one-third of children and adolescents did not receive even 1 outpatient mental health visit (36.86%) during the year-long sampling window. CONCLUSION: Our findings suggest that children and adolescents diagnosed with PTSD may have unmet mental health needs. They are high utilizers of health services overall, but lower utilizers of the sectors that may be most helpful in resolving their symptoms.

Incidence of Lung Cancer Among Never-Smoking Asian American, Native Hawaiian, and Pacific Islander Females.

BACKGROUND: Although lung cancer incidence rates according to smoking status, sex, and detailed race/ethnicity have not been available, it is estimated that more than half of Asian American, Native Hawaiian, and Pacific Islander (AANHPI) females with lung cancer have never smoked. METHODS: We calculated age-adjusted incidence rates for lung cancer according to smoking status and detailed race/ethnicity among females, focusing on AANHPI ethnic groups, and assessed relative incidence across racial/ethnic groups. We used a large-scale dataset that integrates data from electronic health records from 2 large health-care systems-Sutter Health in Northern California and Kaiser Permanente Hawai'i-linked to state cancer registries for incident lung cancer diagnoses between 2000 and 2013. The study population included 1 222 694 females (n = 244 147 AANHPI), 3297 of which were diagnosed with lung cancer (n = 535 AANHPI). RESULTS: Incidence of lung cancer among never-smoking AANHPI as an aggregate group was 17.1 per 100 000 (95% confidence interval [CI] = 14.9 to 19.4) but varied widely across ethnic groups. Never-smoking Chinese American females had the highest rate (22.8 per 100 000, 95% CI = 17.3 to 29.1). Except for Japanese American females, incidence among every never-smoking AANHPI female ethnic group was higher than that of never-smoking non-Hispanic White females, from 66% greater among Native Hawaiian females (incidence rate ratio = 1.66, 95% CI = 1.03 to 2.56) to more than 100% greater among Chinese American females (incidence rate ratio = 2.26, 95% CI = 1.67 to 3.02). CONCLUSIONS: Our study revealed high rates of lung cancer among most never-smoking AANHPI female ethnic groups. Our approach illustrates the use of innovative data integration to dispel the myth that AANHPI females are at overall reduced risk of lung cancer and demonstrates the need to disaggregate this highly diverse population.

Predicting suicide attempts and suicide deaths among adolescents following outpatient visits.

BACKGROUND: Few studies report on machine learning models for suicide risk prediction in adolescents and their utility in identifying those in need of further evaluation. This study examined whether a model trained and validated using data from all age groups works as well for adolescents or whether it could be improved. METHODS: We used healthcare data for 1.4 million specialty mental health and primary care outpatient visits among 256,823 adolescents across 7 health systems. The prediction target was 90-day risk of suicide attempt following a visit. We used logistic regression with least absolute shrinkage and selection operator (LASSO) and generalized estimating equations (GEE) to predict risk. We compared performance of three models: an existing model, a recalibrated version of that model, and a newly-learned model. Models were compared using area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. RESULTS: The AUC produced by the existing model for specialty mental health visits estimated in adolescents alone (0.796; [0.789, 0.802]) was not significantly different than the AUC of the recalibrated existing model (0.794; [0.787, 0.80]) or the newly-learned model (0.795; [0.789, 0.801]). Predicted risk following primary care visits was also similar: existing (0.855; [0.844, 0.866]), recalibrated (0.85 [0.839, 0.862]), newly-learned (0.842, [0.829, 0.854]). LIMITATIONS: The models did not incorporate non-healthcare risk factors. The models relied on ICD9-CM codes for diagnoses and outcome measurement. CONCLUSIONS: Prediction models already in operational use by health systems can be reliably employed for identifying adolescents in need of further evaluation.

Integrating Electronic Health Record, Cancer Registry, and Geospatial Data to Study Lung Cancer in Asian American, Native Hawaiian, and Pacific Islander Ethnic Groups.

BACKGROUND: A relatively high proportion of Asian American, Native Hawaiian, and Pacific Islander (AANHPI) females with lung cancer have never smoked. We used an integrative data approach to assemble a large-scale cohort to study lung cancer risk among AANHPIs by smoking status with attention to representation of specific AANHPI ethnic groups. METHODS: We leveraged electronic health records (EHRs) from two healthcare systems-Sutter Health in northern California and Kaiser Permanente Hawai'i-that have high representation of AANHPI populations. We linked EHR data on lung cancer risk factors (i.e., smoking, lung diseases, infections, reproductive factors, and body size) to data on incident lung cancer diagnoses from statewide population-based cancer registries of California and Hawai'i for the period between 2000 and 2013. Geocoded address data were linked to data on neighborhood contextual factors and regional air pollutants. RESULTS: The dataset comprises over 2.2 million adult females and males of any race/ethnicity. Over 250,000 are AANHPI females (19.6% of the female study population). Smoking status is available for over 95% of individuals. The dataset includes 7,274 lung cancer cases, including 613 cases among AANHPI females. Prevalence of never-smoking status varied greatly among AANHPI females with incident lung cancer, from 85.7% among Asian Indian to 14.4% among Native Hawaiian females. CONCLUSION: We have developed a large, multilevel dataset particularly well-suited to conduct prospective studies of lung cancer risk among AANHPI females who never smoked. IMPACT: The integrative data approach is an effective way to conduct cancer research assessing multilevel factors on cancer outcomes among small populations.

Weighing the Association Between BMI Change and Suicide Mortality.

OBJECTIVE: Suicide rates continue to rise, necessitating the identification of risk factors. Obesity and suicide mortality rates have been examined, but associations among weight change, death by suicide, and depression among adults in the United States remain unclear. METHODS: Data from 387 people who died by suicide in 2000-2015 with a recorded body mass index (BMI) in the first and second 6 months preceding their death ("index date") were extracted from the Mental Health Research Network. Each person was matched with five people in a control group (comprising individuals who did not die by suicide) by age, sex, index year, and health care site (N=1,935). RESULTS: People who died by suicide were predominantly male (71%), White (69%), and middle aged (mean age=57 years) and had a depression diagnosis (55%) and chronic health issues (57%) (corresponding results for the control group: 71% male, 66% White, 14% with depression diagnosis, and 43% with chronic health issues; mean age=56 years). Change in BMI within the year before the index date statistically significantly differed between those who died by suicide (mean change=-0.72±2.42 kg/m2) and the control group (mean change=0.06±4.99 kg/m2) (p<0.001, Cohen's d=0.17). A one-unit BMI decrease was associated with increased risk for suicide after adjustment for demographic characteristics, mental disorders, and Charlson comorbidity score (adjusted odds ratio=1.11, 95% confidence interval=1.05-1.18, p<0.001). For those without depression, a BMI change was significantly associated with suicide (p<0.001). CONCLUSIONS: An increased suicide mortality rate was associated with weight loss in the year before a suicide after analyses accounted for general and mental health indicators.

Serious Suicide Attempts and Risk of Suicide Death.

Background: In the US, more than one million people attempt suicide each year. History of suicide attempt is a significant risk factor for death by suicide; however, there is a paucity of data from the US general population on this relationship. Aim: The objective of this study was to examine suicide attempts needing medical attention as a risk for suicide death. Method: We conducted a case-control study involving eight US healthcare systems. A total of 2,674 individuals who died by suicide from 2000 to 2013 were matched to 267,400 individuals by year and location. Results: Prior suicide attempt associated with a medical visit increases risk for suicide death by 39.1 times, particularly for women (OR = 79.2). However, only 11.3% of suicide deaths were associated with an attempt that required medical attention. The association was the strongest for children 10-14 years old (OR = 98.0). Most suicide attempts were recorded during the 20-week period prior to death. Limitations: Our study is limited to suicide attempts for which individuals sought medical care. Conclusion: In the US, prior suicide attempt is associated with an increased risk of suicide death; the risk is high especially during the period immediately following a nonlethal attempt.

Provider Contributions to Disparities in Mental Health Care.

OBJECTIVE: Disparities in diagnosis of mental health problems and in access to treatment among racial-ethnic groups are apparent across different behavioral conditions, particularly in the quality of treatment for depression. This study aimed to determine how much disparities differ across providers. METHODS: Bayesian mixed-effects models were used to estimate whether disparities in patient adherence to antidepressant medication (N=331,776) or psychotherapy (N=275,095) were associated with specific providers. Models also tested whether providers who achieved greater adherence to treatment, on average, among non-Hispanic white patients than among patients from racial-ethnic minority groups attained lower disparities and whether the percentage of patients from racial-ethnic minority groups in a provider caseload was associated with disparities. RESULTS: Disparities in adherence to both antidepressant medication and psychotherapy were associated with the provider. Provider performance with non-Hispanic white patients was negatively correlated with provider-specific disparities in adherence to psychotherapy but not to antidepressants. A higher proportion of patients from racial-ethnic minority groups in a provider's caseload was associated with lower adherence among non-Hispanic white patients, lower disparities in adherence to psychotherapy, and greater disparities in adherence to antidepressant medication. CONCLUSIONS: Adherence to depression treatment among a provider's patients from racial-ethnic minority groups was related to adherence among that provider's non-Hispanic white patients, but evidence also suggested provider-specific disparities. Efforts among providers to decrease disparities might focus on improving the general skill of providers who treat more patients from racial-ethnic minority groups as well as offering culturally based training to providers with notable disparities.

Substance use disorders and risk of suicide in a general US population: a case control study.

BACKGROUND: Prior research suggests that substance use disorders (SUDs) are associated with risk of suicide mortality, but most previous work has been conducted among Veterans Health Administration patients. Few studies have examined the relationship between SUDs and suicide mortality in general populations. Our study estimates the association of SUDs with suicide mortality in a general US population of men and women who receive care across eight integrated health systems. METHODS: We conducted a case-control study using electronic health records and claims data from eight integrated health systems of the Mental Health Research Network. Participants were 2674 men and women who died by suicide between 2000-2013 and 267,400 matched controls. The main outcome was suicide mortality, assessed using data from the health systems and confirmed by state death data systems. Demographic and diagnostic data on substance use disorders and other health conditions were obtained from each health system. First, we compared descriptive statistics for cases and controls, including age, gender, income, and education. Next, we compared the rate of each substance use disorder category for cases and controls. Finally, we used conditional logistic regression models to estimate unadjusted and adjusted odds of suicide associated with each substance use disorder category. RESULTS: All categories of substance use disorders were associated with increased risk of suicide mortality. Adjusted odds ratios ranged from 2.0 (CI 1.7, 2.3) for patients with tobacco use disorder only to 11.2 (CI 8.0, 15.6) for patients with multiple alcohol, drug, and tobacco use disorders. Substance use disorders were associated with increased relative risk of suicide for both women and men across all categories, but the relative risk was more pronounced in women. CONCLUSIONS: Substance use disorders are associated with significant risk of suicide mortality, especially for women, even after controlling for other important risk factors. Experiencing multiple substance use disorders is particularly risky. These findings suggest increased suicide risk screening and prevention efforts for individuals with substance use disorders are needed.

Chronic pain diagnoses and opioid dispensings among insured individuals with serious mental illness.

BACKGROUND: Individuals with major depressive disorder (MDD) and bipolar disorder (BD) have particularly high rates of chronic non-cancer pain (CNCP) and are also more likely to receive prescription opioids for their pain. However, there have been no known studies published to date that have examined opioid treatment patterns among individuals with schizophrenia. METHODS: Using electronic medical record data across 13 Mental Health Research Network sites, individuals with diagnoses of MDD (N = 65,750), BD (N = 38,117) or schizophrenia or schizoaffective disorder (N = 12,916) were identified and matched on age, sex and Medicare status to controls with no documented mental illness. CNCP diagnoses and prescription opioid medication dispensings were extracted for the matched samples. Multivariate analyses were conducted to evaluate (1) the odds of receiving a pain-related diagnosis and (2) the odds of receiving opioids, by separate mental illness diagnosis category compared with matched controls, controlling for age, sex, Medicare status, race/ethnicity, income, medical comorbidities, healthcare utilization and chronic pain diagnoses. RESULTS: Multivariable models indicated that having a MDD (OR = 1.90; 95% CI = 1.85-1.95) or BD (OR = 1.71; 95% CI = 1.66-1.77) diagnosis was associated with increased odds of a CNCP diagnosis after controlling for age, sex, race, income, medical comorbidities and healthcare utilization. By contrast, having a schizophrenia diagnosis was associated with decreased odds of receiving a chronic pain diagnosis (OR = 0.86; 95% CI = 0.82-0.90). Having a MDD (OR = 2.59; 95% CI = 2.44-2.75) or BD (OR = 2.12; 95% CI = 1.97-2.28) diagnosis was associated with increased odds of receiving chronic opioid medications, even after controlling for age, sex, race, income, medical comorbidities, healthcare utilization and chronic pain diagnosis; having a schizophrenia diagnosis was not associated with receiving chronic opioid medications. CONCLUSIONS: Individuals with serious mental illness, who are most at risk for developing opioid-related problems, continue to be prescribed opioids more often than their peers without mental illness. Mental health clinicians may be particularly well-suited to lead pain assessment and management efforts for these patients. Future research is needed to evaluate the effectiveness of involving mental health clinicians in these efforts.

Association between suicide death and concordance with benzodiazepine treatment guidelines for anxiety and sleep disorders.

OBJECTIVE: Guidelines for management of anxiety and sleep disorders emphasize antidepressant medications and/or psychotherapy as first/second-line and benzodiazepines as third-line treatments. We evaluated the association between suicide death and concordance with benzodiazepine guidelines. METHODS: Retrospective case-control study of patients with anxiety and/or sleep disorders from health systems across 8 U.S. states within the Mental Health Research Network. Suicide death cases were matched to controls on year and health system. Appropriate benzodiazepine prescribing defined as: no monotherapy, no long duration, and/or age < 65 years. The association between guideline concordance and suicide death was evaluated, adjusting for diagnostic and treatment covariates. RESULTS: Sample included 6960 patients with anxiety disorders (2363 filled benzodiazepine) and 6215 with sleep disorders (1237 filled benzodiazepine). Benzodiazepine guideline concordance was associated with reduced odds for suicide in patients with anxiety disorders (OR = 0.611, 95% CI = 0.392-0.953, p = 0.03) and was driven by shorter duration of benzodiazepine use with concomitant psychotherapy or antidepressant medication. The association of benzodiazepine guideline concordance with suicide death did not meet statistical significance in the sleep disorder group (OR = 0.413, 95% CI = 0.154-1.11, p = 0.08). CONCLUSIONS: We found reduced odds for suicide in those with anxiety disorders who filled benzodiazepines in short-moderate duration with concomitant psychotherapy or antidepressant treatment.

Variation in patterns of health care before suicide: A population case-control study.

BACKGROUND: The United States has experienced a significant rise in suicide. As decision makers identify how to address this national concern, healthcare systems have been identified as an optimal location for prevention. OBJECTIVE: To compare variation in patterns of healthcare use, by health setting, between individuals who died by suicide and the general population. DESIGN: Case-Control Study. SETTING: Eight healthcare systems across the United States. PARTICIPANTS: 2674 individuals who died by suicide between 2000 and 2013 along with 267,400 individuals matched on time-period of health plan membership and health system affiliation. MEASUREMENTS: Healthcare use in the emergency room, inpatient hospital, primary care, and outpatient specialty setting measured using electronic health record data during the 7-, 30-, 60-, 90-, 180-, and 365-day time periods before suicide and matched index date for controls. RESULTS: Healthcare use was more common across all healthcare settings for individuals who died by suicide. Nearly 30% of individuals had a healthcare visit in the 7-days before suicide (6.5% emergency, 16.3% outpatient specialty, and 9.5% primary care), over half within 30 days, and >90% within 365 days. Those who died by suicide averaged 16.7 healthcare visits during the year. The relative risk of suicide was greatest for individuals who received care in the inpatient setting (aOR = 6.23). There was both a large relative risk (aOR = 3.08) and absolute utilization rate (43.8%) in the emergency room before suicide. LIMITATIONS: Participant race/ethnicity was not available. The sample did not include uninsured individuals. CONCLUSIONS: This study provides important data about how care utilization differs for those who die by suicide compared to the general population and can inform decision makers on targeting of suicide prevention activities within health systems.

Diagnosed Mental Health Conditions and Risk of Suicide Mortality.

OBJECTIVE: Although mental health conditions are risk factors for suicide, limited data are available on suicide mortality associated with specific mental health conditions in the U.S. population. This study aimed to fill this gap. METHODS: This study used a case-control design. Patients in the case group were those who died by suicide between 2000 and 2013 and who were patients in eight health care systems in the Mental Health Research Network (N=2,674). Each was matched with 100 general population patients from the same system (N=267,400). Diagnostic codes for five mental health conditions in the year before death were obtained from medical records: anxiety disorders, attention deficit-hyperactivity disorder (ADHD), bipolar disorder, depressive disorders, and schizophrenia spectrum disorder. RESULTS: Among patients in the case group, 51.3% had a recorded psychiatric diagnosis in the year before death, compared with 12.7% of control group patients. Risk of suicide mortality was highest among those with schizophrenia spectrum disorder, after adjustment for age and sociodemographic characteristics (adjusted odds ratio [AOR]=15.0) followed by bipolar disorder (AOR=13.2), depressive disorders (AOR=7.2), anxiety disorders (AOR=5.8), and ADHD (AOR=2.4). The risk of suicide death among those with a diagnosed bipolar disorder was higher in women than men. CONCLUSIONS: Half of those who died by suicide had at least one diagnosed mental health condition in the year before death, and most mental health conditions were associated with an increased risk of suicide. Findings suggest the importance of suicide screening and providing an approach to improve awareness of mental health conditions.

Assessing and Minimizing Re-identification Risk in Research Data Derived from Health Care Records.

BACKGROUND: Sharing of research data derived from health system records supports the rigor and reproducibility of primary research and can accelerate research progress through secondary use. But public sharing of such data can create risk of re-identifying individuals, exposing sensitive health information. METHOD: We describe a framework for assessing re-identification risk that includes: identifying data elements in a research dataset that overlap with external data sources, identifying small classes of records defined by unique combinations of those data elements, and considering the pattern of population overlap between the research dataset and an external source. We also describe alternative strategies for mitigating risk when the external data source can or cannot be directly examined. RESULTS: We illustrate this framework using the example of a large database used to develop and validate models predicting suicidal behavior after an outpatient visit. We identify elements in the research dataset that might create risk and propose a specific risk mitigation strategy: deleting indicators for health system (a proxy for state of residence) and visit year. DISCUSSION: Researchers holding health system data must balance the public health value of data sharing against the duty to protect the privacy of health system members. Specific steps can provide a useful estimate of re-identification risk and point to effective risk mitigation strategies.

Self-Reported Suicidal Ideation as a Predictor of Suicidal Behavior Among Outpatients With Diagnoses of Psychotic Disorders.

OBJECTIVE: Individuals with psychotic disorders are at high risk of suicidal behavior. The study examined whether response to item 9 of the Patient Health Questionnaire (PHQ-9), which asks about thoughts of death or self-harm, predicts suicidal behavior among outpatients with diagnoses of psychotic disorders. METHODS: Electronic health records (EHRs) from seven large integrated health systems were used to identify all outpatient visits by adults with a diagnosis of schizophrenia spectrum psychosis or unspecified psychosis from January 1, 2009, to June 30, 2015, during which a PHQ-9 was completed (N=32,982 visits by 5,947 patients). Suicide attempts over the 90 days following each visit were ascertained from EHRs and insurance claims. Suicide deaths were ascertained from state death certificate files. RESULTS: Risk of suicide attempt within 90 days of an outpatient visit was .8% among patients reporting no thoughts of death or self-harm and 3.5% among those reporting such thoughts "nearly every day." Over 90 days of follow-up, 47% of suicide attempts occurred among those who reported any recent thoughts of death or self-harm at the sampled visit. Also, 59% of attempts occurred among those reporting thoughts of death or self-harm at the index visit or any visit in the prior year. The number of suicide deaths within 90 days (N=10) was too small to accurately assess the relationship between PHQ-9 item 9 response and subsequent suicide death. CONCLUSIONS: Among outpatients with psychotic disorders, response to item 9 of the PHQ-9 accurately identified those at increased short-term risk of a suicide attempt.

The Mediating Effect of Sleep Disturbance on the Relationship Between Nonmalignant Chronic Pain and Suicide Death.

IMPORTANCE: Few studies have examined the relationship between nonmalignant chronic pain (NMCP) and suicide death, and even fewer have specifically explored what role sleep disturbance might play in the association between NMCP and suicide death. OBJECTIVE: To assess whether sleep disturbance mediates the relationship between NMCP and suicide death. DESIGN: This case-control study included 2,674 individuals who died by suicide between 2000 and 2013 (cases) and 267,400 matched individuals (controls). SETTING: Eight Mental Health Research Network (MHRN)-affiliated healthcare systems. PARTICIPANTS: All cases and matched controls were health plan members for at least 10 months during the year prior to the index date. MAIN OUTCOMES AND MEASURES: Sociodemographic data and diagnosis codes for NMCP and sleep disorders were extracted from the MHRN's Virtual Data Warehouse. Suicide mortality was identified using International Statistical Classification of Diseases and Related Health Problems (ICD)-10 codes from official government mortality records matched to health system records. RESULTS: After accounting for covariates, there was a significant relationship between NMCP and sleep disturbance; those who were diagnosed with NMCP were more likely to develop subsequent sleep disturbance. Similarly, sleep disturbance was significantly associated with suicide death. Finally, a significant indirect effect of NMCP on suicide death, through sleep disturbance, and a nonsignificant direct effect of NMCP on suicide death provide support for a fully mediated model. CONCLUSIONS AND RELEVANCE: There is a need for clinicians to screen for both sleep disturbance and suicidal ideation in NMCP patients and for health systems to implement more widespread behavioral treatments that address comorbid sleep problems and NMCP.

Predicting Suicide Attempts and Suicide Deaths Following Outpatient Visits Using Electronic Health Records.

OBJECTIVE: The authors sought to develop and validate models using electronic health records to predict suicide attempt and suicide death following an outpatient visit. METHOD: Across seven health systems, 2,960,929 patients age 13 or older (mean age, 46 years; 62% female) made 10,275,853 specialty mental health visits and 9,685,206 primary care visits with mental health diagnoses between Jan. 1, 2009, and June 30, 2015. Health system records and state death certificate data identified suicide attempts (N=24,133) and suicide deaths (N=1,240) over 90 days following each visit. Potential predictors included 313 demographic and clinical characteristics extracted from records for up to 5 years before each visit: prior suicide attempts, mental health and substance use diagnoses, medical diagnoses, psychiatric medications dispensed, inpatient or emergency department care, and routinely administered depression questionnaires. Logistic regression models predicting suicide attempt and death were developed using penalized LASSO (least absolute shrinkage and selection operator) variable selection in a random sample of 65% of the visits and validated in the remaining 35%. RESULTS: Mental health specialty visits with risk scores in the top 5% accounted for 43% of subsequent suicide attempts and 48% of suicide deaths. Of patients scoring in the top 5%, 5.4% attempted suicide and 0.26% died by suicide within 90 days. C-statistics (equivalent to area under the curve) for prediction of suicide attempt and suicide death were 0.851 (95% CI=0.848, 0.853) and 0.861 (95% CI=0.848, 0.875), respectively. Primary care visits with scores in the top 5% accounted for 48% of subsequent suicide attempts and 43% of suicide deaths. C-statistics for prediction of suicide attempt and suicide death were 0.853 (95% CI=0.849, 0.857) and 0.833 (95% CI=0.813, 0.853), respectively. CONCLUSIONS: Prediction models incorporating both health record data and responses to self-report questionnaires substantially outperform existing suicide risk prediction tools.

Treatment Initiation for New Episodes of Depression in Primary Care Settings.

BACKGROUND: Depression is prevalent and costly, but despite effective treatments, is often untreated. Recent efforts to improve depression care have focused on primary care settings. Disparities in treatment initiation for depression have been reported, with fewer minority and older individuals starting treatment. OBJECTIVE: To describe patient characteristics associated with depression treatment initiation and treatment choice (antidepressant medications or psychotherapy) among patients newly diagnosed with depression in primary care settings. DESIGN: A retrospective observational design was used to analyze electronic health record data. PATIENTS: A total of 241,251 adults newly diagnosed with depression in primary care settings among five health care systems from 2010 to 2013. MAIN MEASURES: ICD-9 codes for depression, following a 365-day period with no depression diagnosis or treatment, were used to identify new depression episodes. Treatment initiation was defined as a completed psychotherapy visit or a filled prescription for antidepressant medication within 90 days of diagnosis. Depression severity was measured with Patient Health Questionnaire (PHQ-9) scores on the day of diagnosis. KEY RESULTS: Overall, 35.7% of patients with newly diagnosed depression initiated treatment. The odds of treatment initiation among Asians, non-Hispanic blacks, and Hispanics were at least 30% lower than among non-Hispanic whites, controlling for all other variables. The odds of patients aged ≥ 60 years starting treatment were half those of patients age 44 years and under. Treatment initiation increased with depression severity, but was only 53% among patients with a PHQ-9 score of ≥ 10. Among minority patients, psychotherapy was initiated significantly more often than medication. CONCLUSIONS: Screening for depression in primary care is a positive step towards improving detection, treatment, and outcomes for depression. However, study results indicate that treatment initiation remains suboptimal, and disparities persist. A better understanding of patient factors, and particularly system-level factors, that influence treatment initiation is needed to inform efforts by heath care systems to improve depression treatment engagement and to reduce disparities.