Dataset Information

Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System.

ABSTRACT: There is a very high suicide rate in the year after psychiatric hospital discharge. Intensive postdischarge case management programs can address this problem but are not cost-effective for all patients. This issue can be addressed by developing a risk model to predict which inpatients might need such a program. We developed such a model for the 391,018 short-term psychiatric hospital admissions of US veterans in Veterans Health Administration (VHA) hospitals 2010-2013. Records were linked with the National Death Index to determine suicide within 12 months of hospital discharge (n=771). The Super Learner ensemble machine learning method was used to predict these suicides for time horizon between 1 week and 12 months after discharge in a 70% training sample. Accuracy was validated in the remaining 30% holdout sample. Predictors included VHA administrative variables and small area geocode data linked to patient home addresses. The models had AUC=.79-.82 for time horizons between 1 week and 6 months and AUC=.74 for 12 months. An analysis of operating characteristics showed that 22.4%-32.2% of patients who died by suicide would have been reached if intensive case management was provided to the 5% of patients with highest predicted suicide risk. Positive predictive value (PPV) at this higher threshold ranged from 1.2% over 12 months to 3.8% per case manager year over 1 week. Focusing on the low end of the risk spectrum, the 40% of patients classified as having lowest risk account for 0%-9.7% of suicides across time horizons. Variable importance analysis shows that 51.1% of model performance is due to psychopathological risk factors accounted, 26.2% to social determinants of health, 14.8% to prior history of suicidal behaviors, and 6.6% to physical disorders. The paper closes with a discussion of next steps in refining the model and prospects for developing a parallel precision treatment model.

SUBMITTER: Kessler RC

PROVIDER: S-EPMC7219514 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System.

Kessler Ronald C RC Bauer Mark S MS Bishop Todd M TM Demler Olga V OV Dobscha Steven K SK Gildea Sarah M SM Goulet Joseph L JL Karras Elizabeth E Kreyenbuhl Julie J Landes Sara J SJ Liu Howard H Luedtke Alex R AR Mair Patrick P McAuliffe William H B WHB Nock Matthew M Petukhova Maria M Pigeon Wilfred R WR Sampson Nancy A NA Smoller Jordan W JW Weinstock Lauren M LM Bossarte Robert M RM

Frontiers in psychiatry 20200506

There is a very high suicide rate in the year after psychiatric hospital discharge. Intensive postdischarge case management programs can address this problem but are not cost-effective for all patients. This issue can be addressed by developing a risk model to predict which inpatients might need such a program. We developed such a model for the 391,018 short-term psychiatric hospital admissions of US veterans in Veterans Health Administration (VHA) hospitals 2010-2013. Records were linked with t ...[more]

PMID: 32435212

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Project description:ImportanceThe Veterans Health Administration (VHA) serves a population of veterans with a high prevalence of comorbid health conditions and increased risk for suicide.ObjectiveTo replicate the findings of a previous study and assess whether exposure to angiotensin receptor blockers (ARBs) is associated with differential suicide risk compared with angiotensin-converting enzyme inhibitors (ACEIs) among veterans receiving VHA care.Design, setting, and participantsThis nested case-control design included all suicide decedents from 2015 to 2017 with a VHA inpatient or outpatient encounter in the prior year and with either an active ACEI or ARB prescription in the 100 days prior to death. Using a 4:1 ratio, controls were matched to cases by age, sex, and hypertension and diabetes diagnoses. Controls were alive at the time of the death of the matched case, had a VHA encounter within the previous year, and had either an active ACEI or ARB medication fill within 100 days before the death of the matched case.ExposuresAn active ACEI or ARB prescription within 100 days before the death of the case.Main outcomes and measuresCases were suicide decedents from 2015 to 2017 per National Death Index search results included in the Veteran Affairs/Department of Defense Mortality Data Repository.ResultsAmong 1309 cases, the median (interquartile range [IQR]) age was 68 (60-76) years and among 5217 controls, the median (IQR) age was 67 (60-76) years, and 1.9% of veterans in both groups were female. ARBs were received by 20.2% of controls and 19.6% of cases; ACEIs were received by 79.8% of controls and 80.4% of cases. The crude suicide odds ratio for ARBs vs ACEIs was 0.966 (95% CI, 0.828-1.127). Controlling for covariates, the adjusted odds ratio for ARBs was 0.985 (95% CI, 0.834-1.164). Sensitivity analyses using only those covariates that differed significantly between groups, restricting to veterans ages 65 and older, dropping matching criteria, and adjusting for the quantity and temporal proximity of ACEI and ARB exposure in the 100 days prior to the index date, had consistent findings.Conclusions and relevanceThis case-control study did not identify differences in suicide risk by receipt of ARBs vs ACEIs in analyses specific to veterans receiving VHA care in contrast with findings from the referent study.

Project description:ImportanceUnderstanding the extent to which population-level suicide risk screening facilities follow-up and engagement in mental health treatment is important as engaging at-risk individuals in treatment is critical to reducing suicidal behaviors.ObjectiveTo evaluate mental health follow-up and treatment engagement in the Veterans Health Administration (VHA) following administration of the Columbia-Suicide Severity Rating Scale (C-SSRS) screen, a component of the VHA's universal suicide risk screening program.DesignThis cross-sectional study used data from VA's Corporate Data Warehouse.Settings140 VHA Medical Centers.ParticipantsPatients who completed the C-SSRS screen in ambulatory care between October 1, 2018-September 30, 2020.ExposureStandardized suicide risk screening.Main outcomes and measuresMental health follow-up (one or more visits within 30 days of C-SSRS screening) and treatment engagement (two or more visits within 90 days of C-SSRS screening) were examined.Results97,224 Veterans in Fiscal Year 2019 (FY19) (mean age 51.4 years; 86.8% male; 64.8% white, 22.4% African-American) and 58,693 Veterans in FY20 (mean age 49.6 years; 85.5% male; 63.4% white, 21.9% African-American) received the C-SSRS screen. Across FYs, a positive C-SSRS screen was associated with increased probability of mental health follow-up and treatment engagement. Patients who were not seen in mental health in the year prior to screening had the greatest increase in probability of mental health follow-up and engagement following a positive screen (P<0.001). For FY19, a positive C-SSRS screen in non-mental health connected patients was associated with an increased probability of follow-up from 49.8% to 79.5% (relative risk = 1.60) and engagement from 39.5% to 63.6% (relative risk = 1.61). For mental health-connected patients, a positive C-SSRS screen was associated with a smaller increase in probability of follow-up from 75.8% to 87.6% (relative risk = 1.16) and engagement from 63.3% to 76.4% (relative risk = 1.21). Results for FY20 were similar.Conclusions and relevanceIdentification of suicide risk through population-level screening was associated with increased mental health follow-up and engagement, particularly for non-mental health connected patients. Findings support the use of a standardized, comprehensive suicide risk screening program for managing elevated suicide risk in a large healthcare system.

Project description:BackgroundIdentifying individuals at risk for future hospitalization or death has been a major priority of population health management strategies. High-risk individuals are a heterogeneous group, and existing studies describing heterogeneity in high-risk individuals have been limited by data focused on clinical comorbidities and not socioeconomic or behavioral factors. We used machine learning clustering methods and linked comorbidity-based, sociodemographic, and psychobehavioral data to identify subgroups of high-risk Veterans and study long-term outcomes, hypothesizing that factors other than comorbidities would characterize several subgroups.Methods and findingsIn this cross-sectional study, we used data from the VA Corporate Data Warehouse, a national repository of VA administrative claims and electronic health data. To identify high-risk Veterans, we used the Care Assessment Needs (CAN) score, a routinely-used VA model that predicts a patient's percentile risk of hospitalization or death at one year. Our study population consisted of 110,000 Veterans who were randomly sampled from 1,920,436 Veterans with a CAN score≥75th percentile in 2014. We categorized patient-level data into 119 independent variables based on demographics, comorbidities, pharmacy, vital signs, laboratories, and prior utilization. We used a previously validated density-based clustering algorithm to identify 30 subgroups of high-risk Veterans ranging in size from 50 to 2,446 patients. Mean CAN score ranged from 72.4 to 90.3 among subgroups. Two-year mortality ranged from 0.9% to 45.6% and was highest in the home-based care and metastatic cancer subgroups. Mean inpatient days ranged from 1.4 to 30.5 and were highest in the post-surgery and blood loss anemia subgroups. Mean emergency room visits ranged from 1.0 to 4.3 and were highest in the chronic sedative use and polysubstance use with amphetamine predominance subgroups. Five subgroups were distinguished by psychobehavioral factors and four subgroups were distinguished by sociodemographic factors.ConclusionsHigh-risk Veterans are a heterogeneous population consisting of multiple distinct subgroups-many of which are not defined by clinical comorbidities-with distinct utilization and outcome patterns. To our knowledge, this represents the largest application of ML clustering methods to subgroup a high-risk population. Further study is needed to determine whether distinct subgroups may benefit from individualized interventions.

Project description:ImportanceThe Veterans Health Administration (VHA) implemented a national clinical program using a suicide risk prediction algorithm, Recovery Engagement and Coordination for Health-Veterans Enhanced Treatment (REACH VET), in which clinicians facilitate care enhancements for individuals identified in local top 0.1% suicide risk tiers. Evaluation studies are needed.ObjectiveTo determine associations with treatment engagement, health care utilization, suicide attempts, safety plan documentation, and 6-month mortality.Design, setting, and participantsThis cohort study used triple differences analyses comparing 6-month changes in outcomes after vs before program entry for individuals entering the REACH VET program (March 2017-December 2018) vs a similarly identified top 0.1% suicide risk tier cohort from prior to program initiation (March 2014-December 2015), adjusting for trends across subthreshold cohorts. Subcohort analyses (including individuals from March 2017-June 2018) evaluated difference-in-differences for cause-specific mortality using death certificate data. The subthreshold cohorts included individuals in the top 0.3% to 0.1% suicide risk tier, below the threshold for REACH VET eligibility, from the concurrent REACH VET period and from the pre-REACH VET period. Data were analyzed from December 2019 through September 2021.ExposuresREACH VET-designated clinicians treatment reevaluation and outreach for care enhancements, including safety planning, increased monitoring, and interventions to enhance coping.Main outcomes and measuresProcess outcomes included VHA scheduled, completed, and missed appointments; mental health visits; and safety plan documentation and documentation within 6 months for individuals without plans within the prior 2 years. Clinical outcomes included mental health admissions, emergency department visits, nonfatal suicide attempts, and all-cause, suicide, and nonsuicide external-cause mortality.ResultsA total of 173 313 individuals (mean [SD] age, 51.0 [14.7] years; 161 264 [93.1%] men and 12 049 [7.0%] women) were included in analyses, including 40 816 individuals eligible for REACH VET care and 36 604 individuals from the pre-REACH VET period in the top 0.1% of suicide risk. The REACH VET intervention was associated with significant increases in completed outpatient appointments (adjusted triple difference [ATD], 0.31; 95% CI, 0.06 to 0.55) and proportion of individuals with new safety plans (ATD, 0.08; 95% CI, 0.06 to 0.10) and reductions in mental health admissions (ATD, -0.08; 95% CI, -0.10 to -0.05), emergency department visits (ADT, -0.03; 95% CI, -0.06 to -0.01), and suicide attempts (ADT, -0.05; 95% CI, -0.06 to -0.03). Subcohort analyses did not identify differences in suicide or all-cause mortality (eg, age-and-sex-adjusted difference-in-difference for suicide mortality, 0.0007; 95% CI, -0.0006 to 0.0019).Conclusions and relevanceThese findings suggest that REACH VET implementation was associated with greater treatment engagement and new safety plan documentation and fewer mental health admissions, emergency department visits, and suicide attempts. Clinical programs using risk modeling may be effective tools to support care enhancements and risk reduction.

Project description:OBJECTIVES:Polypharmacy is associated with adverse medication effects. One potential solution is deprescribing, which is the intentional, proactive, rational discontinuation of a medication that is no longer indicated or for which the potential harms outweigh the potential benefits. We identified patient characteristics, attitudes, and health care experiences associated with medication discontinuation. DESIGN, SETTING, AND PARTICIPANTS:We conducted a national mail survey, with the use of the Patient Perceptions of Discontinuation (PPoD) instrument, of 1600 veterans receiving primary care at Veterans Affairs (VA) medical centers and prescribed 5 or more concurrent medications. MAIN OUTCOME MEASURES:The primary outcome was the response to: "Have you ever stopped taking a medicine (with or without your doctor's knowledge)?" The primary predictors of interest were 8 validated attitudinal scales. Other predictors included demographics, health status, and health care experiences. RESULTS:Respondents (n = 803; adjusted response rate 52%) were predominantly male (85%); non-Hispanic white (68%), 65 years of age or older (60%), and with poor (16%) or fair (45%) health. Participant attitudes toward medications and their providers were generally favorable. One in 3 patients (34%) reported having stopped a medicine in the past. In a multivariable logistic regression model (P < 0.001; pseudo-R2 = 0.31; c-statistic = 0.82), factors associated with discontinuation included being told or asking to stop a medicine, greater interest in deprescribing and shared decision making, and higher education. Factors associated with decreased discontinuation were more prescriptions, higher trust in provider, and seeing a VA clinical pharmacist. CONCLUSION:More highly educated patients with interest in deprescribing and shared decision making may be more receptive to discontinuation discussions. Future research evaluating how to incorporate this survey and these findings into clinical workflow through the design of clinical interventions may help to promote safe and rational medication use.

Dataset Information

Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System.

Publications

Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System.

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