Reduced emergency department use among insured individuals receiving extended-release buprenorphine in a health system setting.

Introduction: Extended-release buprenorphine (XR-Bup) is associated with reduced opioid use and opioid negative urine drug screens. Little is known about its use in outpatient addiction care provided within health systems. Methods: Individuals prescribed XR-Bup were identified from electronic health records; chart abstraction was conducted. Primary outcome was all-cause emergency department (ED) use. Secondary outcomes included ED use or inpatient stays for mental health or substance use, ED use for any other cause, discontinuation reasons, and drug substitution. Statistical comparisons used nonparametric tests from related samples (McNemar's test and Wilcoxon matched pair tests) to test outcomes six months prior and 6 months following XR-Bup initiation. Results: 152 individuals had an XR-Bup order, 126 received >1 injection. Among those consistently insured 6 months prior to and following XR-Bup initiation (n=99), the mean number of injections following initiation was 3.95; one-third received 6 doses in the 6 months. The proportion of individuals using ED services for all causes declined (41% prior vs. 28% following XR-Bup initiation, p<.05); similar results were found for secondary ED use outcomes. The proportion of individuals requiring inpatient treatment for mental health or substance use also declined (46% vs. 16%, p<.01). Common reasons for discontinuing XR-Bup included losing insurance (21%) or cost (11%). The most common non-prescribed substances used during treatment were opioids (n=31) and THC (n=20). Conclusions: In this non-randomized retrospective observational study, use of XR-Bup was associated with reduced ED use 6 months following initiation. XR-Bup may help health systems reduce use of costly ED services.

Patient Perspectives on Mental Health and Pain Management Support Needed Versus Received During Opioid Deprescribing.

Prescription opioid tapering has increased significantly over the last decade. Evidence suggests that tapering too quickly or without appropriate support may unintentionally harm patients. The aim of this analysis was to understand patients' experiences with opioid tapering, including support received or not received for pain control or mental health. Patients with evidence of opioid tapering from 6 health care systems participated in semi-structured, in-depth interviews; family members of suicide decedents with evidence of opioid tapering were also interviewed. Interviews were analyzed using thematic analysis. Participants included 176 patients and 16 family members. Results showed that 24% of the participants felt their clinicians checked in with them about their taper experiences while 41% reported their clinicians did not. A majority (68%) of individuals who experienced suicide behavior during tapering reported that clinicians did check in about mood and mental health changes specifically; however, 27% of that group reported no such check-in. More individuals reported negative experiences (than positive) with pain management clinics-where patients are often referred for tapering and pain management support. Patients reporting successful tapering experiences named shared decision-making and ability to adjust taper speed or pause tapering as helpful components of care. Fifty-six percent of patients reported needing more support during tapering, including more empathy and compassion (48%) and an individualized approach to tapering (41%). Patient-centered approaches to tapering include reaching out to monitor how patients are doing, involving patients in decision-making, supporting mental health changes, and allowing for flexibility in the tapering pace. PERSPECTIVE: Patients tapering prescription opioids desire more provider-initiated communication including checking in about pain, setting expectations for withdrawal and mental health-related changes, and providing support for mental health. Patients preferred opportunities to share decisions about taper speed and to have flexibility with pausing the taper as needed.

Impact of Opioid Dose Reductions on Patient-Reported Mental Health and Suicide-Related Behavior and Relationship to Patient Choice in Tapering Decisions.

Mental health and suicide-related harms resulting from prescription opioid tapering are poorly documented and understood. Six health systems contributed opioid prescribing data from January 2016 to April 2020. Patients 18 to 70 years old with evidence of opioid tapering participated in semi-structured interviews. Individuals who experienced suicide attempts were oversampled. Family members of suicide decedents who had experienced opioid tapering were also interviewed. Interviews were analyzed using thematic analysis. The study participants included 176 patients and 16 family members. Patients were 68% female, 80% White, and 15% Hispanic, mean age 58. All family members were female spouses of White, non-Hispanic male decedents. Among the subgroup (n = 60) who experienced a documented suicide attempt, reported experiencing suicidal ideation during tapering, or were family members of suicide decedents, 40% reported that opioid tapering exacerbated previously recognized mental health issues, and 25% reported that tapering triggered new-onset mental health concerns. Among participants with suicide behavior, 47% directly attributed it to opioid tapering. Common precipitants included increased pain, reduced life engagement, sleep problems, withdrawal, relationship dissolution, and negative consequences of opioid substitution with other substances for pain relief. Most respondents reporting suicide behavior felt that the decision to taper was made by the health care system or a clinician (67%) whereas patients not reporting suicide behavior were more likely to report it was their own decision (42%). This study describes patient-reported mental health deterioration or suicide behavior while tapering prescription opioids. Clinicians should screen for, monitor, and treat suicide behavior while assisting patients in tapering opioids. PERSPECTIVE: This work describes changes in patient-reported mental health and suicide behavior while tapering prescription opioids. Recommendations for improving care include mental health and suicide risk screening during and following opioid tapering.

Suicide-related care among patients who have experienced an opioid-involved overdose.

OBJECTIVE: Our objective was to describe suicide prevention care for individuals prescribed opioids or with opioid use disorder (OUD) and identify opportunities for improving this care. METHODS: Adult patients (n = 65) from four health systems with an opioid-involved overdose and clinicians (n = 21) who had contact with similar patients completed 30-60-min semi-structured interviews. A community advisory board contributed to development of all procedures, and interpretation and summary of findings. RESULTS: Patients were mostly female (59%), White (63%) and non-Hispanic (77%); 52 were prescribed opioids, 49% had diagnosed OUD, and 42% experienced an intentional opioid-involved overdose. Findings included: 1) when prescribed an opioid or treated for OUD, suicide risks were typically not discussed; 2) 35% of those with an intentional opioid-involved overdose and over 80% with an unintentional overdose reported no discussion of suicidal ideation when treated for the overdose; and 3) suicide-related follow-up care was uncommon among those with unintentional overdoses despite suicidal ideation being reported by >20%. Clinicians reported that when prescribing opioids or treating OUD, post-overdose suicide-related screening or counseling was not done routinely. CONCLUSIONS: There were several opportunities to tailor suicide prevention care for patients who were treated for opioid-involved overdoses within health systems.

Scaling Family Voices and Engagement to Measure and Improve Systems Performance and Whole Child Health: Progress and Lessons from the Child and Adolescent Health Measurement Initiative.

BACKGROUND: The 1997 legislation authorizing the United States Child Health Insurance Program sparked progress to measure and publicly report on children's healthcare services quality and system performance. To meet the moment, the national Child and Adolescent Health Measurement Initiative (CAHMI) public-private collaboration was launched to put families at the center of defining, measuring and using healthcare performance information to drive improved services quality and outcomes. METHODS: Since 1996 the CAHMI followed an intentional path of collaborative action to (1) articulate shared goals for child health and advance a comprehensive, life-course and outcomes-based healthcare performance measurement and reporting framework; (2) collaborate with families, providers, payers and government agencies to specify, validate and support national, state and local use of dozens of framework aligned measures; (3) create novel public-facing digital data query, collection and reporting tools that liberate data findings for use by families, providers, advocates, policymakers, the media and researchers (Data Resource Center, Well Visit Planner); and (4) generate field building research and systems change agendas and frameworks (Prioritizing Possibilities, Engagement In Action) to catalyze prevention, flourishing and healing centered, trauma-informed, whole child and family engaged approaches, integrated systems and supportive financing and policies. CONCLUSIONS: Lessons call for a restored, sustainable family and community engaged measurement infrastructure, public activation campaigns, and undeterred federal, state and systems leadership that implement policies to incentivize, resource, measure and remove barriers to integrated systems of care that scale family engagement to equitably promote whole child, youth and family well-being. Population health requires effective family engagement.

A Stakeholder-Informed Ethical Framework to Guide Implementation of Suicide Risk Prediction Models Derived from Electronic Health Records.

OBJECTIVE: Develop a stakeholder-informed ethical framework to provide practical guidance to health systems considering implementation of suicide risk prediction models. METHODS: In this multi-method study, patients and family members participating in formative focus groups (n = 4 focus groups, 23 participants), patient advisors, and a bioethics consultant collectively informed the development of a web-based survey; survey results (n = 1,357 respondents) and themes from interviews with stakeholders (patients, health system administrators, clinicians, suicide risk model developers, and a bioethicist) were used to draft the ethical framework. RESULTS: Clinical, ethical, operational, and technical issues reiterated by multiple stakeholder groups and corresponding questions for risk prediction model adopters to consider prior to and during suicide risk model implementation are organized within six ethical principles in the resulting stakeholder-informed framework. Key themes include: patients' rights to informed consent and choice to conceal or reveal risk (autonomy); appropriate application of risk models, data and model limitations and consequences including ambiguous risk predictors in opaque models (explainability); selecting actionable risk thresholds (beneficence, distributive justice); access to risk information and stigma (privacy); unanticipated harms (non-maleficence); and planning for expertise and resources to continuously audit models, monitor harms, and redress grievances (stewardship). CONCLUSIONS: Enthusiasm for risk prediction in the context of suicide is understandable given the escalating suicide rate in the U.S. Attention to ethical and practical concerns in advance of automated suicide risk prediction model implementation may help avoid unnecessary harms that could thwart the promise of this innovation in suicide prevention. HIGHLIGHTSPatients' desire to consent/opt out of suicide risk prediction models.Recursive ethical questioning should occur throughout risk model implementation.Risk modeling resources are needed to continuously audit models and monitor harms.

Clinical implementation of suicide risk prediction models in healthcare: a qualitative study.

BACKGROUND: Suicide risk prediction models derived from electronic health records (EHR) are a novel innovation in suicide prevention but there is little evidence to guide their implementation. METHODS: In this qualitative study, 30 clinicians and 10 health care administrators were interviewed from one health system anticipating implementation of an automated EHR-derived suicide risk prediction model and two health systems piloting different implementation approaches. Site-tailored interview guides focused on respondents' expectations for and experiences with suicide risk prediction models in clinical practice, and suggestions for improving implementation. Interview prompts and content analysis were guided by Consolidated Framework for Implementation Research (CFIR) constructs. RESULTS: Administrators and clinicians found use of the suicide risk prediction model and the two implementation approaches acceptable. Clinicians desired opportunities for early buy-in, implementation decision-making, and feedback. They wanted to better understand how this manner of risk identification enhanced existing suicide prevention efforts. They also wanted additional training to understand how the model determined risk, particularly after patients they expected to see identified by the model were not flagged at-risk and patients they did not expect to see identified were. Clinicians were concerned about having enough suicide prevention resources for potentially increased demand and about their personal liability; they wanted clear procedures for situations when they could not reach patients or when patients remained at-risk over a sustained period. Suggestions for making risk model workflows more efficient and less burdensome included consolidating suicide risk information in a dedicated module in the EHR and populating risk assessment scores and text in clinical notes. CONCLUSION: Health systems considering suicide risk model implementation should engage clinicians early in the process to ensure they understand how risk models estimate risk and add value to existing workflows, clarify clinician role expectations, and summarize risk information in a convenient place in the EHR to support high-quality patient care.

Implementation of Suicide Risk Estimation Analytics to Support Mental Health Care for Quality Improvement.

This quality improvement study describes use of estimation analytics to augment existing suicide prevention practices during routine mental health specialty encounters at a large US health care system.

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

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

Patient expectations of and experiences with a suicide risk identification algorithm in clinical practice.

BACKGROUND: Suicide risk prediction models derived from electronic health records (EHR) and insurance claims are a novel innovation in suicide prevention but patient perspectives on their use have been understudied. METHODS: In this qualitative study, between March and November 2020, 62 patients were interviewed from three health systems: one anticipating implementation of an EHR-derived suicide risk prediction model and two others piloting different implementation approaches. Site-tailored interview guides focused on patients' perceptions of this technology, concerns, and preferences for and experiences with suicide risk prediction model implementation in clinical practice. A constant comparative analytic approach was used to derive themes. RESULTS: Interview participants were generally supportive of suicide risk prediction models derived from EHR data. Concerns included apprehension about inducing anxiety and suicidal thoughts, or triggering coercive treatment, particularly among those who reported prior negative experiences seeking mental health care. Participants who were engaged in mental health care or case management expected to be asked about their suicide risk and largely appreciated suicide risk conversations, particularly by clinicians comfortable discussing suicidality. CONCLUSION: Most patients approved of suicide risk models that use EHR data to identify patients at-risk for suicide. As health systems proceed to implement such models, patient-centered care would involve dialogue initiated by clinicians experienced with assessing suicide risk during virtual or in person care encounters. Health systems should proactively monitor for negative consequences that result from risk model implementation to protect patient trust.

Sex Differences in Comorbid Mental and Substance Use Disorders Among Primary Care Patients With Opioid Use Disorder.

OBJECTIVE: The authors sought to characterize the 3-year prevalence of mental disorders and nonnicotine substance use disorders among male and female primary care patients with documented opioid use disorder across large U.S. health systems. METHODS: This retrospective study used 2014-2016 data from patients ages ≥16 years in six health systems. Diagnoses were obtained from electronic health records or claims data; opioid use disorder treatment with buprenorphine or injectable extended-release naltrexone was determined through prescription and procedure data. Adjusted prevalence of comorbid conditions among patients with opioid use disorder (with or without treatment), stratified by sex, was estimated by fitting logistic regression models for each condition and applying marginal standardization. RESULTS: Females (53.2%, N=7,431) and males (46.8%, N=6,548) had a similar prevalence of opioid use disorder. Comorbid mental disorders among those with opioid use disorder were more prevalent among females (86.4% vs. 74.3%, respectively), whereas comorbid other substance use disorders (excluding nicotine) were more common among males (51.9% vs. 60.9%, respectively). These differences held for those receiving medication treatment for opioid use disorder, with mental disorders being more common among treated females (83% vs. 71%) and other substance use disorders more common among treated males (68% vs. 63%). Among patients with a single mental health condition comorbid with opioid use disorder, females were less likely than males to receive medication treatment for opioid use disorder (15% vs. 20%, respectively). CONCLUSIONS: The high rate of comorbid conditions among patients with opioid use disorder indicates a strong need to supply primary care providers with adequate resources for integrated opioid use disorder treatment.

Patient perspectives on acceptability of, and implementation preferences for, use of electronic health records and machine learning to identify suicide risk.

OBJECTIVE: Assess patient understanding of, potential concerns with, and implementation preferences related to automated suicide risk identification using electronic health record data and machine learning. METHOD: Focus groups (n = 23 participants) informed a web-based survey sent to 11,486 Kaiser Permanente Northwest members in April 2020. Survey items assessed patient preferences using Likert and visual analog scales (means scored from -50 to 50). Descriptive statistics summarized findings. RESULTS: 1357 (12%) participants responded. Most (84%) found machine learning-derived suicide risk identification an acceptable use of electronic health record data; however, 67% objected to use of externally sourced data. Participants felt consent (or opt-out) should be required (mean = -14). The majority (69%) supported outreach to at-risk individuals by a trusted clinician through care messages (57%) or telephone calls (47-54%). Highest endorsements were for psychiatrists/therapists (99%) or a primary care clinician (75-96%); less than half (42%) supported outreach by any clinician and participants generally felt only trusted clinicians should have access to risk information (mean = -16). CONCLUSION: Patients generally support use of EHR data (not externally sourced risk information) to inform automated suicide risk identification models but prefer to consent or opt-out; trusted clinicians should outreach by telephone or care message to at risk individuals.

Suicide Behavior Following PHQ-9 Screening Among Individuals With Substance Use Disorders.

OBJECTIVES: Individuals with substance use disorders (SUD) are at risk for suicide, but no studies have assessed whether routinely administered screeners for suicidal ideation accurately identify outpatients with SUD who are at risk for suicide attempt or death. METHODS: Data from more than 186,000 visits by over 55,000 patients with mental health and SUD diagnoses receiving care in 7 health systems were analyzed to determine whether responses to item 9 of the 9-item Patient Health Questionnaire, which assesses frequency of thoughts of death and self-harm, are associated with suicide outcomes after an outpatient visit. Odds of suicide attempt or death were computed using generalized estimating equations. RESULTS: In bivariate analyses, a nearly 5-fold risk was observed for patients answering "nearly every day" relative to "not at all" among individuals who made a suicide attempt within 90 days (4.9% vs 1.1%; χ2 = 1151, P < 0.0001). At nearly half of visits (46%) followed by a suicide attempt within 90 days, patients responded "not at all." In logistic models, compared to "not at all," all other responses were associated with higher odds of suicide attempt or death within 90 days. Fully adjusted models attenuated results but odds of suicide attempt (AOR = 3.24, CI: 2.69-3.91) and suicide death (AOR = 5.67, CI: 2.0-16.1) remained high for those reporting "nearly every day." CONCLUSIONS: In people with SUD, increasing Patient Health Questionnaire item 9 response predicts increased risk of subsequent suicidal behavior and should prompt intervention. However, clinicians should realize that those reporting "not at all" are not immune from subsequent suicide risk.

Patient characteristics associated with treatment initiation and engagement among individuals diagnosed with alcohol and other drug use disorders in emergency department and primary care settings.

Background: Treatment initiation and engagement rates for alcohol and other drug (AOD) use disorders differ depending on where the AOD use disorder was identified. Emergency department (ED) and primary care (PC) are 2 common settings where patients are identified; however, it is unknown whether characteristics of patients who initiate and engage in treatment differ between these settings. Methods: Patients identified with an AOD disorder in ED or PC settings were drawn from a larger study that examined Healthcare Effectiveness Data and Information Set (HEDIS) AOD treatment initiation and engagement measures across 7 health systems using electronic health record data (n = 54,321). Multivariable generalized linear models, with a logit link, clustered on health system, were used to model patient factors associated with initiation and engagement in treatment, between and within each setting. Results: Patients identified in the ED had higher odds of initiating treatment than those identified in PC (adjusted odds ratio [aOR] = 1.89, 95% confidence interval [CI] = 1.73-2.07), with no difference in engagement between the settings. Among those identified in the ED, compared with patients aged 18-29, older patients had higher odds of treatment initiation (age 30-49: aOR = 1.25, 95% CI = 1.12-1.40; age 50-64: aOR = 1.42, 95% CI = 1.26-1.60; age 65+: aOR = 1.27, 95% CI = 1.08-1.49). However, among those identified in PC, compared with patients aged 18-29, older patients were less likely to initiate (age 30-49: aOR = 0.81, 95% CI = 0.71-0.94; age 50-64: aOR = 0.68, 95% CI = 0.58-0.78; age 65+: aOR = 0.47, 95% CI = 0.40-0.56). Women identified in ED had lower odds of initiating treatment (aOR = 0.80, 95% CI = 0.72-0.88), whereas sex was not associated with treatment initiation in PC. In both settings, patients aged 65+ had lower odds of engaging compared with patients aged 18-29 (ED: aOR = 0.61, 95% CI = 0.38-0.98; PC: aOR = 0.42, 95% CI = 0.26-0.68). Conclusion: Initiation and engagement in treatment differed by sex and age depending on identification setting. This information could inform tailoring of future AOD interventions.

Correlates of Benzodiazepine Use and Adverse Outcomes Among Patients with Chronic Pain Prescribed Long-term Opioid Therapy.

OBJECTIVE: To examine the correlates and odds of receiving overlapping benzodiazepine and opioid prescriptions and whether co-prescription was associated with greater odds of falling or visiting the emergency department. DESIGN: Cross-sectional study. SETTING: A large private integrated health system and a Veterans Health Administration integrated health system. SUBJECTS: Five hundred seventeen adults with musculoskeletal pain and current prescriptions for long-term opioid therapy. METHODS: A multivariate logistic regression model examined correlates of having overlapping benzodiazepine and opioid prescriptions in the year before enrollment in the cross-sectional study. Negative binomial models analyzed the number of falls in the past three months and past-year emergency department visits. In addition to propensity score adjustment, models controlled for demographic characteristics, psychiatric diagnoses, medications, overall comorbidity score, and opioid morphine equivalent dose. RESULTS: Twenty-five percent (N = 127) of participants had co-occurring benzodiazepine and opioid prescriptions in the prior year. Odds of receiving a benzodiazepine prescription were significantly higher among patients with the following psychiatric diagnoses: anxiety disorder (adjusted odds ratio [AOR] = 4.71, 95% confidence interval [CI] = 2.67-8.32, P < 0.001), post-traumatic stress disorder (AOR = 2.24, 95% CI = 1.14-4.38, P = 0.019), and bipolar disorder (AOR = 3.82, 95% CI = 1.49-9.81, P = 0.005). Past-year overlapping benzodiazepine and opioid prescriptions were associated with adverse outcomes, including a greater number of falls (risk ratio [RR] = 3.27, 95% CI = 1.77-6.02, P = 0.001) and emergency department visits (RR = 1.66, 95% CI = 1.08-2.53, P = 0.0194). CONCLUSIONS: Among patients with chronic pain prescribed long-term opioid therapy, one-quarter of patients had co-occurring prescriptions for benzodiazepines, and dual use was associated with increased odds of falls and emergency department visits.

Patient perspectives on how living with a mental illness affects making and maintaining healthy lifestyle changes.

OBJECTIVE: To understand the ways that mental health symptoms interfere with achieving health goals. METHODS: Individuals with mental illness diagnoses and varying levels of preventive service use were recruited from federally qualified health centers and an integrated health care delivery system and interviewed. Thematic analysis was used to characterize descriptions of how mental illness experiences influenced lifestyle change efforts. RESULTS: Three themes described patients' (n = 163) perspectives on barriers to making healthy lifestyle changes: 1) Thinking about making lifestyle changes is overwhelming for individuals already managing the burdens of mental illnesses; 2) Depression makes it difficult to care about a healthy future; and 3) When mental illness symptoms are not adequately treated unhealthy behaviors that provide relief are unlikely to be discontinued. Participants also made suggestions for improving health care delivery to facilitate positive behavior change. CONCLUSION: Patients with mental illnesses need their clinicians to be empathic, help them envision a healthier future, address unmet mental health needs, and provide resources. PRACTICE IMPLICATIONS: Primary care clinicians should encourage their patients with mental illnesses to make healthy lifestyle changes within the context of a supportive relationship. Lifestyle change can be overwhelming; clinicians should acknowledge progress and provide ongoing tangible support.

Preliminary evidence is promising, but challenges remain in providing service dogs to veterans: Commentary on preliminary efficacy of service dogs as a complementary treatment for posttraumatic stress disorder in military members and veterans (O'Haire & Rodriguez, 2018).

OBJECTIVE: Veterans with posttraumatic stress disorder (PTSD) are interested in service dogs to manage or reduce symptoms. Until recently, evidence was anecdotal with few research studies documenting the feasibility or benefits of service dogs for veterans. In the past year, new studies have presented preliminary evidence on the benefits of service dogs. METHOD: Comment on O'Haire and Rodriguez (2018). RESULTS: Positive findings presented in O'Haire and Rodriguez (2018) and Yarborough et al. (2017) included reduced self-reported PTSD symptoms, decreased depression symptoms, improvements in relationships, and increased activity levels. In addition to the benefits, Yarborough, Stumbo, Yarborough, Owen-Smith, and Green (2018) described challenges that veterans had not expected, including the demands of long, intensive training sessions required to receive a service animal, and an increase in unwanted public attention. In light of this preliminary evidence, a critical appraisal and identification of next steps for future research are in order. In this commentary we argue that rigorous randomized controlled trials comparing veterans who receive service dogs with those who do not are due. Such trials should account for self-selection bias and some veterans' overly optimistic expectations for service dogs, both of which have not previously been controlled for in non-randomized studies to date and may present a challenge to trial validity. We also argue that future research needs an adequate long-term follow-up evaluation period and should investigate the specific mechanisms of action at work (i.e. how do service dogs improve PTSD symptoms and quality of life). CONCLUSIONS: Additional trials are needed to establish the efficacy of service dogs for veterans with PTSD. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Assessing long-term sustainment of clinic participation in NIATx200: Results and a new methodological approach.

BACKGROUND: Sustainability frameworks differentiate between sustainability capacity and sustainment of organizational change. Multiple studies have examined sustainability capacity. Methodological approaches to assess long-term sustainment have not been explored. This study addresses this gap by describing the development of a long-term sustainment methodology and evaluating its application utilizing data from substance abuse clinics participating in a quality improvement collaborative. METHODS: The study involved clinics (n = 121) in three states (MI, NY and WA) participating in the 2007-2009 NIATx200 quality improvement (QI) intervention. It extended the primary analysis to focus on clinics' long-term sustainment of wait time, retention and admission improvements. Long-term sustainment was defined as two years post end of the active implementation period (Calendar Years 2010 and 2011). The analysis defined case exclusion criteria and spline "knot" time intervals; allowed for Cp statistic use to address clinic data volatility; established the structure of sustainment plots and explored differences between NIATx implementation strategies. RESULTS: Example spline and sustain plots highlight the application of the long-term sustainment methodology for NIATx200 clinics. In clinics with available longitudinal outcome data, 40.8% (n = 31 of 76 clinics) sustained improvements in wait time, 26.7% (n = 20 of 75 clinics) in retention, and 28.1% (n = 32 of 114 clinics) for admissions. Clinic assignment to a NIATx200 implementation strategy did not significantly influence a clinics' long-term sustainment except for lower wait time changes in the interest circle interventions. Thirty clinics (24.8%) sustained improvements for two outcomes and six clinics (5.0%) did so for all three outcomes. The clinics that sustained multiple outcome improvements were assigned to the interest circle (n = 12), learning session (n = 10), combination (n = 8), and coaching (n = 6) implementation strategies. Guidance for applying the long-term sustainment methodology in other quality improvement settings is described. CONCLUSIONS: Research about sustainability capacity and sustainment of change has become increasingly important in dissemination and implementation research. Assessment of long-term sustainment in a multi-organizational quality improvement collaborative (QIC) is needed to identify when program drift and intervention decay occurs. If "cut-points" indicate when effects diminish, specific sustainability modules could be developed and introduced within the structure of a QIC to improve organizational long-term sustainment. Coaches and change teams could be trained to focus on organizational change sustainment and strengthen the likelihood of institutionalization. ClinicalTrials.gov Identifier: NCT00934141 Registered July 6, 2009. Retrospectively registered.

Perspectives on Providing And Receiving Preventive Health Care From Primary Care Providers and Their Patients With Mental Illnesses.

PURPOSE: Individuals with mental illnesses have higher morbidity rates and reduced life expectancy compared to the general population. Understanding how patients and providers perceive the need for prevention, as well as the barriers and beliefs that may contribute to insufficient care, are important for improving service delivery tailored to this population. DESIGN: Cross-sectional; mixed methods. SETTING: An integrated health system and a network of federally qualified health centers and safety net clinics. PARTICIPANTS: Interviews (n = 30) and surveys (n = 249) with primary care providers. Interviews (n = 158) and surveys (n = 160) with patients diagnosed with schizophrenia, bipolar, anxiety, or major depressive disorders. MEASURES: Semi-structured interviews and surveys. ANALYSIS: Thematic analysis for qualitative data; frequencies for quantitative data. RESULTS: More than half (n = 131, 53%) of clinicians believed patients with mental illnesses care less about preventive care than the general population, yet 88% (n = 139) of patients reported interest in improving health. Most providers (n = 216, 88%) lacked confidence that patients with mental illnesses would follow preventive recommendations; 82% (n = 129) of patients reported they would try to change lifestyles if their doctor recommended. Clinicians explained that their perception of patients' chaotic lives and lack of interest in preventive care contributed to their fatalistic attitudes on care delivery to this population. Clinicians and patients agreed on substantial need for additional support for behavior changes. Clinicians reported providing informational support by keeping messages simple; patients reported a desire for more detailed information on reasons to complete preventive care. Patients also detailed the need for assistive and tangible support to manage behavioral health changes. CONCLUSIONS: Our results suggest a few clinical changes could help patients complete preventive care recommendations and improve health behaviors: improving clinician-patient collaboration on realistic goal setting, increasing visit time or utilizing behavioral health consultants that bridge primary and specialty mental health care, and increasing educational and tangible patient support services.

Benefits and challenges of using service dogs for veterans with posttraumatic stress disorder.

OBJECTIVE: Veterans with posttraumatic stress disorder (PTSD) are increasingly seeking service dogs to help them manage trauma-related symptoms, yet literature describing service dog use in this population is scant. The goal of this study was to document the benefits and challenges experienced by veterans with service dogs trained to assist with PTSD-related needs. METHOD: Participants were veterans (N = 41) with service dogs, and their caregivers (n = 8), recruited through community-based service dog training agencies. We conducted in-depth interviews and observed training sessions as part of a larger study, and used thematic analysis to characterize data. RESULTS: Veterans reported that service dogs reduced hypervigilance by alerting and creating boundaries, and disrupted nightmares, improving sleep quality and duration. Dogs also helped veterans turn their attention away from invasive trauma-related thoughts. Additional reported benefits included improved emotional connections with others, increased community participation and physical activity, and reduced suicidal impulses and medication use. Demands of training, adjustment to life with a service dog, and delayed benefits were challenging for many veterans and caregivers. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Veterans report that service dogs help reduce PTSD symptoms and facilitate recovery and realization of meaningful goals. Service dogs may be a reasonable option for veterans who are reluctant to pursue or persist with traditional evidence-based treatments. Additional rigorous research on the effectiveness of service dogs for this population is warranted. (PsycINFO Database Record