Association of initial opioid prescription duration and an opioid refill by pain diagnosis: Evidence from outpatient settings in ten US health systems.

OBJECTIVE: The Centers for Disease Control and Prevention's 2022 Clinical Practice Guideline for Prescribing Opioids for Pain cautioned that inflexible opioid prescription duration limits may harm patients. Information about the relationship between initial opioid prescription duration and a subsequent refill could inform prescribing policies and practices to optimize patient outcomes. We assessed the association between initial opioid duration and an opioid refill prescription. METHODS: We conducted a retrospective cohort study of adults ≥19 years of age in 10 US health systems between 2013 and 2018 from outpatient care with a diagnosis for back pain without radiculopathy, back pain with radiculopathy, neck pain, joint pain, tendonitis/bursitis, mild musculoskeletal pain, severe musculoskeletal pain, urinary calculus, or headache. Generalized additive models were used to estimate the association between opioid days' supply and a refill prescription. RESULTS: Overall, 220,797 patients were prescribed opioid analgesics upon an outpatient visit for pain. Nearly a quarter (23.5%) of the cohort received an opioid refill prescription during follow-up. The likelihood of a refill generally increased with initial duration for most pain diagnoses. About 1 to 3 fewer patients would receive a refill within 3 months for every 100 patients initially prescribed 3 vs. 7 days of opioids for most pain diagnoses. The lowest likelihood of refill was for a 1-day supply for all pain diagnoses, except for severe musculoskeletal pain (9 days' supply) and headache (3-4 days' supply). CONCLUSIONS: Long-term prescription opioid use increased modestly with initial opioid prescription duration for most but not all pain diagnoses examined.

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.

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.

Prevalence and Medication Treatment of Opioid Use Disorder Among Primary Care Patients with Hepatitis C and HIV.

BACKGROUND: Hepatitis C and HIV are associated with opioid use disorders (OUD) and injection drug use. Medications for OUD can prevent the spread of HCV and HIV. OBJECTIVE: To describe the prevalence of documented OUD, as well as receipt of office-based medication treatment, among primary care patients with HCV or HIV. DESIGN: Retrospective observational cohort study using electronic health record and insurance data. PARTICIPANTS: Adults ≥ 18 years with ≥ 2 visits to primary care during the study (2014-2016) at 6 healthcare systems across five states (CO, CA, OR, WA, and MN). MAIN MEASURES: The primary outcome was the diagnosis of OUD; the secondary outcome was OUD treatment with buprenorphine or oral/injectable naltrexone. Prevalence of OUD and OUD treatment was calculated across four groups: HCV only; HIV only; HCV and HIV; and neither HCV nor HIV. In addition, adjusted odds ratios (AOR) of OUD treatment associated with HCV and HIV (separately) were estimated, adjusting for age, gender, race/ethnicity, and site. KEY RESULTS: The sample included 1,368,604 persons, of whom 10,042 had HCV, 5821 HIV, and 422 both. The prevalence of diagnosed OUD varied across groups: 11.9% (95% CI: 11.3%, 12.5%) for those with HCV; 1.6% (1.3%, 2.0%) for those with HIV; 8.8% (6.2%, 11.9%) for those with both; and 0.92% (0.91%, 0.94%) among those with neither. Among those with diagnosed OUD, the prevalence of OUD medication treatment was 20.9%, 16.0%, 10.8%, and 22.3%, for those with HCV, HIV, both, and neither, respectively. HCV was not associated with OUD treatment (AOR = 1.03; 0.88, 1.21), whereas patients with HIV had a lower probability of OUD treatment (AOR = 0.43; 0.26, 0.72). CONCLUSIONS: Among patients receiving primary care, those diagnosed with HCV and HIV were more likely to have documented OUD than those without. Patients with HIV were less likely to have documented medication treatment for OUD.

Prospective Investigation of Factors Associated with Prescription Opioid Dose Escalation among Patients in Integrated Health Systems.

BACKGROUND: Prior research has identified factors associated with prescription opioid initiation, but little is known about the prevalence or predictors of dose escalation among patients already prescribed long-term opioid therapy (LTOT). OBJECTIVE: This was a 2-year prospective cohort study to examine patient and clinician factors associated with opioid dose escalation. DESIGN: A prospective cohort study. Participants were seen at baseline and every 6 months for a total of 2 years. PARTICIPANTS: Patients prescribed a stable dose of LTOT for musculoskeletal pain were recruited from two integrated health systems (Kaiser Permanente and the Department of Veterans Affairs, respectively). MAIN MEASURES: The prescription opioid dose was based on pharmacy records and self-report. Administrative data were gathered on characteristics of the opioid-prescribing clinician and healthcare utilization. Participants completed measures of pain, functioning, and quality of life. KEY RESULTS: Of enrolled participants (n = 517), 19.5% had an opioid dose increase. In multivariate analyses, patient variables associated with dose escalation were lower opioid dose (hazard ratio [HR] = 0.86, 95% confidence interval [CI] = 0.79-0.94, for every 10-mg increase in baseline dose) and greater pain catastrophizing (HR = 1.03, 95% CI = 1.01-1.05). Other variables associated with dose escalation were as follows: receiving medications from a nurse practitioner primary care provider (HR = 2.10, 95% CI = 1.12-3.96) or specialty physician (HR = 3.18, 95% CI = 1.22-8.34), relative to a physician primary care provider, and having undergone surgery within the past 6 months (HR = 1.80, 95% CI = 1.10-2.94). Other variables, including pain intensity, pain disability, or depression, were not associated with dose escalation. CONCLUSIONS: In this 2-year prospective cohort study, variables associated with opioid dose escalation were lower opioid dose, higher pain catastrophizing, receiving opioids from a medical specialist (rather than primary care clinician) or nurse practitioner, and having recently undergone surgery. Study findings highlight intervention points that may be helpful for reducing the likelihood of future prescription opioid dose escalation.

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.

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.

Healthcare Effectiveness Data and Information Set (HEDIS) measures of alcohol and drug treatment initiation and engagement among people living with the human immunodeficiency virus (HIV) and patients without an HIV diagnosis.

Background: Problematic use of alcohol and other drugs (AOD) is highly prevalent among people living with the human immunodeficiency virus (PLWH), and untreated AOD use disorders have particularly detrimental effects on human immunodeficiency virus (HIV) outcomes. The Healthcare Effectiveness Data and Information Set (HEDIS) measures of treatment initiation and engagement are important benchmarks for access to AOD use disorder treatment. To inform improved patient care, we compared HEDIS measures of AOD use disorder treatment initiation and engagement and health care utilization among PLWH and patients without an HIV diagnosis. Methods: Patients with a new AOD use disorder diagnosis documented between October 1, 2014, and August 15, 2015, were identified using electronic health records (EHR) and insurance claims data from 7 health care systems in the United States. Demographic characteristics, clinical diagnoses, and health care utilization data were also obtained. AOD use disorder treatment initiation and engagement rates were calculated using HEDIS measure criteria. Factors associated with treatment initiation and engagement were examined using multivariable logistic regression models. Results: There were 469 PLWH (93% male) and 86,096 patients without an HIV diagnosis (60% male) in the study cohort. AOD use disorder treatment initiation was similar in PLWH and patients without an HIV diagnosis (10% vs. 11%, respectively). Among those who initiated treatment, few engaged in treatment in both groups (9% PLWH vs. 12% patients without an HIV diagnosis). In multivariable analysis, HIV status was not significantly associated with either AOD use disorder treatment initiation or engagement. Conclusions: AOD use disorder treatment initiation and engagement rates were low in both PLWH and patients without an HIV diagnosis. Future studies need to focus on developing strategies to efficiently integrate AOD use disorder treatment with medical care for HIV.

The prevalence of Healthcare Effectiveness Data and Information Set (HEDIS) initiation and engagement in treatment among patients with cannabis use disorders in 7 US health systems.

Background: Cannabis use disorders (CUDs) have increased with more individuals using cannabis, yet few receive treatment. Health systems have adopted the Healthcare Effectiveness Data and Information Set (HEDIS) quality measures of initiation and engagement in alcohol and other drug (AOD) dependence treatment, but little is known about the performance of these among patients with CUDs. Methods: This cohort study utilized electronic health records and claims data from 7 health care systems to identify patients with documentation of a new index CUD diagnosis (no AOD diagnosis ≤60 days prior) from International Classification of Diseases, Ninth revision, codes (October 1, 2014, to August 31, 2015). The adjusted prevalence of each outcome (initiation, engagement, and a composite of both) was estimated from generalized linear regression models, across index identification settings (inpatient, emergency department, primary care, addiction treatment, and mental health/psychiatry), AOD comorbidity (patients with CUD only and CUD plus other AOD diagnoses), and patient characteristics. Results: Among 15,202 patients with an index CUD diagnosis, 30.0% (95% confidence interval [CI]: 29.2-30.7%) initiated, 6.9% (95% CI: 6.2-7.7%) engaged among initiated, and 2.1% (95% CI: 1.9-2.3%) overall both initiated and engaged in treatment. The adjusted prevalence of outcomes varied across index identification settings and was highest among patients diagnosed in addiction treatment, with 25.0% (95% CI: 22.5-27.6%) initiated, 40.9% (95% CI: 34.8-47.0%) engaged, and 12.5% (95% CI: 10.0-15.1%) initiated and engaged. The adjusted prevalence of each outcome was generally highest among patients with CUD plus other AOD diagnosis at index diagnosis compared with those with CUD only, overall and across index identification settings, and was lowest among uninsured and older patients. Conclusion: Among patients with a new CUD diagnosis, the proportion meeting HEDIS criteria for initiation and/or engagement in AOD treatment was low and demonstrated variation across index diagnosis settings, AOD comorbidity, and patient characteristics, pointing to opportunities for improvement.

The association between medical comorbidity and Healthcare Effectiveness Data and Information Set (HEDIS) measures of treatment initiation and engagement for alcohol and other drug use disorders.

Background: Medical comorbidity may influence treatment initiation and engagement for alcohol and other drug (AOD) use disorders. We examined the association between medical comorbidity and Healthcare Effectiveness Data and Information Set (HEDIS) treatment initiation and engagement measures.Methods: We used electronic health record and insurance claims data from 7 US health care systems to identify patients with AOD use disorders between October 1, 2014, and August 15, 2015 (N = 86,565). Among patients identified with AOD use disorders in outpatient and emergency department (ED) settings, we examined how Charlson/Deyo comorbidity index scores and medical complications of AOD use were associated with treatment initiation. Among those who initiated treatment in inpatient and outpatient/ED settings, we also examined how comorbidity and AOD use-related medical complications were associated with treatment engagement. Analyses were conducted using generalized estimating equation logistic regression modeling.Results: Among patients identified as having an AOD diagnosis in outpatient and ED settings (n = 69,965), Charlson/Deyo comorbidity index scores of 2 or more were independently associated with reduced likelihood of initiation (risk ratio [RR] = 0.80, 95% confidence interval [CI] = 0.74, 0.86; reference score = 0), whereas prior-year diagnoses of cirrhosis (RR = 1.25, 95% CI = 1.12, 1.35) and pancreatic disease (RR = 1.34, 95% CI = 1.15, 1.56) were associated with greater likelihood of initiation. Among those who were identified in outpatient/ED settings and initiated, higher comorbidity scores were associated with lower likelihood of engagement (score 1: RR = 0.85, 95% CI = 0.76, 0.94; score 2+: RR = 0.61, 95% CI = 0.53, 0.71).Conclusion: Medical comorbidity was associated with lower likelihood of initiating or engaging in AOD treatment, but cirrhosis and pancreatic disease were associated with greater likelihood of initiation. Interventions to improve AOD treatment initiation and engagement for patients with comorbidities are needed, such as integrating medical and AOD treatment.

Factors associated with Healthcare Effectiveness Data and Information Set (HEDIS) alcohol and other drug measure performance in 2014-2015.

Background: Only 10% of patients with alcohol and other drug (AOD) disorders receive treatment. The AOD Initiation and Engagement in Treatment (AOD-IET) measure was added to the national Healthcare Effectiveness Data and Information Set (HEDIS) to improve access to care. This study identifies factors related to improving AOD-IET rates. Methods: We include data from 7 health systems with differing geographic, patient demographic, and organizational characteristics; all used a common Virtual Data Warehouse containing electronic health records and insurance claims data. Multilevel logistic regression models examined AOD-IET among adults (18+). Results: A total of 86,565 patients had an AOD diagnosis qualifying for the HEDIS denominator. The overall initiation rate was 27.9% with wide variation; the overall engagement rate was 11.5% and varied from 4.5% to 17.9%. Women versus men (odds ratio [OR] = 0.81, 95% confidence interval [CI] = 0.76-0.86); Hispanics (OR = 0.85, 95% CI = 0.79-0.91), black/African Americans (OR = 0.82, 95% CI = 0.75-0.90), and Asian Americans (OR = 0.83, 95% CI = 0.72-0.95) versus whites; and patients aged 65+ versus 18-29 (OR = 0.82, 95% CI = 0.74-0.90) had lower odds of initiation. Patients aged 30-49 versus 18-29 (OR = 1.11, 95% CI = 1.04-1.19) and those with prior psychiatric (OR = 1.26, 95% CI = 1.18-1.35) and medical (OR = 1.18, 95% CI = 1.10-1.26) conditions had higher odds of initiation. Identification in primary care versus other departments was related to lower odds of initiation (emergency department [ED]: OR = 1.55, 95% CI = 1.45-1.66; psychiatry/AOD treatment: OR = 3.58, 95% CI = 3.33-3.84; other outpatient: OR = 1.19, 95% CI = 1.06-1.32). Patients aged 30-49 versus 18-29 had higher odds of engagement (OR = 1.26, 95% CI = 1.10-1.43). Patients aged 65+ versus 18-29 (OR = 0.51, 95% CI = 0.43-0.62) and black/African Americans versus whites (OR = 0.64, 95% CI = 0.53-0.77) had lower odds. Those initiating treatment in psychiatry/AOD treatment versus primary care (OR = 7.02, 95% CI = 5.93-8.31) had higher odds of engagement; those in inpatient (OR = 0.40, 95% CI = 0.32-0.50) or other outpatient (OR = 0.73, 95% CI = 0.59-0.91) settings had lower odds. Discussion: Rates of initiation and engagement varied but were low. Findings identified age, race/ethnicity, co-occurring conditions, and department of identification as key factors associated with AOD-IET. Focusing on these could help programs develop interventions that facilitate AOD-IET for those less likely to receive care.

Predictors of Healthcare Effectiveness Data and Information Set (HEDIS) treatment initiation and engagement among patients with opioid use disorder across 7 health systems.

Background: The prevalence of opioid use disorder (OUD) has increased rapidly in the United States and improving treatment access is critical. Among patients with OUD, we examined factors associated with the Healthcare Effectiveness Data and Information Set (HEDIS) performance measures of alcohol and other drug (AOD) treatment initiation and engagement. Methods: Electronic health record and claims data between October 1, 2014, and August 15, 2015, from 7 health systems were used to identify patients (n = 11,490) with a new index OUD diagnosis (no AOD diagnosis prior <60 days) based on International Classification of Diseases (ICD)-9 codes. Multivariable generalized linear models with a logit link clustered on health system were used to examine the associations of patient demographic and clinical characteristics, and department of index diagnosis, with HEDIS measures of treatment initiation and engagement. Results: The prevalence of OUD among all AOD diagnoses varied across health systems, as did rates of AOD initiation (5.7%-21.6%) and engagement (7.6%-24.6%). Those diagnosed in the emergency department (adjusted odds ratio [aOR] = 1.58, 95% confidence interval [CI] = 1.27,1.97) or psychiatry/AOD treatment (aOR = 2.92, 95% CI = 2.47,3.46) were more likely to initiate treatment compared with primary care. Older patients were less likely to initiate (age 50-64 vs. age 18-29: aOR = 0.42, 95% CI = 0.35, 0.51; age 65+ vs. age 18-29: aOR = 0.34, 95% CI = 0.26, 0.43), as were women (aOR = 0.72, 95% CI = 0.62, 0.85). Patients diagnosed in psychiatry/AOD treatment (aOR = 2.67, 95% CI = 1.98, 3.60) compared with primary care were more likely to engage in treatment. Those identified in an inpatient setting (aOR = 0.19, 95% CI = 0.14, 0.27 vs. primary care), those with medical comorbidity (aOR = 0.70, 95% CI = 0.52, 0.95), and older patients (age 50-64 vs. 18-29: aOR = 0.64, 95% CI = 0.46, 0.88; age 65+ vs. 18-29: aOR = 0.36, 95% CI = 0.22, 0.57) were less likely to engage in treatment. Conclusions: Rates of initiation and engagement for OUD patients vary widely with noticeable room for improvement, particularly in this critical time of the opioid crisis. Targeting patient and system factors may improve health system performance, which is key to improving patient outcomes.

Psychiatric comorbidity and Healthcare Effectiveness Data and Information Set (HEDIS) measures of alcohol and other drug treatment initiation and engagement across 7 health care systems.

Background: Psychiatric comorbidity is common among patients with alcohol and other drug (AOD) use disorders. To better understand how psychiatric comorbidity influences AOD treatment access in health care systems, the present study examined treatment initiation and engagement among a large, diverse sample of patients with comorbid psychiatric and AOD use disorders. Methods: This study utilized data from a multisite observational study examining Healthcare Effectiveness Data and Information Set (HEDIS) measures of initiation and engagement in treatment (IET) among patients with AOD use disorders from 7 health care systems. Participants were aged 18 or older with at least 1 AOD index diagnosis between October 1, 2014, and August 15, 2015. Data elements extracted from electronic health records and insurance claims data included patient demographic characteristics, ICD-9 (International Classification of Diseases, Ninth Revision) diagnostic codes, and procedure codes. Descriptive analyses and multivariate logistic regression models were used to examine the relationship between patient-level factors and IET measures. Results: Across health care systems, out of a total of 86,565 patients who had at least 1 AOD index diagnosis during the study period, 66.2% (n = 57,335) patients also had a comorbid psychiatric disorder. Among patients with a comorbid psychiatric disorder, 34.9% (n = 19,998) initiated AOD treatment, and of those, 10.3% (n = 2,060) engaged in treatment. After adjusting for age, sex, and race/ethnicity, patients with comorbid psychiatric disorders were more likely to initiate (odds ratio [OR] = 3.20, 95% confidence interval [CI] = 3.08, 3.32) but no more likely to engage (OR = 0.56, 95% CI = 0.51, 0.61) in AOD treatment, compared with those without a comorbid psychiatric disorder. Conclusions: Findings suggest that identification of comorbid psychiatric disorders may increase initiation in AOD treatment. However, innovative efforts are needed to enhance treatment engagement both generally and especially for individuals without diagnosed psychiatric conditions.

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 Use and Perceived Effectiveness of Non-pharmacologic Strategies for Chronic Pain Among Patients Prescribed Long-term Opioid Therapy.

OBJECTIVE: Non-pharmacologic treatments (NPTs) are recommended for chronic pain. Information is limited on patient use or perceptions of NPTs. We examined the frequency and correlates of use and self-rated helpfulness of NPTs for chronic pain among patients who are prescribed long-term opioid therapy (LTOT). METHODS: Participants (n = 517) with musculoskeletal pain who were prescribed LTOT were recruited from two integrated health systems. They rated the frequency and utility of six clinician-directed and five self-directed NPTs for chronic pain. We categorized NPT use at four levels based on number of interventions used and frequency of use (none, low, moderate, high). Analyses examined clinical and demographic factors that differed among groups for both clinician-directed and self-directed NPTs. RESULTS: Seventy-one percent of participants reported use of any NPT for pain within the prior 6 months. NPTs were rated as being helpful by more than 50% of users for all treatments assessed (range 51-79%). High users of clinician-directed NPTs were younger than non-users or low-frequency users and had the most depressive symptoms. In both clinician-directed and self-directed categories, high NPT users had significantly higher pain disability compared to non-NPT users. No significant group differences were detected on other demographic or clinical variables. In multivariable analyses, clinician-directed NPT use was modestly associated with younger age (OR = 0.97, 95% CI = 0.96-0.98) and higher pain disability (OR = 1.01, 95% CI = 1.00-1.02). Variables associated with greater self-directed NPT use were some college education (OR = 1.80, 95% CI = 1.13-2.84), college graduate or more (OR = 2.02, 95% CI = 1.20-3.40), and higher pain disability (OR = 1.01, 95% CI = 1.01-1.02). CONCLUSIONS: NPT use was associated with higher pain disability and younger age for both clinician-directed and self-directed NPTs and higher education for self-directed NPTs. These strategies were rated as helpful by those that used them. These results can inform intervention implementation and be used to increase engagement in NPTs for chronic pain.

Effects of primary care clinician beliefs and perceived organizational facilitators on the delivery of preventive care to individuals with mental illnesses.

BACKGROUND: Although many studies have documented patient-, clinician-, and organizational barriers/facilitators of primary care among people with mental illnesses, few have examined whether these factors predict actual rates of preventive service use. We assessed whether clinician behaviors, beliefs, characteristics, and clinician-reported organizational characteristics, predicted delivery of preventive services in this population. METHODS: Primary care clinicians (n = 247) at Kaiser Permanente Northwest (KPNW) or community health centers and safety-net clinics (CHCs), in six states, completed clinician surveys in 2014. Using electronic health record data, we calculated preventive care-gap rates for patients with mental illnesses empaneled to survey respondents (n = 37,251). Using separate multi-level regression models for each setting, we tested whether survey responses predicted preventive service care-gap rates. RESULTS: After controlling for patient-level characteristics, patients of clinicians who reported a greater likelihood of providing preventive care to psychiatrically asymptomatic patients experienced lower care-gap rates (KPNW γ= - .05, p = .041; CHCs γ= - .05, p = .033). In KPNW, patients of female clinicians had fewer care gaps than patients of male clinicians (γ= - .07, p = .011). In CHCs, patients of clinicians who had practiced longer had fewer care gaps (γ= - .004, p = .010), as did patients whose clinicians believed that organizational quality goals facilitate preventive service provision (γ= - .06, p = .006). Case manager availability in CHCs was associated with higher care-gap rates (γ=.06, p = .028). CONCLUSIONS: Clinicians who report they are likely to address preventive concerns when their mentally ill patients present without apparent psychiatric symptoms had patients with fewer care gaps. In CHCs, care quality goals may facilitate preventive care whereas case managers may not.

A qualitative analysis of family involvement in prescribed opioid medication monitoring among individuals who have experienced opioid overdoses.

BACKGROUND: Little is known about the role, extent, or effects of family member involvement in monitoring and managing opioid analgesics. Knowing when or how family members monitor prescribed opioid medication taking, whether it is acceptable to patients, or how family relationships may be affected by monitoring, are not well documented. METHODS: The study was conducted at Kaiser Permanente Northwest, an integrated health plan in Oregon and Washington. Semistructured in-depth interviews (N = 87) assessed circumstances surrounding overdose events among individuals who either experienced an opioid-related overdose or were family members of patients who died as a result of such an overdose. A subset of participants (n = 20) described family members' roles in monitoring opioid medications before or after overdoses. Interviews were transcribed verbatim and coded using Atlas.ti. We used a modified grounded theory approach to categorize emergent data and to identify common themes. RESULTS: When family members played roles in monitoring and managing opioid medications, clinicians were often unaware of their involvement. Patients and family members reported better outcomes when the patient, caregiver, and clinician developed a shared treatment plan. Negative outcomes included relationship stress, particularly when patients and caregivers had differing perspectives about what constituted effective pain management versus misuse and abuse. CONCLUSIONS: When families are concerned about opioid medications, coordination between clinicians, patients, and family carers appears to clarify roles and foster better outcomes. Increased stress and worse outcomes were reported when clinicians were not actively involved and when they did not attend to carers' concerns.

An Economic Evaluation of a Weight Loss Intervention Program for People with Serious Mental Illnesses Taking Antipsychotic Medications.

Individuals with serious mental illnesses suffer from obesity and cardiometabolic diseases at high rates, and antipsychotic medications exacerbate these conditions. While studies have shown weight loss and lifestyle interventions can be effective in this population, few have assessed intervention cost-effectiveness. We present results from a 12-month randomized controlled trial that reduced weight, fasting glucose, and medical hospitalizations in intervention participants. Costs per participant ranged from $4365 to $5687. Costs to reduce weight by one kilogram ranged from $1623 to $2114; costs to reduce fasting glucose by 1 mg/dL ranged from $467 to $608. Medical hospitalization costs were reduced by $137,500.

Dual recovery among people with serious mental illnesses and substance problems: a qualitative analysis.

OBJECTIVE: Individuals with serious mental illnesses are more likely to have substance-related problems than those without mental health problems. They also face more difficult recovery trajectories as they cope with dual disorders. Nevertheless, little is known about individuals' perspectives regarding their dual recovery experiences. METHODS: This qualitative analysis was conducted as part of an exploratory mixed-methods study of mental health recovery. Members of Kaiser Permanente Northwest (a group-model, not-for-profit, integrated health plan) who had serious mental illness diagnoses were interviewed four times over two years about factors affecting their mental health recovery. Interviews were recorded, transcribed, and coded with inductively derived codes. Themes were identified by reviewing text coded "alcohol or other drugs." RESULTS: Participants (N = 177) had diagnosed schizophrenia/schizoaffective disorder (n = 75, 42%), bipolar I/II disorder (n = 84, 48%), or affective psychosis (n = 18, 10%). At baseline, 63% (n = 112) spontaneously described addressing substance use as part of their mental health recovery. When asked at follow-up, 97% (n = 171) provided codeable answers about substances and mental health. We identified differing pathways to recovery, including through formal treatment, self-help groups or peer support, "natural" recovery (without the help of others), and continued but controlled use of alcohol. We found three overarching themes in participants' experiences of recovering from serious mental illnesses and substance-related problems: Learning about the effects of alcohol and drugs provided motivation and a foundation for sobriety; achieving sobriety helped people to initiate their mental health recovery processes; and achieving and maintaining sobriety built self-efficacy, self-confidence, improved functioning and a sense of personal growth. Non-judgmental support from clinicians adopting chronic disease approaches also facilitated recovery. CONCLUSIONS: Irrespective of how people achieved sobriety, quitting or severely limiting use of substances was important to initiating and continuing mental health recovery processes. Substance abuse treatment approaches that are flexible, reduce barriers to engagement, support learning about effects of substances on mental health and quality of life, and adopt a chronic disease model of addiction may increase engagement and success. Peer-based support like Alcoholics or Narcotics Anonymous can be helpful for people with serious mental illnesses, particularly when programs accept use of mental health medications.

A 12-week weight reduction intervention for overweight individuals taking antipsychotic medications.

People taking antipsychotic medications are at increased risk for obesity, diabetes, and early mortality. Few weight loss interventions have targeted this population. Thirty-six individuals were randomized to an evidence-based 12-week weight loss intervention (PREMIER with DASH diet, n = 18) or to usual care (n = 18) in this feasibility trial. Average attendance was 8.6 of 12 sessions. Intent-to-treat analyses of covariance, adjusted for baseline weight, showed significant changes in weight: Mean weight in intervention participants declined from 213.3 to 206.6 pounds, while control participants' weight was unchanged. It is possible to recruit, assess, intervene with, and retain participants taking antipsychotic medications in a dietary and exercise lifestyle change trial. Participants reported high levels of satisfaction with the intervention.