Factors associated with COVID-19 Infection among a national population of individuals with schizophrenia or schizoaffective disorder in the United States.

BACKGROUND: Individuals with schizophrenia are a vulnerable and under-served population who are also at risk for severe morbidity and mortality following COVID-19 infection. Our research was designed to identify factors that put individuals with schizophrenia at increased risk of COVID-19 infection. METHODS: This study was a retrospective cohort analysis of medical and pharmacy claims among 493,796 individuals residing in the United States with schizophrenia or schizoaffective disorder, between January 1, 2019 and June 30, 2020. A confirmed diagnosis of COVID-19 infection by September 30, 2020 was regressed on demographics, social determinants, comorbidity, and pre-pandemic (December 2019 - February 2020) healthcare utilization characteristics. RESULTS: A total of 35,249 (7.1%) individuals were diagnosed with COVID-19. Elevated odds of COVID-19 infection were associated with age, increasing consistently from 40-49 years (OR: 1.16) to 80+ years (OR:5.92), male sex (OR: 1.08), Medicaid (OR: 2.17) or Medicare (OR: 1.23) insurance, African American race (OR: 1.42), Hispanic ethnicity (OR: 1.23), and higher Charlson Comorbidity Index. Select psychiatric comorbidities (depressive disorder, adjustment disorder, bipolar disorder, anxiety, and sleep-wake disorder) were associated with elevated odds of infection, while alcohol use disorder and PTSD were associated with lower odds. A pre-pandemic psychiatry (OR:0.56) or community mental health center (OR:0.55) visit were associated with lower odds as was antipsychotic treatment with long-acting injectable antipsychotic (OR: 0.72) and oral antipsychotic (OR: 0.62). CONCLUSIONS: Among individuals with schizophrenia, risk of COVID-19 infection was substantially higher among those with fewer economic resources, with greater medical and psychiatric comorbidity burden, and those who resided in African American or Hispanic communities. In contrast, individuals actively engaged in psychiatric treatment had substantially lower likelihood of infection. These results provide insights for healthcare providers that can translate into improved identification of at-risk individuals and interventions to reduce the risk and consequences of COVID-19 infection.

Adherence to augmentation therapy for the treatment of major depressive disorder.

BACKGROUND: Inadequate response to antidepressant medication is common. Often, adjunctive pharmacotherapy or psychotherapy is recommended. OBJECTIVE: To measure adherence to adjunctive pharmacotherapy and psychotherapy among individuals with major depressive disorder (MDD). METHODS: Retrospective cohort study of individuals with MDD on antidepressant monotherapy who added adjunctive pharmacotherapy and/or psychotherapy. Medication adherence was measured by proportion of days covered (PDC) with optimal adherence defined as PDC≥0.80 and psychotherapy adherence defined by count of visits (optimal 8+ visits). Factors associated with optimal adherence were assessed by logistic regression. RESULTS: Among 218,192 individuals with adjunctive therapy, 185,349 added pharmacotherapy and 32,843 added psychotherapy. In the subsequent 12 months, 36.2% and 54.9% achieved optimal adherence to adjunctive pharmacotherapy and psychotherapy, respectively. Adherence to adjunctive pharmacotherapy was associated with adding psychotherapy, index antidepressant adherence, medical comorbidities, and MDD severity codes. Adherence to adjunctive psychotherapy was associated with adding another medication, previous psychiatry visit and psychiatric comorbidities. CONCLUSION: Adjunctive psychotherapy appears under-utilized and adherence to adjunctive therapy was low. Low adherence to adjunctive therapy reinforces challenges in managing MDD. That a second adjunctive therapy enhanced adherence to the initial adjunctive therapy indicates an opportunity to explore alternative adjunctive therapies.

Compliance with buprenorphine medication-assisted treatment and relapse to opioid use.

Opioid dependence (OD), often characterized as a chronic relapsing disorder, affects millions of people worldwide. The purpose of this study was to examine the effect of compliance with buprenorphine on reducing relapse among a sample of patients in treatment for OD. Patients new to buprenorphine (N = 703) completed the Addiction Severity Index (ASI) at baseline, and at 1, 2, and 3 months postbaseline. The ASI is a semistructured interview designed to measure problem severity in seven functional areas known to be affected by alcohol and drug dependence. Compliance was defined as taking buprenorphine medication on at least 22 of the past 28 days (80%), while relapse classification was based on resumed use of opioids during the follow-up period (months 2 and 3). Relapse was regressed onto demographic indicators, baseline ASI composite scores, and compliance with buprenorphine. Noncompliant patients were over 10 times more likely to relapse than those who were compliant (exp ß= 10.55; p < .001). Neither demographics nor baseline ASI composite scores were predictive of relapse (p's > .05). Compliance with medication-assisted treatment supports abstinence, essential for patient recovery. Understanding the factors that drive treatment compliance and noncompliance may assist providers in supporting patient compliance and recovery.

Measuring Response to Adjunctive Therapy Among Individuals with Major Depressive Disorder.

Background: Incomplete or inadequate response to first-line antidepressant therapy (ADT) for major depressive disorder (MDD) is common. Response to adjunctive therapy is less understood. Objective: To estimate response to adjunctive pharmacotherapy or psychotherapy among individuals with MDD on an antidepressant using the PHQ-9 questionnaire. Methods: This was a retrospective cohort analysis using medical and pharmacy insurance claims among individuals with MDD or ADT who initiated adjunctive pharmacotherapy, psychotherapy, or both (dual). Eligible individuals initiated adjunctive therapy between 7/1/2014-12/31/2018. Symptom severity was measured by PHQ-9 score in the 6-month baseline and 12-month follow up. Multivariate logistic regression identified factors associated with improved symptom severity. Results: Most (81.8%) of the 2389 participants initiated adjunctive pharmacotherapy, followed by psychotherapy (12.7%) and dual adjunctive (5.5%). Only 30.2% had both a baseline and follow-up PHQ-9 score. Among those with mild or more severe PHQ-9 baseline scores, 36.7% had the same or worse MDD severity during follow-up. Among those with moderate or more severe baseline scores, 28.1% had the same or worse MDD severity during follow-up. Conclusion: Most individuals with moderate-to-severe MDD did not receive a follow-up questionnaire, suggesting incomplete monitoring of treatment response. Among those with a PHQ-9 following initiation of adjunctive therapy, many continued to report impactful symptoms. Future studies should explore alternate treatment approaches and methods to support the utilization of the PHQ-9 for monitoring treatment response.

Real-World Reductions in Healthcare Resource Utilization over 6 Months in Patients with Substance Use Disorders Treated with a Prescription Digital Therapeutic.

BACKGROUND AND AIMS: Substance use disorders (SUDs) affect approximately 40.3 million people in the USA, yet only approximately 19% receive evidence-based treatment each year. reSET® is a prescription digital therapeutic (PDT) and the only FDA-authorized treatment for patients with cocaine, cannabis, and stimulant use disorders. This study evaluated real-world healthcare resource utilization (HCRU) and associated costs 6 months after initiation of reSET in patients with SUD. METHODS: A retrospective analysis of HealthVerity PrivateSource20 data compared the 6-month incidence of all-cause hospital facility encounters and clinician services in patients treated with reSET (re-SET cohort) before (pre-index period) and after (post-index period) reSET initiation (index). Incidence was compared using incidence rate ratios (IRR). HCRU-related costs were also assessed. RESULTS: The sample included 101 patients (median age 37 years, 50.5% female, 54.5% Medicaid-insured). A statistically significant decrease of 50% was observed in overall hospital encounters from pre-index to post-index (IRR 0.50; 95% CI 0.37-0.67; P < 0.001), which included inpatient stays (56% decrease; IRR 0.44; 95% CI 0.26-0.76; P = 0.003), partial hospitalizations (57% decrease; IRR 0.43; 95% CI 0.21-0.88; P = 0.021), and emergency department visits (45% decrease; IRR 0.55; 95% CI 0.38-0.80; P < 0.004). Additionally, some clinician services declined significantly including pathology and laboratory services: other (54% decrease; IRR 0.46; 95% CI 0.28-0.76; P = 0.003); pathology and laboratory services: drug assays prior to opioid medication prescription (37% decrease; IRR 0.63; 95% CI 0.41-0.96; P = 0.031); and alcohol and drug abuse: medication services (46% decrease; IRR 0.54; 95% CI 0.41-0.70; P < 0.001). Reductions in facility-encounters drove 6-month reSET per-patient cost reductions of $3591 post-index compared to pre-index. CONCLUSIONS: Use of reSET by patients with SUD is associated with durable reductions in HCRU and lower healthcare costs over 6 months compared to the 6 months before PDT treatment, after adjusting for covariates, providing an economic benefit to the healthcare system.

Impact of the COVID-19 Pandemic on Healthcare Resource Utilization in Individuals with Major Depressive Disorder.

Objective: To estimate the impact COVID-19 pandemic on healthcare resource utilization (HCRU) among individuals with major depressive disorder (MDD). Method: A retrospective cohort study was conducted to compare HCRU in the twelve months prior to and six months following pandemic onset among 1,318,709 individuals with MDD and propensity-score matched controls. Outcomes were monthly rates of all-cause and MDD-specific outpatient, inpatient, and prescription medication HCRU. Piecewise random effects models were used to adjust for patient-level clustering, trends over time, and pre-pandemic factors. Results: In the first month following onset, outpatient HCRU declined with primary care visits down 25.1%. Following this initial decline, outpatient HCRU increased, exceeding pre-pandemic rates within three months. By April 2020, three quarters of all psychotherapy sessions were delivered by telehealth, followed by psychiatry (62.3%), and primary care visits (30.1%). The use of telehealth remained highest for psychotherapy and psychiatry (representing 67.6% and 54.2% of visits, respectively, in September 2020). All-cause partial-day hospitalizations declined 50.5% and remained depressed through July 2020 (down 18.3%). Beginning in the first month post-onset, prescription medication HCRU increased for all antidepressant and antipsychotic medication classes: serotonin modulators ( + 11.8%), bupropion ( + 10.4%), SSRIs ( + 9.0%), SNRIs ( + 8.6%), and atypical antipsychotics ( + 7.5%). Conclusions: Following pandemic onset, individuals with MDD realized an immediate, but short-lived, reduction in primary care HCRU. Telehealth use remained elevated through the first six months. The most significant and sustained reduction in HCRU was noted for partial-day hospitalizations and all-cause ED visits.

Mental Health Care Provider's Perspectives Toward Adopting a Novel Technology to Improve Medication Adherence.

Objective: To understand perspectives of mental health care providers regarding barriers and drivers of adopting a medication ingestible event monitoring (IEM) system in clinical practice. Methods: Between April and October 2019, a cross-sectional, online survey was conducted among 131 prescribing clinicians and 119 non-prescribing clinicians providing care to patients with major depressive disorder, bipolar disorder, and schizophrenia. Results: Most prescribing clinicians were physicians (79.4%) while most non-prescribing clinicians (52.9%) were licensed clinical social workers, followed by counselors (30.8%), clinical psychologists (13.4%), and case managers (2.5%). Most respondents (93.2%) reported that clinicians can influence adherence, that the IEM technology was in their patients' best interest (63.6%), and a willingness to beta test the technology (54.8%). Support was positively associated with prescribing clinicians (OR: 2.2; 95% CI: 1.1, 4.5), belief that antipsychotics reduce the health, social, or financial consequences of the condition (OR: 3.8; 95% CI: 1.3, 11.0), concern for patients' well-being without monitoring (OR: 3.3; 95% CI: 1.2, 8.7), and belief the technology will enhance clinical alliance (OR: 3.1; 95% CI: 1.5, 6.3) or improve patient engagement (OR: 3.0; 95% CI: 1.5, 6.2). Support was inversely related to concerns about appropriate follow-up actions (OR: 0.4; 95% CI: 0.2, 0.9) and responsibilities (OR: 0.3; 95% CI: 0.1, 0.8) when using the technology. Conclusions: Our results suggest that IEM sensor technology adoption will depend upon additional evidence that patients will actively engage in the use of the technology, will benefit from the technology through improved outcomes, and that the additional burden placed upon providers is minimal compared to the potential benefit.

Reduced Healthcare Resource Utilization in Patients with Opioid Use Disorder in the 12 Months After Initiation of a Prescription Digital Therapeutic.

BACKGROUND AND AIMS: reSET-O, an FDA-authorized prescription digital therapeutic (PDT) delivering cognitive behavioral therapy and contingency management to patients with opioid u®se disorder (OUD), may help improve clinical outcomes. One-year differences in healthcare resource utilization (HCRU) and costs post-PDT initiation were evaluated. METHODS: Retrospective analysis of healthcare claims data compared all-cause HCRU (across hospital facility encounters [sum of inpatient stays, treat-and-release emergency department [ED] visits, partial hospitalizations, and hospital outpatient department visits] and clinician services [procedure categories]) after PDT initiation (index) between reSET-O patients and controls. Overall and Medicaid-specific differences in HCRU, costs, and buprenorphine adherence were evaluated. FINDINGS: Cohorts included 901 reSET-O patients (median age 36 years, 62.4% female, 73.9% Medicaid) and 978 controls (median age 38 years, 51.1% female, 65.4% Medicaid). Compared to the control group, the reSET-O group experienced 12% fewer total unique hospital encounters (non-significant), driven by 28% fewer inpatient stays (IRR 0.72; 95% CI 0.55-0.96; P = 0.02), 56% fewer hospital readmissions [IRR 0.44; 95% CI 0.20-0.93; P = 0.033]), and 7% fewer ED visits (IRR 0.93; 95% CI 0.79-1.09; P = 0.386). Total clinician services increased by 1391 events versus controls. Differences were greater among the Medicaid patients. Adjustment for concomitant baseline substance use and mental health disorders resulted in similar HCRU incidence rate ratios. Changes in all-cause HCRU drove per-patient per-year cost differences of - $2791 versus controls (- $3832 versus Medicaid controls). Adjusted mean medication possession ratio was 0.848 (SE 0.0118) at 12 months for reSET-O patients, which was significantly higher than controls (0.761 [SE 0.0108]; P < 0.001). CONCLUSIONS: Use of reSET-O is associated with significant and durable real-world reductions in ED and inpatient (including readmissions) utilization, reduced net costs, and increased clinician services and buprenorphine adherence. Differences in costs versus controls were greatest among Medicaid patients. INFOGRAPHIC.

Real-world reduction in healthcare resource utilization following treatment of opioid use disorder with reSET-O, a novel prescription digital therapeutic.

INTRODUCTION: Buprenorphine medication assisted treatment (B-MAT) adherence for opioid use disorder (OUD) is suboptimal. reSET-O, an FDA-cleared prescription digital therapeutic, delivers neurobehavioral therapy (community-reinforcement approach+fluency training+contingency management) to B-MAT-treated OUD patients. METHODS: This retrospective claims study (10/01/2018-10/31/2019) evaluated healthcare resource utilization up to 6 months before/after reSET-O initiation. Repeated-measures negative binomial models compared incidences of encounters/procedures. Net change in costs was assessed. RESULTS: Among 351 patients (mean age 37; 59.5% female; 82.6% Medicaid), 334 had pharmacy claims and 240 (71.9%) received buprenorphine pre-/post-index (medication possession ratio 0.73 and 0.82, respectively; P = 0.004). Facility encounters decreased, with 45 fewer inpatient (P = 0.024) and 27 fewer emergency department (ED) visits (P = 0.247). Clinical encounters with largest changes were drug testing (638 fewer; P < 0.001), psychiatry (349 fewer; P = 0.036), case management (176 additional; P = 0.588), other pathology/laboratory (166 fewer; P = 0.039), office/other outpatient (154 fewer; P = 0.302), behavioral rehabilitation (111 additional; P = 0.124), alcohol/substance rehabilitation (96 fewer; P = 0.348), other rehabilitation (66 fewer; P = 0.387), mental health rehabilitation (61 additional; P = 0.097), and surgery (60 fewer; P = 0.070). Changes in facility/clinical encounters saved $2,150/patient. CONCLUSION: reSET-O initiation was associated with fewer inpatient, ED, and other clinical encounters, increased case management/rehabilitative services, and lower net costs over six months. EXPERT OPINION: Real-world evidence is helpful in evaluating the effectiveness of interventions in usual-care conditions, outside of controlled research environments. Large observational studies based on health care claims are important to understand the actual pharmacoeconomic and outcomes impact of interventions at the health care system and population level.

The Association between Patient Activation and Outcomes among Severely Mentally Ill Patients.

Little is known about the association between patient activation, health, service utilization, and cost among mental health (MH) patients. Patients aged 18 to 64 with schizophrenia (Sz, n = 43), bipolar disorder (BD, n = 59), or major depressive disorder (MDD, n = 34) completed the Patient Activation Measure for Mental Health (PAM-MH), the Colorado Symptom Index, demographic, socioeconomic, treatment, and social support questionnaire items. Average PAM-MH score indicated BD patients the most activated (66.6 ± 17.5), Sz (57.4 ± 10.4) less activated, and MDD the least activated (55.4 ± 14.6). The MDD cohort had the highest ($27,616 ± 26,229) and the BD had the lowest total annual healthcare cost ($18,312 ± 25,091). PAM-MH score was inversely correlated with healthcare costs and regression analysis showed a PAM-MH score × gender interaction. The strongest negative relationship between PAM and cost was for males. These analyses support the inverse association between PAM-MH and healthcare service utilization and cost.

Comparison of Healthcare Resource Utilization Between Patients Who Engaged or Did Not Engage With a Prescription Digital Therapeutic for Opioid Use Disorder.

BACKGROUND: A prescription digital therapeutic (PDT) (reSET-O®) may expand access to behavioral treatment for patients with opioid use disorder (OUD) treated with buprenorphine, but long-term data on effectiveness are lacking. OBJECTIVE: To compare real-world healthcare resource utilization (HCRU) among patients who engaged with reSET-O and buprenorphine compared to similar patients in recovery treated with buprenorphine who did not fill their reSET-O script or engage with the PDT beyond week one. METHODS: A retrospective analysis of facility and clinical service claims data was conducted in adults with PDT initiation and between 12 weeks and 9 months of continuous enrollment in a health plan after initiation. Patients who filled their prescription and engaged with the therapeutic were compared to patients who filled the prescription but did not engage beyond week one (NE), and patients who did not fill the prescription (NR) (the latter two groups combined into one group hereafter referred to as "non-engagers"). Comparisons were analyzed using a repeated-measures negative binomial model of encounters/procedures, adjusted for number of days in each period. Associated cost trends assessed using current Medicare reimbursement rates. RESULTS: A total of 444 patients redeemed a prescription and engaged with the PDT (mean age 37.5 years, 63.1% female, 84% Medicaid), and 64 patients did not engage with the PDT (mean age 39.5 years, 32.8% female, 73.4% Medicaid). Total cost of hospital facility encounters was $2693 for engaged patients vs $6130 for non-engaged patients. Engaged patients had somewhat higher rates of certain clinician services. Total facility and clinician services costs for engaged vs non-engaged patients were $8733 vs $11,441, for a net cost savings over 9 months of $2708 per patient who engaged with reSET-O. CONCLUSION: Patients who engaged with an OUD-specific PDT had a net cost reduction for inpatient and outpatient services of $2708 per patient over 9 months compared to patients who did not engage with the PDT, despite similar levels of buprenorphine adherence.

Prescriber Attitudes, Experiences, and Proclivities Toward Digital Medicine and How They Influence Adoption of Digital Medicine Platforms.

BACKGROUND: Psychiatric prescribers (prescribers) typically assess medication adherence by patient or caregiver self-report. Despite likely clinical benefit of a new digital medicine technology, the role of specific prescriber attitudes, behaviors, and experiences in the likelihood of adoption is unclear. OBJECTIVE: To identify prescriber characteristics that may affect adoption of the ingestible event marker (IEM) platform. DESIGN: A survey of prescribers treating seriously mentally ill patients was conducted. Factor analysis was performed on 11 items representing prescriber characteristics believed to be related to endorsement of the IEM platform. Four factors were extracted. Regression analysis was used to test the strength of the relationships between the factors and likelihood of adoption of the IEM platform. RESULTS: A total of 131 prescribers completed the survey. Most (84%) agreed that visits allow enough time to monitor adherence. Factor analysis revealed four underlying dimensions: 1) perspectives on the value of adherence; 2) concerns about measuring adherence; 3) views toward digital health technologies; and 4) views on payer role/reimbursement. Factors 1 and 3 were related to gender, the belief that computerization benefits prescribers, the presence of office support staff, and the belief that new digital medicine (DM) technology will be cost prohibitive. Willingness to adopt the IEM platform was related to gender (p < 0.05) and perspectives on the value of adherence (p < 0.05), with those scoring higher on that measure also being more likely to adopt. CONCLUSION: Psychiatric prescribers are concerned about medication adherence, perceive current monitoring tools to be problematic, and are open to using digital technologies to improve accuracy of adherence assessment. Relationships among prescriber characteristics, beliefs, and experiences should be considered when developing educational materials, particularly when the goal is to encourage adoption and use of the IEM platform.

Empirical view of opioid dependence.

BACKGROUND: The impact of opioid dependence on employers, managed care, and society is significant. Inappropriate use of narcotic analgesics leads to uncontrolled pain management, dependence, and may lead to patient deaths, creating a tremendous cost burden to the health care system. OBJECTIVE: To provide an overview of the clinical and economic impact of treating opioid dependence on managed care, employers, and society. SUMMARY: An estimated 6% to 15% of people in the United States abuse drugs, and approximately 20% of Americans report using prescription opioids for nonmedical use. This is associated with an annual cost of nearly half a trillion dollars, taking into account the medical, economic, social, and criminal impact of this abuse. A recent study showed that patients who abuse opioids generate mean annual direct health care costs 8.7 times higher than nonabusers. The National Survey on Drug Use and Health (NSDUH), conducted by the Substance Abuse and Mental Health Services Administration (SAM HSA), found that patients who report opioid abuse miss more than 2.2 days of work monthly, compared with the 0.83 days per month reported for the average person. Presenteeism and productivity are also affected by misuse and dependence on opioids. CONCLUSION: The costs associated with opioid dependence are significant. Physicians, employers, and managed care organizations must be proactive in appropriately diagnosing and treating patients who suffer from substance abuse disorders in order to lessen this economic burden.

Practice strategies to improve compliance and patient self-management.

BACKGROUND: Failure in treating opioid dependence is costly to the patient, the employer, managed care organizations, and the overall health care system. Opioid dependent patients tend to be less productive at work and in society and utilize a great many health care resources. Optimizing outcomes is essential. OBJECTIVE: To introduce the benefit of integrated strategies and patient support in the treatment of opioid dependence. SUMMARY: Health Analytics is currently studying the benefit of HereToHelp, a behavioral support program in which registered nurses or addiction treatment counselors with specialized training in addiction education provide information and encouragement to patients receiving pharmacologic treatment for opioid dependence. A total of 470 physicians in 41 states have been enlisted to participate in this patient support study. The study hypothesis is that patients who receive behavioral support and encouragement will be more compliant with their opioid replacement therapy, leading to better outcomes. Additional treatment strategies are also being developed to minimize the risk of abuse and diversion. Prodrugs and vaccines are also being investigated. CONCLUSION: A coordinated team approach is essential in treating pain patients and opioid-dependent patients. Offering behavior modification in addition to pharmacotherapy and utilizing strategies such as prescription monitoring programs, pain contracts, and screening are all vital components necessary for positive outcomes.

Opioid dependence treatment and guidelines.

BACKGROUND: In response to the growing incidence of opioid dependence, guidelines have been created, and new treatments are being developed to assist physicians in treating dependence and withdrawal of opioids. OBJECTIVE: To review treatment modalities and guidelines utilized in opioid dependence. SUMMARY: Guidelines for the treatment of opioid dependence have been developed by organizations such as the American Society of Interventional Pain Physicians (ASIPP) and the American Psychiatric Association (APA). Current guidelines recommend comprehensive treatment with pharmacological agents such as methadone, buprenorphine, or buprenorphine combined with naloxone as well as psychosocial therapy. These guidelines stress the need for an integrated approach to treatment. Office-based opioid treatment is currently being utilized to treat opioid dependent patients in a physician's office setting with buprenorphine/naloxone replacement therapy as an alternative to entering patients into a methadone clinic. These office-based programs offer a breakthrough in access to care for dependent patients. CONCLUSION: Physicians need to be aware of and adhere to currently accepted guidelines and recommendations for treating opioid dependent patients, including integrating psychosocial treatments and behavior modification strategies for optimal results. Clinicians must be educated on the new treatment modalities and regulations surrounding the use of these therapies.

Predicting Incident Treatment-Resistant Depression: A Model Designed for Health Systems of Care.

BACKGROUND: Major depressive disorder (MDD) is a prevalent and debilitating condition. While numerous treatment options are available, low treatment response and high remission rates remain common, leading to the concept of treatment-resistant depression (TRD): a classification applied to patients who fail multiple courses of therapy. A patient with TRD can only be identified after repeated, and often prolonged, therapeutic efforts. OBJECTIVE: To use data readily available to integrated delivery networks to identify characteristics predictive of TRD among patients initiating pharmacotherapy for MDD. METHODS: Decision Resources Group Real-World Data, an integrated medical/pharmacy claims and electronic health record dataset, was used to conduct a retrospective, longitudinal cohort study of patients with MDD who initiated antidepressant treatment between July 1, 2014, and December 31, 2015. Individuals were followed for 24 months to determine treatment resistance. Eligible individuals had integrated claims and electronic health record data available, completed at least 1 course of therapy of adequate dose and duration to achieve response, and had 30 months of continuous benefits eligibility (6 months before and 24 months after treatment initiation). Stepwise logistic regression and demographic, health history, health care utilization, medication, provider, and related characteristics were used to predict onset of TRD. RESULTS: 35,246 people met eligibility and 7,098 (20.1%) met TRD criteria after an average of 402 days. Significant predictors of TRD included patient age, diagnosis of insomnia and hypertension, psychiatric office visits, nurse telephonic encounters, anticonvulsant medication use, suicidality, physician specialty associated with index prescription, total prescription drug claims, unique antidepressants attempted, and duration of untreated illness (the lag between diagnosis and index prescription). The final model achieved an area under the curve (AUC) = 0.83. Structured patient-generated health data, specifically, the Patient Health Questionnaire-2 and the Patient Health Questionnaire-9 were only reported for 542 patients (1.5%). CONCLUSIONS: TRD transition occurs after a prolonged treatment period, suggesting clinical inertia. Using data routinely available to integrated delivery networks and accountable care organizations, it is feasible to identify patients likely to qualify as treatment resistant. Monitoring risk factors may allow health systems to identify patients at risk for TRD earlier, potentially improving outcomes. Early identification of this at-risk population can allow for targeted resources for earlier intervention, more aggressive follow-up, and alternative treatment options. Furthermore, this model can be used to estimate future demand for specialized care resources, such as those delivered by mood disorder clinics. DISCLOSURES: This project was sponsored by Janssen Scientific Affairs. Pesa, Chow, and Verbanac are employed by Janssen Scientific Affairs and report stock ownership in Johnson & Johnson. Liberman, Davis, Heverly-Fitt, and Ruetsch are employed by Health Analytics, which received funding from Janssen Scientific Affairs for work on this project. This study was presented as a poster at the U.S. Psych Congress; October 3-6, 2019; San Diego, CA.

Association between meaningful use of electronic health records and patient health outcomes in schizophrenia: a retrospective database analysis.

Improvements in patient outcomes from the meaningful use of electronic health records (EHRs) have not been extensively studied among patients with schizophrenia. This study assessed the association between EHR use, provision of quality care, and patient outcomes. Providers who were at least 50% compliant with current requirements for the CMS Electronic Health Records Incentive Program were classified as EHR providers. Quality of mental health care was assessed using 4 Healthcare Effectiveness Data and Information Set indicators. Patient outcomes included inpatient admissions and emergency department visits. A total of 18,305 providers (EHR: 16.6%; non-EHR: 83.3%) treated 27,153 patients with schizophrenia. EHR use was associated with improved rates of diabetes screening (77.9% vs 72.4%; P <.001), diabetes monitoring (72.1% vs 61.4%; P <.001), and better antipsychotic adherence (54.7% vs 36.6%; P <.001). EHR use was also associated with fewer inpatient admissions (15.9% vs 25.2%; P <.001) and emergency department visits (32.2% vs 49.3%; P <.001). These data suggest that EHR use may have a positive influence on the process and outcomes of psychiatric care when treating patients with schizophrenia. More research is needed to identify the drivers of the influence of EHRs and to develop programs that ensure all EHR users enjoy the same potential benefits as demonstrated here.

A claims-based algorithm to reduce relapse and cost in schizophrenia.

OBJECTIVES: To refine a payer algorithm identifying patients with schizophrenia at high risk of relapse within a managed Medicaid population and evaluate its effectiveness in a case management (CM) program. STUDY DESIGN: Cross-sectional and longitudinal study design. METHODS: The algorithm used a single payer's Medicaid medical and pharmacy claims (August 1, 2009, to July 31, 2014) for patients with schizophrenia (N = 12,353) to predict those at high risk for hospitalization. The final algorithm was used in a CM program (outbound communication to providers) at 3 payer service centers in 3 states. Based on the algorithm, 60 patients (20 from each site) with the highest risk scores were targeted for CM (CM group) and 60 (those patients ranked 21st-40th most at-risk at each site) comprised the control group. Chi-square tests compared groups on frequency measures (hospitalizations, emergency department [ED] visits). Pre- to postimplementation differences were tested using McNemar's test. A pre-post analysis of variance assessed mean numbers of inpatient admissions, inpatient days, and ED visits for both groups. RESULTS: The algorithm had good positive predictive power (64.0%), negative predictive power (94.7%), sensitivity (40.2%), and specificity (97.9%). Following CM, the proportion of patients with at least 1 inpatient admission in the CM group decreased (23.3% to 13.3%), as did the rate of ED visits per month (by approximately 15%), whereas increases were observed in the control group. CONCLUSIONS: Although not all of these differences were statistically significant, they suggest that the algorithm may be an effective case-finding tool for plans attempting to mitigate hospitalizations among high-risk patients with schizophrenia.

Claims-based proxies of patient instability among commercially insured adults with schizophrenia.

OBJECTIVE: Schizophrenia (Sz) patients are among the highest utilizers of hospital-based services. Prevention of relapse is in part a treatment goal in order to reduce hospital admissions. However, predicting relapse is a challenge, particularly for payers and disease management firms with only access to claims data. Understandably, such organizations have had little success predicting relapse. A tool that allows payers to identify patients at elevated risk of relapse could facilitate targeted interventions prior to relapse and avoid rehospitalization. In this study, a series of proxy measures of patient instability, calculated from claims data were examined for their utility in identifying Sz patients at elevated risk of relapse. METHODS: Aetna claims were used to assess the relationship between instability of Sz patients and valence and magnitude of antipsychotic (AP) medication change during a 2-year period. Six proxies of instability including hospital admissions, emergency department visits, medication utilization patterns, and use of outpatient services were identified. Results were replicated using claims data from Truven MarketScan®. RESULTS: Patients who switched AP ingredient had the highest overall instability at the point of switch and the second steepest decline in instability following switch. Those who changed to a long-acting injectable AP showed the second highest level of instability and the steepest decrease in instability following the change. Patients augmented with a second AP showed the smallest increase in instability, up to the switch. Results were directionally consistent between the two data sets. CONCLUSION: Using claims-based proxy measures to estimate instability may provide a viable method to better understand Sz patient markers of change in disease severity. Also, such proxies could be used to identify those individuals with the greatest need for treatment modification preventing relapse, improving patient outcomes, and reducing the burden of illness.