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.

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.

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.

Network meta-analysis comparing the effectiveness of a prescription digital therapeutic for chronic insomnia to medications and face-to-face cognitive behavioral therapy in adults.

OBJECTIVE: The purpose of this study was to compare the effectiveness of the only Food and Drug Administration-authorized prescription digital therapeutic (PDT) Somryst versus face-to-face cognitive behavioral therapy for insomnia (CBT-I), or FDA-approved prescription medications for insomnia. METHODS: A systematic literature review was undertaken to identify relevant studies. A Bayesian network meta-analysis (NMA) was conducted to examine (1) mean change in insomnia severity index (ISI); (2) proportional change in ISI remitters; (3) mean change in wake after sleep onset (WASO); and (4) mean change in sleep onset latency (SOL). RESULTS: Twenty studies provided data on the PDT, CBT-I, CBT-I in combination with self-help (SH), or two prescription medications (eszopiclone and zolpidem). The PDT was associated with significant mean change in ISI (-5.77, 95% Credible Interval [CrI] - 8.53, -3.07) and ISI remitters (OR 12.33; 95% CrI 2.28, 155.91) compared to placebo, and had the highest probability of being the most effective treatment overall for ISI mean change (56%), and ISI remitters (64%). All evaluated interventions significantly outperformed placebo for WASO but no significant differences were observed for SOL (five interventions). Sensitivity analyses excluding medications and meta-regression (assessing type, duration, delivery method for CBT-I) did not affect NMA results. CONCLUSIONS: This network meta-analysis demonstrated that a PDT delivering CBT-I had the highest probability of being most effective compared to face-to-face CBT-I, prescription sleep medications, or placebo, as measured by reductions in mean ISI score from baseline and ISI-determined remittance.

Chronic insomnia is the long-term inability to fall asleep easily or to stay asleep. This condition is much more serious than most people realize, raising the risk of many health problems including depression, heart disease, and injuries.Although sleep medications are commonly used to treat insomnia, these drugs may not be effective and can lead to harms such as accidents or clouded thinking. Clinical guidelines recommend a treatment called cognitive behavioral therapy for insomnia (CBT-I) that is safe and effective. Unfortunately, there is a shortage of clinicians trained to provide CBT-I.Prescription digital therapeutics (PDTs) are FDA-approved software programs available on mobile devices such as smartphones. A PDT for insomnia (Somryst) delivers CBT-I and can overcome barriers to access for this important type of therapy. To compare the effectiveness of this PDT with FDA-approved sleep medications and face-to-face CBT-I a special kind of study was conducted called a network meta-analysis. This is a statistical method of combining data from numerous studies in a way that allows the results to be fairly compared.This network meta-analysis of 20 studies found that the PDT was more effective at reducing insomnia symptoms than any of the sleep medications studied and was even more effective than face-to-face CBT-I as measured by scores on a clinically valid scale of insomnia symptoms. These results are encouraging because they suggest that digital delivery of CBT-I could help the millions of people who currently do not have access to this effective treatment.

Real-World Evaluation of Clinical Response and Long-Term Healthcare Resource Utilization Patterns Following Treatment with a Digital Therapeutic for Chronic Insomnia.

Background and Objectives: This analysis evaluated insomnia severity and long-term impact on healthcare resource utilization (HCRU) and costs after treatment with Somryst® (previously called SHUTi), a digital therapeutic delivering cognitive behavioral therapy for insomnia (CBT-I). Methods: Change from baseline in insomnia severity index (ISI) score was assessed using last observed ISI score. A pre/post analysis of claims data was conducted, comparing HCRU in patients with self-identified sleep problems who successfully initiated the therapeutic (index date) between June 1, 2016 and December 31, 2018. Results: A total of 248 patients were analyzed (median age 56.5 years, 57.3% female, mean ISI score 19.13, 52.4% treated with sleep aid medications pre-index). After 9 weeks, mean ISI score declined by 37.2% from baseline (19.1 vs 12.0), 58.8% of patients achieved ISI responder status (ISI score improved by =>7; NNT: 1.7), and 26.6% of patients achieved insomnia remission (ISI score <8; NNT for remission: 3.8). After two-year follow-up, post-index events were reduced (compared to 2 years pre-index) for emergency department visits (-53%; IRR: 0.47; 95% CI 0.27, 0.82; P=0.008), hospiatizations (-21%; IRR: 0.79; 95% CI 0.46, 1.35; P=0.389) and hospital outpatient visits (-13%; IRR: 0.87; 95% CI 0.66, 1.14; P=0.315). Slightly increased rates were observed for ambulatory surgical center visits (2%; IRR: 1.02; 95% CI 0.73, 1.44; P=0.903) and office visits (2%; IRR: 1.02; 95% CI 0.92, 1.14; P=0.672). The number of patients treated with sleep aid medications dropped 18.5% (52.4% pre-index vs 42.7% post-index). Average number of prescriptions decreased from 3.98 pre-index to 3.73 post-index (P= 0.552). Total two-year cost reduction post-index vs pre-index was $510,678, or -$2059 per patient. Conclusion: In a real-world cohort of patients with chronic insomnia, treatment with a digital therapeutic delivering CBT-I was associated with reductions in insomnia severity, emergency department visits, and net costs.

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.

Are caregivers ready for digital? Caregiver preferences for health technology tools to monitor medication adherence among patients with serious mental illness.

Background: Adherence to antipsychotic medication is critical for bipolar disorder (BPD), major depression (MDD) and schizophrenia (SCZ) patients. Digital tools have emerged to monitor medication adherence along with tracking general health. Evidence on physician or patient preferences for such tools exists but is limited among caregivers. The study objective was to assess preferences and willingness-to-pay (WTP) for medication adherence monitoring tools among caregivers of SMI patients. Methods: A web-based survey was administered to caregivers of adult SMI patients. Twelve discrete choice questions comparing adherence monitoring tools that varied across two attribute bundles: (1) tool attributes including source of medication adherence information, frequency of information updates, access to adherence information, and physical activity, mood, and rest tracking, and (2) caregiver monthly out-of-pocket cost attribute were administered to caregiver respondents. Attributes were parameterized for both digital and non-digital tools. Random utility models were used to estimate caregivers' preferences and WTP. Results: Among 184 study-eligible caregivers, 57, 61 and 66 participants cared for BPD, MDD, and SCZ patients, respectively. Caregivers highly preferred (odds ratio (OR): 7.34, 95% confidence interval (CI): 5.00-10.79) a tool that tracked medication ingestion using a pill embedded with an ingestible event market (IEM) sensor and tracked patients' physical activity, mood, and rest than a non-digital pill organizer. Additionally, caregivers were willing to pay $255 per month (95% CI: $123-$387) more for this tool compared to a pill organizer. Conclusion: Caregivers of SMI patients highly preferred and were willing to pay more for digital tools that not only measures medication ingestion but also tracks general health.

Treatment Compliance Communications Between Patients with Severe Mental Illness and Treating Healthcare Providers: A Retrospective Study of Documentation Using Healthcare Reimbursement Claims and Medical Chart Abstraction.

INTRODUCTION: Successful treatment for serious mental illnesses (SMIs) requires a good therapeutic alliance with healthcare providers and compliance with prescribed therapies such as antipsychotic medications. This retrospective study, which utilized administrative claims linked with abstracted medical chart data, addressed a data gap regarding compliance-related discussions between providers and patients. METHODS: Commercially insured patients in ambulatory care post-acute (emergency or inpatient) event were eligible. Criteria included age 18-65 years; schizophrenia, bipolar disorder, or major depressive disorder diagnoses; continuous enrollment 6 months before to 12 months after the first acute event claim dated 01/01/2014 to 12/31/2015; and antipsychotic medication prescription. Demographic and clinical data, and patient-provider discussions about treatment compliance were characterized from claims and abstracted medical charts. RESULTS: Ninety patients (62% female, mean age 41 years) were included and 680 visits were abstracted; only 58% had first-visit antipsychotic compliance discussions. Notably, 18% of patients had discussions using the specific terms "compliance," "persistence," or "adherence," whereas half were identified by more general terms. Compliance discussions were observed least often among the patients with schizophrenia, as compared with bipolar or major depressive disorders-a counterintuitive finding. DISCUSSION: Compliance discussions may represent intervention opportunities to optimize treatment, yet their study is a complex endeavor. The results of this study show an opportunity to improve this valuable treatment step.

Impact of treatment-related discussions on healthcare resource use and costs among patients with severe mental illness.

OBJECTIVE: Serious mental illnesses (SMIs), including schizophrenia, bipolar disorder, and major depressive disorder (MDD), are often treated with antipsychotic medications. Unfortunately, medication non-adherence is widespread and is associated with serious adverse outcomes. However, little real-world data are available describing adherence, compliance, or other medication-taking-related discussions between providers and patients. This study described these communications in ambulatory care. METHODS: Commercially insured patients having acute (emergency or inpatient) behavioral health (BH) events were included by specific criteria: age 18-65 years; diagnoses of schizophrenia, bipolar disorder, or MDD; continuous health insurance coverage 6 months before to 12 months after the first claim (index) date during 01/01/2014‒12/31/2015; and prescribed antipsychotic medication. Medical charts were abstracted for ambulatory visits with a BH diagnosis through 12 months after the acute event, describing any treatment compliance discussions that occurred. BH-related healthcare utilization and costs were measured via insurance claims. Results were analyzed by observation of an antipsychotic medication taking-related (i.e. compliance or adherence) discussion at the initial abstracted visit. RESULTS: Ninety patients were included: 62% female, mean age 41 years. Only 58% had antipsychotic compliance discussions during the first abstracted ambulatory visit. A total of 680 BH-related visits were abstracted for the 90 patients. Providers frequently discussed any psychotropic medication use (97% of all visits abstracted); however, discussion of compliance with BH talk therapies was less common (49% of visits among patients with a first visit antipsychotic discussion and 23% without, p < .001). Follow-up BH-related healthcare utilization and costs were not significantly different by cohort. Patients with ≥2 compliance discussions had a significantly lower risk of follow-up acute events, which are the costliest components of healthcare for SMI (p = .023). CONCLUSION: Increasing the frequency of antipsychotic treatment-related adherence/compliance discussions may represent an opportunity to improve the quality of care for these vulnerable patients and reduce the overall economic burden associated with the treatment of SMI diagnosis.

Adoption of Digital Health Technologies in the Practice of Behavioral Health: Qualitative Case Study of Glucose Monitoring Technology.

BACKGROUND: Evaluation of patients with serious mental illness (SMI) relies largely on patient or caregiver self-reported symptoms. New digital technologies are being developed to better quantify the longitudinal symptomology of patients with SMI and facilitate disease management. However, as these new technologies become more widely available, psychiatrists may be uncertain about how to integrate them into daily practice. To better understand how digital tools might be integrated into the treatment of patients with SMI, this study examines a case study of a successful technology adoption by physicians: endocrinologists' adoption of digital glucometers. OBJECTIVE: This study aims to understand the key facilitators of and barriers to clinician and patient adoption of digital glucose monitoring technologies to identify lessons that may be applicable across other chronic diseases, including SMIs. METHODS: We conducted focus groups with practicing endocrinologists from 2 large metropolitan areas using a semistructured discussion guide designed to elicit perspectives of and experiences with technology adoption. The thematic analysis identified barriers to and facilitators of integrating digital glucometers into clinical practice. Participants also provided recommendations for integrating digital health technologies into clinical practice more broadly. RESULTS: A total of 10 endocrinologists were enrolled: 60% (6/10) male; a mean of 18.4 years in practice (SD 5.6); and 80% (8/10) working in a group practice setting. Participants stated that digital glucometers represented a significant change in the treatment paradigm for diabetes care and facilitated more effective care delivery and patient engagement. Barriers to the adoption of digital glucometers included lack of coverage, provider reimbursement, and data management support, as well as patient heterogeneity. Participant recommendations to increase the use of digital health technologies included expanding reimbursement for clinician time, streamlining data management processes, and customizing the technologies to patient needs. CONCLUSIONS: Digital glucose monitoring technologies have facilitated more effective, individualized care delivery and have improved patient engagement and health outcomes. However, key challenges faced by the endocrinologists included lack of reimbursement for clinician time and nonstandardized data management across devices. Key recommendations that may be relevant for other diseases include improved data analytics to quickly and accurately synthesize data for patient care management, streamlined software, and standardized metrics.

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.

Antipsychotic Medication Adherence and Healthcare Services Utilization in Two Cohorts of Patients with Serious Mental Illness.

OBJECTIVE: To evaluate differences in patient characteristics and real-world outcomes in two distinct high-risk cohorts of patients with serious mental illness (SMI). METHODS: Retrospective cross-sectional analysis using a national multi-payer claims database. Two SMI cohorts identified by a technical expert panel-patients recently discharged (RD) from an SMI-related hospitalization and early episode (EE) patients-were evaluated for antipsychotic medication adherence, healthcare utilization, and spending patterns. RESULTS: The analysis included 51,705 patients with bipolar disorder, major depressive disorder, and schizophrenia. More than half were over age 46 and >60% were female. Adherence to psychiatric medications was low (52.5% RD and 16.1% EE). More than half of RD and 100% of EE patients switched medications at least once annually, but 19% of RD patients switched ≥2 times compared to 14% of EE. The RD cohort (generally older and sicker) had higher psychiatric related utilization and higher annual costs (US$21,171 versus US$15,398). In both cohorts, women were more likely to have an emergency department (ED) and primary care provider (PCP) visit, but less likely to be hospitalized. Patients age <46 were less likely to have a PCP visit and more likely to have an ED visit, but younger patients age 18-24 were less likely to be hospitalized. CONCLUSION: Efforts to manage SMI are confounded by heterogeneity and low adherence to treatment. By better understanding which patients are at higher risk for specific adverse outcomes, clinicians can target interventions more appropriately to reduce the significant burden of SMI.

Using Information on Patient Adherence to Antipsychotic Medication to Understand Their Adherence to Other Medications.

PURPOSE: To assess how patient adherence to atypical antipsychotic medications is associated with adherence to concurrently used medications that treat other serious mental illnesses (SMIs), type-2 diabetes, or hypertension. METHODS: Among patients who had been diagnosed with an SMI (i.e., bipolar disorder, major depressive disorder, or schizophrenia) in the previous year, we used health-insurance claims data to measure adherence based on medication fills. Patients diagnosed with an SMI were required to have 1) a prescription for an atypical oral antipsychotic, and 2) another SMI therapy or oral anti-diabetic or antihypertensive agent in the same year. The patient's concurrent adherence to an antipsychotic and one of 23 other medications was measured by the proportion of days covered (PDC) over a one-year period. Patients were considered adherent when the PDC was ≥ 80%. The strength of the association between their atypical antipsychotic adherence and their concurrent medication adherence was evaluated using the following metrics: accuracy, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: The average (standard deviation) age of patients (N = 129,614) was 44.8 (14.8) years and 62.2% of patients were female. The median accuracy based on atypical antipsychotic adherence to the other 23 medications was 67% (range, 55-71%; statistically different from 50% accuracy in all cases, P < 0.001). Accuracy was higher than physician predictions of adherence from previous studies (53%). The negative predictive value of antipsychotic adherence (75%; range, 62-88%) was generally higher than the PPV (62%; range, 43-67%; all, P < 0.001). CONCLUSION: Information on patient adherence to antipsychotics provides significant insight into adherence to other medications often used by patients with SMI. Because NPV is higher than PPV, adherence to antipsychotics is likely to be a necessary but not sufficient condition for patients with SMI regarding adherence to non-SMI medications.

Does knowledge of patient non-compliance change prescribing behavior in the real world? A claims-based analysis of patients with serious mental illness.

BACKGROUND: New digital technologies offer providers the promise of more accurately tracking patients' medication adherence. It is unclear, however, whether access to such information will affect provider treatment decisions in the real world. METHODS: Using prescriber-reported information on patient non-compliance from health insurance claims data between 2008 and 2014, we examined whether prescribers' knowledge of non-compliance was associated with different prescribing patterns for patients with serious mental illness (SMI). We examined patients who initiated an oral atypical antipsychotic, but were later objectively non-adherent to this treatment, defined as proportion of days covered (PDC) <0.8. We examined how a physician's awareness of patient non-compliance (ICD-9 diagnosis code: V15.81) was correlated with the physician's real-world treatment decisions for that patient. Treatment decisions studied included the share of patients who increased antipsychotic dose, augmented treatment, switched their antipsychotic, or used a long-acting injectable (LAI). RESULTS: Among the 286,249 patients with SMI who initiated an antipsychotic and had PDC <0.8, 4,033 (1.4%) had documented non-compliance. When prescribers documented non-compliance, patients were more likely to be switched to another antipsychotic (32.8% vs 24.7%, P<0.001), have their dose increased (24.4% vs 22.1%, P=0.004), or receive an LAI (0.09% vs 0.04%, P=0.008), but were less likely to have augmented therapy with another antipsychotic (1.1% vs 1.3%, P=0.035) than patients without documented non-compliance. CONCLUSION: Among SMI patients with documented non-compliance, the frequency of dose, medication switches, and LAI use were higher and augmentation was lower compared to patients without documented non-compliance. Access to adherence information may help prescribers more rapidly switch ineffective medications as well as avoid unnecessary medication augmentation.

Medication Adherence Patterns Among Patients with Multiple Serious Mental and Physical Illnesses.

INTRODUCTION: Patients with mental and physical health conditions are complex to treat and often use multiple medications. It is unclear how adherence to one medication predicts adherence to others. A predictive relationship could permit less expensive adherence monitoring if overall adherence could be predicted through tracking a single medication. METHODS: To test this hypothesis, we examined whether patients with multiple mental and physical illnesses have similar adherence trajectories across medications. Specifically, we conducted a retrospective cohort analysis using health insurance claims data for enrollees who were diagnosed with a serious mental illness, initiated an atypical antipsychotic, as well as an SSRI (to treat serious mental illness), biguanides (to treat type 2 diabetes), or an ACE inhibitor (to treat hypertension). Using group-based trajectory modeling, we estimated adherence patterns based on monthly estimates of the proportion of days covered with each medication. We measured the predictive value of the atypical antipsychotic trajectories to adherence predictions based on patient characteristics and assessed their relative strength with the R-squared goodness of fit metric. RESULTS: Within our sample of 431,591 patients, four trajectory groups were observed: non-adherent, gradual discontinuation, stop-start, and adherent. The accuracy of atypical antipsychotic adherence for predicting adherence to ACE inhibitors, biguanides, and SSRIs was 44.5, 44.5, and 49.6%, respectively (all p < 0.001 vs. random). We also found that information on patient adherence patterns to atypical antipsychotics was a better predictor of patient adherence to these three medications than would be the case using patient demographic and clinical characteristics alone. CONCLUSION: Among patients with multiple chronic mental and physical illnesses, patterns of atypical antipsychotic adherence were useful predictors of adherence patterns to a patient's adherence to ACE inhibitors, biguanides, and SSRIs. FUNDING: Otsuka Pharmaceutical Development & Commercialization, Inc.

Digital health technology for use in patients with serious mental illness: a systematic review of the literature.

BACKGROUND: As the capabilities and reach of technology have expanded, there is an accompanying proliferation of digital technologies developed for use in the care of patients with mental illness. The objective of this review was to systematically search published literature to identify currently available health technologies and their intended uses for patients with serious mental illness. MATERIALS AND METHODS: The Medline, Embase, and BIOSIS Previews electronic databases were searched to identify peer-reviewed English language articles that reported the use of digital, mobile, and other advanced technology in patients with schizophrenia/schizoaffective disorder, bipolar disorder, and major depressive disorder. Eligible studies were systematically reviewed based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS: Eighteen studies that met the inclusion criteria were identified. Digital health technologies (DHTs) assessed in the selected studies included mobile applications (apps), digital medicine, digital personal health records, and an electronic pill container. Smartphone apps accounted for the largest share of DHTs. The intended uses of DHTs could be broadly classified as monitoring to gain a better understanding of illness, clinical assessment, and intervention. Overall, studies indicated high usability/feasibility and efficacy/effectiveness, with several reporting validity against established clinical scales. Users were generally engaged with the DHT, and mobile assessments were deemed helpful in monitoring disease symptoms. CONCLUSION: Rapidly proliferating digital technologies seem to be feasible for short-term use in patients with serious mental illness; nevertheless, long-term effectiveness data from naturalistic studies will help demonstrate their usefulness and facilitate their adoption and integration into the mental health-care system.

The cost of adherence mismeasurement in serious mental illness: a claims-based analysis.

OBJECTIVES: To quantify how adherence mismeasurement affects the estimated impact of adherence on inpatient costs among patients with serious mental illness (SMI). STUDY DESIGN: Proportion of days covered (PDC) is a common claims-based measure of medication adherence. Because PDC does not measure medication ingestion, however, it may inaccurately measure adherence. We derived a formula to correct the bias that occurs in adherence-utilization studies resulting from errors in claims-based measures of adherence. METHODS: We conducted a literature review to identify the correlation between gold-standard and claims-based adherence measures. We derived a bias-correction methodology to address claims-based medication adherence measurement error. We then applied this methodology to a case study of patients with SMI who initiated atypical antipsychotics in 2 large claims databases. RESULTS: Our literature review identified 6 studies of interest. The 4 most relevant ones measured correlations between 0.38 and 0.91. Our preferred estimate implies that the effect of adherence on inpatient spending estimated from claims data would understate the true effect by a factor of 5.3, if there were no other sources of bias. Although our procedure corrects for measurement error, such error also may amplify or mitigate other potential biases. For instance, if adherent patients are healthier than nonadherent ones, measurement error makes the resulting bias worse. On the other hand, if adherent patients are sicker, measurement error mitigates the other bias. CONCLUSIONS: Measurement error due to claims-based adherence measures is worth addressing, alongside other more widely emphasized sources of bias in inference.

Access to credible information on schizophrenia patients' medication adherence by prescribers can change their treatment strategies: evidence from an online survey of providers.

OBJECTIVE: Overestimating patients' medication adherence diminishes the ability of psychiatric care providers to prescribe the most effective treatment and to identify the root causes of treatment resistance in schizophrenia. This study was conducted to determine how credible patient drug adherence information (PDAI) might change prescribers' treatment decisions. METHODS: In an online survey containing 8 clinical case vignettes describing patients with schizophrenia, health care practitioners who prescribe antipsychotics to patients with schizophrenia were instructed to choose a preferred treatment recommendation from a set of predefined pharmacologic and non-pharmacologic options. The prescribers were randomly assigned to an experimental or a control group, with only the experimental group receiving PDAI. The primary outcome was the prescribers' treatment choice for each case. Between-group differences were analyzed using multinomial logistic regression. RESULTS: A convenience sample (n=219) of prescribers completed the survey. For 3 nonadherent patient vignettes, respondents in the experimental group were more likely to choose a long-acting injectable antipsychotic compared with those in the control group (77.7% experimental vs 25.8% control; P<0.001). For 2 adherent but poorly controlled patient vignettes, prescribers who received PDAI were more likely to increase the antipsychotic dose compared with the control group (49.1% vs 39.1%; P<0.001). For the adherent and well-controlled patient vignette, respondents in both groups made similar treatment recommendations across all choices (P=0.099), but respondents in the experimental arm were more likely to recommend monitoring clinical stability (87.2% experimental vs 75.5% control, reference group). CONCLUSION: The results illustrate how credible PDAI can facilitate more appropriate clinical decisions for patients with schizophrenia.

Estimating the Value of New Technologies That Provide More Accurate Drug Adherence Information to Providers for Their Patients with Schizophrenia.

BACKGROUND: Nonadherence to antipsychotic medication among patients with schizophrenia results in poor symptom management and increased health care and other costs. Despite its health impact, medication adherence remains difficult to accurately assess. New technologies offer the possibility of real-time patient monitoring data on adherence, which may in turn improve clinical decision making. However, the economic benefit of accurate patient drug adherence information (PDAI) has yet to be evaluated. OBJECTIVE: To quantify how more accurate PDAI can generate value to payers by improving health care provider decision making in the treatment of patients with schizophrenia. METHODS: A 3-step decision tree modeling framework was used to measure the effect of PDAI on annual costs (2016 U.S. dollars) for patients with schizophrenia who initiated therapy with an atypical antipsychotic. The first step classified patients using 3 attributes: adherence to antipsychotic medication, medication tolerance, and response to therapy conditional on medication adherence. The prevalence of each characteristic was determined from claims database analysis and literature reviews. The second step modeled the effect of PDAI on provider treatment decisions based on health care providers' survey responses to schizophrenia case vignettes. In the survey, providers were randomized to vignettes with access to PDAI and with no access. In the third step, the economic implications of alternative provider decisions were identified from published peer-reviewed studies. The simulation model calculated the total economic value of PDAI as the difference between expected annual patient total cost corresponding to provider decisions made with or without PDAI. RESULTS: In claims data, 75.3% of patients with schizophrenia were found to be nonadherent to their antipsychotic medications. Review of the literature revealed that 7% of patients cannot tolerate medication, and 72.9% would respond to antipsychotic medication if adherent. Survey responses by providers (n = 219) showed that access to PDAI would significantly alter treatment decisions for nonadherent or adherent/poorly controlled patients (P < 0.001). Payers can expect to save $3,560 annually per nonadherent patient who would respond to therapy if adherent. Savings increased to $9,107 per nonadherent patient when PDAI was given to providers who frequently augmented therapy for these patients. Among all poorly controlled patients (i.e., the nonadherent or those who were adherent but unresponsive to therapy), access to PDAI decreased annual patient cost by $2,232. Savings for this group increased to $7,124 per patient when PDAI was given to providers who frequently augmented therapy. CONCLUSIONS: Access to PDAI significantly improved provider decision making, leading to lower annual health care costs for patients who were nonadherent or adherent but poorly controlled. Additional research is warranted to evaluate how new technologies that accurately monitor adherence would affect health and economic outcomes among patients with serious mental illness. DISCLOSURES: This study and medical writing assistance was funded by Otsuka Pharmaceutical Development & Commercialization. Shafrin and Schwartz are employees of Precision Health Economics, which received funding from Otsuka Pharmaceutical Development & Commercialization in support of this study. Lakdawalla is Chief Scientific Officer and a founding partner of Precision Health Economics. Schwartz is a consultant for Otsuka Pharmaceutical Development & Commercialization, and Forma is an employee of Otsuka Pharmaceutical Development & Commercialization. The authors presented the abstract for this study as a poster presentation at the AMCP Managed Care & Specialty Pharmacy Annual Meeting, April 19-22, 2016, San Francisco, California. All authors contributed equally to the study design, data collection and analysis, and the writing and revision of the manuscript.

Patterns of Adherence to Oral Atypical Antipsychotics Among Patients Diagnosed with Schizophrenia.

BACKGROUND: Poor medication adherence contributes to negative treatment response, symptom relapse, and hospitalizations in schizophrenia. Many health plans use claims-based measures like medication possession ratios or proportion of days covered (PDC) to measure patient adherence to antipsychotics. Classifying patients solely on the basis of a single average PDC measure, however, may mask clinically meaningful variations over time in how patients arrive at an average PDC level. OBJECTIVE: To model patterns of medication adherence evolving over time for patients with schizophrenia who initiated treatment with an oral atypical antipsychotic and, based on these patterns, to identify groups of patients with different adherence behaviors. METHODS: We analyzed health insurance claims for patients aged ≥ 18 years with schizophrenia and newly prescribed oral atypical antipsychotics in 2007-2013 from 3 U.S. insurance claims databases: Truven MarketScan (Medicaid and commercial) and Humana (Medicare). Group-based trajectory modeling (GBTM) was used to stratify patients into groups with distinct trends in adherence and to estimate trends for each group. The response variable was the probability of adherence (defined as PDC ≥ 80%) in each 30-day period after the patient initiated antipsychotic therapy. GBTM proceeds from the premise that there are multiple distinct adherence groups. Patient demographics, health status characteristics, and health care resource use metrics were used to identify differences in patient populations across adherence trajectory groups. RESULTS: Among the 29,607 patients who met the inclusion criteria, 6 distinct adherence trajectory groups emerged from the data: adherent (33%); gradual discontinuation after 3 months (15%), 6 months (7%), and 9 months (5%); stop-start after 6 months (15%); and immediate discontinuation (25%). Compared to patients 18-24 years of age in the adherent group, patients displaying a stop-start pattern after 6 months had greater odds of having a history of drug abuse (OR = 1.46; 95% CI = 1.26-1.66; P < 0.001), alcohol abuse (OR = 1.34; 95% CI = 1.14-1.53; P< 0.001), and a codiagnosis of major depressive disorder (OR = 1.24; 95% CI = 1.05-1.44; P < 0.001) and were less likely to be aged 35-54 years (OR = 0.66; 95% CI = 0.46-0.85; P < 0.001). CONCLUSIONS: Longitudinal medication adherence patterns can be expressed as distinct trajectories associated with specific patient characteristics and health care utilization patterns. We found 6 distinct patterns of adherence to antipsychotics over 12 months. Patients in different groups may warrant different types of clinical interventions to prevent hospitalizations, longer hospital stays, and increased clinical complexity. For example, clinicians may consider regular home visits, assertive community treatment, and other related interventions for patients at high risk of immediate discontinuation. Health plans should consider supplementing claims-based adherence measures with new technologies that are able to track patient adherence patterns over time. DISCLOSURES: Otsuka Pharmaceutical Development & Commercialization provided support for this research. MacEwan and Shafrin are employees of Precision Health Economics, which was contracted by Otsuka Pharmaceutical Development & Commercialization to conduct this study. Lakdawalla is the Chief Scientific Officer and a founding partner of Precision Health Economics. Forma is an employee of Otsuka Pharmaceutical Development & Commercialization. Hatch is a former employee of Otsuka Pharmaceutical Development & Commercialization and is a current employee of ODH, Inc. Lindenmayer has received grant/research support from Janssen, Lilly, AstraZeneca, Johnson & Johnson, Pfizer, BMS, Otsuka, Dainippon, and Roche and is a consultant for Janssen, Lilly, Merck, Shire, and Lundbeck. Portions of this study were presented as a poster at the American Society of Clinical Psychopharmacology Annual Meeting in Miami Beach, Florida; June 23, 2015; and at the 28th Annual U.S. Psychiatric and Mental Health Congress; San Diego, California; September 12, 2015. Study concept and design were contributed by Forma, Ladkawalla, MacEwan, and Shafrin, along with Hatch and Lindenmayer. MacEwan, Shafrin, Forma, and Lakdawalla collected the data, along with Hatch and Lindenmayer. Data interpretation was performed by Hatch, Lindenmayer, MacEwan, and Shafrin, assisted by Forma and Lakdawalla. The manuscript was written and revised by MacEwan, Forma, and Shafrin, along with Hatch Lakdawalla, and Lindenmayer.