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.

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.

The impact of insulin type on severe hypoglycaemia events requiring inpatient and emergency department care in patients with type 2 diabetes.

AIMS: To evaluate the risk from different insulin types on severe hypoglycaemia (SHG) events requiring inpatient (IP) or emergency department (ED) care in patients with type 2 diabetes. METHODS: Type 2 diabetes patients newly started on insulin in a large commercial claims database were evaluated for SHG events. Patients were classified into an insulin group based on their most frequently used insulin type. Multivariable Cox models assessed the association between insulin type and the risk of SHG events. RESULTS: We identified 8626 patients (mean age 53.5 years; 55% female) with type 2 diabetes followed for an average of 4.0 years after insulin initiation. Of these, 161 (1.9%) had a SHG event at an average of 3.1y after insulin initiation. Patients with SHG events were slightly older (56.4 vs. 53.4 years), used a similar number of OADs (1.1 vs. 1.2) but had more co-morbidities compared with those without SHG events. In multivariate Cox models, premixed insulin (HR 2.12; p<0.01), isophane insulin (NPH) (HR 2.02; p<0.01), and rapid acting insulin (HR 2.75; p<0.01) had significantly higher risks of SHG events compared with glargine. No statistically significant difference in SHG events was seen with detemir (HR 1.20; p=0.73). CONCLUSIONS: Among patients with type 2 diabetes, the use of newer basal insulin analogues was associated with lower rates of SHG events requiring IP or ED care compared with users of other insulin formulations. Future research should examine the impact of hypoglycaemia events of different severity levels.