Joanna P MacEwan1, Felicia M Forma2, Jason Shafrin1, Ainslie Hatch2, Darius N Lakdawalla3, Jean-Pierre Lindenmayer4. 1. 1 Precision Health Economics, Los Angeles, California. 2. 2 Otsuka Pharmaceutical Development & Commercialization, Rockville, Maryland. 3. 3 Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, California. 4. 4 Department of Psychiatry, New York University, New York, New York.
Abstract
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.
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.
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