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THE PEER-REVIEWED FORUM FOR REAL-WORLD EVIDENCE IN BENEFIT DESIGN ™ APRIL 2015

VOLUME 8, NUMBER 2

FOR PAYERS, PURCHASERS, POLICYMAKERS, AND OTHER HEALTHCARE STAKEHOLDERS

EDITORIAL

A Silver Anniversary of the Internet David B. Nash, MD, MBA CLINICAL

Cardiovascular-Related Healthcare Resource Utilization and Costs in Patients with Hypertension Switching from Metoprolol to Nebivolol Stephanie Chen, PhD; An-Chen Fu, MS, BSPharm; Rahul Jain, PhD; Hiangkiat Tan, MS, BSPharm ™

Stakeholder Perspective: Moving Beyond Measures into Outcomes in Hypertension Research By Michael F. Murphy, MD, PhD BUSINESS

The Cost of Unintended Pregnancies for Employer-Sponsored Health Insurance Plans Gabriela Dieguez, FSA, MAAA; Bruce S. Pyenson, FSA, MAAA; Amy W. Law, PharmD; Richard Lynen, MD; James Trussell, PhD Stakeholder Perspective: A Call to Action to Address Burden of Unintended Pregnancies in Plans’ Benefit Design By F. Randy Vogenberg, PhD, RPh

Economic Burden of Opioid-Induced Constipation Among Long-Term Opioid Users with Noncancer Pain Yin Wan, MS, BPharm; Shelby Corman, PharmD, MS, BCPS; Xin Gao, PhD; Sizhu Liu, MS; Haridarshan Patel, PharmD; Reema Mody, PhD, MBA Stakeholder Perspective: Opioid-Induced Constipation Associated with Considerable Economic Burden By Matthew Mitchell, PharmD, MBA, FAMCP

8 8 © 2015 Engage Healthcare Communications, LLC

www.AHDBonline.com


DISCOVERING HOW FAR THERAPY CAN GO IMPORTANT SAFETY INFORMATION WARNINGS AND PRECAUTIONS Hemorrhage - Fatal bleeding events have occurred in patients treated with IMBRUVICA®. Grade 3 or higher bleeding events (subdural hematoma, gastrointestinal bleeding, hematuria, and post-procedural hemorrhage) have occurred in up to 6% of patients. Bleeding events of any grade, including bruising and petechiae, occurred in approximately half of patients treated with IMBRUVICA®. The mechanism for the bleeding events is not well understood. IMBRUVICA® may increase the risk of hemorrhage in patients receiving antiplatelet or anticoagulant therapies. Consider the benefit-risk of withholding IMBRUVICA® for at least 3 to 7 days pre and post-surgery depending upon the type of surgery and the risk of bleeding. Infections - Fatal and non-fatal infections have occurred with IMBRUVICA® therapy. Grade 3 or greater infections occurred in 14% to 26% of patients. Cases of progressive multifocal leukoencephalopathy (PML) have occurred in patients treated with IMBRUVICA®. Monitor patients for fever and infections and evaluate promptly.

Cytopenias - Treatment-emergent Grade 3 or 4 cytopenias including neutropenia (range, 19 to 29%), thrombocytopenia (range, 5 to 17%), and anemia (range, 0 to 9%) occurred in patients treated with IMBRUVICA®. Monitor complete blood counts monthly. Atrial Fibrillation - Atrial fibrillation and atrial flutter (range, 6 to 9%) have occurred in patients treated with IMBRUVICA®, particularly in patients with cardiac risk factors, acute infections, and a previous history of atrial fibrillation. Periodically monitor patients clinically for atrial fibrillation. Patients who develop arrhythmic symptoms (eg, palpitations, lightheadedness) or newonset dyspnea should have an ECG performed. If atrial fibrillation persists, consider the risks and benefits of IMBRUVICA® treatment and dose modification. Second Primary Malignancies - Other malignancies (range, 5 to 14%) including non-skin carcinomas (range, 1 to 3%) have occurred in patients treated with IMBRUVICA®. The most frequent second primary malignancy was non-melanoma skin cancer (range, 4 to 11%).


IMBRUVICA® (ibrutinib) is the first and only FDA-approved therapy for use in patients with Waldenström’s macroglobulinemia (WM) IMBRUVICA® is approved for use in 4 indications IMBRUVICA® is indicated for the treatment of patients with Mantle cell lymphoma (MCL) who have received at least one prior therapy.

Accelerated approval was granted for this indication based on overall response rate. Continued approval for this indication may be contingent upon verification of clinical benefit in confirmatory trials.

Chronic lymphocytic leukemia (CLL) who have received at least one prior therapy. Chronic lymphocytic leukemia with 17p deletion. Waldenström’s macroglobulinemia (WM).

Tumor Lysis Syndrome - Tumor lysis syndrome has been reported with IMBRUVICA® therapy. Monitor patients closely and take appropriate precautions in patients at risk for tumor lysis syndrome (e.g. high tumor burden).

DRUG INTERACTIONS

Embryo-Fetal Toxicity - Based on findings in animals, IMBRUVICA® can cause fetal harm when administered to a pregnant woman. Advise women to avoid becoming pregnant while taking IMBRUVICA®. If this drug is used during pregnancy or if the patient becomes pregnant while taking this drug, the patient should be apprised of the potential hazard to a fetus.

CYP3A Inducers - Avoid co-administration with strong CYP3A inducers.

ADVERSE REACTIONS The most common adverse reactions (≥25%) in patients with B-cell malignancies (MCL, CLL, WM) were thrombocytopenia, neutropenia, diarrhea, anemia, fatigue, musculoskeletal pain, bruising, nausea, upper respiratory tract infection, and rash. Seven percent of patients receiving IMBRUVICA® discontinued treatment due to adverse events.

CYP3A Inhibitors - Avoid co-administration with strong and moderate CYP3A inhibitors. If a moderate CYP3A inhibitor must be used, reduce the IMBRUVICA® dose.

SPECIFIC POPULATIONS Hepatic Impairment - Avoid use in patients with moderate or severe baseline hepatic impairment. In patients with mild impairment, reduce IMBRUVICA® dose. Please review the Brief Summary of full Prescribing Information on the following page.

To learn more, visit

www.IMBRUVICA.com © Pharmacyclics, Inc. 2015 © Janssen Biotech, Inc. 2015 1/15 PRC-00770


Brief Summary of Prescribing Information for IMBRUVICA® (ibrutinib) IMBRUVICA® (ibrutinib) capsules, for oral use See package insert for Full Prescribing Information INDICATIONS AND USAGE Mantle Cell Lymphoma: IMBRUVICA is indicated for the treatment of patients with mantle cell lymphoma (MCL) who have received at least one prior therapy. Accelerated approval was granted for this indication based on overall response rate. Continued approval for this indication may be contingent upon verification of clinical benefit in confirmatory trials [see Clinical Studies (14.1) in Full Prescribing Information]. Chronic Lymphocytic Leukemia: IMBRUVICA is indicated for the treatment of patients with chronic lymphocytic leukemia (CLL) who have received at least one prior therapy [see Clinical Studies (14.2) in Full Prescribing Information]. Chronic Lymphocytic Leukemia with 17p deletion: IMBRUVICA is indicated for the treatment of patients with chronic lymphocytic leukemia (CLL) with 17p deletion [see Clinical Studies (14.2) in Full Prescribing Information]. Waldenström’s Macroglobulinemia: IMBRUVICA is indicated for the treatment of patients with Waldenström’s macroglobulinemia (WM) [see Clinical Studies (14.3) in Full Prescribing Information]. CONTRAINDICATIONS None WARNINGS AND PRECAUTIONS Hemorrhage: Fatal bleeding events have occurred in patients treated with IMBRUVICA. Grade 3 or higher bleeding events (subdural hematoma, gastrointestinal bleeding, hematuria and post procedural hemorrhage) have occurred in up to 6% of patients. Bleeding events of any grade, including bruising and petechiae, occurred in approximately half of patients treated with IMBRUVICA. The mechanism for the bleeding events is not well understood. IMBRUVICA may increase the risk of hemorrhage in patients receiving antiplatelet or anticoagulant therapies. Consider the benefit-risk of withholding IMBRUVICA for at least 3 to 7 days pre and post-surgery depending upon the type of surgery and the risk of bleeding [see Clinical Studies (14) in Full Prescribing Information]. Infections: Fatal and non-fatal infections have occurred with IMBRUVICA therapy. Grade 3 or greater infections occurred in 14% to 26% of patients. [See Adverse Reactions]. Cases of progressive multifocal leukoencephalopathy (PML) have occurred in patients treated with IMBRUVICA. Monitor patients for fever and infections and evaluate promptly. Cytopenias: Treatment-emergent Grade 3 or 4 cytopenias including neutropenia (range, 19 to 29%), thrombocytopenia (range, 5 to 17%), and anemia (range, 0 to 9%) occurred in patients treated with IMBRUVICA. Monitor complete blood counts monthly. Atrial Fibrillation: Atrial fibrillation and atrial flutter (range, 6 to 9%) have occurred in patients treated with IMBRUVICA, particularly in patients with cardiac risk factors, acute infections, and a previous history of atrial fibrillation. Periodically monitor patients clinically for atrial fibrillation. Patients who develop arrhythmic symptoms (e.g., palpitations, lightheadedness) or new onset dyspnea should have an ECG performed. If atrial fibrillation persists, consider the risks and benefits of IMBRUVICA treatment and dose modification [see Dosage and Administration (2.3) in Full Prescribing Information]. Second Primary Malignancies: Other malignancies (range, 5 to 14%) including non-skin carcinomas (range, 1 to 3%) have occurred in patients treated with IMBRUVICA. The most frequent second primary malignancy was non-melanoma skin cancer (range, 4 to 11 %). Tumor Lysis Syndrome: Tumor lysis syndrome has been reported with IMBRUVICA therapy. Monitor patients closely and take appropriate precautions in patients at risk for tumor lysis syndrome (e.g. high tumor burden). Embryo-Fetal Toxicity: Based on findings in animals, IMBRUVICA can cause fetal harm when administered to a pregnant woman. Ibrutinib caused malformations in rats at exposures 14 times those reported in patients with MCL and 20 times those reported in patients with CLL or WM, receiving the ibrutinib dose of 560 mg per day and 420 mg per day, respectively. Reduced fetal weights were observed at lower exposures. Advise women to avoid becoming pregnant while taking IMBRUVICA. If this drug is used during pregnancy or if the patient becomes pregnant while taking this drug, the patient should be apprised of the potential hazard to a fetus [see Use in Specific Populations]. ADVERSE REACTIONS The following adverse reactions are discussed in more detail in other sections of the labeling: • Hemorrhage [see Warnings and Precautions] • Infections [see Warnings and Precautions] • Cytopenias [see Warnings and Precautions] • Atrial Fibrillation [see Warnings and Precautions] • Second Primary Malignancies [see Warnings and Precautions] • Tumor Lysis Syndrome [see Warnings and Precautions]

IMBRUVICA® (ibrutinib) capsules Because clinical trials are conducted under widely variable conditions, adverse event rates observed in clinical trials of a drug cannot be directly compared with rates of clinical trials of another drug and may not reflect the rates observed in practice. Clinical Trials Experience: Mantle Cell Lymphoma: The data described below reflect exposure to IMBRUVICA in a clinical trial that included 111 patients with previously treated MCL treated with 560 mg daily with a median treatment duration of 8.3 months. The most commonly occurring adverse reactions (≥ 20%) were thrombocytopenia, diarrhea, neutropenia, anemia, fatigue, musculoskeletal pain, peripheral edema, upper respiratory tract infection, nausea, bruising, dyspnea, constipation, rash, abdominal pain, vomiting and decreased appetite (see Tables 1 and 2). The most common Grade 3 or 4 non-hematological adverse reactions (≥ 5%) were pneumonia, abdominal pain, atrial fibrillation, diarrhea, fatigue, and skin infections. Fatal and serious cases of renal failure have occurred with IMBRUVICA therapy. Increases in creatinine 1.5 to 3 times the upper limit of normal occurred in 9% of patients. Adverse reactions from the MCL trial (N=111) using single agent IMBRUVICA 560 mg daily occurring at a rate of ≥ 10% are presented in Table 1. Table 1: Non-Hematologic Adverse Reactions in ≥ 10% of Patients with MCL (N=111) System Organ Class Gastrointestinal disorders

Infections and infestations

General disorders and administrative site conditions Skin and subcutaneous tissue disorders Musculoskeletal and connective tissue disorders Respiratory, thoracic and mediastinal disorders Metabolism and nutrition disorders Nervous system disorders

Preferred Term Diarrhea Nausea Constipation Abdominal pain Vomiting Stomatitis Dyspepsia Upper respiratory tract infection Urinary tract infection Pneumonia Skin infections Sinusitis Fatigue Peripheral edema Pyrexia Asthenia Bruising Rash Petechiae Musculoskeletal pain Muscle spasms Arthralgia Dyspnea Cough Epistaxis Decreased appetite Dehydration Dizziness Headache

All Grades (%) 51 31 25 24 23 17 11

Grade 3 or 4 (%) 5 0 0 5 0 1 0

34 14 14 14 13 41 35 18 14 30 25 11 37 14 11 27 19 11 21 12 14 13

0 3 7 5 1 5 3 1 3 0 3 0 1 0 0 4 0 0 2 4 0 0

Table 2: Treatment-Emergent* Decrease of Hemoglobin, Platelets, or Neutrophils in Patients with MCL (N=111)

Platelets Decreased Neutrophils Decreased Hemoglobin Decreased

Percent of Patients (N=111) All Grades Grade 3 or 4 (%) (%) 57 17 47 29 41 9

* Based on laboratory measurements and adverse reactions Ten patients (9%) discontinued treatment due to adverse reactions in the trial (N=111). The most frequent adverse reaction leading to treatment discontinuation was subdural hematoma (1.8%). Adverse reactions leading to dose reduction occurred in 14% of patients.


IMBRUVICA® (ibrutinib) capsules

IMBRUVICA® (ibrutinib) capsules

Patients with MCL who develop lymphocytosis greater than 400,000/mcL have developed intracranial hemorrhage, lethargy, gait instability, and headache. However, some of these cases were in the setting of disease progression. Forty percent of patients had elevated uric acid levels on study including 13% with values above 10 mg/dL. Adverse reaction of hyperuricemia was reported for 15% of patients. Chronic Lymphocytic Leukemia: The data described below reflect exposure to IMBRUVICA in an open label clinical trial (Study 1) that included 48 patients with previously treated CLL and a randomized clinical trial (Study 2) that included 391 randomized patients with previously treated CLL or SLL. The most commonly occurring adverse reactions in Study 1 and Study 2 (≥ 20%) were thrombocytopenia, neutropenia, diarrhea, anemia, fatigue, musculoskeletal pain, upper respiratory tract infection, rash, nausea, and pyrexia. Approximately five percent of patients receiving IMBRUVICA in Study 1 and Study 2 discontinued treatment due to adverse events. These included infections, subdural hematomas and diarrhea. Adverse events leading to dose reduction occurred in approximately 6% of patients. Study 1: Adverse reactions and laboratory abnormalities from the CLL trial (N=48) using single agent IMBRUVICA 420 mg daily occurring at a rate of ≥ 10% are presented in Tables 3 and 4. Table 3: Non-Hematologic Adverse Reactions in ≥ 10% of Patients with CLL (N=48) in Study 1 System Organ Class Gastrointestinal disorders

Infections and infestations

General disorders and administrative site conditions Skin and subcutaneous tissue disorders Respiratory, thoracic and mediastinal disorders Musculoskeletal and connective tissue disorders Nervous system disorders Metabolism and nutrition disorders Neoplasms benign, malignant, unspecified Injury, poisoning and procedural complications Psychiatric disorders Vascular disorders

All Grades (%)

Grade 3 or 4 (%)

Diarrhea Constipation Nausea Stomatitis Vomiting Abdominal pain Dyspepsia Upper respiratory tract infection Sinusitis Skin infection Pneumonia Urinary tract infection Fatigue Pyrexia Peripheral edema Asthenia Chills Bruising Rash Petechiae Cough Oropharyngeal pain Dyspnea Musculoskeletal pain Arthralgia Muscle spasms Dizziness Headache Peripheral neuropathy Decreased appetite

63 23 21 21 19 15 13

4 2 2 0 2 0 0

48 21 17 10 10 31 25 23 13 13 54 27 17 19 15 10 27 23 19 21 19 10 17

2 6 6 8 0 4 2 0 4 0 2 0 0 0 0 0 6 0 2 0 2 0 2

Second malignancies*

10*

0

Laceration

10

2

Anxiety Insomnia Hypertension

10 10 17

0 0 8

Preferred Term

*One patient death due to histiocytic sarcoma.

Table 4: Treatment-Emergent* Decrease of Hemoglobin, Platelets, or Neutrophils in Patients with CLL (N=48) in Study 1 Percent of Patients (N=48) All Grades Grade 3 or 4 (%) (%) Platelets Decreased 71 10 Neutrophils Decreased 54 27 Hemoglobin Decreased 44 0 * Based on laboratory measurements per IWCLL criteria and adverse reactions Study 2: Adverse reactions and laboratory abnormalities described below in Tables 5 and 6 reflect exposure to IMBRUVICA with a median duration of 8.6 months and exposure to ofatumumab with a median of 5.3 months in Study 2. Table 5: Non-Hematologic Adverse Reactions ≥ 10% Reported in Study 2

System Organ Class ADR Term Gastrointestinal disorders Diarrhea Nausea Stomatitis* Constipation Vomiting General disorders and administration site conditions Fatigue Pyrexia Infections and infestations Upper respiratory tract infection Pneumonia* Sinusitis* Urinary tract infection Skin and subcutaneous tissue disorders Rash* Petechiae Bruising* Musculoskeletal and connective tissue disorders Musculoskeletal Pain* Arthralgia Nervous system disorders Headache Dizziness Injury, poisoning and procedural complications Contusion Eye disorders Vision blurred

IMBRUVICA (N=195) All Grade Grades 3 or 4 (%) (%)

Ofatumumab (N=191) All Grade Grades 3 or 4 (%) (%)

48 26 17 15 14

4 2 1 0 0

18 18 6 9 6

2 0 1 0 1

28 24

2 2

30 15

2 1

16 15 11 10

1 10 1 4

11 13 6 5

2 9 0 1

24 14 12

3 0 0

13 1 1

0 0 0

28 17

2 1

18 7

1 0

14 11

1 0

6 5

0 0

11

0

3

0

10

0

3

0

Subjects with multiple events for a given ADR term are counted once only for each ADR term. The system organ class and individual ADR terms are sorted in descending frequency order in the IMBRUVICA arm. * Includes multiple ADR terms


IMBRUVICA® (ibrutinib) capsules

IMBRUVICA® (ibrutinib) capsules

Table 6: Treatment-Emergent* Decrease of Hemoglobin, Platelets, or Neutrophils in Study 2

Neutrophils Decreased Platelets Decreased Hemoglobin Decreased

IMBRUVICA (N=195) Grade All 3 or 4 Grades (%) (%) 51 23 52 5 36 0

Ofatumumab (N=191) Grade All 3 or 4 Grades (%) (%) 57 26 45 10 21 0

* Based on laboratory measurements per IWCLL criteria Waldenström’s Macroglobulinemia The data described below reflect exposure to IMBRUVICA in an open label clinical trial that included 63 patients with previously treated WM. The most commonly occurring adverse reactions in the WM trial (≥ 20%) were neutropenia, thrombocytopenia, diarrhea, rash, nausea, muscle spasms, and fatigue. Six percent of patients receiving IMBRUVICA in the WM trial discontinued treatment due to adverse events. Adverse events leading to dose reduction occurred in 11% of patients. Adverse reactions and laboratory abnormalities described below in Tables 7 and 8 reflect exposure to IMBRUVICA with a median duration of 11.7 months in the WM trial. Table 7: Non-Hematologic Adverse Reactions in ≥ 10% of Patients with Waldenström’s Macroglobulinemia (N=63) System Organ Class Gastrointestinal disorders

Skin and subcutaneous tissue disorders General disorders and administrative site conditions Musculoskeletal and connective tissue disorders Infections and infestations

Respiratory, thoracic and mediastinal disorders Nervous system disorders Neoplasms benign, malignant, and unspecified (including cysts and polyps)

All Grades (%) 37 21 16

Grade 3 or 4 (%) 0 0 0

13 22 16 11 21

0 0 0 0 0

Muscle spasms Arthropathy

21 13

0 0

Upper respiratory tract infection Sinusitis Pneumonia* Skin infection* Epistaxis Cough

19 19 14 14 19 13

0 0 6 2 0 0

Dizziness Headache Skin cancer*

14 13 11

0 0 0

Preferred Term Diarrhea Nausea Stomatitis* Gastroesophageal reflux disease Rash* Bruising* Pruritus Fatigue

The system organ class and individual ADR terms are sorted in descending frequency order. * Includes multiple ADR terms. Table 8: Treatment-Emergent* Decrease of Hemoglobin, Platelets, or Neutrophils in Patients with WM (N=63)

Platelets Decreased Neutrophils Decreased Hemoglobin Decreased

Percent of Patients (N=63) All Grades (%) Grade 3 or 4 (%) 43 13 44 19 13 8

* Based on laboratory measurements.

Postmarketing Experience: The following adverse reactions have been identified during post-approval use of IMBRUVICA. Because these reactions are reported voluntarily from a population of uncertain size, it is not always possible to reliably estimate their frequency or establish a causal relationship to drug exposure. Hypersensitivity reactions including anaphylactic shock (fatal), urticaria, and angioedema have been reported. DRUG INTERACTIONS Ibrutinib is primarily metabolized by cytochrome P450 enzyme 3A. CYP3A Inhibitors: In healthy volunteers, co-administration of ketoconazole, a strong CYP3A inhibitor, increased Cmax and AUC of ibrutinib by 29- and 24-fold, respectively. The highest ibrutinib dose evaluated in clinical trials was 12.5 mg/kg (actual doses of 840 – 1400 mg) given for 28 days with single dose AUC values of 1445 ± 869 ng • hr/mL which is approximately 50% greater than steady state exposures seen at the highest indicated dose (560 mg). Avoid concomitant administration of IMBRUVICA with strong or moderate inhibitors of CYP3A. For strong CYP3A inhibitors used short-term (e.g., antifungals and antibiotics for 7 days or less, e.g., ketoconazole, itraconazole, voriconazole, posaconazole, clarithromycin, telithromycin) consider interrupting IMBRUVICA therapy during the duration of inhibitor use. Avoid strong CYP3A inhibitors that are needed chronically. If a moderate CYP3A inhibitor must be used, reduce the IMBRUVICA dose. Patients taking concomitant strong or moderate CYP3A4 inhibitors should be monitored more closely for signs of IMBRUVICA toxicity [see Dosage and Administration (2.4) in Full Prescribing Information]. Avoid grapefruit and Seville oranges during IMBRUVICA treatment, as these contain moderate inhibitors of CYP3A [see Dosage and Administration (2.4), and Clinical Pharmacology (12.3) in Full Prescribing Information]. CYP3A Inducers: Administration of IMBRUVICA with rifampin, a strong CYP3A inducer, decreased ibrutinib Cmax and AUC by approximately 13- and 10-fold, respectively. Avoid concomitant use of strong CYP3A inducers (e.g., carbamazepine, rifampin, phenytoin and St. John’s Wort). Consider alternative agents with less CYP3A induction [see Clinical Pharmacology (12.3) in Full Prescribing Information]. USE IN SPECIFIC POPULATIONS Pregnancy: Pregnancy Category D [see Warnings and Precautions]. Risk Summary: Based on findings in animals, IMBRUVICA can cause fetal harm when administered to a pregnant woman. If IMBRUVICA is used during pregnancy or if the patient becomes pregnant while taking IMBRUVICA, the patient should be apprised of the potential hazard to the fetus. Animal Data: Ibrutinib was administered orally to pregnant rats during the period of organogenesis at oral doses of 10, 40 and 80 mg/kg/day. Ibrutinib at a dose of 80 mg/kg/day was associated with visceral malformations (heart and major vessels) and increased post-implantation loss. The dose of 80 mg/kg/day in animals is approximately 14 times the exposure (AUC) in patients with MCL and 20 times the exposure in patients with CLL or WM administered the dose of 560 mg daily and 420 mg daily, respectively. Ibrutinib at doses of 40 mg/kg/day or greater was associated with decreased fetal weights. The dose of 40 mg/kg/day in animals is approximately 6 times the exposure (AUC) in patients with MCL administered the dose of 560 mg daily. Nursing Mothers: It is not known whether ibrutinib is excreted in human milk. Because many drugs are excreted in human milk and because of the potential for serious adverse reactions in nursing infants from IMBRUVICA, a decision should be made whether to discontinue nursing or to discontinue the drug, taking into account the importance of the drug to the mother. Pediatric Use: The safety and effectiveness of IMBRUVICA in pediatric patients has not been established. Geriatric Use: Of the 111 patients treated for MCL, 63% were 65 years of age or older. No overall differences in effectiveness were observed between these patients and younger patients. Cardiac adverse events (atrial fibrillation and hypertension), infections (pneumonia and cellulitis) and gastrointestinal events (diarrhea and dehydration) occurred more frequently among elderly patients. Of the 391 patients randomized in Study 2, 61% were ≥ 65 years of age. No overall differences in effectiveness were observed between age groups. Grade 3 or higher adverse events occurred more frequently among elderly patients treated with IMBRUVICA (61% of patients age ≥ 65 versus 51% of younger patients) [see Clinical Studies (14.2) in Full Prescribing Information]. Of the 63 patients treated for WM, 59% were 65 years of age or older. No overall differences in effectiveness were observed between these patients and younger patients. Cardiac adverse events (atrial fibrillation and hypertension), and infections (pneumonia and urinary tract infection) occurred more frequently among elderly patients. Renal Impairment: Less than 1% of ibrutinib is excreted renally. Ibrutinib exposure is not altered in patients with Creatinine clearance (CLcr) > 25 mL/min. There are no data in patients with severe renal impairment (CLcr < 25 mL/min) or patients on dialysis [see Clinical Pharmacology (12.3) in Full Prescribing Information].


IMBRUVICA® (ibrutinib) capsules Hepatic Impairment: Ibrutinib is metabolized in the liver. In a hepatic impairment study, data showed an increase in ibrutinib exposure. Following single dose administration, the AUC of ibrutinib increased 2.7-, 8.2- and 9.8-fold in subjects with mild (Child-Pugh class A), moderate (Child-Pugh class B), and severe (Child-Pugh class C) hepatic impairment compared to subjects with normal liver function. The safety of IMBRUVICA has not been evaluated in patients with hepatic impairment. Monitor patients for signs of IMBRUVICA toxicity and follow dose modification guidance as needed. It is not recommended to administer IMBRUVICA to patients with moderate or severe hepatic impairment (Child-Pugh classes B and C) [see Dosage and Administration (2.5) and Clinical Pharmacology (12.3) in Full Prescribing Information]. Females and Males of Reproductive Potential: Advise women to avoid becoming pregnant while taking IMBRUVICA because IMBRUVICA can cause fetal harm [see Use in Specific Populations]. Plasmapheresis: Management of hyperviscosity in patients with WM may include plasmapheresis before and during treatment with IMBRUVICA. Modifications to IMBRUVICA dosing are not required. PATIENT COUNSELING INFORMATION See FDA-approved patient labeling (Patient Information). • Hemorrhage: Inform patients of the possibility of bleeding, and to report any signs or symptoms (blood in stools or urine, prolonged or uncontrolled bleeding). Inform the patient that IMBRUVICA may need to be interrupted for medical or dental procedures [see Warnings and Precautions]. • Infections: Inform patients of the possibility of serious infection, and to report any signs or symptoms (fever, chills, weakness, confusion) suggestive of infection [see Warnings and Precautions]. • Atrial Fibrillation: Counsel patients to report any signs of palpitations, lightheadedness, dizziness, fainting, shortness of breath, and chest discomfort [see Warnings and Precautions]. • Second primary malignancies: Inform patients that other malignancies have occurred in patients who have been treated with IMBRUVICA, including skin cancers and other carcinomas [see Warnings and Precautions]. • Tumor lysis syndrome: Inform patients of the potential risk of tumor lysis syndrome and report any signs and symptoms associated with this event to their healthcare provider for evaluation [see Warnings and Precautions]. • Embryo-fetal toxicity: Advise women of the potential hazard to a fetus and to avoid becoming pregnant [see Warnings and Precautions]. • Inform patients to take IMBRUVICA orally once daily according to their physician’s instructions and that the capsules should be swallowed whole with a glass of water without being opened, broken, or chewed at approximately the same time each day [see Dosage and Administration (2.1) in Full Prescribing Information]. • Advise patients that in the event of a missed daily dose of IMBRUVICA, it should be taken as soon as possible on the same day with a return to the normal schedule the following day. Patients should not take extra capsules to make up the missed dose [see Dosage and Administration (2.5) in Full Prescribing Information]. • Advise patients of the common side effects associated with IMBRUVICA [see Adverse Reactions]. Direct the patient to a complete list of adverse drug reactions in PATIENT INFORMATION. • Advise patients to inform their health care providers of all concomitant medications, including prescription medicines, over-the-counter drugs, vitamins, and herbal products [see Drug Interactions]. • Advise patients that they may experience loose stools or diarrhea, and should contact their doctor if their diarrhea persists. Advise patients to maintain adequate hydration. Active ingredient made in China. Distributed and Marketed by: Pharmacyclics, Inc. Sunnyvale, CA USA 94085 and Marketed by: Janssen Biotech, Inc. Horsham, PA USA 19044 Patent http://www.imbruvica.com IMBRUVICA® is a registered trademark owned by Pharmacyclics, Inc. © Pharmacyclics, Inc. 2015 © Janssen Biotech, Inc. 2015 PRC-00786


EDITORIAL BOARD EDITOR-IN-CHIEF

David B. Nash, MD, MBA Founding Dean, The Dr Raymond C. and Doris N. Grandon Professor, Jefferson School of Population Health Thomas Jefferson University, Philadelphia, PA DEPUTY EDITORS

Joseph D. Jackson, PhD Program Director, Applied Health Economics and Outcomes Research, Jefferson School of Population Health, Thomas Jefferson University Laura T. Pizzi, PharmD, MPH, RPh Professor, Dept. of Pharmacy Practice, Jefferson School of Pharmacy, Thomas Jefferson University AGING AND WELLNESS

Eric G. Tangalos, MD, FACP, AGSF, CMD Professor of Medicine Mayo Clinic, Rochester, MN CANCER RESEARCH

Al B. Benson, III, MD, FACP, FASCO Professor of Medicine, Associate Director for Clinical Investigations Robert H. Lurie Comprehensive Cancer Center Northwestern University, IL Samuel M. Silver, MD, PhD, FASCO Professor of Internal Medicine, Hematology/Oncology Assistant Dean for Research, Associate Director Faculty Group Practice, University of Michigan Medical School EMPLOYERS

Gregory Shaeffer, MBA, RPh, FASHP Vice President, Consulting Pharmacy Healthcare Solutions AmerisourceBurgen, Harrisburg, PA Arthur F. Shinn, PharmD, FASCP President, Managed Pharmacy Consultants, LLC, Lake Worth, FL F. Randy Vogenberg, RPh, PhD Principal, Institute for Integrated Healthcare Greenville, SC ENDOCRINOLOGY

James V. Felicetta, MD Chairman, Dept. of Medicine Carl T. Hayden Veterans Affairs Medical Center, Phoenix, AZ Quang Nguyen, DO, FACP, FACE Medical Director, Las Vegas Endocrinology Adjunct Associate Professor Endocrinology Touro University Nevada EPIDEMIOLOGY RESEARCH

Joshua N. Liberman, PhD Executive Director, Research, Development & Dissemination, Sutter Health, Concord, CA GOVERNMENT

Kevin B. “Kip” Piper, MA, FACHE President, Health Results Group, LLC Washington, DC HEALTH INFORMATION TECHNOLOGY

Kelly Huang, PhD Operating Partner, Spindletop Capital Austin, TX HEALTH OUTCOMES RESEARCH

Russell Basser, MBBS, MD, FRACP Senior Vice President Global Clinical Research and Development CSL Behring, King of Prussia, PA Diana Brixner, RPh, PhD Professor & Chair, Dept. of Pharmacotherapy Executive Director, Outcomes Research Center Director of Outcomes, Personalized Health Care Program, University of Utah, Salt Lake City

60

l

Joseph E. Couto, PharmD, MBA Clinical Program Manager Cigna Corporation, Bloomfield, CT Steven Miff, PhD Senior Vice President VHA, Inc., Irving, TX Kavita V. Nair, PhD Professor and Director, Graduate Program Track in Pharmaceutical Outcomes Research Skaggs School of Pharmacy and Pharmaceutical Sciences University of Colorado, Aurora Gary M. Owens, MD President, Gary Owens Associates Ocean View, DE Andrew M. Peterson, PharmD, PhD Dean, Mayes School of Healthcare Business and Policy, Associate Professor, University of the Sciences, Philadelphia Sarah A. Priddy, PhD Director, Competitive Health Analytics Humana, Louisville, KY Timothy S. Regan, BPharm, RPh, CPh Executive Director, Strategic Accounts Xcenda, Palm Harbor, FL Vincent J. Willey, PharmD, BCACP Staff Vice President HealthCore, Inc., Wilmington, DE David W. Wright, MPH President, Institute for Interactive Patient Care Bethesda, MD HEALTH & VALUE PROMOTION

Craig Deligdish, MD Hematologist/Oncologist Oncology Resource Networks, Orlando, FL Thomas G. McCarter, MD, FACP Chief Clinical Officer Executive Health Resources, PA Byron C. Scott, MD, MBA Medical Director National Clinical Medical Leader Truven Health Analytics, Chicago, IL Albert Tzeel, MD, MHSA, FACPE Regional Medical Director Medicare Operations, North Florida Humana, Jacksonville MANAGED MARKETS

Jeffrey A. Bourret, PharmD, MS, BCPS, FASHP Senior Director, North America Medical Affairs Medical Lead, Specialty Payer & Channel Customer Strategy, Pfizer Inc Richard B. Weininger, MD Chairman, CareCore National, LLC Bluffton, SC PATIENT ADVOCACY

Mike Pucci Sr VP, Commercial Operations and Business Development, PhytoChem Pharmaceuticals Lake Gaston, NC

Jeff Jianfei Guo, BPharm, MS, PhD Professor of Pharmacoeconomics & Pharmacoepidemiology, College of Pharmacy Univ. of Cincinnati Medical Center, OH PHARMACY BENEFIT DESIGN

Joel V. Brill, MD, AGAF, CHCQM Chief Medical Officer, Predictive Health, Phoenix, AZ Teresa DeLuca, MD, MBA Assistant Clinical Professor, Psychiatry, Mount Sinai School of Medicine, New York, NY Leslie S. Fish, PharmD Vice President of Clinical Programs Fallon Community Health Plan, MA John Hornberger, MD, MS Cedar Associates, LLC CHP/PCOR Adjunct Associate, Menlo Park, CA Michael S. Jacobs, RPh MSJ Associates, Sandy Springs, GA Matthew Mitchell, PharmD, MBA, FAMCP Director, Pharmacy Services SelectHealth, Murray, UT Paul Anthony Polansky, BSPharm, MBA PAPRx, LLC Gulph Mills, PA Christina A. Stasiuk, DO, FACOI Senior Medical Director Cigna, Philadelphia, PA POLICY & PUBLIC HEALTH

Joseph R. Antos, PhD Wilson H. Taylor Scholar in Health Care Retirement Policy, American Enterprise Institute Washington, DC Robert W. Dubois, MD, PhD Chief Science Officer National Pharmaceutical Council, Washington, DC Jack E. Fincham, PhD, RPh Professor of Pharmacy, School of Pharmacy Presbyterian College, Clinton, SC Walid F. Gellad, MD, MPH Assistant Professor of Medicine, University of Pittsburgh, Staff Physician, Pittsburgh VA Medical Center, Adjunct Scientist, RAND Health Paul Pomerantz, MBA CEO, American Society of Anesthesiologists Park Ridge, IL J. Warren Salmon, PhD Professor of Health Policy & Administration School of Public Health University of Illinois at Chicago Raymond L. Singer, MD, MMM, CPE, FACS Chief, Division of Cardiothoracic Surgery Vice Chair, Department of Surgery for Quality & Patient Safety and Outreach Lehigh Valley Health Network, PA RESEARCH & DEVELOPMENT

PAYER-PROVIDER FINANCES

Bruce Pyenson, FSA, MAAA Principal & Consulting Actuary Milliman, Inc, New York, NY

Christopher (Chris) P. Molineaux President, Pennsylvania BIO Malvern, PA Michael F. Murphy, MD, PhD Chief Medical Officer and Scientific Officer Worldwide Clinical Trials King of Prussia, PA

PERSONALIZED MEDICINE

SPECIALTY PHARMACY

Amalia M. Issa, PhD, MPH Director, Program in Personalized Medicine & Targeted Therapeutics, University of the Sciences, Philadelphia PHARMACOECONOMICS

Josh Feldstein President & CEO, CAVA, The Center for Applied Value Analysis, Inc, Norwalk, CT

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Atheer A. Kaddis, PharmD Senior Vice President Sales and Business Development Diplomat Specialty Pharmacy, Flint, MI James T. Kenney, Jr, RPh, MBA Pharmacy Operations Manager, Harvard Pilgrim Health Care, Wellesley, MA Michael Kleinrock Director, Research Development IMS Institute for Healthcare Informatics

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VOLUME 8, NUMBER 2 THE PEER-REVIEWED FORUM FOR REAL-WORLD EVIDENCE IN BENEFIT DESIGN ™

TABLE OF CONTENTS

PUBLISHING STAFF Senior Vice President/Group Publisher Nicholas Englezos nenglezos@the-lynx-group.com Directors, Client Services Joe Beck jbeck@the-lynx-group.com Zach Ceretelle zceretelle@the-lynx-group.com Ron Gordon rgordon@the-lynx-group.com Editorial Director Dalia Buffery dbuffery@the-lynx-group.com Senior Associate Editor Lilly Ostrovsky Associate Editor Lara J. Lorton Editorial Assistant Cara Guglielmon Production Manager Cara Nicolini Founding Editor-in-Chief Robert E. Henry

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A Silver Anniversary of the Internet David B. Nash, MD, MBA

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 ardiovascular-Related Healthcare Resource Utilization and Costs in Patients C with Hypertension Switching from Metoprolol to Nebivolol Stephanie Chen, PhD; An-Chen Fu, MS, BSPharm; Rahul Jain, PhD; Hiangkiat Tan, MS, BSPharm 79

 takeholder Perspective: Moving Beyond Measures into Outcomes in S Hypertension Research By Michael F. Murphy, MD, PhD

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T he Cost of Unintended Pregnancies for Employer-Sponsored Health Insurance Plans Gabriela Dieguez, FSA, MAAA; Bruce S. Pyenson, FSA, MAAA; Amy W. Law, PharmD; Richard Lynen, MD; James Trussell, PhD 90

 takeholder Perspective: A Call to Action to Address Burden of Unintended S Pregnancies in Plans’ Benefit Design By F. Randy Vogenberg, PhD, RPh Continued on page 62

Mission Statement American Health & Drug Benefits is founded on the concept that health and drug benefits have undergone a transformation: the econo­metric value of a drug is of equal importance to clinical outcomes as it is to serving as the basis for securing coverage in formularies and benefit designs. Because benefit designs are greatly affected by clinical, business, and policy conditions, this journal offers a forum for stakeholder integration and collaboration toward the im­provement of healthcare. This publication further provides benefit design de­cision makers the integrated industry information they require to devise formularies and benefit designs that stand up to today’s special healthcare delivery and business needs. Contact Information: For subscription information and edi­torial queries, please contact: editorial@engagehc.com; phone: 732-992-1892; fax: 732-992-1881.

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THE LYNX GROUP President/CEO Brian Tyburski Chief Operating Officer Pam Rattananont Ferris Vice President of Finance Andrea Kelly Human Resources Jennine Leale Director, Strategy & Program Development John Welz Director, Quality Control Barbara Marino Quality Control Assistant Theresa Salerno Director, Production & Manufacturing Alaina Pede Director, Creative & Design Robyn Jacobs Creative & Design Assistants Lora LaRocca Wayne Williams Director, Digital Media Anthony Romano Jr Digital Media Specialist Charles Easton IV Web Content Manager Anthony Trevean Digital Programmer Michael Amundsen Meeting & Events Planner Linda Mezzacappa Project Managers Deanna Martinez Jeremy Shannon Project Coordinator Rachael Baranoski IT Manager Kashif Javaid Administrative Team Leader Allison Ingram Administrative Assistant Amanda Hedman Office Coordinator Robert Sorensen Engage Healthcare Communications, LLC 1249 South River Road - Ste 202A Cranbury, NJ 08512 phone: 732-992-1880 fax: 732-992-1881

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FOR PAYERS, PURCHASERS, POLICYMAKERS, AND OTHER HEALTHCARE STAKEHOLDERS

TABLE OF CONTENTS

(Continued)

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Economic Burden of Opioid-Induced Constipation Among Long-Term Opioid Users with Noncancer Pain Yin Wan, MS, BPharm; Shelby Corman, PharmD, MS, BCPS; Xin Gao, PhD; Sizhu Liu, MS; Haridarshan Patel, PharmD; Reema Mody, PhD, MBA 102 S  takeholder Perspective: Opioid-Induced Constipation Associated with Considerable Economic Burden By Matthew Mitchell, PharmD, MBA, FAMCP

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EDITORIAL

A Silver Anniversary of the Internet David B. Nash, MD, MBA Editor-in-Chief, American Health & Drug Benefits; Founding Dean, J ­ efferson School of Population Health, Philadelphia, PA

N

ow that we are solidly into 2015, we should take some time to reflect on a recent special silver anniversary and contemplate what this means for health and healthcare in the next few years. The birthday of the Web is often cited as March 12, 1989.1 On that date, Sir Tim Berners-Lee produced a document that became the foundation for the Web. Back then, Berners-Lee was “a young software consultant for CERN, a large physics lab in Geneva with over 5,000 scientists, many working in remote offices around ­ was hampered in his research by the world.” 1 Berners-Lee the challenges posed by the company’s multiple global offices. “He wrote a proposal for ‘an information management’ system to efficiently link research and documents across countries” and time zones.1 He set about to write the code for the World Wide Web and released it on Christmas Day 19901 (in my view, the real anniversary!), thereby launching his creation—the Web—which was originally called Mesh.1 (Please note that experts, even those at the Pew Research Center’s Internet and American Life Project, use the terms Web and Internet interchangeably, but these are not the same.1 The Internet facilitates the exchange of information, serving as the transport mechanism for content. Web content is one way to make information available for viewing.) Although we can all extol the virtues of Amazon, Google, Facebook, and the like, what may be some of the important medically related advances that we can tie to the anniversary of the Internet in the next few years? Many experts believe that the $3-billion effort to decode the human genome could not have taken place without the existence of the Internet, which enabled scientists to share gigantic data sets as they attempted to sequence the entire genome. Without that instant capability to compare their progress worldwide, it might have taken many more decades. Gene sequencing is becoming routine; it can be done quickly, in large numbers, and relatively inexpensively. As reported in the Economist in January 1, 2015, “a new gene-sequencing machine developed by Illumina, a San Diego–based company, can mint a genome every 25 minutes.” 2 According to Illumina President Francis de-

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Souza, “the number of whole genomes sequenced will double in 2015.” 2 In 2014, 229,000 whole genomes were sequenced; in 2015, we will exceed 422,000, and are predicted to reach 952,000 by 2016.2 This silver anniversary is, therefore, directly connected to some science fiction–like benefits in the next 2 years. For example, “the earliest beneficiaries of cheap, fast sequencing will be pregnant women. Prenatal diagnosis is about to undergo a dramatic change, as sequencing can detect fetal abnormalities in maternal blood samples. This does away with the need for invasive (and potentially harmful) tests for disorders such as Down’s Syndrome. Mr deSouza says that in two to three years non-invasive tests will become the norm for pregnancies of average risk, and within two to three years babies will be sequenced at birth.” 2 He further claims that “it will also become routine to sequence the genomes of tumours,” and that within a decade such sequencing will make cancer a chronic disease.2 Lee Rainie, Director of the Pew Internet Project, contends that “in the next 10 years, we’ll wear the Internet. We’ll walk into rooms that are connected rooms. We’ll walk down streets that are full of connected devices. We’ll drive in the Internet. It will be so embedded in our lives that we’ll be less and less aware of it. Like electricity, we’ll only notice it when it’s not working.” 1 This notion of the “Internet of Things” is now widely regarded as the most likely scenario to describe the maturation of the Web.1 From a healthcare perspective, this “Internet of Things” will enable us to personally tailor our diet and exercise regimens, test our urine and feces through Web-enabled laboratory devices, and enable physicians to target drugs with a laser beam–like focus, having fully analyzed a patient-specific genome. Some experts also believe we will see an explosion in wearable devices that will provide early detection for disease risks, not just the disease itself. “We may literally be able to adjust both medications and lifestyle changes on a day-to-day basis, thus enormously magnifying the effectiveness of an ever more understaffed medical delivery system.” 1 More than 1600 experts participate regularly in the

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EDITORIAL

Pew research regarding the influence of the Internet on the life of Americans. These prognosticators also believe that dramatic advances in robotics and telemedicine are just around the corner. For example, they see that some chronically ill or elderly patients will be re-

These prognosticators also believe that dramatic advances in robotics and telemedicine are just around the corner. Others believe that the drugstore and other cliniclike settings will house “booths that function as remote examining, treatment and simple surgery rooms,” all because of the “Internet of Things.” leased from healthcare institutions back to their home with a kit of sensors that home nurses can use. Others believe that the drugstore and other cliniclike settings will house “booths that function as remote examining, treatment and simple surgery rooms,” all because of the “Internet of Things.” 1 The Pew researchers also note that tests are under way, in our country and around the world, “to achieve

network speeds that are 50-100 times faster than today’s average high-speed connection.” 1 This speed, and its increase in bandwidth, “will play the same kind of transformational role in reshaping society that railroads and freeways played in our past.” 1 I am fascinated by the possibilities beyond the silver anniversary of the Internet. Imagine what practicing medicine will be like for one of my twin daughters, now an intern in a Medicine training program, 25 years from now! Her silver anniversary is hard to visualize today. Increasing bandwidth, genome sequencing in 25 minutes for less than $1000, targeted drug therapy, the Internet of Things, and complete patient engagement in their personal care, are well under way right now. To learn more about the Pew study, I recommend that you visit pewresearch.org/web25. Take a look at some of the reports at this site, including the Web25, Digital Life in 2025, and the Internet of Things. I am curious as to how you may view digital life in 2015 and beyond, and as always, I am interested in your views. You can reach me at david.nash@jefferson.edu. n

References

1. Maloney J. Our lives online. November 21, 2014. http://magazine.pewtrusts.org/­­ en/archive/fall-2014/our-lives-online. Accessed March 10, 2015. 2. Loder N. Genes, unzipped: so much genetic data; so many uses. The Economist. The World in 2015 [Special Issue]. January 1, 2015; p 150.

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Consider once-weekly TANZEUM for formulary inclusion

• The lowest Wholesale Acquisition Cost (WAC) in the GLP-1 receptor agonist class1,a — WAC comparison does not imply comparable safety or effectiveness and does not imply identical indications — No Phase III clinical trial data are available comparing the efficacy of TANZEUM to Bydureon® (exenatide extended-release for injectable suspension), Byetta® (exenatide) Injection, or Trulicity™ (dulaglutide) injection, for subcutaneous use. In a head-to-head trial of TANZEUM vs Victoza, TANZEUM provided less HbA1c reduction than Victoza® (liraglutide [rDNA origin] injection), solution for subcutaneous use and the treatment difference was statistically significant • Available in 2 dosage strengths at the same WAC price1,2: 30-mg and 50-mg, single-dose pens • The safety and efficacy for TANZEUM have been evaluated in a clinical trial program comprising 8 Phase III studies and 2365 patients who received TANZEUM2 a

WAC is the listed price to wholesalers and warehousing chains, not including prompt pay, stocking or distribution allowances, or other discounts, rebates, or chargebacks. The listed price may not represent prices charged to other customers, including specialty distributors. WAC does not reflect the price paid by consumers.1

Indications and Usage for TANZEUM TANZEUM is indicated as an adjunct to diet and exercise to improve glycemic control in adults with type 2 diabetes mellitus.

Limitations of Use: • TANZEUM is not recommended as first-line therapy for patients inadequately controlled on diet and exercise. • TANZEUM has not been studied in patients with a history of pancreatitis. Consider other antidiabetic therapies in patients with a history of pancreatitis. • TANZEUM is not indicated in the treatment of patients with type 1 diabetes mellitus or for the treatment of patients with diabetic ketoacidosis. TANZEUM is not a substitute for insulin in these patients. • TANZEUM has not been studied in patients with severe gastrointestinal disease, including severe gastroparesis. The use of TANZEUM is not recommended in patients with pre-existing severe gastrointestinal disease. • TANZEUM has not been studied in combination with prandial insulin.

Important Safety Information for TANZEUM WARNING: RISK OF THYROID C-CELL TUMORS Thyroid C-cell tumors have been observed in rodent studies with glucagon-like peptide-1 (GLP-1) receptor agonists at clinically relevant exposures. It is unknown whether TANZEUM causes thyroid C-cell tumors, including medullary thyroid carcinoma (MTC), in humans. TANZEUM is contraindicated in patients with a personal or family history of MTC or in patients with Multiple Endocrine Neoplasia syndrome type 2 (MEN 2). Routine serum calcitonin or thyroid ultrasound monitoring is of uncertain value in patients treated with TANZEUM. Patients should be counseled regarding the risk and symptoms of thyroid tumors.

CONTRAINDICATIONS Hypersensitivity: TANZEUM is contraindicated in patients with a prior serious hypersensitivity reaction to albiglutide or to any of the product components. Continued on next page


Important Safety Information for TANZEUM (contâ&#x20AC;&#x2122;d) WARNINGS AND PRECAUTIONS Risk of Thyroid C-cell Tumors: Counsel patients regarding the risk for MTC with the use of TANZEUM and inform them of symptoms of thyroid tumors (e.g., a mass in the neck, dysphagia, dyspnea, persistent hoarseness). Patients with thyroid nodules noted on physical examination or neck imaging should be referred to an endocrinologist for further evaluation. Routine monitoring of serum calcitonin or using thyroid ultrasound is of uncertain value for early detection of MTC in patients treated with TANZEUM. If serum calcitonin is measured and found to be elevated, the patient should be referred to an endocrinologist for further evaluation. Acute Pancreatitis: In clinical trials, acute pancreatitis has been reported in association with TANZEUM. After initiation of TANZEUM, observe patients carefully for signs and symptoms of pancreatitis (including persistent severe abdominal pain, sometimes radiating to the back and which may or may not be accompanied by vomiting). If pancreatitis is suspected, promptly discontinue TANZEUM. If pancreatitis is confirmed, TANZEUM should not be restarted. TANZEUM has not been studied in patients with a history of pancreatitis to determine whether these patients are at increased risk for pancreatitis. Consider other antidiabetic therapies in patients with a history of pancreatitis. Hypoglycemia with Concomitant Use of Insulin Secretagogues or Insulin: The risk of hypoglycemia is increased when TANZEUM is used in combination with insulin secretagogues (e.g., sulfonylureas) or insulin. Therefore, patients may require a lower dose of sulfonylurea or insulin to reduce the risk of hypoglycemia in this setting. Hypersensitivity Reactions: Across 8 Phase III clinical trials, a serious hypersensitivity reaction with pruritus, rash, and dyspnea occurred in a patient treated with TANZEUM. If hypersensitivity reactions occur, discontinue use of TANZEUM; treat promptly per standard of care and monitor until signs and symptoms resolve. Renal Impairment: In patients treated with GLP-1 receptor agonists, there have been postmarketing reports of acute renal failure and worsening of chronic renal failure, which may sometimes require hemodialysis. Some of these events were reported in patients without known underlying renal disease. A majority of reported events occurred in patients who had experienced nausea, vomiting, diarrhea, or dehydration. In a trial of TANZEUM in patients with renal impairment, the frequency of such gastrointestinal reactions increased as renal function declined. Because these reactions may worsen renal function, use caution when initiating or escalating doses of TANZEUM in patients with renal impairment. Monitor renal function in patients with renal impairment reporting severe adverse gastrointestinal reactions. Macrovascular Outcomes: There have been no clinical trials establishing conclusive evidence of macrovascular risk reduction with TANZEUM or any other antidiabetic drug.

ADVERSE REACTIONS The most common adverse reactions, excluding hypoglycemia, reported in â&#x2030;Ľ5% of patients treated with TANZEUM and more commonly than in patients treated with placebo, are: upper respiratory tract infection (14.2 vs 13.0); diarrhea (13.1 vs 10.5); nausea (11.1 vs 9.6); injection site reaction (10.5 vs 2.1); cough (6.9 vs 6.2); back pain (6.7 vs 5.8); arthralgia (6.6 vs 6.4); sinusitis (6.2 vs 5.8); influenza (5.2 vs 3.2).

DRUG INTERACTIONS TANZEUM delays gastric emptying and may impact absorption of concomitantly administered oral medications. Caution should be exercised when oral medications are concomitantly administered with TANZEUM.

USE IN SPECIFIC PATIENT POPULATIONS Pediatric Use: Safety and effectiveness of TANZEUM have not been established in pediatric patients (younger than 18 years). A1C = glycosylated hemoglobin; GLP-1 = glucagon-like peptide-1. References: 1. Data on file. GSK. 2. Prescribing Information for TANZEUM.

Please see Brief Summary of Prescribing Information, including Boxed Warning, for TANZEUM on the following pages. www.GSKSource.com Bydureon and Byetta are registered trademarks of the AstraZeneca group of companies. Trulicity is a trademark of Eli Lilly and Company. Victoza is a registered trademark of Novo Nordisk A/S.


TANZEUM

BRIEF SUMMARY

(albiglutide) for injection, for subcutaneous use

The following is a brief summary only; see full prescribing information for complete product information. WARNING: RISK OF THYROID C-CELL TUMORS • Thyroid C-cell tumors have been observed in rodent studies with glucagon-like peptide-1 (GLP-1) receptor agonists at clinically relevant exposures. It is unknown whether TANZEUM™ causes thyroid C-cell tumors, including medullary thyroid carcinoma (MTC), in humans [see Warnings and Precautions (5.1)]. • TANZEUM is contraindicated in patients with a personal or family history of MTC or in patients with Multiple Endocrine Neoplasia syndrome type 2 (MEN 2). Routine serum calcitonin or thyroid ultrasound monitoring is of uncertain value in patients treated with TANZEUM. Patients should be counseled regarding the risk and symptoms of thyroid tumors [see Contraindications (4.1), Warnings and Precautions (5.1)]. 1 INDICATIONS AND USAGE TANZEUM is indicated as an adjunct to diet and exercise to improve glycemic control in adults with type 2 diabetes mellitus [see Clinical Studies (14) of full prescribing information]. Limitations of Use: TANZEUM is not recommended as first-line therapy for patients inadequately controlled on diet and exercise [see Warnings and Precautions (5.1)]. TANZEUM has not been studied in patients with a history of pancreatitis [see Warnings and Precautions (5.2)]. Consider other antidiabetic therapies in patients with a history of pancreatitis. TANZEUM is not indicated in the treatment of patients with type 1 diabetes mellitus or for the treatment of patients with diabetic ketoacidosis. TANZEUM is not a substitute for insulin in these patients. TANZEUM has not been studied in patients with severe gastrointestinal (GI) disease, including severe gastroparesis. The use of TANZEUM is not recommended in patients with pre-existing severe gastrointestinal disease [see Adverse Reactions (6.1)]. TANZEUM has not been studied in combination with prandial insulin. 4 CONTRAINDICATIONS 4.1 Medullary Thyroid Carcinoma: TANZEUM is contraindicated in patients with a personal or family history of medullary thyroid carcinoma (MTC) or in patients with Multiple Endocrine Neoplasia syndrome type 2 (MEN 2) [see Warnings and Precautions (5.1)]. 4.2 Hypersensitivity: TANZEUM is contraindicated in patients with a prior serious hypersensitivity reaction to albiglutide or to any of the product components [see Warnings and Precautions (5.4)]. 5 WARNINGS AND PRECAUTIONS 5.1 Risk of Thyroid C-cell Tumors: Nonclinical studies in rodents of clinically relevant doses of GLP-1 receptor agonists showed dose-related and treatment-duration-dependent increases in the incidence of thyroid C-cell tumors (adenomas and carcinomas). Carcinogenicity studies could not be conducted with TANZEUM because drug-clearing, anti-drug antibodies develop in animals used for these types of studies [see Nonclinical Toxicology (13.1)]. It is unknown whether GLP-1 receptor agonists are associated with thyroid C-cell tumors, including MTC in humans [see Boxed Warning, Contraindications (4.1)]. Across 8 Phase III clinical trials [see Clinical Studies (14) of full prescribing information], MTC was diagnosed in 1 patient receiving TANZEUM and 1 patient receiving placebo. Both patients had markedly elevated serum calcitonin levels at baseline. TANZEUM is contraindicated in patients with a personal or family history of MTC or in patients with MEN 2. Counsel patients regarding the risk for MTC with the use of TANZEUM and inform them of symptoms of thyroid tumors (e.g., a mass in the neck, dysphagia, dyspnea, persistent hoarseness). The clinical value of routine monitoring of serum calcitonin to diagnose MTC in patients at risk for MTC has not been established. Elevated serum calcitonin is a biological marker of MTC. Patients with MTC usually have calcitonin values >50 ng/L. Patients with thyroid nodules noted on physical examination or neck imaging should be referred to an endocrinologist for further evaluation. Routine monitoring of serum calcitonin or using thyroid ultrasound is of uncertain value for early detection of MTC in patients treated with TANZEUM. Such monitoring may increase the risk of unnecessary procedures, due to the low specificity of serum calcitonin testing for MTC and a high background incidence of thyroid disease. If serum calcitonin is measured and found to be elevated, the patient should be referred to an endocrinologist for further evaluation. 5.2 Acute Pancreatitis: In clinical trials, acute pancreatitis has been reported in association with TANZEUM. Across 8 Phase III clinical trials [see Clinical Studies (14) of full prescribing information], pancreatitis adjudicated as likely related to therapy occurred more frequently in patients receiving TANZEUM (6 of 2,365 [0.3%]) than in patients receiving placebo (0 of 468 [0%]) or active comparators (2 of 2,065 [0.1%]). After initiation of TANZEUM, observe patients carefully for signs and symptoms of pancreatitis (including persistent severe abdominal pain, sometimes radiating to the back and which may or may not be accompanied by vomiting). If pancreatitis is suspected, promptly discontinue TANZEUM. If pancreatitis is confirmed, TANZEUM should not be restarted. TANZEUM has not been studied in patients with a history of pancreatitis to

determine whether these patients are at increased risk for pancreatitis. Consider other antidiabetic therapies in patients with a history of pancreatitis. 5.3 Hypoglycemia with Concomitant Use of Insulin Secretagogues or Insulin: The risk of hypoglycemia is increased when TANZEUM is used in combination with insulin secretagogues (e.g., sulfonylureas) or insulin. Therefore, patients may require a lower dose of sulfonylurea or insulin to reduce the risk of hypoglycemia in this setting [see Adverse Reactions (6.1)]. 5.4 Hypersensitivity Reactions: Across 8 Phase III clinical trials [see Clinical Studies (14) of full prescribing information], a serious hypersensitivity reaction with pruritus, rash, and dyspnea occurred in a patient treated with TANZEUM. If hypersensitivity reactions occur, discontinue use of TANZEUM; treat promptly per standard of care and monitor until signs and symptoms resolve [see Contraindications (4.2)]. 5.5 Renal Impairment: In patients treated with GLP-1 receptor agonists, there have been postmarketing reports of acute renal failure and worsening of chronic renal failure, which may sometimes require hemodialysis. Some of these events were reported in patients without known underlying renal disease. A majority of reported events occurred in patients who had experienced nausea, vomiting, diarrhea, or dehydration. In a trial of TANZEUM in patients with renal impairment [see Clinical Studies (14.3) of full prescribing information], the frequency of such gastrointestinal reactions increased as renal function declined [see Use in Specific Populations (8.6)]. Because these reactions may worsen renal function, use caution when initiating or escalating doses of TANZEUM in patients with renal impairment [see Dosage and Administration (2.3) of full prescribing information, Use in Specific Populations (8.6)]. 5.6 Macrovascular Outcomes: There have been no clinical trials establishing conclusive evidence of macrovascular risk reduction with TANZEUM or any other antidiabetic drug. 6 ADVERSE REACTIONS The following serious reactions are described below or elsewhere in the labeling: Risk of Thyroid C-cell Tumors [see Warnings and Precautions (5.1)], Acute Pancreatitis [see Warnings and Precautions (5.2)], Hypoglycemia with Concomitant Use of Insulin Secretagogues or Insulin [see Warnings and Precautions (5.3)], Hypersensitivity Reactions [see Warnings and Precautions (5.4)], Renal Impairment [see Warnings and Precautions (5.5)]. 6.1 Clinical Trials Experience: Because clinical trials are conducted under widely varying conditions, adverse reaction rates observed in the clinical trials of a drug cannot be directly compared with rates in the clinical trials of another drug and may not reflect the rates observed in practice. Pool of Placebo-Controlled Trials: The data in Table 1 are derived from 4 placebo-controlled trials. TANZEUM was used as monotherapy in 1 trial and as add-on therapy in 3 trials [see Clinical Studies (14) of full prescribing information]. These data reflect exposure of 923 patients to TANZEUM and a mean duration of exposure to TANZEUM of 93 weeks. The mean age of participants was 55 years, 1% of participants were 75 years or older and 53% of participants were male. The population in these studies was 48% white, 13% African/African American, 7% Asian, and 29% Hispanic/Latino. At baseline, the population had diabetes for an average of 7 years and had a mean HbA1c of 8.1%. At baseline, 17% of the population in these studies reported peripheral neuropathy and 4% reported retinopathy. Baseline estimated renal function was normal or mildly impaired (eGFR >60 mL/min/1.73 m2) in 91% of the study population and moderately impaired (eGFR 30 to 60 mL/min/1.73 m2) in 9%. Table 1 shows common adverse reactions excluding hypoglycemia associated with the use of TANZEUM in the pool of placebo-controlled trials. These adverse reactions were not present at baseline, occurred more commonly on TANZEUM than on placebo, and occurred in at least 5% of patients treated with TANZEUM. Table 1. Adverse Reactions in Placebo-controlled Trials Reported in ≥5% of Patients Treated with TANZEUMa Placebo TANZEUM Adverse Reaction (N=468) (N=923) % % Upper respiratory tract infection 13.0 14.2 Diarrhea

10.5

13.1

Nausea

9.6

11.1

Injection site reaction

2.1

10.5

Cough

6.2

6.9

Back pain

5.8

6.7

Arthralgia

6.4

6.6

Sinusitis

5.8

6.2

Influenza

3.2

5.2

b

Adverse reactions reported includes adverse reactions occurring with the use of glycemic rescue medications which included metformin (17% for placebo and 10% for TANZEUM) and insulin (24% for placebo and 14% for TANZEUM). b See below for other events of injection site reactions reported. a

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Adverse Reactions (cont’d) Gastrointestinal Adverse Reactions: In the pool of placebo-controlled trials, gastrointestinal complaints occurred more frequently among patients receiving TANZEUM (39%) than patients receiving placebo (33%). In addition to diarrhea and nausea (see Table 1), the following gastrointestinal adverse reactions also occurred more frequently in patients receiving TANZEUM: vomiting (2.6% versus 4.2% for placebo versus TANZEUM), gastroesophageal reflux disease (1.9% versus 3.5% for placebo versus TANZEUM), and dyspepsia (2.8% versus 3.4% for placebo versus TANZEUM). Constipation also contributed to the frequently reported reactions. In the group treated with TANZEUM, investigators graded the severity of GI reactions as “mild” in 56% of cases, “moderate” in 37% of cases, and “severe” in 7% of cases. Discontinuation due to GI adverse reactions occurred in 2% of individuals on TANZEUM or placebo. Injection Site Reactions: In the pool of placebo-controlled trials, injection site reactions occurred more frequently on TANZEUM (18%) than on placebo (8%). In addition to the term injection site reaction (see Table 1), the following other types of injection site reactions also occurred more frequently on TANZEUM: injection site hematoma (1.9% versus 2.1% for placebo versus TANZEUM), injection site erythema (0.4% versus 1.7% for placebo versus TANZEUM), injection site rash (0% versus 1.4% for placebo versus TANZEUM), injection site hypersensitivity (0% versus 0.8% versus for placebo versus TANZEUM), and injection site hemorrhage (0.6% versus 0.7% for placebo versus TANZEUM). Injection site pruritus also contributed to the frequently reported reactions. The majority of injection site reactions were judged as “mild” by investigators in both groups (73% for TANZEUM versus 94% for placebo). More patients on TANZEUM than on placebo: discontinued due to an injection site reaction (2% versus 0.2%), experienced more than 2 reactions (38% versus 20%), had a reaction judged by investigators to be “moderate” or “severe” (27% versus 6%) and required local or systemic treatment for the reactions (36% versus 11%). Pool of Placebo- and Active-controlled Trials: The occurrence of adverse reactions was also evaluated in a larger pool of patients with type 2 diabetes participating in 7 placebo- and active-controlled trials. These trials evaluated the use of TANZEUM as monotherapy, and as add-on therapy to oral antidiabetic agents, and as add-on therapy to basal insulin [see Clinical Studies (14) of full prescribing information]. In this pool, a total of 2,116 patients with type 2 diabetes were treated with TANZEUM for a mean duration of 75 weeks. The mean age of patients treated with TANZEUM was 55 years, 1.5% of the population in these studies was 75 years or older and 51% of participants were male. Forty-eight percent of patients were white, 15% African/African American, 9% Asian, and 26% were Hispanic/Latino. At baseline, the population had diabetes for an average of 8 years and had a mean HbA1c of 8.2%. At baseline, 21% of the population reported peripheral neuropathy and 5% reported retinopathy. Baseline estimated renal function was normal or mildly impaired (eGFR >60 mL/min/1.73 m2) in 92% of the population and moderately impaired (eGFR 30 to 60 mL/min/1.73 m2) in 8% of the population. In the pool of placebo- and active-controlled trials, the types and frequency of common adverse reactions excluding hypoglycemia were similar to those listed in Table 1. Other Adverse Reactions: Hypoglycemia: The proportion of patients experiencing at least one documented symptomatic hypoglycemic episode on TANZEUM and the proportion of patients experiencing at least one severe hypoglycemic episode on TANZEUM in clinical trials [see Clinical Studies (14) of full prescribing information] is shown in Table 2. Hypoglycemia was more frequent when TANZEUM was added to sulfonylurea or insulin [see Warnings and Precautions (5.3)]. Table 2. Incidence (%) of Hypoglycemia in Clinical Trials of TANZEUMa TANZEUM Monotherapyb Placebo 30 mg Weekly N = 101 (52 Weeks) N = 101 Documented symptomaticc Severed In Combination with Metformin Trial (104 Weeks)e Documented symptomatic Severe In Combination with Pioglitazone ± Metformin (52 Weeks) Documented symptomatic Severe In Combination with Metformin and Sulfonylurea (52 Weeks) Documented symptomatic Severe In Combination with Insulin Glargine (26 Weeks) Documented symptomatic Severe

2% Placebo N = 101 4% Placebo N = 151 1% Placebo N = 115 7% Insulin Lispro N = 281 30% 0.7%

2% TANZEUM N = 302 3% TANZEUM N = 150 3% 1% TANZEUM N = 271 13% 0.4% TANZEUM N = 285 16% -

Table 2. Incidence (%) of Hypoglycemia in Clinical Trials of TANZEUMa (cont’d) Insulin In Combination with TANZEUM Glargine N = 504 Metformin ± Sulfonylurea (52 Weeks) N = 241 Documented symptomatic Severe In Combination with OADs in Renal Impairment (26 Weeks) Documented symptomatic Severe

27% 0.4%

17% 0.4%

Sitagliptin N = 246

TANZEUM N = 249

6% 0.8%

10% -

OAD = Oral antidiabetic agents. Data presented are to the primary endpoint and include only events occurring on-therapy with randomized medications and excludes events occurring after use of glycemic rescue medications (i.e., primarily metformin or insulin). bIn this trial, no documented symptomatic or severe hypoglycemia were reported for TANZEUM 50 mg and these data are omitted from the table. cPlasma glucose concentration ≤70 mg/dL and presence of hypoglycemic symptoms. dEvent requiring another person to administer a resuscitative action. eRate of documented symptomatic hypoglycemia for active controls 18% (glimepiride) and 2% (sitagliptin).

a

Pneumonia: In the pool of 7 placebo- and active-controlled trials, the adverse reaction of pneumonia was reported more frequently in patients receiving TANZEUM (1.8%) than in patients in the all-comparators group (0.8%). More cases of pneumonia in the group receiving TANZEUM were serious (0.4% for TANZEUM versus 0.1% for all comparators). Atrial Fibrillation/Flutter: In the pool of 7 placebo- and active-controlled trials, adverse reactions of atrial fibrillation (1.0%) and atrial flutter (0.2%) were reported more frequently for TANZEUM than for all comparators (0.5% and 0%, respectively). In both groups, patients with events were generally male, older, and had underlying renal impairment or cardiac disease (e.g., history of arrhythmia, palpitations, congestive heart failure, cardiomyopathy, etc.). Appendicitis: In the pool of placebo- and active-controlled trials, serious events of appendicitis occurred in 0.3% of patients treated with TANZEUM compared with 0% among all comparators. Immunogenicity: In the pool of 7 placebo- and active-controlled trials, 116 (5.5%) of 2,098 patients exposed to TANZEUM tested positive for anti-albiglutide antibodies at any time during the trials. None of these antibodies were shown to neutralize the activity of albiglutide in an in vitro bioassay. Presence of antibody did not correlate with reduced efficacy as measured by HbA1c and fasting plasma glucose or specific adverse reactions. Consistent with the high homology of albiglutide with human GLP-1, the majority of patients (approximately 79%) with anti-albiglutide antibodies also tested positive for anti-GLP-1 antibodies; none were neutralizing. A minority of patients (approximately 17%) who tested positive for anti-albiglutide antibodies also transiently tested positive for antibodies to human albumin. The detection of antibody formation is highly dependent on the sensitivity and specificity of the assay. Additionally, the observed incidence of antibody (including neutralizing antibody) positivity in an assay may be influenced by several factors including assay methodology, sample handling, timing of sample collection, concomitant medications, and underlying disease. For these reasons, the incidence of antibodies to albiglutide cannot be directly compared with the incidence of antibodies of other products. Liver Enzyme Abnormalities: In the pool of placebo- and active-controlled trials, a similar proportion of patients experienced at least one event of alanine aminotransferase (ALT) increase of 3-fold or greater above the upper limit of normal (0.9% and 0.9% for all comparators versus TANZEUM). Three subjects on TANZEUM and one subject in the all-comparator group experienced at least one event of ALT increase of 10-fold or greater above the upper limit of normal. In one of the 3 cases an alternate etiology was identified to explain the rise in liver enzyme (acute viral hepatitis). In one case, insufficient information was obtained to establish or refute a drug-related causality. In the third case, elevation in ALT (10 times the upper limit of normal) was accompanied by an increase in total bilirubin (4 times the upper limit of normal) and occurred 8 days after the first dose of TANZEUM. The etiology of hepatocellular injury was possibly related to TANZEUM but direct attribution to TANZEUM was confounded by the presence of gallstone disease diagnosed on ultrasound 3 weeks after the event. Gamma Glutamyltransferase (GGT) Increase: In the pool of placebo-controlled trials, the adverse event of increased GGT occurred more frequently in the group treated with TANZEUM (0.9% and 1.5% for placebo versus TANZEUM). Heart Rate Increase: In the pool of placebo-controlled trials, mean heart rate in patients treated with TANZEUM was higher by an average of 1 to 2 bpm compared with mean heart rate in patients treated with placebo across study visits. The long-term clinical effects of the increase in heart rate have not been established [see Warnings and Precautions (5.6)].

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7 DRUG INTERACTIONS TANZEUM did not affect the absorption of orally administered medications tested in clinical pharmacology studies to any clinically relevant degree [see Clinical Pharmacology (12.3) of full prescribing information]. However, TANZEUM causes a delay of gastric emptying, and thereby has the potential to impact the absorption of concomitantly administered oral medications. Caution should be exercised when oral medications are concomitantly administered with TANZEUM.

50 mg/kg/day (39 times clinical exposure based on AUC). In pregnant mice given SC doses of 1, 5, or 50  mg/kg/day from gestation Day 6 through 15 (organogenesis), embryo-fetal lethality (post-implantation loss) and bent (wavy) ribs were observed at 50 mg/kg/day (39 times clinical exposure based on AUC), a dose associated with maternal toxicity (body weight loss and reduced food consumption). Pregnant mice were given SC doses of 1, 5, or 50 mg/kg/day from gestation Day 6 to 17. Offspring of pregnant mice given 50 mg/kg/day (39 times clinical exposure based on AUC), a dose associated with maternal toxicity, had reduced body weight pre-weaning, dehydration and coldness, and a delay in balanopreputial separation. Pregnant mice were given SC doses of 1, 5, or 50 mg/kg/day from gestation Day 15 to lactation day 10. Increased mortality and morbidity were seen at all doses (≥1 mg/kg/ day) in lactating females in mouse pre- and postnatal development studies. Mortalities have not been observed in previous toxicology studies in nonlactating or non-pregnant mice, nor in pregnant mice. These findings are consistent with lactational ileus syndrome which has been previously reported in mice. Since the relative stress of lactation energy demands is lower in humans than mice and humans have large energy reserves, the mortalities observed in lactating mice are of questionable relevance to humans. The offspring had decreased pre-weaning body weight which reversed post-weaning in males but not females at ≥5 mg/kg/day (2.2 times clinical exposure based on AUC) with no other effects on development. Low levels of albiglutide were detected in plasma of offspring. Lactating mice were given SC doses of 1, 5, or 50 mg/kg/day from lactation day 7 to 21 (weaning) under conditions that limit the impact of lactational ileus (increased caloric intake and culling of litters). Doses ≥1  mg/kg/day (exposures below clinical AUC) caused reduced weight gain in the pups during the treatment period.

8 USE IN SPECIFIC POPULATIONS 8.1 Pregnancy: Pregnancy Category C: There are no adequate and wellcontrolled studies of TANZEUM in pregnant women. Nonclinical studies have shown reproductive toxicity, but not teratogenicity, in mice treated with albiglutide at up to 39 times human exposure resulting from the maximum recommended dose of 50 mg/week, based on area under the time-concentration curve (AUC) [see Nonclinical Toxicology (13.1,13.3)]. TANZEUM should not be used during pregnancy unless the expected benefit outweighs the potential risks. Due to the long washout period for TANZEUM, consider stopping TANZEUM at least 1 month before a planned pregnancy. There are no data on the effects of TANZEUM on human fertility. Studies in mice showed no effects on fertility [see Nonclinical Toxicology (13.1)]. The potential risk to human fertility is unknown. 8.3 Nursing Mothers: There are no adequate data to support the use of TANZEUM during lactation in humans. It is not known if TANZEUM is excreted into human milk during lactation. Given that TANZEUM is an albumin-based protein therapeutic, it is likely to be present in human milk. Decreased body weight in offspring was observed in mice treated with TANZEUM during gestation and lactation [see Nonclinical Toxicology (13.3)]. A decision should be made whether to discontinue nursing or to discontinue TANZEUM, taking into account the importance of the drug 17 PATIENT COUNSELING INFORMATION See FDA-approved patient labeling (Medication Guide and Instructions for to the mother and the potential risks to the infant. 8.4 Pediatric Use: Safety Use). The Medication Guide is contained in a separate leaflet that accompanies and effectiveness of TANZEUM have not been established in pediatric patients the product. Inform patients about self-management practices, including the (younger than 18 years). 8.5 Geriatric Use: Of the total number of patients importance of proper storage of TANZEUM, injection technique, timing of dosage (N = 2,365) in 8 Phase III clinical trials who received TANZEUM, 19% (N = of TANZEUM and concomitant oral drugs, and recognition and management of 444) were 65 years and older, and <3% (N = 52) were 75 years and older. hypoglycemia. Inform patients that thyroid C-cell tumors have been observed No overall differences in safety or effectiveness were observed between in rodents treated with some GLP-1 receptor agonists, and the human these patients and younger patients, but greater sensitivity of some older relevance of this finding is unknown. Counsel patients to report symptoms of individuals cannot be ruled out. 8.6 Renal Impairment: Of the total number thyroid tumors to their physician [see Warnings and Precautions (5.1)]. Advise of patients (N = 2,365) in 8 Phase III clinical trials who received TANZEUM, patients that persistent, severe abdominal pain that may radiate to the back 54% (N = 1,267) had mild renal impairment (eGFR 60 to 89 mL/min/1.73 and which may (or may not) be accompanied by vomiting is the hallmark m2), 12% (N = 275) had moderate renal impairment (eGFR 30 to 59 mL/ symptom of acute pancreatitis. Instruct patients to discontinue TANZEUM min/1.73 m2) and 1% (N = 19) had severe renal impairment (eGFR 15 to <30 promptly and to contact their physician if persistent, severe abdominal pain mL/min/1.73 m2). No dosage adjustment is required in patients with mild occurs [see Warnings and Precautions (5.2)]. The risk of hypoglycemia is (eGFR 60 to 89 mL/min/1.73 m2), moderate (eGFR 30 to 59 mL/min/1.73 m2), increased when TANZEUM is used in combination with an agent that induces or severe (eGFR 15 to <30 mL/min/1.73 m2) renal impairment. Efficacy of hypoglycemia, such as sulfonylurea or insulin. Instructions for hypoglycemia TANZEUM in patients with type 2 diabetes and renal impairment is described should be reviewed with patients and reinforced when initiating therapy with elsewhere [see Clinical Studies (14.3) of full prescribing information]. There TANZEUM, particularly when concomitantly administered with a sulfonylurea or is limited clinical experience in patients with severe renal impairment (19 insulin [see Warnings and Precautions (5.3)]. Advise patients on the symptoms subjects). The frequency of GI events increased as renal function declined. of hypersensitivity reactions and instruct them to stop taking TANZEUM and For patients with mild, moderate, or severe impairment, the respective seek medical advice promptly if such symptoms occur [see Warnings and event rates were: diarrhea (6%, 13%, 21%), nausea (3%, 5%,16%), and Precautions (5.4)]. Instruct patients to read the Instructions for Use before vomiting (1%, 2%, 5%). Therefore, caution is recommended when initiating starting therapy. Instruct patients on proper use, storage, and disposal of or escalating doses of TANZEUM in patients with renal impairment [see the pen [see How Supplied/Storage and Handling (16.2) of full prescribing Dosage and Administration (2.3) of full prescribing information, Warnings and information, Patient Instructions for Use of full prescribing information]. Precautions (5.5), Clinical Pharmacology (12.3) of full prescribing information]. Instruct patients to read the Medication Guide before starting TANZEUM and to 10 OVERDOSAGE read again each time the prescription is renewed. Instruct patients to inform No data are available with regard to overdosage in humans. Anticipated their doctor or pharmacist if they develop any unusual symptom, or if any symptoms of an overdose may be severe nausea and vomiting. In the event known symptom persists or worsens. Inform patients not to take an extra of an overdose, appropriate supportive treatment should be initiated as dose of TANZEUM to make up for a missed dose. If a dose is missed, instruct dictated by the patient’s clinical signs and symptoms. A prolonged period of patients to take a dose as soon as possible within 3 days after the missed observation and treatment for these symptoms may be necessary, taking into dose. Instruct patients to then take their next dose at their usual weekly time. account the half-life of TANZEUM (5 days). If it has been longer than 3 days after the missed dose, instruct patients to wait and take TANZEUM at the next usual weekly time. 13 NONCLINICAL TOXICOLOGY 13.1 Carcinogenesis, Mutagenesis, Impairment of Fertility: As albiglutide TANZEUM is a trademark of the GSK group of companies. is a recombinant protein, no genotoxicity studies have been conducted. Carcinogenicity studies have not been performed with albiglutide because such studies are not technically feasible due to the rapid development of drug-clearing, anti-drug antibodies in rodents. Thyroid C-cell tumors were observed in 2-year rodent carcinogenicity studies with some GLP-1 receptor agonists. The clinical relevance of rodent thyroid findings observed with GLP-1 Manufactured by GlaxoSmithKline LLC receptor agonists is unknown. In a mouse fertility study, males were treated Wilmington, DE 19808 with subcutaneous (SC) doses of 5, 15, or 50 mg/kg/day for 7 days prior to U.S. Lic. No. 1727 cohabitation with females, and continuing through mating. In a separate fertility Marketed by GlaxoSmithKline study, females were treated with SC doses of 1, 5, or 50 mg/kg/day for 7 days Research Triangle Park, NC 27709 prior to cohabitation with males, and continuing through mating. Reductions ©2014, the GSK group of companies. All rights reserved. in estrous cycles were observed at 50  mg/kg/day, a dose associated with August 2014, TNZ:2BRS maternal toxicity (body weight loss and reduced food consumption). There were no effects on mating or fertility in either sex at doses up to 50 mg/kg/ ©2014 GSK group of companies. day (up to 39 times clinical exposure based on AUC). 13.3 Reproductive All rights reserved. Printed in USA. BIG159R0 November 2014 and Developmental Toxicity: In order to minimize the impact of the drug-clearing, anti-drug antibody response, reproductive and developmental toxicity assessments in the mouse were partitioned to limit the dosing period to no more than approximately 15 days in each study. In pregnant mice given SC doses of 1, 5, or 50  mg/kg/day from gestation Day 1 to 6, there were no adverse effects on early embryonic development through implantation at


CLINICAL

ORIGINAL RESEARCH

Cardiovascular-Related Healthcare Resource Utilization and Costs in Patients with Hypertension Switching from Metoprolol to Nebivolol Stephanie Chen, PhD; An-Chen Fu, MS, BSPharm; Rahul Jain, PhD; Hiangkiat Tan, MS, BSPharm BACKGROUND: The prevalence of hypertension is increasing in the United States and the associated costs are soaring. Despite the many treatment options, only approximately 50% of Americans with hypertension achieve optimal control. Patients receiving nebivolol, a third-generation beta-blocker, have fewer adverse events and better treatment persistence compared with patients receiving other antihypertensive agents. Little is known about the impact of switching from a second-generation beta-blocker, such as CLINICAL metoprolol, to nebivolol on healthcare resource utilization and costs. OBJECTIVE: To assess the impact of switching patients with hypertension from metoprolol to nebivolol on the associated healthcare resource utilization and cost. METHOD: This retrospective claims-based analysis included 765 adults aged ≥18 years who were diagnosed with hypertension between January 1, 2008, and December 31, 2012. Data were extracted from the HealthCore Integrated Research Database; the study was conducted between July 1, 2007, and June 30, 2013. To be included in the study, patients had to receive metoprolol for ≥6 months before switching from metoprolol to nebivolol (the preperiod), and continue to use nebivolol for an additional 6 months after switching (the postperiod). Patients with compelling indications for metoprolol but not for nebivolol were excluded from the study. The primary outcome measures were healthcare resource utilization and costs for cardiovascular (CV)-related events. The CV-related resource utilization was calculated based on 100 patients per month; the CV-related costs were calculated per patient per month (PPPM) in 2013 US dollars. RESULTS: A total of 765 patients were included in the analysis. Compared with the preperiod, patients switching to nebivolol had significantly fewer CV-related emergency department visits (0.2 [standard deviation (SD), 1.9] vs 0.04 [SD, 0.8], respectively; P = .012) and fewer CV-related outpatient visits (9.2 [SD, 19.9] vs 6.7 [SD, 17.5], respectively; P <.001). The numbers of inpatient visits in the preperiod and postperiod were similar (0.3 [SD, 2.4] vs 0.1 [SD, 1.5], respectively; P = .164). Patients switching to nebivolol also had significantly lower CV-related emergency department costs ($6 [SD, $78] vs $1 [SD, $27] PPPM, respectively; P = .028) and lower CV-related total medical costs ($94 [SD, $526] vs $54 [SD, $266] PPPM, respectively; P = .020). CONCLUSION: This analysis of real-world data suggests that patients with hypertension who switch from the second-generation antihypertensive metoprolol to the third-generation hypertensive nebivolol have significantly lower CV-related healthcare resource utilization (eg, emergency department and outpatient visits) and lower CV-related medical costs. KEY WORDS: nebivolol, metoprolol, hypertension, healthcare resource utilization, cardiovascular events

A

ccording to the Eighth Joint National Committee, hypertension is a known risk factor for cardiovascular (CV) events, such as stroke, myo-

Dr Chen was Associate Director, Health Economics and Outcomes Research, Forest Research Institute, Inc, Jersey City, NJ, when this study was conducted; Ms Fu is Senior Research Analyst, HealthCore, Inc; Dr Jain is Research Manager, HealthCore, Inc; Mr Tan is Director, HealthCore, Inc, Wilmington, DE.

Vol 8, No 2

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April 2015

Stakeholder Perspective, page 79

Am Health Drug Benefits. 2015;8(2):71-80 www.AHDBonline.com Received December 11, 2014 Accepted in final form February 17, 2015

Disclosures are at end of text

cardial infarction, and heart failure.1 Hypertension is defined as persistent systolic/diastolic blood pressure of at least 140/90 mm Hg among patients aged <60 years or at least 150/90 mm Hg among patients aged ≥60 years for ­6 months.1 The prevalence of hypertension is steadily increasing in the United States and is projected to rise from 29% of the US adult population in 2006 to approximately 38% by 2030, and the associated costs soaring from $70 billion in 2010 to an estimated $200 billion ­­in 2030.2,3

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CLINICAL

KEY POINTS By 2030, the prevalence of hypertension, a key risk factor for CV events, is projected to rise to approximately 38% of the US population, and the associated costs to soar to an estimated $200 billion ➤ Despite many treatment options, hypertension management remains inadequate: only approximately 50% of US adults with hypertension achieve optimal control ➤ Metoprolol, a second-generation beta-blocker, is the most often prescribed beta-blocker in the United States ➤ Nebivolol is a cardioselective third-generation betablocker with good efficacy and a reduced side-effects profile compared with other antihypertensives ➤ Using real-world claims-based data, this study investigated the impact of switching from metoprolol to nebivolol on cost and healthcare resource utilization ➤ Overall, patients with hypertension who switched from metoprolol to nebivolol had significantly lower CV-related healthcare resource utilization ➤ Specifically, patients switching to nebivolol had significantly less CV-related emergency department visits and outpatient visits, and approximately 6-fold lower emergency department costs PPPM than patients receiving metoprolol ➤ The CV-related total medical costs were $40 less PPPM after switching from metoprolol to nebivolol ➤

The goal of therapy in patients with hypertension is to decrease the risk for CV events (such as myocardial infarction or stroke) by reducing and controlling blood pressure.1 A recent study showed that within 5 years, patients with hypertension with an average blood pressure reduction of 3.6/2.4 mm Hg can potentially have a 14% lower odds for overall CV events, 28% lower odds for stroke events, 19% lower odds for coronary events, and 20% lower odds for heart failure.4 Despite the numerous treatment options available, the management of patients with hypertension remains inadequate: only approximately 50% of US adults with hypertension achieve optimal control.5 Inadequately managed hypertension may contribute to several adverse health outcomes, including stroke, myocardial infarction, and heart failure.6-8 Metoprolol was the most often prescribed beta-blocker in the United States in 2011, with 72.3 million prescriptions.9 However, metoprolol and other second-generation beta-blockers have lower efficacy than other classes of antihypertensives, which led to the development of third-­

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generation beta-blockers.10 One of these third-generation beta-blockers, nebivolol, is a cardioselective agent with high selectivity for beta1-adrenergic receptors. Nebivolol also causes vasodilation, by interacting with the endothelial L-arginine/nitric oxide pathway.11-13 Cardioselective beta-blockers not only lower the heart rate by blocking the effect of adrenaline in the heart but also relax and widen blood vessels, improving blood flow.13 Having both properties, nebivolol reduces the peripheral vascular resistance and significantly increases stroke volume while preserving cardiac output.13 Clinical trials have shown that nebivolol is efficacious compared with other classes of antihypertensive medications and is well-tolerated among a wide range of patients.13-16 Moreover, in one study, patients receiving nebivolol reported fewer adverse events (eg, sexual dysfunction, fatigue, depression, and metabolic abnormalities) than patients receiving other beta-blockers.17 The presence of fewer adverse events generally is associated with a lower likelihood of treatment discontinuation,18,19 and a recent study by Signorovitch and colleagues demonstrated that patients with hypertension who received nebivolol had better medication persistence compared with patients receiving other beta-blockers (eg, metoprolol).20 The reduction in adverse events among patients with hypertension who receive nebivolol and their subsequent improved medication persistence may also result in better disease management compared with patients receiving other beta-blockers. Although nebivolol may be a viable treatment alternative for patients with hypertension who do not respond to or cannot tolerate other beta-blockers, little is known about the impact of switching patients from metoprolol to nebivolol on the associated CV-related healthcare resource utilization and costs. This information is especially important to payers, because nebivolol is a branded drug with a significantly higher average wholesale price (listed in the Medi-Span Master Drug Database) than for generic metoprolol.21 Therefore, the aim of this study is to analyze and document the impact on CV-related economic outcomes for patients with hypertension who switch their treatment from metoprolol to nebivolol.

Methods Data Source For this retrospective observational study, medical, pharmacy, and eligibility claims data were extracted from the HealthCore Integrated Research Database. This database contains claims from 14 geographically dispersed US commercial health plans representing more than 45 million lives, making it one of the largest data sets of a commercially insured population. Overall, this database is comparable with the US Census data (the American

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Community Survey) in terms of age and sex, although the population in the database is slightly younger, because all members are commercially insured. All personally identifiable data used in this study were deidentified and were accessed with protocols that are compliant with the Health Insurance Portability ­ and Accountability Act of 1996. Patient confidentiality was preserved, and the anonymity of all patient data w ­ as safeguarded throughout the study. No waiver of i­nformed consent was required from an Institutional R ­ eview Board.

Study Design Claims were obtained from the HealthCore Integrated Research Database during the study period (July 1, 2007-June 30, 2013). Patients included in the analysis were aged ≥18 years with a diagnosis of hypertension (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] 401.xx-404.xx) in an inpatient or outpatient setting during the patient identification period (January 1, 2008-December 31, 2012). To be included in the analysis, patients had to receive metoprolol for at least 6 months before switching to nebivolol, and then receive nebivolol for at least 6 months after switching from metoprolol. A treatment gap of less than 30 days of prescription supply was considered a continuous or stable drug regimen. The index date was defined as the date the patient switched from metoprolol to nebivolol; the preperiod (baseline) was defined as the 6 months of treatment with metoprolol before the index date, and the postperiod was the 6 months of continuous treatment with nebivolol after the index date. All patients were continuously enrolled in a health plan during the preperiod and post­period (Figure 1). Patients were excluded from the analysis if they had any compelling indications identified by ICD-9-CM diagnosis codes, for which metoprolol but not nebivolol is an approved treatment (eg, angina [ICD-9-CM codes 411.1x and 413.xx], myocardial infarction [410.xx and 412.xx], or congestive heart failure [428.xx, 402.01, 402.11, 402.91, 404.x1, and 404.x3]). Patients were also excluded if they did not maintain a stable background treatment of other classes of antihypertensive medications (eg, angiotensin-II receptor blockers) during the preperiod and postperiod (Figure 2). Outcome Measures The primary outcome measures were healthcare resource utilization and costs associated with specific CV events, including cerebrovascular disease (including stroke), chronic ischemic heart disease, acute coronary syndrome, peripheral vascular disease, valvular disease, arrhythmia, and aortic aneurysm. CV-related resource utilization was calculated as the

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number of times a healthcare resource was utilized divided by the number of months of follow-up during the preperiod or postperiod, multiplied by 100 patients, to reach the healthcare resource utilization per 100 patients per month. Figure 1 Study Design Study period July 1, 2007-June 30, 2013 Patient identification period January 1, 2008-December 31, 2012 Index date (initial fill date for nebivolol)

X

Preindex period (6 months)

Postindex period (6 months)

Metoprolol users who switched to nebivolol

Outcomes of interest: 1. P reindex to postindex change in CV-related PPPM healthcare costs 2. P reindex to postindex change in CV-related healthcare utilization

CV indicates cardiovascular; PPPM, per-patient per-month.

Figure 2 Study Cohort Selection

4,326,022

Included patients aged ≥18 years at the first observed diagnosis of hypertension in the inpatient or outpatient setting during the patient identification period

1751

Included patients who started using metoprolol and continued for at least 6 months, then switched to nebivolol and continued for at least 6 months during the continuous health plan enrollment

1547

Excluded patients with any compelling indications for metoprolol (eg, angina, myocardial infarction, congestive heart failure) during the preperiod and postperiod

765

Excluded patients who did not maintain a stable background treatment of other classes of antihypertensive medication, for example, angiotensin-II receptor blockers (defined as prescription supply gap of <30 days), during the preperiod and postperiod

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 aseline Patient Demographics and Table 1 B Characteristics Patients, N (%) Demographics/characteristics (N = 765) Mean age, yrs (SD)

55 (11)

Sex Male

449 (59)

Female

316 (41)

Table 2 Major Comorbid Conditions at Baseline

Anemia

34 (4)

Asthma/COPD

51 (7)

Cardiovascular disease Arrhythmia

495 (65)

HMO

187 (24)

Consumer-directed health plan

52 (7)

Medicare Advantage

36 (5)

Fee-for-service

31 (4)

Region of patient residence South

267 (35)

Midwest

202 (26)

Northeast

156 (20)

West

140 (18)

Year of index datea 2008

123 (16)

2009

295 (39)

2010

133 (17)

2011

122 (16)

2012

92 (12)

The index date is defined as the first date of initiation of nebivolol therapy. SD indicates standard deviation.

11 (1)

Cerebrovascular disease, including stroke

21 (3)

Chronic ischemic heart disease

130 (17)

Peripheral vascular disease

25 (3)

Valvular disease

1 (0.1)

Depression

47 (6)

Diabetes

108 (14)

Erectile dysfunction

40 (5)

Fatigue

127 (17)

Hypothyroidism

51 (7)

Obesity

43 (6)

Renal disease

25 (3)

Charlson Deyo Comorbidity Index score Overall score, mean (SD)

0.5 (0.9)

Score categories

a

74

107 (14)

Aortic aneurysm

Insurance plan type PPO

Patients, N (%) (N = 765)

Comorbidities

0

533 (70)

1

143 (19)

â&#x2030;Ľ2

89 (12)

COPD indicates chronic obstructive pulmonary disease; SD, standard deviation.

The CV-related healthcare costs were presented in 2013 US dollars per patient per month (PPPM); that is, the costs were calculated by dividing the CV-related cost (the total, inpatient, and outpatient costs) by the number of months during the preperiod or postperiod (6 months each). Healthcare resource utilization and costs were then categorized by the setting of service (ie, inpatient, emergency department visit, and outpatient office visit). CV-related events in the inpatient or emergency department settings were identified from the primary diagnosis; because the primary diagnosis was unavailable in outpatient claims, patients receiving care in outpatient settings were identified using all diagnosis positions.

who switched from metoprolol to nebivolol were analyzed. The results of the main analysis may overstate the impact of switching to nebivolol, because only the patients who are likely to benefit the most from switching are included. To evaluate this possibility, a sensitivity analysis was conducted, in which the same outcomes were evaluated and compared between matched cohorts of patients who switched from metoprolol to nebivolol and those who did not switch but continued to receive metoprolol. The patients who switched from metoprolol to nebivolol were matched to those who did not switch and continued treatment with metoprolol, using propensity score matching on baseline demographic and clinical characteristics.

Sensitivity Analysis In the main analysis, the change in the healthcare resource utilization and costs of patients with hypertension

Statistical Analysis Unadjusted differences between the preperiod and postperiod were assessed using McNemarâ&#x20AC;&#x2122;s test for nom-

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inal variables and a bootstrap paired t-test for continuous variables. All data analyses were conducted using SAS version 9.2 (SAS Institute; Cary, NC) or Stata version 12.0 (Stata Corporation; College Station, TX). All statistical tests were 2-sided hypothesis tests performed at a 5% level of significance.

Results Patient Characteristics A total of 765 patients were included in the analysis; the patients’ mean age was 55 years, and 59% were men (Table 1). At baseline, the majority (70%) of patients had a Charlson Deyo Comorbidity Index score of 022; only 12% had a score of 2 or higher (Table 2). The most common comorbidities of interest at baseline were chronic ischemic heart disease and fatigue (17% each), followed by arrhythmia and diabetes (14% each). Medication Use and Resource Utilization The mean (standard deviation [SD]) number of all prescription medications increased from 21 (14%) in the preperiod to 23 (14%; P <.001) in the postperiod (Table 3). Fewer treatment gaps were observed in the postperiod during treatment with nebivolol than in the preperiod while patients were receiving metoprolol (mean, 12.8 days vs 15.9 days, respectively). Compared with the preperiod, patients switching to nebivolol in the postperiod had significantly fewer CV-related emergency department visits (0.2 [SD, 1.9] vs 0.04 [SD, 0.8], respectively; P = .012) and CV-related outpatient visits (9.2 [SD, 19.9] vs 6.7 [SD, 17.5], respectively; P <.001). These results are shown in Figure 3. The number of inpatient visits in the preperiod and postperiod were similar (0.3 [SD, 2.4] vs 0.1 [SD, 1.5], respectively; P = .164). The proportion of patients with at least 1 CV-related outpatient visit was significantly lower in the postperiod than in the preperiod (21% vs 27%, respectively; P <.001), as shown in Figure 4. CV-related healthcare costs followed a pattern similar to healthcare utilization (Table 4). Compared with the preperiod, patients switching to nebivolol had significantly lower CV-related emergency department mean costs ($6 [SD, $78] vs $1 [SD, $27] PPPM, respectively; P = .028) and lower CV-related total medical costs ($94 [SD, $526] vs $54 [SD, $266] PPPM, respectively; P = .020), as shown in Figure 5. The CV-related inpatient costs were similar between the preperiod and postperiod ($29 [SD, $324] vs $13 [SD, $161] PPPM, respectively; P =.173), as were the CV-related outpatient costs ($59 [SD, $400] vs $40 [SD, $206] PPPM, respectively; P = .130). The sensitivity analysis produced results similar to the primary analysis, although the differences between the

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patients who switched to nebivolol and those who did not switch were not statistically significant (Table 5).

Discussion The results of this analysis of real-world data demonTable 3 Preperiod and Postperiod Medication Use Preperiod Postperiod P (N = 765) (N = 765) valuea

Medication use

Antihypertensive medications,b N (%) ACE inhibitors

121 (16)

121 (16)

NA

69 (9)

69 (9)

NA

765 (100)

765 (100)

NA

Calcium channel blockers

79 (10)

79 (10)

NA

Loop diuretics

20 (3)

20 (3)

NA

Thiazide diuretics

54 (7)

54 (7)

NA

Other antihypertensive agents

16 (2)

16 (2)

NA

164 (21)

164 (21)

NA

21 (14)

23 (14)

<.001

ACE inhibitors

0.7 (1.8)

0.7 (1.8)

.705

ARBs

0.4 (1.4)

0.4 (1.4)

.746

4.4 (1.8)

NA

ARBs Beta-blockersc

Single-pill combination Prescription fills, mean, N (SD) All medications Antihypertensive medications

Beta-blockers

c

Metoprolol Nebivolol

5.6 (1.5)

NA

Calcium channel blockers

0.5 (1.6)

0.5 (1.7)

.026

Loop diuretics

0.1 (0.8)

0.1 (0.8)

.784

Thiazide diuretics

0.3 (1.3)

0.3 (1.3)

.061

Other antihypertensive agents

0.1 (0.9)

0.1 (1.0)

.085

Single-pill combination

1.1 (2.3)

1.1 (2.3)

.097

Refill gap of metoprolol/nebivolol, mean (SD) Sum of gaps in days of metoprolol fills in the preperiod

15.9 (13.2)

NA

Sum of gaps in days of nebivolol fills in the postperiod

12.8 (12.2)

NA

McNemar’s test was used for nominal variables; Wilcoxon signed rank test was used for ordinal variables or discrete/nonnormal continuous variables; paired t-test was used for continuous variables. b P values for antihypertensive medications were not applicable because the study inclusion criteria required patients to have a stable antihypertensive medication regimen during the pre- and postperiod. c No other beta-blockers should have been used along with metoprolol during the preperiod or nebivolol during the postperiod. ACE indicates angiotensin converting enzyme; ARBs, angiotensin-II receptor blockers; NA, not applicable; SD, standard deviation. a

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strates that patients with hypertension who switched from metoprolol to nebivolol and who continued the drug regimen for at least 6 months had significantly fewer CV-related emergency department and outpatient visits, as well as lower CV-related emergency department and total medical costs in the 6 months after switching. Our results add to clinical studies,16 and suggest that the use of nebivolol has

10 9 8 7 6 5 4 3 2 1 0

9.2b 6.7b

Preperiod Postperiod

0.3 0.1

0.2b 0.04b

Inpatient Emergency hospitalization visits department visits

Outpatient visits

Healthcare resource utilization was calculated on a basis of 100 patients per month. b P <.05; bootstrapping paired t-test was used for continuous variables between the preperiod and the postperiod. a

M  ain Analysis Results: Healthcare Resource Table 4 Utilization and Costs in the Preperiod and Postperiod Healthcare resource utilization/costs

l

30

Preperiod Postperiod P (N = 765) (N = 765) value

CV-related emergency department visits, N (SD)

0.2 (1.9)

CV-related outpatient visits, N (SD)

9.2 (19.9)

6.7 (17.5)

<.001

CV-related inpatient visits, N (SD)

0.3 (2.4)

0.1 (1.5)

.164

Patients with â&#x2030;Ľ1 CVrelated outpatient visit, %

27

21

<.001

CV-related emergency department costs, PPPM, $ (SD)

6 (78)

1 (27)

.028

CV-related outpatient costs, PPPM, $ (SD)

59 (400)

40 (206)

.130

CV-related inpatient costs, PPPM, $ (SD)

29 (324)

13 (161)

.173

CV-related total medical costs, PPPM, $ (SD)

94 (526)

54 (266)

.020

0.04 (0.8)

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27%b 25

.012

CV indicates cardiovascular; PPPM, per patient per month; SD, standard deviation.

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Healthcare Figure 4 Cardiovascular-Related Resource Utilizationa

CV-related resource utilization, %

Cardiovascular-related visits, N

 ardiovascular-Related Healthcare Figure 3 C Resource Utilizationa

a role in improving hypertension disease management by reducing the likelihood of CV-related utilization and costs when patients switched from metoprolol for any reasons. The reduction in CV-related emergency department and outpatient visits after the treatment regimen switch could be in part a result of the pharmacologic features of nebivolol. This third-generation medication has been shown to activate endothelial nitric oxide production, which helps to reduce the peripheral resistance of the blood vessels, leading to improved stroke volume with a neutral impact on cardiac output.16,23,24 This effect is useful in the treatment of hypertension, heart failure, ischemia reperfusion injury, and stroke. It is possible that the observed reduction in the CV-related emergency department and outpatient visits for patients who switched to nebivolol may be related to better management of hypertension and CV disease, which is consistent with previously published clinical trials.25 In addition, the higher treatment persistence observed among patients receiving nebivolol, as measured by fewer days of treatment gaps compared with patients receiving metoprolol, may also contribute to better disease management and potentially lower CV-related emergency department and outpatient visits after switching drugs. Consistent with the results of this present study, in a previously published study, patients receiving nebivolol

21%b 20

15

10

5

0

Preperiod

Postperiod

Healthcare resource utilization was calculated on a basis of 100 patients per month. b P <.05; bootstrapping paired t-test was used for continuous variables between the preperiod and the postperiod. a

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were found to have better persistence relative to metopro­ lol.20 Yang and colleagues recently showed that patients with hypertension who were nonpersistent with therapy had significantly more CV-related hospitalizations and emergency department visits than patients who were persistent with therapy.26 Costs of Cardiovascular-Related Care, by Type Figure 5 of Service, Per Patient Per Month (in 2013 US Dollars)a CV-related outpatient costs  C  V-related emergency department costs CV-related inpatient costs

100 90 80

CV-related costs, $

70 $59

60 50

$94b

40 $40

$6b

30

$54b

20 $29

10

$1b $13

0

Preperiod

Postperiod

Bootstrapping paired t-test was used for continuous variables between the preperiod and postperiod. b P <.05. CV indicates cardiovascular. a

Together, the lower CV-related healthcare resource utilization and lower CV-related emergency department costs observed with nebivolol may translate into the significantly lower CV-related medical costs seen after switching from metoprolol. Hypertension is a known risk factor for severe CV diseases such as stroke, myocardial infarction, and heart failure, which result in substantial medical expenditures and account for a large proportion of the total healthcare costs. For example, the estimated direct costs of CV disease-related complications that were attributable to hypertension were predicted to increase from $130.7 billion in 2010 to $389 billion in 2030.3 Added to that were indirect costs (eg, lost productivity) that were estimated to be $25.4 billion in 2010 and were expected to rise to $42.8 billion by 2030.3 The more effectively a treatment can manage hypertension, the lower the risk for severe CV diseases in the near future,26 which can possibly lead to better health and can minimize CV-related resource utilization as well as overall healthcare resource utilization and costs. In this study, we evaluated the CV-related healthcare resource utilization and the costs of patients with hypertension who switched from metoprolol to nebivolol; therefore, the preperiod and postperiod approach was the appropriate study design for the main analysis. Although the reasons for switching from metoprolol to nebivolol were not evaluated, it was recognized that only patients who were likely to benefit the most from switching were likely to switch. To test the importance of this assumption, we performed a sensitivity analysis to evaluate the outcomes that were the same between matched patients receiving metoprolol and those receiving nebivolol. The results of the sensitivity analysis were similar to those of the primary analysis, although the differences were not statistically significant. In other words, the point estimate of reduc-

 ensitivity Analysis Results: Healthcare Resource Utilization and Costs in Patients Who Did and Did Not Table 5 S Switch to Nebivolol Patients who did not Patients who switched Healthcare resource switch to nebivolol to nebivolol utilization/costs (N = 764) (N = 764) P value CV-related emergency department visits, N (SD)

0.2 (2.0)

0.04 (0.8)

.098

CV-related outpatient visits, N (SD)

7.8 (22.3)

6.7 (17.5)

.278

CV-related inpatient visits, N (SD)

0.2 (1.6)

0.1 (1.5)

.787

Patients with â&#x2030;Ľ1 CV-related outpatient visit, %

24

21

.153

4 (61)

1 (27)

.173

CV-related outpatient costs, PPPM, $ (SD)

44 (351)

40 (206)

.768

CV-related inpatient costs, PPPM, $ (SD)

49 (1045)

13 (161)

.315

CV-related total medical costs, PPPM, $ (SD)

97 (1115)

54 (266)

.227

CV-related emergency department costs, PPPM, $ (SD)

CV indicates cardiovascular; PPPM, per patient per month; SD, standard deviation.

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tion in the healthcare resource utilization and costs were consistent with that of the main analysis, but with larger variation, and were therefore statistically insignificant. Taken together, these results demonstrate that switching from the second-generation beta-blocker metoprolol to the third-generation beta-blocker nebivolol is likely to reduce CV-related resource utilization and costs. To quantify the impact of switching to nebivolol, this study was limited to patients who consistently took metoprolol and nebivolol for 6 months or more.

Limitations

The findings of this study may also not be generalizable to patients who are not taking metoprolol and nebivolol consistently. It is possible that the observed effect of cost-saving in this study is more applicable to patients who respond and adhere to their antihypertension medications. This study is subject to limitations similar to other retrospective database studies, including coding errors or omissions, incomplete claims, unreliable clinical coding, and unobservable factors that may have influenced the outcomes. However, there is no evidence that this potential measurement error would be nonrandom between the periods that patients were receiving metoprolol and nebivolol. The impact of the measurement error is likely to increase the confidence interval of the estimate (results toward nonsignificant), but the point estimate is likely to be robust. Because this study was based on a commercially insured population in the United States, the results may not be generalizable to patients with other types of health insurance or those living outside of the United States.

Conclusion

In this analysis, which was based on real-world data, patients with hypertension who switched from metoprolol to nebivolol had significantly lower CV-related healthcare resource utilization (eg, emergency department and outpatient visits) and lower CV-related healthcare costs. The lower CV-related resource utilization and costs may indicate improved disease management of hypertension. Additional studies are needed to identify these key drivers and to quantify the long-term economic impact of switching from other antihypertensive agents to nebivolol. ■ Funding Source Funding for this study was provided by Forest Research Institute, Inc. Author Disclosure Statement Dr Chen was an employee of Forest Research Institute at

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the time of this study; Ms Fu, Dr Jain, and Mr Tan are employees of HealthCore, Inc, a consultancy whose activities on the project were funded by Forest Research Institute.

References

1. James PA, Oparil S, Carter BL, et al. 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). JAMA. 2014; 311:507-520. Erratum in: JAMA. 2014;311:1809. 2. Go AS, Mozaffarian D, Roger VL, et al; for the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation. 2013;127:e6-e245. Erratum in: Circulation. 2013;127:e841. 3. Heidenreich PA, Trogdon JG, Khavjou OA, et al; for the American Heart Association Advocacy Coordinating Committee; Stroke Council; Council on Cardiovascular Radiology and Intervention; Council on Clinical Cardiology; Council on Epidemiology and Prevention; Council on Arteriosclerosis, Thrombosis and Vascular Biology; Council on Cardiopulmonary, Critical Care, Perioperative and Resuscitation; Council on Cardiovascular Nursing; Council on the Kidney in Cardiovascular Disease; Council on Cardiovascular Surgery and Anesthesia; Interdisciplinary Council on Quality of Care and Outcomes Research. Forecasting the future of cardiovascular disease in the United States: a policy statement from the American Heart Association. Circulation. 2011;123:933-944. 4. Sundström J, Arima H, Jackson R, et al; for the Blood Pressure Lowering Treatment Trialists’ Collaboration. Effects of blood pressure reduction in mild hypertension: a systematic review and meta-analysis. Ann Intern Med. 2015;162:184-191. 5. National Center for Health Statistics. Health, United States, 2013: With Special Feature on Prescription Drugs. Hyattsville, MD: US Department of Health & Human Services; 2014. www.cdc.gov/nchs/data/hus/hus13.pdf. Accessed October 14, 2014. 6. Aiyagari V, Gorelick PB. Management of blood pressure for acute and recurrent stroke. Stroke. 2009;40:2251-2256. 7. Kaplan RC, Psaty BM, Heckbert SR, et al. Blood pressure level and incidence of myocardial infarction among patients treated for hypertension. Am J Public Health. 1999;89:1414-1417. 8. Manickavasagam S, Merla R, Koerner MM, et al. Management of hypertension in chronic heart failure. Expert Rev Cardiovasc Ther. 2009;7:423-433. 9. Clinton P, Cacciotti J. Pharm Exec 50: growth from the bottom up. Pharm Exec. May 1, 2012. www.pharmexec.com/pharmexec/Top+Feature/Pharm-Exec-­ 50-Growth-from-the-Bottom-Up/ArticleStandard/Article/detail/773562. Accessed October 14, 2014. 10. Toblli JE, DiGennaro F, Giani JF, Dominici FP. Nebivolol: impact on cardiac and endothelial function and clinical utility. Vasc Health Risk Manag. 2012;8:151-160. 11. Bystolic (nebivolol) tablets [prescribing information]. St Louis, MO: Forest Laboratories, Inc; January 2014. 12. Grassi G, Trevano FQ, Facchini A, et al. Efficacy and tolerability profile of nebivolol vs atenolol in mild-to-moderate essential hypertension: results of a double-blind randomized multicentre trial. Blood Press Suppl. 2003;2:35-40. 13. Cheng JW. Nebivolol: a third-generation β-blocker for hypertension. Clin Ther. 2009;31:447-462. 14. Van Nueten L, Taylor FR, Robertson JIS. Nebivolol vs atenolol and placebo in essential hypertension: a double-blind randomised trial. J Hum Hypertens. 1998;12:135-140. 15. Weiss RJ, Saunders E, Greathouse M. Efficacy and tolerability of nebivolol in stage I-II hypertension: a pooled analysis of data from three randomized, placebo-controlled monotherapy trials. Clin Ther. 2011;33:1150-1161. 16. Weiss R. Nebivolol: a novel beta-blocker with nitric oxide-induced vasodilatation. Vasc Health Risk Manag. 2006;2:303-308. 17. Wojciechowski D, Papademetriou V. Beta-blockers in the management of hypertension: focus on nebivolol. Expert Rev Cardiovasc Ther. 2008;6:471-479. 18. Veronesi M, Cicero AF, Prandin MG, et al. A prospective evaluation of persistence on antihypertensive treatment with different antihypertensive drugs in clinical practice. Vasc Health Risk Manag. 2007;3:999-1005. 19. Grégoire JP, Moisan J, Guibert R, et al. Determinants of discontinuation of new courses of antihypertensive medications. J Clin Epidemiol. 2002;55:728-735. 20. Signorovitch JE, Samuelson TM, Ramakrishnan K, et al. Persistence with nebivolol in the treatment of hypertension: a retrospective claims analysis. Curr Med Res Opin. 2012;28:591-599. 21. Wolters Kluwer Health. Medi-Span Master Drug Database. www.medispan.

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com/drug-information-products/. Accessed October 14, 2014. 22. D’Hoore W, Bouckaert A, Tilquin C. Practical considerations on the use of the Charlson Comorbidity Index with administrative data bases. J Clin Epidemiol.­ 1996;49:1429-1433. 23. Weber MA. The role of the new β-blockers in treating cardiovascular disease. Am J Hypertens. 2005;18(12 pt 2):169S-176S. 24. Kamp O, Sieswerda GT, Visser CA. Comparison of effects on systolic and diastolic left ventricular function of nebivolol versus atenolol in patients with

uncomplicated essential hypertension. Am J Cardiol. 2003;92:344-348. 25. Celik T, Iyisoy A, Kursaklioglu H, et al. Comparative effects of nebivolol and metoprolol on oxidative stress, insulin resistance, plasma adiponectin and soluble P-selectin levels in hypertensive patients. J Hypertens. 2006;24: 591-596. 26. Yang W, Kahler KH, Fellers T, et al. Copayment level, treatment persistence, and healthcare utilization in hypertension patients treated with single-­ pill combination therapy. J Med Econ. 2011;14:267-278.

STAKEHOLDER PERSPECTIVE

Moving Beyond Measures into Outcomes in Hypertension Research By Michael F. Murphy, MD, PhD Chief Medical Officer and Scientific Officer, Worldwide Clinical Trials, King of Prussia, PA

N

ovel chemical and biological entities herald significant advances in disease management across a variety of therapeutic areas. Too frequently, however, therapeutic novelty becomes an impetus for expanded clinical use without an attendant demonstration of benefits in health-related outcomes across representative patients in diverse environments. When “novelty is not enough,” this becomes a clarion call for outcome studies initiated in tandem, or subsequent to prototypical registration programs.1 In their article in this issue of the journal, Chen and colleagues provide a methodologically rigorous example of a retrospective/prospective claims-based analysis for a cardioselective beta-blocker indicated for the treatment of hypertension that complements and validates results from controlled clinical studies. Data suggest that a newer class of agent, in contrast to a widely utilized second-generation beta-blocker, may improve disease management and reduce overall cardiovascular utilization and cost. RESEARCHERS: Properly designed observational studies yield estimates of treatment effect comparable to randomized controlled trials.2 They also generate ecologically relevant data to support marketing authorization with costs/resource utilization estimates to inform formulary placement and extent of coverage. Differentiation of therapy in a more heterogeneous population of patients and physicians is more feasible in observational research3 compared with ­designs necessitated by a preregistration program in which patient and research center characteristics are optimized under the constraints of a protocol design for the purposes of demonstrating evidence of efficacy and safety.

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Observational research permits sampling of a larger spectrum of clinical end points to measure effectiveness and efficiency in typical practice settings compared with current standards of care. Conclusions are framed with limitations common to observational studies using a claims database. However, sensitivity analyses in this report used propensity score matching of prognostically important variables can mute criticisms associated with nonrandomized trials that provide insights not available in traditional randomized prospective clinical trials.4 PAYERS: Policy mandates, accelerating costs, and changes in demographics require that new therapeutic entities submitted for marketing to prevalent, chronic conditions demonstrate effects on outcomes, as well as measures as part of their development program. Moving beyond measures into outcomes is a prerequisite for informing decisions in benefit design. Studies that provide insights on healthcare resource utilization and cost as part of a switching strategy in hypertension move the discussion from being focused on compound attributes to one that is focused on clinical attributes, within a time frame of patient exposure that is sufficient for inferring clinically relevant outcomes. With adherence dictating reduction in longer-­ term outcomes, a cardioselective agent with fewer reported adverse events bodes well for reduction in significant drivers of cost. Copayment can be a strong predictor of adherence to antihypertensive therapy, indicating that price sensitivity of patients dictates medication adherence.5 Observational studies suggesting that a pharmacologically sophisticated agent may displace second-generation medications inform this consideration.

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STAKEHOLDER PERSPECTIVE Continued PATIENTS: Medication-related issues represent one of many dimensions modifying overall antihypertensive medication adherence.6 Across cultures, an appreciation of the rationale for chronic therapy in the absence of symptoms is also an important modifying variable, which is entwined with the quality of the physician–patient relationships.7 The significance of medication attributes as represented by a novel cardioselective antihypertensive agent lies in its being a modifiable risk factor in contrast to social, economic, and other condition-related dimensions that may prove to be too distant or too intractable for effective intervention. ■

1. Shah NR. Evidence standards in the era of comparative effectiveness. Am Health Drug Benefits. 2009;2(1 suppl):S41-S48. 2. Concato J, Shah N, Horwitz RI. Randomized, controlled trials, observational studies, and the hierarchy of research designs. N Engl J Med. 2000;342: 1887-1892. 3. Marko NF, Weil RJ. The role of observational investigations in comparative effectiveness research. Value Health. 2010;13:989-997. 4. Newgard CD, Hedges JR, Arthur M, Mullins RJ. Advanced statistics: the propensity score—a method for estimating treatment effect in observational research. Acad Emerg Med. 2004;11:953-961. 5. Taira DA, Wong KS, Frech-Tamas F, Chung RS. Copayment level and compliance with antihypertensive medication: analysis and policy implications for managed care. Am J Manag Care. 2006;12:678-683. 6. Krousel-Wood M, Joyce C, Holt E, et al. Predictors of decline in medication adherence: results from the cohort study of medication adherence among older adults. Hypertension. 2011;58:804-810. 7. Gascón J, Sánchez-Ortuño M, Llor B, et al. Why hypertensive patients do not comply with the treatment: results from a qualitative study. Fam Pract. 2004;21:125-130.

Call for Papers American Health & Drug Benefits offers an open forum for all healthcare participants to exchange

ideas and present their data, innovations, and initiatives to facilitate patient-centered healthcare and benefit design models that meet the needs of all stakeholders—Distributors, Employers, Manufacturers, Patients, Payers, Policymakers, Providers, Purchasers, and Researchers. Topics and type of articles of high interest include:

• Adherence Concerns • Benefit Design • Case Studies • Comorbidities and Cost Issues • Comparative Effectiveness Research • Decision-Making Tools • Ethics in Medicine • Health Economics Outcomes

• Health Information Exchange • Health Plan Initiatives • Innovations in Healthcare • Literature Reviews • Managed Care • Medicare/Medicaid • Patient Outcomes/Advocacy • Pharmacoeconomics

• Pharmacogenomics • Policy Issues • Prevention Initiatives • Real-World Evidence • Reimbursement Strategies • Social Media in Healthcare • Survey Results • Value-Based Healthcare

SUBMIT articles to editorial@engagehc.com or at www.AHDBonline.com Articles must follow the Manuscript Instructions for Authors, available online

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What is the value of one year on velCaDe (bortezomib)? ®

for patients with previously untreated multiple myeloma, 1 year of treatment with velCaDe in combination with MP* delivered a >1-year sustained median overall survival (os) advantage.1† At 60.1-month median follow-up: VELCADE (bortezomib)+MP provided a median OS of 56.4 months vs 43.1 months with MP alone (HR=0.695 [95% CI, 0.57-0.85]; p<0.05) At 3-year median follow-up: VELCADE+MP provided an OS advantage over MP that was not regained with subsequent therapies Of the 69% of MP patients who received subsequent therapies, 50% received VELCADE or a VELCADE-containing regimen1 Results were achieved using VELCADE twice weekly followed by a weekly dosing for a median of 50 weeks (54 weeks planned)1

the additional value of choice of administration. Subcutaneous VELCADE demonstrated efficacy consistent with IV for the primary endpoints2‡: At 12 weeks, subcutaneous VELCADE: 43% achieved overall response rate (ORR) and 7% complete response (CR) vs IV: 42% ORR and 8% CR §II

The median age of patients in the VISTA† trial was 71 years (range: 48-91).

At 24 weeks, subcutaneous VELCADE ± dexamethasone: 53% achieved ORR and 11% CR vs IV: 51% ORR and 12% CR§II More than 80% of previously untreated patients starting on VELCADE receive subcutaneous administration 3¶

Indication and Important Safety Information for VELCADE® (bortezomib) INDICATION VELCADE (bortezomib) is indicated for the treatment of patients with multiple myeloma. CONTRAINDICATIONS VELCADE is contraindicated in patients with hypersensitivity (not including local reactions) to bortezomib, boron, or mannitol, including anaphylactic reactions. VELCADE is contraindicated for intrathecal administration. Fatal events have occurred with intrathecal administration of VELCADE. WARNINGS, PRECAUTIONS, AND DRUG INTERACTIONS ▼ Peripheral neuropathy: Manage with dose modification or discontinuation. Patients with preexisting severe neuropathy should be treated with VELCADE only after careful risk-benefit assessment. ▼ hypotension: Use caution when treating patients taking antihypertensives, with a history of syncope, or with dehydration. ▼ Cardiac toxicity: Worsening of and development of cardiac failure have occurred. Closely monitor patients with existing heart disease or risk factors for heart disease. ▼ Pulmonary toxicity: Acute respiratory syndromes have occurred. Monitor closely for new or worsening symptoms.

▼ Posterior reversible encephalopathy syndrome: Consider MRI imaging for onset of visual or neurological symptoms; discontinue VELCADE if suspected. ▼ Gastrointestinal toxicity: Nausea, diarrhea, constipation, and vomiting may require use of antiemetic and antidiarrheal medications or fluid replacement. ▼ thrombocytopenia or neutropenia: Monitor complete blood counts regularly throughout treatment. ▼ tumor lysis syndrome: Closely monitor patients with high tumor burden. ▼ hepatic toxicity: Monitor hepatic enzymes during treatment. ▼ embryo-fetal risk: Women should avoid becoming pregnant while being treated with VELCADE. Advise pregnant women of potential embryo-fetal harm. ▼ Closely monitor patients receiving VELCADE in combination with strong CyP3a4 inhibitors. Avoid concomitant use of strong CyP3a4 inducers. ADVERSE REACTIONS Most commonly reported adverse reactions (incidence ≥20%) in clinical studies include nausea, diarrhea, thrombocytopenia, neutropenia, peripheral neuropathy, fatigue, neuralgia, anemia, leukopenia, constipation, vomiting, lymphopenia, rash, pyrexia, and anorexia. Please see Brief Summary for VELCADE adjacent to this advertisement. For Reimbursement Assistance, call 1-866-VELCADE (835-2233), Option 2, or visit VELCADE-HCP.com.

*Melphalan+prednisone. † VISTA TRIAL: a randomized, open-label, international phase 3 trial (N=682) evaluating the efficacy and safety of VELCADE administered intravenously in combination with MP vs MP in previously untreated multiple myeloma. The primary endpoint was TTP. Secondary endpoints were CR, ORR, PFS, and overall survival. At a prespecified interim analysis (median follow-up 16.3 months), VELCADE+MP resulted in significantly superior results for TTP (median 20.7 months with VELCADE+MP vs 15.0 months with MP [p=0.000002]), PFS, overall survival, and ORR. Further enrollment was halted and patients receiving MP were offered VELCADE in addition. Updated analysis was performed. ‡ SuBCuTAnEouS VS IV was a randomized (2:1), open-label, non-inferiority phase 3 trial (N=222) in patients with relapsed multiple myeloma designed to establish whether subcutaneous VELCADE (bortezomib) was non-inferior to intravenous administration.2 Non-inferiority was defined as retaining 60% of the intravenous treatment effect, measured by ORR, at the end of 4 cycles.2 The primary endpoint was ORR at 4 cycles. The secondary endpoints were response rate at 8 cycles, median TTP and PFS (months), 1-year OS, and safety. § Responses were based on criteria established by the European Group for Blood and Marrow Transplantation.2 II 82 patients (55%) in the subcutaneous VELCADE group and 39 patients (53%) in the IV group received dexamethasone. ¶ Out of 275 estimated unique patients receiving VELCADE as of May 2013.3 References: 1. Mateos MV, Richardson PG, Schlag R, et al. Bortezomib plus melphalan and prednisone compared with melphalan and prednisone in previously untreated multiple myeloma: updated follow-up and impact of subsequent therapy in the phase III VISTA trial. J Clin Oncol. 2010;28(13):2259-2266. 2. Moreau P, Pylypenko H, Grosicki S, et al. Subcutaneous versus intravenous administration of bortezomib in patients with relapsed multiple myeloma: a randomised, phase 3, non-inferiority study. Lancet Oncol. 2011;12(5):431-440. 3. Data on file 59, Millennium Pharmaceuticals, Inc.


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Brief Summary

VELC3X0043_A_Velcade_BS_7x10_r3.indd 1

Embryo-fetal: Pregnancy Category D. Women of reproductive potential should avoid becoming pregnant while being treated with VELCADE. Bortezomib administered to rabbits during organogenesis at a dose approximately 0.5 times the clinical dose of 1.3 mg/m2 based on body surface area caused post-implantation loss and a decreased number of live fetuses. ADVERSE EVENT DATA: Safety data from phase 2 and 3 studies of single-agent VELCADE 1.3 mg/m2/dose administered intravenously twice weekly for 2 weeks followed by a 10-day rest period in 1163 patients with previously-treated multiple myeloma (N=1008) and previously-treated mantle cell lymphoma (N=155) were integrated and tabulated. In these studies, the safety profile of VELCADE was similar in patients with multiple myeloma and mantle cell lymphoma. In the integrated analysis, the most commonly reported (≥10%) adverse reactions were nausea (49%), diarrhea NOS (46%), fatigue (41%), peripheral neuropathies NEC (38%), thrombocytopenia (32%), vomiting NOS (28%), constipation (25%), pyrexia (21%), anorexia (20%), anemia NOS (18%), headache NOS (15%), neutropenia (15%), rash NOS (13%), paresthesia (13%), dizziness (excl vertigo 11%), and weakness (11%). Eleven percent (11%) of patients experienced at least 1 episode of ≥Grade 4 toxicity, most commonly thrombocytopenia (4%) and neutropenia (2%). A total of 26% of patients experienced a serious adverse reaction during the studies. The most commonly reported serious adverse reactions included diarrhea, vomiting, and pyrexia (3% each), nausea, dehydration, and thrombocytopenia (2% each), and pneumonia, dyspnea, peripheral neuropathies NEC, and herpes zoster (1% each). In the phase 3 VELCADE+melphalan and prednisone study in previously untreated multiple myeloma, the safety profile of VELCADE administered intravenously in combination with melphalan/prednisone is consistent with the known safety profiles of both VELCADE and melphalan/prednisone. The most commonly reported adverse reactions in this study (VELCADE+melphalan/prednisone vs melphalan/prednisone) were thrombocytopenia (48% vs 42%), neutropenia (47% vs 42%), peripheral neuropathy (46% vs 1%), nausea (39% vs 21%), diarrhea (35% vs 6%), neuralgia (34% vs <1%), anemia (32% vs 46%), leukopenia (32% vs 28%), vomiting (26% vs 12%), fatigue (25% vs 14%), lymphopenia (23% vs 15%), constipation (23% vs 4%), anorexia (19% vs 6%), asthenia (16% vs 7%), pyrexia (16% vs 6%), paresthesia (12% vs 1%), herpes zoster (11% vs 3%), rash (11% vs 2%), abdominal pain upper (10% vs 6%), and insomnia (10% vs 6%). In the phase 3 VELCADE subcutaneous vs intravenous study in relapsed multiple myeloma, safety data were similar between the two treatment groups. The most commonly reported adverse reactions in this study were peripheral neuropathy NEC (37% vs 50%), thrombocytopenia (30% vs 34%), neutropenia (23% vs 27%), neuralgia (23% vs 23%), anemia (19% vs 23%), diarrhea (19% vs 28%), leukopenia (18% vs 20%), nausea (16% vs 14%), pyrexia (12% vs 8%), vomiting (9% vs 11%), asthenia (7% vs 16%), and fatigue (7% vs 15%). The incidence of serious adverse reactions was similar for the subcutaneous treatment group (20%) and the intravenous treatment group (19%). The most commonly reported SARs were pneumonia and pyrexia (2% each) in the subcutaneous treatment group and pneumonia, diarrhea, and peripheral sensory neuropathy (3% each) in the intravenous treatment group. DRUG INTERACTIONS: Bortezomib is a substrate of cytochrome P450 enzyme 3A4, 2C19 and 1A2. Co-administration of ketoconazole, a strong CYP3A4 inhibitor, increased the exposure of bortezomib by 35% in 12 patients. Monitor patients for signs of bortezomib toxicity and consider a bortezomib dose reduction if bortezomib must be given in combination with strong CYP3A4 inhibitors (eg, ketoconazole, ritonavir). Co-administration of omeprazole, a strong inhibitor of CYP2C19, had no effect on the exposure of bortezomib in 17 patients. Co-administration of rifampin, a strong CYP3A4 inducer, is expected to decrease the exposure of bortezomib by at least 45%. Because the drug interaction study (n=6) was not designed to exert the maximum effect of rifampin on bortezomib PK, decreases greater than 45% may occur. Efficacy may be reduced when VELCADE is used in combination with strong CYP3A4 inducers; therefore, concomitant use of strong CYP3A4 inducers is not recommended in patients receiving VELCADE. St. John’s wort (Hypericum perforatum) may decrease bortezomib exposure unpredictably and should be avoided. Co-administration of dexamethasone, a weak CYP3A4 inducer, had no effect on the exposure of bortezomib in 7 patients. Co-administration of melphalan-prednisone increased the exposure of bortezomib by 17% in 21 patients. However, this increase is unlikely to be clinically relevant. USE IN SPECIFIC POPULATIONS: Nursing Mothers: It is not known whether bortezomib is excreted in human milk. Because many drugs are excreted in human milk and because of the potential for serious adverse reactions in nursing infants from VELCADE, a decision should be made whether to discontinue nursing or to discontinue the drug, taking into account the importance of the drug to the mother. Pediatric Use: The safety and effectiveness of VELCADE in children has not been established. Geriatric Use: No overall differences in safety or effectiveness were observed between patients ≥age 65 and younger patients receiving VELCADE; but greater sensitivity of some older individuals cannot be ruled out. Patients with Renal Impairment: The pharmacokinetics of VELCADE are not influenced by the degree of renal impairment. Therefore, dosing adjustments of VELCADE are not necessary for patients with renal insufficiency. Since dialysis may reduce VELCADE concentrations, VELCADE should be administered after the dialysis procedure. For information concerning dosing of melphalan in patients with renal impairment, see manufacturer’s prescribing information. Patients with Hepatic Impairment: The exposure of bortezomib is increased in patients with moderate and severe hepatic impairment. Starting dose should be reduced in those patients. Patients with Diabetes: During clinical trials, hypoglycemia and hyperglycemia were reported in diabetic patients receiving oral hypoglycemics. Patients on oral antidiabetic agents receiving VELCADE treatment may require close monitoring of their blood glucose levels and adjustment of the dose of their antidiabetic medication. Please see full Prescribing Information for VELCADE at VELCADEHCP.com.

VELCADE, MILLENNIUM and are registered trademarks of Millennium Pharmaceuticals, Inc. Other trademarks are property of their respective owners. Millennium Pharmaceuticals, Inc., Cambridge, MA 02139 Copyright © 2013, Millennium Pharmaceuticals, Inc. V-12-0306a All rights reserved. Printed in USA V-14-0258

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INDICATIONS: VELCADE® (bortezomib) for Injection is indicated for the treatment of patients with multiple myeloma. VELCADE for Injection is indicated for the treatment of patients with mantle cell lymphoma who have received at least 1 prior therapy. CONTRAINDICATIONS: VELCADE is contraindicated in patients with hypersensitivity (not including local reactions) to bortezomib, boron, or mannitol, including anaphylactic reactions. VELCADE is contraindicated for intrathecal administration. Fatal events have occurred with intrathecal administration of VELCADE. WARNINGS AND PRECAUTIONS: Peripheral Neuropathy: VELCADE treatment causes a peripheral neuropathy that is predominantly sensory; however, cases of severe sensory and motor peripheral neuropathy have been reported. Patients with pre-existing symptoms (numbness, pain, or a burning feeling in the feet or hands) and/or signs of peripheral neuropathy may experience worsening peripheral neuropathy (including ≥Grade 3) during treatment with VELCADE. Patients should be monitored for symptoms of neuropathy, such as a burning sensation, hyperesthesia, hypoesthesia, paresthesia, discomfort, neuropathic pain or weakness. In the Phase 3 relapsed multiple myeloma trial comparing VELCADE subcutaneous vs intravenous, the incidence of Grade ≥2 peripheral neuropathy events was 24% for subcutaneous and 39% for intravenous. Grade ≥3 peripheral neuropathy occurred in 6% of patients in the subcutaneous treatment group, compared with 15% in the intravenous treatment group. Starting VELCADE subcutaneously may be considered for patients with pre-existing or at high risk of peripheral neuropathy. Patients experiencing new or worsening peripheral neuropathy during VELCADE therapy may require a decrease in the dose and/or a less dose-intense schedule. In the VELCADE vs dexamethasone phase 3 relapsed multiple myeloma study, improvement in or resolution of peripheral neuropathy was reported in 48% of patients with ≥Grade 2 peripheral neuropathy following dose adjustment or interruption. Improvement in or resolution of peripheral neuropathy was reported in 73% of patients who discontinued due to Grade 2 neuropathy or who had ≥Grade 3 peripheral neuropathy in the phase 2 multiple myeloma studies. The long-term outcome of peripheral neuropathy has not been studied in mantle cell lymphoma. Hypotension: The incidence of hypotension (postural, orthostatic, and hypotension NOS) was 8%. These events are observed throughout therapy. Caution should be used when treating patients with a history of syncope, patients receiving medications known to be associated with hypotension, and patients who are dehydrated. Management of orthostatic/postural hypotension may include adjustment of antihypertensive medications, hydration, and administration of mineralocorticoids and/or sympathomimetics. Cardiac Toxicity: Acute development or exacerbation of congestive heart failure and new onset of decreased left ventricular ejection fraction have occurred during VELCADE therapy, including reports in patients with no risk factors for decreased left ventricular ejection fraction. Patients with risk factors for, or existing, heart disease should be closely monitored. In the relapsed multiple myeloma study of VELCADE vs dexamethasone, the incidence of any treatment-related cardiac disorder was 8% and 5% in the VELCADE and dexamethasone groups, respectively. The incidence of adverse reactions suggestive of heart failure (acute pulmonary edema, pulmonary edema, cardiac failure, congestive cardiac failure, cardiogenic shock) was ≤1% for each individual reaction in the VELCADE group. In the dexamethasone group, the incidence was ≤1% for cardiac failure and congestive cardiac failure; there were no reported reactions of acute pulmonary edema, pulmonary edema, or cardiogenic shock. There have been isolated cases of QT-interval prolongation in clinical studies; causality has not been established. Pulmonary Toxicity: Acute Respiratory Distress Syndrome (ARDS) and acute diffuse infiltrative pulmonary disease of unknown etiology, such as pneumonitis, interstitial pneumonia, and lung infiltration have occurred in patients receiving VELCADE. Some of these events have been fatal. In a clinical trial, the first two patients given high-dose cytarabine (2 g/m2 per day) by continuous infusion with daunorubicin and VELCADE for relapsed acute myelogenous leukemia died of ARDS early in the course of therapy. There have been reports of pulmonary hypertension associated with VELCADE administration in the absence of left heart failure or significant pulmonary disease. In the event of new or worsening cardiopulmonary symptoms, consider interrupting VELCADE until a prompt, comprehensive, diagnostic evaluation is conducted. Posterior Reversible Encephalopathy Syndrome (PRES): Posterior Reversible Encephalopathy Syndrome (PRES; formerly termed Reversible Posterior Leukoencephalopathy Syndrome (RPLS)) has occurred in patients receiving VELCADE. PRES is a rare, reversible, neurological disorder, which can present with seizure, hypertension, headache, lethargy, confusion, blindness, and other visual and neurological disturbances. Brain imaging, preferably MRI (Magnetic Resonance Imaging), is used to confirm the diagnosis. In patients developing PRES, discontinue VELCADE. The safety of reinitiating VELCADE therapy in patients previously experiencing PRES is not known. Gastrointestinal Toxicity: VELCADE treatment can cause nausea, diarrhea, constipation, and vomiting, sometimes requiring use of antiemetic and antidiarrheal medications. Ileus can occur. Fluid and electrolyte replacement should be administered to prevent dehydration. Interrupt VELCADE for severe symptoms. Thrombocytopenia/Neutropenia: VELCADE is associated with thrombocytopenia and neutropenia that follow a cyclical pattern, with nadirs occurring following the last dose of each cycle and typically recovering prior to initiation of the subsequent cycle. The cyclical pattern of platelet and neutrophil decreases and recovery remained consistent over the 8 cycles of twice-weekly dosing, and there was no evidence of cumulative thrombocytopenia or neutropenia. The mean platelet count nadir measured was approximately 40% of baseline. The severity of thrombocytopenia was related to pretreatment platelet count. In the relapsed multiple myeloma study of VELCADE vs dexamethasone, the incidence of bleeding (≥Grade 3) was 2% on the VELCADE arm and <1% on the dexamethasone arm. Complete blood counts (CBC) should be monitored frequently during treatment with VELCADE. Platelet counts should be monitored prior to each dose of VELCADE. Patients experiencing thrombocytopenia may require change in the dose and schedule of VELCADE. Gastrointestinal and intracerebral hemorrhage has been reported in association with VELCADE. Transfusions may be considered. Tumor Lysis Syndrome: Tumor lysis syndrome has been reported with VELCADE therapy. Patients at risk of tumor lysis syndrome are those with high tumor burden prior to treatment. Monitor patients closely and take appropriate precautions. Hepatic Toxicity: Cases of acute liver failure have been reported in patients receiving multiple concomitant medications and with serious underlying medical conditions. Other reported hepatic reactions include hepatitis, increases in liver enzymes, and hyperbilirubinemia. Interrupt VELCADE therapy to assess reversibility. There is limited re-challenge information in these patients.


BUSINESS

ORIGINAL RESEARCH

The Cost of Unintended Pregnancies for Employer-Sponsored Health Insurance Plans Gabriela Dieguez, FSA, MAAA; Bruce S. Pyenson, FSA, MAAA; Amy W. Law, PharmD; Richard Lynen, MD;­ James Trussell, PhD BACKGROUND: Pregnancy is associated with a significant cost for employers providing health insurance benefits to their employees. The latest study on the topic was published in 2002, estimating the unintended pregnancy rate for women covered by employer-sponsored insurance benefits to be approximately 29%. OBJECTIVES: The primary objective of this study was to update the cost of unintended pregnancy to employer-sponsored health insurance plans with current data. The secondary objective was to develop a regression model to identify the factors and associated magnitude that contribute to unintended pregnancies in the employee benefits population. METHODS: We developed stepwise multinomial logistic regression models using data from a national survey on maternal attitudes about pregnancy before and shortly after giving birth. The survey was conducted by the Centers for Disease Control and Prevention through mail and via telephone interviews between 2009 and 2011 of women who had had a live birth. The regression models were then applied to a large commercial health claims database from the Truven Health MarketScan to retrospectively assign the probability of pregnancy intention to each delivery. RESULTS: Based on the MarketScan database, we estimate that among employer-sponsored health insurance plans, 28.8% of pregnancies are unintended, which is consistent with national findings of 29% in a survey by the Centers for Disease Control and Prevention. These unintended pregnancies account for 27.4% of the annual delivery costs to employers in the United States, or approximately 1% of the typical employer’s health benefits spending for 1 year. Using these findings, we present a regression model that employers could apply to their claims data to identify the risk for unintended pregnancies in their health insurance population. CONCLUSION: The availability of coverage for contraception without employee cost-sharing, as was required by the Affordable Care Act in 2012, combined with the ability to identify women who are at high risk for an unintended pregnancy, can help employers address the costs of unintended pregnancies in their employee benefits population. This can also help to bring contraception efforts into the mainstream of other preventive and wellness programs, such as smoking cessation, obesity management, and diabetes control programs.

Stakeholder Perspective, page 90

Am Health Drug Benefits. 2015;8(2):83-91 www.AHDBonline.com Received December 19, 2014 Accepted in final form March 2, 2015

Supplemental material online KEY WORDS: unintended pregnancy, costs, PRAMS questionnaire, delivery, contraception, employer-­ Disclosures are at end of text sponsored health insurance, employee benefits

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regnancy and delivery is the single largest group of diagnoses, by cost, for employers providing health insurance benefits, accounting for approximately

Ms Dieguez is Principal and Consulting Actuary, Milliman, Inc; Mr Pyenson is Principal and Consulting Actuary, Milliman, Inc, New York, NY; Dr Law is Deputy Director, Health Economics and Outcomes Research, Bayer HealthCare Pharmaceuticals; Dr Lynen is Medical Director, Bayer HealthCare Pharmaceuticals, Whippany, NJ; Dr Trussell is Professor of Economics and Public Affairs, Office of Population Research, Princeton University, NJ.

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$30 billion in hospital bills in 2008.1 Avoiding unnecessary hospitalizations is a focus for insurers and employers of cost reduction, but hospital admissions for deliveries are inherently different from other admissions, because they cannot be avoided or substituted with other appropriate outpatient care. Although the majority of pregnancies are planned, many are not; in fact, the unintended pregnancy rate among employer-sponsored health insurance plans was as high as 29% in 2002.2 In this study, we use the term “employer,” because employer-­ sponsored health programs cover the majority of commercially insured lives, but the results are applicable to other forms of

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KEY POINTS Unintended pregnancies carry a major cost burden for employer-sponsored health insurance ➤ The most recent studies were published in 2002 based on data from the mid-1990s ➤ This new study analyzed real-world data from a large national database and a large commercial database ➤ Among employer-sponsored health plans, 28.8% of pregnancies were unintended, accounting for 27.4% of the delivery costs to employers, or approximately 1% of an employer’s total healthcare annual costs ➤ In women aged 15 to 19 years, the rate of unintended pregnancies was 78%, almost 4 times higher than in women aged 35 to 39 years ➤ Increasing access to and reducing out-of-pocket costs of oral contraceptives have been suggested as potential strategies to reduce unintended pregnancies and the associated costs ➤ Promoting contraception by employers can also potentially reduce unintended pregnancies ➤ The high cost of unintended pregnancy for employers highlights a need for the identification and stratification of at-risk patients, as well as the need for patient education ➤

commercial insurance (eg, policies sold directly to individuals on exchanges or union-sponsored plans). Unintended pregnancy is a major cost component for employer-sponsored health benefits; yet, the most recent cost estimates are based on surveys that were conducted almost 20 years ago (in the mid-1990s), and do not reflect the socioeconomic and healthcare changes that have occurred since that time.2 Furthermore, published analyses of the costs of unintended pregnancy have often focused on spending by public insurance programs, perhaps because approximately 66% of births resulting from unintended pregnancies are paid for by such programs.3 Analyses based on spending by public insurance programs suggest that implementing or expanding public policies to prevent unintended pregnancies has the potential to provide substantial savings to the public.4 In addition, findings from a recent literature review on the costs of pregnancy suggest that reducing unintended pregnancies may lower the overall economic burden of pregnancy on the US healthcare system.3 Given all these factors, we sought to update the cost to employers of unintended pregnancy with more current data. The purpose of our study was to model pregnancy intention in a large commercial claims database and to

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provide aggregate estimates about the typical employer cost and healthcare resource utilization of deliveries associated with unintended pregnancy. An additional goal was to create a model that could be applied retrospectively to a health plan’s claims data to identify the risk that the pregnancies covered by the health plan were unintended. Such identification could be used as an aid for population health efforts to reduce unintended pregnancy.

Methods Data Sources We used 2009-2011 data from the Centers for Disease Control and Prevention’s Pregnancy Risk Assessment Monitoring System (PRAMS) database5 and 2010-2011 data from the Truven Health MarketScan Commercial Claims and Encounters Database6 (hereafter Market­ Scan) to estimate the probability of unintended pregnancy in a commercially insured population. The 2009-2011 PRAMS database contains population-based data on maternal attitudes and experiences before, during, and shortly after a live birth from approximately 50,000 women in approximately 40 states.5 The PRAMS questionnaire has more than 300 questions, which focus on topics such as pregnancy intention, contraception use, patient demographic characteristics, health insurance status, and the general health of the mother before and during pregnancy. Researchers can gain access to the PRAMS database through the Centers for Disease Control and Prevention5 and through individual state health departments. The MarketScan database, which includes medical and pharmacy claims for approximately 50 million commercially insured lives, contains International Classification of Diseases, Ninth Edition diagnosis codes, Current Procedural Terminology codes, National Drug Codes, identifiers of individuals associated with homogeneous benefit design groups, and identifiers that allow longitudinal studies of individuals.6 MarketScan is a proprietary database of Truven Health. Definitions The PRAMS questionnaire contains 2 parts. Part 1 is a set of 56 core questions asked in all participating states. Part 2 is a set of standard state-specific questions. In our analysis, we used 7 of the core questions and 4 of the standard state-specific questions. The questions were chosen based on our ability to find responses from the PRAMS questionnaire (eg, the mother’s age) in the MarketScan claims data. Commercial claims databases, such as MarketScan, do not include most socioeconomic information (eg, income and race). We identified pregnancy intention by core question 11 in the PRAMS questionnaire, which asks, “Thinking

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Cost of Unintended Pregnancy for Employers

back to just before you got pregnant with your new baby, how did you feel about becoming pregnant?” We mapped the respondent answers to either intended or unintended categories. Unintended pregnancy was defined as a mistimed (“I wanted to be pregnant later”) or unwanted (“I didn’t want to be pregnant then or at any time in the future”) pregnancy. Pregnancy intended at the time of conception (“I wanted to be pregnant then”) or earlier (“I wanted to be pregnant sooner”) was defined as intended. These definitions based on the PRAMS questionnaire are consistent with definitions found in other publications.7 In our analysis, we excluded women who were uninsured or covered by Medicaid, to remain consistent with the MarketScan database, which contains data only from employer-sponsored plans. We included only women with employer-sponsored health insurance plans who indicated that they were covered by health insurance from their job (or the job of their husband, partner, or parents), using respondent answers to PRAMS core question 2, which asks, “During the month before you got pregnant with your new baby, were you covered by any of these health insurance plans?” The age distribution of the survey respondents we considered closely resembled the age distribution of the study population in the MarketScan database, which we used to estimate the employer cost burden of unintended pregnancies.

Statistical Methods We conducted 2 distinct analyses. First, we developed regression models from survey data that assigned the probability that each pregnancy was unintended. We used stepwise multinomial logistic regressions to select the variables from the PRAMS data that were significant at the 0.1 level for explaining pregnancy intention. The resulting models retroactively assigned the probability that a given delivery was the result of an unintended pregnancy. Then, we applied the regression model to claims data in MarketScan to estimate the unintended pregnancy rate in employer-sponsored health benefits and normalized the coefficients of the model to closely replicate the unintended pregnancy rate in the PRAMS data. Table 1 shows the unintended pregnancy rates by the mother’s age. The explanatory variables in the PRAMS database were selected from survey questions that could be identified using information in typical claims and exposure data. This approach was taken so that model equations developed using PRAMS data could be applied to the MarketScan data. In subsequent steps, we further excluded explanatory variables based on odds ratios and clinical reasonability. The variables used in the regression were the mother’s age; prescription drug contraception use; existence of a previous live birth; number of dependents

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in the family; marital status at the time of conception; medical conditions (ie, hypertension, anemia, heart problems, seizures, thyroid problems, and mental health problems); the use of progesterone, Gestiva (trade name has since changed; generic hydroxyprogesterone caproate), or a 17-alpha hydroxyprogesterone shot; a previous miscarriage, fetal death, or stillbirth; and the state’s unintended pregnancy rate (low, medium, high). The logistic regression models were applied to inpatient deliveries that were identified in the 2011 MarketScan database. For deliveries in 2011 that might have begun as pregnancies in 2010, we looked back to 2010 claims, as needed, to obtain the data (eg, existing medical conditions) required for the regression models. The probability that the pregnancy was unintended was determined by the results of the regression formulas. We applied the results of the regression models to deliveries from the MarketScan database to develop the average cost of unintended pregnancies to employers. By applying the adjusted regression coefficients, each live delivery was assigned a probability of being unintended or intended. The average delivery costs weighted by pregnancy intention were developed by the cost component, which was done separately for unintended and intended pregnancies. The average claim costs per delivery were developed for the following categories: • Anesthesia • Caesarean section delivery • Vaginal delivery • Newborn without complications • Newborn with complications Comparison of Unintended Pregnancy Rates for the Table 1 MarketScan Unadjusted, MarketScan Adjusted, and PRAMS Databases Average probability of unintended pregnancy MarketScan MarketScan Mother’s before after age-group, normalization, normalization, yrs % % PRAMS, % 15-19

100.4

78.0

79.3

20-24

64.1

49.8

56.3

25-29

41.6

32.3

30.2

30-34

30.7

23.8

23.0

35-39

27.0

20.9

21.1

40-44

31.3

24.3

23.4

45-49

28.9

22.4

22.4

Total

36.9

28.7

28.4

PRAMS indicates Pregnancy Risk Assessment Monitoring System. Sources: 2011 Truven Health MarketScan database; 2009-2011 PRAMS database.

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Table 2 2011 Average Allowed Costs per Delivery Average allowed costs per deliveryb Service a category Unintended,c $ Intended, $ Total, $ 3925 4101 4051 Caesarian section delivery 3956 3888 3907 Vaginal delivery 560 684 650 Newborn without complications 4342 5102 4889 Newborn with complications 2688 2780 2754 Delivery (professional) 987 997 994 Anesthesiad 528 573 560 Inpatient visits 972 1116 1076 All other delivery costs 17,958 19,241 18,881 Total a Service categories are mutually exclusive. b Amounts are average allowed costs (eg, plan-paid amounts plus patient cost-sharing). c Excludes deliveries that occurred in December 2011; because of claims coding practices, not all deliveries were associated with newborn and professional claims. d Professional cost component only. Source: 2010-2011 Truven Health MarketScan database.

Table 3 Frequency and Cost of Deliveries Delivery variable

Deliveries Unintended Intended

Total

Annual frequency of deliveries for WCBA 11.89 29.38 41.27 Deliveries per 1000 WCBA per month, N 28.8 71.2 100.0 Total deliveries, % Monthly costs of deliveries for WCBA 17.80 47.10 64.90 Allowed delivery costs per WCBA per month, $ 27.4 72.6 100.0 Total allowed delivery costs, % Monthly costs of deliveries for all plan members 5.07 13.41 18.48 Allowed delivery costs PMPM, $ Costs of deliveries as portion of total plan costs allowed 1.4 3.7 5.1 Deliveries portion of total healthcare costs,a % a Percent of total costs was calculated as the allowed delivery costs PMPM, divided by $358.95, which is the total monthly allowed costs per member for all medical and prescription drug services for a typical population covered by employer-sponsored health benefits. PMPM indicates per member per month; WCBA, women of childbearing age. Source: 2010-2011 Truven Health MarketScan database.

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• Delivery (professional) • Inpatient visits • All other delivery costs (between the maternity admission and the discharge dates). Because some payer systems show costs separately for the baby and for the mother, we included all claims for babies that occurred within 30 days of the delivery date. To arrive at the average cost by pregnancy intention, the costs for these categories were weighted by the probability that the pregnancy was unintended.

Results We estimated that among employer-sponsored health insurance plans in the United States, 28.8% of pregnancies are unintended, and these pregnancies account for 27.4% of the employers’ maternity delivery costs. The model’s estimate of the portion of deliveries associated with unintended pregnancies is very close to that observed in the PRAMS database. The probability that a pregnancy is unintended is generally much higher in younger women than in older women, as shown in Table 1. In women aged 15 to 19 years, the proportion of unintended pregnancies was 78%, which is almost 4 times higher than in women aged 35 to 39 years. In addition, we observed an increase in the unintended pregnancy rate in women aged 40 to 44 years and in women aged 45 to 49 years relative to that in women aged 35 to 39 years. This increase could potentially reflect pregnancies in women who might have already had the number of children they wanted and who did not intend to become pregnant at these relatively older ages. Table 2 shows the average allowed costs per delivery in 2011, which are summarized by major categories of care for unintended and intended pregnancies. The allowed costs include amounts paid by the health plan and by the patient (eg, deductibles and coinsurance). In per-member per-month (PMPM) terms, unintended pregnancies for a typical employer-sponsored insured population cost approximately $5 of a total monthly medical allowed amount of $359, including prescription drug coverage, based on our analysis of data in the MarketScan database. Table 3 compares the annual frequency of deliveries per 1000 women of childbearing age resulting from unintended and intended pregnancies, ­ presents the monthly allowed costs of these deliveries (including patient cost-sharing), and contrasts these costs to the total monthly allowed medical cost for a typical population covered by employer-sponsored insurance. Table 4 represents the secondary objective of this study, using a regression model to identify the factors and associated magnitude that contribute to unintended pregnancies in the employee benefits population.

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Table 4 P RAMS Survey Variable Logistic Regression Coefficientsa PRAMS survey question Variable Variable Variable description Intercept Age-group, yrs 15-19 20-24 25-29 30-34 35-39 40-44 45-49 Contraception use Contraception use

N/A

Core 6 and core 36

b

Core 13

Family-related variables Previous live birth Core 8 1 dependent 2 dependents 3 dependents Core 55 4 dependents 5 dependents 6+ dependents Husband/partner Standard P2 Boyfriend/other c Medical conditions variables Hypertension Anemia Heart problems Standard L11 Seizures Thyroid Mental health Other variables Progesterone use Previous miscarriage Low state unintended pregnancy rate Medium state unintended pregnancy rate High state unintended pregnancy rateb

Regression coefficient (95% CI) Mistimed Unwanted

Standard N5 Standard FF1

N/A

–5.96 (–6.47 to –5.45)

–2.06 (–2.14 to –1.99)

Mother’s age between 15 and 19 yrs Mother’s age between 20 and 24 yrs

7.02 (6.5 to 7.53)

1.27 (1.21 to 1.34)

6.02 (5.51 to 6.54)

–0.14 (–0.2 to –0.08)

Mother’s age between 25 and 29 yrs

4.96 (4.44 to 5.47)

–0.91 (–0.97 to –0.85)

Mother’s age between 30 and 34 yrs Mother’s age between 35 and 39 yrs

4.48 (3.97 to 5)

–1.17 (–1.23 to –1.1)

3.96 (3.45 to 4.48)

–0.92 (–0.98 to –0.86)

Mother’s age between 40 and 44 yrs

3.54 (3.03 to 4.05)

–0.37 (–0.43 to –0.31)

b

Mother’s age between 45 and 49 yrs

Reference group

Reference group

Prescription drug contraception use (pill, patch, diaphragm, vaginal ring, or emergency contraception)

2.59 (2.56 to 2.63)

2.9 (2.86 to 2.94)

Mother had other babies born alive

0.43 (0.42 to 0.44)

1.37 (1.35 to 1.39)

Number of people, including mother, depending on household income during the 12 months before the birth of the new baby

Relationship to new baby’s father at time of conception

0.9 (0.88 to 0.93)

1.1 (1.06 to 1.14)

–0.32 (–0.34 to –0.29)

–0.49 (–0.53 to –0.46)

–0.51 (–0.53 to –0.49)

–1.23 (–1.26 to –1.19)

–0.08 (–0.11 to –0.06)

–0.03 (–0.06 to 0.01)

–0.04 (–0.07 to –0.01)

0.34 (0.3 to 0.37)

0.29 (0.26 to 0.32)

0.52 (0.48 to 0.56)

–0.44 (–0.47 to –0.42)

–0.21 (–0.25 to –0.17)

0.93 (0.88 to 0.99)

0.78 (0.71 to 0.85)

0.19 (0.15 to 0.24)

0.22 (0.17 to 0.28)

0.2 (0.18 to 0.22)

0.45 (0.42 to 0.48)

0.76 (0.7 to 0.82)

0.11 (0.01 to 0.21)

–0.5 (–0.6 to –0.4)

–1.18 (–1.41 to –0.96)

–0.36 (–0.39 to –0.33)

–0.53 (–0.59 to –0.48)

0.25 (0.23 to 0.28)

0.18 (0.14 to 0.22)

–0.38 (–0.42 to –0.35)

–0.74 (–0.8 to –0.68)

–0.69 (–0.75 to –0.62)

–0.2 (–0.28 to –0.12)

–0.39 (–0.39 to –0.38)

–0.41 (–0.43 to –0.4)

–0.18 (–0.19 to –0.17)

–0.19 (–0.2 to –0.17)

Reference group

Reference group

Mother’s health problems during the 3 months before pregnancy

Mother received weekly shots of progesterone, Gestiva, or 17 alphahydroxyprogesterone Mother had a miscarriage, fetal death, or stillbirth during the 12 months before pregnancy State’s unintended pregnancy rate (low, medium, high) for mothers with job-related insurance, PRAMS (2009-2011)

a The logistic regression formula details are provided online in the Technical Appendix (www.AHDBonline.com). b“Mother’s age between 45 and 49 yrs” and “high state unintended pregnancy rate” were used as the reference groups for the logistic regression analysis. cLookback for MarketScan contraception claims is 1 month before pregnancy date. CI indicates confidence interval; N/A, not applicable; PRAMS, Pregnancy Risk Assessment Monitoring System.

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Discussion The most recent article on the cost to employers and commercial insurers of unintended pregnancy was published in 2002 and was based on maternal delivery dates that occurred between October 1, 1995, and March 31, 1996.2 This 2002 survey-based study by Green and colleagues reported that the unintended pregnancy rate among commercially insured lower-risk women was 29%.2 One purpose of our analysis was to update this outdated information for employers and commercial insurers, and to provide a tool for health plans to determine the extent of unintended pregnancies in their population. Based on our analysis of the MarketScan database, we estimated that 28.8% of deliveries in employer-­sponsored health insurance plans in 2011 were a result of unintended pregnancies, and that the direct cost associated with these deliveries, which was mostly borne by employers, was approximately $5 PMPM before cost-sharing (or approximately 1% of the typical employer’s annual spending on health benefits, including patient cost-sharing, as shown in Table 3). The $5 PMPM cost, in the authors’ experience, is comparable with the cost of other conditions that employers attempt to manage.8 These findings suggest an opportunity for better health management, and we argue that the target population for appropriate intervention can be easily identified through methods that we developed and presented in this article. Much of the literature pertaining to unintended pregnancy involves the impact of contraception. In particular, the available estimates of the cost-effectiveness of public spending on contraception and family planning indicate that there is significant opportunity to reduce the taxpayers’ cost of maternal and newborn care that is associated with unintended pregnancy.9,10 Numerous studies describe how access to contraception varies as a result of cost or convenience.11,12 The American College of Obstetricians and Gynecologists recommends making oral contraceptives available over the counter to increase contraception access and use, and to possibly reduce unintended pregnancy rates.13 Cost barriers, such as out-of-pocket expenses, have also been identified as major deterrents to the use of contraception.14,15 More effective long-acting reversible contraception tends to be associated with higher upfront patient cost-sharing,11,12 but it is more cost-effective than short-acting reversible methods.16 The Affordable Care Act (ACA), which requires that commercial health plans cover certain women’s health medical services without cost-sharing, has ameliorated concerns over out-of-pocket expenses for contraception services.14 The federal Health Resources and Services Administration (an organization that is responsible for improving access to healthcare)17 used recommendations

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from the Institute of Medicine (an independent nonprofit organization that provides unbiased and authoritative advice to decision makers and the public) to establish the provision that women with reproductive capacity will have access to all US Food and Drug Administration–approved contraceptive methods, sterilization procedures, and patient education and counseling.14,17 This provision applies to all nongrandfathered plans starting with the plan year that began on or after August 1, 2012. Plans eligible to be grandfathered have been in existence since March 23, 2010 (and, in some cases, even earlier), and have not made significant benefit reductions or beneficiary cost increases since that time.14,18 Exceptions also exist for nonprofit religious organizations under certain circumstances.14 Because of the time lapse between the provision of health services (including contraception) and the availability of claims data, it was too early at the time of this study to tell whether the ACA’s no cost-sharing contraception mandate has had any effect on the use of contraception services or on the rate of unintended pregnancy. We believe this topic merits follow-up research. Lower cost-sharing is associated with increased use of services,15 including preventive services. Despite this effect, there are well-known examples of the suboptimal use of other preventive services (eg, cancer screening).19 It is likely that contraceptive preventive services are similarly underutilized or misused,20 suggesting the need to further promote awareness of the availability of contraceptives via communication efforts by providers and health insurers.21 The financial case for employers to promote contraception as a preventive service has not been made, although the prevention of unintended pregnancies is a public health goal and benefit. Our findings suggest that employer promotion of contraception has the potential to reduce unintended pregnancies, as well as costs to employer-sponsored insurance. We argue that family planning fits well into broader wellness and health promotion efforts that are aimed at improving overall employee well-being and health cost-savings. The high cost of unintended pregnancy for employers suggests the need for research that identifies the most effective patient management methods.22 Typical processes used for managing patients who are at risk for medical conditions include identifying at-risk patients; stratifying at-risk patients so that certain patients receive more intense or less intense outreach efforts; and offering patient education, reminders, or other services aimed at the patient’s clinical situation.

Limitations We acknowledge several limitations in our analysis, which include potential survey bias, our inability to cap-

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ture socioeconomic status in the MarketScan database, potential mismatches between PRAMS survey-reported conditions and diagnosis codes in claims, and the exclusion of terminated pregnancies. In the PRAMS database, the survey responses pertaining to pregnancy intention are self-reported; intention is inherently subjective, and our results may have been affected by respondent bias. Nonetheless, the rates of unintended pregnancy in the PRAMS database, overall and specific by demographic characteristics, are consistent with those noted by other reputable sources, such as the Guttmacher Institute and the Centers for Disease Control and Prevention.23,24 None of the known major risk factors associated with unintended pregnancy (eg, minority race, fewer years of education, and lower income)25 is available in typical commercial claims data. We therefore attempted to capture some of the differences in socioeconomic factors by adding a regional variable to the regression analysis based on the mother’s state of residence. We ranked states according to their unintended pregnancy rate for mothers with employer-sponsored health insurance, as defined earlier. We established 3 categories to reflect the aggregate unintended pregnancy rates, and then assigned each state to 1 of 3 categories (low, medium, or high), with an equal number of states in each category. Although our goal was to reflect race or income regional differences, we did not expect this simple approach to capture the full range of socioeconomic influences. Although the MarketScan database is representative of the national aggregate employee and dependent population that is typically covered by employer-sponsored health insurance with regard to size, geography, and health benefits, the conclusions of this study may vary from the experience of any given employer-sponsored health plan. Finally, the way in which we identified the medical conditions and events used in our model differed by database. In the PRAMS database, medical conditions and events are self-reported, whereas in the MarketScan database, they are based on diagnoses and procedure codes that appear in the administrative claims data. We also recognized the potential for varying degrees of accuracy or underreporting or overreporting in the 2 databases. For example, in the PRAMS database, a woman may have self-­reported elevated blood pressure, whereas in the Market­Scan database, a woman with hypertension was identified based on the diagnosis codes in her medical claims. Pregnancies that ended in abortion or miscarriage are not included in the PRAMS database, because this database contains only data on live birth deliveries. We were therefore able to estimate the probabilities and cost burdens of live births only.

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Conclusion In our analysis, we used the 2009-2011 PRAMS database and the 2010-2011 MarketScan database to demonstrate that a significant proportion of pregnancies recently paid for by employer-sponsored health insurance programs are unintended. We also developed a regression model that can be applied to readily available medical claims databases to retrospectively quantify the cost and aggregate risk for unintended pregnancies, in particular, employer-­sponsored health insurance benefit populations. The cost-effectiveness of contraceptive methods has been widely demonstrated elsewhere.9,10 Population health management efforts for other conditions are popular with employers. The ability to identify women who are at high risk for an unintended pregnancy, as demonstrated in this analysis, is one important step toward promoting proved methods to address unintended pregnancies in the employee benefits population. When this study was conducted, it was too early to assess whether the ACA’s no cost-sharing contraception mandate has had any effect on the use of contraception services or on the rate of unintended pregnancy. We believe this topic merits follow-up research. ■ Acknowledgments This work was supported in part by the Eunice Kennedy Shriver National Institute of Child Health and Human Development grant for Infrastructure for Population Research at Princeton University, Grant R24HD047879. Funding Source This study was funded by Bayer Pharmaceuticals. Author Disclosure Statement Ms Dieguez and Mr Pyenson received actuarial consulting fees for this research from Bayer Pharmaceuticals; Dr Law is an employee of Bayer Pharmaceuticals; Dr Lynen is an employee and stockholder of Bayer HealthCare; Dr Trussell reported no conflicts of interest.

References

1. Wier LM, Andrews RM. The national hospital bill: the most expensive conditions by payer, 2008. Statistical brief #107. March 2011. In: Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Rockville, MD: Agency for Healthcare Research and Quality (US). www.ncbi.nlm.nih.gov/books/ NBK53976/. Accessed November 3, 2014. 2. Green DC, Gazmararian JA, Mahoney LD, Davis NA. Unintended pregnancy in a commercially insured population. Matern Child Health J. 2002;­6: 181-187. 3. Sonfield A, Kost K, Gold RB, Finer LB. The public costs of births resulting from unintended pregnancies: national and state-level estimates. Perspect Sex Reprod Health. 2011;43:94-102. 4. Monea E, Thomas A. Unintended pregnancy and taxpayer spending. Perspect Sex Reprod Health. 2011;43:88-93. 5. Centers for Disease Control and Prevention. What is PRAMS? Updated May 14, 2014. www.cdc.gov/prams/index.htm. Accessed November 3, 2014. 6. Truven Health Analytics. MarketScan databases and tools: better understand health economics and treatment outcomes. http://truvenhealth.com/

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your-healthcare-focus/Life-Sciences/MarketScan-Databases-and-Online-Tools. Accessed November 5, 2014. 7. Centers for Disease Control and Prevention (CDC). Prepregnancy contraceptive use among teens with unintended pregnancies resulting in live births— Pregnancy Risk Assessment Monitoring System (PRAMS), 2004-2008. MMWR Morb Mortal Wkly Rep. 2012;61:25-29. 8. Fitch K, Iwasaki K. Ambulatory-care-sensitive admission rates: a key metric in evaluating health plan medical-management effectiveness. January 2009. http://us.milliman.com/insight/research/health/pdfs/Ambulatory-care-sensitive-­ admission-rates-A-key-metric-in-evaluating-health-plan-medical-management/. Accessed December 19, 2014. 9. Foster DG, Biggs MA, Malvin J, et al. Cost-savings from the provision of specific contraceptive methods in 2009. Womens Health Issues. 2013;­­23: e265-e271. 10. Burlone S, Edelman AB, Caughey AB, et al. Extending contraceptive coverage under the Affordable Care Act saves public funds. Contraception. 2013;87:143-148. 11. Eisenberg D, McNicholas C, Peipert JF. Cost as a barrier to long-acting reversible contraceptive (LARC) use in adolescents. J Adolesc Health. 2013;52 ­(4 suppl):S59-S63. 12. Peipert JF, Madden T, Allsworth JE, Secura GM. Preventing unintended pregnancies by providing no-cost contraception. Obstet Gynecol. 2012;120:12911297. 13. American College of Obstetricians and Gynecologists’ Committee on Gynecologic Practice. Committee Opinion Number 544: over-the-counter access to oral contraceptives. Obstet Gynecol. 2012;120:1527-1531. 14. Health Resources and Services Administration. Women’s preventive services guidelines: Affordable Care Act expands prevention coverage for women’s health and well-being. www.hrsa.gov/womensguidelines/. Accessed November 3, 2014. 15. Chernew ME, Newhouse JP. What does the RAND Health Insurance Experiment tell us about the impact of patient cost sharing on health outcomes? Am J Manag Care. 2008;14:412-414.

16. Trussell J, Hassan F, Henry N, et al. Cost-effectiveness analysis of levonorgestrel-­ releasing intrauterine system (LNG-IUS) 13.5 mg in contraception. Contraception. 2014;89:451-459. 17. Health Resources and Services Administration. About HRSA: tens of millions of Americans get affordable health care and other help through HRSA’s 80-plus programs and more than 3,000 grantees. www.hrsa.gov/about/index. html. Accessed November 4, 2014. 18. US Department of Health & Human Services. Fact sheet: keeping the health plan you have: the Affordable Care Act and “grandfathered” health plans. www.healthreform.gov/newsroom/keeping_the_health_plan_you_have. html. Accessed November 3, 2014. 19. American Cancer Society. Cancer prevention & early detection facts & figures 2013. 2013. www.cancer.org/acs/groups/content/@epidemiologysurveilance/­ documents/document/acspc-037535.pdf. Accessed November 3, 2014. 20. Crosignani PG, Glasier A; for the ESHRE Capri Workshop Group. Family planning 2011: better use of existing methods, new strategies and more informed choices for female contraception. Hum Reprod Update. 2012;18:670-681. 21. Weisman CS, Chuang CH. Making the most of the Affordable Care Act’s contraceptive coverage mandate for privately-insured women. Womens Health Issues. 2014;24:465-468. 22. Health Enhancement Research Organization; Population Health Alliance. Program measurement and evaluation guide: core metrics for employee health management. Executive summary. www.populationhealthalliance.org/publications/­ program-measurement-evaluation-guide-core-metrics-for-employee-health-man agement-executive-summary.html. Accessed November 5, 2014. 23. Guttmacher Institute. Unintended pregnancy in the United States. Fact sheet. February 2015. www.guttmacher.org/pubs/FB-Unintended-Pregnancy-US­. html. Accessed March 9, 2015. 24. Centers for Disease Control and Prevention. Unintended pregnancy prevention. Updated February 12, 2013. www.cdc.gov/reproductivehealth/unintended pregnancy/. Accessed November 3, 2014. 25. Mosher WD, Jones J, Abma JC. Intended and unintended births in the United States: 1982-2010. Natl Health Stat Report. 2012:1-28.

STAKEHOLDER PERSPECTIVE

A Call to Action to Address Burden of Unintended Pregnancies in Plans’ Benefit Design By F. Randy Vogenberg Partner, Access Market Intelligence and Principal, Institute for Integrated Healthcare, Greenville, SC

EMPLOYERS/HEALTH PLANS: Self-funded or fully funded employers who provide health insurance to their employees have significant costs related to unintended pregnancies, as Dieguez and colleagues point out in their article in this issue of American Health & Drug Benefits.1 The risk (ie, cost) associated with providing health insurance is affected by a variety of factors that may be controllable, as well as a variety of options for managing that risk in an imperfect US health insurance world. Unintended pregnancies remain a significant problem to be tackled through the input and involvement of purchasers of care, such as self-funded employers. As seen in the current analysis by Dieguez and colleagues, the consistently high estimate of unintended pregnancies that

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persist today represents a general call to action for those in charge of benefit plan design in terms of the medical and the pharmacy benefits provided to plan members. The US Centers for Disease Control and Prevention (CDC) Division of Reproductive Health monitors maternal and infant health, including many issues related to the consequences of pregnancy.2 A number of surveillance systems, data, and reports are available to inform benefit decision makers on the CDC’s website about the extent and potential burden of unintended pregnancies in the United States.3 In terms of maternal and fetal medicine, for example, neonatal costs continue to be a significant economic driver for variations in clinical care. The March of Dimes produces an annual scorecard of premature births in each

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STAKEHOLDER PERSPECTIVE Continued state, with a corresponding letter grade assigned to it.4 In 2014, South Carolina was tied with Florida for the fourth highest rate of preterm births in the country.4 Also in 2014, South Carolina received a “D” grade rating from the March of Dimes.5 The South Carolina Birth Outcomes Initiative, a collaborative effort that focused on population health and engages all healthcare stakeholders, including employers and commercial insurance plans, was launched in July 2011.6 The South Carolina Birth Outcomes Initiative is now a best practices case study for turning the situation around with regard to the elimination of elective inductions for nonmedically indicated deliveries before 39 weeks, unless medically necessary.6 The change in rating in 1 year alone can represent a true quality shift in Leapfrog Hospital Safety scores for participating hospitals, in addition to Medicaid and commercial health insurance benefits, in terms of avoiding maternal or infant complications, as well as preventing increased medical costs.7 The multistakeholder South Carolina Birth Outcomes Initiative illustrates how population health can include the employer as part of the solution, as well as contribute to improving the health of a population.

EMPLOYERS/PROVIDERS/PATIENTS: The article by Dieguez and colleagues can assist employers as purchasers of care, along with providers (ie, medical groups, hospitals, and health systems) and patients (ie, employee plan members) to know all the factors and options related to dealing with value-based decision-making on an important issue such as unintended pregnancies.1 These factors include economics as well as clinical outcomes from various stakeholder perspectives related to today’s shared responsibility of managing healthcare risk. ■ 1. Dieguez G, Pyenson BS, Law AW, et al. The cost of unintended pregnancies for employer-sponsored health insurance plans. Am Health Drug Benefits. 2015; 8:83-91. 2. US Centers for Disease Control and Prevention. Maternal and infant health. Updated March 11, 2015. www.cdc.gov/reproductivehealth/MaternalInfantHealth/ index.htm. Accessed March 20, 2015. 3. US Centers for Disease Control and Prevention. Reproductive health: data and statistics. Updated February 4, 2015. www.cdc.gov/reproductivehealth/ Data_Stats/index.htm. Accessed March 20, 2015. 4. March of Dimes. 2014 Premature birth report cards. www.marchofdimes.org/ mission/prematurity-reportcard.aspx. Accessed March 20, 2015. 5. March of Dimes. 2014 Premature birth report card: South Carolina. www. marchofdimes.org/materials/premature-birth-report-card-south-carolina.pdf. Accessed March 20, 2015. 6. South Carolina Healthy Connections. South Carolina Birth Outcomes Initiative. www.scdhhs.gov/organizations/boi. Accessed March 20, 2015. 7. The Leapfrog Group. Early elective deliveries fact sheet. https://leapfroghospital survey.org/web/wp-content/uploads/FSdeliveries.pdf. Accessed March 20, 2015.

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AN RSARY VE NI

ANNUAL CONFERENCE

Monday, May 4, 2015 Co-chairs:

John Fox, MD, MHA Senior Medical Director Associate Vice President Medical Affairs Priority Health Alex Jung Principal, Global Strategic Advisory Services Ernst & Young LLP

Tuesday, May 5, 2015 Co-chairs:

Sanjiv S. Agarwala, MD Professor of Medicine Temple University School of Medicine Chief, Medical Oncology & Hematology St. Lukeâ&#x20AC;&#x2122;s Cancer Center

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ORIGINAL RESEARCH

Economic Burden of Opioid-Induced Constipation Among Long-Term Opioid Users with Noncancer Pain Yin Wan, MS, BPharm; Shelby Corman, PharmD, MS, BCPS; Xin Gao, PhD; Sizhu Liu, MS; Haridarshan Patel, PharmD; Reema Mody, PhD, MBA BACKGROUND: Opioid-induced constipation (OIC) can be a debilitating side effect of opioid therapy and may result in increased medical costs. The published data on the economic burden of OIC among longterm opioid users are limited. OBJECTIVE: To assess the economic burden of OIC in patients with noncancer pain in a managed care population in the United States. METHODS: This retrospective study used 2007-2011 data from the Truven Health MarketScan Commercial CLINICAL and Medicare databases. The study included adults with ≥12 months of insurance enrollment before and after starting long-term (≥90 days) use of opioids. Patients were excluded if they had cancer or a diagnosis of drug abuse or drug dependence during the study period, or if they had constipation or bowel obstruction within 90 days before starting opioid therapy during the study period. OIC was identified by International Classification of Diseases, Ninth Edition codes for constipation (564.0) or bowel obstruction (560.x) within 12 months of the initiation of an opioid. Patients with OIC were identified in the non­elderly, elderly (age ≥65 years), and long-term care populations. Differences in costs and healthcare resource utilization were calculated using propensity scoring. RESULTS: A total of 13,808 nonelderly (age, 48.6 ± 10.4 years; female, 50%) and 2958 elderly patients (age, 78.7 ± 8.1 years; female, 70%) met the study inclusion criteria. Of 401 nonelderly and 194 elderly patients with OIC, 85 patients initiated opioid therapy in a long-term care facility (age, 80.7 ± 11.6 years; female, 77%). After matching by key covariates, patients with OIC had significantly more hospital admissions than patients without OIC (nonelderly, 33% vs 22%, respectively; P <.001; elderly, 51% vs 31%, Stakeholder Perspective, respectively; P <.001) and longer inpatient stays (nonelderly, 3.0 ± 8.4 days vs 1.0 ± 3.0 days, respective- page 102 ly; P <.001; elderly, 5.2 ± 12.2 days vs 2.1 ± 4.0 days, respectively; P <.001). The group with OIC had significantly higher total healthcare costs than the group without OIC in all 3 study cohorts (nonelderly, $23,631 ± $67,209 vs $12,652 ± $19,717, respectively; elderly, $16,923 ± $38,191 vs $11,117 ± $19,525, respectively; long-term care, $16,000 ± $22,897 vs $14,437 ± $25,690, respectively; all P <.05). CONCLUSION: To the best of our knowledge, this is the first study to analyze the economic impact of Am Health Drug Benefits. 2015;8(2):93-102 long-term use of opioids among patients with OIC, using real-world data. The findings underscore the www.AHDBonline.com significant economic burden associated with long-term opioid use for noncancer pain in a managed care population. Effective therapies for OIC may reduce the associated economic burden and improve quality Received December 12, 2014 of life for long-term opioid users. Accepted in final form February 17, 2015

KEY WORDS: opioid-induced constipation, long-term opioid use, constipation, economic burden, elderly, healthcare resource utilization, long-term care, pain management

Ms Wan is Associate Scientist, Pharmerit International; ­­ Dr Corman is Senior Clinical Outcomes Scientist, Pharmerit International; Dr Gao is Senior Director, Pharmerit International; Mr Liu is Outcomes Research Analyst, Pharmerit International, Bethesda, MD; Dr Patel is a fellow in Global Outcomes Research, Takeda Pharmaceuticals International, Inc, Deerfield, and Consultant, Immensity Consulting, Inc, Chicago, IL; Dr Mody is Associate Director, Outcomes Research, Takeda Pharmaceuticals International, Inc, Deerfield, IL.

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pioids are the most frequently prescribed medications for the treatment of severe pain in developed countries, with up to 90% of American patients who present to specialized pain centers being treated with opioids.1 The frequency of opioid use is increasing, with one study reporting that opioid prescriptions for chronic abdominal pain in the United States more than doubled between 1997 and 2008.2 In addition to this rise in opioid use, the incidence of opioid-related adverse events has increased as well, with opioid-induced

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KEY POINTS Opioids are the most often prescribed medications for the treatment of severe pain ➤ Opioid-induced constipation (OIC) is a debilitating side effect of opioid therapy, with symptoms persisting for the duration of treatment ➤ This is the first study to assess the economic impact of OIC among elderly, nonelderly, and long-term care populations with noncancer pain ➤ Patients with OIC had significantly more hospital admissions and longer inpatient stays ➤ Based on this study, OIC is associated with significantly increased healthcare resource utilization and total healthcare costs among the elderly, nonelderly, and long-term care populations ➤ Effective therapies and more research are needed to reduce the economic burden of OIC on pain management among long-term opioid users ➤

constipation (OIC) being one of the most common and persistent events.1 In a systematic review of randomized trials of the use of oral opioids for chronic noncancer pain, the reported incidence of OIC from individual trials was as high as 71%.3 OIC has been associated with significant clinical and economic burdens, and may have a negative impact Figure 1 Study Design Baseline preindex period is fixed; patients must have at least 12 months of continuous enrollment before the index

Case selection window where index diagnosis must occur between January 2008 and December 2010

Postindex period is fixed; patients must have at least 12 months of continuous enrollment postindex January 1, 2007

January 1, 2008

December 31, 2010

December 31, 2011

NOTE: The length of the preindex and postindex periods is fixed (12 months).

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on patients’ quality of life.4 In addition, persistent constipation may lead to serious medical sequelae, such as bowel obstruction and fecal impaction,5 resulting in the increased use of medical services and decreased productivity loss.6 To the best of our knowledge, this is the first study using real-world administrative data to have quantified the economic burden of OIC in patients who are receiving a variety of opioids specifically for noncancer pain. Previous studies have shown that OIC is associated with significant economic burden; however, these studies have included only patients with cancer-related pain7 or have included cancer and noncancer pain without differentiating between them.8 Because of the exponential growth in opioid use for noncancer pain9,10 and the lack of real-world data on its gastrointestinal-related risks, it is important to quantify the economic impact of OIC in this population. One study concluded that the economic burden of gastrointestinal events among opioid users for noncancer pain is substantial, but that study included only patients receiving immediate-release oxycodone or hydrocodone.11 In addition, that study followed patients only for a relatively short period (90 days), which may underestimate the burden of OIC, a chronic condition in many cases. Furthermore, previous literature has focused on commercially insured and relatively young populations; therefore, the generalizability of their results to an older population is questionable. It is widely reported in the literature that elderly patients and long-term care residents are more likely to develop OIC than younger patients.12,13 However, little is known about the economic burden associated with OIC in these 2 patient populations. The paucity and limitations of the existing evidence underscore the need for new studies to evaluate the economic burden for long-term opioid users who have noncancer pain. The objective of our study was to estimate the healthcare resource utilization and costs associated with OIC among patients who are receiving an opioid long term for noncancer pain. To better understand the economic burden among various populations, 3 subpopulations were involved in our analysis, including nonelderly patients, elderly patients, and patients who received opioids in long-term care facilities.

Methods Study Design This study was a retrospective claims analysis using data from the Truven Health MarketScan Commercial Claims and Encounters Database and Truven Health MarketScan Medicare Supplemental and Coordination of Benefits Database from January 1, 2007, through

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­ ecember 31, 2011. The index event window, preindex D window, and postindex follow-up window are defined in Figure 1. The initial opioid prescription during the index period (January 2008-December 2010) was assigned as the study index date. Patients had continuous plan insurance coverage for 1 year before (baseline period) and after the index date.

Sample Selection Patients were included if they had at least 1 claim for an opioid prescription during the study period. To be included as long-term opioid users, patients had to have at least a 90-day supply of opioids11 with ≤15 days between prescription refills. Patients were excluded if they were aged <18 years or were diagnosed with cancer (International Classification of Diseases, Ninth Edition [ICD-9] code, 140.x-239.x), or had drug abuse (ICD-9 code, 305.x) or drug dependence (ICD-9 code, 304.x) during the study period. Patients who had claims for opioids during the 12-month preindex period were excluded from the study. In addition, patients were excluded if

they had a primary or secondary diagnosis of constipation (ICD-9 code, 564.0) or bowel obstruction (ICD-9 code, 560.x) within the 3 months before the index date.14 This study identified 3 study cohorts—nonelderly patients (aged 18-64 years); elderly patients (aged ≥65 years); and patients who used opioids at long-term care facilities, who could be either elderly or nonelderly. Each study cohort was divided into a constipation group, which included long-term opioid users who developed constipation, and a group without constipation, which included long-term opioid users who did not develop constipation.

Measurement and Outcomes OIC was defined as at least 1 claim with a primary or secondary ICD-9 diagnosis code for constipation (564.0) or intestinal obstruction without hernia (560.x) in the 12-month postindex period after the initiation of longterm opioid therapy. Healthcare resource utilization and healthcare costs were measured for a 12-month period after the initiation of opioids. All costs were presented as annual costs per

Figure 2 Flowchart of Cohort Selection with Sample Size Exclusion criteriaa (N = 11,200,262): • Patients aged <18 years (N = 2,104,254) • Patients with cancer (N = 7,751,898) • Patients with a diagnosis for drug abuse (N = 2,075,347)

Patients starting a new opioid medication during study period (2007-2011), N = 24,703,286

Patients aged ≥18 years as of index date, N = 13,503,024

Exclusion criteria (N = 2,649,255): • Patients with opioid use <90 days (N = 2,649,047) • Patients who had constipation within 90 days before index date (N = 208)

Patients with a continuous enrollment for ≥12 months before and after the index date, N = 2,666,021

Patients identified as long-term (>90 days) opioid users, N = 16,766

Elderly cohort (≥65 years) N = 2958

OIC N = 194

Non-OIC N = 2764

Long-term care cohort N = 566

OIC N = 85

Nonelderly cohort N = 13,808

Non-OIC N = 481

OIC N = 401

Non-OIC N = 13,407

NOTE: Exclusion criteria were not mutually exclusive; patients may have been excluded for multiple reasons. OIC indicates opioid-induced constipation.

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Table 1 Patient Characteristics of the Nonelderly, Elderly, and Long-Term Care Cohorts, by Constipation Statusa Nonelderly Elderly Long-term care OIC Non-OIC OIC Non-OIC OIC Non-OIC Characteristics (N = 401) (N = 13,407) (N = 194) (N = 2764) (N = 85) (N = 481) Mean age at index date, yrs (SD)

49.5 (10.5)

48.6 (10.4)

81.0 (7.4)b

78.5 (8.2)b

80.5 (9.2)

80.8 (12.0)

Sex, female, N (%)

253 (63)b

6680 (50)b

133 (69)

1934 (70)

68 (80)

368 (77)

Mean Charlson comorbidity score, N (SD)

0.7 (1.0)b

0.4 (0.7)b

1.5 (1.8)b

1.0 (1.3)b

2.1 (2.0)b

1.6 (1.6)b

Obesity, N (%)

33 (8)b

683 (5)b

6 (3)

55 (2)

3 (4)

16 (3)

Depression, N (%)

b

77 (19)

1437 (11)

22 (11)

191 (7)

15 (18)

73 (15)

Anxiety, N (%)

58 (14)b

1208 (9)b

13 (7)

111 (4)

8 (9)

38 (8)

Multiple sclerosis, N (%)

c

7 (2)

108 (1)

1 (1)

9 (0)

1 (1)

5 (1)

Spinal cord injury, paraplegia, quadriplegia, N (%)

b

5 (1)

45 (0)

0 (0)

5 (0)

0 (0)

3 (1)

Parkinson’s disease, N (%)

4 (1)b

18 (0)b

17 (9)b

51 (2)b

8 (9)

25 (5)

Conditions related to constipation b

c

c

b

c

Mean morphine-equivalent daily dose, mg (SD)

55.7 (80.6)

49.8 (79.5)

30.5 (28.8)

30.5 (34.7)

27.6 (25.0)

27.5 (29.2)

Mean duration of opioid use during follow-up, days (SD)

271.3 (86.2)

269.0 (89.1)

237.1 (89.7)

230.3 (93.3)

248.8 (83.7)

259.0 (85.7)

Had ≥1 hospitalizations during preindex period, N (%)

83 (21)b

1358 (10)b

71 (37)b

690 (25)b

48 (56)

264 (55)

Had nausea or vomiting during follow-up, N (%)

74 (18)b

600 (4)b

46 (24)b

148 (5)b

25 (29)b

47 (10)b

c

c

Using appropriate statistical tests, patient characteristics were compared between long-term opioid users with constipation and long-term opioid users without evidence of constipation within the nonelderly, elderly, and long-term care cohorts, respectively. Chi-square tests were used for categorical variables, whereas Fisher’s exact tests were used as the count of a cell <5. Student t-tests were used for the continuous variables. b P <.01. c P <.05. OIC indicates opioid-induced constipation; SD, standard deviation. a

patient and were adjusted to 2011 values based on the current Consumer Price Index information provided by the US Bureau of Labor Statistics.15 The analysis of the healthcare resource utilization included the inpatient admission rate, inpatient length of stay, emergency department visit rate, the number of emergency department visits, office visit rate, and the number of physician visits during the follow-up period. The total healthcare costs included costs for inpatient, pharmacy, outpatient, emergency department, long-term care facility, and other costs.

Statistical Analyses The demographics, comorbidities, opioid use patterns, and resource utilization were compared between patients who developed OIC and those who did not using a bivariate approach. The comparisons of continuous variables were performed using t-tests and categorical variables using Pearson chi-square tests. The cost data were com-

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pared between the groups with and without OIC using a nonparametric test (Wilcoxon rank sum test), because cost data are often heavily skewed.16 Patients with OIC were matched by propensity score to patients without OIC with a 1:1 ratio using the nearest-neighbor matching algorithm. All the baseline demographic and clinical variables were used for the matching method. The differences in healthcare resource utilization, total costs, and cost components were calculated between the patients with OIC and their propensity score–matched group without OIC during the 12-month follow-­up period. A generalized linear model with gamma distribution and log link was performed to estimate the impact of OIC on total cost, which was adjusted for covariates (age, sex, insurance plan, region, dose and duration of opioid use, baseline hospitalization, Charlson comorbidity score, nausea/vomiting, and constipation-related d­ isease conditions). The analyses were performed using Statistical Analysis

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Software version 9.2.3 (SAS Institute; Cary, NC). P <.05 was considered to indicate statistical significance.

Results Patient Cohorts Figure 2 outlines the selection of study cohorts. More than 24 million patients who started therapy with an opioid during the study period were identified. Of the 16,766 long-term opioid users who met the study criteria, 13,808 patients were classified as nonelderly and 2958 patients were classified as elderly; among these, 566 patients received an opioid medication in a long-term care facility. Baseline characteristics of the study cohorts are summarized in Table 1. The mean patient ages were 48.6 years (standard deviation [SD], 10.4); 78.7 years (SD, 8.1); and 80.7 years (SD, 11.6) for the nonelderly, elderly, and long-term care cohorts, respectively. A total of 50%, 70%, and 77% of the patients, respectively, were female. Among the nonelderly cohort, a higher proportion of patients with OIC were female (63.1%) compared with patients without OIC (49.8%). Patients with OIC had significantly higher comorbidity scores, and more patients experienced nausea or vomiting compared with the patients without OIC across all 3 study cohorts. In the nonelderly and elderly cohorts, depression and Parkinson disease were more prevalent among patients with OIC than among those without OIC. No signifi-

cant differences in any constipation-associated conditions were found between those with and without OIC in the long-term care cohort.

Healthcare Resource Utilization After propensity score matching, the nonelderly and elderly patients with OIC had more hospital admissions and a longer inpatient length of stay than the group without OIC. Among the nonelderly patients, the group with OIC had more patients with physician office visits and a higher annual number of office visits than the group without OIC. Among the elderly patients, more patients with OIC had emergency department visits, and the group with OIC had a higher mean annual number of emergency department visits than the patients without OIC. In the long-term care cohort, there was no significant difference in healthcare resource utilization between the groups with and without OIC (Table 2). Healthcare Costs After matching by key covariates, patients with OIC had significantly higher total healthcare costs than patients without OIC in all 3 cohorts, including the non­ elderly population ($23,631 ± $67,209 vs $12,652 ± $19,717, respectively; P <.001 [Figure 3]), the elderly population ($16,923 ± $38,191 vs $11,117 ±$19,525, respectively; P = .009 [Figure 4]), and the long-term care

Table 2 Healthcare Resource Utilization by Constipation Statusa in the Nonelderly, Elderly, and Long-Term Care Cohorts Nonelderly Elderly Long-term care Healthcare resource utilization

OIC Non-OIC P OIC Non-OIC P OIC Non-OIC P (N = 400)b (N = 400) value (N = 190)b (N = 190) value (N = 79) (N = 79) value

Patients with ≥1 hospital admissions, N (%)

131 (33)

78 (20)

<.001

96 (51)

58 (31)

<.001

46 (58)

39 (49)

.264

Inpatient length of stay, days, mean (SD)

3.0 (8.4)

1.0 (3.0)

<.001

5.2 (12.2)

2.1 (4.0)

<.001

6.0 (8.0)

6.1 (15.8)

.939

Patients with ≥1 emergency department visits, N (%)

172 (43)

114 (29)

<.001

98 (52)

69 (36)

.003

41 (52)

36 (46)

.426

Emergency department visits, mean, N (SD)

1.0 (2.4)

0.7 (2.5)

.119

1.6 (2.5)

0.8 (1.4)

<.001

1.5 (2.3)

1.2 (2.0)

.302

Patients with ≥1 office visits, N (%)

395 (99)

384 (96)

.015

175 (92)

166 (87)

.128

62 (78)

70 (89)

.086

Physician office visits, mean, N (SD)

20.7 (16.6)

15.9 (16.6)

<.001

14.1 (14.7)

12.4 (13.7)

.247

10.5 (12.6)

7.2 (10.9)

.078

Healthcare resource utilization was compared between long-term opioid users with constipation and a propensity score– matched cohort (1:1 ratio) of long-term opioid users without evidence of constipation during the 12-month follow-up period within the nonelderly, elderly, and long-term care cohorts, respectively. b As a result of incomplete matching (the exact match cannot be found given the greedy matching algorithm), 1 patient was excluded from the nonelderly population and 4 patients were excluded from the elderly population. OIC indicates opioid-induced constipation; SD, standard deviation. a

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Figure 3 T otal Costs and Cost Components in Nonelderly Patients, by Constipation Statusa P <.001 $25,000

$23,631 OIC cohort (N = 400) Matched non-OIC cohort (N = 400)

Cost, 2011 dollars

$20,000

$15,000

$12,652

P = .029 P <.001

$10,230 $10,000

P = .039

$7169

$5328 $5393

$4790

$5000

$1992 $0

$79 Total

Inpatient

Outpatient

P <.001

P >.999 $105

Long-term care

P = .035

$654 $313 Emergency

$170 Pharmacy

$59

Other

Cost by department and total

Within the nonelderly patients, the cost components were compared between long-term opioid users with constipation and a propensity scoreâ&#x20AC;&#x201C;matched cohort (1:1 ratio) of long-term opioid users without evidence of constipation during 12-month follow-up. OIC indicates opioid-induced constipation.

a

Figure 4 T otal Costs and Cost Components in Elderly Patients, by Constipation Statusa P = .009 $18,000

$16,923

$16,000 OIC cohort (N = 190) Matched non-OIC cohort (N = 190)

Cost, 2011 dollars

$14,000 $12,000

$11,117

$10,000 $8000

P <.001

$6000

$5223 $5060

$5105 $3329

$4000

$4044

P = .003 P = .004 $1664

$2000 $0

P = .352

P = .144

$739 Total

Outpatient

Inpatient

P = .650

$1640

Long-term care

$750

$275

Emergency

$160 Pharmacy

$51

Other

Total cost and cost by department

Within the elderly patients, cost components were compared between long-term opioid users with constipation and a propensity scoreâ&#x20AC;&#x201C;matched cohort (1:1 ratio) of long-term opioid users without evidence of constipation during 12-month follow-up. OIC indicates opioid-induced constipation.

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Table 3 Predictors of Total Cost in Elderly and Nonelderly Populations Nonelderly

Long-term care

Coefficient estimatea

P value

Coefficient estimatea

P value

Constipation vs nonconstipation

1.52

<.001

1.89

<.001

Age

1.00

.177

1.01

<.001

Sex (female vs male)

1.03

.420

1.05

.021

Comprehensive

0.78

<.001

1.03

.716

HMO

0.61

<.001

0.97

.294

Missing

1.47

.002

0.90

.010

Other

0.87

.187

1.03

.584

North Central

0.93

.136

1.13

.062

Northeast

1.13

.037

0.97

<.001

Unknown

0.91

.701

0.74

.408

West

0.95

.428

1.23

.001

Morphine equivalent daily dose, mg

1.00

<.001

1.00

<.001

Duration of opioid use during follow-up, days

1.00

.310

1.00

<.001

Had ≥1 hospitalizations during preindex period

1.32

<.001

1.38

<.001

Had nausea/vomiting during follow-up

1.25

.003

1.15

<.001

Charlson comorbidity score

1.06

<.001

1.21

<.001

Depression

1.08

.321

1.23

<.001

Obesity

0.88

.015

1.17

<.001

Anxiety

0.79

.105

0.78

<.001

Multiple sclerosis

1.68

.094

2.17

<.001

Spinal cord injury, paraplegia, quadriplegia

0.47

.178

1.20

.264

Parkinson’s disease

0.84

<.001

2.35

<.001

Variable

Category

Insurance plan (ref, PPO)

Region (ref, South)

Other comorbidity conditions

The coefficient estimate represents the exponential of the maximum likelihood estimates of the coefficients. HMO indicates health maintenance organization; PPO, preferred provider organization.

a

population ($16,000 ± $22,897 vs $14,437 ± $25,690, respectively; P = .049). Patients with OIC had significantly higher inpatient and emergency department costs among the elderly and nonelderly patients compared with their matched patients without OIC. In addition, nonelderly patients with OIC had significantly higher outpatient costs and other costs; however, the same trend was not found for the pharmacy cost. Among elderly patients, those with OIC had significantly higher long-term care costs compared with their matched counterparts without OIC. Among patients initiating opioids in long-term care facilities, patients with OIC had higher total costs than patients without OIC. The results from the multivariate regression (general-

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ized linear model) suggest that elderly patients with OIC had 89% higher costs than elderly patients without OIC ($19,963 vs $10,556, respectively; P <.001), whereas costs were 52% higher in nonelderly patients with OIC than in those without OIC ($15,737 vs $10,332; P <.001). In the long-term care cohort, the healthcare costs were not significantly different between the patients with and without OIC (P = .42). Among the nonelderly and elderly patients, the common cost drivers included baseline hospitalizations, having nausea or vomiting during follow-up, and the higher Charlson comorbidity score. In addition, for the nonelderly patients, there are several significant cost drivers, including comorbid conditions, such as obesity, depression, multiple sclerosis, and Parkinson’s disease (Table 3).

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Discussion Our study is the first to examine the economic burden associated with constipation among subpopulations (nonelderly, elderly, and long-term care cohorts) of long-term opioid users for noncancer pain using data from a real-world setting. We found that for patients treated with opioids for ≥90 days, constipation after opioid therapy significantly increased the healthcare resource utilization and the total healthcare costs among all 3 subpopulations. In particular, OIC is associated with significantly increased inpatient, outpatient, and emergency department costs among the nonelderly population. Among the elderly population, OIC is associated with significantly increased inpatient and emergency department costs. In addition, our study identified significant and clinically important cost drivers, such as certain comorbid conditions, having vomiting or nausea, and having baseline hospitalization. This finding is consistent with previous studies that evaluated the healthcare resource utilization of groups with OIC versus groups without OIC among opioid users.7,8,11 The studies by Iyer and colleagues and Candrilli and colleagues assessed the economic burden of OIC among patients receiving opioid therapy.7,8 However, these 2 studies did not differentiate between patients with cancer-related pain and those without cancer.7,8 Kwong and colleagues identified the substantial economic burden of gastrointestinal events among users of opioids for noncancer pain, but the target population was limited to patients receiving immediate-release oxycodone or hydrocodone, and the costs were evaluated for only 90 days after the initiation of an opioid.11 Outside of the United States, there were few published studies assessing the economic burden of OIC.17,18 For example, a Swedish study examined the indirect and direct medical costs associated with OIC using survey data, and the researchers concluded that OIC imposed substantial costs to society, especially for patients with severe OIC.18 However, these studies did not differentiate among subpopulations (eg, the elderly and patients residing in longterm care facilities) with different levels of risk for OIC that may lead to varied levels of resource utilization. The burden of OIC reported in literature varies widely across studies, and can rise to approximately $40,000 as a result of the difference in service type, study perspectives (ie, societal, payer, provider), inclusion of patients with cancer, or severity of the disease. Our study found a $10,979 incremental cost of OIC in the nonelderly population, which is higher than the finding by Kwong and colleagues.11 The differences between both studies may result from our focus on long-term opioid users only, whereas their study included patients who used opioids

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for a short time only (ie, 90 days). We also found that the elderly population and patients residing in long-term care facilities had a higher rate of identified cases of OIC. It is well-documented in the literature that older adults are more likely to develop OIC than younger patients.12 The reasons may include age-related physiologic changes in the gastrointestinal tract; comorbidities (eg, Parkinson’s disease); concomitant use of multiple medications, especially other medications that cause constipation; difficulty reaching the bathroom; low fluid and fiber intake; and inadequate time and privacy to defecate.12,13 Such factors may be especially prevalent in the elderly and in patients residing in long-term care facilities. Therefore, it is particularly important to study the elderly population and the population residing in long-term care facilities, which are disproportionately affected by OIC. The significant impact of OIC may not be well-­ recognized by healthcare professionals. Most symptoms of OIC persist for the length of treatment with an opioid, which has a substantially negative impact on patients’ quality of life and could reduce treatment adherence with pain management.19 For example, one survey-based study found that more than 33% of the population had missed, decreased, or stopped using opioids to reduce their OIC.20 The trade-off between optimal opioid use for pain therapy and the risk for the discontinuation of opioids as a result of OIC poses a challenge in reaching the pain management goal. Thus, the alleviation of constipation may help optimize treatment with an opioid, help with pain control, and reduce economic burden. OIC is a persistent condition.21 Although over-the-counter laxatives can address some of the symptoms of OIC, alternative therapies are needed to relieve constipation and its related symptoms for patients receiving long-term treatment with an opioid. The results of our study highlight the importance of providing patients with OIC with effective and available treatment options, as well as enhancing patient–­ physician communication regarding the management of OIC. Such efforts may reduce the economic burden of OIC in the population of long-term opioid users. Future research should focus on identifying the risk factors for and measuring the impact of OIC on pain management.

Limitations Our study is subject to several limitations that are inherent to the use of a claims analysis, which might have influenced our estimates of the economic burden of OIC. First, the patients we identified as having OIC may represent more severe constipation cases as a result of our reliance on ICD-9 codes and not on patient-reported measures. Therefore, our estimated economic burden may reflect more severe cases of OIC.

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Economic Burden of Opioid-Induced Constipation

Second, nonreimbursable items, such as over-thecounter medications, were not captured in the commercial insurance claims. As a result, the estimated pharmaceutical costs associated with OIC may be conservative. Third, healthcare resource utilization and cost data should be interpreted carefully, because these were not limited to visits and costs resulting only from OIC. In other words, it is not possible to establish the causality relationship between the incremental economic burden and OIC given the nature of retrospective study design. Fourth, OIC is also likely to be associated with productivity loss, which cannot be assessed through claims database analyses. Fifth, patients with OIC may have had more serious or severe medical comorbidities, and may have used more healthcare resources and medical services that cannot be attributed to OIC. Propensity score matching cannot correct bias from unmeasured confounders, and this may tend to overestimate the magnitude of the difference in economic costs between patients who did and did not experience OIC.

Conclusions Notwithstanding these limitations, results from this study are consistent with previous findings that OIC is associated with significant economic burden.5 Our study adds to the current knowledge by assessing elderly patients, nonelderly patients, and residents in long-term care facilities who receive long-term opioid therapy for the management of noncancer pain. Our findings show that OIC is associated with increased healthcare costs in all 3 subpopulations that were included in the study. The economic burden of OIC should be considered when evaluating the cost-effectiveness of treatments for the management of pain. The use of effective therapies may reduce the economic burden that is associated with OIC. Future research using alternative sources, such as patient surveys or chart reviews, is needed to better determine the burden of OIC on pain management from a societal perspective, especially in high-risk subpopulations, such as the elderly. ■ Acknowledgment The authors would like to thank Shawn Yu of Takeda Pharmaceuticals International, Inc, for his contribution in reviewing the manuscript and commenting on the statistical methods before submission. Funding Source This study was funded by Takeda Pharmaceuticals International, Inc.

Author Disclosure Statement Ms Wan, Dr Corman, Dr Gao, and Mr Liu are employees of Pharmerit International, which received research funding from Takeda Pharmaceuticals International, Inc. Dr Patel is an employee of Immensity Consulting, and a contractor for Takeda Pharmaceuticals International, Inc; Dr Mody is an employee of Takeda Pharmaceuticals International, Inc.

References

1. Trescot A, Glaser SE, Hansen H. Effectiveness of opioids in the treatment of chronic non-cancer pain. Pain Physician. 2008;11:S181-S200. 2. Dorn SD, Meek PD, Shah ND. Increasing frequency of opioid prescriptions for chronic abdominal pain in US outpatient clinics. Clin Gastroenterol Hepatol. 2011;9:1078-1085.e1. Erratum in: Clin Gastroenterol Hepatol. 2012;10:332. 3. Moore RA, McQuay HJ. Prevalence of opioid adverse events in chronic non-malignant pain: systematic review of randomised trials of oral opioids. ­Arthritis Res Ther. 2005;7:R1046-R1051. 4. Bell T, Milanova T, Grove G, et al. OBD symptoms impair quality of life and daily activities, regardless of frequency and duration of opioid treatment: results of a US patient survey (PROBE survey). J Pain. 2007;8(4 suppl 1). Abstract 882. 5. Singh G, Kahler K, Bharathi V, et al. Constipation in adults: complications and comorbidities. Gastroenterology. 2005;128(4 suppl 2). Abstract S960. 6. Bell T, Annunziata K, Freedman D, et al. Opioid-induced constipation increases healthcare resource use and impairs work productivity: comparison with other patient groups with and without constipation. J Pain. 2007;8(4 suppl 1). Abstract 897. 7. Candrilli SD, Davis KL, Iyer S. Impact of constipation on opioid use patterns, health care resource utilization, and costs in cancer patients on opioid therapy. J Pain Palliat Care Pharmacother. 2009;23:231-241. 8. Iyer S, Davis KL, Candrilli S. Opioid use patterns and health care resource utilization in patients prescribed opioid therapy with and without constipation. Manag Care. 2010;19:44-51. 9. Caudill-Slosberg MA, Schwartz LM, Woloshin S. Office visits and analgesic prescriptions for musculoskeletal pain in US: 1980 vs. 2000. Pain. 2004; 109:514-519. 10. Sullivan MD, Edlund MJ, Fan M-Y, et al. Trends in use of opioids for non-cancer pain conditions 2000-2005 in commercial and Medicaid insurance plans: the TROUP study. Pain. 2008;138:440-449. 11. Kwong WJ, Diels J, Kavanagh S. Costs of gastrointestinal events after outpatient opioid treatment for non-cancer pain. Ann Pharmacother. 2010;44:­630-640. 12. Max EK, Hernandez JJ, Sturpe DA, Zuckerman IH. Prophylaxis for opioid-­ induced constipation in elderly long-term care residents: a cross-sectional study of Medicare beneficiaries. Am J Geriatr Pharmacother. 2007;5:129-136. 13. McMillan SC. Assessing and managing opiate-induced constipation in adults with cancer. Cancer Control. 2004;11(3 suppl 1):3-9. 14. Staats PS, Markowitz J, Schein J. Incidence of constipation associated with long-acting opioid therapy: a comparative study. South Med J. 2004;97:129-134. 15. National Center for Health Statistics. Health, United States, 2010: with special feature on death and dying. February 2011. www.cdc.gov/nchs/data/hus/ hus10.pdf#specialfeature. Accessed March 10, 2015. 16. Kessler ER, Shah M, Gruschkus SK, Raju A. Cost and quality implications of opioid-based postsurgical pain control using administrative claims data from a large health system: opioid-related adverse events and their impact on clinical and economic outcomes. Pharmacotherapy. 2013;33:383-391. 17. Takemoto MLS, Fernandes RA, Almeida GR, et al. Health care resource use and costs in opioid-treated patients with and without constipation in Brazil. Value Health. 2011;14(5 suppl 1):S78-S81. 18. Hjalte F, Berggren A-C, Bergendahl H, Hjortsberg C. The direct and indirect costs of opioid-induced constipation. J Pain Symptom Manage. 2010;40:696-703. 19. Goodheart CR, Leavitt SB. Managing opioid-induced constipation in ambulatory-care patients. Pain Treatment Topics. August 2006. http://paincommu nity.org/blog/wp-content/uploads/Managing_Opioid-Induced_Constipation. pdf. Accessed March 2, 2015. 20. Bell TJ, Panchal SJ, Miaskowski C, et al. The prevalence, severity, and impact of opioid-induced bowel dysfunction: results of a US and European Patient Survey (PROBE 1). Pain Med. 2009;10:35-42. 21. Panchal SJ, Müller-Schwefe P, Wurzelmann JI. Opioid-induced bowel dysfunction: prevalence, pathophysiology and burden. Int J Clin Pract. 2007;61: 1181-1187.

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BUSINESS

STAKEHOLDER PERSPECTIVE

Opioid-Induced Constipation Associated with Considerable Economic Burden By Matthew Mitchell, PharmD, MBA, FAMCP Director, Pharmacy Services, SelectHealth, Murray, UT

PAYERS/EMPLOYERS: Opioid-induced constipation (OIC) is not a top-of-mind concern for health plans as they evaluate their top 10 classes of drugs in terms of total spending, an approach that helps payers determine their priorities and opportunities for cost management. However, pain medications, especially opioids, are always a hot topic and are on the radar for providers of health insurance, including large, self-funded employer groups who consider that medication category as a major concern. Based on my professional experience, employers are concerned about the substantial costs associated with employees’ use of pharmaceutical therapies for pain management, the associated costs for medical procedures (eg, lower back pain), and other costs related to pain management. In particular, the frequent use of opioids may involve the concern for decreased work productivity, as well as the risks for overuse, misuse, and/or abuse associated with pain medications. Furthermore, there are other direct medical adverse events associated with opioid use, including constipation, irritable bowel disease, and even irritable bowel syndrome, may also be relevant in this context. The topic of OIC may not get the “respect” it deserves, perhaps because until recently few prescription medications have been approved by the US Food and Drug Administration (FDA) for OIC, and, therefore, the majority of drugs used for that condition have been overthe-counter therapies. The present study by Wan and colleagues featured in this issue of American Health & Drug Benefits is the first published analysis to demonstrate the economic burden of long-term constipation among long-term users of opioids.1 This study is a step in the right direction to enhance our understanding of OIC, and the way it fits into the entire cost spectrum of

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pain management for payers, including employers. PATIENTS: Unfortunately, many patients who require high doses of opioids may also have other factors contributing to their constipation, such as limited mobility, nonopioid-constipating drugs, and/or other contributing medical conditions. A sick patient population and an ambiguous coding for constipation make specific, nonconfounding analysis of the impact of OIC difficult. Because as payers we consider the real-world environment of our members, we also need to consider the entire patient population who may be using drugs intended for the management of OIC. This may include patients using opioids on a short-term basis, patients using opioids on an as-needed basis, patients who require around-the-clock opioid dosing for pain, and patients with cancer or other terminal conditions who require very high opioid doses. RESEARCHERS: With newly available FDA-­ approved OIC agents, more data will likely be generated that will not only demonstrate the economic burden associated with OIC, but also help to better define the most appropriate patient subpopulations for these therapies. Another helpful tool will be clinical guidelines to further differentiate the appropriate timing, sequencing, and treatment combinations to regulate the management of OIC. Although the overall financial burden of OIC may not seem impactful on the surface, OIC is something that needs to be addressed by payers and other stakeholders, and will likely become a more prevalent topic for consideration as more therapies become commercially available for patient use. ■ 1. Wan Y, Corman S, Gao X, et al. Economic burden of opioid-induced constipation among long-term opioid users with noncancer pain. Am Health Drug Benefits. 2015;8:41-50.

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