OffLabel Prescription among Outpatient Physicians

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Off-Label Prescription among Outpatient Physicians

• David Radley, Dartmouth College
• Randall Stafford, Stanford University
• Stan Finkelstein, Massachusetts Institute of
  Technology
• Iain Cockburn, Boston University

This research was supported by a research grant
from the Agency for Healthcare Research and
Quality (AHRQ) (R01-HS013405). Merck and
Company, Inc. and IMS HEALTH provided access to
the data used in analysis.
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Background

• Off-label prescription - a drug is prescribed for
  uses not indicated in its FDA approved product
  label
• Controversy from recent media attention
• FDA regulations prohibit DTC promotion restrict
  physiciantargeted promotion
• Unproven off-label use may be costly and threaten
  patient safety

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Why are drugs used Off-Label?

• FDA
• focuses on market entry
• historically maintained a hands-off approach
• Sometimes little market incentive to seek
  approval for additional indications
• older generically available drugs
• population size too small
• No alternatives exist orphan drugs pediatric
• Desire to broaden therapeutic alternatives
• failure of standard therapy
• innovation seeking behavior

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Off-Label Prescription

• Clinically Reasonable
• Well-known evidence based-therapy
• Supported by treatment guidelines
• Other drugs in class are indicated
• Cost-effective generic substitution
• Innovative approaches when standard therapies
  fail

• Concerns
• May not bear same degree of clinical or
  scientific scrutiny as labeled indications
• Inconclusive evidence
• contraindications
• safe dose
• ADRs
• Over use of unproven off-label therapies may be
  costly

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Methods

• Data Source 2001 National Disease Therapeutic
  Index (NDTI)
• 160 Commonly prescribed Rx medications
• drug mentions stratified by ICD-9-CM
• Classified drug mentions for each drug
• Labeled
• Off-Label with strong scientific support
• Off-Label with little or no scientific support
• Characteristic profile defined for each drug

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NDTI

• Nationally representative cross sectional
  physician survey conducted quarterly by IMS
  HEALTH (Plymouth Meeting, PA)
• Diagnostic treatment data for physician/patient
  encounters
• Drug Mention Estimated national number of
  occurrences of newly prescribed or continued
  medication

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Sampled medications

• 160 systematically sampled medications
• top 100 by NDTI drug mentions
• 60 randomly selected from among the next most
  common 150 prescription medications
• account for 56 of all Rx drug use in 2001
• Over-the-counter excluded
• Many therapeutic contexts represented
• 13 distinct functional categories
• preponderance hypertension drugs, antibiotics,
  and analgesics

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Drug / diagnosis combinations

• 40 most frequent primary diagnoses for each drug
• Diagnoses identified by ICD-9 codes
• DRUGDEX used to classify mentions for each
  drug/diagnosis combination
• Labeled ICD-9 matched to FDA-approved indication
• Off-label with strong support ICD-9 not matched,
  therapeutic goals achieved in clinical settings
• Off-label with little/no support ICD-9 not
  matched, insufficient or inconsistent evidence of
  therapeutic goals

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Drug mention classification

• Published pharmaceutical compendia used to assess
  degree of evidence supporting off-label uses
• primary reference MICROMEDEX systems DRUGDEX
  Drug Evaluations File
• used by Medicaid and many private insurers to
  authorize payment for unapproved uses
• ranks efficacy (effective, possibly effective,
  ineffective) and degree of scientific
  documentation (excellent, good, fair, poor) for
  off-label uses of each drug

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Drug characteristic profile

• Predictive effect of drug characteristics
  assessed
• Drug characteristics describe
• functional class
• formulations (tablet, capsule, inhaler, etc)
• duration of use (chronic vs. acute)
• drug age in 2001
• generic availability in 2001
• degree of DTC promotion
• manufacturer
• Data collected from published pharmaceutical
  compendia

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Analysis

• Unit of Analysis drug mention
• Outcomes
• Frequency (number) of off-label drug mentions
• Proportion of off-label drug mentions
• scientifically supported vs. not supported
• Predictors of off-label prescription
• Multivariate Regression
• Log-linear model fit with negative binomial (NB)
  distribution to account for overdispersion
• Clustered according to chemical class to account
  for non-independence among drugs in each class
• Incidence Risk Ratios (IRR)
• represent the independent relative risk for
  off-label prescription associated with each
  characteristic

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Key Findings

• 722 million total drug mentions (95 CI 611-835
  M)
• 572 million (95CI 484-661 M) Labeled (79)
• 150 million (95CI 127-174 M) Off-Label (21)
• Off-Label Mentions
• 42 million (95CI 36 - 49 M) with strong
  scientific support (28)
• 108 million (95CI 91 - 124 M) with little/no
  scientific support (72)
• Off-Label use exceeded 50 for 16 (of 160)
  medications under study

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Figure 1. Proportion of Off-Label Prescribing by
Functional Class
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Drugs with highest proportion Off-Label use

• gabapentin (anticonvulsant)
• 83 20 supp. / 80 no supp.
• amitriptyline (psychiatric tri-cyclic
  antidepressant) 79 25 supp. / 75 no supp.
• isosorbide mononitrate (cardiac nitrate)
• 75 64 supp. / 36 no supp.
• digoxin (cardiac dysrythmia)
• 66 38 supp. / 62 no supp.
• risperidone (psychiatric anti-psychotic)
• 66 lt1 supp. / 99 no supp.

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Top 5 Medications with Strong and Little/No
Scientific Support
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Indications most Frequently Treated Off-Label
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Characteristics Predictive of Off-Label
Prescription

• RR 95CI
• Functional Class
• Analgesics 1.0 (ref.)
• Antimicrobials 2.6 (1.1, 6.3)
• Anticonvulsants 4.1 (1.5, 10.8)
• Diabetes Therapy 0.07 (0.01, 0.45)
• Lipid Lowering 0.35 (0.17, 0.75)
• Other Cardiac 3.1 (1.1, 8.9)

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Characteristics Predictive of Off-Label
Prescription

• RR 95 CI
• Generic availability 1.9 (1.0, 3.5)
• High degree of DTC 2.0 (1.0, 4.1)
• Drug form topical 0.40 (0.17, 0.91)
• Drug age gt15 years 1.8 (1.1, 3.2)
• Non significant predictors
• manufacturer, frequency of dosing, chronic
  therapy, drug form (capsule, inhaler),
  combination drug

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Summary

• Off-Label Prescription is common in out-patient
  clinical care
• Most off-label prescription occurs without
  scientific support, but among drug diagnosis
  combinations that may not necessarily be of great
  clinical concern
• Off-label prescription varies greatly among
  specific medications and drug classes
• Few drug-specific characteristics are
  consistently associated with increased off-label
  prescription

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Clinical Policy Implications

• Physicians should Recognize Off-Label use in
  their own Practice
• They should choose evidence-based medication
  therapies
• Many off-label uses are not of great concern
• Broad, unselective strategies to constrain
  off-label use would eliminate clinically
  beneficial evidence-based treatments
• Selective efforts should be made to constrain
  unproven off-label use among costly medications