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The American Journal of Managed Care January 2011
Hypertension Treatment and Control Within an Independent Nurse Practitioner Setting
Wendy L. Wright, MS; Joan E. Romboli, MSN; Margaret A. DiTulio, MS, MBA; Jenifer Wogen, MS; and Daniel A. Belletti, MA
Relationship Between Short-Acting β-Adrenergic Agonist Use and Healthcare Costs
Harris S. Silver, MD; Christopher M. Blanchette, PhD; Shital Kamble, PhD; Hans Petersen, MS; Matthew A. Letter, BS; David Meddis, PhD; and Benjamin Gutierrez, PhD
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Healthcare Costs and Nonadherence Among Chronic Opioid Users
Harry L. Leider, MD, MBA; Jatinder Dhaliwal, MBA; Elizabeth J. Davis, PhD; Mahesh Kulakodlu, MS; and Ami R. Buikema, MPH
Relationship Between Patient Satisfaction With Inpatient Care and Hospital Readmission Within 30 Days
William Boulding, PhD; Seth W. Glickman, MD, MBA; Matthew P. Manary, MSE; Kevin A. Schulman, MD; and Richard Staelin, PhD
Effects of Health Savings Account Eligible Plans on Utilization and Expenditures
Mary E. Charlton, PhD; Barcey T. Levy, PhD, MD; Robin R. High, MBA, MA; John E. Schneider, PhD; and John M. Brooks, PhD
Health Plan Resource Use Bringing Us Closer to Value-Based Decisions
Sally Elizabeth Turbyville, MA, MS; Meredith B. Rosenthal, PhD; L. Gregory Pawlson, MD; and Sarah Hudson Scholle, DrPH
Telephone-Based Disease Management: Why It Does Not Save Money
Brenda R. Motheral, PhD
Economic Model for Emergency Use Authorization of Intravenous Peramivir
Bruce Y. Lee, MD, MBA; Julie H. Y. Tai, MD; Rachel R. Bailey, MPH; Sarah M. McGlone, MPH; Ann E. Wiringa, MPH; Shanta M. Zimmer, MD; Kenneth J. Smith, MD, MS; and Richard K. Zimmerman, MD, MPH
High-Deductible Health Plans and Costs and Utilization of Maternity Care
Katy Backes Kozhimannil, PhD, MPA; Haiden A. Huskamp, PhD; Amy Johnson Graves, MPH; Stephen B. Soumerai, ScD; Dennis Ross-Degnan, ScD; and J. Frank Wharam, MB, BCh, MPH
High-Deductible Health Plans and Costs and Utilization of Maternity Care
Katy Backes Kozhimannil, PhD, MPA; Haiden A. Huskamp, PhD; Amy Johnson Graves, MPH; Stephen B. Soumerai, ScD; Dennis Ross-Degnan, ScD; and J. Frank Wharam, MB, BCh, MPH
Telephone-Based Disease Management: Why It Does Not Save Money
Brenda R. Motheral, PhD
Economic Model for Emergency Use Authorization of Intravenous Peramivir
Bruce Y. Lee, MD, MBA; Julie H. Y. Tai, MD; Rachel R. Bailey, MPH; Sarah M. McGlone, MPH; Ann E. Wiringa, MPH; Shanta M. Zimmer, MD; Kenneth J. Smith, MD, MS; and Richard K. Zimmerman, MD, MPH

Healthcare Costs and Nonadherence Among Chronic Opioid Users

Harry L. Leider, MD, MBA; Jatinder Dhaliwal, MBA; Elizabeth J. Davis, PhD; Mahesh Kulakodlu, MS; and Ami R. Buikema, MPH

Healthcare costs are elevated for patients on chronic opioid therapy; nonadherence to the opioid regimen, based on urine drug monitoring results, further increases costs.

Objectives: To assess the health economic burden of chronic opioid users and to determine whether opioid regimen nonadherence contributes to increased healthcare costs.


Study Design: Retrospective claims-based analysis of patients with long-term prescription opioid use (>120 days of supply over 6 months).


Methods: Twelve-month healthcare utilization and costs were compared for chronic opioid users (n = 49,425) and, among chronic opioid users with urine drug-monitoring results (n = 2100), between adherent patients versus patients with evidence of nonadherence to their opioid regimen. Likely nonadherence was based on urine test results indicating absence of the prescribed drug, higher or lower than expected drug levels based on a proprietary algorithm, or presence of unprescribed or illegal drugs. The influence of nonadherence on total healthcare costs was assessed using multivariate models.


Results: Prevalence of chronic opioid use was 1.3%. Chronic opioid users had significantly greater healthcare utilization and costs than matched nonusers ($23,049 vs $4975; P <.001). Adherent patients (n = 442) had lower total healthcare costs than likely nonadherent patients (n = 1658; $23,160 vs $26,433; P = .036). After adjustment for demographics, likely nonadherence was significantly associated with elevated total healthcare costs (cost ratio [CR] 1.136; 95% confidence interval [CI] 1.00, 1.29; P = .048). When adjusting for other types of nonadherence, the presence of higher than expected levels of the prescribed opioid was associated with significantly elevated costs (CR 1.121; 95% CI 1.01, 1.25; P = .039).


Conclusions: Chronic opioid users represent a substantial cost burden relative to similar patients without evidence of chronic pain. Among likely nonadherent chronic opioid users, those with evidence of opioid overuse had significantly elevated healthcare costs.


(Am J Manag Care. 2011;17(1):32-40)

Healthcare utilization and costs were compared between patients on chronic opioid therapy and matched controls, and between chronic opioid users who were likely nonadherent based on urine drug monitoring results versus adherent users.


  • Over 1 year of follow-up, chronic opioid users had more ambulatory, emergency, and hospital visits than controls, and higher annual healthcare costs.


  • Likely nonadherent chronic opioid users were predicted to be 14% more expensive than adherent patients, and had significantly more hospital days.


  • Nonadherence to the opioid regimen, likely overuse of the prescribed drug, appears to contribute to elevated costs.
Pain is a common reason to seek medical care. In contrast to acute pain, chronic pain ceases to serve a protective purpose, is persistent, and disrupts normal living.1 Chronic pain is highly prevalent by some estimates; in a US survey, 42% of participants aged >20 years and 57% of those aged >65 years reported pain lasting 1 year or more.2 Patients experiencing chronic pain have been found to use healthcare services more frequently than those without pain.3,4 Opioid analgesics have a recognized role in pain  management.5-10 For chronic pain, opioids are often effective when prescribed and used appropriately as part of a structured pain management plan.7,8,10 Current pain management recommendations include periodic monitoring of pain control and functional goal achievement, as well as monitoring medication use and aberrant behaviors.7,10,11

The need for oversight of prescription opioid use is supported by multiple recent studies. One 2010 report indicated that nearly 10% of patients admitted for substance abuse treatment in 2008 reported prescription pain reliever abuse—an increase from 2% among admissions in 1998.12 The 2008 National Survey on Drug Use and Health reported that among Americans aged >12 years, the prevalence of nonmedical use of prescriptions (ie, pain relievers, tranquilizers, stimulants, sedatives) was second only to marijuana use  among types of illicit drug use.13 Changes in the prevalence of prescription pain reliever abuse paralleled an increase in hospitalizations for poisoning by prescription opioids, sedatives, and tranquilizers: from 1999 to 2006, US hospitalizations for these medications increased by 65%.14

Monitoring adherence, or the accuracy and consistency with which a patient follows the pharmacological regimen, is an important aspect of a chronic pain management plan. Nonadherence could include taking too much of the prescribed medication, diverting medication to other individuals, self-medicating with unprescribed or illegal drugs, or taking medication inconsistently.15-17 Urine toxicology testing is one means of monitoring opioid adherence and assessing whether the prescribed regimen is being followed.7,10,17-20

In order to understand the health economic burden of patients with chronic opioid regimens, we assessed the costs and utilization of chronic opioid users (who are presumably being treated for chronic pain) relative to similar patients without evidence of chronic pain. In a separate analysis of chronic opioid users with urine drug testing results, we explored whether opioid regimen nonadherence contributed to an increase in annual healthcare costs.


Data Sources

Data were obtained from a managed care claims database including geographically diverse commercial, Medicare Advantage, and Medicaid health plan members in the United States. Approximately 18 million people were enrolled in the health plans during the study period from July 1, 2005, through September 30, 2008. Data for adherence classification were obtained from an independent database of urine drug testing results.

Identification of Chronic Opioid Users and Matched Controls

Patients with evidence of long-term prescription opioid use during January 1, 2006, through September 30, 2007 (identification period) were selected for the study. Chronic opioid use was defined as at least 120 days of a qualifying opioid (eAppendix A available at over any consecutive 6 months during the identification period. The date of the first qualifying opioid fill was the index date.

A control cohort of patients with no evidence of chronic pain or chronic opioid use was also identified (eAppendix B at Patients in the control cohort could have no more than 1 claim for any opioid, no more than 2 claims for any other pain-related medications (nonsteroidal anti-inflammatory drugs including salicylates and COX-2 inhibitors, and migraine therapies), and no diagnosis for chronic pain (International Classification of Diseases, Ninth Revision, Clinical Modification codes 338.0, 338.2x-338.4, 780.96) during the study period. An index date was randomly assigned during the identification period. Both chronic opioid users and matched controls were required to have continuous medical and pharmacy benefits coverage for 6 months prior to (baseline period) through 1 year following (follow-up period) the index date (eAppendix B).

The chronic opioid and control cohorts were matched 1 to 1 based on age (±1 year), sex, geographic region, insurance type, mental health benefit, and preindex Charlson  comorbidity score21 (±2). Patients who could not be matched were excluded. All data were de-identified and accessed with protocols compliant with the Health Insurance Portability and Accountability Act.22

Identification of Chronic Opioid Users With Urine Drug Testing and Adherence Classification

A subset of chronic opioid users with 4 or more claims with codes indicating urine drug testing for opiates, benzodiazepines, barbiturates, and amphetamines on the same date of service was identified. These patients were matched with a database of urine drug test results based on patient date of birth, sex, 5-digit zip code, and testing date (±3 days). Privacy board approval was obtained for the use of protected health information for database matching purposes.

For patients with urine drug monitoring results, results from the first test following the index date were used to assign patients to adherent and likely nonadherent cohorts. Nonadherence was determined using urine testing data, which indicated whether individual assay results aligned with reported medication type (ie, presence of prescribed opioid, absence of unprescribed controlled or illegal drugs).23 Patients were also classified as likely nonadherent if their urine drug levels were not within the concentration ranges expected for their prescribed regimen (eg, total daily dose) after adjustment for physiologic factors as determined by applying a proprietary algorithm (Rx Guardian, Ameritox, Ltd, Baltimore, MD) to the urine assay.23-26 The likely nonadherent classification is not synonymous with substance abuse, although certain types of nonadherence could suggest abuse or misuse of controlled or illicit drugs.

For patients with urine drug monitoring results, the baseline period was the 6 months before the test, and the followup period was the year following the test.

Determination of Cohort Characteristics

Enrollment and claims information were used to determine baseline demographic information, comorbid conditions, and medication use for the cohorts of interest. The Charlson comorbidity score, an estimate of comorbidity burden, was calculated.21,27,28 General comorbid conditions in the baseline period were identified from claims using Healthcare Cost & Utilization Project Comorbidity Software, version 3.2 (Agency for Healthcare Research and Quality, Rockville, MD). Opioid (including heroin) abuse/dependence, opioid  overdose/poisoning, alcoholism or drug abuse, depression,and anxiety during the baseline period were detected using the codes listed in eAppendix C at

Determination of Healthcare Utilization and Costs

Healthcare resource utilization during the follow-up period was calculated for each patient as number of office visits, outpatient visits, emergency department visits, inpatient admissions, and hospital days.

Pharmacy costs and medical costs, including ambulatory, emergency service, inpatient, and other medical costs, were tabulated from claims in the follow-up period and adjusted to 2008 dollars.29 Healthcare costs included both health plan and patient-paid amounts. “Other” medical costs include costs associated with durable medical equipment, home care, and services such as laboratory testing (including urine drug testing).

Claims with codes for pain-related services and procedures (eAppendix D at were used to determine pain-related costs.


Baseline characteristics, healthcare utilization, and costs were analyzed descriptively, comparing the chronic opioid cohort with the matched control cohort, and the cohorts of adherent and likely nonadherent chronic opioid users. Significance was determined as P <.05.

The relationship between 12-month follow-up total healthcare costs and likely nonadherence to the prescribed opioid treatment regimen was modeled using a generalized linear model with gamma distribution and log link,30 controlling for demographics, mental health benefit, insurance type, and index month. A similar model was developed to assess the relationship between individual categories of nonadherence and total healthcare costs while controlling for each type of nonadherence.


Chronic Opioid Users Versus Matched Controls

The prevalence of chronic opioid use was 1.3% among enrollees meeting the 18-month continuous enrollment requirement. Characteristics of chronic opioid and matched control cohorts are shown in Table 1. Most patients were commercially insured and, consistent with the health plan distribution, the South was the most heavily represented geographic region.

The number of unique medications and total medication dispensings in the baseline period was greater among chronic opioid users than matched control patients (Table 1). Eighteen of the 20 most common comorbidities identified in the cohorts occurred more frequently among chronic opioid users and are often associated with pain, including disorders such as spondylosis, intervertebral disc disorders, and other back problems; nontraumatic joint disorders; and mood disorders (eAppendix E at Chronic opioid users had a greater frequency of alcoholism or other drug abuse than matched control patients, and although the proportion of patients with evidence of opioid  abuse/dependence or overdose/poisoning was low overall, it was significantly greater for chronic opioid users compared with matched control patients (eAppendix E).

Chronic opioid users had more ambulatory and emergency visits, and more hospital admissions than nonusers (Table 2). Total healthcare costs were more than 4 times higher for the chronic opioid cohort compared with matched nonusers ($23,049 ± $42,798 vs $4975 ± $13,185; P <.001), with medical costs approximately 5 times greater and pharmacy costs 3.5 times greater for chronic opioid users (Table 3).

Adherent Versus Likely Nonadherent Chronic Opioid Users

Cohort Characteristics. The selection of the patient population with urine testing results is shown in eAppendix B. Baseline characteristics of this subsample (n = 2100)

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