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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.

according to adherent/likely nonadherent classification are shown in Table 1. The adherent cohort comprised 21.1% of tested patients, but most patients were likely nonadherent (Figure). Nonadherence due to a higher than expected level of the prescribed opioid was the type observed most frequently (Figure).

During the baseline period, likely nonadherent patients filled a significantly higher number of unique prescriptions and had a greater total number of medication dispensings than  adherent patients (Table 1). They also filled significantly more unique opioid types, had a greater number of opioid dispensings, and had more days of supply of opioids (Table 1). Hydrocodone and oxycodone were the most commonly filled opioids (eAppendix F at

Comorbidity scores were higher for likely nonadherent patients (Table 1). These patients had a greater prevalence of mood-related disorders and alcoholism/other drug abuse, whereas prevalences of both opioid abuse/dependence and opioid overdose/poisoning were low and did not differ significantly between the cohorts (eAppendix E).

Healthcare Utilization in the Follow-up Period. The mean number of ambulatory and emergency department visits per patient did not differ significantly between adherent and likely nonadherent cohorts (Table 2); nor did the mean number of hospital admissions. However, the number of hospital days was significantly greater for likely nonadherent patients    (2370 days per 1000 patients) compared with adherent patients (1753 days per 1000 patients; P <.001) because a greater percentage of patients in the likely nonadherent cohort  had a hospital admission (24.3% vs. 19.5%; P = 0.032) with longer average length of stay per admission (6.2 ± 5.1 days vs. 5.7± 6.1 days; P = 0.049). Likely nonadherent patients  continued to have significantly more opioid dispensings (20.7 ± 11.1 vs 18.2 ± 8.6; P <.001) and more days of supply of opioids (414.9 ± 169.0 vs 391.8 ± 146.3; P = .004) than adherent patients in the follow-up period.

Healthcare Costs. Among chronic opioid users with urine testing results, total healthcare costs per patient during the follow-up period were approximately 14% higher for likely nonadherent patients, a statistically significant difference from the adherent cohort (Table 3).

No statistically significant cost differences were observed for pain-related services between the adherent and likely nonadherent cohorts, although the relative magnitude of spending was notable: costs for surgery of the spine among patients with at least 1 relevant service date were $33,290 for adherent patients (n = 28) and 23% higher for likely nonadherent patients (n = 83) ($40,893; P = .468); mean cost for intrathecal or epidural drug infusion pump implantation and maintenance among patients with at least 1 relevant claim was $10,896 for adherent patients (n = 6) and 64% higher for likely nonadherent patients (n = 41) ($17,959; P = .370).

The relationship between adherence and total follow-up costs was further assessed using multivariate models (Table 4). Consistent with the unadjusted mean costs, costs predicted based on the adjusted model were also approximately 14% higher for the likely nonadherent cohort ($26,419) than for the adherent cohort ($23,263); this difference was significant (Table 4, Model 1).

Of the possible test results, patients with lower-thanexpected urine drug levels and those with higher than expected levels had the highest predicted costs ($27,752 and $27,631, respectively), but only having higher than expected levels of the prescribed opioid was associated with statistically significantly greater predicted total healthcare expense in the adjusted model (Table 4, Model 2). Based on Model 2, patients with higher than expected opioid levels were predicted to have follow-up healthcare costs that were 12% higher than those of other patients. Predicted total healthcare costs for patients with evidence of an illegal drug ($18,606) were significantly lower than costs predicted for other patients.


Although the prevalence of chronic opioid therapy is not high, total medical spending on chronic opioid users is likely to be substantial in most managed care plans. Chronic opioid users had elevated healthcare resource use and incurredsubstantially greater healthcare costs than nonusers. Furthermore, some chronic opioid users generated higher costs  than others and these excess costs were associated with indicators of nonadherence determined by urine drug monitoring. This was particularly evident in the cohort of patients with higher than expected drug levels.

Our results are consistent with previous studies suggesting that patients who use opioids for long-term pain incur greater healthcare costs than patients who are not on opioid therapy.4,31 Higher costs in the chronic opioid population are likely related to moderate to severe chronic pain as well as pain-related comorbidities such as arthritis or diabetic  neuropathy. Other possible explanations for the reported cost differences include disproportionate use of expensive services or increased risk of unintentional effects of opioid use, such as overdose.

The overall prevalence of nonadherence, while consistent with the finding of a previous study using the same urine drug testing database,23 is higher than nonadherence rates typically found in studies of drug treatment for other disease states. For example, nonadherence with treatment for chronic conditions such as diabetes, hypertension, and hyperlipidemia has been reported to range from approximately 22% to 50%.32,33 Multiple reasons are likely to contribute to the higher proportion of likely nonadherent patients that we observed. First, nonadherence with therapy in most disease states refers exclusively to underuse or discontinuation of a drug. With respect to chronic opioid therapy, nonadherence includes underuse34 as well as drug abuse, supplementation with additional opioids, potential diversion, illicit drug use, and the concomitant use of other controlled drugs unbeknownst to the provider ordering the opioid.17,23 Criteria to detect abnormal results based on an expected range have not been applied in all studies of opioid nonadherence, and these additional criteria may also account for differences in the reported prevalence of nonadherence.17

Second, although opioid urine drug monitoring is an integral part of current pain management recommendations7, patients with urine toxicology results in this study might have been selected for testing because they were perceived to be at high risk for misuse. The data in eAppendix E suggest that mood disorders and substance abuse were more prevalent among patients with urine testing than among the population of chronic opioid users as a whole. Since patients with these comorbidities are more likely to be nonadherent, testing bias could also contribute to the high overall rate of nonadherence among tested patients.

Finally, clinicians who ordered urine drug testing were asked to indicate on the lab requisition form whether patients were taking a controlled drug on an “as needed” basis. It is possible that this was not consistently documented, which could increase the rate of nonadherence in the categories of “no prescribed opioid” or “lower than expected” drug level.

Detection of higher than expected drug levels appears to be a useful addition to criteria for defining abnormal results, as likely overuse was found to be associated with increased costs. Higher than expected levels of the prescribed opioid could indicate inadequate pain control (requiring additional use of opioid medication) or potential abuse. This behavior could put patients at risk for side effects or overdose, further increasing their need for healthcare services and leading to higher costs. Overuse constituting abuse has been associated with increased costs,35 but due to limitations of healthcare claims research, abuse was not specifically investigated here.

In contrast to the increased costs associated with overuse, use of illegal drugs was associated with lower healthcare costs. Possible explanations for this finding are that individuals who use illicit drugs might be less likely to seek healthcare,36,37 they might be less likely to have commercial insurance (which could in turn affect costs associated with their care), or they might require fewer healthcare services because their pain is fictitious. It is also possible that clinician mistrust of patients with evidence of illicit drug use influences treatment plans. Further investigation is needed to confirm and explore reasons for this finding.

Our findings suggest that appropriate use of an opioid regimen moderates excess costs. Identifying nonadherent patients, particularly those with high urine drug levels, for treatment plan adjustments and care management interventionscould help to improve pain control, reduce drug misuse, and reduce excess costs associated with nonadherence. Other strategies to monitor opioid use (eg, use of screening instruments to identify aberrant behaviors, other risk assessment tools, online prescription databases) complement urine testing, and determining concordance between these measures could be of value to physicians.7,38,39 Additional research is needed to determine whether feedback to  clinicians provided by drug monitoring directly reduces costs or guides care practices.


All claims-based analyses are subject to certain limitations, such as possible coding errors, undercoding, and lack of generalizability. In this study, the classification of adherence was limited by possible misinformation provided to the testing facility regarding the prescribed opioid regimen. Determination of adherence based on expected urine drug levels was dependent on receipt of accurate information concerning the patient’s opioid regimen prescriptions as well as clinical information such as sex, height, and weight. If incomplete  or inaccurate information was provided, some patients identified as nonadherent could have in fact been following their prescribed regimen. In addition, although the  study samples comprised all available patients who fulfilled the inclusion criteria, the comparisons may not have been powered to detect moderate differences.


A high level of healthcare resource use and costs was generated by patients on chronic opioid regimens in comparison with patients who did not use opioid medications or have evidence of chronic pain. Urine drug testing can identify patients who are likely to be nonadherent and have significantly higher healthcare costs. In particular, patients with urine drug levels that were higher than expected using a proprietary algorithm were predicted to have significantly higher costs than patients whose test results were within an expected range. Improving adherence could reduce costs incurred by patients with chronic pain.

Author Affiliations: From Ameritox Ltd (HLL, JD), Baltimore, MD; and i3 Innovus (EJD, MK, ARB), Eden Prairie, MN.


Funding Source: This study was funded Ameritox Ltd.


Author Disclosures: Dr Leider and Mr Dhaliwal are employees of Ameritox Ltd, the funder of the study. Dr Davis, Mr Kulakodlu, and Ms Buikema are employees of i3 Innovus, which was contracted by Ameritox to conduct the study. Preliminary findings from this study were presented at the American Academy of Pain Medicine (AAPM) 26th Annual Meeting, San Antonio, TX, February 3-6, 2010, and the Academy of Managed Care Pharmacy (AMCP) 22nd Annual Meeting, San Diego, CA, April 7-10, 2010.


Authorship Information: Concept and design (HLL, JD, MK, ARB); analysis and interpretation of data (HLL, JD, EJD, MK, ARB); drafting of the manuscript (HLL, JD, EJD, MK, ARB); critical revision of the manuscript for important intellectual content (HLL, JD, EJD, MK, ARB); statistical analysis (MK); obtaining funding (HLL, JD); and supervision (HLL, JD, ARB).


Address correspondence to: Harry L. Leider, MD, MBA, Ameritox, Ltd, 300 E Lombard St, Ste 1610, Baltimore, MD 21202. E-mail: harry.leider@

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