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The American Journal of Managed Care November 2013
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Opioid Analgesic Treated Chronic Pain Patients at Risk for Problematic Use
Joseph Tkacz, MS; Jacqueline Pesa, PhD, MPH; Lien Vo, PharmD, MPH; Peter G. Kardel, MA; Hyong Un, MD; Joseph R. Volpicelli, MD, PhD; and Charles Ruetsch, PhD
Depression Self-Management Assistance Using Automated Telephonic Assessments and Social Support
John D. Piette, MSc, PhD; James E. Aikens, PhD; Ranak Trivedi, PhD; Diana Parrish, MSW; Connie Standiford, MD; Nicolle S. Marinec, MPH; Dana Striplin, MHSA; and Steven J. Bernstein, MD, MPH
Creating Peer Groups for Assessing and Comparing Nursing Home Performance
Margaret M. Byrne, PhD; Christina Daw, PhD; Ken Pietz, PhD; Brian Reis, BE; and Laura A. Petersen, MD, MPH
Upcoding Emergency Admissions for Non-Life-Threatening Injuries to Children
Zachary Pruitt, MHA; and Etienne Pracht, PhD
Variations in the Service Quality of Medical Practices
Dan P. Ly, MD, MPP; and Sherry A. Glied, PhD
Using Health Outcomes to Validate Access Quality Measures
Julia C. Prentice, PhD; Michael L. Davies, MD; and Steven D. Pizer, PhD
Collecting Mortality Data to Drive Real-Time Improvement in Suicide Prevention
Brian K. Ahmedani, PhD; M. Justin Coffey, MD; and C. Edward Coffey, MD

Opioid Analgesic Treated Chronic Pain Patients at Risk for Problematic Use

Joseph Tkacz, MS; Jacqueline Pesa, PhD, MPH; Lien Vo, PharmD, MPH; Peter G. Kardel, MA; Hyong Un, MD; Joseph R. Volpicelli, MD, PhD; and Charles Ruetsch, PhD
A large proportion of opioid analgesic treated chronic pain patients exhibited behaviors indicative of potentially problematic opioid use, which significantly affected healthcare costs.
Objectives: To characterize potentially problematic opioid use (PPOU) among opioid analgesic–treated chronic pain (OAT-CP) patients and to compare their healthcare service utilization and expenditures with those of a control group of OAT-CP patients not exhibiting these behaviors.

Study Design: Cross-sectional, retrospective analysis of health claims data.

Methods: Members of a national health plan (n = 3891) with chronic pain and an opioid prescription were categorized into 3 groups: PPOU group (n = 1499), those displaying evidence of doctor shopping or rapid opioid dose escalation; buprenorphine/naloxone group (n =199), those who filled a prescription for buprenorphine/naloxone, which served as a proxy for opioid dependence; and control group (n = 2193), those not meeting either of the above criteria. Groups were compared on 1-year healthcare service utilization and costs.

Results: The PPOU group made up more than one-third of the study sample. Compared with the control group, they incurred significantly greater 1-year adjusted mean pharmacy costs ($6573 vs $6160), office costs ($5705 vs $4479), emergency department (ED) costs ($835 vs $388), inpatient costs ($15,646 vs $7445), and total healthcare costs ($39,048 vs $26,171) (all P <.05). The buprenorphine/naloxone group incurred significantly greater 1-year pharmacy costs ($6981 vs $6160) and ED costs ($1126 vs $388) (both P <.05) than the control group.

Conclusions: The PPOU group had the highest healthcare service utilization and costs. Although drivers of elevated service utilization and cost among this population are not clear, health plans may want to focus on PPOU case identification and development of interventions.

Am J Manag Care. 2013;19(11):871-880
The data available in medical and pharmacy claims were used to characterize a sample of opioid analgesic–treated chronic pain patients in a national health plan.
  • One-third exhibited evidence of potential problematic opioid use in the absence of a clinical diagnosis of abuse or dependence.

  • The potentially problematic opioid users had significantly higher healthcare service utilization and costs compared with nonproblematic opioid-using patients.

  • Health plans may be able to use these methods to identify members with chronic pain who exhibit similar problematic behaviors in order to effectively intervene and optimize resource allocation.
Opioid utilization, especially in the United States, has been increasing for several years; Americans now consume 80% of the global opioid supply despite representing only 4.6% of the world’s population.1 The rise in opioid use can be attributed to a number of factors including changes in prescribing practices, suboptimal addiction risk factor screening, an increase in the aged population, and an increased availability of opioid medications.2 The opioid abuse and addiction literature indicates that the behaviors of some chronic pain patients on opioid therapy put them at greater risk of consequences often associated with addiction, including elevated  use of healthcare services, crime, and death.3,4

Studies based on both clinical trial data5 and administrative claims databases6 have reported the incidence of diagnosed opioid abuse and/or addiction among the opioid analgesic–treated chronic pain (OATCP) population to be approximately 3%. However, there is disagreement among opioid prescribers on what constitutes aberrant drug-related behavior,7 compounded by OAT-CP patients’ tendency to underreport these behaviors.8 The evidence on the prediction and identification of potentially problematic opioid use (PPOU) among OAT-CP patients is limited due to a number of factors including poor instrumentation, disagreement of terms across studies, and methodological shortcomings,9 though Rice and colleagues10 recently demonstrated that exposure to  buprenorphine and diagnoses of nonopioid drug abuse and mental illness were predictive of opioid abuse. One review estimated addiction rates among the OAT-CP population to be between 0% and 50% depending on criteria used to define addiction.11 Taken together, these findings highlight the need to build upon existing methods for identifying potentially problematic opioid users among the OAT-CP population.

Although opioid addiction results in well-documented societal costs,12 undertreated chronic pain and associated costs cannot be  ignored. Current algorithms and instruments tend to focus on either the quality of pain management or the emergence of opioid addiction and its consequences. Health plans and providers require algorithms with greater precision than those currently available to facilitate effective treatment of chronic pain patients based on both their need for analgesia and their probability of developing problematic use or addiction. The purpose of the present study was to use a national managed care organization’s administrative medical and pharmacy claims database to characterize OAT-CP patients who may meet our definition of PPOU and to compare

the healthcare service utilization and costs of these members with those of a control group of OAT-CP patients without evidence of PPOU or addiction. It was hypothesized that the identified PPOU subsample would have significantly greater healthcare service utilization and costs compared with OATCP patients without evidence of PPOU.


Sample Selection

Aetna (Blue Bell, Pennsylvania) provided medical, pharmacy, and provider data for their chronic pain population during calendar years 2009 through 2011. To identify the study sample, the following inclusion and exclusion criteria were imposed:

1. Adults (aged 18-64 years) with chronic pain were defined as those with either
a. at least 1 medical claim with a diagnosis of chronic pain, or
b. during a period of at least 3 months, 3 claims with a primary diagnosis of low back pain, 3 claims with a primary diagnosis of osteo-arthritis, or 3 claims with a primary diagnosis of diabetic peripheral neuropathy (first claim serving as the index event).

2. 6 months “chronic pain naïve” prior to the index date.

3. >90 days of supply of any opioids prescribed within a 180-day window around the index date (90 days preindex, 90 days postindex), including both long- and short-acting formulations, as well as combination products.
4. Continuous eligibility for 18 months around the index date (6 months pre-index, 12 months postindex).

5. Absence of mood disorder or drug dependence diagnoses. (In the current sample, 5 patients had a diagnosis of opioid dependence while meeting no other group assignment criteria.)

The International Classification of Diseases, Ninth Revision, Clinical Modification, (ICD-9-CM) chronic pain diagnosis code (338.xx) is underutilized; therefore, 3 specific groups of diagnoses associated with chronic pain were used in case finding: neuropathic (diabetic peripheral neuropathy), inflammatory (osteoarthritis), and functional (low back pain).13,14 Chronic low back pain may be categorized in any of the 3 pain classes,13 but was primarily selected because a significant portion of the chronic pain population has this condition.15 A conservative rule for defining chronic pain was applied, which required 3 claims within a specific diagnosis group to appear, spanning at least a 3-month period.16 The Figure details the attrition of the sample at each imposition of the inclusion and exclusion criteria, resulting in the final study sample (N = 3891).

Placement Into Opioid Use Groups

Each member of the study sample was assigned hierarchically to 1 of 3 mutually exclusive groups based on their 1-year postindex claims. Members who qualified for multiple groups were placed in the highest ranking group.

1. Buprenorphine/naloxone group. Members of this group had at least 1 fill for buprenorphine/naloxone.

2. PPOU group. Members of this group had a pattern of opioid prescription fills that reflected either doctor shopping, defined as receiving opioid fills from 5 or more different prescribers within 1 year,17 or rapid dose escalation, defined as either a 50% increase in opioid dose (combined standard morphine units across any long- or short-acting opioids prescriptions18) during the first 3 months or a 100% increase in dose at any time during the course of the follow-up period. Both short- and long-acting opioids were used in the dose escalation calculation, and methods were implemented to prevent double counting of day.

3. Control group. Members of this group did not meet any of the above criteria.

The buprenorphine/naloxone group was created as a proxy for opioid abuse or dependence.10 The PPOU group served as the main study group of interest, as these members provided no direct evidence of opioid abuse or addiction. Both the buprenorphine/ naloxone and PPOU groups were compared with the control group, which was composed of normal-functioning OAT-CP patients without any discernible signs of problematic use, abuse, or addiction.


The following health services  and expenditure outcomes were measured and compared across groups:

  • Total prescription fills and costs.
  • Opioid fills and costs.
  • Inpatient hospital admissions, days, and costs.
  • Emergency department (ED) visits and costs.
  • Physician office visits and costs.
  • Outpatient hospital visits and costs.
  • Total medical costs.
  • Total healthcare costs (medical plus pharmacy costs).
Bivariate Analyses

Study groups were compared on demographic variables including age, sex, chronic pain diagnosis, preindex Charlson Comorbidity Index score19 (a measure of overall health), and region of residence. Groups were then compared on 1-year postindex period healthcare service utilization and cost variables. Chi-square tests of equality of proportions were used for categorical variables, and 1-way analysis of variance was used for continuous variables. Tukey’s post hoc tests were conducted to examine group differences when omnibus tests were statistically significant.

Multivariate Analyses

Differences in residualized postindex period service utilization counts and costs were adjusted for sex, region of residence, age, and the Charlson Comorbidity Index score using 2-step regression models. Outpatient, inpatient, and ED measures are typically zero-inflated distributions and were therefore dichotomized into no/any utilization or spending and served as dependent variables in logistic regression models. Next, the subset of cases with any utilization/expenditure on these measures was selected and entered into generalized linear models. For service utilization counts, Poisson log-linear models were estimated. For cost variables, gamma models with a log link were estimated. Pharmacy utilization and costs, office utilization and costs, and total costs did not present zero-inflated distributions and were directly regressed onto the predictors via generalized linear models. All data management and analyses were conducted using SPSS version 20 (IBM Corp, Armonk, New York).


A total of 3891 OAT-CP patients were included in the analyses. Of them, 199 (5.1%) had a fill for buprenorphine/naloxone during the postindex period. Evidence of PPOU was found in 38.5% (n = 1499) of the sample, with 25.4% (n = 989) having rapid dose escalation, 21.3% (n = 827) engaging in doctor shopping, and 8.1% (n = 317) exhibiting both behaviors. The remaining 56.4% of members (n =2193) comprised the control group, with no evidence of PPOU, abuse, or addiction.

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