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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Pain Symptom Manage. Author manuscript; available in PMC Aug 1, 2011.
Published in final edited form as:
PMCID: PMC2921474
NIHMSID: NIHMS203911
An Analysis of Heavy Utilizers of Opioids for Chronic Non-Cancer Pain in the TROUP Study
Mark J. Edlund, MD, PhD, Bradley C. Martin, PharmD, PhD, Ming-Yu Fan, PhD, Jennifer Brennan Braden, MD, MPH, Andrea Devries, PhD, and Mark D. Sullivan, MD, PhD
Division of Health Services Research (M.J.E.), Department of Psychiatry, College of Medicine, and Division of Pharmaceutical Evaluation and Policy (B.C.M.), University of Arkansas for Medical Sciences, Little Rock, Arkansas; Department of Psychiatry and Behavioral Sciences (M.-Y.F., J.B.B., M.D.S.), University of Washington, Seattle, Washington; and HealthCore, Inc. (A.D.), Wilmington, Delaware, USA
Address correspondence to: Mark J. Edlund, MD, PhD Division of Health Services Research University of Arkansas for Medical Sciences 4301 W. Markham, Slot 554 Little Rock, AR 72205 ; mjedlund/at/uams.edu
Context
While opioids are increasingly used for chronic non-cancer pain (CNCP), we know little about opioid dosing patterns among individuals with CNCP in usual care settings, and how these are changing over time.
Objectives
To investigate the distribution of mean daily dose and mean days supply among patients with CNCP in two disparate populations, one national and commercially-insured population (HealthCore) and one state-based and publicly-insured (Arkansas Medicaid), for years 2000 and 2005.
Methods
For individuals with any opioid use, we calculated the distribution of mean daily dose (in milligram morphine equivalents), mean days supply in a year, mean annual dose, and patient characteristics associated with heavy utilizers of opioids.
Results
Between 2000 and 2005, across all percentiles, there was little change in the mean daily opioid dose. In HealthCore, mean days supply increased most rapidly at the top end of the days supply distribution, while in Arkansas Medicaid the greatest increases were near the median of days supply. In HealthCore the top 5% of users accounted for 70% of total use (measured in milligram morphine equivalents), and the top 5% of Arkansas Medicaid users accounted for 48% of total use. The likelihood of heavy opioid utilization was increased among individuals with multiple pain conditions, and in HealthCore, among those with mental health and substance use disorders.
Conclusion
Opioid use is heavily concentrated among a small percent of patients. The characteristics of these high utilizers need to be further established, and the benefits and risks of their treatment evaluated.
Keywords: Opioids, chronic non-cancer pain, pharmaco-epidemiology
Approximately one in five primary care patients have significant chronic non-cancer pain (CNCP) (1,2). Common conditions include back pain, arthritis, and recurrent migraines (3,4). Complete recovery from CNCP is uncommon with most patients having recurrent episodes of pain (5). The social burden of CNCP is large because it often occurs during adulthood when individuals typically assume parenting and care giving roles and participate in the paid labor force at high rates. Thirteen percent of headache patients and 18% of back pain patients in the U.S. report that they have been unable to work full-time because of pain (6).
Prescription opioids are increasingly used for CNCP (7,8) with wide variation from state to state (9-11). Increased use of opioids for CNCP has been accompanied by a parallel increase in opioid use disorders (OUDs) and accidental overdose (7, 12-19). Thus, a key issue in prescribing opioids for CNCP is balancing the possible benefits of pain relief and improved quality of life with the risks of addiction, overdose, reduced quality of life, and other negative outcomes such as diversion. Reflecting the key issue of balancing the benefits and risks, the Food and Drug Administration recently indicated that manufacturers of long-acting opioid formulations will be required to have a Risk Evaluation and Mitigation Strategy to “ensure that the benefits of the drugs continue to outweigh the risks” (20).
The TROUP (Trends and Risks of Opioid Use for Pain) study was designed to assess trends in (years 2000 to 2005) and risks of opioid therapy for CNCP in two disparate populations, a national commercially-insured population (HealthCore Blue Cross and Blue Shield plans) and a state-based publicly-insured population (Arkansas Medicaid). In our first series of reports, we found that between 2000 and 2005, the proportion of enrollees receiving a CNCP diagnosis increased by 33% in HealthCore and 9% in Arkansas Medicaid. The proportion of enrollees who received opioids increased by 38% in HealthCore and by 37% in Arkansas Medicaid (21). Among CNCP patients being treated with opioids, the mean days supply of opioids in a year increased 23% in HealthCore and 34% in Arkansas Medicaid, although mean dose was relatively constant (21). Rates of opioid use were similar across common pain conditions (22), but any opioid use, and chronic opioid use was much more frequent in CNCP patients with mental health and substance use disorders (23).
In the current paper, our objective was to characterize in greater detail patterns of opioid use for CNCP, including the distribution of mean daily dose and mean days supply in our sample. Opioids are unique in the modern pharmacopeia in that there is no absolute limit to dose (due to the development of tolerance) or to days supplied (due to their use for chronic conditions). Therefore, we hypothesized that a high percentage of total opioid use would be attributable to high utilizers of opioids. We sought to test this hypothesis and to describe the characteristics of these high utilizers of opioids for CNCP. Such information is important for policy makers and clinicians, as it helps define patient groups that may be at high risk for adverse outcomes of opioid use and helps target harm reduction strategies.
Study Populations
Arkansas Medicaid
Arkansas is one of the poorest states, with 26% of the Arkansas population qualifying for Medicaid benefits in 2005 (24). The 2005 total expenditures for the program were $3.0 billion dollars or $4,368 per recipient. Arkansas Medicaid serves a disadvantaged and vulnerable population situated in the geographic region with the highest opioid use in the country (25). Arkansas Medicaid covers all federally-mandated services and nearly all federal optional services, including prescription drug, ambulatory surgical center services, and 44 other optional services. Most Arkansas Medicaid enrollees participate in the primary care physician program where recipients utilize a primary care provider to coordinate care. The Arkansas Medicaid program imposes some benefit limitations: twelve physician, clinic, and/or outpatient visits per year, six prescriptions per month, maximum of 24 inpatient days per year, and some co-insurance and co-payments for prescription drugs and other services depending on eligibility type. Analyses of Medicaid claims indicate that Medicaid data are generally valid and suitable for epidemiologic uses (26).
HealthCore
The HealthCore Integrated Research Database contains medical and pharmacy administrative claims and health plan eligibility data from five commercial health plans representing the West, Mid-West, and South-East regions. Data come from health plan members who are fully insured via several commercial insurance products including health maintenance organizations, preferred provider organizations, and point of service providers. Health plan members all have full medical and pharmacy coverage, with a range of co-pay and deductibles. Claims submitted with partial or complete subscriber liability (due to co-pay or deductible requirements) are captured.
Study Sample
The study sample consisted of enrollees in the two health plans in 2000 and 2005 who met the following inclusion criteria: 1) one or more recorded CNCP diagnosis based on ICD codes for back pain, neck pain, arthritis/joint pain, headache/migraine, and HIV/AIDS. We could not verify that these conditions were chronic or that these were the conditions for which opioids were prescribed, but chronic forms of these conditions are the most common indication for long-term opioid use in a general medical population; 2) received at least one opioid prescription in the given year, either 2000 or 2005; 3) age 18 or older; 4) enrolled and eligible for benefits for at least nine months in the given year, either 2000 or 2005. Exclusion criteria included: 1) cancer diagnosis at any time in 2000-2005 other than non-melanoma skin cancer; 2) resident of nursing home; or 3) receiving hospice benefits. These criteria allow us to focus on outpatient enrollees likely receiving opioids for the treatment of CNCP.
Opioid Use
Data included all opioid prescriptions (including date, dose, and type of opioid) regardless of indication for opioid use. For years 2000 and 2005, we formed an analytic file including all individuals with one of our tracer CNCP diagnoses in one of those years who received any prescribed opioids. We recorded the total number of opioid prescription fills for each patient within the calendar year and calculated the number of days supplied in the year, as recorded by the dispensing pharmacist. Total morphine equivalents for each prescription were calculated by multiplying the quantity of each prescription by the strength of the prescription (milligrams of opioid per unit dispensed). The quantity-strength product was then multiplied by conversion factors derived from published sources to estimate the milligrams of morphine equivalent to the opioids dispensed in the prescription (27-29). The total opioid dose in a year was obtained by summing across all prescriptions. The mean dose in morphine equivalents per day supplied was calculated by summing the morphine equivalents for each prescription filled during the year for each patient, and dividing by the number of days supplied.
In some cases, days supplied might exceed 365 days for the year. First, patients might be on two different opioids, for example a long-acting Schedule II and a short-acting Schedule III for breakthrough pain. We counted the days supplied for both medications. Second, a patient might take more medication than prescribed, run out of a prescription early, and request and be given a new prescription.
Mental Health and Substance Use Disorders
Using ICD-9-CM codes we created a dichotomous variable for six types of disorders--adjustment disorders, anxiety disorders, mood disorders, personality disorders, substance use disorders, and miscellaneous disorders (e.g., eating disorders, somatoform disorders)--using validated grouping software developed by the Agency for Healthcare Research and Quality (30).
Data Quality Issues
To protect against data entry errors, for each prescription we treated any value for days supply or quantity greater than two times the 99th percentile value for the particular opioid as a potentially invalid value. These values were then handled as if they were missing data. If either quantity or days supply was missing for a particular prescription, then morphine equivalents were not calculated for that prescription. The estimate of total morphine equivalents was inflated by the total number of prescriptions in the year (including those with missing data) divided by the number of prescriptions with valid data (i.e., not counting the ones with missing/potentially invalid data). This approach conservatively estimates the morphine equivalents for prescriptions with missing/potentially invalid data as being equal to the mean prescription in the year. In both samples, total missing was less than 1.5% and potentially invalid values excluded were less than 0.5%
Analyses
Among individuals with a CNCP diagnosis and any opioid use in 2000, we calculated the 10th, 20th, 30th, 40th, 50th, 60th, 70th, 80th, 90th, 95th and 99th percentiles for (i) mean daily dose in year 2000, in morphine equivalents, (ii) total days of opioids supplied in year 2000, (iii) and total dose in year 2000, in morphine equivalents. That is, for (i), using the distribution of mean daily opioid dose in individuals with CNCP and any opioid use, we determined the mean daily dosage for the 10th percentile, the 20th percentile, etc. This was done separately for the Arkansas Medicaid and HealthCore populations. We performed analogous calculations using our year 2005 data, and divided each percentile in year 2005 by the corresponding percentile for year 2000 to calculate the percentage change. In a similar fashion, we calculated the various percentiles for total days supply of opioids in a year, and total dose of opioids, in morphine equivalents in a year.
We also calculated the percentage of the total sample opioid days supply consumed by the individuals within the various days supply percentiles. For example, to determine what fraction of the total opioid days were consumed by all individuals in the 21st to 30th percentile we first summed the days supply across all patients in the 21st to 30th percentile, and divided this by the total days supply of opioids in the entire sample. In a similar fashion we calculated the total opioid dose that was accounted for by individuals in the various percentiles of total opioid dose. That is, we calculated the total morphine equivalents consumed by all individuals in the 21st to 30th percentile of total dose, then divided this by total morphine equivalents consumed by the entire sample.
Finally, we investigated the characteristics of individuals who were high utilizers of opioids; all data for these analyses (opioid utilization, age, mental health diagnoses, substance abuse diagnoses, CNCP diagnoses) were derived from year 2005. We defined high utilizers to be those individuals in the top 5% of total opioid use, and regressed this binary variable on age, gender, the chronic pain diagnoses, and the mental health and substance use diagnoses.
Between 2000 and 2005 there was little change in the mean daily opioid dose of a prescription for any of the percentiles in HealthCore (Table 1). For example, in the distribution of mean daily dose, in year 2000 the mean daily dose for an individual in the 50th percentile was 37.5 mg (morphine equivalents), while the mean daily dose for an individual in the 50th percentile in 2005 was 38.1 mg (morphine equivalents). The mean daily dose for an individual in the 99th percentile was 230.0 mg in both 2000 and 2005. In Arkansas Medicaid the mean daily dose did increase substantially for those in the 99th percentile of mean daily dose, from 158.7 mg in year 2000 to 191.9 mg in year 2005, but was generally slightly lower lower than the mean daily dose observed in HealthCore. Mean daily doses remained relatively constant or decreased slightly over the five year period in Arkansas Medicaid for other percentiles.
Table 1
Table 1
Distribution of Mean Daily Dose in Milligram Morphine Equivalents for Patients with CNCP and Any Opioid Use
In the HealthCore distribution of days supply, the days supply did not increase for the 10th, 20th, 30th, or 40th percentiles, and only increased modestly (i.e., percentage increases from 10% to 18%) for the 50th, 60th, and 70th percentiles (Table 2). On the other hand, days supply increased from 23% to 33% for the 80th to 99th percentiles. In Arkansas Medicaid, the days supplied increased across all percentiles, with increases typically ranging from 10% to 70%, with largest percentage increases near the median.
Table 2
Table 2
Distribution of Opioids Day Supply in a Calendar Year for Patients with CNCP and Any Opioid Use
In HealthCore and Arkansas Medicaid, the total yearly dose increased for all percentiles except the 10th percentile, and the largest percentage increases were for the highest percentiles (Table 3). For example, in both HealthCore and Arkansas Medicaid the total yearly dose increased 55% for individuals in the 99th percentile between 2000 and 2005. In HealthCore, the percentage increases in the annual total dose for the other percentiles were more modest, ranging from 0% to 26%. In Arkansas Medicaid, the increases ranged from 0% to 47% for the other percentiles.
Table 3
Table 3
Distribution of Total Opioid Dose for a Calendar Year in Morphine Equivalents (Milligrams) for Patients with CNCP and Any Opioid Use
The 4% of the individuals in the 95th to 99th percentiles of days supply accounted for 32.5% of the total opioid days in HealthCore in 2000 and 32.3% of the total opioid days in 2005, while individuals in the 99th to 100th percentiles accounted for 15.7% of total opioid days in both years (Table 4). Thus, in HealthCore in 2005, 48% of the days supply was accounted for by those in the top 5% of days supply. In Arkansas Medicaid, individuals in the 95th to 99th percentiles accounted for 20% of total opioid days in 2000 and 17.6% of the total opioid days in 2005; individuals in the 99th to 100th percentiles accounted for 8.3% and 6.7% of the total opioid days in years 2000 and 2005 respectively. Therefore, in Arkansas Medicaid in year 2005, 24% of the days supply was accounted for by those in the top 5%.
Table 4
Table 4
Percentage of Total Days Supply Consumed by Individuals Within a Given Percentile.
For HealthCore, the 4% of individuals in the 95th to 99th percentile accounted for 27.5% of total opioids utilized in 2000 and 27.3% of the total opioids utilized (morphine equivalents) in 2005 (Table 5). The top 1% (99th to 100th percentile) accounted for another 37.3% of total opioid use in 2000, and 43.3% of total opioid use in 2005. In Arkansas Medicaid, the 4% of individuals in the 95th to 99th percentile accounted for 25.5% of total opioid use in 2000, and 27.2% percent of total opioid use in 2005. The top 1% accounted for 20.5% of total opioid use in 2000, and 20.6% in 2005.
Table 5
Table 5
Percentage of Total Opioids (Milligrams of Morphine Equivalents) Consumed by Individuals Within a Given Percentile
In HealthCore adjusted logistic regression results, older individuals were more likely to be heavy utilizers of opioids (top 5% of total opioid use), with the odds increasing for each age group; there were no gender differences. Regarding CNCP diagnoses, individuals with back pain and headache were especially likely to be heavy utilizers, although the odds ratios for all the CNCP conditions were greater than 1.00. (Individuals without the given CNCP condition comprised the reference group). Among the mental health and substance use disorders, individuals with mood disorders and substance use disorders were especially likely to be heavy utilizers. (Again, those without the given mental health or substance use disorder comprised the reference group). In Arkansas Medicaid, the middle-aged, and men were more likely to be heavy utilizers. All CNCP conditions had odds ratios greater than 1.00, but the largest effects were for arthritis and back pain. Among the mental health and substance use diagnoses, individuals with substance use disorders and miscellaneous MH disorders were more likely to be heavy utilizers.
We also conducted multiple logistic regressions utilizing the total number of CNCP diagnoses instead of the actual CNCP diagnoses, and the total number of MH/SUD diagnoses, rather than the actual diagnoses to predict heavy opioid utilization (Table 6 and Table 7). In HealthCore, the likelihood of being a heavy opioid user increased monotonically with the number of CNCP diagnoses, and the number of MH/SUD diagnoses. On the other hand, the likelihood of being a heavy opioid utilizer in Arkansas Medicaid increased with the number of CNCP diagnoses but did not consistently increase with the number of mental health disorders. While those with a MH/SUD diagnosis were more likely to be heavy utilizers than those without an MH/SUD, the likelihood did not increase with the number of MH/SUD diagnoses.
Table 6
Table 6
Characteristics Associated with Heavy Opioid Utilization
Table 7
Table 7
Association Between Heavy Opioid Utilization, and Number of CNCP and MH/SUD Diagnosesa
Our analyses suggest an unprecedented concentration of opioid use among enrollees with CNCP diagnoses who have used some opioids. In HealthCore enrollees, the top 5% of users accounted for over 70% of total opioids used in 2005. The top 1% accounted for 43% of total opioids used. Between 2000 and 2005, opioid use became more concentrated in these groups. In Arkansas Medicaid enrollees, the top 5% of users accounted for 47% of total opioids used and the top 1% accounted for 21% of total opioids used. This concentration of use is not seen with any other prescribed medication and raises unique challenges and opportunities for reducing the risks of chronic opioid use. The use of opioids was more heavily concentrated in the top 1% of users in HealthCore (as defined by total morphine equivalents of opioid use in the year) than in the top 1% of Arkansas Medicaid users not because the top 1% in HealthCore were heavier utilizers than the top 1% in Arkansas Medicaid. Rather, the top 1% in HealthCore had a higher mean daily dose than those in Arkansas Medicaid but a lower days supply, however both had days of supply >365 days indicating the use of multiple concurrent opioids. The mean user in Arkansas Medicaid actually had heavier use as defined by total morphine equivalents received in the year than the mean user in HealthCore. Indeed, individuals in the top 1% of use in Arkansas Medicaid utilized on mean 88,275 mg of morphine equivalents in year 2005, while individuals in the top 1% of HealthCore utilized on mean 55,800 mg of morphine equivalents.
In both groups, the likelihood of heavy opioid use increased with the number of CNCP diagnoses. Only in the HealthCore group did heavy use increase with increasing number of mental health and substance abuse diagnoses. The higher prevalence of mental health and substance abuse comorbidity in the Medicaid sample may account for less concentration of opioid use in this group. High opioid utilization was associated with age 41-60 in both samples and also age >60 in HealthCore. High utilization was associated with all the CNCP diagnoses and substance abuse disorders in both samples.
Our results suggest that among individuals in the HealthCore sample with CNCP diagnoses who receive opioids, most are not chronic users of opioids. For example, in HealthCore in 2005, it appears that between 10% and 20% percent of the sample were chronic users, as the 80th percentile for days supply was 43 days, which is clearly not chronic use, while the 90th percentile for days supply was 150 days, which might be considered chronic use. On the other hand, in Arkansas Medicaid, chronic use was more common, with 30% of the Arkansas Medicaid sample utilizing 136 days or more.
Our study suggests that mean daily dose did not change between 2000 and 2005. A current controversy in pain management is whether there should be any upper limit or cautionary range for daily opioid dose. The Washington State AMDG opioid dosing guidelines for primary care physicians suggest that “rarely, and only after pain management consultation, should the total daily dose of opioid be increased above 120 milligram oral morphine equivalents” 31 While our study was not designed to investigate what constitutes an appropriate upper limit of daily opioid use, we can assess what percentage of individuals with CNCP disorders and opioid use exceed given thresholds. For example, in HealthCore, in both 2000 and 2005 about 8% of individuals with CNCP who were on opioids had mean daily doses greater than 120 mg oral morphine equivalents, and in Arkansas Medicaid about 5% of individuals exceeded this threshold.
Our findings suggest several key areas for future research. First, given that opioid use is so heavily concentrated among relatively few users, are the negative outcomes from opioids similarly concentrated among relatively few users, and are these groups the same? If this is the case, efforts to minimize the negative outcomes of opioid use might have to focus only on a relatively small percentage of opioid users. We also need to better understand the characteristics of heavy utilizers. While our results suggest that heavy opioid utilization is associated with more CNCP and MH/SUD diagnoses, clearly additional research is needed to define the characteristics of these individuals, and this might need to be done for different sub-populations. For example, in Healthcore, the elderly were more likely to be heavy utilizers, and there were no gender differences. On the other hand, in Arkansas Medicaid, middle age individuals and men were more likely to be heavy utilizers. Also, there may be other important factors associated with high utilization of opioids that our data do not allow us to address. For example, work (or disability) status, social support, and physical health status might all influence use of opioids, and this should be pursued in future studies.
We need to better understand the patterns of utilization of these individuals. We speculate that there may be distinct, clinically important patterns of use, and that some patterns may be more suggestive of misuse than other patterns. For example, some high utilizers may be patients on high, but stable, doses of opioids, prescribed by one clinician. These individuals might be on two regularly prescribed opioids, such as long acting opioid for control of baseline pain, and a short acting opioid for breakthrough pain, but with no early re-fills. Other high utilizers may have pharmaco-epidemiological profiles which are characterized by increasing dosages, early re-fills, and prescriptions from multiple clinicians and pharmacies—all patterns suggestive of misuse, or possibly pseudo-addiction. We believe a key step in this line of research is determining what percentage of individuals have pharmacy profiles that fit into the first category, and what percentage have pharmacy profiles that are consistent with the second category, as this will give us valuable insight into the magnitude of the possible abuse problem. We need to better understand whether these pharmacy profiles are actually associated with better or worse outcomes. For those individuals whose pharmacy use patterns suggest abuse or misuse, we need to know whether pseudo-addiction associated with poor pain control might be responsible for the observed patterns.
Finally, we need to better understand the pharmaco-epidemiology of opioid use from the vantage point of the clinician. In particular, it would be helpful to know to what extent opioid prescribing is concentrated among a relatively small percentage of physicians. Obviously pain specialists will have high rates of opioid prescribing, but among primary care physicians, is opioid prescribing highly concentrated? Our results are helpful to some degree in this regard, as they allow individual clinicians to compare their own opioid prescribing patterns with the distributions of days supply and mean daily dose in two large, diverse populations. Days supply showed more variation than daily dose and might be considered as a target for harm reduction efforts.
A primary goal of this report was to determine whether the observed increases in opioid use in the TROUP populations between 2000 and 2005 were attributable to high utilizers of opioids. In Arkansas Medicaid, we found that the increased use (total annual milligrams of morphine equivalents) occurred over all percentiles, suggesting that the observed increases were not attributable to high utilizers of opioids. On the other hand, in HealthCore, we found that opioid use increased generally across all percentiles, but the greatest increases occurred among the heaviest users who were older and likely to have multiple chronic pain conditions.
Limitations
Our results should be interpreted in light of the following limitations. Our data are from diverse sources, but are not nationally representative, so the generalizability to the larger population is unknown. Arkansas Medicaid serves a low income, vulnerable population in the South-Central U.S. HealthCore has enrollees in many states in the Midwest, West, and Southeast, most of whom are working, middle, or upper class. Our intent in investigating these two dissimilar populations was not to draw contrasts, but to describe the current range of opioid prescribing practices. Indeed, the much higher burden of disease in Arkansas Medicaid, measured in terms of both rates of CNPC and MH/SUD diagnoses, makes comparisons problematic.
Because the samples were dissimilar in virtually every way (geographically, sociodemographically, burden of illness) we cannot say what the observed differences in opioid utilization between Healthcore and Arkansas Medicaid were due to. Further, we know of no system factors such as re-imbursement policies or insurance restrictions that might have led to differences among these groups. HealthCore is primarily fee-for-service, and Arkansas Medicaid is entirely so. Co-pays and deductibles are generally be less in Arkansas Medicaid than in HealthCore, but Arkansas Medicaid recipients on average have fewer resources. Further, Arkansas Medicaid recipients are limited to six prescriptions per month.
Although we relied upon conversion factors from published sources to derive morphine equivalents (27, 28, 29) there are no canonical conversion tables, and estimates of conversion factors differ, generally by small amounts. Differences between conversion tables were resolved by consensus among the clinicians on this study, in collaboration with other researchers and clinicians.
Methadone presents two challenges for a study such as ours. First, published estimates of conversion factors for methadone to morphine equivalents differ considerably (more so than conversion factors for other opioids to morphine equivalents). Second, we were not able to separate methadone used for pain from methadone used for methadone maintenance. However, methadone accounted for a relatively small percentage of total opioid use in our samples, and methadone maintenance is not common. For example, there is only one methadone clinic in all of Arkansas.
We relied upon administrative data for diagnoses and for pharmacy records. No independent clinical assessment of patients to confirm diagnoses could be done. Pain diagnoses have high specificity, although sensitivity is likely lower (33). Our study does not reflect opioids paid for out of pocket or those bought over the internet without a prescription (34) or bought illegally or diverting them. As we analyzed each year's data separately and did not track individual subjects’ status from year to year our results represent population trends and not the trends of individual enrollees.
In conclusion, opioid use, as measured by days supply and cumulative yearly opioid dose, is heavily concentrated among a small percent of users. It is increasing broadly across all types of users of opioids, and in the HealthCore commercially insured population, the largest increases were generally among those individuals who are already the heaviest users. The characteristics of these high utilizers need to be further established, and the benefits and risks of their treatment evaluated.
Acknowledgments
This work was supported by NIDA R01 DA022560-01.
Footnotes
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