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The Economic Burden of Diagnosed Opioid Abuse Among Commercially Insured Individuals J. Bradford Rice PhD, Noam Y. Kirson PhD, Amie Shei PhD, Caroline J. Enloe BS, Alice Kate G. Cummings BA, Howard G. Birnbaum PhD, Pamela Holly JD & Rami Ben-Joseph PhD To cite this article: J. Bradford Rice PhD, Noam Y. Kirson PhD, Amie Shei PhD, Caroline J. Enloe BS, Alice Kate G. Cummings BA, Howard G. Birnbaum PhD, Pamela Holly JD & Rami Ben-Joseph PhD (2014) The Economic Burden of Diagnosed Opioid Abuse Among Commercially Insured Individuals, Postgraduate Medicine, 126:4, 53-58 To link to this article: http://dx.doi.org/10.3810/pgm.2014.07.2783

Published online: 13 Mar 2015.

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C L I N I C A L F O C U S : P A I N M A N A G E M E N T, R A R E D I S E A S E S , A N D A L L E R G I E S

The Economic Burden of Diagnosed Opioid Abuse Among Commercially Insured Individuals

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DOI: 10.3810/pgm.2014.07.2783

J. Bradford Rice, PhD 1 Noam Y. Kirson, PhD 1 Amie Shei, PhD 2 Caroline J. Enloe, BS 3 Alice Kate G. Cummings, BA 4 Howard G. Birnbaum, PhD 5 Pamela Holly, JD 6 Rami Ben-Joseph, PhD 7 Manager, Analysis Group, Inc., Boston, MA; 2Associate, Analysis Group, Inc., Boston, MA; 3Analyst, Analysis Group, Inc., Boston, MA; 4Senior Analyst, Analysis Group, Inc., Boston, MA; 5Principal, Analysis Group, Inc., Boston, MA; 6Manager of Health Outcomes and Pharmacoeconomics, Purdue Pharma L.P., Stamford, CT; 7 Head of Health Outcomes and Pharmacoeconomics, Purdue Pharma L.P., Stamford, CT 1

Abstract: The abuse of prescription opioids imposes a substantial public health and economic burden. Recent research using administrative claims data has substantiated the prevalence and cost of opioid abuse among commercially insured individuals. Although administrative claims data are readily available and have been used to effectively answer research questions about the burden of illness for many different conditions, an important issue is the reliability, replicability, and generalizability of estimates derived from different databases. Therefore, this study sought to assess whether the findings of a recently published study of opioid abuse in a commercial claims database (original analysis) could be replicated in a different commercial claims database. The original analysis, which analyzed the prevalence and excess health care costs of diagnosed opioid abuse in the OptumHealth Reporting and Insights Database, was replicated by applying the same approach to the Truven MarketScan Commercial Claims and Encounters Database (replication analysis). In the replication analysis, the prevalence of diagnosed opioid abuse increased steadily from 15.8 diagnosed opioid abusers per 10 000 in 2009, to 26.6 diagnosed opioid abusers per 10 000 in 2012. Although the prevalence of diagnosed opioid abuse was higher than reported in the original analysis, the trend of increasing prevalence over time was consistent across analyses. Additionally, diagnosed abusers had excess annual per patient health care costs of $11 376 in the replication analysis, which was consistent with the excess annual per patient health care costs of diagnosed abuse of $10 627 reported in the original analysis. The replication analysis also found an upward trend in the prevalence of diagnosed opioid abuse over time and substantial excess annual per patient health care costs of diagnosed opioid abuse among commercially insured individuals, suggesting that these findings are generalizable to other commercially insured populations. Keywords: opioid abuse; health care costs; prevalence; claims data analyses

Introduction

Correspondence: J. Bradford Rice, PhD, Analysis Group, Inc., 111 Huntington Avenue, Tenth Floor, Boston, MA 02199. Tel: 617-425-8247 Fax: 617-425-8001 E-mail: [email protected]

A large portion of the recent literature documents the need to treat patients who suffer from chronic pain1–5 and the substantial economic costs of opioid abuse in the United States.6–11 Physicians have increasingly used prescription opioid analgesics for the treatment of pain, which are among the most effective drugs for pain management.12–15 These medications, however, also carry the risk of potential abuse, unintended misuse, and dependence.1,2 In 2012, 2.1 million Americans aged $ 12 years experienced prescription pain reliever abuse or dependence (defined as abuse).3 Recent research using commercial claims databases has documented the prevalence and economic burden of prescription opioid abuse. Using 2006–2012 data from a large administrative claims database, Rice et al estimated that the prevalence of diagnosed opioid abuse was 18.6 diagnosed opioid abusers per 10 000 in 2011, and that the excess annual

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Rice et al

per patient health care cost of diagnosed opioid abuse was $10 627 (adjusted to 2012 US dollars).4,5 Such claims-based estimates are important tools for discussions among public policy makers as well as physicians, pharmaceutical companies, payers, and employers regarding approaches to addressing the serious risks of opioid abuse and its associated economic costs. Although administrative claims data are readily available and have been effectively used to answer research questions about the burden of illness for many different conditions, an important issue is the reliability, replicability, and generalizability of estimates derived from different claims databases. This issue is particularly relevant in the context of opioid abuse, because many payers are not aware of the substantial burden of prescription opioid abuse6 and may not consider the results of a particular claims data analysis generalizable to their plan. The objective of this study was to assess whether the findings of a recently published study on opioid abuse in a commercial claims database could be replicated in a different commercial claims database. Specifically, this study sought to replicate findings of the Rice et al study on the prevalence and excess health care costs of diagnosed opioid abuse, which analyzed the OptumHealth Reporting and Insights (Optum) Database7 (original analysis).19 The original analysis was replicated using the same methodology and empirical approach but applied to the Truven MarketScan Commercial Claims and Encounters (Truven) Database8 (the replication analysis).

Materials and Methods

The replication analysis reported here was conducted using the de-identified Truven commercial claims database. The Truven and Optum datasets both cover a diverse set of commercially insured beneficiaries from a wide range of commercial health plans across the United States. For the prevalence calculations, diagnosed abusers were identified as patients with $ 1 diagnosis for opioid abuse or dependence, defined using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for opioid abuse or dependence (304.0×, 304.7×, 305.5×, 965.00, 965.02, and 965.09) during the first quarter of 2009 through the fourth quarter of 2012. Use of both opioid abuse and dependence ICD-9-CM diagnosis codes is consistent with the National Survey on Drug Use and Health,4 the Diagnostic and Statistical Manual of Mental Disorders, 5th edition, and several published studies on opioid abuse.2,24–29 Therefore, both the original and replication analyses use ICD-9-CM diagnosis codes for opioid abuse and dependence, hereafter 54

referred to as opioid abuse. Prevalence rates of diagnosed abuse were estimated annually as the proportion of patients with $  1 abuse diagnosis among all beneficiaries with $ 1 month of eligibility during the year. For the estimation of the excess costs of diagnosed opioid abuse, diagnosed abusers were identified using the ICD-9-CM opioid abuse diagnosis codes discussed previously. Potential comparison patients were identified as those who did not submit any medical claims with opioid abuse diagnosis codes during the first quarter of 2009 through the fourth quarter of 2012. All patients were aged 12 to 64 years and continuously eligible with non-HMO coverage during the 18-month study period to ensure that all relevant prescription drug and medical claims were captured for the final sample. The 18-month study period consisted of a 12-month observation period centered on the index date, and a 6-month baseline period preceding the observation period that was used for propensity score matching. The 12-month observation period included the 6-month period before the index date because medical costs for abuse may be incurred before a formal diagnosis of abuse is made. For abusers, the index date was defined as the date of the first abuse diagnosis. For the comparison patients, the index date was assigned as the date of a random medical claim. To account for baseline differences in demographics, comorbidities, and health care resource use, abusers were matched 1:1 to comparison patients based on index year, baseline health care costs, and propensity score (estimated using a logistic regression model for all study patients based on sex, age, US Census region, selected baseline comorbidities likely to affect health care costs, and baseline health care resource use). Health care use and costs in the 12-month observation period were compared between abusers and matched comparison patients to determine the excess annual per patient health care use and costs of diagnosed abuse. The cost and health care use measures were total health care (ie, medical and prescription drug) costs, total medical costs, medical costs by places of service (ie, inpatient, emergency department [ED], outpatient), total prescription drug costs, medical resource use by places of service, and prescription drug use. Costs were calculated as the total payments from insurers to providers and were inflated to 2012 US dollars using the medical care component of the Consumer Price Index. The replication analysis mirrored the methodology used in the original analysis, with a few exceptions. The original analysis covered a longer period (Q1 2006–Q1 2012) than the replication analysis (Q1 2009–Q4 2012) because of data availability. The categorization of geographic

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Diagnosed Opioid Abuse Among Commercially Insured Patients

locations differed between the original analysis (9 US Census divisions) and the replication analysis (4 US Census regions). In the original analysis, abusers and comparison patients were matched 1:1 based on whether the patients had work-loss data; because work-loss data were not available for the replication analysis, the criterion implemented in the original analysis to match patients based on availability of these data was not applicable. Finally, the original analysis included more categories of places of service (inpatient, ED, outpatient, rehabilitation facility, other) than the replication analysis (inpatient, ED, outpatient), which precluded cost comparisons by place of service. These differences between the 2 analyses were not expected to have a substantive impact on the results. Details regarding the original analysis, including details of the propensity score matching algorithm used to account for observable differences between abusers and comparison patients, is available. 19 All analyses were performed in SAS version 9.3 (SAS Institute Inc., Cary, NC).

Discussion

In the replication analysis, the prevalence of diagnosed opioid abuse increased steadily from 15.8 diagnosed opioid abusers per 10 000 in 2009, to 26.6 diagnosed opioid abusers per 10 000 in 2012 (Figure 1). While the prevalence of diagnosed opioid abuse in the replication analysis was higher than reported in the original analysis, the trend of increasing prevalence over time was consistent across analyses. In the original analysis, the prevalence of diagnosed abuse increased from 11.8 diagnosed opioid abusers per 10 000 in 2009, to 18.6 diagnosed opioid abusers per 10 000 in 2011 (the last full year of claims data in the original analysis).19 The higher prevalence of diagnosed abuse in the replication analysis likely reflects the different patient compositions between the Truven and Optum samples. While both databases cover a diverse set of commercially insured beneficiaries from a wide range of commercial health plans across the United States, differences in underlying comorbidities likely to influence the prevalence of opioid abuse are present. Specifically, the rates of non-opioid substance abuse diagnoses (11.3% vs 5.5%) and other mental disorders (26.3% vs 21.8%) during the baseline period among abusers were higher in the Truven sample compared with Optum sample, respectively. Prior research has determined that having $ 1 diagnosis of nonopioid drug abuse or mental illness is a key characteristic of patients at increased risk for opioid abuse.7 For the estimation of the excess costs of diagnosed opioid abuse in the replication analysis, 38 876 abusers and 903 415 comparison patients met the inclusion criteria (Table 1). Of

Figure 1.  Prevalence of diagnosed opioid abuse over time in the replication analysis.

those, 35 857 (92.2%) abusers were matched with comparison patients. Because the Truven database was much larger than the Optum database, the analytic sample of diagnosed abusers and matched comparison patients was larger in the replication analysis (35 857 matched pairs) than in the original analysis (7658 matched pairs). Before matching, abusers had significantly higher rates of baseline comorbidities (eg, congestive heart failure, renal disease, psychotic disorders, and other mental disorders), than comparison patients (Table 2). Among the matched sample, baseline characteristics were well-balanced. During the 12-month observation period, abusers had significantly more days in the ED (1.7 vs 0.6), days hospitalized (5.5 vs 0.9), and outpatient visits (24.1 vs 16.5) compared with matched comparison patients (all P , 0.001; Table 3). As a result of their greater health care resource use, abusers had $11 376 higher annual per patient health care costs than matched Table 1.  Replication Analysis Summary of Sample Selection Criteria Number of Patients Patients with $ 1 medical claim in Q1 2009–Q4 2012 Abusers $ 1 diagnosis for opioid abuse/dependencea Continuous (non-HMO) coverage during the 6-mo   baseline and 12-mo observation periods Aged 12–64 years during the 6-mo baseline   and 12-mo observation periods Comparison patients No diagnoses for opioid abuse/dependencea   (since Q1 2009) 50% random sample Continuous (non-HMO) coverage during the 6-mo   baseline and 12-mo observation periods Aged 12–64 years during the 6-mo baseline and   12-mo observation periods

8 424 331 280 709 41 896 38 876

8 143 622 4 071 811 1 225 458 903 415

Opioid abuse/dependence was identified using the following ICD-9-CM diagnosis codes: 304.0x, 304.7x, 305.5x, 965.00, 965.02, 965.09. a

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Table 2.  Replication Analysis Demographics, Comorbidities, and Health Care Resource Use and Costsa During the Baseline Period Characteristics

Age on index date, mean (SD)

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Male US Census region Northeast Midwest South West Unknown CCI, mean (SD) Patient Conditions Chronic pulmonary disease Mild to moderate diabetes Rheumatologic disease Congestive heart failure Any malignancy including   leukemia and lymphoma Cerebrovascular disease Diabetes with chronic  complication Peripheral vascular disease Mild liver disease Renal disease Peptic ulcer disease Myocardial infarction Hemiplegia or paraplegia Metastatic solid tumor HIV/AIDS Moderate or severe liver disease Dementia Other selected comorbidities Non-opioid substance abuse  diagnoses Psychotic disorders Other mental disorders Health care resource use Inpatient days, mean (SD) ED days, mean (SD) Outpatient visits, mean (SD) Number of prescriptions filled,   mean (SD) Number of unique NDCs filled,   mean (SD) Total health care costs,a   mean (SD)

Unmatched Sample

Matched Sample

Abusers (n = 38 876)

Comparison Patients (n = 903 415)

P Value

Abusers (n = 35 857)

Comparison Patients (n = 35 857)

P Value

37.4 (14.4) 54.6%

40.8 (15.0) 44.6%

, 0.001

37.1 (14.5) 55.5%

37.4 (15.6) 56.1%

, 0.001

18.5% 24.2% 37.1% 19.6% 0.6% 0.34 (0.9)

15.1% 27.9% 40.2% 15.8% 1.0% 0.20 (0.7)

, 0.001 , 0.001 , 0.001 , 0.001 , 0.001 , 0.001

18.7% 24.3% 37.3% 19.2% 0.6% 0.29 (0.8)

18.8% 24.5% 37.4% 18.6% 0.7% 0.31 (0.8)

0.589 0.462 0.820 0.049 0.082

7.5% 5.0% 2.2% 1.6% 1.7%

3.4% 4.9% 0.9% 0.7% 1.6%

, 0.001 0.812

6.8% 5.4% 1.9% 1.2% 1.6%

0.272

, 0.001 , 0.001 0.178

6.6% 4.7% 1.9% 1.2% 1.5%

1.6% 1.5%

0.7% 0.9%

, 0.001 , 0.001

1.3% 1.2%

1.3% 1.5%

0.465

1.2% 1.0% 0.9% 0.7% 0.5% 0.4% 0.3% 0.3% 0.2% 0.1%

0.5% 0.4% 0.5% 0.2% 0.2% 0.1% 0.2% 0.1% 0.0% 0.0%

, 0.001 , 0.001 , 0.001 , 0.001 , 0.001 , 0.001 , 0.001 , 0.001 , 0.001 , 0.001

1.0% 0.8% 0.7% 0.5% 0.3% 0.3% 0.2% 0.3% 0.1% 0.1%

1.1% 0.8% 0.8% 0.5% 0.4% 0.2% 0.2% 0.3% 0.1% 0.0%

0.211 0.590 0.138 0.483 0.655 0.491 0.476 0.614 0.592 0.732

14.0%

1.6%

, 0.001

11.3%

10.7%

0.002

16.8% 29.3%

2.8% 8.2%

, 0.001 , 0.001

14.2% 26.3%

13.8% 27.1%

0.087 0.004

1.0 (5.0) 0.6 (1.8) 8.9 (10.5) 17.8 (18.8) 8.0 (7.8) $8088 ($27 089)

0.2 (1.7) 0.1 (0.5) 4.2 (6.4) 6.3 (9.1) 3.2 (4.0) $2344 ($11 161)

, 0.001

0.4 (2.4) 0.4 (1.1) 7.7 (9.0) 15.4 (16.1) 7.0 (6.6) $4626 ($9299)

0.4 (2.3) 0.4 (1.0) 7.9 (9.5) 15.2 (17.4) 6.9 (6.8) $4626 ($9299)

0.003

, 0.001

, 0.001 , 0.001 , 0.001 , 0.001 , 0.001

0.047

, 0.001

, 0.001 0.912 0.311 0.202

, 0.001

, 0.001 0.230 , 0.001 , 0.001 0.860

Costs are reported in 2012 US dollars. Abbreviations: AIDS, acquired immunodeficiency syndrome; CCI, Charlson Comorbidity Index; ED, emergency department; HIV, human immunodeficiency virus; NDC, National Drug Code. a

56

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Diagnosed Opioid Abuse Among Commercially Insured Patients

Table 3.  Health Care Resource Use and Costsa Among Matched Abusers and Comparison Patients During the Replication Analysis Observation Periodb

Health care resource use, mean (SD) Medical resource use   Inpatient days   ED days

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  Outpatient visits Prescription drug use  Number of prescriptions filled  Number of unique NDCs filled Health care costs, mean (SD) Total health care costs Medical costs Inpatient costs ED costs Outpatient costs Prescription drug costs

Abusers (n = 35 857)

Comparison Patients (n = 35 857)

Excess Resource Use/Costs

5.5 (11.7) 1.7 (3.2) 24.1 (20.8)

0.9 (4.6) 0.6 (1.6) 16.5 (18.7)

4.7

36.0 (32.6) 14.2 (11.6)

29.2 (33.8) 10.1 (9.7)

6.7

$22 301 ($48 876) $19 282 ($47 886) $10 011 ($41 249) $1885 ($5047) $7386 ($14 882) $3019 ($5762)

$10 925 ($31 363) $8431 ($29 880) $3212 ($23 465) $616 ($2444) $4603 ($12 582) $2494 ($6123)

$11 376

1.1 7.7

4.1

abusers also had a significantly higher health care resource use, leading to $10 627 in excess annual per patient health care costs (Table 4). Thus, the replication analysis found that the annual excess per patient health care costs of diagnosed opioid abuse were 7.0% higher than those reported in the original analysis. In both the replication and original analyses, abusers had higher costs than matched comparison patients in terms of prescription drug and medical costs. Excess medical costs accounted for 95.4% in the replication analysis compared with 92.7% in the original analysis. Excess prescription drug costs accounted for 4.6% in the replication analysis compared with 7.3% in the original analysis. As previously noted, differences in the way the databases categorized places of service precluded a meaningful comparison of medical costs by place of service.

Summary $10 851 $6798 $1269 $2784 $525

Costs are reported in 2012 US dollars. All P values , 0.001. Abbreviations: ED, emergency department; NDC, National Drug Code. a

b

comparison patients ($22 301 vs $10 925; P , 0.001) in the 12-month observation period. These results were consistent with the original analysis, in which abusers also had higher health care resource use. In the original analysis, relative to comparison patients,

Both the original analysis and the replication analysis found an upward trend in the prevalence of diagnosed opioid abuse over time. In addition, this analysis also found substantial excess annual per patient health care costs of diagnosed opioid abuse in a commercially insured population, which are similar in magnitude to the results reported in previous research.19,28 The fact that both the prevalence and excess health care costs are similar in direction and magnitude between the 2 analyses suggests that, at least in the case of opioid abuse, the results of claims data analyses of commercially insured populations are reliable, replicable, and generalizable to other populations of commercially insured individuals.

Conflict of Interest Statement

The research for this article was carried out by Analysis Group, Inc., which received funding for this research from Purdue Pharma L.P.

Table 4.  Per Patient Excess Health Care Costsa During the 12-Month Observation Periodb Original Analysis (Optum Database)

Total health care costs, mean Medical costs Prescription drug costs

Replication Analysis (Truven Database)

Abusers (n = 7658)

Comparison Patients (n = 7658)

Excess Costs

Abusers (n = 35 857)

Comparison Patients (n = 35 857)

Excess Costs

$20 343 $17 518 $2826

$9716 $7671 $2045

$10 627 $9847 $781

$22 301 $19 282 $3019

$10 925 $8431 $2494

$11 376 $10 851 $525

Costs are reported in 2012 US dollars. All P values , 0.001.

a

b

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Rice et al

J. Bradford Rice, PhD, Noam Y. Kirson, PhD, Amie Shei, PhD, Caroline J. Enloe, BS, Alice Kate G. Cummings, BA, and Howard G. Birnbaum, PhD, are employees of Analysis Group, Inc. Pamela Holly, JD, Rami Ben-Joseph, PhD, are employees of Purdue Pharma L.P.

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The economic burden of diagnosed opioid abuse among commercially insured individuals.

The abuse of prescription opioids imposes a substantial public health and economic burden. Recent research using administrative claims data has substa...
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