This study identified inefficiencies in drug and medical service utilization related to pain management among Medicare members with osteoarthritis and chronic low back pain.
Objective: To identify inefficiencies in drug and medical service utilization related to pain management in patients with osteoarthritis and chronic low back pain.
Study Design: This retrospective cohort study applied revised measures of pain management inefficiencies to Humana Medicare members with osteoarthritis and/or chronic low back pain.
Methods: Subjects had either 2 or more claims for osteoarthritis on different days or 2 or more claims for low back pain 90 or more days apart, from January 1, 2008, to June 30, 2010, with the first occurrence assigned the index date. Inefficiencies were identified for 365 days postindex.Pain-related healthcare costs postindex were compared between members with and without inefficiencies. A generalized linear model calculated adjusted costs per member controlling for age, sex, and comorbidities.
Results: Most members diagnosed with osteoarthritis, chronic low back pain, or both (N = 68,453) had at least 1 inefficiency measure (n = 37,863) during the postindex period. High per member costs were for repeated surgical procedures ($26,451) and inpatient admissions ($19,372) compared with members without inefficiencies ($781; P <.0001). High total costs (prevalence times per member cost) were for repeated diagnostic testing and excessive office visits. Members with an inefficiency had adjusted pain-related costs 5.42 times higher than those of members without an inefficiency (P <.0001).
Conclusions: Pain management inefficiencies are common and costly among Humana Medicare members with osteoarthritis and/or chronic low back pain. Further work by providers and payers is needed to determine benefits of member identification and early intervention for these inefficiencies.
Am J Manag Care. 2013;19(10):816-823This study demonstrates that most Medicare members with osteoarthritis or chronic low back pain experience inefficient pain management. High-cost inefficiencies need to be further explored to determine benefits of identifying members experiencing pain and providing early intervention.
Two forms of chronic pain, osteoarthritis (OA) and chronic low back pain (CLBP), have exceptionally high prevalence and associated healthcare costs in the United States. An estimated 13.9% of adults 25 years and older and 33.6% of adults 65 years and older are affected by OA1; and roughly a quarter of all adults in the United States suffer from CLBP during their lifetime.2 Associated healthcare costs for these diseases have been estimated at $48 billion for OA and $40 billion for back problems in 2005; collectively, musculoskeletal conditions ranked as the third-highest spending category among medical conditions in the United States.3 Given the high prevalence of and healthcare spending on musculoskeletal conditions, it is crucial for providers and payers to provide appropriate and adequate pain management for these conditions. Exposure of potentially inefficient provider practices or patient behavior can aid providers and payers in providing appropriate care and reducing costs.
Sources of inefficiencies in pain management include underdiagnosis or inappropriate diagnosis, use of unnecessary procedures and tests,4-6 and improper use of medications.7 Although examples of such inefficiencies are numerous, very few studies have indicated how to identify patients experiencing suboptimal pain management and to quantify their associated healthcare costs. Goldberg and colleagues8 published measures of inefficiencies developed by an expert clinical panel (Patient Population Assessment to Identify Need [PAIN]) concerning pain management in patients with OA and/or low back pain (LBP). These indicators were then overlaid on a managed care database to identify members who were likely in need of better pain management.8 Additional analysis by Goldberg and colleagues9 demonstrated that total per member per month pain-related costs of members identified with any PAIN indicator of inefficiency ($843) were significantly higher than those for members not identified with an indicator of inefficiency ($121).
The objective of the current study was to use Goldberg and colleagues’ PAIN indicators as a guide to develop inefficiency measures applicable to a specific health insurance provider for Medicare members with OA and/or CLBP. With the information provided in this study, providers and payers will be able to focus on identified members to determine whether their painful conditions are being managed adequately and efficiently.
This study utilized data from Humana’s SAS database, containing enrollment, medical, and pharmacy claims data for Humana’s Medicare membership. All data sources were merged using de-identified member identification. The finalized protocol was approved by an independent institutional review board.
This was a retrospective cohort study using a claims database to evaluate OA and CLBP patients identified as having suboptimal management of pain based on inefficiency measures of prescription drug and medical service use (Table 1). Internal experts from Humana’s clinical and drug utilization review programs were consulted to review the Goldberg PAIN indicators8 and revise them based on clinical judgment. Patients with OA and/or CLBP were identified from January 1, 2008, to June 30, 2010. Each member’s date of first OA or LBP diagnosis was considered to be the index date, and members were required to have 365 days of continuous enrollment preindex and 365 days postindex.
Members 18 years and older in Humana’s Medicare Advantage plans were included if they were identified with 2 or more claims for OA on different days and/or 2 or more claims for LBP in the primary diagnosis position that were 90 or more days apart to ensure the “chronic” nature of pain. The following International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code was used to identify OA: 715.xx. ICD-9-CM codes for LBP were 721.3, 721.42, 721.5-721.9x, 722.1x-722.2, 722.30, 722.32, 722.52, 722.6, 722.70, 722.73, 722.80, 722.83, 722.90, 722.93, 724.00, 724.02, 724.09, 724.2-724.6, 724.8, 724.9, 737.10-737.19, 737.2x, 737.3, 737.30, 738.4, 738.5, 793.3, 793.4, 756.10-756.19, 805.4, 805.6, 805.8, 846.x, 847.2, 847.3, 847.9, 739.3, 739.4, and 996.4.
Members were excluded if they had pregnancy (ICD-9- CM 630.xx-679.xx, V22.xx, and V23.xx), cancer (ICD-9- CM 140.xx-172.xx and 174.xx-208.xx), organ transplant (ICD-9-CM V42.xx), rheumatoid arthritis (ICD-9-CM 714. xx), ankylosing spondylitis (ICD-9-CM 720.xx), human immunodeficiency virus infection (ICD-9-CM 042.xx), or sickle cell anemia (ICD-9-CM 282.6x) in the first or second diagnosis position. Members were also excluded if they resided in skilled nursing homes.
Claims data of Humana Medicare members with OA, CLBP, or both conditions (hereafter OA/CLBP) were analyzed to identify individuals who met criteria for any inefficiency measures in Table 1. The count and percentage of members identified with each inefficiency measure during the postindex period were determined. Demographic and clinical characteristics ascertained during the preindex period were compared between members with and without inefficiencies identified postindex. Variables included age, sex, geographic region, top 10 comorbidities by 4-digit ICD-9-CM codes, psychiatric comorbidities, and the RxRisk-V comorbidity score.The RxRisk-V score10-14 is derived from drug claims data and thus can be applied to data from a narrow window of claims rather than the broader window typically necessary for medical—claims-based comorbidity scores.15 Accordingly, means were compared using 2-sample t tests, and count variables were compared using x2 tests.
Pain-related costs were defined for all provider, facility, and pharmacy claims categories and summed over 365 days postindex for each of the 22 inefficiencies identified during the postindex period. For professional and facility claims, 100% of costs were counted if the OA or LBP diagnosis was documented in the primary position. For claims with OA or LBP in any secondary position, costs were apportioned based on number of listed diagnostic conditions that were consistent with the study by Goldberg and colleagues.8 For pharmacy claims, all postindex costs were summed for opioids, nonsteroidal anti-inflammatory drugs, antimigraine agents, antidepressants, antiepileptics/anticonvulsants, muscle relaxants, and steroids.
Once members with inefficiencies and associated costs were identified, the inefficiencies were rank-ordered from costliest to least costly. A member was allowed to have 1 or more inefficiencies; hence, mean costs of inefficiencies were not independent of each another. In order to determine whether the rankings were statistically significant, pairwise Wilcoxon signed rank tests were performed first within each inefficiency category and then for high-cost inefficiencies overall. The alpha value to achieve significance was adjusted via the Bonferroni method for the number of comparisons within each inefficiency category (15 comparisons for opioid use, 21 for miscellaneous drug use, and 28 for medical service use). In addition, members with and without inefficiencies were compared with respect to their postindex total pain-related healthcare costs. Generalized linear modeling of the log of the mean total 365-day postindex cost (adjusted to 2010 dollars) was performed on the following covariates: age, sex, geographic region, baseline RxRisk-V score, presence of an inefficiency, disease state (OA/CLBP and CLBP versus OA), presence of any of the top 10 medical comorbidities, and psychiatric comorbidity. The parameter estimates, Wald 95% confidence limits, exponentiated estimates, and Wald x2 test results were computed using the SAS PROC GENMOD procedure with log link function and gamma distribution (SAS Institute Inc, Cary, North Carolina). This approach to linear modeling is appropriate when modeling claim costs as both the gamma and claim costs distributions tend to approach a normal distribution.
Final sample sizes of 51,773 OA, 11,510 CLBP, and 5170 OA/CLBP members were available for analysis. We identified 46.9% of OA, 80.1% of CLBP, and 84.8% of OA/CLBP members as having at least 1 inefficiency measure postindex (Table 2). The mean number of inefficiencies per member with at least 1 inefficiency was highest for members with OA/ CLBP (2.65), followed by CLBP (2.23) and OA (1.73). Overall, 71% of the members with inefficiencies were identified as having an inefficiency from 1 category rather than multiple categories; only 4% of members with inefficiencies were identified with an inefficiency from all 3 categories.
Demographic and clinical characteristics of members with any of the inefficiencies versus members without inefficiencies are compared in Table 3. The mean age of members identified with inefficiencies was approximately 2 years less than the mean age of those without inefficiencies (P <.0001), and a lower percentage of these members lived in the South compared with members without inefficiencies (P <.0001). Members with inefficiencies had a significantly higher RxRisk-V score (P <.0001) and rates of psychiatric disorders than members without inefficiencies (in each case, P <.0001 except for alcohol abuse, for which there was no statistical difference between members). Among the top 10 comorbidities, rates of pain in joint (ICD-9-CM 719.4), as well as malaise and fatigue (ICD-9-CM 780.7) were higher among members with inefficiencies versus without (P <.0001).
The rank ordering by pain-related per member cost for the 7 highest cost inefficiency measures is reported in Table 4A. The highest cost inefficiency was repeated OA/LBP-related surgical procedures followed by OA/LBP-related inpatient admissions and excessive postsurgical opioid use. Per member costs for all 7 of the highest cost inefficiencies were significantly different than costs for members without inefficiencies (P <.0001 for all comparisons). Similar results were obtained when analyzing OA, CLBP, or OA/CLBP conditions separately (data not shown).
The rank ordering by total cost to the plan of inefficiency measures (Table 4B) resulted in a different set of high-cost measures driven by prevalence of inefficiencies in the population. Repeated diagnostic testing ($6215) and excessive OA/ LBP-related office visits ($4999) ranked in the top 2 positions, even though their per member costs were relatively low compared with the top 7 inefficiencies based on per member cost. Excessive postsurgical opioid use ($18,294) ranked high in terms of both per member cost and total plan cost.
Parameter estimates (including exponentiated for ease of interpretation) from generalized linear modeling for pain-related costs are reported in Table 5. The presence of any inefficiency was associated with adjusted costs that were more than 5-fold higher than costs associated with not having an inefficiency (P <.0001). Having OA/CLBP was associated with 16% higher pain-related costs than having OA (P <.0001), and CLBP was associated with 27% lower costs than OA (P <.0001). The presence of one of the top 10 comorbidities was associated with slightly lower costs (P <.0001), as was the presence of any one of the psychiatric comorbidities (P = .017).
Among 22 prespecified measures of inefficiencies deemed to have clinical significance in the context of pain management, this study has elucidated inefficiencies accompanied by high per member and total pain-related cost. Specifically, excessive postsurgical opioid use ranked high in both categories.While associated costs of this measure included the costs of surgery, and therefore inflated the dollar amount, taking opioids beyond 90 days postsurgery is concerning from both the provider and payer perspectives. Alam and colleagues16 reported that among elderly patients undergoing low-risk surgeries, 8% who received an opioid for acute pain postoperatively at 7 days were still receiving an opioid 1 year later. Although 90 days of postoperative opioid use for an OA-related or back surgery may be clinically appropriate, strategies to limit transition from postoperative analgesia to long-term opioid use are warranted. These may include models of care that enhance coordination from the hospital to the community, administrative regulations on postsurgical opioid use, and drug utilization reviews.16
Within the opioid use category, concomitant long-acting opioid use ranked high in terms of per member cost, and uncoordinated opioid use ranked high in terms of total plan cost. Published data from 2 health plans reported a doubling of long-term opioid use between 1997 and 2005, reaching 4% to 5% among all enrolled adult members.17 This rise in opioid use has been accompanied by statistics indicating that inappropriate prescription, misuse, and abuse of opioids are widespread.4 dentifying members with excessive or uncoordinated opioid use could signal improper provider or patient behavior, or indicate that such members’ pain has not been controlled adequately.
Whereas repeated OA/LBP-related surgical procedures and nonsurgical inpatient admissions ranked highest by per member costs, inefficiencies in the outpatient setting (repeated diagnostic testing and excessive office visits) were the top cost drivers by total plan costs. Similar to excessive postoperative opioid use, care coordination techniques could curtail the prevalence of these inefficiencies. For example, as integrated electronic medical records become commonplace, it may be possible to reduce the duplication of the same diagnostic tests performed by different providers.
As for the outpatient inefficiency measure related to excessive office visits, these may or may not signal the inefficient management of pain, which is a limitation of this inefficiency measure. That is, excessive office visits may in fact be a less expensive way of preventing more costly, inappropriate surgical procedures. In the case of CLBP, studies have questioned the benefit of surgical stabilization of the lumbar spine over intense rehabilitation for CLBP18,19 and generated evidence for the high failure rate of back surgeries.20 Given the high costs of treatment for patients with failed back surgery,21 investing up front to pinpoint the etiology of the pain is of utmost importance to identify appropriate candidates for surgery.22
In addition, the fact that repeated surgical procedures and inpatient admissions do not rank high from the point of view of total costs may be an indication that a thorough review of such members is already in place. However, close monitoring of CLBP patients with repeated office visits and/or diagnostic tests may help contribute to the evidence base for best practices, as well as to help eliminate those practices associated with poorer outcomes. As with office visits, other inefficiency measures may signal the severity of the pain condition or the existence of more than 1 pain type (eg, concurrent adjuvant therapies). Although it is not possible to draw immediate conclusions about members identified with any of these other inefficiency measures, investigation is warranted to determine whether providers’ and patients’ behaviors are justified.
One limitation of the study is the appropriateness of the comparator group. Although it may be possible to define a narrower comparator group for each of the drug-related inefficiencies, this would not be possible for medical service utilization measures such as emergency department visits. Future work focused on 1 specific inefficiency measure could further examine the appropriateness of the comparator group. An additional limitation of the study is that high-cost outliers, commonly found in administrative claims data, are known to skew the cost data. Rather than attempting to remove the outliers, our approach was to report the median and standard devian tions alongside the mean costs to provide the reader with the degree of uncertainty in the data. Other limitations include lack of certain data (lab results, patient behavior) and error in claims coding (misclassification bias). Additionally, data were generated from 1 health plan, which implies that results are not generalizable to the general US population. However, members reside in a broad array of geographic US regions.
This study is the first to examine the prevalence of inefficiencies in pain management among Humana Medicare members with OA and/or CLBP and to rank healthcare costs associated with these inefficiencies. Inefficiencies in pain management are common and are associated with higher healthcare expenditures. These findings call for further work by providers and payers to determine the benefits of member identification and early intervention for these inefficiencies.Author Affiliations: From Comprehensive Health Insights, Inc (MKP, RD, NCP), Louisville, KY; Pfizer Inc (AVJ, DS, JM, JH), New York, NY; Humana Inc (ATR, GAA), Indianapolis, IN.
Funding Source: This study was funded jointly by Pfizer Inc and Humana Inc.
Author Disclosures: Drs Pasquale, Dufour, and Patel report employment with Comprehensive Health Insights, Inc, a wholly owned subsidiary of Humana Inc, who were paid consultants to Pfizer in connection with the development of the manuscript. Drs Andrews and Reiners report employment with Humana Inc. Drs Schaaf, Mardekian, and Harnett report employment with Pfizer Inc, and have stock ownership in the company. Dr Joshi was a full-time employee of Pfizer Inc at the time of study and drafting of the manuscript, and also has stock ownership in Pfizer Inc.
Authorship Information: Concept and design (MKP, RD, AVJ, ATR, DS, JM, GAA, NCP, JH); acquisition of data (RD); analysis and interpretation of data (MKP, RD, AVJ, ATR, DS, JM, GAA, NCP); drafting of the manuscript (MKP, RD, NCP); critical revision of the manuscript for important intellectual content (MKP, RD, AVJ, ATR, DS, GAA, NCP, JH); statistical analysis (RD, JM); obtaining funding (AVJ, NCP, JH); administrative, technical, or logistic support (MKP, AVJ); and supervision (AVJ, GAA, NCP, JH).
Address correspondence to: Margaret K. Pasquale, PhD, Principal Researcher, Comprehensive Health Insights, Inc, 325 W Main St WFP6W, Louisville, KY 40202. E-mail: email@example.com. Lawrence RC, Felson DT, Helmick CG, et al; National Arthritis Data Workgroup. Estimates of the prevalence of arthritis and other rheumatic conditions in the United States Part II. Arthritis Rheum. 2008;58(1); 26-35.
2. Balagué F, Mannion AF, Pellise F, Cedraschi C. Non-specific low back pain. Lancet. 2012;379(9814):482-491.
3. Roehrig C, Miller G, Lake C, Bryant J. National health spending by medical condition, 1996-2005. Health Aff (Millwood). 2009;28(2): W358-W367.
4. Deyo RA, Mirza SK, Turner JA, Martin BI. Overtreating chronic back pain: time to back off? J Am Board Fam Med. 2009;22(1):62-68.
5. Rao JK, Kroenke K, Mihaliak KA, Eckert GJ, Weinberger M. Can guidelines impact the ordering of magnetic resonance imaging studies by primary care providers for low back pain? Am J Manag Care. 2002; 8(1):27-35.
6. Swedlow A, Johnson G, Smithline N, Milstein A. Increased costs and rates of use in the California workers’ compensation system as a result of self-referral by physicians. N Engl J Med. 1992;327(21):1502-1506.
7. US Food and Drug Administration. FDA acts to reduce harm from opioid drugs. http://www.fda.gov/ForConsumers/ConsumerUpdates/ucm251830.htm. Published April 19, 2011. Accessed September 5, 2012.
8. Goldberg GA, Kim SS, Seifeldin R, Haberman M, Robinson D. Identifying suboptimal management of persistent pain from integrated claims data: a feasibility study. Manag Care. 2003;12(8)(suppl improving pain):8-13.
9. Goldberg GA, Kim SS, Seifeldin R, Haberman M, Robinson D. Health care costs associated with suboptimal management of persistent pain. Manag Care. 2003;12(8)(suppl improving pain):14-17.
10. Fishman PA, Goodman MJ, Hornbrook MC, Meenan RT, Bachman DJ, O’Keeffe Rosetti MC. Risk adjustment using automated ambulatory pharmacy data: the RxRisk model. Med Care. 2002;41(1):84-99.
11. Sloan KL, Sales AE, Liu CF, et al. Construction and characteristics of the RxRisk-V: a VA-adapted pharmacy-based case-mix instrument. Med Care. 2003;41(6):761-774.
12. Sales AE, Liu CF, Sloan KL, et al. Predicting costs of care using a pharmacy-based measure risk adjustment in a veteran population. Med Care. 2003;41(6):753-760.
13. Von Korff M, Wagner EH, Saunders K. A chronic disease score from automated pharmacy data. J Clin Epidemiol. 1992;45(2):197-203.
14. Farley JF, Harley CR, Devine JW. A comparison of comorbidity measurements to predict healthcare expenditures. Am J Manag Care. 2006;12(2):110-119.
15. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613-619.
16. Alam A, Gomes T, Zheng H, Mamdani MM, Juurlink DN, Bell CM. Long-term analgesic use after low risk surgery: a retrospective cohort study. Arch Intern Med. 2012;172(5):425-430.
17. Boudreau D, Von Korff M, Rutter CM, et al. Trends in long-term opioid therapy for chronic non-cancer pain. Pharmacoepidemiol Drug Saf. 2009;18(12):1166-1175.
18. Gibson J, Grant I, Waddell G. The Cochrane review of surgery for lumbar disc prolapse and degenerative lumbar spondylosis. Spine (Phila Pa 1976). 1999;24(17):1820-1832.
19. Fairbank J, Frost H, Wilson-MacDonald J, Yu LM, Barker K, Collins R; Spine Stabilization Trial Group. Randomised controlled trial to compare surgical stabilization of the lumbar spine with an intensive rehabilitation programme for patients with chronic low back pain: the MRC spine stabilisation trial [published correction appears in BMJ. 2005;330(7506):1485]. BMJ. 2005;330(7502):1233.
20. Nguyen TH, Randolph DC, Talmage J, Succop P, Travis R. Long-term outcomes of lumbar fusion among workers compensation subjects: a historical cohort study. Spine (Phila Pa 1976). 2011;36(4):320-321.
21. Hollingworth W, Turner JA, Welton NJ, Comstock BA, Deyo RA. Cost and cost-effectiveness of spinal cord stimulation (SCS) for failed back surgery syndrome: an observational study in a workers’ compensation population. Spine (Phila Pa 1976). 2011;36(24):2076-2083.
22. Rathmell JP. A 50-year-old man with chronic low back pain. JAMA. 2008;299(17):2066-2077.