Inefficiencies in Osteoarthritis and Chronic Low Back Pain Management | Page 2
Published Online: October 23, 2013
Margaret K. Pasquale, PhD; Robert Dufour, PhD; Ashish V. Joshi, PhD; Andrew T. Reiners, MD; David Schaaf, MD; Jack Mardekian, PhD; George A. Andrews, MD, MBA, CPE; Nick C. Patel, PharmD, PhD, BCPP; and James Harnett, PharmD, MS
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.
1. 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.