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The American Journal of Managed Care March 2015
Evaluation of Care Management Intensity and Bariatric Surgical Weight Loss
Sarit Polsky, MD, MPH; William T. Donahoo, MD; Ella E. Lyons, MS; Kristine L. Funk, MS, RD; Thomas E. Elliott, MD; Rebecca Williams, DrPh, MPH; David Arterburn, MD, MPH; Jennifer D. Portz, PhD, MSW; and Elizabeth Bayliss, MD, MSPH
Potential Savings From Increasing Adherence to Inhaled Corticosteroid Therapy in Medicaid-Enrolled Children
George Rust, MD, MPH, FAAFP, FACPM; Shun Zhang, MD, MPH; Luceta McRoy, PhD; and Maria Pisu, PhD
Innovation in Plain Sight
Karen Ignagni, MBA, President and Chief Executive Officer, America's Health Insurance Plans
Early Changes in VA Medical Home Components and Utilization
Jean Yoon, PhD, MHS; Chuan-Fen Liu, PhD, MPH; Jeanie Lo, MPH; Gordon Schectman, MD; Richard Stark, MD; Lisa V. Rubenstein, MD, MSPH; and Elizabeth M. Yano, PhD, MSPH
Are Healthcare Quality "Report Cards" Reaching Consumers? Awareness in the Chronically Ill Population
Dennis P. Scanlon, PhD; Yunfeng Shi, PhD; Neeraj Bhandari, MD; and Jon B. Christianson, PhD
Developing a Composite Weighted Quality Metric to Reflect the Total Benefit Conferred by a Health Plan
Glen B. Taksler, PhD; and R. Scott Braithwaite, MD, MSc, FACP
Insurance Impact on Nonurgent and Primary Care-Sensitive Emergency Department Use
Weiwei Chen, PhD; Teresa M. Waters, PhD; and Cyril F. Chang, PhD
Cost Differential by Site of Service for Cancer Patients Receiving Chemotherapy
Jad Hayes, MS, ASA, MAAA; J. Russell Hoverman, MD, PhD; Matthew E. Brow, BA; Dana C. Dilbeck, BA; Diana K. Verrilli, MS; Jody Garey, PharmD; Janet L. Espirito, PharmD; Jorge Cardona, BS; and Roy Beveridge, MD
The Combined Effect of the Electronic Health Record and Hospitalist Care on Length of Stay
Jinhyung Lee, PhD; Yong-Fang Kuo, PhD; Yu-Li Lin, MS; and James S. Goodwin, MD
Strategy for a Transparent, Accessible, and Sustainable National Claims Database
Robin Gelburd, JD, BA
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Treatment Patterns, Healthcare Utilization, and Costs of Chronic Opioid Treatment for Non-Cancer Pain in the United States
David M. Kern, MS; Siting Zhou, PhD; Soheil Chavoshi, MS; Ozgur Tunceli, PhD; Mark Sostek, MD; Joseph Singer, MD; and Robert J. LoCasale, PhD
Long-Term Outcomes of Analogue Insulin Compared With NPH for Patients With Type 2 Diabetes Mellitus
Julia C. Prentice, PhD; Paul R. Conlin, MD; Walid F. Gellad, MD, MPH; David Edelman, MD; Todd A. Lee, PharmD, PhD; and Steven D. Pizer, PhD
Factors Affecting Medication Adherence Trajectories for Patients With Heart Failure
Deborah Taira Juarez, ScD; Andrew E. Williams, PhD; Chuhe Chen, PhD; Yihe Goh Daida, MS; Sara K. Tanaka, MPH; Connie Mah Trinacty, PhD; and Thomas M. Vogt, MD, MPH

Treatment Patterns, Healthcare Utilization, and Costs of Chronic Opioid Treatment for Non-Cancer Pain in the United States

David M. Kern, MS; Siting Zhou, PhD; Soheil Chavoshi, MS; Ozgur Tunceli, PhD; Mark Sostek, MD; Joseph Singer, MD; and Robert J. LoCasale, PhD
Healthcare utilization and costs increased in the 6 months after patients started opioid therapy for chronic pain; they then decreased but never reverted to baseline levels.

To evaluate treatment patterns, healthcare resource utilization, and costs among patients within a large managed care population chronically using opioids for non-cancer pain.

Study Design: Retrospective cohort study.

Methods: Patients aged ≥18 years with ≥1 prescription initiat-ing opioids between January 1, 2007, and December 31, 2011, who also had 12 months of continuous pre-index health plan enrollment, were identified. Patients with pre-index opioid use or cancer diagnosis were excluded. Opioid exposure was stratified by treatment duration—short-term (30-182 days) versus chronic (≥183 days)—and by index opioid type (weak vs strong).

Results: A total of 2.9 million patients initiating opioids were identified, of which 257,602 had at least 30 days of continuous use and were included in the study. The mean age was 51 years and 52% were female. Overall, 239,998 (93%) patients had short-term opioid use, and 17,604 (7%) had chronic use; 215,424 (84%) initiated treatment with a weak opioid, and 44,712 (17%) with a strong opioid. The specialty most associated with the use of less potent opioids was general/family practice (28%), and for more potent opioids it was surgery (22%). Large increases in health-care utilization were reported between the pre-index and first 6-month post initiation periods for chronic users. Utilization rates decreased after the first 6 months but never reverted to baseline levels. Costs mirrored utilization trends, more than doubling between baseline and the first 6 months of treatment for phar-macy ($2029 vs $4331) and all-cause medical ($11,430 vs $27,365). Costs declined after the first 6 months of opioid use but remained above pre-index levels.

Conclusions: These results demonstrated that healthcare resource utilization and costs increased during the first 6 months following clinical scenarios that necessitated opioid initiation and subse-quently declined, suggesting the need to monitor patients beyond the acute care period.

Am J Manag Care. 2015;21(3):e222-e234

  • The high rates of morbidity, healthcare utilization, and costs among patients chronically using opioids varied with treatment duration, type of index opioid, and length of persistent exposure.
  • Costs and healthcare utilization remained at increased levels beyond the acute treatment phase compared with the pre-index level, suggesting the need to moni-tor patients’ opioid treatment and the targeted condition for longer periods follow-ing the initiation of opioids.
  • These findings have important clinical and cost implications for stakeholders— namely patients, providers, and payers—for clinical conditions associated with pro-longed use of opioid therapy.

The Institute of Medicine estimates that 100 million Americans are affected by chronic pain, with annual direct and indirect economic burdens, accompanied by high social costs, exceeding $600 billion.1 In March 2013, the American Academy of Pain Medicine acknowledged the absence of consensus on the treatment of chronic non-cancer pain, and suggested that opioid therapy is appropriate when conservative approaches are ineffective, as well as when treatment plans are designed to sidestep diversion, abuse and addiction, and serious side effects.2 Currently, opioids are prescribed for approximate-ly 90% of patients with chronic pain in the United States3; about 90% of patients presenting at pain management cen-ters already receive an opioid.4 Such high and escalating prescription rates are aimed at improving the management of nonmalignant pain, and an evaluation of the manage-ment of chronic and acute non-cancer pain in ambulatory and office-based settings in the United States showed that opioid prescribing almost doubled (from 11.3% to 19.6% of all pain-related visits) between 2000 and 2010.5

Prior studies have produced varied results regarding the effect of opioid therapy use for the treatment of non-can-cer pain. Most studies and clinical trials with opioids have yielded unfavorable results in the short term (eg, 12 weeks), demonstrated greater healthcare resource utilization (HRU), and indicated considerable risk-benefit concerns.6-12 Howev-er, there has been considerably less research regarding long-term outcomes of opioid use on HRU.

Continued opioid treatments have common side effects such as sedation, dizziness, nausea, vomiting, and respiratory distress,3 with the most prevalent and worst tolerated being constipation.3,13 These side effects interfere with treatment adherence, work productivity, and health-related quality of life, and are associated with escalated HRU and costs.3,13,14 Medication abuse15 and inadequate adherence and dosing limits3 stemming from side effects can un-dermine the analgesic goals of pain control, and also result in greater use of emergency department (ED) and outpatient services, along with substantial cost increases.13,16

A handful of studies have reported on the overall trends and characteristics of patients initiating opioid therapy, including long-term use14,17; dosing patterns and length of expo-sure18; the comparison of chronic, acute, and non-opioid users19; and prescribing trends based on differing pain types,20 among other areas. Over-all, there has been little systematic coordination of these disparate aspects in the literature; it is still not known how healthcare costs change over the duration of opioid therapy for pain management. The few studies that ex-amined opioid usage patterns and HRU focused on gas-trointestinal-related complications13; as of now, there has been no longitudinal analysis based on more than 1 year of continuous opioid therapy focused on enrollees in a national database of commercially insured patients.

The objective of this study was to describe patient char-acteristics, treatment patterns, HRU, and costs among patients newly initiating opioid therapy. Subgroups were examined separately: those patients with at least 6 months of opioid exposure (chronic users) and those with 30 to 182 days of opioid use (intermediate-term users), combined with indexing on weak or strong opioids. 


Data Source and Study Design

This retrospective cohort study, which utilized an in-ception cohort (new users) design,21 queried the Health-Core Integrated Research Environment (HIRE) to identify patients with ≥1 prescription (first fill defined as index date) for opioids during the period January 1, 2007, to December 31, 2011. A repository of more than 40 mil-lion researchable lives, the HIRE contains medical, phar-macy, and other administrative claims data originating in 14 geographically dispersed commercial health plans, yielding coverage across the continental United States. This nonexperimental study, which did not require inves-tigational review board review, complied with all appli-cable provisions of the Health Insurance Portability and Accountability Act. Patient confidentiality was preserved throughout and data remained anonymous; researchers only had access to relevant data sets, from which indi-vidual patient identifiers were purged.

Inclusion/Exclusion Criteria

To be included in the study, patients were required to have ≥1 prescription fill for an opioid during the patient identification period, and ≥30 days of continuous opioid use starting at index, defined as the first prescription fill for an opioid medication. In addition, patients were re-quired to be 18 years or older on the index date and have at least 12 months of pre-index health plan enrollment. Patients with any prescription fill for opioids during the 12-month pre-index period were excluded from the study; also excluded were patients with a diagnosis of cancer during the 12-month pre-index period. A cancer diag-nosis (International Classification of Diseases, Ninth Re-vision, Clinical Modification [ICD-9-CM] diagnosis codes 140.xx-209.3x, 230.xx-234.xx) was based on at least 2 claims on distinctly different dates for the same type of cancer (identified with 3-digit ICD-9-CM diagnosis codes) occurring within 60 days of each other.

Study Measures

Chronic opioid use and patient follow-up. Opioid us-ers were separated into subgroups based on their duration of continuous opioid treatment (30-182 days, considered intermediate-term use; and 183+ days, considered chronic use) and analyzed separately. Opioid treatment was consid-ered continuous if an opioid prescription was filled within 30 days after the prescription date plus the days supply of the previous prescription fill. The duration of therapy was calculated as the number of days from opioid initiation to the date of the last fill considered to be continuous use, plus the number of days supply of that fill. All patients were required to have at least 30 days of opioid use as a means to exclude acute opioid use, while 6 months was used to separate chronic opioid use from that of interme-diate-term ailments such as surgery and fractures, and to provide sufficient time for measuring healthcare utilization and costs. The 30-day and 6-month thresholds have also been used in prior studies of opioid use.13,18

To investigate how healthcare utilization and costs changed for chronic opioid users during an extended peri-od of continuous use (in excess of 6 months) after the initia-tion of opioid therapy, the subgroup of chronic use patients were analyzed in greater detail before and after the 183rd day of opioid treatment. The rationale for this extended analysis is based on the hypothesis that considerable costs could accrue shortly after the initiation of opioids to ad-dress the condition that warranted the opioid use, but may decrease over time. All comorbid conditions were identi-fied via ICD-9-CM diagnosis codes, while pharmacy and medical records were used to determine medication use.

The observation period of all patients included the 12 months prior to opioid initiation through the last day of continuous opioid therapy.

Comorbidities and prior medication use. Use of medi-cation other than opioids was identified during the 12 months prior to opioid initiation. The proportion of pa-tients with at least 1 prescription fill for each of the classes of interest was captured. Comorbid conditions were also identified in the 12 months prior to starting opioid ther-apy, and were based on the presence of at least 1 medical claim including an ICD-9-CM diagnosis code of the con-dition of interest.

HRU and costs. Evaluations of HRU included office, outpatient, ED, and inpatient encounters, as well as the length of stay of inpatient hospitalizations. Costs were calculated using the common health economics measure, per patient per year,22,23 which reflected the total accumu-lated costs divided by number of patient-years of obser-vation. Included cost categories were total healthcare, including inpatient, outpatient, office visit, ED, and phar-macy. Costs were reported by plan paid, patient paid, and total (plan paid + patient paid).

Proportion of days covered (PDC). PDC was used to measure opioid use of medications, and was calculated as the ratio of the number of days covered by any opioid prescription filled during the post index period divided by the number of days from index date to end of follow-up. Days covered included the date an opioid prescription was filled plus the days supply on that prescription minus 1. If 1 day was covered by multiple opioid medications it was only counted once. PDC could range from >0 to 1.0 for patients not taking any medication, and to 1 for pa-tients who had all post index days covered. Only patients with at least 1 post index opioid fill were included in this calculation.

Opioid strength. The mechanism of action of the opi-oid class of compounds relies on the ability to bind to the opioid receptors in the brain, spinal column, and sur-rounding tissues. The strength of opioids, strong or weak, is based on their affinity for binding with the μ, κ, and δ receptors.9 Opioid users were analyzed according to the strength of the index opioid: weak (codeine, dihydroco-deine, hydrocodone, propoxyphene, tapentadol, trama-dol) versus strong or potent (fentanyl, hydromorphone, levorphanol tartrate, meperidine, methadone, morphine, oxycodone, oxymorphone).

Statistical Analysis

Analyses were conducted on new opioid users based on the first observed prescription fill of opioid medication and were stratified based on duration of use (intermediate-term and chronic use). Additionally, chronic opioid users were examined in more detail. Descriptive statistics, in-cluding proportions and means, are reported throughout. Unadjusted bivariate statistical tests (χ2 and t tests) were used to compare patient demographics and opioid use between those initiating strong versus weak opioids and intermediate-term users versus chronic users.

Costs per patient-year, allowing for unequal follow-up times, were reported for chronic users across 3 time points: during the 12-month pre-index period, the first 6 months of therapy, and the period after the first 6 months of therapy. To obtain generalized estimating equations to compare costs across each time point, we used repeated measures gamma regression analyses (to account for with-in-patient correlation and the skewed nature of cost data), weighted for follow-up times.

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