The Focus on the Big Trends Starts with Cost

Unless you’ve been hiding under a rock, it would not be a surprise to hear that the top growth category in the pharma space is specialty pharmacy. The trends are very clear: On a per-patient basis, the spending on traditional products is declining, while spending is rising on the specialty side (Figure 1).

Finding a specialty pharmacy definition on which everyone can agree is challenging. The disease states contained within specialty pharmacy include cancer, rheumatoid arthritis, and multiple sclerosis. Knowing that specialty disease states are complicated, it is safe to assume that these complex drugs can be very expensive. CMS, in response to the growing number of Medicare Part D plans that were using specialty tiers, established a $670 minimum cost threshold that drugs must meet before plans can place them on a specialty tier. Although cost is an important factor to consider, cost alone could reduce the important service aspects behind successfully caring for a specialty pharmacy patient.

Once a product has been identified as being specialty, another question arises: What is the best practice setting or distribution strategy for that product? Those decisions are most often made by the manufacturer well in advance of the product’s approval. The growth in specialty products has closely paralleled the growth in specialty pharmacies; however, we as a profession, in the view of many, lag behind in clearly defining what is, or is not, a specialty pharmacy. Like all good markets, however, the business finds a way to create differentiation. In specialty, a key indicator is having obtained accreditation from 1 of the 4 leading accrediting bodies.

By the Numbers, Accredited Pharmacies and Drug Spend

Growth in accredited specialty pharmacies has been remarkable and pharmacies are chasing the specialty dollar by investing in accreditation (Figure 2).

Specialty pharmacies owned by pharmacy benefit managers and health plans fill more than 60% of the total specialty drug spend, principally because of their alignment with a patient’s prescription benefit coverage. Specialty is quickly approaching 50% of the spend from less than 3% of the total prescriptions filled, a concentrated mix (Figure 3). The more significant the spend on a particular specialty therapy, the greater the probability that the share of dispensing of that product will be disproportionately higher in payer-owned specialty pharmacies.

Follow the Patient and Their Disease State

In specialty, the common denominator is providing the best patient services possible to assure the best outcomes. It does not matter what “category” of specialty pharmacy one is in, because every operation is committed to being the best they can be.

While we continue to be blessed with existing and emerging products to treat many disease states, the reality, according to IMSQuintiles, is that the top 5 disease states constitute 75% of drug spend (Figure 4).

It stands to reason that if a pharmacy is going to be successful, it must incorporate some of these disease states. This is especially true in payer-owned specialty pharmacies, which, unlike the independents, likely offer a broad spectrum of disease programs.

Measuring Outcomes: Setting Specialty Up for Success

One mantra in specialty pharmacy is, “If you didn’t document it, you did not do it.” Well, that goes for measuring value, often in the form of quality metrics. Only in this way will specialty pharmacy be in a position to demonstrate its positive impact. Each therapeutic class or disease state presents itself with potential measures. Those measures can be based on multiple factors, including quality, improvement/cure, outcomes, adherence, cost, and potentially so many more. Often the measures can be subjective based on how the patient feels.

In most cases, the services provided by specialty go far beyond dispensing, and that value must be measured. While focused specialty pharmacies concentrate on key measures in their business, as a whole, standards have not been clearly established as an industry. The Pharmacy Quality Alliance (PQA) released its Specialty Core Measure Set to assess adherence and persistence for several important specialty therapies. These health-plan-level measures fill a gap in the market and will have a positive impact on appropriate medication use and patient outcomes.

The set of PQA-endorsed measures include:

1. Treatment of Chronic Hepatitis C: Completion of Therapy

2. Adherence to Antiretroviral Medications

3. Adherence to Non-Infused Disease-Modifying Agents Used to Treat Multiple Sclerosis

Right around the corner for PQA is Adherence to Non-Infused Biologic Medications Treating Rheumatoid Arthritis (autoimmune). The objective of the Specialty Core Measure Set used in its entirety, or individually, will enable key stakeholders such as specialty pharmacies, health plans, and pharmacy benefit managers to understand the impact of their quality initiatives to support the health and outcomes of specialty patient populations. Patient adherence to drug therapy is the top mission for all specialty pharmacies. By measuring adherence and persistence on 4 of the 5 top disease states, pharmacies can best assure optimal outcomes for patients. The plan is to leverage these core data sets and pair them with a payer’s service-level metrics, patient experience metrics, or other metrics, to holistically assess plan members’ medication use.

Specialty pharmacy has become a focal point of data convergence. In the case of specialty products, the interpretation and action taken by specialty pharmacy in the management of pharmaceutical care—using their professional expertise to reduce abandonment and improve adherence&mdash;is the point of differentiation.

The definition of value is generally understood as the result of quality divided by cost, or the health outcomes achieved per dollar spent. Value-based contracting involves payment or reimbursement based on indicators of value, such as patient health outcomes, efficiency, and quality. To get there, we must focus on key elements, such as: the number of dollars spent on a medication versus other associated healthcare costs.

Finding specific measures and calculating how those measures can be tracked against key benchmarks. For example, one could measure the sustained virologic response test results for hepatitis C products over a period of time to measure cure rates.

Providing portability in these measures as patients may go from one plan to another over the course of their treatment.

The 4 P’s: Patient, Providers, Pharma, and Payers

All 4 of the P’s fit in the data mix. Pharma and payers are interested in seeing measurable outcomes for the drugs prescribed to patients. Pharma want to claim that their product—if used correctly&mdash;will decrease the progression of disease, reduce visits to the emergency department, and achieve positive outcomes. Pharma wants to compare their product with others on the market to point out its efficacy.

The aligned interests of payers and their specialty pharmacy operations are key. They look for products that decrease cost, reduce hospital admissions, and improve patients’ quality of life by lessening their disability and reducing absences from work due to illness. Both pharma and payer stakeholders need access to comprehensive internal data, and they look to specialty pharmacies to provide that service. Specialty pharmacies must improve their ability to better measure outcomes if they want to make valid claims that because of their actions, the health outcome for the patient was improved. Lastly, both pharma and payers are reliant on the data specialty pharmacies provide to demonstrate that a product is having a positive, not negative, impact on the patient’s condition. 

The American Journal of Pharmacy Benefits® 

) is committed to providing you with peer-written and reviewed articles focused on the “real world” of managed markets specialty pharmacy practice. We invite you to enjoy this issue and pass it on!

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®. Please reach out to us at dsteiber@specialtypharmacytimes.com or djames@ajpb.com. And we also encourage you and your colleagues to subscribe to this unique journal by logging onto 

 is executive vice president and editor-in-chief of The American Journal of Pharmacy Benefits®. Professionally, he is an industry-leading consultant and has been responsible for managed markets, commercial operations, and supply chain strategy. Mr Steiber has served in several senior positions in pharmacy, distribution, and industry over the course of his 40-year career.

Mr Steiber is a licensed pharmacist in Texas, Washington, California, and Pennsylvania. He is affiliated with several professional associations and publications and is a frequent speaker on behalf of many professional organizations. He graduated from Washington State University College of Pharmacy and has participated in a variety of postgraduate programs in law and business development/marketing at Harvard University and Northwestern University. Mr Steiber currently resides in Highland Village, Texas.

Articles

From the Editor

Focusing on cost alone could diminish the vital service aspects behind successfully caring for a patient in a specialty pharmacy.

Proving Value in Specialty Pharmacy Practice: A New Yardstick

The provision of high-quality healthcare is a priority for US health plans and the CMS. Health plans can implement quality initiatives to ensure appropriate healthcare for their beneficiaries and use quality measures in their various initiatives to assess quality of care and outcomes.

 We reviewed publicly reported diabetes quality measures for Medicaid fee-for-service (FFS) programs in all 50 states and the District of Columbia. Our objective was to provide insights into the extent of diabetes quality measure reporting (ie, which diabetes quality measures are being reported and what percentage of states are using the diabetes Healthcare Effectiveness Data and Information Set [HEDIS] measures) and into the quality of care being provided (ie, achievement of established goals in diabetes care and state-level performance on diabetes quality measures).

 Overall, the number of Medicaid FFS programs reporting these data was low: 23 states reported data on at least 1 HEDIS measure. The most common measures reported were process measures (eg, retinal eye exams and HbA1c testing); outcomes measures such as the proportion of patients achieving goal were much less frequently available. Few states had historical data available, which limits the opportunity for retrospective analyses or quality benchmarking.

 This research is important, as an understanding of the current landscape of quality measures for the Medicaid diabetes population is critical to designing and implementing initiatives aimed at improving the quality of care for individuals living with diabetes.

The provision of high-quality healthcare is a priority for the United States, in large part as a result of the Affordable Care Act (ACA) and its focus on 3 goals to improve the experience of care, improve the health of populations, and reduce the per capita costs of healthcare. These triple aims also guide the National Quality Strategy that was established as part of the ACA, which describes the national priorities and strategic plan for quality improvement in healthcare. 

This reformative health policy environment has spurred many organizations to call for methods to measure, track, and improve the provision of healthcare and engage in the nationwide conversation regarding the definition of “value.”

Medicaid is responsible for providing healthcare coverage to more than 72 million beneficiaries.

 The population served by Medicaid will continue to expand under the ACA. The CMS has been a major forerunner in implementing quality healthcare measures and has implemented ongoing initiatives to ensure quality healthcare for both Medicare and Medicaid beneficiaries through accountability and public disclosure.

The ACA required the implementation of a core set of healthcare quality measures for adults participating in Medicaid. The initial set of measures was released in 2013.

 Reporting by states is voluntary; however, many states now require Medicaid managed care plans to report quality measures using the Healthcare Effectiveness Data and Information Set (HEDIS) tool. Although Medicaid is shifting away from a traditional fee-for-service (FFS) model, a substantial number of complex beneficiaries remain within this structure (eg, those with multiple chronic conditions and high resource utilization) and account for a disproportionate amount of total Medicaid expenditures.

 As such, we sought to explore the availability and breadth of data from quality measurement programs that traditional FFS Medicaid plans and Medicaid managed care organizations (MCOs) have instituted for patients with diabetes.

Diabetes-related quality data were obtained as of July 2015 through a Web-based search of publicly reported data, such as state Medicaid and related websites (list of websites provided in Table 1). This research included a review of all 50 states’ Medicaid FFS programs, the District of Columbia, and the largest Medicaid MCO in the 10 states with the highest Medicaid enrollment through May 2015: California, New York, Texas, Florida, Illinois, Ohio, Pennsylvania, Michigan, Georgia, and New Jersey. For purposes of this research, a Medicaid MCO was defined as a contracted entity that administers Medicaid benefits on behalf of a state. Because of the inconsistencies observed in the degree and time frames with which Medicaid FFS plans reported their diabetes-related quality data on public websites, the investigators reached out personally to a 10% sample of states to validate the completeness of data that were captured after the initial Web-based research was completed. Medical and quality program directors at 5 state Medicaid FFS programs (California, New York, Florida, Ohio, and Michigan) were contacted via phone and/or e-mail by the research team. For each state, a director of policy and quality, or similar role, was sought to provide confirmation on the data. These 5 states were chosen for the sample because they include the greatest number of covered lives under their FFS models.

The extent of diabetes-related quality measures found included mostly HEDIS Comprehensive Diabetes Care indicators (both current and previous measures), such as:

Blood pressure (BP) control (<140/90 mmHg)

Low-density lipoprotein cholesterol (LDL-C) screening

Other non-HEDIS diabetes-related quality measures were also identified and documented when found. When provided, the percentage of Medicaid patients meeting each quality indicator in each program was captured. Quality data were analyzed by examining the states’ individually published information for trends in Medicaid program reporting and performance while also noting those Medicaid plans that did not report any data.

All 50 states and Washington, DC, reported using HEDIS as their quality reporting system for either Medicaid FFS or MCO patients. For FFS programs, publicly reported data on diabetes quality measures were poor, with only 23 states (45%) having published data on at least 1 HEDIS measure for their FFS Medicaid lives or total Medicaid lives (Figure 1). We also found that the data-reporting period (ie, the date of the data available on the websites) varied widely, from as early as 2002 through March 2015. Only 6 states reported data from the previous year (2014; Colorado, Illinois, Tennessee, Florida, Vermont, and Connecticut). Only Colorado, Florida, and Vermont reported 7 or more HEDIS diabetes quality measures. Other non-HEDIS diabetes measures identified included hospital admissions rate for short-term complications associated with diabetes, hospital admissions rate for long-term complications associated with diabetes, proportion of patients with microalbuminuria, proportion of patients receiving screening for microalbuminuria, proportion of patients classified as obese or overweight, receipt of influenza vaccine in adults aged 50 to 64 years, health literacy, tobacco use, and composite measure of HbA1c, low-density lipoprotein cholesterol (LDL-C), blood pressure, and tobacco use.

Of the 23 states with publicly reported data for the FFS programs, the most commonly reported process measure was “percentage of patients receiving HbA1c testing” (21 of 23 states), followed by the other screening measures: LDL-C screening (19 of 23 states) and eye examination (12 of 23 states). The least frequently reported measures were those that require states to conduct medical record reviews or additional data analysis (eg, outcome measures such as percentage of patients achieving HbA1c <7%, proportion of patients achieving BP control, and proportion of patients achieving LDL-C <100 mg/dL).

 Moreover, few states had published consecutive data year over year that would enable examination of changes in reporting following Medicaid expansion as a result of the ACA, and only 5 states had historical FFS program quality data available (Colorado [2013], Illinois [2013], Kansas [2003-2008], North Carolina [2010], and Vermont [2012]). It is worth noting that the 5 states that had historical data generally reported more measures (ie, at least 3 measures: Colorado [9 measures], Illinois [3 measures], Kansas [4 measures], North Carolina [3 measures], and Vermont [10 measures]). As shown in Figure 1, the overall quality of diabetes care was low, although generalizations should be made with caution because of the small number of states with publicly reported data and the variance in the age of the reported data. In comparison, however, national benchmark data from the National Committee for Quality Assurance, the organization responsible for the development and maintenance of the HEDIS measures, shows that the proportion of members receiving HbA1c testing was 94% in 2014; the percentage of members with poor control (HbA1c >9%) was 19%.

Contrary to FFS programs, in the assessment of the 10 Medicaid MCOs, they all used the HEDIS core set of measures and had more recent data reporting time frames (all but 1 MCO reported data from 2013 or 2014). The most commonly reported measures were the process (screening) measures: HbA1c testing, eye examination, and LDL-C screening.

The degree and timing of reporting diabetes quality measures differed significantly among states, and historical data were found to be scarce. There was sometimes a significant lag in the time from measurement to reporting of data (eg, 3 years from time of data measurement to report date in Wyoming, Louisiana, and Arizona). Overall, states did not consistently report quality measures on an annual or other regular interval. Moreover, a significant difference exists between Medicaid FFS program populations and MCO populations in terms of their available data. Despite many states’ transitioning their Medicaid population from FFS plans to MCO plans that monitor performance via well-established HEDIS quality measures, the current state of reporting for Medicaid FFS programs remains inadequate. The issue of poor or low levels of public reporting of quality measures is an important one. One of the strategic goals and objectives of the US Department of Health and Human Services (HHS) plan for 2014 through 2018 is to strengthen and improve healthcare quality and patient safety.

 However, improvements can be recognized only if the measures are standardized across the various stakeholders and organizations and the performance data on quality measures are shared regularly. It is not known whether the states identified in this report were influenced by the ACA to report quality measures; although the ACA states that the HHS must publish and periodically update the Medicaid Adult Core Set, participation and reporting by Medicaid programs is voluntary. Although only 4 states had public reports that predated the availability of the Medicaid Adult Core Set (2012), it is not known whether the remaining 19 states that reported ≥1 measure began reporting because of this or whether the timing was coincidental.

There are ongoing initiatives that seek to enhance reporting within various Medicaid populations and more, such as the CMS Innovation Center’s State Innovation Models (SIMs) and other state-level multipayer initiatives, which, perhaps, indicates that changes are on the horizon. In this assessment, 40 states were identified as either participating in SIMs or otherwise establishing larger statewide quality initiatives, with 22 of them tracking at least 1 diabetes-related measure. Because funding for these plans began in April 2013, these initiatives are still in their infancy, with only baseline outcomes data available for review at the time of publication. However, it is anticipated that the level of visibility for progress in diabetes care will continue to increase as states continue to leverage the availability of these funding awards to test innovative health system management approaches.

With the continued expansion of Medicaid and the increasing number of eligible beneficiaries, continuous quality improvement and the impact on outcomes for patients will remain a top priority in the US healthcare system. The recognition of improvement, however, will begin only with the establishment of quality-driven benchmarks and consistent reporting within Medicaid programs.

: Robert Bailey and Janice Lopez are employees of Janssen Scientific Affairs. Marie Smith is a paid consultant to Janssen Scientific Affairs. Herman Chen is an employee of Xcenda, which received funding from Janssen Scientific Affairs to conduct this research.

: Jessica Black, Xcenda, for research and Evelyn Sarnes, Xcenda, for manuscript development assistance.

: Concept and design (JMSL, RB, HC); acquisition of data (HC); analysis and interpretation of data (HC, JMSL, RB, MS); drafting of the manuscript (HC, MS, RB, JMSL); critical revision of the manuscript for important intellectual content (HC, JMSL, RB, MS); statistical analysis (HC); provision of patients or study materials (HC); obtaining funding (JMSL, RB); administrative, technical, or logistic support (HC); and supervision (JMSL, MS, RB).

1. Centers for Medicare & Medicaid Services. Medicaid & CHIP: August 2015 monthly applications, eligibility determinations and enrollment report. https://www.medicaid.gov/medicaid/program-information/medicaid-and-chip-enrollment-data/monthly-reports/index.html. Published October 26, 2015. Accessed November 4, 2015.

2. National Quality Forum; Measure Applications Partnership. Strengthening the core set of healthcare quality measures for adults enrolled in Medicaid, 2014. Final report. http://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=78283. Published August 29, 2014. Accessed November 5, 2015. 

3. The Henry J. Kaiser Family Foundation. Medicaid and managed care: key data, trends, and issues. https://kaiserfamilyfoundation.files.wordpress.com/2012/

02/8046-02.pdf. Published February 2012. Accessed November 4, 2015.

4. US Department of Health & Human Services. 2014 annual report on the quality of health care for adults enrolled in Medicaid. https://www.medicaid.gov/medicaid/quality-of-care/downloads/2014-adult-sec-rept.pdf. Published November 2014. Accessed November 5, 2015.

5. National Committee for Quality Assurance. 2014 accreditation benchmarks and thresholds — mid-year update. http://ww2.ncqa.org/Portals/0/PolicyUpdates/Trending%20and%20Benchmarks/2014_BENCHMARKSANDTHRESHOLDS.pdf. Accessed November 3, 2015.

6. US Department of Health & Human Services. Strategic goal 1: strengthen health care. http://www.hhs.gov/about/strategic-plan/strategic-goal-1/index.html. Updated February 9, 2016. Accessed November 3, 2015.

Original Research

In a review of publicly reported diabetes quality measures for Medicaid fee-for-service, overall reporting was low and reports of outcomes measures were rarely available.

Low Reporting of Medicaid Diabetes Quality Measures: Room for Improvement?

: Little evidence has been available, from real-world clinical practice, on how the use of pharmacotherapy with pain medications changes in patients who begin using hyaluronic acid (HA) injections. We aimed to examine the impact of the initiation of HA injections on the utilization patterns of pain management modalities in patients with knee osteoarthritis (OA).

: Retrospective pre-post design utilizing Truven Marketscan Commercial Claims and Encounters data from 2004 to 2011.

: The first HA injections received by patients aged between 18 and 64 years during the study period were identified from the data. Utilizations of 3 pharmacotherapies commonly used in the treatment of knee OA—nonsteroidal anti-inflammatory drugs (NSAIDs), corticosteroid injections, and opioids&mdash;in the 6-month pre- and post periods were assessed. These were measured as an average number of prescriptions per patient, and as proportions of patients using at least 1 of these agents. The independent variable in the study was a use of HA injection. Paired sample t test and McNemar’s test were used to assess pre-post changes.

: There were statistically significant decreases in the utilization of NSAIDs and in steroid prescriptions, along with a significant reduction in the proportion of patients filling these prescriptions during the post period (P <.001). In addition, the number of patients getting any opioid prescriptions reduced significantly during the post period (P <.001).

: The study results suggest that HA treatment may help reduce the use of other pain medications such as steroids, NSAIDs, and opioids.

In the United States, nearly 30 million adults suffer from various forms of osteoarthritis (OA), of which knee OA is among the leading causes of disability.

 Patients with knee OA may encounter severe pain, leading to disability-related productivity losses,

 reduced quality of life,4 and increased financial burden.

 A number of treatment options are available for these patients to mitigate pain, improve daily functioning and disability, and perhaps delay progression of knee OA.

 Treatment for pain management in knee OA includes oral medications such as nonsteroidal anti-inflammatory drugs (NSAIDs) and opioids, and intraarticular injections such as hyaluronic acid (HA).

HA is a complex glycosaminoglycan sugar, a normal component of synovial fluid and cartilage in the knee.

 The purpose of HA in the knee joints is to provide viscoelastic properties to the synovial fluid—lubrication, shock absorption, and the prevention of wear of articular cartilage.

 In many OA patients, synovial inflammation is an important component of the osteoarthritic cascade. In these patients, concentration of HA polymer in the joints decreases as the knee OA progresses, leading to lower ranges of HA molecular weight. These lower ranges of HA lead to inflammation.

 Treatment with HA injections (viscosupplementation) has been shown to help restore synovial fluid properties in the knee, leading to less pain and improved clinical outcomes.

 A recent network meta-analysis by Bannuru et al, which included 137 studies with 33,243 patients with knee OA, reported that HA injections were the most efficacious treatment for reducing pain-related outcomes.

 Additionally, an economic benefit of HA injections, post treatment of knee OA, has been reported in a prospective multi-center study in France

 and in other studies with decision analytic models comparing costs of HA therapy with those of conventional care.

 Results of a recent study have suggested that a longer time to total knee replacement (TKR) is associated with use of HA injections; on average, patients who used HA had a longer time to TKR, and the time to TKR increased with additional courses of HA injections.

However, little evidence exists on how treatment with pain medications and anti-inflammatories (NSAIDs, opioids, and corticosteroid injections) is affected when patients receive HA injections in real-world clinical practice. This study aimed to examine the impact of initiating HA injections on the utilization patterns of 3 other pain-management modalities commonly used in patients with knee OA: NSAIDs, corticosteroid injections, and opioids.

This retrospective pre-post study utilized Truven Marketscan Commercial Claims and Encounters data. All commercially insured patients who received an HA injection between January 1, 2004, and December 31, 2011, were identified. The first HA injection received by patients during this time was described as the index HA injection. The time period of 30 days after the index HA injection was categorized as the treatment period. The 6-month periods before and after the treatment period were categorized as the pre- and posttreatment periods, respectively.

Patients were included if they: (a) were aged between 18 and 64 years on the date of their index HA injection (as the database did not cover Medicare-eligible patients); (b) were continuously eligible for the overall study period (the year before and the year after HA injection); and (c) had an outpatient visit with a knee OA diagnosis during the overall study period. Patients who had a TKR during the 1-year period before the index HA injection were excluded from the study; however, the study did include patients who had a TKR in the post treatment period.

The main outcomes of interest were the utilization patterns of 3 pharmacotherapies commonly used in treating knee OA: NSAIDs, corticosteroid injections, and opioids. These patterns were analyzed in the 6-month pre- and post periods. Utilization was measured as an average number of prescriptions per patient, and as a proportion of patients using at least 1 of these agents. The independent variable in the study was the use of an HA injection. Demographic variables such as age, gender, and region were also assessed.

Descriptive statistics were used for demographic variables. Age was categorized into 3 groups (18-44, 45-54, and 55-64 years) and regions were categorized as Northeast, North Central, South, and West. In order to assess the average number of prescriptions per patient, a paired sample t test was used to analyze a pre-post change. A McNemar’s test was used to assess a pre-post change in proportion of patients using at least 1 of these agents (NSAIDs, corticosteroid injections, or opioids). In addition, analysis was stratified based on patients who had TKR and those who had no TKR in the posttreatment period. All these analyses were performed using SAS for Windows, Version 9.4 (SAS Institute Inc, Cary, North Carolina).

Of the 386,636 identified patients who received HA injections between January 1, 2004, and December 31, 2011, a total of 152,953 patients were included in the final analysis after applying the inclusion and exclusion criteria. The majority of the patients (53.4%) were aged 55-64 years; 35.4% and 11.2% were aged 45-54 years and 18-44 years, respectively. Females and males represented 62.1% and 37.9% of the patients, respectively. The Southern region was most prominently represented with 43.7% of the patients, while the North Central, West, and East regions had 31.3%, 14.2%, and 5.8%, respectively.

During the 30-day initial treatment period, 436,000 HA injections were administered. Most patients received either 1 or 3 injections, per treatment recommendations. Further, an additional 50,000 injections were administered in the follow-up period, which were mostly carried over from the initial treatment period.

Results of the overall analysis are presented in the Table. The monthly trends in number of prescriptions filled and proportions of patients filling the prescriptions over time are shown in the Figure. Overall, our results demonstrated a statistically significant decrease in the utilization of NSAID prescriptions during the post period. Analysis showed a 10% reduction in the average number of NSAID prescriptions filled (P <.001) and a 15% reduction in the number of patients getting any NSAID prescriptions (P <.001). In addition, the average number of NSAID prescriptions among TKR patients and non-TKR patients went down significantly, by 11% and 9% respectively (P <.001). The number of TKR patients and non-TKR patients getting any NSAID prescriptions was also reduced by 5% and 16% respectively (P <.001). There was a consistent increase in the number of NSAID prescriptions filled and in the proportion of patients filling prescriptions during the pre-period leading up to HA index date, followed by a consistent decrease in the post period (Figure).

Likewise, a significant decrease in the utilization of steroid injections was found during the post period. The average number of steroid injections filled and the number of patients getting any steroid injections went down by 55% and 57%, respectively (both P <.001). Further, the average number of steroid injections among TKR patients and non-TKR patients was reduced by 63% and 54%, respectively (both P <.001). The proportions of TKR and non-TKR patients getting any steroid injections also decreased, by 60% and 56%, respectively (both P <.001). The Figure shows similar trends for both, the number of steroid prescriptions filled and proportion of patients filling the prescription. There was a consistent steep increase during the pre-period followed by a consistent steep decrease during the post period.

Interestingly, utilization of opioids increased in the post period. Overall, the average number of opioid prescriptions filled increased by 12% (P <.001) but the number of patients getting any opioid prescriptions fell by 6% (P <.001). Subgroup analysis indicated that there was a 175% (P <.001) increase in the average number of opioid prescriptions among TKR patients (representing nearly 7.5% of the study population) and a 6% decrease (P <.001) among non-TKR patients (representing nearly 92.5% of the study population). The proportion of TKR patients getting any opioid prescription increased by 85%, while the proportion of non-TKR patients using opioids decreased by 15% (P <.001). The explanation for the sharp increase in the opioid use among patients who had TKR surgery during the post period is that in the United States, a majority of patients receive a short-term opioid prescription following orthopedic surgery for postoperative pain management. The Figure shows that both the 6-month run-up in opioid prescriptions and the proportion of patients filling those prescriptions plateau after initiation of HA injection. Similar trends were observed in patients with no TKR. However, there were increases in the number of prescriptions filled and in the proportion of patients filling the prescriptions among TKR patients after the initiation of HA injection.

This was a retrospective observational study with pre- and post-intervention design. Its findings should be considered in light of certain limitations. Since this study is observational in nature, it would be difficult to draw any causal inferences. Limited details were available about each intervention. Furthermore, the use of Truven MarketScan data restricted the study population to those aged between 18 and 64 years. Hence, findings of this study cannot be generalized to patients 65 years and older. Further research in the form of randomized control trials should study the impact of HA injections, to substantiate the causal inferences.

Overall, the results of this study suggest that in the real-world setting, HA treatment may help reduce the use of anti-inflammatories and pain medications, such as steroids, NSAIDs, and opioids, among patients with knee OA. Although an increase in opioid use was found during the post period in the overall study population, this appeared to be predominantly driven by patients who used opioids for postoperative pain following TKR surgery. Nevertheless, an analysis of the patients without TKR during the post period, representing 92.5 % of the main study population, demonstrated lower opioid use compared to the pre-period. This study provides a real-world perspective around the utilization of pharmacotherapeutic options for pain management among patients with knee OA following HA treatment.

We would like to thank Surbhi Shah, MS, and Monali Bhosle, PhD, both from Outcomes, Inc, for providing medical writing services.

: Concept and design (SY, MK, SB, BB,LEM,WB); acquisition of data (SY, MK, SB, BB); analysis and interpretation of data (SY, MK, SB, BB); drafting of the manuscript (SY, MK, SB, BB); critical revision of the manuscript for important intellectual content (MK, LM, SB, BB,LEM,WB); statistical analysis (MK); obtaining funding (SB); and supervision (MK, SB).

: Northwell Physician Partners (LFM), Sleepy Hollow, NY; Tuckahoe Orthopedics (WB), Richmond, VA; DePuy Synthes Mitek Sports Medicine, Johnson & Johnson (SB, BB), Raynham, MA; Medical Devices — Epidemiology, Johnson & Johnson (SY, MK), New Brunswick, NJ.

: Samir Bhattacharyya, PhD, DePuy Synthes Mitek Sports Medicine, Johnson & Johnson, 325 Paramount Dr, Raynham, MA 02767. E-mail: 

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7. Altman RD, Manjoo A, Fierlinger A, Niazi F, Nicholls M. The mechanism of action for hyaluronic acid treatment in the osteoarthritic knee: a systematic review. BMC Musculoskelet Disord. 2015;16:321. doi: 10.1186/s12891-015-0775-z.

8. Bannuru RR, McAlindon TE, Sullivan MC, Wong JB, Kent DM, Schmid CH. Effectiveness and Implications of Alternative Placebo Treatments: A Systematic Review and Network Meta-analysis of Osteoarthritis Trials. Ann Intern Med. 2015;163(5):365-72.  doi: 10.7326/M15-0623.

9. Vincent HK, Percival SS, Conrad BP, Seay AN, Montero C, Vincent KR. Hyaluronic acid (HA) viscosupplementation on synovial fluid inflammation in knee osteoarthritis: a pilot study. Open Orthop J. 2013;7:378-384. doi: 10.2174/1874325001307010378.

10. Bannuru RR, Schmid CH, Kent DM, Vaysbrot EE, Wong JB, McAlindon TE. Comparative effectiveness of pharmacologic interventions for knee osteoarthritis: a systematic review and network meta-analysis. Ann Intern Med. 2015;162(1):46-54.

11. Mazières B, Bard H, Ligier M, Bru I, d’Orsay GG, Le Pen C. Medicoeconomic evaluation of hyaluronic acid for knee osteoarthritis in everyday practice: the MESSAGE study. Joint Bone Spine. 2007;74(5):453-460.

12. Hatoum HT, Fierlinger AL, Lin SJ, Altman RD. Cost-effectiveness analysis of intra-articular injections of a high molecular weight bioengineered hyaluronic acid for the treatment of osteoarthritis knee pain. J Med Econ. 2014;17(5):326-337.

13. Altman R, Fredericson M, Bhattacharyya SK, et al. Association between hyaluronic acid injections and time-to-total knee replacement surgery. J Knee Surg. 2016;29(7):564-570.

This study assessed the impact of initiating hyaluronic acid injections on pain management pharmacotherapies utilization patterns in patients with knee osteoarthritis.

Impact of Hyaluronic Acid Injections on Pain Management Medications Utilization

: To evaluate treatment initiation timing and healthcare costs among newly diagnosed fibromyalgia patients receiving pain medications commonly used in fibromyalgia treatment.

: Retrospective cohort study (IMS Pharmetrics Plus).

: Eligible patients had ≥2 International Classification of Diseases, Ninth Revision, Clinical Modification codes 729.1X within a year (first [index] diagnosis in 2012; none in 12 months prior to index diagnosis [baseline]), and no baseline use of medications commonly used in fibromyalgia management (pregabalin, duloxetine, milnacipran, gabapentin, tramadol, venlafaxine, amitriptyline, cyclobenzaprine). “Early-” (≤60 days after index diagnosis) and “late-” treated cohorts were propensity score (PS) matched using patient characteristics and other pain-related medications. Total healthcare costs during the 12-month follow-up period were compared for early- versus late-treated cohorts and were adjusted for baseline costs, unbalanced comorbidities, and follow-up use of other pain medications.

: Among 33,470 newly diagnosed fibromyalgia patients identified, 5208 (15.6%) received common fibromyalgia medications. Median age for PS matched cohorts (n = 2019 each) was 47 years, and 72.2% were female. Most frequent comorbidities were rheumatic conditions, hyperlipidemia, back and neck pain, and depression. Proportion of patients receiving other pain-related medications was similar in both cohorts, with >20% receiving opioid, anti-inflammatory, anti-anxiety, and selective serotonin reuptake inhibitor medications, respectively. Total follow-up costs were 15% lower ($1970 less) in the early-treated cohort (adjusted mean costs: $11,287 versus $13,258; P <.0001).

: Total healthcare costs were significantly lower among early-treated patients, suggesting that prompt use of fibromyalgia-related medications is a potential cost-saving strategy. Patients with fibromyalgia had a substantial comorbidity burden.

Fibromyalgia is a chronic pain syndrome characterized by widespread neuromuscular pain and generalized tender points.

 It is estimated that fibromyalgia affects as many as 6% of US adults and 3% of adults worldwide.

 Symptoms of fibromyalgia may be subjective, and diagnosis may be difficult and may vary between practitioners. Frequent misdiagnosis of fibromyalgia results in underdiagnosis in the general population, since fibromyalgia may often be confused with other regional pain syndromes and systemic disorders that share pathophysiologic features with fibromyalgia.

 Although early treatment is optimal, current diagnosis time averages about 5 years.

 Patients with fibromyalgia have frequent comorbidities, including other pain disorders, sleep disorders, depression, and mood/anxiety disorders,

 and fibromyalgia patients average 4.2 comorbid conditions each year.

 Studies estimate that up to half of fibromyalgia patients also suffer from obesity, and an additional 21% to 30% are overweight.

Fibromyalgia is associated with significant direct and indirect costs,

 and total annual costs among patients with fibromyalgia have been estimated at $17,165 (2012 US$), with approximately 68% of total costs attributed to indirect costs due to pain.

 High prescription costs may be due to polypharmacy, attributable not only to fibromyalgia management but also reflective of the high comorbidity burden associated with fibromyalgia.

 Fibromyalgia is associated with significant indirect costs since it has an impact on patient productivity.

 Furthermore, fibromyalgia has a significant impact on patient quality of life, as it can impair the patient’s ability to participate in everyday activities and affect relationships with family, friends, and employers.

Fibromyalgia treatment recommendations have been put forth by the European League Against Rheumatism (EULAR)

; however, the APS cautions that its treatment guidelines are outdated and should not be used for current medical practice.

 EULAR 2016 guidelines recommend the use of amitriptyline (at low dose), duloxetine, milnacipran, tramadol, pregabalin, and cyclobenzaprine for pharmacological management of fibromyalgia, although initial non-pharmacological management strategies are encouraged. Absent from the EULAR recommendation is gabapentin, which, despite not having a specific indication for fibromyalgia, is often used in this patient population.

 The American College of Rheumatology summarizes medications for fibromyalgia treatment on its patient/caregiver website; this list includes antidepressants and gabapentin.

 In 2012, Canadian Guidelines for the Diagnosis and Management of Fibromyalgia were developed

; however, milnacipran, which is FDA approved for fibromyalgia treatment, is not available in Canada. Only 3 medications (pregabalin, duloxetine, and milnacipran) are approved by the FDA in the United States to treat fibromyalgia pain.

 The use of nonsteroidal anti-inflammatory drugs (NSAIDs) and/or opioid medications to manage fibromyalgia is common, despite a lack of evidence supporting their use in fibromyalgia.

 While there is some consensus that opioids are not recommended options for treating fibromyalgia, tramadol, a weak opioid with mild serotonin-noradrenaline reuptake inhibitor activity, is supported for short-term use if the prescriber believes that opioid treatment is needed to treat fibromyalgia pain.

Previous research has suggested that fibromyalgia treatment rates are low, and that more than two-thirds of patients newly diagnosed with fibromyalgia are not treated with prescription pain medication within 1 year of diagnosis.

 Furthermore, among patients who are treated pharmacologically, discontinuation of prescribed pain medications is common,

 and doses of medications prescribed to patients with fibromyalgia are frequently lower than recommended.

 While underdiagnosis, undertreatment, and delayed diagnosis are described in the fibromyalgia literature,

 little is known regarding the impact of treatment initiation timing on patient outcomes or healthcare cost.

The primary objective of this study was to assess the impact of prompt fibromyalgia treatment on total healthcare costs. Secondary objectives included the evaluation of the initial pain management therapy for newly diagnosed fibromyalgia patients, and to estimate the proportion of patients treated with common fibromyalgia-related medications.

This was a retrospective cohort study that used administrative medical and pharmacy claims for commercial, employer self-insured, and Medicare risk plans from the IMS Pharmetrics Plus™ database. Patients who were aged ≥18 years at the time of first diagnosis (index date) of fibromyalgia (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] 729.1) during 2012, with a second fibromyalgia diagnosis within 1 year of the index date, were identified. Patients were required to have continuous health plan enrollment for both medical and pharmacy coverage during the 12 months prior to and following the index date, with no fibromyalgia diagnosis during 1 year prior to the index date. The 12-month period preceding the index date was used as the study baseline period. Patients were excluded if they received any of the following common fibromyalgia-related prescription medications during the 12-month study baseline period: amitriptyline, cyclobenzaprine, venlafaxine, duloxetine, milnacipran, pregabalin, gabapentin, or tramadol.

Patient demographic information was obtained from claims data and subsequently assessed. Comorbid conditions were evaluated at baseline using ICD-9-CM codes from medical claims. Study endpoints included the proportion of patients treated with pain medications within 1 year of index date. These included prescription medications commonly prescribed to treat fibromyalgia in the United States (amitriptyline, cyclobenzaprine, venlafaxine, duloxetine, milnacipran, pregabalin, gabapentin, tramadol),

 as well as other prescription medications which may be used in fibromyalgia, including opioid (excluding tramadol) and non-opioid pain medications, muscle relaxants, antidepressants, sleep aids, attention-deficit/hyperactivity disorder (ADHD) medications, and anti-anxiety medications (eAppendix Table; eAppendices available at www.ajpb.com). Time to initial prescription fill of common fibromyalgia-related medications from the index date was evaluated, and patients were categorized and placed in an “early-treated” (≤60 days) or “late-treated” (>60 days) cohort. A sensitivity analysis was performed to evaluate the impact of using an alternate threshold for the early-treated cohort (<90 days). Total healthcare costs for all healthcare services during 1 year following index date were obtained from administrative claims data, including inpatient, outpatient, and pharmacy total costs, and these costs were compared between early- and late-treated patient groups. Pharmacy costs represent costs associated with prescriptions filled outside a hospital setting; inpatient costs represent the total cost associated with hospitalizations; and all other costs (not pharmacy or inpatient) were attributed to outpatient costs (eg, emergency department visits, physician office visits, outpatient surgical or diagnostic procedures, laboratory, pathology, or radiology encounters).

Descriptive statistics (means, SDs, and frequencies) were calculated for patient characteristics. These characteristics and baseline healthcare costs during the follow-up period were compared using t tests for continuous variables, and χ2 and Fisher’s exact tests for categorical variables, between early- and late-treated patient groups. Early- and late-treated groups were propensity score matched to ensure comparability of baseline characteristics and to reduce the possibility of selection bias.

 Propensity scores were calculated using logistic regression, with patient demographics, comorbidities, health plan payer type, and other pain-related therapies at baseline as independent variables. Chi-square and Fisher’s exact tests were used to assess the balance between groups across all independent variables at P >.1. Generalized linear regression with a gamma distribution and log link function was used to compare costs between early- and late-treated patient groups, adjusting for baseline costs, unbalanced baseline comorbidities, and other pain medication use during the follow-up period.

Preliminary inclusion criteria were met by 33,470 newly diagnosed fibromyalgia patients (Figure 1). During the follow-up period, 15.6% (5208) received common fibromyalgia-related medications; 39.9% of these patients started therapy within 60 days and 47.7% within 90 days, of diagnosis. During the baseline period, 45.8% of the 33,470 newly diagnosed patients were prescribed >1 other pain medication. Almost half (49.4%) of all patients were prescribed other pain medications during follow-up.

Among all patients treated with a fibromyalgia-related medication (N = 5208), median age was 47 years and 70.0% were female (Table 1). Female patients were more likely to be early-treated than late (72.6% vs 68.3%; P <.05). The majority of patients (65.3%) were covered by a health maintenance organization health plan. The most frequent comorbid conditions were rheumatic conditions, hyperlipidemia, back/neck pain, and depression. Late-treated patients had higher rates of back/neck pain and lumbago (Table 1). Characteristics for the propensity score matched cohort are also included in Table 1 (N = 2019 for each group). The median age for the propensity score matched cohort was 47 years, and 72.2% were female. Subsequent to propensity score matching, only depression remained slightly unbalanced between groups (P = .09).

The most commonly prescribed fibromyalgia-related treatment was cyclobenzaprine, prescribed to slightly more than 4 in 10 study patients (Table 2). Many patients who were treated with a fibromyalgia-related treatment were also treated with NSAIDs, anti-anxiety medications, and antidepressants during both baseline and during follow-up. Among all patients who were treated with a fibromyalgia-related medication, the most commonly used other pain medications at baseline were opioids (40.7%), anti-inflammatory medications (25.9%), selective serotonin reuptake inhibitors (SSRIs) (23.9%), and anti-anxiety medications (21.0%). Early- and late-treated patients had similar baseline rates of use of most other pain medications; however, early-treated patients were more likely to have used opioids and anti-inflammatory medications (P <.05 for both). Among propensity matched groups, baseline use of all other pain medications was similar. However, during follow-up, late-treated patients were more likely to be treated with SSRI antidepressants (P = .001). Use of opioids, anti-inflammatory medications, and, to a lesser extent, anti-anxiety medications, increased from baseline to follow-up. Among propensity matched groups, only SSRI use during follow-up differed between early- and late-treated patients (23.7% vs 28.5%; P <.01). Overall, about 1 in 10 patients were prescribed sleep aids.

Figure 2 includes unadjusted baseline and follow-up healthcare costs by category for propensity matched groups. Both baseline and follow-up total healthcare costs were higher among late-treated patients. Figure 2B shows that total healthcare costs were higher during the first year following initial fibromyalgia diagnosis for late-treated patients, across all categories of healthcare spend (outpatient, inpatient, and pharmacy). Outpatient costs represented the largest driver of total healthcare costs for both treatment groups across both baseline and follow-up periods. Outpatient costs during the follow-up period represented 62.9% of total direct costs among early-treated patients, and 67.2% of total direct costs among late-treated patients. Pharmacy costs represented 17.4% and 15.4% of total healthcare costs among early- and late-treated patients, respectively. Mean total healthcare costs during follow-up, adjusted for log-transformed baseline cost, baseline depression, and follow-up SSRI antidepressant and ADHD drug use, are shown in Figure 3. Adjusted total healthcare costs during the 1-year follow-up period were estimated at $11,287 for the early-treated group and $13,258 for the late-treated group, and costs incurred by the early-treated group were 14.9% lower ($1970) than those of the late-treated group.

To examine the impact of our study’s definition of “early-treated” patients, we analyzed the impact of using a 90-day threshold instead of 60 days (“early-treated,” ≤90 days; “late-treated,” >90 days). After propensity score matching, 2337 patients were included in the early- and late-treated patient groups. Adjusted total healthcare costs during the 1-year follow-up period were estimated at $11,281 for the early-treated group and $12,803 for the late-treated group (difference = $1522; P <.0001).

Our study results, in agreement with those of previously published research,

 indicated low fibromyalgia treatment rates, as about half of newly diagnosed patients with fibromyalgia (47.9%) did not receive medications commonly used in fibromyalgia management in the United States, or other pain medications which may be used to treat fibromyalgia. A high comorbidity burden was associated with fibromyalgia. Rheumatic conditions, back/neck pain, and depression were common at baseline, and many patients were prescribed opioids, pain medications, and depression/anxiety medications during the year preceding the fibromyalgia diagnosis. While we were unable to evaluate sleep-related comorbidities from medical claims data, about 10% of patients were prescribed sleep aids.

Early initiation of fibromyalgia treatment was associated with lower total healthcare costs, suggesting that prompt initiation of these medications may be a cost-saving strategy. During the first year following the initial fibromyalgia diagnosis, the early initiation of treatment was associated with 15% lower ($1970) adjusted mean total healthcare costs, despite additional medication costs related to the earlier initiation of fibromyalgia-related medications during the study follow-up period. These cost savings appeared to be largely driven by lower outpatient costs among the early-treated patient group during the follow-up period.

In our study, 16% of fibromyalgia patients received treatment with either a recommended medication

 or a medication found to be commonly used in fibromyalgia management.

 studied 240,144 patients who were diagnosed with fibromyalgia in the United States between 2009 and 2011, and found that more than two-thirds of patients (69%) were not pharmacologically treated within a year of diagnosis. Among patients who were treated with pain medication, about half filled an initial prescription within 1 month of diagnosis, and many patients who filled a prescription for fibromyalgia management discontinued its use shortly after initiation.

 the use of opioids in our fibromyalgia patient population was common, despite the lack of evidence or treatment guidelines supporting their use in fibromyalgia. More than half of our fibromyalgia patient population filled at least 1 prescription for an opioid other than tramadol during the 12-month follow-up period. The proportion of patients using opioids increased after fibromyalgia diagnosis in both early- and late-treated groups. The common use of opioids in fibromyalgia may be concerning given their high potential for abuse, especially given the lack of evidence supporting their effectiveness in fibromyalgia management. However, a large proportion of our study population suffered from comorbid conditions with chronic pain (eg, rheumatic conditions, back/neck pain), and since claims data were used for this study, we cannot ascertain whether opioids were prescribed for fibromyalgia or other comorbid chronic pain conditions.

Several other studies have evaluated the direct cost of fibromyalgia. Total annual direct adjusted costs in our study were $11,287 for early-treated and $13,258 for late-treated patients during 2012-2013. A study by Schaefer and colleagues that examined the cost of fibromyalgia in 2012 US$ estimated the 3-month direct cost of fibromyalgia at $1394, which would annualize to approximately $5576.

 Schaefer’s lower direct cost estimates may be related to study design, as costs were captured via patient questionnaire, rather than the administrative claims used for the current study. Chandran et al examined the impact of fibromyalgia severity on costs, and estimated direct annualized costs that ranged from $4854 to $9318 for patients with mild to severe fibromyalgia (2009 US$).

 The current study’s estimates are also somewhat higher than the mean direct annual costs observed by White and colleagues ($7286) in 2008, but this difference may be attributable to differences in the time periods studied.

 White and colleagues partially attributed high prescription drug costs to polypharmacy, and in our study, polypharmacy was also common, as the majority of patients treated with a fibromyalgia-related medication also received other pain medications. In 2011, the observations of Sanchez et al, similar to results of the current study, were that outpatient costs were the largest driver of the total direct cost burden, accounting for about half.

 They found that the highest outpatient utilization occurred within the first 6 months of fibromyalgia diagnosis before decreasing and subsequently leveling off, with the initial utilization peak possibly associated with increased patient monitoring and with therapy changes to optimize initial management.

 Finally, though it was not assessed in the current study, it is important to recognize that fibromyalgia may impact patient productivity and the associated economic consequences. Schaefer and colleagues estimated that approximately 68% of total costs were indirect, while White and colleagues estimated the annual indirect cost burden of fibromyalgia at $2983.

 Chandran et al found that fibromyalgia severity influenced the contribution of indirect costs, and estimated that mean total indirect costs accounted for 53%-78% of mean total costs for patients with mild to severe fibromyalgia, respectively.

This retrospective study of a geographically diverse population allowed the assessment of “usual care” in a large cohort of patients with fibromyalgia with good generalizability of results, although our results may not be reflective of those without health insurance coverage. However, it is important to consider our findings in the context of several important limitations. This study used administrative claims to assess fibromyalgia-related prescriptions filled using a pharmacy benefit, and prescriptions obtained outside the pharmacy benefit (out-of-pocket) would not be captured. Thus, use of over-the-counter medications, particularly nonprescription NSAIDs, was not assessed due to our use of pharmacy claims. We were also unable to capture information related to use of non-pharmacologic treatments (eg acupuncture, massage) which may be used for fibromyalgia management. Some prior research suggested that the majority of people with fibromyalgia use over-the-counter medications to treat fibromyalgia pain.

 The patient diagnosis of fibromyalgia was obtained from claims with the fibromyalgia ICD-9-CM codes, and due to the nature of claims data, a fibromyalgia diagnosis could not be verified and mis- and underdiagnosis of fibromyalgia may have played roles. A sizable proportion of our study population was prescribed antidepressants, despite a relatively low prevalence of depression or anxiety as classified by medical claims history; the frequency of these conditions in medical claims data may be lower than what may be observed among the results of other studies that used patient self-report to obtain this information. We were unable to assess the severity of fibromyalgia, or provider characteristics, and both of these factors may have impacted our study findings. Obesity may play a role in fibromyalgia management, and patient body mass index was not available for inclusion in the analysis. Finally, our study only captured direct costs; it did not capture the indirect costs associated with productivity loss, which have been shown to be considerable in other studies.

Patients with fibromyalgia have a substantial comorbidity burden and considerable annual healthcare costs. The majority of newly diagnosed fibromyalgia patients did not receive pharmacological treatment with a fibromyalgia-related medication. Treatment of fibromyalgia with opioids and other classes of medications not recommended for use in fibromyalgia appears to be relatively common. Total healthcare costs were significantly lower among early-treated patients, suggesting that despite the increased pharmacy costs, early use of fibromyalgia-related pharmacological therapy may be a cost-saving strategy. Future research should investigate whether early treatment of fibromyalgia may also be associated with indirect cost savings, lower patient out-of-pocket costs for alternative care (eg, massage, acupuncture), and improved patient quality of life.

: Concept and design (CQ, MG, QZ, FF); acquisition of data (CQ, FF); analysis and interpretation of data (CQ, MG, QZ, FF); drafting of the manuscript (CQ, FF); critical revision of the manuscript for important intellectual content (CQ, QZ); statistical analysis (CQ, MG, FF); obtaining funding (FF); administrative, technical, or logistic support (FF); and supervision (QZ, FF).

FF and QZ were employed by Daiichi Sankyo, Inc at the time of the study but are no longer employed by Daiichi Sankyo, Inc.

Avalon Health Solutions, Inc, Philadelphia, PA.

Chunlin Qian, PhD, Health Economics & Outcomes Research, Daiichi Sankyo, Inc, 211 Mount Airy Road, Basking Ridge, NJ 07920;

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15. Chandran A, Schaefer C, Ryan K, Baik R, McNett M, Zlateva G. The comparative economic burden of mild, moderate, and severe fibromyalgia: results from a retrospective chart review and cross-sectional survey of working-age U.S. adults. J Manag Care Pharm. 2012;18(6):415-426.

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Initiation of treatment with guideline-recommended medications within 60 days of fibromyalgia diagnosis was associated with immediate annual healthcare cost savings.

November/December 2017

Proving Value in Specialty Pharmacy Practice: A New Yardstick

Low Reporting of Medicaid Diabetes Quality Measures: Room for Improvement?

Impact of Hyaluronic Acid Injections on Pain Management Medications Utilization

Cost Implications of Early Treatment Initiation Among Patients With Newly Diagnosed Fibromyalgia

Pharmacotherapy Timing in Heart Failure and Impact on Healthcare Costs

Working Toward Better Value in Prescription Drug Benefits Management