Gandhi R. Bhattarai, PhD

Oncology management has changed dramatically over the last decade with the approval and rapid adoption of newly targeted oral oncology medications.

 These medications have moved much cancer therapy away from the inpatient setting or outpatient infusion therapy care setting and into the home, offering patients greater convenience and flexibility of timing and location of treatment.

 Oral oncology therapy represents one of the fastest growing expenditures for payers, costing up to $8000 per month per prescription.

The movement to oral oncology medications has altered the balance of risks and adverse effects in the treatment of cancer care. These treatments are associated with a different constellation of adverse effects and drug interactions. Moreover, delivery of these medications at home has raised the possibility of nonadherence to therapy.

 Many studies have indicated that nonadherence is associated with treatment failures and downstream healthcare costs in oncology patients.

 There are also studies that indicate that high patient copayment burden is one of the main reasons for poor medication adherence, with a higher rate of discontinuation and non-adherence as the cost-sharing amount increases.

 Our research will focus on another aspect of benefit design, where designated specialty pharmacies are selected to deliver pharmaceutical care and distribution services to patients taking oral oncology medications.

Specialty pharmacies, in addition to providing basic dispensing and counseling services, use trained nurses and pharmacists to educate patients and more actively manage their care.

 Specialty pharmacies aim to reduce variability in care delivery, improve appropriate medication use and the quality of care, and reduce costs of cancer care.

However, little is known about the effect of specialty pharmacy services in the marketplace.

Accordingly, we compared the effectiveness of a specialty pharmacy program implemented by a large commercial national health plan through specialty pharmacies to improve cancer care compared with services through retail pharmacies in the same insured population.

UnitedHealthcare Pharmacy implemented a s pecialty pharmacy program for specifi c oral oncology medications for its commercial patients in August 2007. The program required the contracted specialty pharmacy to provide clinical expertise and patient education in oncology medications and comorbid conditions, a monthly proactive adherence program including refi ll reminders, adherence screenings, and interventions with the patient and physician if nonadherence was detected. Additionally, an oncology clinical management program of telephonic clinical counseling sessions was required to provide extensive patient education, assessment of disease-specifi c parameters, depression screening, pharmaceutical care interventions, and provider outreach and referral to health resources.

The specialty pharmacy program had requirements for contracted medication reimbursement rates, staff expertise, operational services, and clinical programs that were met by the contracted specialty pharmacies. The interventional adherence program included reminder calls to the patient to coordinate medication refills, with assessment during the call for medication nonadherence in the past 30 days of therapy. If nonadherence was suspected, clinical counseling with specialty-trained pharmacists was provided to address any adherence-related issues through patient education and support strategies, identifi cation of financial assistance opportunities, and/or engagement with the physician.

Consultations were conducted biweekly to monthly for the first 3 months and then approximately every 3 months thereafter while the patient was in the program. Patients were also advised to contact their specialty pharmacist with questions as needed, and this support was available 24 hours a day/7 days a week. When appropriate, the pharmacist engaged in communication with the healthcare provider about their intervention recommendations or immunosuppressive therapy clinical concerns

that were identified in the consultations.

The insurance coverage was offered through employers and consisted of both self-insured and fully insured employers. For those employer groups who enrolled in the specialty pharmacy program, 2 specialty pharmacy vendors meeting the above program requirements were designated as the sole providers of prescriptions for the specific oral oncology medications. Competitive negotiated drug rates incorporated the cost of the additional services and no additional costs were passed on to employers or patients. 

 summarizes the specialty program flow of interactions between the patients and the specialty pharmacies.

The data source was an administrative claims database that included data for approximately 14 million enrollees from United Healthcare with both medical and pharmacy benefits. The claims were de-identifi ed and compliant with the provisions of the Health Insurance Portability and Accountability Act (HIPAA) of 1996.

We included patients that had pharmacy and medical benefits through this health plan and filled 1 or more prescriptions for an oral oncology study drug between August 1, 2007, the program initiation date, and December 31, 2007, providing a 5-month index period to capture 60-day or 90-day fills through either retail or mail order. During the identifi cation period, each patient was assigned an index date (the first prescription fill date) and an oncology index drug (all study drugs listed in 

). Study patients were required to be continuously enrolled for at least 1 year before the index date (baseline period) and 1 year after (follow-up period). The first 2 prescriptions, regardless of where they were filled, were dropped for each patient, due to the existence of a transition period. Each patient was then assigned to the specialty pharmacy network or retail pharmacy network cohort. Patients who subsequently fi lled 80% or more of their oral oncology prescriptions from the 2 designated specialty pharmacies were classifi ed as specialty pharmacy patients; those filling 80% or more of their oral oncology prescriptions from other pharmacies were assigned to the retail pharmacy cohort. Those patients (8% of the study population) who did not meet either criterion were omitted. Specialty pharmacy program participation was also assessed; after applying our assignment, in 98% of employer groups, all patients within a group were assigned to either a specialty pharmacy or retail pharmacy, indicating limited selection bias. The details of sample attrition are shown in 

A retrospective matched cohort study was designed to control for differences in healthcare costs and health services utilization between the 2 groups. The primary outcome measurement was financial: the overall costs (pharmacy and medical). Secondary financial measures include total outpatient costs, total medical costs (inpatient, outpatient hospital and office, and emergency department), and oral oncology pharmacy costs. Secondary clinical resource utilization outcomes based on reimbursement structure include hospitalizations, inpatient and outpatient hospital visits, office visits, emergency department visits, cancer-specific total medical and pharmacy costs, and cancer-specific resource utilization outcomes. Cancer- related medical costs are the costs from physician and facility claims with any of the International 

Classification of Diseases, Ninth Revision, Clinical Modification

) cancer diagnosis codes 140 to 239.99. Oncologyrelated pharmaceutical costs include all oral oncology medications in the analysis as defined by selected Hierarchical Ingredient Code List (HICL) codes (eAppendix A). Additionally, we evaluated medication adherence using 3 methods: 1) the total number of monthly prescriptions filled for all of the study medications, including switches, additions, and deletions; 2) weighted medication possession ratio (MPR) methodology in 

; and 3) number of medication gaps (MGs) per patient, defi ned as a period of at least 60 days without a study medication in the post period.

In order to control for confounding variables, the 2 cohorts were balanced using propensity score matching. The propensity score was derived from a logistic regression, which was then used to construct matched samples from the 2 cohorts.

We used a 1-on-1 greedy matching technique with 3 digits matched at 0.005 to derive the propensity score matched-pair sample.25 The logistic regression used patients’ demographics; baseline costs (medical and pharmaceutical); an indicator for start time of oral oncology agent; baseline comorbidities using the Charlson Comorbidity Index

; 5 cancer drug groups; and baseline cancer complexity score based on methodology adapted from Darkow et al.

 Darkow’s methodology defines complexity by diagnoses that suggest that a cancer patient’s health will be more diffi cult to manage than the typical patient with a particular condition, either because of: a) intrinsic activity of the given condition, or b) the difficulty of managing associated complications, comorbidities, and adverse effects of the treatments used for the management of that condition. Complexity variables in our study were built using primary and secondary diagnosis codes on patients’ medical claims, and patients were classified as having either moderate or high complexity as specified in

. If none of these were present, the patient was considered to have usual complexity.

Additionally, after the matching, the following factors were compared at baseline to assess comparability of cohorts: total costs, medical costs, and pharmacy costs, resource utilization variables such as hospitalization, inpatient and outpatient hospital visits, office visits, and emergency department visits. Post hoc analyses matching on individual cancer diagnoses and cancer drugs were also conducted to validate the cancer groupings utilized (

Statistical tests on the means of outcome measures in the follow-up periods between the 2 cohorts utilizing 

 tests were conducted. Subsequently, sensitivity analyses were conducted on the same outcomes after varying follow-up times to 3, 6, and 9 months to assess whether variations in mortality rates and/or disenrollment from plans and associated cost differences were contributing to results.

The effect of the program on medication adherence, measured as prescriptions filled, weighted MPR, and number of MGs per patient, was compared using 

 <.05 was considered statistically signifi cant. SAS version 9.1 (Cary, North Carolina) was utilized for all statistical analyses. For costs, all outliers were eliminated if over 5 times the standard deviation.

A total of 4165 unique patients were identified (Figure 2) who filled 1 or more oral oncology prescriptions between August 1, 2007, and December 31, 2007. After applying continuous enrollment and prescription filling inclusion criteria, 1588 patients were identified, of whom 502 used specialty pharmacy. Subsequent propensity matching procedures yielded 464 patients in each of the specialty pharmacy and retail pharmacy cohorts with no statistically signifi cant differences at the 5% level for the 2 groups. The patient selection flow chart is shown in Figure 1, and the matching and baseline evaluations are shown in 

The comparison of follow-up costs, healthcare utilization measures, and weighted MPR between the matched specialty pharmacy and retail pharmacy cohorts is presented in 

. For the primary outcome of total healthcare costs (the sum of pharmacy, outpatient, and inpatient medical costs), there were statistically signifi cant differen ces between the 2 groups. The mean total cost per patient per year was 13% lower in the specialty pharmacy group ($84,105 vs $97,196, difference = —$13,092; 

 = .02). Similarly, mean outpatient hospital cost ($16,777 vs $28,629, difference = —$11,852; 

 <.01) was lower by 41% in the specialty pharmacy group. Total medical cost (outpatient and inpatient costs) was 25% lower in the specialty pharmacy group ($45,696 vs $61,137, difference = —$15,440; 

 <.01). The number of oral oncology prescriptions dispensed per patient in the specialty pharmacy group was higher (10.32 vs 7.78, difference = 2.53;

 <.001). The associated overall oral oncology pharmacy costs trended higher in the specialty arm, ($32,545 vs $29,654, difference = $2891; 

The number of outpatient hospital visits was significantly lower in the specialty pharmacy cohort (15.75 vs 19.66, difference = —3.90; 

 <.01), but not for inpatient hospital visits or for total days of inpatient stay. The differences in cancer-related expenditures and healthcare utilization show that cancer-related outpatient hospital visits were significantly lower for the specialty pharmacy patients (11.08 vs 13.86, difference = —2.78; 

 = .02), as were the associated outpatient hospital costs ($14,624 vs $23,606, difference = —$8982; 

 <.01). Total cancerrelated medical costs ($41,175 vs $53,554, difference = —$12,369; 

 = .01) were also lower, largely driven by the outpatient hospital costs. Additionally, 36 patients (7.76%) in the specialty group versus 57 patients (12.28%) in the retail group had codes for IV chemotherapy agents (difference = 58%; 

The weighted MPR was higher in the specialty pharmacy patients (0.657 vs 0.580; 

 <.001), indicating greater adherence to oral oncology therapy. Similarly, the mean number of MGs per patient was signifi cantly lower in the specialty pharmacy patients (0.369 vs 0.571, difference 35%; 

The sensitivity analysis using 3-, 6-, and 9-month follow-up periods of outcomes found the results to be consistent with our main findings, as shown in 

As we broadly expand coverage to patients in the United States while attempting to contain costs, it is essential to identify approaches that improve quality and reduce costs. The recent movement of cancer care to the outpatient setting and the new challenges that patients face in appropriately administering complex regimens in their own homes provides a unique opportunity to examine the effect of different models of care delivery on healthcare efficiency.

Our findings indicate that specialty pharmacy programs are associated with improved medication adherence to oncology medications and reduction in costs of select resources. Substantial increases in oral oncology prescriptions filled and several measures of adherence were better in patients who used specialty pharmacies. Specialty pharmacy programs were associated with 13% reductions in overall healthcare costs, largely driven by an over 40% reduction in outpatient hospital costs. These findings suggest that specialty pharmacy services may represent an important opportunity to improve quality and reduce healthcare costs for patients requiring therapy for cancer.

There are a number of reasons why patients in the specialty pharmacy cohort were associated with exhibiting higher medication adherence. The specialty pharmacy program aims to improve education and adherence, to deliver positive reinforcement from a clinician with interventions and follow-up as needed, and to provide directed management of expected adverse effects. Greater adherence in the specialty cohort, though resulting in higher specialty medication costs, was also most likely responsible for reduced healthcare costs. Those who did not adhere more frequently may have required intravenous (IV) chemotherapy. Approximately 58% more patients started IV chemotherapy in the retail setting than in the specialty setting. Increased use of chemotherapy likely represents an important component of the greater outpatient hospital resource utilization that drove increased overall healthcare costs. Unfortunately, due to the retrospective nature of our study, the exact reasons for the decreased use of IV chemotherapy and biological therapy cannot be elucidated. However, it has been shown that improved adherence does lead to decreased need for further add-on or a switch in treatment regimens to IV agents, which increase costs due to the fact they are administered in hospital and/or ambulatory settings.

Reduced rates of outpatient visits were also associated with the specialty pharmacy cohort. Again, this difference could be due to a variety of reasons, including better management from specialty pharmacists of adverse effects of the oral chemotherapy agents. Many of the expected adverse effects of oral oncology agents are vastly different from those of the injectable chemotherapy, and close monitoring and care of these effects lead to better adherence. It is possible that better preparing patients for the expected adverse effects of oral oncology medications and appropriate methods to manage these adverse effects at home would mean less need for additional visits to address these concerns. Other reasons may include better management of comorbid conditions such as management of hypertension. As such, the educational component of specialty pharmacy care and the continuous 24/7  team, may play an important role in improving care effi ciency. Therefore, even in the presence of increased pharmacy cost , the overall total medical costs were statistically lower for the specialty pharmacy cohort.

There are important limitations to this study. Confounding may have resulted from selection bias, as patients or employers may have self-selected into either the specialty or retail pharmacy benefi t programs. Approximately 8% of the possible sample was omitted from the analytic data set because patients did not fi ll 80% of their prescriptions at either retail or specialty pharmacies. There are several reasons why patients may have fi lled in both channels. The most likely reason is that self-insured employers changed their preferred pharmacy outlet during the course of the study period; in this case patient selection would not have infl uenced pharmacy choice. However, some patients may have not used the pharmacyselected by their insurer and would have been omitted if they filled less than 80% from either channel. After omitting that 8% of the sample, we evaluated whether patients within each employer chose the same channel. We found variability in pharmacy choice in only 2% of employers, indicating that the overwhelming majority of benefi ciaries we included in this analysis used the preferred pharmacy channel of their employer, limiting the possibility that selection bias by patients was responsible for the fi ndings here. The selection bias of employers was not explored and remains a limitation of the study.

We were unable to identify dates of death using administrative claims, and had to choose a specific time frame in which to follow patients. Our base case included only patients who were consistently enrolled in the health benefit plan for 1 year after initiation of therapy. By excluding those who did not survive the year or disenrolled within the post—follow-up period, we enriched the sample with healthier, consistently treated patients and excluded the costs associated with death for those who did not survive and/or switch providers and plans.

We would not expect higher mortality rates in the specialty pharmacy cohort, and expect that this would lead to conservative estimates of healthcare cost reductions that resulted from specialty pharmacy care. However, we addressed this limitation by conducting sensitivity analysis requiring only 3, 6, and 9 months of continuous enrollment. The results were qualitatively unchanged in each of the time periods, suggesting that our fi ndings were not influenced by censoring due to death and/or disenrollment. Furthermore, we were not able to match on duration of therapy with the oral oncology agent prior to the study period, leading to potential residual confounding. However, after the match, there was no statistical difference in the number of new starts within 3, 6, and 9 months of the baseline period, mitigating the impact of this variable. Follow-up period beyond 1 year was not within the scope of this study and death and/or disenrollment post 1 year could not infl uence results and is a limitation of the study period we chose.

There may have been confounding related to stage of disease and cancer type. Based on previous studies, some of these limitations are inherent to retrospective claims studies.

We attempted to address this issue by matching on multiple variables that served as a proxy for disease complexity, including type of cancer drug, comorbidity score, and a calculated cancer complexity disease score. Additionally, the post-hoc analysis matching on cancer diagnoses yielded similar results. We have used a comprehensive medical claims—based proxy variable for cancer complexity developed by Darkow et al in our econometric models.

 However, the key factor for complexity is the diffi culty of managing the patient and, as such, is a clinical construct. Note that complexity is not health status, nor severity, nor disease stage; instead, complexity is shorthand for the clinical challenges posed by an individual patient with a particular disease state. However, we admit that the different stage and trajectory of disease progression for different cancers can impact costs as well as outcomes, and remains a limitation of the study.

Additionally, we were not able to capture quality of life measures in this study setting, which would have provided information from a patient perspective and may have significant policy implications.

Articles

Our research focuses on the clinical and economic outcomes of patients receiving care from a payer-designated specialty pharmacy program compared with regular retail pharmacies.