Abstract
Aims: Switching drug manufacturers in transplant patients may require an increased intensity of therapeutic monitoring, leading to additional healthcare visits, associated laboratory tests, and perhaps hospitalizations. As real-world studies examining the interchangeability of tacrolimus from different manufacturers are limited, the purpose of this study was to examine the healthcare resource utilization (HRU) and economic impact of tacrolimus-switching in kidney transplantation.
Materials and methods: This cross-sectional, retrospective study examined HRU and healthcare costs (HCCs) among patients with a kidney transplant who were prescribed tacrolimus from fixed-source (FS) vs variable-source (VS) manufacturers using claims data from the large US health plan Humana from October 1, 2012, to December 31, 2013.
Results: Overall, 1,024 patients were identified (FS: n = 674, 66%; VS: n = 350, 34%). The number of therapeutic drug monitoring (TDM) events for the VS group was 13% greater than for the FS group after controlling for demographics, comorbidity score, and number of medications (incidence rate ratio = 1.13, p = .033). Adjusted total HCCs were 9% lower for VS (US$28,054 vs US$30,823, p = .045). In the unadjusted analysis, VS had greater emergency department (ED) utilization (45% vs 35%, p < .002). In the VS group, the mean (standard deviation [SD]) number of days from manufacturer switch to first outpatient visit was 23.8 (33.6), and the number of days (SD) to first TDM event was 43.6 (56.2).
Limitations: Study limitations include the lack of availability of many transplant-specific variables within the Humana database, potential errors/omissions in claims coding, and restriction of cross-sectional data examination to a 1-year period.
Conclusions: VS patients had greater TDM and lower total HCCs. Further research is warranted to understand the drivers of ED use among the VS group, and to determine factors associated with delayed TDM after regimen modification. Opportunities may exist to improve the quality of care for patients receiving immunosuppressant treatment with tacrolimus.
Introduction
Generic drugs are accepted and utilized by physicians across a broad range of therapeutic areas. Regulatory approval for these generics is often predicated on their demonstrating bioequivalence to a reference product’s pharmacokinetic measurementsCitation1,Citation2. However, anecdotal evidence and testimonials from practicing physicians suggest that, in some drug classes, particularly for narrow therapeutic index (NTI) drugs, switching between branded and generic products, or among generics, may lead to adverse effectsCitation3–7.
Even though studies of NTI drugs have investigated drug-switching patterns and subsequent healthcare outcomes, few drug-switching studies have been conducted in the immunosuppressant therapeutic area. Those studies that do exist indicate that transplant patients currently taking a branded tacrolimus product may safely switch to a genericCitation8–10, but there remains conflicting evidence regarding whether there exists a need for additional monitoring of tacrolimus trough concentrations following a switchCitation8,Citation9,Citation11. Pharmacokinetic data suggest that the bioequivalence of generic tacrolimus may be challenged in populations such as the elderly, with particular concern regarding the possibility that monitoring trough concentrations cannot reveal pharmacokinetically meaningful differences in the rate and extent of drug absorptionCitation12.
Tacrolimus prescribing information states: “Monitoring of tacrolimus blood concentrations in conjunction with other laboratory and clinical parameters is considered an essential aid to patient management for the evaluation of rejection, toxicity, dose adjustments and compliance”Citation13. Furthermore, the clinical practice guidelines for the care of kidney transplant recipients issued by the Kidney Disease: Improving Global Outcomes Transplant Working Group recommend measuring calcineurin inhibitor blood levels (level of evidence 1B), and suggest doing so whenever there is a change in medication or patient status that may affect those blood levels (Level 2C)Citation14. The half-life of tacrolimus is ∼20 h in patients with a kidney transplantCitation13. As such, following dose adjustment, steady-state concentrations are established within ∼5–7 days. One protocol for switching tacrolimus, with clinical evidence supporting safe transition between brand and generic formulations, suggests therapeutic drug monitoring (TDM) and biochemical monitoring on the day of switch, and at 1, 2, and 4 weeks after a switchCitation15.
In the US healthcare environment, changes in the manufacturer of chronic-use generic drugs at the pharmacy level often go unnoticed by the patient and/or the prescriber. A study by Hauch et al.Citation11 examined patients who were being converted from branded to generic tacrolimus and their associated outcomes during the first year post-transplant. The study reported increased drug variability and more episodes of rejection, ultimately leading to increased costs of care. In addition, although the literature is replete with calls for closer monitoring of patients who switch from brand to genericCitation3–8,Citation10,Citation16, studies that examine the therapeutic effect of switching products at the manufacturer level are scarce. As switching manufacturers in transplant patients may require an increased intensity of therapeutic monitoring, leading to additional healthcare visits, associated laboratory tests, and perhaps hospitalizations, the purpose of this present study was to examine the healthcare resource utilization (HRU) and economic impact of tacrolimus-switching in patients with a kidney transplant. Differences in HRU and healthcare costs (HCCs) for individuals on fixed-source (FS) and variable-source (VS) tacrolimus were examined, with particular analytic focus on tacrolimus TDM and total HCCs. The VS group was expected to result in more frequent tacrolimus TDM and higher total HCCs.
Materials and methods
This study was a cross-sectional, retrospective analysis based on claims data from Humana, a health and well-being company serving millions of people across the US through Medicare Advantage, stand-alone prescription drug plans, and commercial plan offerings. The research database contains integrated medical claims, pharmacy claims, and enrollment data representing more than 20 million individuals currently or formerly enrolled. An independent institutional review board approved the protocol with a waiver of informed consent (Schulman Associates Institutional Review Board, #201404461).
Patient identification and cohort construction
Patients with a history of kidney transplantation using tacrolimus (branded and/or generic) during 2013 were selected for inclusion using drug national drug codes (NDCs). Patients were required to have had at least one prescription for tacrolimus in both the first and fourth quarters of 2013 in order to increase the likelihood that the patients had been continuously on therapy. Patients were required to be 16–89 years of age, and to have a history of kidney transplantation, indicated by at least one of the following codes in any position on any medical claim: a current procedural terminology (CPT) procedure code for kidney transplantation (50360, 50365); an International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) procedure code for kidney transplantation (55.6, 55.69); or a diagnosis code for a kidney replaced by transplant or kidney complication (v42.0, 996.81). Patients with evidence of pregnancy as indicated by claims were excluded from the analysis (ICD-9-CM 630.xx–679.xx, V22.xx and V23.xx).
The full study data period was from October 1, 2012 to December 31, 2013 (). A staggered identification period (October 1, 2012 to September 30, 2013) was used to assign patients to FS and VS groups based on patterns of tacrolimus utilization. Outcomes were measured during the 2013 measurement period (January 1, 2013, to December 31, 2013). The identification period was staggered to begin in the fourth quarter (Q4) of 2012, so as to appropriately attribute outcomes at the beginning of 2013 to switches that may have occurred toward the end of 2012.
Figure 1. Study design.
All patients who had their first switch in Q4 in the 2013 calendar year were removed from the analysis. This exclusion was based on the inability to observe meaningful outcomes for these patients switched within the same calendar year (i.e. a Q4 switch in 2013 will not allow for an informative measurement of impact on the 2013 outcomes observation period, per the study design).
Switching was conceptually defined as tacrolimus manufacturer substitution at the outpatient pharmacy level. Comparator group assignment (FS vs VS) was based on pharmacy claims data. Consistency of the manufacturer for a specific strength of tacrolimus was the crucial component of the FS definition, not whether the manufacturer was a brand or generic. Manufacturers were identified using the manufacturer code component of NDCs for each tacrolimus dispensing. Within each of the formulation strengths (0.5 mg, 1 mg, 5 mg) supplied to an individual, if the prescription claims indicated a consistent manufacturer of tacrolimus, the individual was assigned to the FS group. The VS group included patients identified as having at least one tacrolimus manufacturer change for a specific formulation strength during the identification period. Changes in manufacturer due to dose adjustments were assessed based on the treatment regimen prior to the dosage change. For example, a dose change from 6 mg to 7 mg was not considered to be a switch if both the NDCs for the continuing 5 mg and 1 mg strengths remained the same, but was considered as such if either the NDC for the 5 mg and/or 1 mg strengths changed at the time of dosage adjustment. Patients maintained on multiple strengths (e.g. 0.5 mg, 1 mg, and 5 mg) of tacrolimus from different manufacturers were assigned to the FS group, as long as the manufacturer of each strength remained unchanged.
Study measures
The study outcomes were measured using medical and pharmacy claims data during calendar year 2013. HRU included inpatient hospital, emergency department (ED), and outpatient utilization, which were identified using place-of-service codes associated with medical claims. Consecutive hospital claims representative of a hospital transfer were considered to be a single hospitalization. ED claims with the same service date as a hospitalization were not counted as a separate ED visit. ED visits adjacent to a hospitalization were considered a hospitalization and were not counted as an ED visit. Outpatient utilization included physician office visits, urgent care visits, outpatient hospital visits, ambulatory center visits, use of end-stage renal disease treatment facilities, and independent laboratory visits. Tacrolimus TDM was of particular interest and was identified using medical claims from any place of treatment where a CPT code was included that indicated a tacrolimus laboratory test had been performed.
All-cause HCCs included medical, laboratory, pharmacy, and total HCCs. Medical costs were calculated based on financial data associated with medical claims, and included both health plan-paid and patient out-of-pocket cost components. As a subset of the medical costs, tacrolimus TDM laboratory costs were also specifically examined, and were defined as the sum of medical claim costs associated with a tacrolimus laboratory CPT code. Total pharmacy costs were defined as the sum of costs associated with all outpatient pharmacy claims, inclusive of both health plan- and patient-paid cost components. Similarly, tacrolimus pharmacy costs were reported separately as a subset of the total pharmacy costs, and were defined as the sum of costs associated with all pharmacy claims for any oral tacrolimus prescription during the observation period. All-cause total HCCs were defined as the sum of medical, laboratory, and pharmacy costs. Only direct healthcare costs were measured; indirect healthcare costs such as lost productivity or missed work were not considered.
Additional measures included the number of tacrolimus switches, the number of tacrolimus NDCs, the time from switch to tacrolimus TDM, and the time from switch to outpatient encounter. The number of tacrolimus switches was defined as the number of manufacturer switches counted during the observation period for the VS group. The number of tacrolimus NDCs was defined as the absolute number of tacrolimus NDCs counted for the FS and VS group during the identification period. For VS patients, the time to the first outpatient visit was defined as the number of days from first observed switch to the first subsequent outpatient visit, and the time to the TDM was defined as the number of days from first observed switch to the first subsequent tacrolimus TDM.
Demographics, health plan type, benefit type, and clinical characteristics were measured as covariates. Demographics (age, sex, and geographic region) were based on information contained in the patient enrollment file as of January 1, 2013. Geographic region was reported based on the US Census Bureau division classifications. Health plan type was defined as commercial or MAPD. Benefit type was defined as health maintenance organization, preferred-provider organization, point-of-service, or other. Clinical characteristics included the total number of unique medications and the number of Elixhauser comorbidities. The number of unique medications was identified using generic product identifier codes. Elixhauser comorbidities were identified based on diagnosis codes associated with inpatient and outpatient medical claimsCitation17,Citation18. An Elixhauser comorbidity score was calculated as the number of Elixhauser conditionsCitation19.
Statistical analysis
All analyses were conducted using SAS version 9.2, including descriptive statistics (mean, median, standard deviation [SD], interquartile range) and inferential statistics (t-tests and Chi-squared tests). The a priori alpha level for all inferential analyses was 0.05, and all statistical tests were two-tailed. Data were evaluated for violation of assumptions underlying the associated statistical tests.
Demographics, clinical characteristics, HCCs, and HRU were compared between the FS and VS groups using bivariate statistical tests. Total HCCs, total medical costs, and total pharmacy costs were compared between groups. Medical costs were further broken down into ED, inpatient, outpatient, and tacrolimus TDM costs, and these cost components were compared. Pharmacy costs for tacrolimus were separately examined and compared between groups. Inpatient use (dichotomous variable), number of inpatient encounters, ED use, number of ED encounters, outpatient use and number of outpatient encounters, tacrolimus TDM use, and number of tacrolimus TDM encounters were compared between groups. Within the VS group, descriptive statistics were used to examine the time from switch to outpatient visit, and the time from switch to tacrolimus TDM encounter.
Regression analyses were conducted to examine all-cause HCCs and tacrolimus TDM for FS and VS groups after controlling for potential confounders. Individual cost components and the non-TDM utilization measures were not modeled in adjusted analyses. All models were adjusted for demographics and health plan characteristics, number of Elixhauser conditions, and number of unique medications. To model total HCCs, a generalized linear model with log link and gamma distribution was fitted. To examine tacrolimus TDM (as a number of encounters), negative binomial regression was used.
To further understand the relationship observed between tacrolimus utilization patterns in the VS group and the extent of tacrolimus TDM, two post hoc exploratory analyses were conducted: (1) to examine the relationship between the number of switches and the number of tacrolimus TDM encounters, a variable capturing the number of switches was added to the TDM model; and (2) to examine the relationship between the number of tacrolimus NDCs and the number of tacrolimus TDM encounters, a variable capturing the number of unique NDCs was added to the TDM model.
Results
A total of 1,024 patients met the inclusion criteria (). Approximately two-thirds of subjects met the FS definition (FS: n = 674, 66%; VS: n = 350, 34%). Demographic and clinical characteristics were similar between the two groups, with the exception of plan type (). The VS group had significantly more MAPD patients compared with the FS group (87% vs 78%, p = .001). The average age was ∼60 years, and the majority of patients in both groups were male. The number of Elixhauser comorbidities and the number of unique medications were similar in both groups. The median number of switches observed in VS patients was one (IQR = 1–9), with 63% of the VS group having only one switch.
Figure 2. Attrition flow.
Abbreviations. CPT, current procedural terminology; MAPD, Medicare Advantage prescription drug plan.
Table 1. Demographic and clinical characteristics of recipients of variable-source and fixed-source tacrolimus.
VS patients were more likely to have an ED encounter (45% vs 35%, p < .01) and had a greater number of tacrolimus TDM encounters (mean [SD] = 8.62 [8.33] vs 7.49 [7.17], p = .03) compared with FS patients (). Statistically significant differences in overall use and frequency of use of inpatient and outpatient services were not observed. For the VS group, the mean (SD) number of days from switch to outpatient visit was 23.8 (33.6) days, with half (50%) of the visits occurring within 14 days of the switch (). Despite the presence of an outpatient visit claim, the mean (SD) number of days from switch date to tacrolimus TDM event was 43.6 (56.2). In addition, 50% of subjects did not have a tacrolimus TDM event for at least 29 days, and 42% did not have a tacrolimus TDM event for at least 43 days ().
Figure 3. Number of days from tacrolimus formulation switch to first subsequent outpatient visit for subjects with variable-source tacrolimus prescription. Abbreviation. SD, standard deviation.
Figure 4. Number of days from tacrolimus formulation switch to tacrolimus TDM event for subjects with variable-source tacrolimus prescriptions. Abbreviation. SD, standard deviation; TDM, therapeutic drug monitoring event.
Table 2. Healthcare resource utilization for tacrolimus prescription recipients with an encounter by variable-source and fixed-source users during calendar year 2013.
After controlling for demographic, clinical, and health plan characteristics, the rate of TDM encounters for the VS group was 13% greater than the FS group (incidence rate ratio [IRR] 1.13, p = .033). Within the VS group, neither the number of tacrolimus switches, nor the number of tacrolimus NDCs, was associated with increased TDM. The TDM rate was not significantly different for subjects who had two or more tacrolimus switches vs those who had just one (IRR 0.86, p = .127). The number of tacrolimus TDM events in subjects with three or more tacrolimus NDCs was also not statistically different from those who had two or fewer tacrolimus NDCs (IRR 1.14, p = .175).
Unadjusted mean total HCCs () were not significantly different between the FS and VS groups (US$34,071 vs US$33,470; p = .851). ED, inpatient, and outpatient cost components were also similar for both groups. There was no statistically significant difference in total pharmacy costs, although tacrolimus medication costs were significantly lower in the VS group compared with the FS group ($3,135 vs $3,629; p = .007) during the 1-year observation period. After adjusting for demographic, clinical, and health plan characteristics, the FS group also demonstrated significantly higher total HCCs compared with the VS cohorts ($30,823 vs $28,054, p = .045; ).
Table 3. Unadjusted healthcare costs by variable-source and fixed-source tacrolimus prescription recipients during calendar year 2013.
Table 4. Adjusted average total healthcare cost during calendar year 2013 across fixed- and variable-source tacrolimus prescription recipients.
Discussion
Switching between various manufacturers of tacrolimus is permitted on the basis of bioequivalence to the reference product. When switching does occur, from brand to generic or from one generic to another, the switch may be related to a number of factors, including patient preferences, financial considerations, product availability at the pharmacy, and/or drug formulary coverage. Although manufacturer substitutions of tacrolimus can be managed with attentive TDM testing and appropriate dose titration, the extent that such monitoring occurs in real-world clinical practice—where the prescriber may not be aware that the substitution has occurred—is not clear.
This study found a greater overall rate of TDM and lower total HCCs among patients receiving VS tacrolimus. Of note, there was a substantial period of time between the “switch date” and subsequent TDM. This delay in TDM may be the result of factors at the physician, pharmacy, or patient level. The prescriber may not be aware that a manufacturer substitution has been implemented at the point of dispensing, and that TDM is necessary. VS patients used ED services to a greater extent than FS patients, although this analysis did not adjust for potential confounders. Among the VS group, a greater proportion of patients used ED services; however, among the subjects in both groups who used ED services, the frequency of ED visits was similar. It is important to note that, although ED utilization was higher among VS patients, the reason for this use was not assessed, and therefore it is not known whether the encounter was related to a transplant complication or other causes. Interestingly, greater ED use among the VS group did not translate into greater ED costs.
To our knowledge, no previous research has examined patterns of ED use among individuals who switch between tacrolimus manufacturers. More frequent TDM in the VS group, combined with greater ED use, may suggest that the ED is being used as a portal to obtain TDM in the VS group. The place of treatment associated with tacrolimus TDM was not evaluated in the current study, and further research is warranted to determine drivers of ED utilization among switch patients.
The therapeutic benefit of tacrolimus TDM (i.e. avoiding toxicity) is well establishedCitation8–10,Citation12,Citation15,Citation20. Although there are no guidelines to definitively inform the appropriate frequency of tacrolimus TDM, guidance exists regarding its necessity, particularly after a medication change or a change in a patient’s statusCitation14. Additionally, the package insert establishes TDM as an essential component of patient care and as a useful adjunct for evaluating effectiveness, tolerability, dose adjustments, and complianceCitation13. To this end, the VS group had a TDM rate that was 13% greater than that in the FS group in the analysis modeling tacrolimus TDM rates, accounting for approximately one additional encounter. Nearly all patients (94%) had at least one tacrolimus TDM encounter, and the overall mean number of such encounters was approximately eight. However, only 50% of the patients in the VS group had a tacrolimus TDM encounter within 4 weeks of their switch date, and 42% did not have a TDM encounter for at least 43 days, despite half of VS users having an outpatient visit within 14 days of a switch in tacrolimus manufacturer. Given the importance of TDM and the signal seen with ED use among the VS group, these data reveal an opportunity to improve engagement between pharmacy stakeholders who make drug dispensing decisions and clinicians who care for patients and make decisions regarding TDM after drug modifications.
Most US states require patient notification when a generic substitution is made at the point of dispensing; however, outside of some specific situations (e.g. dispense-as-written requirements), provider-notification is typically not required. An opportunity may exist to provide for additional channels of communication between the pharmacy and the prescriber when formulation switches of NTI drugs occur. In addition, payers have unique visibility into medication use patterns, and the ability to signal the need for communication between the pharmacy and the prescriber. Further research is warranted to determine the factors associated with delayed TDM after medication regimen modifications, and to identify potential opportunities to improve quality of care.
Unadjusted total HCCs were not significantly different between the FS and VS groups; however, tacrolimus medication costs, which are included in the total HCCs, were significantly lower in the VS group. Adjusted total HCCs were 9% lower for the VS group vs the FS group, despite higher unadjusted ED utilization rates. Lower tacrolimus medication costs may have contributed to this difference. In addition, although the imbalance between MAPD and commercial patients was adjusted for in the multivariate analysis, it is possible that the inclusion of other relevant covariates with the potential to impact downstream cost and HRU—such as transplantation-related factors, socioeconomic status, baseline HRU, and baseline costs—would have resulted in a more robust analysis.
Limitations
This study utilized data from the Humana database. Individuals included in this administrative claims database have either commercial group-based insurance coverage or MAPD coverage. As such, the transplant population may systematically differ from registry sources such as the United States Renal Data System (USRDS) registry, which includes all end stage renal disease patients in the US without regard to health insurance coverage or age. In addition, registries are focused on collecting data for a specific disease state and will have more comprehensive and detailed demographic and transplant-related clinical information than administrative data sources will contain. For example, many transplant-specific variables were not available for this analysis, including donor and recipient characteristics, relevant laboratory values, and time since transplant. In addition, claims data may not accurately reflect important transplant-related outcomes such as acute rejection and chronic renal allograft nephropathy, and the duration of study follow-up may not adequately capture long-term economic impact resulting from deteriorating graft function. Although the study population was sourced from the administrative records of a large national health plan, a large proportion of its patients reside in the Southern US and had Medicare coverage. Thus, the results may not be generalizable to the broader US population or individuals with different types of health insurance coverage. As expected, limitations that are common to studies using administrative claims data apply to this studyCitation21–24, including a lack of certain information retained in the database, and errors/omissions in claims coding.
The cross-sectional examination of this data was restricted to a 1-year time period, and thus long-term clinical and/or economic outcomes were not captured. In addition, given the cross-sectional design, temporality between the timing of medication switching and measurement of the study outcomes was not enforced; alternative study designs (e.g. historical cohort) would be required to address this limitation. Additional unmeasured confounders may have limited the findings and restricted their interpretation. While the actual date of kidney transplant is an important variable, it was not available for patients in this study. However, the selection criteria applied did include requirements for the medication as well as procedure codes to identify patients with kidney transplants. Most of the endpoints were designed as all-cause utilization, so HRU and HCCs were not all specific to transplantation. Finally, a greater number of HRU events are likely to have been captured in patients who interfaced with the healthcare system earlier and more frequently; as such, an observational bias may have existed. While this study did not specifically examine the clinical impact of switching between manufacturers of an NTI drug, switching manufacturers is also a factor for which the risk for negative patient outcomes must be evaluated.
Conclusion
In this study, approximately one-third of tacrolimus users had evidence of a switch between tacrolimus manufacturers, often with a protracted delay in tacrolimus TDM testing after the switch. While studies have shown that patients can be safely managed in a controlled environment over the short-term using any approved tacrolimus formulationCitation20, the literature is devoid of reports that are representative of the real-world setting in which patients may be cycled through products from multiple manufacturers. Even less clear is what happens to these patients over longer time-horizons.
The concern over a switch is ultimately the potential to exacerbate variability in tacrolimus exposure. While the present study was not designed to examine causal relationships or long-term clinical outcomes, it does suggest that, over 1 year, patients who switched had significantly more tacrolimus TDM and lower total costs, likely due to lower tacrolimus costs. The unadjusted findings also indicated that the switch group had more ED visits without a corresponding increase in ED costs. Further research is warranted to understand the reasons for ED use among patients in the VS group, and to determine the factors associated with delayed TDM after medication regimen modification. Opportunities may exist to improve the quality of care for patients receiving immunosuppressant treatment with tacrolimus.
Transparency
Declaration of funding
This study was funded by Astellas Pharma Global Development, Inc.
Declaration of financial/other interests
The authors of this manuscript have conflicts of interests to disclose. This research was conceived, funded, and carried out collaboratively by Humana Inc., Astellas Pharma Global Development, Inc., and Comprehensive Health Insights, Inc. (CHI). The research concept was approved by the Joint Research Governance Committee of the Astellas–Humana Research Collaboration, comprised of Astellas, Humana, and CHI employees. Comprehensive Health Insights, Inc., a wholly-owned subsidiary of Humana Inc., received compensation from Astellas in connection with the conduct of this research and development of this manuscript. SB is employed by CHI and reports grants from various pharmaceutical companies; EL is a former employee of Astellas; PK is a former employee of CHI; BS is employed by CHI, is a stakeholder in Humana Inc., and in the course of his employment has conducted research sponsored by Pfizer, Novo Nordisk, Shire, Janssen, and Allergan; BF, JSc, GT, and JSp are employees of Astellas Pharma Global Development, Inc. JME peer reviewers on this manuscript have no relevant financial or other relationships to disclose.
Previous presentations
These data have been previously presented as a poster at the 2015 American Transplant Congress in Philadelphia.
Acknowledgments
This study was funded by Astellas Pharma Global Development, Inc. James Wallis, MRes, of Cello Health MedErgy provided editorial support during the development of the manuscript. Editorial support was funded by Astellas Pharma, Inc.
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