: To assess comparative resource use for patients given a new diagnosis of nonvalvular atrial fibrillation (NVAF) and newly treated with dabigatran or warfarin in a US real-world setting.

: A retrospective cohort analysis was conducted using de-identified electronic health record data from a large, nationwide database of integrated delivery networks (IDNs).

: We identified newly diagnosed NVAF patients, newly treated with dabigatran or warfarin ≤90 days of diagnosis between October 1, 2010, and September 30, 2012. Patients had ≥1 year of data prior to initiating therapy (index date). Patients in the warfarin cohort were propensity score matched 1:1 to patients in the dabigatran cohort and were followed up to 1 year after index to assess all-cause, stroke-related, and bleed-related healthcare resource use.

: A total of 3890 patients (1945, dabigatran; 1945, warfarin) were included and balanced through the matching process. The probability of an all-cause hospitalization was significantly lower in the dabigatran cohort compared with the warfarin cohort (42.8% vs 47.5%; P = .007). The dabigatran cohort had significantly lower per-patient per-year (PPPY) hospital (1.07 vs 1.20), emergency department (ED) (0.36 vs 0.51), and physician office visits (10.64 vs 18.13), as well as mean length of hospital stay (4.0 vs 4.6 days), compared with the warfarin cohort (all P <.001). Total PPPY stroke-related hospitalizations and physician office visits, and total PPPY bleed-related ED visits, were also significantly lower for the dabigatran cohort compared with the warfarin cohort (all P <.05).

: Newly diagnosed NVAF patients, newly treated with dabigatran and receiving care in US IDNs, experienced fewer all-cause healthcare resources than patients initiating warfarin.

Nonvalvular atrial fibrillation (NVAF), which refers to atrial fibrillation (AF) without valvular disease, accounts for more than 95% of all cases of AF

 in the United States. Clinical trial data have shown that oral anticoagulation therapy substantially reduces the risk of stroke in patients with AF.

Warfarin is the most prevalent anticoagulant prescribed in the United States today.

 The drug has been shown to reduce the risk of stroke by about two-thirds compared with placebo.

 However, warfarin is associated with a number of therapeutic challenges. For instance, warfarin requires frequent monitoring to maintain international normalized ratio (INR) within a narrow therapeutic range (2.0-3.0).

For INR values that fall outside of the optimal therapeutic range, the risk of hemorrhage increases with the level of INR,

 with yearly rates of major bleeding ranging from 0.5% to 6.5% and fatal bleeding ranging from 0.1% to 1.0% among warfarin-treated patients.

 Additional challenges of warfarin use include the ability of dietary vitamin K intake and concomitant medications to interact with warfarin, which may influence INR maintenance and the time spent within therapeutic range.

Dabigatran, approved by the FDA in October 2010, is the first novel oral anticoagulant (NOAC) to become available for clinical use in more than 50 years.

 It has been shown to be associated with significantly lower rates of stroke, systemic embolism, and intracranial hemorrhages compared with warfarin.

Due to the recent emergence of pharmaceutical alternatives to warfarin, payers in the United States have been faced with the option of placing NOACs on their formularies, thereby intensifying the need for evaluations of the overall economic impact—particularly healthcare resource utilization and direct medical costs&mdash;across treatment types.

Limited real-world data exist on healthcare utilization associated with NOAC therapies; as such, the primary objective of this analysis was to assess comparative resource use for patients receiving a new diagnosis of NVAF, and newly treated with dabigatran or warfarin, using de-identified electronic health record (EHR) data from integrated delivery networks (IDNs). With an emphasis on the continuum of healthcare services, this unique data source provides additional insight on real-world estimates of resource utilization for this patient population.

A retrospective cohort analysis was conducted using de-identified EHR data from October 1, 2010, through September 30, 2013. During the study period, the Humedica EHR database contained de-identified data on more than 30 million individuals across 38 states. Humedica integrates claims, prescription, and practice management data by partnering with medical groups, IDNs, and hospital chains to directly obtain data from EHRs in real time.

Approximately 40% of patients in the Humedica database have linked inpatient and outpatient records, allowing for a complete analysis of a patient’s full experience in the IDN system. This subset of patients in the EHR who are from an IDN setting formed the basis for the patient population in this study.

The population of interest for this analysis included adult patients ≥18 years, newly diagnosed with NVAF and newly treated with an oral anticoagulant (OAC), either dabigatran or warfarin. The date of the first OAC prescription was defined as the index date, which must have occurred between October 1, 2010, and September 30, 2012, to allow for 1-year baseline and follow-up periods.

To ensure cohorts of newly treated patients, patients were only included if they had no prescriptions for any OAC during the 12-month pre-index period. Additionally, to ensure cohorts of newly diagnosed NVAF patients, the first observed NVAF diagnosis (identified by International Classification of Diseases, Ninth Revision, Clinical Modification code 427.31 in any position) during the 12-month pre-index period was required to appear on the index date or within 3 months prior to the index date.

Patients were required to have ≥1 EHR encounter in the 12 months prior to the index date (defined as the baseline period) and ≥1 EHR encounter following the index date (defined as the follow-up period). Furthermore, patients must have had integrated inpatient and outpatient data, which allow for a complete picture of resource utilization in the IDN setting. Patients in the warfarin cohort were propensity score matched 1:1 to patients in the dabigatran cohort, and only matched pairs were included in the final data set.

Patients were required to have ≥2 prescriptions of the index OAC (including the prescription on the index date) for study inclusion. Additionally, on the index date or within 3 months prior to the index date, patients were required to have ≥1 inpatient, ≥2 physician office visits, ≥2 emergency department (ED) visits, or ≥1 physician office visit and ≥1 ED visit on distinct service dates with an NVAF diagnosis.

Patients with the following were excluded from the study sample: diagnosis of hyperthyroidism (potentially reversible underlying cause of AF) or valvular heart disease during the 12 months pre-index or an indication of cardiac surgery or diagnosis of pericarditis, myocarditis, or pulmonary embolism within 3 months prior to the first NVAF diagnosis.

Patients were followed for a maximum of 12 months. The follow-up period commenced on the index date and ended on the earlier of the following: (a) last EHR encounter date in the observed data or (b) the last day of the 12-month postindex period.

Additional Details on the Matching Process

Patients in the warfarin cohort were matched 1:1 to patients in the dabigatran cohort using the propensity score matching (PSM) method. The propensity score was defined as the probability of being treated with dabigatran (dependent variable) based on a set of baseline characteristics, including sex, age, geographic region, month of index date, Deyo-Charlson comorbidity index, CHADS2 stroke risk score, HEMORR2HAGES bleeding risk score, and selected comorbidities (ie, acute myocardial infarction, cardiomyopathy, congestive heart failure, chronic obstructive pulmonary disease/emphysema, diabetes, hyperlipidemia, hypertension, peptic ulcer/gastroesophageal reflux disease, ischemic stroke/transient ischemic attack, venous thromboembolism). PSM by logistic regression, with the Nearest Neighbor method within a caliper of 0.20 of the standard deviation of the estimated logit, was used to match dabigatran and warfarin cohorts.

Comparative healthcare resource utilization during the follow-up period was the primary outcome of interest for this analysis. Incidence rates for the first healthcare resource use were reported by type of service (ie, hospitalization, ED visit, physician office visit) for all-cause, stroke-related, and bleed-related events.

The total number of all-cause, stroke-related, and bleed-related resource utilization events were assessed and reported by type of service (ie, hospitalization, ED visit, physician office visit). Among patients with at least 1 hospitalization, the average length of stay per all-cause hospitalization was estimated.

Kaplan-Meier survival analyses were conducted to assess the time to first all-cause hospitalization, time to first stroke-related hospitalization, and time to first bleed-related hospitalization following index for each cohort. A log-rank sum test was used to test the significant differences between survival curves.

In addition to using a PSM method for identifying matched patients, a sensitivity analysis was conducted using a doubly robust approach in the form of a multivariate Cox proportional hazards model; this was to assess the association between OAC treatment and risk of first all-cause hospitalization, first all-cause ED visit, and first all-cause physician office visit.

 The primary covariate used in the model included the presence of dabigatran as the index therapy. Incidence rates were presented as a rate per 100 patient-years to allow for variable follow-up. Incidence rates were calculated using the number of patients identified in each cohort having an event during the follow-up period as the numerator. The denominator consisted of all patients with NVAF in the cohort (the denominator was censored based on the date of first all-cause or disease-specific event, last EHR encounter date in the observed data, or the last day of the 12-month postindex period). For each incidence rate, a 95% confidence interval was derived assuming a binomial distribution.

Outcomes tested for statistical significance (defined as P <.05) between the dabigatran and warfarin cohorts, including baseline demographic and clinical characteristics and number of healthcare resource utilization events. Chi-squared contingency tests were used for comparison of proportions, while nonparametric Wilcoxon rank sum tests were used to evaluate the statistical significance of differences in continuous variables.

Baseline Demographic and Clinical Characteristics

Prior to matching, 1945 patients with NVAF who met the inclusion criteria initiated treatment on dabigatran, while 11,473 initiated treatment on warfarin. The matched analysis included 1945 patients in the dabigatran and warfarin cohorts, nearly 58% of them male and with a mean age of approximately 70 years (Table 1).

Following the PSM process, mean CHADS2 and CHA2DS2-VASc scores were approximately 1.3 and 2.5, indicating moderate to high stroke risk, respectively.

 Additionally, the HEMORR2HAGES score was approximately 1.50, indicating low to moderate bleeding risk.

Coronary artery disease, diabetes, hyperlipidemia, and hypertension were the most common comorbid conditions identified among the matched study population. Dabigatran and warfarin patients were well matched on demographic and clinical characteristics, with no significant differences observed in age, sex, region, selected comorbidities, Deyo-Charlson comorbidity index, Elixhauser comorbidity index, stroke risk (CHADS2), and bleeding risk (HEMORR2HAGES). The average duration of follow-up was 342 days (±64) for the dabigatran index cohort and 332 days

Results from the Kaplan-Meier analyses demonstrated the time to first all-cause hospitalization to be significantly (P = .007) longer for the dabigatran cohort compared with the warfarin cohort (Figure 1). In fact, only 42.8% of the dabigatran cohort had an all-cause hospitalization at the end of 1 year compared with 47.5% of the warfarin cohort.

Similarly, the time to first stroke-related hospitalization was significantly (P = .024) longer for the dabigatran cohort (Figure 2). Moreover, just 3.5% of the dabigatran cohort had a stroke-related hospitalization at the end of 1 year compared with 4.9% of the warfarin cohort. Results from the analysis of time to first bleed-related hospitalization were not significant.

First all-cause hospitalization and ED visits per 100 patient-years were considerably lower in the dabigatran cohort compared with the warfarin cohort (Table 2). Incidence rates for first stroke-related and bleed-related events (hospitalizations, ED visits, physician office visits) were also lower in the dabigatran cohort.

The per-patient per-year (PPPY) total number of all-cause hospitalizations, ED visits, and physician office visits were all significantly (P <.001) lower for the dabigatran cohort compared with the warfarin cohort (mean all-cause PPPY hospitalizations, 1.07 vs 1.20; mean all-cause PPPY ED visits, 0.36 vs 0.51; mean all-cause PPPY physician office visits, 10.64 vs 18.13) (Table 2).

Additionally, the dabigatran cohort had a significantly lower mean length of stay for all-cause inpatient admissions (4.0 vs 4.6 days; P <.001) compared with the warfarin cohort. Moreover, the PPPY total of stroke-related hospitalizations and physician office visits was significantly lower for the dabigatran cohort compared with the warfarin cohort (mean stroke-related PPPY hospitalizations, 0.06 vs 0.10, P = .03; mean stroke-related PPPY ED visits, 0.00 vs 0.01, P = .65; mean stroke-related PPPY physician office visits, 0.16 vs 0.29, P = .02) (Table 2).

The PPPY total number of bleed-related ED visits were significantly (P = .02) lower for the dabigatran cohort, while the bleed-related hospitalizations and physician office visits were not statistically significantly different between cohorts (mean bleed-related PPPY hospitalizations, 0.05 vs 0.03, P = .49; mean bleed-related PPPY ED visits, 0.01 vs 0.03, P = .02; mean bleed-related PPPY physician office visits, 0.05 vs 0.15, P = .57) (Table 3).

Results of the sensitivity analysis using adjusted Cox regression models to predict risk of first all-cause hospitalization, ED visit, or physician office visit were consistent with the unadjusted matched results. In particular, treatment with dabigatran was associated with a significantly (P <.01) lower likelihood of a first all-cause event.

A total of 1945 matched patients, newly diagnosed with NVAF and newly treated with dabigatran or warfarin, were analyzed from the de-identified EHR IDN dataset. The time to first all-cause hospitalization and time to first stroke-related hospitalization were significantly longer for the dabigatran cohort.

Moreover, total PPPY all-cause resource use (hospitalizations, ED visits, and physician office visits), total PPPY stroke-related hospitalizations and physician office visits, and total PPPY bleed-related ED visits were significantly lower for the dabigatran cohort compared with the warfarin cohort during the 1-year follow-up period.

To the best of our knowledge, no other published articles of retrospective database design have compared resource utilization using IDN data in this population of newly diagnosed NVAF patients who have been newly treated with dabigatran or warfarin. Other recently published retrospective database studies of dabigatran users have focused on characteristics affecting OAC treatment selection among patients with NVAF,

 thus demonstrating an important need to measure economic outcomes in this population.

Using prospectively collected data from the RE-LY (Randomized Evaluation of Long-term anticoagulant therapY: dabigatran vs. warfarin) clinical trial, Connolly et al estimated the risk of stroke or systemic embolism among patients with AF randomized to 1 of 2 fixed dose arms of dabigatran (110 or 150 mg twice daily) or adjusted-dose warfarin.

Compared with the warfarin arm, the authors found a statistically significant reduction in the primary outcome measure associated with the 150-mg dose of dabigatran. Despite differences in sample selection criteria between the RE-LY trial and our EHR-based study, we, too, found a statistically significant reduction in stroke events (ie, stroke-related hospitalizations) among patients newly treated with dabigatran.

Our study is subject to certain limitations that are inherent in EHR data in general, primarily the lack of continuous eligibility/enrollment data, which potentially underestimates the level of observed utilization events. As a proxy for ensuring continuous eligibility, patients were required to have at least 1 medical encounter in the baseline period and at least 1 follow-up encounter, which assumes continuous enrollment in the health plan before and after index.

Additionally, National Drug Code (NDC) information was not reliably provided by data sources contributing to the EHR database, making it difficult to identify prescriptions. To avoid misclassifying prescriptions, this study used the drug name field, rather than the NDC field, for dabigatran or warfarin identification.

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Web Exclusives

This study assesses comparative resource use for patients given a new diagnosis of nonvalvular atrial fibrillation treated with dabigatran or warfarin in a real-world setting.

Resource Use Among Nonvalvular Atrial Fibrillation Patients

: This retrospective study compared the treatment patterns of ustekinumab (UST), with recommended maintenance administration every 12 weeks, with adalimumab (ADA) and etanercept (ETA), administered weekly or every other week, for the treatment of plaque psoriasis.

: Persons with psoriasis ≥18 years and having ≥1 medical or pharmacy claim for UST or ≥1 pharmacy claim for ADA or ETA, from February 8, 2010, to January 31, 2011, were selected from the MarketScan databases and assigned to mutually exclusive cohorts. Patient characteristics and dose escalation were described during the 12-month pre-index period and 12-month follow-up period, respectively. Differences in baseline characteristics were adjusted using inverse probability of treatment weights. Pairwise comparisons of rates of discontinuations, restarts, and switches were made between ADA and UST and between ETA and UST.

: A total of 2933 ADA, 4011 ETA, and 583 UST patients were selected. Patients in the UST cohort had higher baseline comorbidity scores and greater exposure to multiple psoriasis drugs at baseline. Dose escalation was observed in 7.8% of ADA patients, 30.9% of ETA patients, and 18.2% of UST patients. Discontinuations were seen in 38.6% of UST patients, 53.3% of ADA patients, and 56.2% of ETA patients. Restarts were seen in 8.9% of UST patients, 17.5% of ADA patients, and 23.1% of ETA patients. Switching to a nonindex medication occurred in 14.8% of UST patients compared with 22.0% of ETA patients and in 14.4% of UST patients compared with 20.8% of ADA patients.

: Discontinuations, restarts, and switches were common for all 3 biologics, but were significantly lower among patients with psoriasis receiving UST compared with those receiving ADA or ETA.

Psoriasis is a chronic, immune-mediated, inflammatory skin disease affecting as many as 7.4 million Americans, or 3.2%of the United States population 20 years and older.

 Plaque psoriasis is the most common form and manifests as elevated red lesions covered by scaling silvery plaques. Psoriasis affects health-related quality of life, treatments can be costly and time-consuming, and patients with more severe psoriasis experience a greater negative impact on their quality of life.

Three subcutaneously administered biologic drugs, adalimumab (ADA),

 are effective for use in the treatment of moderate to severe plaque psoriasis and improve health-related quality of life.

 ADA and ETA block the cytokine tumor necrosis factor-alpha (TNF-alpha) that is associated with inflammation.

ADA is recommended for administration as an initial 80-mg dose (induction dose) followed by 40 mg every other week starting 1 week after the initial dose (maintenance period).

 ETA is recommended for administration as a 50-mg dose twice per week for 3 months (induction period) followed by a step down to a 50-mg dose once per week (maintenance period).

UST, the most recently approved of the 3 biologics (approved September 25, 2009), selectively targets the cytokines interleukin 12 and 23 that are associated with psoriasis inflammation.

 UST is recommended to be administered at weeks 0 and 4, and then every 12 weeks thereafter. It is dosed by weight: 45 mg for patients ≤100 kg and 90 mg for patients >100 kg.

All 3 products can be administered by healthcare professionals or self-administered by patients. Adjustments to the dosing and administration schedules of ADA and ETA are necessary for those responding suboptimally to the standard dosing regimen.

Currently, few studies have been published comparing UST with the other subcutaneous biologic medications used to treat psoriasis in real-world clinical practice.

 The objectives of this study were to examine the dosing and timing of UST and to compare patient characteristics, dose escalation, discontinuation, restarts, and switching with the subcutaneous TNF-alpha inhibitors, ADA and ETA.

This retrospective analysis used the Truven Health MarketScan Commercial Claims and Encounters and Medicare Supplemental Databases, which contain administrative healthcare claims for inpatient and outpatient medical and outpatient pharmacy services. Individuals in the databases are insured through a variety of private health plans, including preferred provider organizations, indemnity plans, and health maintenance organizations.

The Medicare database contains claims for individuals insured through Medicare supplemental commercial insurance. Both Medicare-covered and insurer-covered services are included due to coordination of benefits. These databases include enrollment data from approximately 150 large employers and health plans across the United States, which provide private healthcare coverage and contain more than 60 million covered lives per year.

Data are de-identified and are fully compliant with the Health Insurance Portability and Accountability Act of 1996. As this study did not involve the collection, use, or transmittal of individually identifiable data, it did not require Institutional Review Board review or approval.

Persons who were included in the analyses were ≥18 years; had ≥1 pharmacy claim for ADA, ETA, or UST, or had ≥1 medical claim for UST, between February 8, 2010, and January 31, 2011 (index date); had at least 12 months of pre- and postindex continuous enrollment in medical and pharmacy benefits; and had at least 1 nondiagnostic claim with a diagnosis of psoriasis (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] 696.1) on the index date or during the 12-month pre-index period.

Patients with a diagnosis of any of the following conditions on the index date, or during the 12-month pre-index period, were excluded: rheumatoid arthritis, psoriatic arthritis, ulcerative colitis, Crohn’s disease, ankylosing spondylitis. Patients were selected into 3 mutually exclusive cohorts (ADA, ETA, UST) based the index drug.

Measures Assessed During the 12-month Pre-index Period

 and individual comorbidities were assessed using the ICD-9-CM codes on nondiagnostic medical claims during the baseline period. Psoriasis treatments were also assessed during the baseline period. Per-patient healthcare expenditures were measured using paid amounts for adjudicated claims. Psoriasis-related expenditures were measured using medical claims with a diagnosis of psoriasis and pharmacy claims for any psoriasis treatment.

For UST medical claims, the dose was imputed using the amount paid on the claim and data for the wholesale acquisition cost at the time of the claim. This imputation was applied in <5% of the UST cohort. The dose for pharmacy claims was computed by multiplying drug strength (specified by the National Drug Code) by the quantity.

Dose escalation for UST was defined as the presence of any claim with a dose higher than the patient’s initial dose. For ADA and ETA, maintenance dose escalation was defined as an average weekly dose ([total dispensed dose]/[days’ supply/7]) in the maintenance period >15% higher than the maintenance dose recommended on the product label. The ADA maintenance period was defined as starting with the second ADA claim, and the ETA maintenance period was defined as the period starting 91 days after the index date.

Discontinuation was defined as a treatment gap ≥90 days after the end of the days’ supply on a pharmacy claim for ADA and ETA, which allowed for refill of a 90-day mail order pharmacy claim before a patient discontinued. For UST, discontinuation was defined as a treatment gap ≥168 days after the end of the days’ supply for pharmacy claims or the effective days’ supply for medical claims (28 days for the first claim and 84 days for subsequent claims). A treatment gap of twice the dosing interval for UST (168 days) was selected after a review of the literature.

Treatment restart was defined as the presence of a prescription or medical claim for the index medication following discontinuation of the index medication. Treatment switch was defined by the presence of a prescription claim for a nonindex biologic medication following discontinuation of the index medication.

Pairwise descriptive comparisons of demographics, comorbidities, healthcare expenditures, and psoriasis treatments in the pre-index period were made between ADA and UST and between ETA and UST. Chi-squared tests were conducted for differences in dichotomous or categorical variables, and t tests were conducted for comparisons of continuous variables.

To address potential selection bias, a propensity-score—based inverse probability of treatment weighting (IPTW) was used. IPTW was used to reduce the observed differences in the outcomes due to the drug assignment rather than other underlying differences, such as age and comorbidities.

Two logistic regression models were estimated: 1 to compute the propensity score for the choice of ADA versus UST and 1 for ETA versus UST. The variables used in the models included demographics, pre-period comorbidities and expenditures, and the counts of pre-period psoriasis treatments.

The results of these models were used to assign a weight (inverse of the propensity score) to each patient. The comparisons of the proportions of patients who discontinued, restarted, or switched were conducted using the weighted data. To further control for remaining imbalance in baseline characteristics, Cox proportional hazards models were used to estimate the hazard ratios for discontinuation comparing ADA versus UST and ETA versus UST.

There were 2933 ADA, 4011 ETA, and 583 UST patients who received a psoriasis diagnosis who met the selection criteria (Figure 1).

Pre-period Demographic and Clinical Characteristics

Patients in the UST cohort were slightly older, and there was a higher proportion of UST (10.5%) patients ≥65 years compared with ADA (7.8%; P = .033). UST patients had a higher average CCI (0.46) compared with ADA (0.35; P = .004) and ETA (0.37; P = .02).

The UST cohort had a higher proportion of patients with dyslipidemia (23.3%) compared with ADA (18.9%; P = .015) and ETA (20.2%; P = .083), and a higher proportion of patients who had been given an obesity diagnosis (6.3%) compared with ADA (3.8%; P = .004) and ETA (3.5%; P <.001). Patients in the UST cohort had significantly higher pre-period total and psoriasis-related healthcare expenditures than the ADA cohort, but expenditures were not significantly different than those of the ETA cohort (Table 1).

Most UST patients (93.1%), 78.4% of ADA patients, and 72% of ETA patients had a previous topical, systemic, or biologic treatment (P <.001 in all comparisons). Receipt of the index treatment as third-line or higher (2+ prior biologic therapies other than the index drug) was observed in 29.2% of UST patients compared with 8.5% of ADA (P <.001) and 2.4% of ETA (P <.001) patients. The UST patients were all taking UST for the first time, whereas 70.0% of the ADA patients (P <.001) and 82.3% of the ETA patients (P <.001) had taken their respective drugs during the pre-period (Table 2).

The median number of days between UST doses 1 and 2 was 28, as recommended in the drug’s label (Figure 2). The mean and median number of days between subsequent doses were slightly higher than the 84 days in the prescribing guidelines.

Overall, dose escalation was 18.2% among UST patients (Figure 3). At the second dose, only 1.3% of patients had escalated from 45 mg to 90 mg. At dose 3, 12.2% of patients who had initiated at 45 mg were taking the 90-mg dose. The proportion of escalators at doses 4, 5, and 6 were 15.5%, 16.9%, and 18.5%, respectively. Maintenance dose escalation was 7.8% among ADA patients and 30.9% among ETA patients.

IPTW Comparisons of Baseline Characteristics

The propensity models of UST versus ADA and UST versus ETA resulted in weighted cohorts that were well balanced in terms of demographics, CCI, comorbidities, and pre-period healthcare expenditures. The major differences that remained after weighting were the proportions with various types of pre-period treatments.

The weighted UST cohorts had higher proportions of patients with any pre-period biologic, systemic, or topical therapy other than the index drug (90.5% of UST vs 80.3% of ADA; P <.001; 91.6% of UST vs 74.2% of ETA; P <.001) and higher proportions of patients with prior phototherapy (9.4% of UST vs 6.4% of ADA; P = .008; 11.3% of UST vs 5.0% of ETA; P <.001).

IPTW Comparisons of Discontinuations, Restarts, and Switches

Smaller proportions of the UST cohort discontinued, restarted, or switched than in the other cohorts (P <.001 for all comparisons). Specifically, 38.6% of UST discontinued versus 53.3% of ADA, 8.9% of UST restarted versus 17.5% of ADA, and 14.4% of UST switched versus 20.8% of ADA.

Also, 38.6% of UST discontinued versus 56.2% of ETA, 8.9% of UST restarted versus 23.1% of ETA, and 14.8% of UST switched versus 22.0% ETA (Table 2). The results of the multivariate proportional hazard models indicated a lower hazard of discontinuing for UST compared with either drug. The hazard ratio of discontinuing and 95% CI for UST versus ADA was 0.626 (95% CI, 0.580-0.676) and for UST versus ETA, 0.586 (95% CI, 0.545-0.629).

This study found that prior to starting UST treatment, patients with psoriasis initiating UST treatment had higher comorbidity rates and higher baseline healthcare costs compared with patients who were initiating or continuing ADA or ETA. Also, a higher proportion of UST patients were on a third-line biologic therapy.

After using the IPTW technique to balance the baseline characteristics of the 3 cohorts, the UST cohort had the lowest rates of discontinuation, restarts, or switches during 1 year of follow-up. Cox proportional hazards models adjusted for any remaining imbalances and showed that UST patients had a significantly lower hazard of discontinuation compared with ADA or ETA patients.

Clinical studies have shown that dose escalation may improve effectiveness for those with severe disease who are inadequately or only partially responding to the standard dosing regimens of all 3 of the drugs.

 The dosing intervals of UST were consistent with the prescribing guidelines, and dose escalation occurred in 18.2% of UST patients.

 Maintenance dose escalation occurred in 7.8% of ADA patients and 30.9% of ETA patients. This study did not examine whether shortening or lengthening the interval between doses contributed to dose escalation, nor did we have clinical information to understand the reason(s) for dose escalation.

One study used a physician survey to evaluate the baseline characteristics of patients with psoriasis treated with ADA, ETA, or UST and found that UST had a higher proportion of patients with prior biologic use: 62.5% of UST 90 mg, 55.4% of UST 45 mg, 14.4% of ADA, and 25.0% of ETA.

 The highest average number of baseline comorbidities was found in the UST 90-mg group (1.6), while the UST 45-mg group (1.1) was similar to ADA (1.2) and ETA (1.1).

Several studies have examined therapy discontinuation among patients with psoriasis treated with all 3 of our study drugs, each showing that discontinuation of psoriasis treatments was common, but that discontinuation rates were lowest among UST patients.

Feldman and colleagues used administrative claims data to evaluate medication utilization patterns of first-line ADA, ETA, and UST patients.

 In that study, discontinuation rates over a 12-month follow-up period post titration were lowest for UST (16%) and higher for ADA (27%) and ETA (35%). Another study used chart data to follow psoriasis patients for 4 years.

The UST group had the lowest discontinuation rate (39%) compared with 50% for ADA and 60% for ETA. Another study using administrative claims data included 8 patients with psoriasis treated with UST, 195 treated with ADA, and 369 treated with ETA.

Also in that study, discontinuation was evaluated among new initiators and existing users, and was defined as a gap ≥45 days of therapy without a restart or switch during the year. No UST patients discontinued treatment. Discontinuation rates were higher among initiators (ADA, 31%; ETA, 33%) than among existing users (ADA, 13%; ETA, 18%). Note that in our study, all of the UST patients were initiators of UST (see Table 2), while in the weighted cohorts, 68% of ADA and 80% of ETA patients had received the index drug during the baseline year.

Original Research

This retrospective study compared the treatment patterns of ustekinumab with recommended maintenance administration every 12 weeks, with adalimumab and etanercept, administered weekly or every other week, for the treatment of plaque psoriasis.

Comparative Treatment Patterns Among Psoriasis Patients Using Adalimumab, Etanercept, or Ustekinumab

: The study aimed to determine the impact of a Medicare Part D formulary tier change, specifically with respect to branded insulin products, on insulin adherence, hemoglobin A1C (A1C), and diabetes-related events in elderly diabetic beneficiaries.

: Retrospective database analysis based on pharmacy claims from a Medicare Advantage Prescription Drug (MAPD) plan in Texas.

: Elderly (≥65 years) beneficiaries with diabetes who were continuously enrolled from January 1, 2013, to December 31, 2014, with at least 2 prescription fills of any insulin product each year, were included. Outcomes included changes in the following, before and after the tier change (January 1, 2014): adherence to insulin medications (proportion of days covered for insulin products [PDC-i]); A1C; and diabetes-related events (hyperglycemia [790.21, 790.22, and 790.29], hyperosmolar hyperglycemic state [250.2-0.23], or diabetic ketoacidosis [249.1, 249.11, and 250.1-0.13]). For change in A1C, a minimum of 2 records of A1C measurement, 1 each in 2013 and after May 2014, was required.

: The sample consisted of 3285 beneficiaries, with the majority female (57.17%), aged 65-74 years (61.10%), and receiving the low-income subsidy (51.29%). Decline in PDC-i (mean = 0.48; SD = 0.25; P <.001) was observed among 2333 beneficiaries (71%). Only 1516 beneficiaries met the criteria for calculating change in A1C, out of which 755 (49.80%) had an increase in A1C (mean = 0.92; SD = 0.98; P <.001) after the tier change. Diabetes-related events were observed in 19 beneficiaries before and 32 beneficiaries after the tier change.

: Change in tier of insulin medications was associated with a statistically significant decline in insulin adherence and increase in A1C within the MAPD population.

Implemented in 2006, Medicare Part D was established to provide prescription drug coverage for Medicare beneficiaries. Medicare beneficiaries who receive Medicare Part D have had improved adherence to medications, increased rates of medication fills, decreased out-of-pocket expenses, decreased medical spending, and fewer avoidable hospitalizations.

 However, the design of the Part D benefit introduced a unique feature known as the coverage gap, or “donut hole.”

In 2013, Medicare beneficiaries paid an initial $325 deductible (or a deductible set by the health plan), followed by 25% of covered costs (or a co-payment set by the health plan) until the initial coverage limit of $2970 was met, as represented in Figure 1. The limit is reached by totaling the amounts paid by the beneficiary and the payer.

After the initial coverage limit was met, the beneficiary entered the coverage gap, during which they paid 47.5% of the cost on brand-name drugs and 79% of the cost on generic drugs until their total out-of-pocket spending reached a catastrophic limit of $4750. However, the amount of money that the payer pays does not contribute toward reaching the catastrophic limit. The catastrophic coverage period is when the beneficiary is paying the greater of 5% or $2.65 for generic/preferred drugs and $6.60 for other drugs.

Guidance provided by CMS plays a significant role in Medicare Part D sponsor formulary decisions. Many Medicare Part D sponsors place branded medications that do not have generic therapeutic equivalences—ie, insulin products&mdash;on a generic tier to decrease the cost burden of these medications for their Medicare beneficiaries. Per CMS, brand medications offered on a generic tier leave the health plan open to tiering exceptions for any brand medication in a higher tier.

CMS defines a tiering exception as a drug plan’s decision to charge a lower amount for a drug that is on its nonpreferred drug tier, based on specific criteria.

 CMS further states, within Chapter 18 of its Prescription Drug Benefit Manual, that if a beneficiary requests that a branded medication be provided at a generic cost, these medications do not meet the tiering exception criteria unless the health plan covers other brands at the generic tier and/or they meet specific criteria.

 This means that all Medicare Part D sponsors who were placing branded medications on a generic tier to provide coverage through the coverage gap for their Medicare beneficiaries were susceptible to having any branded medication that has alternatives on generic tiers to be potentially approved for generic cost sharing. On January 1, 2014, many Medicare Part D sponsors moved brand name products from a generic drug tier to a brand drug tier.

As a result of this change, co-payments increased significantly for diabetic beneficiaries prior to entering the coverage gap. In addition, insulin products became more susceptible to the coverage gap and Medicare beneficiaries who required insulin therapy had to pay 47.5% out-of-pocket for these maintenance medications. Our study is the first known study to assess the effect of this change on patient outcomes.

In 2012, diabetes affected 25.9% of US residents 65 years or older, or 11.2 million residents. As a result, the estimated total cost of diagnosed diabetes increased by 41%: from $174 billion in 2007 to $245 billion in 2012.

 In addition, the majority (62.4%) of the cost for diabetes care in the United States is provided by government insurance (including Medicare and Medicaid).

Further, between 2010 and 2012, 6 million adults 18 years or older with diagnosed diabetes reported using insulin and pills, or insulin only, for the treatment of diabetes.

 Currently, there are no available generic therapeutic alternatives for insulin products in the United States. Although the cost of insulin can be expensive, anti-diabetic agents and diabetes supplies make up only 12% of medical expenditures for diagnosed diabetes.

The 2 largest components that contribute to diabetes medical expenditures are prescription medications to treat complications of diabetes (18% of total medical cost) and hospital in-patient care (43%).

 The National Diabetes Statistic Report of 2014 found that “about 175,000 emergency room visits had a hyperglycemic crisis (eg, diabetic ketoacidosis and hyperglycemic hyperosmolar state) as the first-listed diagnosis.”

In addition, diabetes was listed 44% of the time as the primary cause of all new cases of kidney failure in the United States in 2011.

 The American Diabetes Association estimated the cost of diagnosed diabetes, in 2012, to be $245 billion, including $176 billion in direct medical costs and $69 billion in reduced productivity.

 As for hospital in-patient care, in 2009 in the United States, the CDC found that the average length of stay for a patient with diabetes as any-listed diagnosis was 4.8 days.

 These reasons, and more, are why controlling the cost of anti-diabetes medications, especially insulin therapy, is important to help control the cost of healthcare overall.

For Medicare beneficiaries who live with diabetes, poor adherence to medications can lead to a worsening of glycemic control, increased comorbidities, and diabetes-related hospitalizations. Zhang et al reported that entering the coverage gap reduced medication use (mainly brand name drugs) among Medicare beneficiaries who had heart failure and/or diabetes.

In addition, Gu et al found that the coverage gap significantly decreased adherence to diabetes medications by 39% and 30% for those beneficiaries in the coverage gap who had no coverage or generic coverage only, respectively.

 These reductions in medication use and adherence can negatively impact medical costs (ie, disease progression can lead to comorbidities and more hospitalizations).

 In this study, we examined the effects of changing the tier status of insulin on adherence to insulin medications (aspart, glulisine, lispro, glargine, detemir, and regular insulin), hemoglobin A1C (A1C), and diabetes-related events. In the light of existing literature, we predicted that Medicare beneficiaries who have higher co-payments but no coverage through the coverage gap would have decreased adherence to insulin therapy, an increase in A1C, and an increase in diabetes-related events compared with Medicare beneficiaries who had lower co-payments and coverage through the coverage gap. Evidence of any significant negative impact on health outcomes due to the policy changes may help in future investigations for creating better access to essential insulin medications.

The study was a retrospective database analysis conducted using data obtained from the pharmacy and medical claims of a Medicare Advantage Prescription Drug (MAPD) plan in Texas. The study population consisted of diabetes beneficiaries enrolled in an MAPD 1 year before (January 1, 2013-December 31, 2013) and 1 year after (January 1, 2014-December 31, 2014) the insulin tier status change, which was implemented on January 1, 2014.

Inclusion criteria were as follows: (a) aged ≥65; (b) continuously enrolled in an MAPD plan for 2 years, 1 year before and 1 year after the tiering change, in the southeast Texas region (South Texas and greater Houston areas); (c) diabetes diagnosis (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] codes 249.XX and 250.XX); and (d) at least 2 claims, in each year, for 1 or more of the following insulin medications: aspart, glulisine, lispro, glargine, detemir, or regular insulin (identified using Generic Product Identifiers, 14-digit codes that each contain 7 pairs of digits).

The calculation of change in A1C required additional criteria of having 2 A1C measurements, 1 before and 1 after the tiering change. “Before the tier change measurement” was defined as an A1C measurement any time in 2013 and “after the tier change measurement” was defined as an A1C measurement after May 2014, to account for the effect of the tier change. Exclusion criteria were: (a) beneficiaries receiving prescription drugs from a Veterans Affairs health provider, (b) hospice members, (c) those with cancer diagnoses (ICD-9-CM codes 140.XX-239.XX), (d) those with a change of low-income subsidy (LIS) status during the study period, and (e) those having Medicare without prescription coverage within the same health plan.

Insulin can be used, and is sometimes the sole therapy, to maintain glycemic control and slow the progression of diabetes. Poor adherence to insulin medications results in insufficient glycemic control, micro- and macrovascular complications leading to higher probabilities of hospitalization, higher medical cost, and increased chance of mortality.

 Hence, a change in medication adherence was evaluated only for insulin medications.

Adherence to insulin medication was calculated as the proportion of days covered for insulin products (PDC-i). Very little literature exists about the use of PDC as a measure of adherence for injectable products, due to its complex dosing based on a sliding scale: the quantity of insulin dispensed to a patient is calculated according to the days of supply.

Because a patient using the maximum dose daily will use the dispensed quantity within the calculated days of supply, any deviations in the refill time will be considered nonadherence, just as in the case of oral medications. Therefore, PDC-i was defined as the number of days the insulin medication was possessed by the beneficiary divided by the total number of days between the first insulin fill and the end of study period.

Change in PDC-i was calculated, for each beneficiary, as the difference between PDC-i values for the year before and the year after the tier change. PDC-i has been used in many studies as a standard measure for medication adherence.

 We used a PDC-i cutoff value of 0.8, with beneficiaries having PDC-i ≥0.8 considered as being adherent to insulin medications.

A1C is a clinical test widely used to indicate the mean blood glucose concentration over the lifespan of a red blood cell. The measure correlates best with mean blood glucose over the previous 8 to 12 weeks.

 A1C was calculated as at least 1 value during the indicated study period. If a beneficiary had more than 1 measurement for either of the 2 time periods, an average value of the measurements was taken and used for further calculations. Change in A1C was calculated by a difference between the 2 measurements of A1C for each beneficiary, 1 before and 1 after the tiering change.

Diabetes-related events were defined as having a diagnosis, reported within a healthcare setting, for any of the following events (designated by ICD-9-CM codes): hyperglycemia (790.21, 790.22, and 790.29), hyperosmolar hyperglycemic state (250.2-0.23), or diabetic ketoacidosis (249.1, 249.11, and 250.1-0.13). These complications were included because they can be directly correlated with nonadherence to insulin therapies. Percent change in the average number of diabetes-related events after the tier status change was calculated. Any increase in the number of events by more than 5% was classified as significant.

Participants’ age, gender, and LIS status were also obtained. The subjects were divided into 2 groups: those aged 65-74 years and those 75 years or more. Beneficiaries were divided into those who were non-LIS and received no “extra help” outside of their purchased insurance through the MAPD plan and those who had full LIS status, receiving “extra help” from a government program.

Descriptive analyses were conducted by calculating the mean for continuous variables and frequencies for categorical variables. To evaluate changes in adherence to insulin medications, in A1C, and in the number of diabetes-related events before and after the tier change, t tests were conducted. Logistic regression analyses were conducted to test the associations between: (a) change in adherence to insulin medications and age, gender, and LIS status; and (b) change in A1C and adherence, age, gender, and LIS status. All analyses were conducted using SAS version 9.3. The study was approved by the University of Houston’s Institutional Review Board.

The final sample consisted of beneficiaries continuously enrolled during the 2 time periods: before the tier-status change (2013) and after the tier-status change (2014). After applying inclusion and exclusion criteria, 3285 beneficiaries were identified and included in the final analyses. Figure 2 represents the flow diagram for selection of study participants.

A total of 5006 beneficiaries were initially selected who met the inclusion criteria and exclusion criteria of being enrolled throughout the study period, were located within the study region, had a diagnosis of diabetes and 2 prescription claims for insulin within each year; were not hospice members or receiving prescription drugs from Veterans Affairs, and did not have a change in full LIS status during the study period. From the 5006 beneficiaries, 1130 and 591 were then excluded due to age (being <65 years) or a cancer diagnosis, respectively.

The final cohort of 3285 beneficiaries was included for analyses. Table 1 displays the sample baseline characteristics for beneficiaries. The mean age of the sample was 73.73 years (± 6.24), with a majority being female (57.17%) and within the younger group (aged 65-74 years).

Table 2 represents the PDC-i, A1C, and diabetes-related event values for before and after the tier change. As indicated in Table 2, 1784 (54.31%) of the beneficiaries had PDC-i ≥0.8 before the tiering change compared with only 761 (23.17%) after the tiering change. Of the total of 3285 beneficiaries, 2333 (71.02%) had a decline in PDC-i after the tier change, with an average decline of 0.48 (P <.001; 95% CI, 0.47-0.49).

Logistic regression analyses (Table 3) indicated that beneficiaries with LIS were 17 times less likely to show a decline in PDC-i compared with those without LIS (odds ratio [OR], 0.828; 95% CI, 0.710-0.965). Beneficiaries 75 years or older were 17 times less likely to show a decline in PDC-i compared with those aged 65-75 years (OR, 0.829; 95% CI, 0.710-0.968).

Of the total 3285 beneficiaries, 1516 met the criteria for inclusion in the analyses for change in A1C (Table 2). The average values for A1C were 8.19% (standard deviation [SD] = 1.46%) before the tier change and 8.17% (SD = 1.54%) after the tier change. Although the overall average A1C remained constant, 755 (49.80%) beneficiaries had a statistically significant increase in A1C after the tier change, with an average increase of 0.91% (95% CI, 0.85%-0.99%; P <.001) (Figure 3). This change in A1C, however, was not associated with gender, age, LIS status, or change in adherence (Table 3).

A total of 22 beneficiaries had diabetes-related events before the tier change: 21 beneficiaries had 1 event and 1 beneficiary had 2 events. After the tier change, 36 beneficiaries had diabetes-related events: 32 beneficiaries had 1 event, 3 had 2 events, and 1 had 3 events. It was observed that among these beneficiaries, 35 did not have any such event before the tier change, indicating an almost 100% increase in the number of diabetes-related events for those patients after the tier change. A t test indicated that the increase in the number of diabetes-related events was significant (P <.001).

Medicare beneficiaries’ insulin adherence decreased after the tier status change, when all branded insulin products were switched from a generic drug tier with gap coverage and lower co-payments to a brand drug tier with no gap coverage and higher co-payments. The study followed the same beneficiaries before and after the insulin tier change. After the tier change, these beneficiaries had an increase in initial co-payment and were susceptible to the coverage gap.

When these beneficiaries entered the coverage gap, their co-payments substantially increased, to paying 47.5% of the cost for insulin. This noticeably large increase in cost for these insulin therapies creates barriers for beneficiaries in obtaining their insulin therapy, which can transpire to nonadherence and a progression of their diabetes. These findings are similar to those of other studies that noted a drop in adherence for beneficiaries who had an increase in the co-payment for their diabetes medications.

However, unlike most of the previous studies, which involved oral medications, the current study assessed adherence and associated outcomes for injectable insulin products. It may be argued that measuring adherence for injectable insulin is difficult using a claims database due to the variable sliding scale regimen. However, in practice, a quantity appropriate to cover patients’ needs for the amount of insulin as per the dosing regimen is calculated and dispensed by the pharmacist. Thus, when dosed on a schedule, insulin has the same adherence measurement parameters as oral medications (ie, number of days’ supply [numerator] over days covered [denominator]).

The study results indicated that beneficiaries with full LIS status were less likely to be nonadherent. LIS is a government program that assists Medicare beneficiaries who have limited resources and income; they qualify to get “extra help” to pay for the costs—monthly premiums, annual deductibles, and prescription co-payments&mdash;related to a Medicare prescription drug plan.

Eligibility for LIS status includes: being dual-eligible (beneficiaries who have both Medicare and Medicaid), beneficiaries who receive Social Security income (even in states in which they do not qualify for Medicaid), beneficiaries eligible for 1 of the Medicare Saving Programs, or beneficiaries who meet the financial eligibility criteria to become eligible.

Although beneficiaries with LIS status have the additional challenge of having a low income, they did not see changes in cost for their prescription medications ($2.65 for generic and $6.60 for brand name medication),

 and they continued to have coverage through the gap after the insulin tier change. The correlation we found between having LIS status and becoming less likely to decline in adherence to insulin therapies further shows that beneficiaries who had an increase in their co-payment and no coverage through the coverage gap were more likely to be nonadherent to insulin therapies.

It is important to note: for beneficiaries whose A1C increased, that increase was significant, at an average 0.9%. These beneficiaries’ average A1C after the insulin tier change was 8.7%, which is trending toward poorer compliance with the CMS Part C A1C measure goal of <9.0% and is thus a significant finding. The use of this clinical test, as a measure for the control of diabetes, has been proposed by the National Quality Measures Clearinghouse (NQMC) and implemented as a Part C measure.

 It was included as a Part C measure because the NQMC noted that diabetes is an increasingly common, complex, serious disease that not only imposes economic burdens, but frequently leads to human suffering from the disease’s complications.

Moreover, the burden of diabetes and its complications largely affect minorities and the elderly, and it will likely increase as minority populations grow and as the US population ages.

 As of 2015, the National Committee for Quality Assurance’s Healthcare Effectiveness Data and Information Set measures have posited that an A1C >9.0% is poorly controlled. A Medicare Part D sponsor would find an increasing trend of A1C concerning because poor compliance to this measure can affect its Star ratings and reimbursements. Furthermore, beneficiaries with uncontrolled diabetes are at an increased risk of macro- and microvascular complications, which lead to increased cost due to worsening comorbidities and increased risk of mortality.

The analysis also revealed an increase in the number of diabetes-related events when comparing the months before and after the change in insulin tier. We anticipated a larger increase in diabetes-related events; however, we also hypothesized some potential reasons for lower numbers.

These included: beneficiaries could be using noninsulin anti-diabetes medications in addition to insulin therapy or beneficiaries could have failed to report diabetes-related events, possibly due to the cost of a hospitalization. Due to small sample size, no claims could be made regarding an association between an increase in the number of events and the lessened adherence to insulin.

There were several limitations to this study. Insulin adherence was determined using pharmacy claims data to obtain the days’ supply for our PDC-i calculations. Some insulin regimens are not represented correctly by days’ supplies, such as sliding scale regimens. We were unable to obtain individual regimens, and thus used the days’ supply provided within the pharmacy claims data to determine insulin adherence. In addition, the use of PDC-i may overestimate the actual adherence. We did not note if the beneficiaries were using multiple insulins simultaneously for therapy. If a beneficiary is prescribed to take multiple insulins but only takes 1, the PDC-i calculation will note the beneficiary as adherent.

Also, beneficiaries could have received samples from providers or through patient assistance programs; pharmacy claims data do not account for those doses. We also did not collect data about concurrent use of noninsulin anti-diabetes medications. Many beneficiaries who live with diabetes use both insulin therapies and noninsulin anti-diabetes medications for glycemic control. Additionally, we did not randomize the beneficiaries based on their use of noninsulin anti-diabetes medications. The concurrent use of these medications can affect A1C levels and diabetes-related events.

This study explores the impact of a Medicare Part D formulary tier change, specifically with respect to branded insulin products, on insulin adherence, hemoglobin A1C, and diabetes-related events in elderly diabetic beneficiaries strategies for selection of appropriate and cost-effective therapy will also be discussed.

Medicare Part D Insulin Tiering Change: Impact on Health Outcomes

: The objective of this study is to present the first information available regarding enablement and use by state and area characteristics of electronic prescriptions of controlled substances (EPCS) by prescribers and pharmacy systems in the United States using 15 months of transactional data, from January 2014 to March 2015, from an e-prescribing network.

: The data used in this study represent electronic transactions of controlled substances from all states and the District of Columbia, from Surescripts. Data in this analysis include prescriber enablement for EPCS, pharmacies with certified and audit-approved software enabled to receive EPCS transactions from prescribers, and number of EPCS transactions. In addition, area characteristics were determined using the Area Health Resource File to define metropolitan and non-metropolitan areas.

: During the study period from January 2014 to March 2015, pharmacies enabled for EPCS grew minimally, from 78% to 79% nationally. National EPCS enablement among prescribers increased 3.7%, from 0.6% to 4.3%. Differences were observed between metropolitan and rural counties. The percent of transactions nationally grew from 0.16% to 1.8% during the study period, more than a 10-fold increase in transactions.

: There has been progress in the adoption and use of EPCS. However, there are marked differences at the state level and between urban and rural areas. EPCS is an important tool to help prescribers provide better care for their patients. Additionally, EPCS provides a complete electronic record of controlled substance prescriptions, critical for public health and law enforcement initiatives that address the misuse and diversion of opioid medications.

The growth of the prescribing of controlled substances over the past decade has paralleled the growth of prescription drug addictions and overdoses in the United States.

There is an increasing association of controlled substances, especially opioid analgesics, with drug overdose deaths, misuse, and drug diversion. The highest rates of death, opioid sales, and nonmedical use are clustered in the Southeast, especially the Appalachian region, and in the Northwest.

 Overdose rates also vary by urban-rural county type, with more rural areas having higher overdose death rates for prescription opioids compared with urban areas.

Until recently, controlled substances were not permitted to be e-prescribed.

 Currently, a prescriber is able to issue electronic prescriptions for controlled substances (EPCS) only when the electronic prescription or electronic health record (EHR) application the practitioner is using complies with the requirements set forth in The Drug Enforcement Administration Interim Final Rule (IFR) passed in 2010.

The rule requires prescribers to apply to certain federally approved credential service prescribers or certification authorities to obtain their 2-factor authentication credential or digital certificate. Additionally, the IFR set out security requirements for providers and pharmacies to lawfully send and receive EPCS.

These security requirements include: (a) an in-person or remote knowledge-based identity proofing process for providers, (b) use of a 2-factor authentication procedure by providers when creating and transmitting an EPCS, (c) use of a digital signature associated with each EPCS transaction, (d) pharmacy capability to digitally sign or receive digitally signed EPCS and store records of such transactions, and (e) the required completion of a third-party audit by both e-prescribing vendors and pharmacy system vendors. If these requirements are met, prescribers are now legally able to electronically send controlled substance prescriptions to pharmacies for dispensing.

Currently, all 50 states and the District of Columbia allow EPCS. Missouri was the most recent state to allow EPCS, with updated controlled substance rules and regulations enacted in July 2015.

 In states where EPCS is legal, all states allow the electronic prescribing of Schedules II-V. New York is the only state that has required mandatory EPCS.

The New York legislature required the date of implementation of this public health law begin March 2016. This will require practitioners to become compliant with the requirements, including the registration of their software applications with the Bureau of Narcotic Enforcement.

 This mandate applies to all New York pharmacies, including rural areas.

Health information technology (health IT), and specifically EPCS, offers the potential to help enhance prescription monitoring and support patient care. EPCS can play an important role in delivery of care by eliminating paper-based fraud and improving work flow to reduce error.

The ability to electronically prescribe controlled substances provides prescribers and their patients the same benefits made possible by e-prescribing, most importantly access to enhanced safety features and clinical decision support. From a public health perspective, the additional security requirements necessary for EPCS are a crucial step towards curbing fraud and diversion that contribute to prescription drug abuse.

 This is because EPCS can support data sharing by prescribers to facilitate appropriate prescribing and improve clinical decision making.

Due to the federal and state-level policy initiatives, it is important to explore the national and state level trends of EPCS. The objective of this study is to present the first information available regarding enablement and use by state and area characteristics of EPCS by prescribers and pharmacy systems in the United States, using 15 months of transactional data from January 2014 to March 2015 from an e-prescribing network.

This study presents the first information available regarding enablement and use by state and area characteristics of electronic prescribing of controlled substances by prescribers and pharmacy systems in from an e-prescribing network.

September/October 2016

Resource Use Among Nonvalvular Atrial Fibrillation Patients

Comparative Treatment Patterns Among Psoriasis Patients Using Adalimumab, Etanercept, or Ustekinumab

Medicare Part D Insulin Tiering Change: Impact on Health Outcomes

Electronic Prescribing of Controlled Substances: A Tool to Help Promote Better Patient Care

Introduction to the Hospital-Acquired Condition Reduction Program for Pharmacy

The Pharmacy and Therapeutics Committee Revisited: Transparency in Focus