Steven Kymes, PhD

 Medication discrepancies at the time of hospital discharge are common and can harm patients. Medication reconciliation by pharmacists has been shown to prevent such discrepancies and the adverse drug events (ADEs) that can result from them. Our objective was to estimate the economic value of nontargeted and targeted medication reconciliation conducted by pharmacists and pharmacy technicians at hospital discharge versus usual care.

 We developed a discrete-event simulation model to prospectively model the incidence of drug-related events from a hospital payer’s perspective. The model assumptions were based on data published in the peer-reviewed literature. Incidences of medication discrepancies, preventable ADEs, emergency department visits, rehospitalizations, costs, and net benefit were estimated.

 The expected total cost of preventable ADEs was estimated to be $472 (95% credible interval [CI], $247-$778) per patient with usual care. Under the base-case assumption that medication reconciliation could reduce medication discrepancies by 52%, the cost of preventable ADEs could be reduced to $266 (95% CI, $150-$423), resulting in a net benefit of $206 (95% CI, $73-$373) per patient, after accounting for intervention costs. A medication reconciliation intervention that reduces medication discrepancies by at least 10% could cover the initial cost of intervention. Targeting medication reconciliation to high-risk individuals would achieve a higher net benefit than a nontargeted intervention only if the sensitivity and specificity of a screening tool were at least 90% and 70%, respectively.

 Our study suggests that implementing a pharmacist-led medication reconciliation intervention at hospital discharge could be cost saving compared with usual care.

		Medication discrepancies at the time of hospital discharge are common and can harm patients. 

		Little is known about the economic value of implementing medication reconciliation by pharmacists and whether this intervention is more cost-effective when targeted to those most at risk of medication discrepancies. 

		A medication reconciliation intervention that reduces medication discrepancies by at least 10% could cover the initial cost of intervention. 

		Our results suggest that, despite the use of relatively costly pharmacists and pharmacy technicians, the cost savings from avoided rehospitalizations would more than offset the cost of the intervention.

Disruptions to, and changes in, a patient’s outpatient medication regimen occur frequently during hospitalization. This often results in discrepancies between drugs prescribed at discharge and the medications outpatient providers believe that patients should be on.

 Although the majority of such discrepancies do not have clinically important effects, their consequences can be profound. These events are defined as preventable adverse drug events (ADEs) because they are caused by medication discrepancies that could have been avoided. It is estimated that 2.4% to 4.1% of all hospital admissions are directly related to ADEs, and up to 69% of those ADEs are preventable.

 There are other circumstances in which the presence of a medication discrepancy has not yet resulted in an ADE, but may still be costly and/or may expose patients to the risks of additional testing or monitoring.

Medication reconciliation by pharmacists at hospital discharge is a possible strategy to reduce medication discrepancies and subsequent ADEs. Several systematic reviews have found that medication reconciliation significantly reduces the risk of medication discrepancies.

 Despite this, not all studies evaluating the impact of medication reconciliation on health resource utilization (HRU) have found beneficial effects.

 These findings naturally raise questions about the economic value of this intervention and how the balance between the costs and benefits of the intervention could be optimized by, for example, selectively targeting high-risk patients.

Accordingly, we conducted a simulation-based cost-benefit analysis to estimate and compare the economic value of 3 strategies at hospital discharge: a) usual care (no intervention), b) nontargeted medication reconciliation for all patients, and c) targeted medication reconciliation that uses a screening tool to identify patients at high risk of postdischarge ADEs.

We developed a 2-part discrete-event simulation to prospectively model the sequence of events that occur within the 30 days after hospital discharge for a hypothetical cohort of patients who did, and did not, receive medication reconciliation. Consistent with the existing literature,

 we categorized medication discrepancies that were associated with harm as preventable ADEs and those not associated with harm as “potential” ADEs. We categorized both preventable ADEs and potential ADEs into those that were (or were potentially) life-threatening, serious, or significant. Nonsignificant events were considered, by definition, to not be a medication discrepancy.

Our model first estimated the incidence of medication discrepancies, preventable ADEs, their associated major HRUs (ie, emergency department [ED] visits and rehospitalizations), and costs among patients who did not undergo reconciliation (

[A]). We then used the distribution of outcomes from the initial phase of the simulation to compare the impact of 3 interventions (Figure 1 [B]): a) a nontargeted pharmacist-led medication reconciliation intervention for all patients, b) a targeted pharmacist-led medication reconciliation intervention in which only patients at high risk of ADEs were intervened upon, and c) usual care (no intervention). Our modeling approach and structure have been explained in the eAppendix (

We used a cost-benefit analysis framework and conducted our analysis from the perspective of a hospital that was at risk for the added cost of any intervention and the savings from avoiding HRUs due to preventable ADEs. All costs were estimated in 2014 US dollars and, when necessary, unit costs were converted to 2014 prices using reported changes in the consumer price index.

 Considering the short period of analysis, no discount rate was applied. All simulations were developed using Arena version 12.00 (Rockwell Automation Technologies, Inc, Milwaukee, Wisconsin).

The characteristics of the simulated cohort were assumed to be similar to populations in the studies that were the basis of our assumptions about event probabilities (

 The model assumptions and event probabilities are based on data from the Pharmacist Intervention for Low Literacy in Cardiovascular Disease (PILL-CVD) study.

 In this randomized clinical trial, 851 patients hospitalized with acute coronary syndromes or acute decompensated heart failure were randomized to receive either a pharmacist-assisted intervention (ie, medication reconciliation, inpatient pharmacist counseling, low-literacy adherence aids, and individualized telephone follow-up) or usual care after hospital discharge. Similar to the trial population, the simulated cohort was assumed to have an average age of 60 years, have a median 14 years of education, and to be 60% male. We assumed that approximately 10% of the hypothetical cohort had inadequate health literacy and 8.7% had marginal, and that 11.5% had some level of cognitive impairment. We also assumed that approximately 60% of patients were hospitalized with acute coronary syndrome, 30% with acute heart failure, and 10% with both diagnoses.

Estimates of Medication Discrepancies and Subsequent Events

The incidences of medication discrepancies, preventable ADEs, and potential ADEs for patients not undergoing medication reconciliation were estimated using the distribution of observed events in the control arm of the PILL-CVD study.

 The distribution of events in the usual-care arm of PILL-CVD is summarized in eAppendix Figure A. In this study, 25 of the 125 (20%) patients with preventable ADEs were rehospitalized or visited an ED. Based on a case-by-case analysis of these patients by 1 study author (JLS), we determined that 24 of these ADEs were potentially amenable to medication reconciliation activities at the time of hospital discharge (as opposed to postdischarge activities, such as monitoring for ADEs).

The proportion of patients who had rehospitalizations, ED visits, or both events, was based on the observed rates in this trial

 In the base-case analysis, we assumed that the intervention could reduce the risk of rehospitalization due to preventable ADEs, amenable to medication reconciliation, by 52% (ie, a relative risk of 0.48). This was based on an average effectiveness rate of medication reconciliation observed in 4 published studies of at least moderate methodological quality.

 The estimated effectiveness of the intervention may vary as a function of care setting, patient characteristics, composition of the healthcare team responsible for delivery of the intervention, and services that were bundled with medication reconciliation.

 As a result, we varied our assumptions of effectiveness extensively in the sensitivity analysis.

In order to estimate the impact of limiting medication reconciliation to high-risk patients (ie, those patients most likely to have a postdischarge ADE due to a medication discrepancy), we assumed that patients underwent screening with a tool that could predict, with some accuracy, the risk of ADEs based on the patient’s characteristics at the time of hospital discharge. A perfect screening tool could exactly discriminate patients with and without preventable ADEs, based on the information available at discharge, and would only assign the former to receive the intervention.

Given the sensitivity and specificity of this tool, we assigned patients into 4 categories: a) true positive, who would have had a preventable ADE and were assigned to receive the intervention; b) false positive, who would not have had a preventable ADE but were assigned to the intervention; c) false negative, who would have had a preventable ADE but were not assigned to receive the intervention; and d) true negative, who would not have had a preventable ADE and were not assigned to receive the intervention. We assumed that the effectiveness of targeted interventions was similar to that of the nontargeted strategy, in which medication reconciliation was offered to all patients.

Average cost per rehospitalization was based on recent estimates by the Agency for Health Research and Quality.

 The cost per ED visit was based on estimated charges in the same report, which were calculated by applying an estimated cost-to-charge ratio of 0.55.

 A 3% penalty rate was applied to all rehospitalization costs to reflect the current policies for readmission payment adjusted amount.

Intervention costs were calculated using estimated average pharmacist time per reconciliation

 and average salary and benefit payments to pharmacists and pharmacy technicians.

 We assumed that pharmacy technicians, rather than pharmacists, conducted 50% of medication reconciliation-related tasks (eg, taking medication histories) in order to reduce the intervention cost. Pharmacists were assumed to supervise technicians and do some of the tasks, like patient counseling or order review, themselves. This assumption was varied in the sensitivity analysis. We also assumed that a medication history conducted by a pharmacy technician under a pharmacist’s supervision was as effective as that of one conducted by a pharmacist alone.

We performed extensive 1-way and 2-way sensitivity analyses to investigate the net benefit of medication reconciliation interventions under different assumptions for our model parameters, screening properties, and patients’ characteristics. A probabilistic sensitivity analysis also was performed to examine distribution of our point estimates, given uncertainty of the model parameters, by varying all of our model parameters simultaneously.

 We also reported credible intervals based on probabilistic sensitivity analyses that reflect uncertainty of simulated point estimates.

In a cohort of 10,000 patients not undergoing medication reconciliation, we estimated that 5090 would have at least 1 medication discrepancy within the first 30 days after hospital discharge. These medication errors would result in 3807 preventable ADEs and 5230 potential ADEs. Preventable ADEs would result in 421 rehospitalizations and 496 ED visits. Overall, the average cost of preventable ADEs was estimated to be $472 (95% credible interval [CI], $247-$778) per patient in the usual care strategy.

Medication Reconciliation for All Patients

Assuming that pharmacist-led medication reconciliation for all patients at hospital discharge could reduce medication errors by 52%, and therefore would reduce HRU due to preventable ADEs amenable to medication reconciliation by the same proportion, the number of rehospitalizations and ED visits related to preventable ADEs would be reduced to 199 and 215, respectively, with this strategy. This reduction in hospitalizations and ED visits would reduce the overall cost per patient to $266 (95% CI, $150-$423). This estimate includes the cost of medication reconciliation of approximately $39 per patient. Therefore, performing medication reconciliation for all patients at the time of hospital discharge would result in a significant net benefit of $206 (95% CI, $73-$373) per patient.

The impact of altering our assumptions about the effectiveness of medication reconciliation are presented in 

 and eAppendix Figure B. We found that the initial cost of the intervention would be more than offset by the savings from averted events as long as it reduced medication errors by at least 10%. The results of 1-way sensitivity analysis suggest that the net benefit is most sensitive to the effectiveness of intervention, the proportion of preventable ADEs that result in a rehospitalization or ED visit, and the average length of stay for patients who had an ADE-related rehospitalization (

The results of our probabilistic sensitivity analysis are presented in eAppendix Figure C. We found that the uncertainty around net benefit ranges between —$2 and $577 per patient, with 99% of the simulated trials resulting in a positive net benefit.

Medication Reconciliation for Targeted Patients

The net benefit from targeting medication reconciliation under our base-case assumptions of 70% sensitivity and 70% specificity was $146 savings per patient, which was not superior to the savings from a nontargeted approach ($206 per patient). The impact of alternating our assumptions about sensitivity and specificity of the screening tool are presented in 

 and eAppendix Figure D. We found that the net benefit could be as high as $232 per patient, if the specificity and sensitivity of a screening tool were 100%. The net benefit could exceed that which we obtained in the base-case analysis as long as the sensitivity and specificity of the screening tool were at least 90% and 70%, respectively. However, in general, improving the sensitivity of the screening tool has larger impact on net benefits than improving specificity. For example, a screening tool with 100% sensitivity resulted in a net benefit of $222 per patient even if the specificity was only 60%.

The added value of a targeted intervention increased as the effectiveness of the intervention decreased and the cost of intervention increased. For example, if medication reconciliation reduced the risk of medication discrepancy by 20% (rather than 52%) and is more expensive (eg, because it is performed by pharmacists rather than a combination of pharmacist technicians and pharmacists), then a targeted intervention is the preferred approach.

Although pharmacist-led medication reconciliation reduces medication errors that commonly occur after hospital discharge, its economic value has not been completely evaluated. We found that this intervention, when performed at the time of hospital discharge, reduced the cost of rehospitalizations and ED visits that resulted from medication discrepancies within 30 days after hospital discharge from $472 per patient to $266. This resulted in a net savings of $206 per patient, after accounting for the cost of the intervention. The magnitude of this savings could be increased if a screening tool, with very high sensitivity and modest specificity, was used to identify and target patients at higher risk of preventable postdischarge ADEs.

More than half of hospitalized patients are estimated to have 1 or more medication discrepancy after hospital discharge.

 ADEs resulting from these discrepancies result in the rehospitalization of approximately 4% of discharged patients.

 This highlights the opportunity for interventions that can prevent medication discrepancies to both improve quality and lower costs. Medication reconciliation at the time of hospital discharge involves a bundle of activities including patient counseling and communication with outpatient providers.

Although careful medication reconciliation at hospital discharge can potentially eliminate many discrepancies in discharge medication orders,

 it, not surprisingly, has a more limited effect on other types of medication errors that occur during the postdischarge period. For example, patient misunderstanding of their medication regimen could lead to additional discrepancies after discharge.

 Likely for this reason, reconciliation programs that include a telephone follow-up after discharge—along with other, more conventional features such as coordination with physicians, patient education, and transition coaching&mdash;have, in general, been more effective.

Our results suggest that despite the use of relatively costly pharmacists and pharmacy technicians, the cost savings from avoided rehospitalizations would more than offset the cost of the intervention. This is consistent with the limited existing cost-effectiveness evidence.

 The value of medication reconciliation could be increased if it were preferentially used for individuals at the highest risk of ADEs. This is consistent with the fact that many of the most effective medication reconciliation interventions reported in the literature are those that were targeted interventions, rather than administered to all patients.

 Several studies have attempted to develop risk prediction models for hospital readmissions with acceptable accuracy.

 However, these models tend to predict risk of all rehospitalizations in the first 30 days after discharge and, therefore, are not necessarily calibrated to predict the risk of readmission specifically resulting from medication errors.

The estimates of costs and benefits of medication reconciliation in our study assume that those paying for medication reconciliation interventions are the same ones that share the savings from reduced postdischarge HRUs. The extent to which this is true varies tremendously at present; however, it most certainly will increase dramatically in the near future. For example, in 2015, approximately 20% of Medicare payments are based on non—fee-for-service payment models. This share is expected to increase to 30% by 2016 and to 50% in 2018.

 These models included bundled payments, in which providers are at risk for the cost of any readmissions occurring in the 30-to-90-day period after discharge.

Similarly, accountable care organizations put providers at risk for the total cost of care of the populations they serve; these incentives have encouraged providers to target readmissions as an opportunity to improve care and reduce costs. Moreover, Medicare has introduced readmission penalties broadly, and these penalties have led to much greater focus on readmission rates. As a whole, these payment models are critical to creating the business case for hospitals to invest in interventions such as mediation reconciliation.

Factors other than the intervention cost might limit the implementation of medication reconciliation. Pharmacists or other healthcare professionals conducting this intervention must have access to accurate and timely information about inpatient and outpatient mediations. As such, the availability of electronic health records

 that can facilitate obtaining medication records from different points of care will directly affect the feasibility and success of medication reconciliation.

 Patient-specific barriers, such as lower education level and language and other communication barriers, could limit the successful implementation of medication reconciliation in some healthcare settings.

Our study should be viewed in light of its limitations. Only a handful of studies have shown the direct impact of medication reconciliation interventions on postdischarge HRU. As a result, our model was based on a chain of events whereby medication discrepancies lead to postdischarge ADEs and HRU, and on studies that show reductions in these discrepancies and in some of the downstream events. Although each link in the chain can be supported by data, we acknowledge that estimates of effectiveness on patient outcomes may be inaccurate. It is reassuring, however, that our results were largely unchanged in the extensive sensitivity analyses we conducted. In addition, the studies we used to derive our base-case estimates were based on specific patient populations, and thus may not be generalizable to all other patient groups or care settings.

Our study suggests that a pharmacist-led medication reconciliation can be a cost-saving strategy compared with usual care. The net benefit of this intervention could be enhanced using a highly sensitive screening tool for patients at high risk for postdischarge ADEs. Future studies should more directly make the link between interventions and reductions in postdischarge healthcare utilization in order to increase the precision of cost-benefit estimates.

Articles

The results of this simulation model suggest that implementing a pharmacist-led medication reconciliation intervention at hospital discharge could be cost-saving compared with usual care.

Economic Value of Pharmacist-Led Medication Reconciliation for Reducing Medication Errors After Hospital Discharge

 To develop a cost-offset model from a US payer perspective comparing glaucomatous progression and costs among primary open-angle glaucoma (POAG) patients using bimatoprost, latanoprost, or travoprost.

 A Markov cohort model was used to estimate glaucomatous progression for POAG patients over 7 years. The model assumed bimatoprost-treated patients had lower resulting intraocular pressure (IOP) (by 1 mm Hg) for all presenting IOP categories than latanoprost- or travoprost-treated patients. Patients with lower IOP were assumed to have lower probability of progression. Those that progressed were assumed to do so at a rate of —0.6 dB per year. Direct costs associated with mean deviation score categories were applied to each treatment cohort to calculate the expected 7-year costs of treating patients with each prostaglandin analogue (PGA). Literature was used to support assumptions. A budget impact analysis was conducted where all travoprost patients switched to generic latanoprost and where all bimatoprost patients switched to generic latanoprost. The base case market share was 22% bimatoprost, 23% travoprost, and 55% latanoprost.

 Model results demonstrate that for a managed care plan with 9500 PGA-treated glaucoma patients, exclusive bimatoprost use would prevent progression in 136 additional individuals compared with exclusive travoprost or latanoprost treatment. Model results demonstrate that greater IOP reduction from bimatoprost is associated with increased cost savings compared with latanoprost or travoprost treatments.

 Model results demonstrate that greater IOP reduction from bimatoprost could reduce managed care spending.

	Greater intraocular pressure (IOP) reduction with bimatoprost is associated with cost savings compared with treatment with latanoprost or travoprost.

		In a health plan with 9500 prostaglandin analogue—treated glaucoma patients, exclusive bimatoprost use would prevent progression in an estimated 136 incremental patients over 7 years compared with exclusive travoprost or latanoprost treatment.

		Exclusive treatment with bimatoprost would generate cost savings from reduced resource utilization estimated at $4009 per avoided progression of early glaucoma and $4543 per avoided progression of advanced glaucoma over 7 years.

		Cost benefits of lower IOP provide incentive for health system—level policies to promote bimatoprost use.

	More than 2 million people in the United States 40 years and older have glaucoma.

 Primary open-angle glaucoma (POAG) is the most common form of the disease. While there are numerous risk factors associated with glaucoma progression,

 intraocular pressure (IOP) is one of the only known modifiable risk factors that can be controlled to prevent progressive optic neuropathy.

	The incidence of glaucoma is expected to rise with the continued growth of the elderly population and is a leading cause of preventable blindness in the United States.

 Glaucoma costs the healthcare system an estimated $2.9 billion annually in direct medical expenditures.

 An analysis using a Markov model to replicate health events over the remaining lifetime of someone newly diagnosed with glaucoma estimated that the cost of care for people with POAG was higher than that of people without the disease by $1688 over their expected lifetime, or approximately $137 per year.

 Lee et al estimated the average direct cost of treatment for glaucoma to range from $523 per patient per year to $2125 per patient per year depending upon the stage of disease (early vs severe).

 Given the burden of glaucoma on the healthcare system, there is a financial incentive to delay or prevent glaucomatous progression and associated visual field loss.

	Multiple medical therapies that are intended to lower IOP are available, the most common of which are prostaglandin analogues (PGAs).

 and are not associated with the significant systemic side effects associated with topical beta blockers, such as bronchospasm, shortness of breath, depression, fatigue, impotence, hair loss, heart failure, and bradycardia.

	Latanoprost, travoprost, and bimatoprost are once-daily PGAs that lower IOP effectively and continuously, allowing for flat diurnal curves with few systemic side effects.

 Numerous randomized clinical trials have demonstrated that patients achieve greater IOP lowering with bimatoprost than either latanoprost or travoprost.

 Furthermore, a recent meta-analysis of multiple studies concluded that bimatoprost has greater efficacy than latanoprost or travoprost.

 reported on the latanoprost to bimatoprost switching patterns of an HMO. The study found that the HMO achieved a high switch rate, with few patients switching back. Moreover, there was a statistically significant reduction in mean IOP for patients who switched from latanoprost to bimatoprost. Similar studies that examined high switch rates from latanoprost to travoprost did not demonstrate a statistically significant reduction in mean IOP.

 The impact of this superior efficacy of bimatoprost on direct medical costs has not been evaluated to date.

	The primary objective of this study was to develop a cost-offset model from a payer perspective to compare glaucomatous progression and costs among POAG patients treated with bimatoprost, latanoprost, or travoprost in the United States. The analysis aimed to support the hypothesis that a reduction in IOP leads to a decreased risk of glaucoma progression, which in turn leads to cost savings from less healthcare resource utilization; specifically, due to bimatoprost’s greater efficacy in IOP reduction compared with travoprost and latanoprost, its use should lead to cost savings for payers. Fewer advanced glaucoma patients would result in lower expenses as increased resources needed for advanced disease stages are avoided.

) was constructed (1) to evaluate the difference in glaucomatous progression rates with bimatoprost, travoprost, or latanoprost; and (2) to provide a cost-offset analysis for these PGAs. This analysis was driven by the link between the treatments’ clinical efficacy, as represented by IOP after treatment (called the resultant IOP) and resource utilization. The model accounted for various healthcare resources (detailed below), the use of which increased with the resultant IOP. As the model assumed that IOP increase is associated with increased glaucomatous progression risk (detailed below), this analysis demonstrated the cost savings that arise from decreasing resource utilization due to the prevention of disease progression. While a cost-offset analysis provides useful insight into whether an intervention is worth financing from a budgetary standpoint, it is based on efficacy and resource utilization assumptions that may differ from real world rates and practices.

	The model calculated results over a 7-year period in a hypothetical health plan of 1,000,000 members. Given the slow-progressing nature of glaucoma, a 7-year time horizon was utilized, as in previous studies.

 Progression was defined as the loss of visual field as it is measured by the mean deviation (MD) score. POAG prevalence was assumed to be 1.9% (ie, 19,000 plan members had glaucoma).

 the PGA treatment rate was estimated at 50% (N = 9500).

	A subsequent budget impact analysis assumed bimatoprost as a first-line treatment with PGA market share as follows: 22% bimatoprost, 23% travoprost, and 55% latanoprost.

 This budget impact analysis compared the total cost to the payer, including the cost of the PGAs, medical costs (such as physician visits), and other glaucoma medication costs between different market share scenarios. In general, these budget impact analyses are useful for estimating systemwide (eg, medical and pharmacy) budget impact; they are often used by managed care organizations

 to justify therapy reimbursement. While these analyses are limited in that they do not account for the full impact of clinical and patient outcomes and therefore do not establish the overall value of the product, they are not intended to do so; budget impact analyses are intended to present a financial perspective when considering a particular therapeutic decision.

	Two scenarios were explored: In scenario A, all bimatoprost patients were switched to generic latanoprost, resulting in a market share of 0% bimatoprost, 23% travoprost, and 77% latanoprost. In scenario B, all travoprost patients were switched to generic latanoprost; market shares were 22% bimatoprost, 0% travoprost, and 78% latanoprost.

	This model accounted for 3 different payer situations: all Medicare Part D patients, all commercial patients, and a mix of Medicare Part D and commercial patients. Medicare Part D patients included the assumed number of individuals covered by the plan who were 65 years or older. Commercial patients included the assumed number of individuals on the plan who were younger than 65 years.

	Intraocular pressure ranges 19-21, 22, 23, 24, 25, and 26 (all mm Hg) and the percentage of patients allocated to them were based on a population-based study (the Beaver Dam Eye Study).

 The resultant IOP with bimatoprost treatment was calculated by reducing the presenting IOP by 27%.

 As the model assumes a 1 mm Hg reduction advantage for bimatoprost over latanoprost or travoprost,

 the resultant IOPs for the latter PGAs were calculated by adding 1 mm Hg to the resultant IOP scores calculated for bimatoprost. This resultant IOP value was the basis of the patients’ probability of progression (discussed in the Progression Rate section).

	As supported by literature, a baseline mean deviation (MD) score of —4 decibels

 (dB) for early glaucoma patients or —10 dB

 for advanced glaucoma patients was assigned to the “worse seeing eye” of each patient cohort.

 Baseline MD score for the “better seeing eye” was estimated based upon a correlation with the worse seeing eye using the following equation:

	BetterEYEMD = (WorseEYEMD —1.83) / 1.307.

	The annual probability of progression was estimated using the resultant IOP following PGA treatment. It was estimated from literature that a 1 mm Hg decrease in IOP would reduce the probability of glaucoma progression by 10%

 was the benchmark. Based on the Early Manifest Glaucoma Trial data, the probability that a patient with an IOP of 21 mm Hg would progress was 10.2%.

 Therefore, the calculation for progression probability for IOP values other than 21 mm Hg was estimated using the following equation:

	After PGA treatment, the probability of progression remained constant throughout the model. Based on data from the Early Manifest Glaucoma Trial,

 patients who progressed did so at a mean rate of —0.6 dB/year (Table 1).

	The model also considered the impact of IOP fluctuation on probability of progression. The percentage of patients with IOP fluctuation greater than 3 mm Hg was 7.8% for latanoprost and 2.5% for bimatoprost.

 The percentage of patients on travoprost with high fluctuation was assumed to be similar to or greater than the percentage of those on latanoprost

; therefore, the percentage of travoprost patients with IOP fluctuation greater than 3 mm Hg defaulted to 7.8%. Inclusion of IOP fluctuation in the model was supported by a literature review that demonstrated that individuals who had an IOP fluctuation greater than 2 standard deviations would be 3 times more likely to experience glaucomatous progression.

 The risk ratio (progression due to IOP fluctuation) was calculated as follows: 30% (probability of progression among patients with IOP fluctuation of >2 mm Hg)

 / 9.7% (probability of progression among patients with IOP fluctuation of 

	The annual per-patient PGA cost was calculated based on drug cost (average wholesale price [AWP] or Medicare), dispensing fees ($2 for each brand name drug and $3 for generic drugs), copays,

 rebates, and pharmacy discounts (which were 15% of the monthly cost of branded products and 17% of the monthly cost of generic products; Table 1). Rebates and copays were subtracted from the price to get the net cost to the managed care payer. As there were no data for the generic price of latanoprost, it was assumed that the cost of generic latanoprost would be 80% of the branded price. A lower copay for the generic ($10 instead of $25)

 was also assumed. The number of office visits and visual field tests were predicted from the MD score and IOP value.

 The annual number of concomitant glaucoma medications (non-PGA) was predicted from the MD score (Y[number of glaucoma medications] = 0.70 — 0.0455 [MD score]), as derived from literature on the number of medications by stage.

 Surgery was applied as a per-eye cost and the number of surgeries per year was estimated using literature on the annual rate of surgery by stage.

 For nonprogressors, the annual rate of surgery was assumed to be the literature-based rate for the stage of disease corresponding to the MD score in the model. For progressors, this annual rate was assumed to be higher based on literature showing that those who had disease stage progression were more likely to receive surgery (rate ratio of 1.92).

 The model conservatively assumed that patients who underwent surgery discontinued their medication and were therefore dropped out of the cohort. Direct medical and non-PGA pharmacy costs, which were based on resource utilization estimates, were applied to each treatment cohort to calculate the expected 7-year costs (

). Unit costs for office visits, visual field tests, additional non-PGA glaucoma medications, and surgeries were based on resource utilization and 2008 Current Procedural Terminology codes (Table 2).

 A discount rate of 3% for future costs was applied.

 Results were presented as total annual costs and annual per member per month (PMPM) costs.

	Two 1-way sensitivity analyses were conducted for the estimated reduction in risk of progression per 1 mm Hg change in IOP. The base case assumed that there is a 10% reduction in the risk of progression per 1 mm Hg decrease in IOP. The sensitivity analyses assumed that this reduction in risk is 19%. This parameter change was tested for both less severe and more severe glaucoma patients.

	Four 1-way sensitivity analyses were conducted for the price of generic latanoprost. Since the analysis was originally conducted, generic latanoprost has entered the market. Therefore, the sensitivity analyses assessed how the results would change should the price of generic latanoprost be as low as $16.44

 These results were calculated for patients with less severe and more severe disease.

	This study is a health-related outcomes analysis based on available peer-reviewed literature and therefore was not subject to approval by an institutional review board.

Long-Term Progression Rates: Base Case for Early and Advanced Glaucoma Patients

Treatment of glaucoma with bimatoprost is associated with cost savings compared with treatment with latanoprost or travoprost because of greater intraocular pressure reduction.

Steven Kymes, PhD

Articles

Economic Value of Pharmacist-Led Medication Reconciliation for Reducing Medication Errors After Hospital Discharge

Cost-Offset Analysis: Bimatoprost Versus Other Prostaglandin Analogues in Open-Angle Glaucoma