Annual Stroke Costs to Health Plans Among Atrial Fibrillation Patients

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The American Journal of Pharmacy Benefits, May/June 2015, Volume 7, Issue 3

An incidence-based model was developed to estimate the annual ischemic and hemorrhagic stroke–related healthcare costs to a hypothetical US health plan with 1 million enrollees.

Atrial fibrillation (AF), a common risk factor for stroke, is characterized by a rapid, irregular heartbeat that can cause blood to pool in the atria and increase the risk of blood clot formation.1 AF is the most common form of sustained arrhythmia, affecting more than 2 million people in the United States. The prevalence of AF has been increasing, and as the overall US population ages, it is predicted that more than 5.5 million people will be affected by 2050.2 Prevalence of AF among the general population is approximately 1%, but increases with age to approximately 9% for persons 80 years or older.

AF has been shown to increase the risk of ischemic stroke (IS) 5-fold due to the high risk of clotting associated with the condition,3 and furthermore, patients with AF are at higher risk of increased stroke severity.4,5 Consequently, this may lead to higher costs given that the cost of severe stroke has been shown to be 11% to 71% higher when compared with minor stroke.6 Additionally, anticoagulation therapy to prevent IS can increase the risk for hemorrhagic stroke (HS) in patients with AF.7 Total direct costs attributable to AF have been estimated at $6.65 billion annually within the United States, including approximately 350,000 hospitalizations and 5 million office visits per year.8 Moreover, the total societal burden of stroke in the United States (ie, direct and indirect costs for all stroke types) has been estimated at $34.3 billion annually.1 Stroke costs have also been shown to vary across stroke types, with a recent analysis estimating the 4-year medical expenditures for IS and HS to be $39,396 and $48,327, respectively, among Medicare beneficiaries.9

Analyses of long-term observational data have suggested that age-adjusted stroke incidence has declined considerably over the past few decades.10,11 However, limited recent data on stroke incidence among patients with AF exist, particularly from a US perspective.12-14 Similarly, despite the increased risk of stroke among patients with AF, limited data exist on the economic burden of stroke in this patient population. A 2013 literature review on the economics of anticoagulant therapy for stroke prevention in AF noted this gap in stroke-related cost data in the United States.15 Moreover, the literature search and review resulted in no published data comparing the economic burden of IS and HS among patients with AF.

In recent years, the effectiveness of multiple new oral anticoagulants (OACs) for stroke prevention in AF patients has been evaluated in comparison to warfarin.16,17 Health plans would be required to invest considerable time and effort to undertake analyses to understand the epidemiology and costs of stroke in their enrolled populations. Addressing the current epidemiologic and economic data gaps outlined above will provide necessary estimates for conducting risk-benefit analyses for new stroke prevention strategies among an AF population.

To facilitate the understanding of total cost of stroke in AF patients, we created a model based on key estimates derived from a large database that allows for the estimation of both IS and HS among patients with AF from the perspective of a representative health plan.

METHODS

Overview

This study was a model simulation to estimate the cost of IS and HS in AF patients of a hypothetical health plan of 1 million members. The study involved 2 phases of data analysis. In the first analysis, annual stroke incidence rates among patients with AF, with stratification by stroke type (ie, IS and HS), were estimated by a retrospective claims data analysis. In the second analysis, age- and gender-specific annual stroke incidence estimates from the first analysis, along with 1-year resource use and cost estimates associated with IS and HS from a previously published study,18 were applied to estimate the annual cost of stroke care in the hypothetical health plan. Patients who experienced and survived a stroke hospitalization were assigned the cost associated with a nonfatal index event plus 1 year of stroke-related follow-up cost, while patients who did not survive a stroke event were assigned the cost of a fatal ischemic or hemorrhagic index event only. Annual healthcare costs for both stroke types were estimated for the hypothetical health plan by summing costs for all age- and sex-specific cohorts (

Figure 1

).

Data Source

Both the annual stroke incidence analysis and the previously published resource use and cost of stroke study were performed using the combined administrative medical and pharmacy claims data of 2 Truven Health MarketScan databases: 1) the Commercial Claims and Encounters database, and 2) the Medicare Supplemental and Coordination of Benefits database. Data from January 1, 2005, to December 31, 2011, were used for the analyses. The MarketScan Commercial Claims and Encounters database has claims for approximately 100 self-insured employers and 12 health plans representing approximately 40 million covered lives in 2008. The population covers employees and dependents 64 years and younger. The MarketScan Medicare Supplemental and Coordination of Benefits database focuses on patients with Medicare coverage plus employer-paid commercial plans, and includes 2.5 million lives from 2008. The database includes both employer-paid and Medicare-paid components of healthcare.

Parameters for Model Calculation

Incidence of stroke in AF analysis: patient selection. To be eligible for the annual stroke incidence analysis, patients were required to meet the following selection criteria: adult patient (aged ≥18 years); at least 1 inpatient claim or 2 outpatient claims for AF (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] code 427.31) during the identification period (ie, January 1, 2006, to November 30, 2011)19; at least 12 months continuous enrollment prior to the initial AF claim (ie, baseline period); and no stroke diagnoses in the 4-to-12-month period prior to the initial AF claim (a stroke diagnosis may have occurred in the 3 months prior to the index AF diagnosis to allow for undiagnosed AF prior to the stroke event). The date associated with the first AF diagnosis was termed the index date.

Incidence of stroke in AF analysis: study measures & data analysis. Stroke events were identified by the presence of at least 1 inpatient hospitalization claim with a primary diagnosis for stroke (ICD-9-CM codes 433.01, 433.11, 433.31, 434.xx for IS; 430.xx, 431.xx, 432.xx for HS) either in the 3 months prior to the index AF claim or in the 12 months following the index AF claim. Annual incidences were evaluated for each calendar year from 2006 to 2011 as rates per 100 person-years to allow for the fact that some patients might not have enrolled for a full year in the MarketScan database. Incidence rates were stratified based on age, sex, and stroke type. Incidence rates were calculated using the number of patients identified with having a stroke event during a given calendar year (either in the 3 months prior to or during the 12 months following the index AF claim) as the numerator. The denominator consisted of all AF patients identified in a given calendar year (the denominator was censored based on the date of a stroke event or disenrollment from the plan). For each incidence rate, a 95% confidence interval (CI) was derived assuming a binomial distribution. All analyses were performed using SAS version 9.3 (SAS Institute, Cary, North Carolina).

Health Plan Stroke Cost Simulation

Demographic characteristics observed in AF patients eligible for the incidence analysis were applied to a hypothetical health plan population of 1 million members to determine the plan’s AF patient age and gender distribution. Age- and gender-specific incidence rates for IS and HS were then applied to the estimated AF population to establish the expected total number of ISs and HSs experienced by the health plan’s AF population during a given year. An average incidence rate calculated from all years in the analysis (2006-2011) was used in the modeling application.

The number of fatal IS and HS cases among AF patients in the health plan were estimated based on IS and HS mortality rates observed in a previous analysis of the MarketScan databases (2005-2011) aimed at assessing the resource use and cost of stroke in AF patients.18 In that analysis, estimates of index stroke hospitalization mortality rate, index stroke hospitalization costs (ie, all costs occurring throughout the time frame of the index hospitalization, stratified by survival outcome), and stroke-related costs 12 months post index for all components of medical care were available (all 2011 US$). Cost estimates were adjusted, controlling for demographics, comorbidities, baseline anticoagulant use, and baseline resource use.

Table 1

presents a summary of the cost inputs that were used in the current study.

In the cost simulation, AF health plan members who did not survive the index stroke hospitalization were assigned the cost associated with a fatal IS or HS event, while AF members who survived a stroke were assigned the cost associated with a nonfatal index event plus 12 months of stroke-related medical care costs. Costs for all age- and sex-specific cohorts were then summed to capture the total stroke-related costs experienced by the health plan for a given year.

The impact of variations in stroke incidence rates and estimated stroke-related costs were evaluated in 1-way sensitivity analyses. High and low 95% CI estimates for incidence and cost data inputs were entered into the model. Corresponding results demonstrate the potential range of annual stroke-related costs experienced by a typical US health plan.

RESULTS

Incidence of Stroke in AF Population

Based on analyses of the 2 Truven Health MarketScan databases, 20,318 patients with AF experienced a HS or IS from 2006 to 2011. The average (± SD) age of the stroke population was 75.8 (± 11.4) years, with 50.2% of patients being female. Forty-one percent of patients received treatment with an OAC during the baseline period; 65.8% of patients had hypertension, 36.8% had coronary artery disease, 26.7% had diabetes, and 24.3% had congestive heart failure.

Annual IS rates per 100 person-years were considerably higher than HS rates among the cohort of patients with AF. Furthermore, the overall annual rate of IS and HS per 100 person-years tended to decline with time. For instance, rates of IS events per 100 person-years were 1.84 in 2006 and 1.37 in 2011. Similarly, rates of HS events per 100 person-years were 0.37 in 2006 and 0.25 in 2011 (

Figure 2

).

For IS, females experienced a higher incidence rate during each year of analysis (range = 1.62-2.31 per 100 person-years) compared with males (range = 1.17-1.50 per 100 person-years). HS rates were similar between males and females with the incidence of either stroke type tending to increase with age, with the highest rates observed among those 85 years or older (Table 2).

Health Plan Stroke Cost Simulation

Based on previously published data on the prevalence of diagnosed AF among an adult population (ie, age ≥18),20 we estimated that there would be approximately 9500 AF patients per 1 million adult enrollees in a typical health plan. Demographic characteristics (ie, baseline age and sex data) observed in AF patients eligible for the stroke incidence analysis were applied to the hypothetical health plan population, as were age- and sex-specific annual stroke incidence rates for both IS and HS. This resulted in an estimate of 142 and 29 new cases projected of IS and HS for the health plan during 1 year, respectively.

Total annual stroke-related healthcare costs for the health plan were estimated at $4,930,787, with 74% of costs attributable to IS (ie, $3,672,304 annual stroke-related costs attributable to IS vs $1,258,482 attributable to HS) (Figure 3).

Sensitivity Analyses

When the estimated rates representing the lower bound of the 95% CI for annual incidence of both IS and HS were entered into the model, there were 124 and 21 new cases of IS and HS among AF patients in the health plan during 1 year, respectively. This resulted in total annual stroke-related healthcare costs of $4,109,449. When the estimated rates representing the upper bound of the 95% CI for annual incidence were used, there were 159 and 37 new cases of IS and HS, respectively, resulting in total annual stroke-related healthcare costs of $5,727,283.

Applying the estimates representing the lower bound of the 95% CI for all stroke-related costs to base case annual incidence stroke rates resulted in total annual stroke-related costs of $4,775,668 ($3,585,039 attributable to IS and $1,190,629 attributable to HS) for the health plan. Using estimates representing the upper bound of the 95% CI for all stroke-related costs in the model yielded a total annual stroke-related cost of $5,091,968 ($3,761,707 attributable to IS and $1,330,261 attributable to HS).

DISCUSSION

Summary

This study used multi-year data from 2 large administrative claims databases to assess the incidence of stroke, as well as stroke-related medical costs, among patients with AF. Our findings suggest that rates of incidence of IS and HS among patients with AF have declined over the 6-year period from 2006 to 2011, with rates of IS being higher among female patients versus males during each year, and incidence rates of both stroke types increasing with age. Applying observed average incidence rates and costs to a typical health plan of 1 million enrollees resulted in an annual stroke-related healthcare cost of $4,930,787, with 74% of costs being attributable to IS due to higher prevalence rates and lower mortality compared with HS. Resulting total annual costs to the health plan were more sensitive to variations in incidence rates than in stroke-related cost inputs.

Comparison With Other Literature

Our results for stroke incidence among AF patients are comparable to previously published data. For instance, a 2013 study that analyzed the incidence of stroke among patients diagnosed with AF in the French National Hospital Database found 20.4 cases of IS per 1000 patients and 8.0 cases of HS per 1000 patients over the 2-year period from 2008 to 2009.12 These incidence rates are comparable to the cumulative IS (2.78 per 100 person-years [1.35 per 100 person-years in 2008 and 1.43 per 100 person-years in 2009]) and HS (0.61 per 100 person-years [0.29 per 100 person-years in 2008 and 0.32 per 100 person-years in 2009]) rates observed in our analysis during the years 2008 and 2009. From a US perspective, Miyakasa and colleagues found a cumulative incidence of first IS following an AF diagnosis of 10% at 5 years.21 This estimate is slightly higher than our 6-year cumulative incidence rate for IS (9.02 per 100 person-years [1.84, 1.62, 1.35, 1.43, 1.41, and 1.37 per 100 person-years for 2006-2011, respectively]); however, the paper by Miyakasa and colleagues relies on regional and older data (1980-2000).

A recent article by Olsson and colleagues found a cumulative incidence of first HS of 0.57% at 3 years post AF diagnosis.14 While this 3-year cumulative rate is slightly lower than any consecutive 3-year cumulative rate observed in our study (eg, 0.84 from 2009-2011 [0.32, 0.27, and 0.25 per 100 person-years in 2009, 2010, and 2011, respectively]; 0.95 from 2007-2009 [0.34, 0.29, and 0.32 per 100 person-years in 2009, 2010, and 2011, respectively]), the difference is likely attributable to a more narrow set of ICD-9-CM diagnosis codes used to identify HS, and the difference in data source (ie, registry medical record data vs administrative claims data). Similar to our results, other studies have also shown that incidence rates for IS have decreased among patients with AF since the year 2000,14 and that female patients with AF generally experience a higher incidence rate of IS versus males.21

A recent publication by Luengo-Fernandez and colleagues evaluated both acute and long-term healthcare costs among patients with AF experiencing a stroke event. The average annual cost for any stroke, including the costs associated with the acute event, was approximately £13,783 (2009 UK pounds sterling, or approximately $24,174 in 2011 US$) and typically increased with stroke severity.22 Approximately 85% of strokes analyzed were IS, making the estimate reported by Luengo-Fernandez comparable to the 12-month IS cost used in our model ($25,635 [2011 US$]).

Previous studies found medical expenditures for HS to be higher than IS9,18 on a per patient basis. For example, an analysis conducted among Medicare beneficiaries found that patients who experience a subarachnoid hemorrhage accrue higher medical expenditures in the 4 years following an initial event compared with patients experiencing an IS ($48,327 vs $39,396 per patient, respectively [2001 US$]).9 The adjusted cost results from the analysis used to populate this model (Sussman et al) also found higher 12-month stroke-related costs for patients experiencing an HS versus those experiencing an IS (Table 2).8 However, our analysis indicates that the opposite is true on a population basis (ie, higher costs are realized for IS due to a higher prevalence rate and lower mortality rate), which is important for health plan budgeting purposes.

Limitations

This study is subject to the general limitations of basing case ascertainment on ICD-9-CM diagnosis codes from administrative claims data, including coding errors and potential underestimation of event occurrence.23 Particular to this study, the identification of AF was limited to the complete AF diagnosis code (ICD-9-CM 427.31) to ensure the accuracy of the identified patient population. AF cases that were miscoded using the broader 427.3 code (ie, without the fifth digit) were excluded, potentially underestimating the incidence rate of stroke. Moreover, the population 65 years and older in the study database includes patients with Medicare and supplemental employer insurance coverage. This population may not be generalizable to the overall elderly population in the United States. More generally, our model solely relies on data from an insured US patient population, which does not provide information on indirect costs (eg, lost wages, caregiver burden) or the economic burden experienced by uninsured patients. These data points would be necessary for modeling stroke costs from a societal perspective. Regarding the cost analysis, only follow-up costs for medical care that listed a diagnosis of stroke were reported; doing so likely under-estimated the full costs of stroke among patients with AF, since some stroke-related services may have been coded for specific complications rather than directly for IS or HS.

The stroke-related cost and stroke incidence data used in the model were derived from 2 separate patient populations (ie, AF patients in the incidence analysis were not followed to determine their corresponding costs after experiencing a stroke). However, both analyses used the same large, geographically diverse, third-party-payer-perspective data source. Both sets of derived inputs were therefore appropriate for modeling and estimating national health plan expenditures. Furthermore, to obtain the most accurate populations necessary for deriving data on incidence and cost outcomes, 2 separate analyses were required. This is because each outcome required different index events to establish follow-up (ie, index AF event for stroke incidence analysis; index stroke event for stroke cost analysis).

The stroke-related cost and stroke incidence data used in the model were derived from 2 separate patient populations (ie, AF patients in the incidence analysis were not followed to determine their corresponding costs after experiencing a stroke). However, both analyses used the same large, geographically diverse, third-party-payer-perspective data source. Both sets of derived inputs were therefore appropriate for modeling and estimating national health plan expenditures. Furthermore, to obtain the most accurate populations necessary for deriving data on incidence and cost outcomes, 2 separate analyses were required. This is because each outcome required different index events to establish follow-up (ie, index AF event for stroke incidence analysis; index stroke event for stroke cost analysis).

CONCLUSIONS

Despite the elevated risk of stroke among patients with AF, few studies have been published that assess the relative burden of IS to HS among this population. Our study demonstrates a decreased incidence of stroke among AF patients during recent years; the reason(s) for this decline would be important to determine in future analyses. Observational studies that have highlighted a declining stroke incidence in a general population have cited reasons such as increased smoking cessation and blood pressure control.10 In addition to providing contemporary estimates of stroke incidence among a large US payer population, our study found that IS accounted for a significantly higher proportion of annual stroke-related healthcare costs as compared with HS among patients with AF. Our model can be used by health plans to estimate the costs of IS and HS among AF patients. Moreover, these data underscore the importance of stroke prevention in AF patients and provide additional insights into future risk-benefit assessments of oral anticoagulation therapy from an economic perspective.