The Impact of Alteplase Coverage on Health Equity for the Treatment of Ischemic Stroke in the USA: A Distributional Cost-Effectiveness Analysis

Abstract

Objectives

A distributional cost-effectiveness analysis (DCEA) was conducted to evaluate how alteplase for acute ischemic stroke affected overall health and disparities in the USA.

Methods

Using an existing, published, cost-effectiveness analysis, a DCEA was developed from a US payer perspective. The population was divided into 25 equity-relevant subgroups based on race and ethnicity (5 census-based groups), and county-level social vulnerability index (quintiles). Inputs for stroke outcomes, incidence and alteplase utilization varied across subgroups. Opportunity costs were estimated by converting total spend on alteplase into quality-adjusted life-years (QALYs) using an equal distribution across subgroups. Various scenarios explored the impact of health system changes to improve stroke care access.

Results

Alteplase treatment resulted in larger relative QALY gains in more vulnerable versus less vulnerable subgroups owing to increased acute ischemic stroke incidence and lower receipt of thrombolysis. Using an opportunity cost threshold of US$150,000/QALY, alteplase was estimated to improve social welfare by increasing population health (45,606 QALYs gained) and reducing existing overall US inequities by 0.0001% annually. Results were robust across all levels of population inequality aversion and alternate opportunity cost thresholds. Health system scenarios that reduced care gaps promoted additional reductions in existing inequalities, because more patients with lower baseline health were eligible for treatment.

Conclusions

Under current treatment patterns, this DCEA demonstrated that alteplase for acute ischemic stroke increased population health and improved health equity. It is critical to address existing care gaps to enable equitable access to alteplase across race, ethnicity and geography.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40258-025-00985-6.

Key Points for Decision Makers

This distributional cost-effectiveness analysis estimated population effects of ischemic stroke across race, ethnicity and geography to evaluate equity impacts under current alteplase treatment patterns and found that alteplase reduced overall US health disparities and improved overall US population health.

While all subgroups, regardless of race, ethnicity, or geography, benefited from alteplase, larger relative health gains were estimated in more disadvantaged populations compared with fewer disadvantaged populations.

In exploring the influence of health systems interventions to address known care gaps, more timely and equitable access to alteplase led to increases in the benefits of health systems saving and equity gains.

Introduction

In the USA, the mean incidence of ischemic stroke across all ages was 79 per 100,000 in 2022. Compared with women, stroke incidence and prevalence were higher in men; however, mortality was lower [1]. It is well documented that ischemic stroke disproportionately affects racial minorities [2]. Black individuals aged ≥ 65 years have a 1.5- to 2-fold higher risk of ischemic stroke than White individuals. Over the past two decades, mortality associated with ischemic stroke in the USA has increased, and the average annual cost per patient was substantial at over US$59,000 in 2020 [3, 4].

Educational interventions have revolved around reducing stroke risk factors and earlier recognition of symptoms. However, there are substantial racial, ethnic and geographic disparities in the quality of stroke care, health outcomes and treatment access. Evidence indicates timeliness of stroke care in racial and ethnic minority groups is affected by lower use of emergency medical services for transportation and longer emergency department wait times compared with White patients. In addition, minority groups have lower access to specialist care, lower rates of treatment with a tissue-type plasminogen activator (tPA) or mechanical thrombectomy and are more commonly misdiagnosed than White patients [5]. Efforts are being made to better understand and eliminate drivers of disparities, such as improving disease and risk factor awareness within specific racial and ethnic groups and carrying out community-based participatory research studies, to actively engage with community members who collaborate with research teams to ensure clinical research meets the needs of diverse populations [6, 7].

Intravenous thrombolysis with alteplase is an effective therapy for ischemic stroke when administered promptly after symptom onset. Alteplase was approved by the Food and Drug Administration in 1996 to be administered intravenously within 3 hours of ischemic stroke symptom onset [8]. In a meta-analysis of multiple clinical trials, alteplase administration within 3 h of symptom onset reduced the relative risk of post-stroke disability by 38% compared with placebo. This reduced disability risk is particularly important because strokes are associated with long-term reduced quality of life for patients and high associated costs [9, 10]. In fact, these downstream effects are more pronounced in racial and ethnic minority groups who experience a higher cost of acute care and a higher long-term mortality post-stroke than those groups who do not share these experiences [11, 12]. Compared with placebo, administration of alteplase for ischemic stroke results in neurological improvement at 24 h after the event [13], and a 32% relative increase in the number of patients experiencing minimal or no disability after 3 months [14]. However, Black patients were found to be approximately 20% less likely to receive intravenous thrombolysis than White patients [11]. To ensure equitable access to alteplase and other efficacious treatments for ischemic stroke, improvements in time to treatment and other health system factors need to be considered [15]. Further, the role of alteplase in closing these disparities through the reduction of post-stroke disability has yet to be investigated.

Through the promotion of efficient resource allocation under limited budget, cost-effectiveness analyses (CEAs) can be leveraged by health systems to inform decisions on funding and reimbursement for ischemic stroke treatments. Cost-effectiveness analyses seek to maximize efficiency to create the largest gains in health for a population of interest [16, 17]. However, determining who is positively or negatively affected by a cost-increasing health program depends on numerous factors. These include health risks, treatment access, capacity to benefit and who bears the opportunity costs associated with diverting scarce resources away from other uses [18]. Therefore, the diversity of a patient’s journey to receive ischemic stroke care should be considered when assessing how to address inequitable outcomes. Although alteplase has proven dominant (i.e., cost savings and providing more health) versus standard of care (SOC) [16], its cost effectiveness within equity-relevant subgroups with known stroke disparities in place is unknown, thus, the impact of alteplase and health system factors on those disparities and overall equity remains uncertain.

A distributional cost-effectiveness analysis (DCEA) is an extension of CEA that provides information on how interventions will affect overall health and underlying health inequalities. Distributional cost-effectiveness analysis allows us to isolate the health and equity impacts of an intervention in equity-relevant subgroups and enables the modeling of health system scenarios targeting these subgroups [18]. Given the undeniable impact of ischemic stroke on disadvantaged populations [5], it is essential to consider how decisions on funding alteplase treatments affect health disparities. Furthermore, it is important to test different health system modifications surrounding alteplase that could help to address the disparities.

As the equity impact of funding alteplase in the USA has not been established, the purpose of this DCEA was to evaluate how US Medicare funding of alteplase treatment for ischemic stroke would affect health equity annually, considering the cost effectiveness of alteplase treatment and the distribution of opportunity costs. This DCEA aims to predict the equity impacts of alteplase treatment using outcomes from a CEA of alteplase versus SOC and real-world estimates of US ischemic stroke incidence and thrombolytic utilization. A secondary goal of this study was to explore how to best guide efforts aimed at addressing ischemic stroke disparities from a broader public health or health system perspective.

Methods

Underlying Alteplase CEA

This DCEA was built upon a previously published CEA in which alteplase administration within 3 hours of ischemic stroke symptom onset was dominant over SOC treatment [16]. In this underlying lifetime horizon, payer-perspective CEA, alteplase outcomes were estimated through treatment impacts on post-event disability. A hybrid Markov model used short-term parameters in a decision tree to sort patients into Markov annual health states based on post-event disability levels. This was assessed by the modified Rankin Scale (mRS) at 90 days post-event. Thus, all long-term outcomes, utilities and costs were stratified by post-event discharge disability level.

To use the most relevant, contemporary inputs, updates were made to the CEA based on a review of the literature (Online Resource Targeted Literature Search Details and Online Resource Table 1), with key changes documented in Table 1. Equity-relevant subgroup adjustments were included for parameters used in the CEA for which distributional information was identified in the literature.

Table 1.

Key CEA updates and subgroup adjustments

Parameter	Originala CEA estimate	Updateda CEA estimate	Subgroup adjustor	Notes	References
Clinical inputs
Inpatient mortality by SVI quintile
Q2			1.01	SVI adjustments were obtained using American Community Survey data, and were used to calculate the proportion of the SVI quintile within different federal poverty levels that matched the household income levels in Ader et al. (Online Resource Table 2) Reference group: SVI Q1 (least deprived)	[43]
Q3			1.01
Q4			1.02
Q5			1.03
Stroke recurrence	0.05	0.03		Annual recurrence rate was based on the probability of recurrent stroke over 10 years after stroke	[19]
Long-term mortality	2.50	2.89		Hazard ratio of those disabled by stroke compared with those not disabled by stroke	[20]
non-Hispanic Black			1.01	Race and ethnicity adjustments were made to both disability statuses Reference group: non-Hispanic White	[12]
Intracranial hemorrhage
API			1.23	Reference group: non-Hispanic White	[45]
Utilities
Quality of life
Non-disabled by stroke	0.84	0.87		Utilities were weighted averages based on individual modified Rankin scores post-stroke	[10]
Disabled by stroke	0.47	0.42
Costs
Acute care costs
Additional cost of alteplase (US$)	6525	8800			Data on file
Non-disabled by stroke (US$)	7503	30,814		Consumer Prices Index adjusted to 2023 US$; average cost of an ischemic stroke hospitalization was stratified by disability status at discharge	[9]
Disabled by stroke (US$)	11,397	85,532
Fatal stroke (US$)	13,324	86,627
Non-Hispanic Black			1.17	Race and ethnicity adjustments were applied to all stroke disability levels Reference group: non-Hispanic White	[11]
Hispanic			1.47
API			1.51

API Asian/Pacific Islander, CEA cost-effectiveness analysis, Q quintile, SVI social vulnerability index

^aUpdates to Boudreau et al. [16], all other baseline inputs used were sourced from this previously published alteplase CEA, in which all original inputs are detailed

Updates to Underlying Alteplase CEA

Annual stroke recurrence rate was updated based on a recent study of ischemic stroke outcomes reported between 2008 and 2017 [19]. Long-term mortality for patients who were or were not disabled by ischemic stroke was updated based on literature measuring 5-year post-event mortality stratified by 90-day post-event mRS scores [20]. Utility values on the EQ-5D questionnaire of patients who were or were not disabled by ischemic stroke were calculated based on a study that used individual mRS scores [10]. These utilities were then weighted by individual mRS scores of trial participants included in the meta-analysis by Wardlaw et al. [21]. Inpatient care costs were updated based on a US commercial insurance claims analysis that used International Statistical Classification of Diseases and Related Health Problems 9th Revision (ICD-9) codes to identify ischemic stroke hospitalizations between 2006 and 2015. These were stratified by discharge status: disabled, non-disabled and deceased [9]. All costs were inflated to 2023 US$ using the consumer price index for medical care [22]. To account for costs and benefits occurring at different times, all costs and quality-adjusted life-years (QALYs) were discounted at an annual rate of 3%, as recommended by the second Panel on Cost-Effectiveness in Health and Medicine [23].

The relative risks of alteplase lowering disability and increasing symptomatic intracranial hemorrhage (sICH) were not updated owing to a lack of new data comparing alteplase with SOC. Finally, sources and assumptions for long-term health-state costs were not updated from the prior model owing to a lack of studies available with appropriate longitudinal cost data post-ischemic stroke.

Overview of DCEA

Distributional cost-effectiveness analysis captures information on the differences of health impacts across equity-relevant population subgroups to understand the distribution effects of an intervention and the resulting consequences for health equity. Equity-relevant population subgroups are mutually exclusive groups within a population with different baseline health and, therefore, a different capacity to benefit from intervention [18]. In the USA, previous DCEAs have defined these subgroups based on geographic location, level of vulnerability, race and ethnicity [24–28].

The DCEA process started by gathering information on the differences in need for care, receipt of care and short- and long-term effects of care across equity-relevant population subgroups. This is referred to as the “equity pathway” for a given disease area and associated funding decision. When taken together, the differential impacts provide information on whether an intervention has different values across equity-relevant subgroups. Information on the differential impacts was combined with the underlying health disparities that exist within equity-relevant subgroups to estimate how funding an intervention impacts equity in the overall population. The impact is provided as (1) the health benefit provided by the intervention in QALYs and (2) the health impact from resources forgone for healthcare services given the chosen opportunity cost threshold. Using this threshold (US$150,000 per QALY), the health impact is calculated by converting the total cost of funding the intervention into total health losses (QALYs) that are taken from the total health of each subgroup to represent the broader equity consequences of funding decisions. For example, assuming an opportunity cost threshold of US$150,000 per QALY for a treatment that costs the US healthcare system US$2 billion, the health losses resulting from opportunity costs would be 13,333 QALYs across the population. The final step of DCEA uses inequality aversion, or an individual’s preference for addressing health disparities, to weigh health gains in subgroups with lower baseline health—or more health disparities—today.

Following foundational methods outlined by Cookson and colleagues, the impact of funding alteplase on US health equity was estimated by assessing its treatment benefit relative to supportive care for all treated patients. Health losses due to opportunity costs were estimated in the total US population [29]. As this DCEA seeks to estimate the total impact on health inequalities across the USA, the full US population was modeled, with opportunity costs borne by individuals across the USA. However, as most ischemic strokes occur in individuals aged ≥ 65 years, this largely models the equity consequences of US Medicare funding of alteplase treatments.

Baseline Distribution of Health Disparities in the US Population

To assess the equity impact of alteplase, this DCEA used information on the baseline distribution of health disparities in the overall US population. The distribution was organized into 25 equity-relevant subgroups based on race and ethnicity (five census-based groups) and county-level social vulnerability (five geographic quintiles) [26]. Racial and ethnic subgroups consisted of: non-Hispanic American Indian or Alaska Native (AIAN), non-Hispanic Asian and Pacific Islander (API), non-Hispanic Black, non-Hispanic White and Hispanic. The social vulnerability index (SVI) from the Centers for Disease Control and Prevention helped to group US counties according to levels of social vulnerability [30]. Percentile ranking values ranged from 0 to 1, with higher values indicating greater social vulnerability. Counties were grouped into quintiles based on SVI percentile ranking and were further divided by five census-based racial and ethnic subgroups. This approach aligns with that used in other settings to define equity-relevant subgroups [31, 32]. The resulting 25 equity-relevant subgroups enabled improved capture of health outcomes that may be influenced by racial, ethnic and geographic disparities as a result of social determinants of health [33]. Using these baseline distribution of health disparities, an average quality-adjusted life expectancy (QALE) for an individual within that subgroup can be calculated and the population sizes of each equity-relevant subgroup are reflective of combining age and sex within each subgroup (Table 2).

Table 2.

Summary of key ischemic stroke DCEA inputs

Subgroupa (ranked by starting QALE)	Subgroup QALE at birthb	Total populationc	Annual Incidence		Alteplase utilization,e %	Total alteplase-treated annually
			Based on baseline demographics	Race- and ethnicity-adjustedd
AIANQ5	55.90	925,808	1041	1041	9.68	101
AIANQ4	57.51	692,786	765	765	9.68	74
AIANQ3	58.49	476,501	554	554	9.68	54
BQ5	58.67	10,889,324	14,195	15,096	11.85	1789
AIANQ2	59.26	403,697	438	438	9.68	42
WQ5	59.64	22,221,972	45,194	45 194	14.45	6531
AIANQ1	59.84	160,945	192	192	9.68	19
BQ4	60.49	12,856,383	16,205	17,161	11.85	2033
WQ4	61.04	42,971,129	85,344	85,344	14.45	12,332
BQ3	61.38	9,395,736	10,919	11,522	11.85	1365
BQ2	62.12	5,337,989	5877	6178	11.85	732
WQ3	62.21	50,321,633	99,547	99,547	14.45	14,385
WQ2	62.83	46,689,477	87,424	87,424	14.45	12,633
BQ1	63.18	2,391,551	2688	2818	11.85	334
WQ1	63.77	34,823,253	63,153	63,153	14.45	9126
HQ5	69.29	16,591,323	16,422	16,422	14.16	2325
HQ4	68.68	19,317,637	16,132	16,132	14.16	2284
HQ3	68.32	12,335,825	10,214	10,214	14.16	1446
HQ2	67.18	6,124,952	4441	4441	14.16	629
HQ1	65.68	2,960,828	2098	2098	14.16	297
APIQ5	73.93	1,878,977	2362	2362	16.62	392
APIQ4	73.44	5,085,449	7132	7132	16.62	1185
APIQ3	72.11	5,171,947	7389	7389	16.62	1228
APIQ2	71.84	4,053,270	4808	4808	16.62	799
APIQ1	70.03	1,958,708	2097	2097	16.62	348
Total US sample		316,037,100	506,630	509,520		72,484

Discrepancies in sums within the columns are due to the rounding of whole numbers

AIAN American Indian and Alaska Native, API Asian/Pacific Islander, B non-Hispanic Black, H Hispanic, Q quintile, SVI Social Vulnerability Index, W non-Hispanic White

^aQ1, least socially vulnerable; Q5, most socially vulnerable

^bSourced from Kowal et al. [25] American Community Survey data were used to generate QALE estimates for each subgroup according to county-specific SVI

^cSourced from Ramirez et al, National Inpatient Sample data were used to calculate age-adjusted ischemic stroke hospitalization rates per the underlying age makeup of each equity-relevant subgroup [35]

^dSourced from Norris et al, US national cohort data were used to determine that Black individuals aged ≥ 60 years had a higher risk of incident ischemic stroke than White individuals [36]

^eBaseline alteplase utilization was 14.45% of all ischemic strokes as determined using US national hospital registry data per Mendelson et al. [40]; alteplase utilization was adjusted by race and ethnicity according to Kumar et al., who used National Inpatient Sample data to determine the odds of receiving thrombolysis by race and ethnicity [11]

Ischemic Stroke and Alteplase Health Disparities

All health disparity information was obtained through a targeted literature search conducted in March 2022 (Online Resource Table 1) [34]. Google Scholar and MEDLINE were used to identify literature studying the impact of race, ethnicity and various social deprivation indices on selected ischemic stroke outcomes relevant to economic modeling. Important distributional differences were identified and summarized in an equity pathway (Fig. 1).

Fig. 1.

Ischemic stroke equity pathway depicting the need, receipt, short- and long-term effects [18]

Specific individual-level (underlying CEA) and population-level (DCEA) values for each equity-relevant subgroup are documented in Tables 1 and 2, respectively. If possible, unadjusted estimates were used from the studies identified for equity-relevant subgroup health disparities to better reflect real-world conditions that influenced these disparities.

Incidence

Age-adjusted annual incidence of ischemic stroke was based on a study of hospitalization rates using the National Inpatient Sample between 2000 and 2010 among adults aged ≥ 25 years. Ischemic stroke hospitalizations were assessed using ICD-9 codes as the primary discharge diagnosis code [35]. The average age of patients with ischemic stroke was 73, 65, 67, 68 and 70 years for White, Black, Hispanic, AIAN and API individuals, respectively [11]. Information from the American Community Survey on age distribution for each equity-relevant subgroup was combined with age-based incidence rates to create a detailed estimate of ischemic stroke incidence across equity-relevant subgroups [26, 35]. Based on US Veterans Health Administration data, the rate of ischemic stroke is higher in Black individuals than White individuals, especially those aged ≥ 65 years [36]. For this reason, the estimates for Black individuals aged 65–84 and ≥ 85 years were further adjusted based on hazard ratios derived from Norris et al. [36]. The annual rates of ischemic stroke for Black individuals within these age groups were calculated to be 6% and 30%—greater than for individuals of the same age in all other subgroups, respectively (Table 2). In further support of this adjustment, our literature search yielded three other studies that showed increased ischemic stroke incidence in Black populations compared with White populations [37–39].

Receipt of Alteplase

In the original literature search, alteplase utilization was studied by race and ethnicity in four studies [14, 40–42]. In three of these studies, alteplase utilization was lower in at least one non-White population, whereas one study found no difference in administration of alteplase. To avoid modeling the racial and ethnic disparity with conflicting evidence, the literature was searched further and a systematic literature review was obtained reporting that alteplase use was lower in Black individuals compared with non-Hispanic White individuals in 9/11 studies analyzed [5]. Another study found evidence that non-Hispanic Black patients were more likely to decline alteplase treatment compared with non-Hispanic White patients [40]. A decreased receipt of alteplase in non-White individuals was modelled based on the information above and based on a study of > 170,000 patients that used 2011 and 2012 National Inpatient Sample data to show decreased odds of thrombolysis [11]. According to this study, alteplase utilization decreased by 33%, 18% and 2% in AIAN, Black and Hispanic patients, respectively, compared with White patients. In contrast, API individuals had a 15% greater chance of receiving alteplase than White individuals (Table 2). The modeled population reflected the entire US population; however, alteplase treatment was restricted to 14.45% of all ischemic strokes experienced in the USA annually according to a national hospital registry [40].

The comparator for this DCEA was SOC as assessed by the underlying CEA [16], therefore patients who experienced ischemic stroke and were not treated with alteplase did not receive thrombolysis treatments.

Hospitalization Costs

Ischemic stroke hospitalization costs were higher in non-White subgroups in all four relevant studies identified [11, 37, 41, 42]. One study used ischemic stroke ICD-9 codes to capture charges associated with hospitalization and to produce a mean cost per hospitalization [11]. From this study, the difference in costs within the underlying CEA was converted to a ratio multiplier to increase hospitalization costs due to ischemic stroke for API, non-Hispanic Black, and Hispanic individuals versus non-Hispanic White individuals, regardless of disability level post-ischemic stroke (Table 1).

Inpatient Mortality

The effect of level of social deprivation or vulnerability on inpatient mortality due to ischemic stroke was investigated in one study in the literature search results [43]. The study used national hospital registry data between 2015 and 2017 to identify more than 50,000 patients hospitalized for ischemic stroke. These patients were linked to zip code-level median household income, which was divided into 2015 US census-based quintiles. Estimates from this study showed that lower household income was associated with increased inpatient mortality due to ischemic stroke [43]. Based on these estimates, inpatient mortality in the underlying CEA was increased for more socially vulnerable groups compared with less vulnerable groups of all races and ethnicities (Table 1). Based on the median household quintiles in Ader et al, the proportion of patients within SVI quintiles and 2015 US census Federal poverty level cut-offs were used to assign the effect sizes of estimates to SVI quintiles (Online Resource Table 2) [43].

Long-Term Mortality

All three studies evaluated for equity considerations on long-term mortality, within 90 days post-stroke, showed that non-Hispanic Black patients had a higher mortality compared with non-Hispanic White patients [12, 37, 44]. Using ischemic strokes that occurred between 2008 and 2012, a study of over 1 million Medicare fee-for-service beneficiaries showed that non-Hispanic Black beneficiaries had a lower 5-year post-stroke survival probability than White beneficiaries [12]. In the underlying CEA, long-term mortality post-stroke was converted to an annual rate and an increase of 0.8% was applied to Black patients regardless of post-stroke disability level (Table 1).

Symptomatic Intracranial Hemorrhage (sICH)

Three studies evaluating sICH rates showed an increased risk in API patients compared with non-Hispanic White patients [45–47]. Song et al. used national hospital registry data on over 1.7 million ischemic stroke hospitalizations between 2004 and 2016 to show that Asian American patients had greater odds of sICH within 36 hours of receiving alteplase compared with White patients [45]. As such, sICH was increased by 23% for API patients who received alteplase within the underlying CEA.

Modeling Alteplase Impact on Health Disparities

Before assessing health equity implications at the population level using DCEA, the underlying CEA was modified to capture differential impacts on costs and health (QALYs) at the individual level for each equity-relevant subgroup. The CEA results were then fed into the DCEA portion for each subgroup to capture the entire impact of alteplase on health disparities.

The base case DCEA used the differential impacts of ischemic stroke and alteplase health disparities (Table 1) to assess the health equity impacts of funding alteplase treatments annually. A single US payer with a fixed budget, such as Medicare, was assumed, wherein the funding of alteplase treatments for ischemic stroke would result in opportunity costs from choosing not to fund other interventions. This assumption was justified, given that ischemic stroke per 100,000 persons occurs at a 4-fold greater rate in individuals aged 65–84 years versus 45–64 years in the USA [35]. Because no studies determining how opportunity costs were distributed across equity-relevant subgroups were identified, the base case DCEA assumed an equal distribution of opportunity costs across subgroups and an opportunity cost threshold of US$150,000 per QALY, as consistent with a similarly designed US DCEAs [25, 48, 49]. Scenarios were tested to demonstrate the effect of changing the opportunity cost threshold to US$100,000 and US$50,000.

To further assess what affects treatment access, we evaluated how other health factors beyond alteplase could affect ischemic stroke outcomes. Therefore, we evaluated how three different public health or health system interventions could facilitate treatment access within the 3-hour time window. Additional scenario analyses tested how increased alteplase utilization influenced health equity impacts overall and in different subgroups. In the first scenario, overall alteplase utilization was increased by 5% for all patients, therefore 19.45% of all ischemic strokes were treated with alteplase. This was intended to simulate more patients arriving to inpatient care in time to receive alteplase. A second tested scenario was that by eliminating existing health disparities, all subgroups had equal alteplase utilization of 14.45% of all ischemic strokes [40]. Finally, a third scenario was analyzed in which the use of alteplase was increased by 20% in the top 15% of the most deprived subgroups, according to lowest baseline health (QALE). This was done to simulate efforts to close alteplase health disparities focused on the patient subgroups that experienced the greatest disparities in length and quality of life (AIAN quintile 5 [AIANQ5], AIANQ4, AIANQ3, non-Hispanic Black Q5 [BQ5], AIANQ2, non-Hispanic White Q5 [WQ5], AIANQ1 and BQ4) [26]. Using the same inputs as the base case, a subgroup analysis was tested in individuals within the US population aged ≥ 65 years to better estimate the impact on Medicare recipients who were most likely to experience ischemic stroke.

Social welfare impacts were assessed using the net health impact from funding alteplase treatments across all subgroups and the change in Atkinson’s index of inequality at birth (QALE) due to alteplase treatments [18]. To assess the change in Atkinson’s index of inequality, the distribution of health across subgroups must be quantified before and after funding alteplase treatment. This is achieved by estimating the equally distributed equivalent health (EDEH), the equity-weighted mean of the health distribution that considers relative inequality and total health to represent overall social welfare of the US population, using the formula:

$$\text{EDEH}={\left(\frac{1}{N}\sum h_i^{1-\epsilon }\right)}^{\frac{1}{1-\epsilon }},$$ 1

where $N$ is the overall US population, $h_i$ is the individual QALE for each equity-relevant subgroup and $\epsilon$ is the Atkinson index inequality aversion parameters (IAP). Then, the Atkinson’s index of inequality before and after funding alteplase treatment was assessed, using the formula:

$$A\left(\epsilon \right)=1-\frac{\text{EDE}}{\overline{h}},$$ 2

where $A\left(\epsilon \right)$ is Atkinson index of inequality (scaled from 0 to 1, where 0 represents no inequality and 1 represents full inequality), EDE is the result of the previous equation, and $\overline{h}$ is the mean health in the US population. Because US preferences for inequality aversion are unknown, an inequality aversion score of 11 for the Atkinson index was assumed for the base case, aligned with preferences in the general population from the UK [50]. Factoring in this inequality aversion level, the change in Atkinson’s index of inequality before and after funding alteplase treatment can be expressed as a percentage change in existing overall health disparities, using this formula:

$$(A{(\epsilon )}_{\text{post}}-A{(\epsilon )}_{\text{pre}})\times {\text{EDE}}_{\text{pre}}\times N=\%\text{change}\text{in}\text{existing}\text{health}\text{disparities,}$$ 3

where the difference in $A\left(\epsilon \right)$ is the change in Atkinson’s index of inequality, ${\text{EDE}}_{\text{pre}}$ is the social welfare of the health distribution before alteplase treatment, and $N$ is the overall US population. Positive percent changes signal a reduction in overall health disparities in the US population.

To better assess how the base case and scenarios would change with different preferences for addressing health disparities, an EDEH graph was created, in which equity-weighted population-level net health impacts (EDEH QALYs) were plotted against different levels of Atkinson index IAP. Positive values on the y-axis of the EDEH graph represent overall net increases in social welfare. The slope of the graph indicates changes in social welfare across Atkinson aversion parameters. A positive slope suggests the intervention improves equity, because there is more net health gain under increased equity weighting, or higher preference for reducing health disparities [18, 51].

Results

CEA Results

Traditional deterministic CEA results were assessed per patient for all 25 equity-relevant subgroups (Table 3). Subgroups are presented from lowest baseline health (lowest QALE) to highest, from AIANQ5 to APIQ1. Under a willingness-to-pay threshold of US$150,000/QALY [52], alteplase administration within 3 h of ischemic stroke symptom onset dominated best supportive care across all subgroups. Over the span of a lifetime, alteplase was cost saving, with an average cost reduction of US$30,697 per patient. Alteplase improved health compared with best supportive care, with an average incremental QALY gain of 0.51 per patient across all subgroups. These results aligned with the previous CEA and were mainly driven by alteplase-related reduction of post-ischemic stroke disability [16]. Subgroup differences were driven primarily by the differences in acute care costs across racial and ethnic subgroups, because distributional inputs for long-term care were not identified in the published literature.

Table 3.

CEA results at patient and subgroup levels for alteplase versus SOC

Subgroupa (ranked by starting QALE)	Incremental outcomes per patient			Total subgroup outcomes
	Total incremental cost savings (US$)	Inc. QALY	Relative QALE gain per patientb %	Total cost savings (US$)	Total QALYs	QALYs gained per 100,000 persons
AIANQ5	–31,710	0.53	0.95	–3,196 328	53	5.8
AIANQ4	–31,709	0.53	0.92	–2,347,711	39	5.6
AIANQ3	–31,708	0.53	0.90	–1,699,953	28	5.9
BQ5	–38,023	0.56	0.95	–68,013,495	1002	9.2
AIANQ2	–31,707	0.53	0.89	–1,346,015	22	5.6
WQ5	–20,083	0.45	0.76	–131,149,555	2956	13.3
AIANQ1	–31,706	0.53	0.88	–589,378	10	6.1
BQ4	–38,022	0.56	0.93	–77,313,831	1139	8.9
WQ4	–20,081	0.45	0.74	–247,644,865	5583	13.0
BQ3	–38,021	0.56	0.91	–51,906,394	765	8.1
BQ2	–38,020	0.56	0.90	–27,831,726	410	7.7
WQ3	–20,080	0.45	0.73	–288,843,627	6512	12.9
WQ2	–20,079	0.45	0.72	–253,656,577	5719	12.2
BQ1	–38,019	0.56	0.89	–12,695,061	187	7.8
WQ1	–20,078	0.45	0.71	–183,225,042	4131	11.9
HQ5	–33,791	0.53	0.80	–78,580,835	1229	7.4
HQ4	–33,790	0.53	0.79	–77,188,971	1207	6.2
HQ3	–33,789	0.53	0.77	–48,870,677	764	6.2
HQ2	–33,788	0.53	0.77	–21,248,385	332	5.4
HQ1	–33,787	0.53	0.76	–10,037,183	157	5.3
APIQ5	–29,892	0.50	0.72	–11,731,611	198	10.5
APIQ4	–29,890	0.50	0.70	–35,424,974	597	11.7
APIQ3	–29,889	0.50	0.70	–36,697,328	618	12.0
APIQ2	–29,888	0.50	0.69	–23,879,831	402	9.9
APIQ1	–29,886	0.50	0.68	–10,412,302	175	9.0
Total/average	–30,697	0.51	0.81	–1,705,531,654	34,236	8.7

Discrepancies in sums within the columns are due to the rounding of whole numbers

AIAN American Indian and Alaska Native, API Asian/Pacific Islander, B non-Hispanic Black, H Hispanic, CEA cost-effective analysis, Q quintile, QALE quality-adjusted life expectancy, QALY quality-adjusted life-year, SOC standard of care, W non-Hispanic White

^aQ1, least socially vulnerable; Q5, most socially vulnerable

^bThis estimate expresses the relative change in QALE for each patient treated in the subgroup, for which the total QALY gain from alteplase treatment is divided by the baseline QALE for the subgroup. The estimate was included to stress that baseline QALE could affect relative health benefits across subgroups

The relative QALE gain per patient showed that there was interplay between impacts of alteplase treatment and existing disparities. This metric highlighted how health impacts of alteplase treatment brought greater relative benefits to more vulnerable populations. For example, while relative QALE gain per treated patient was 0.81% across the population, estimates for AIAN and non-Hispanic Black subgroups were larger at 0.95% relative gains.

Table 3 displays subgroup CEA results per patient, combined with age-adjusted, race-adjusted and ethnicity-adjusted ischemic stroke incidence. The QALYs gained per 100,000 persons showed a more direct comparison among subgroups, which varied greatly in population size. There was a trend of more socially vulnerable populations benefiting further from alteplase within most racial and ethnic groups; however, there was a larger QALY gain per 100,000 persons in non-Hispanic White subgroups. These results were driven by the larger number of patients aged ≥ 65 years within these subgroups compared with other subgroups with lower QALE; non-Hispanic White individuals had a greater chance of reaching ages at which ischemic stroke incidence was higher than subgroups with lower baseline health. Of non-Hispanic White patients, 19% were aged ≥ 65 years compared with 12%, 11%, 10% and 7% for API, non-Hispanic Black, AIAN and Hispanic subgroups, respectively [26].

DCEA Results

Under current utilization patterns, it was estimated that administration of alteplase within 3 hours of ischemic stroke symptom onset resulted in US$1.7 billion in savings to the US healthcare system annually. Furthermore, alteplase improved the health of 72,484 treated patients by 34,236 QALYs in total each year. Considering opportunity costs at a threshold of US$150,000 per QALY, lifetime health system savings from alteplase treatment led to health gains of 11,370 QALYs under an even distribution of opportunity costs according to subgroup population size. The total net health benefit provided by alteplase treatments was 45,606 QALYs for the overall US population. Positive net health benefits were seen across all equity-relevant subgroups (Table 4). Taken together, these results suggest that alteplase administration within 3 hours of ischemic stroke symptom onset was population health-improving and equity-improving in the overall US population (Online Resource Fig. 1).

Table 4.

DCEA results at the population level

Subgroupsa (ranked by starting QALE)	Total population by subgroups	Average starting patient QALE	Health benefit from alteplaseb	Health benefit from opportunity costsc	Net health benefits	Net health benefit per 100,000 personsd
AIANQ5	925,808	55.9	53	33	87	9.35
AIANQ4	692,786	57.5	39	25	64	9.24
AIANQ3	476,501	58.5	28	17	45	9.54
BQ5	10,889,324	58.7	1002	392	1394	12.80
AIANQ2	403,697	59.3	22	15	37	9.15
WQ5	22,221,972	59.6	2956	799	3756	16.90
AIANQ1	160,945	59.8	10	6	16	9.70
BQ4	12,856,383	60.5	1139	463	1602	12.46
WQ4	42,971,129	61.0	5583	1546	7129	16.59
BQ3	9,395,736	61.4	765	338	1103	11.74
BQ2	5,337,989	62.1	410	192	602	11.28
WQ3	50,321,633	62.2	6512	1810	8322	16.54
WQ2	46,689,477	62.8	5719	1680	7398	15.85
BQ1	2,391,551	63.2	187	86	273	11.42
WQ1	34,823,253	63.8	4131	1253	5384	15.46
HQ5	16,591,323	65.7	1229	597	1825	11.00
HQ4	19,317,637	67.2	1207	695	1902	9.85
HQ3	12,335,825	68.3	764	444	1208	9.79
HQ2	6,124,952	68.7	332	220	553	9.02
HQ1	2,960,828	69.3	157	107	263	8.90
APIQ5	1,878,977	70.0	198	68	265	14.11
APIQ4	5,085,449	71.8	597	183	780	15.33
APIQ3	5,171,947	72.1	618	186	804	15.55
APIQ2	4,053,270	73.4	402	146	548	13.52
APIQ1	1,958,708	73.9	175	70	246	12.55
Total/average	316,037,100	63.4	34,236	11,370	45,606	12.31

Discrepancies in sums within the columns are due to the rounding of whole numbers

AIAN American Indian and Alaska Native, API Asian/Pacific Islander, B non-Hispanic Black, DCEA distributional cost-effective analysis, H Hispanic, Q quintile, QALE quality-adjusted life expectancy, QALY quality-adjusted life-year, W non-Hispanic White

^aQ1, least socially vulnerable; Q5, most socially vulnerable

^bIncremental QALY gains per patient with ischemic stroke treated with alteplase, scaled to the total QALY gain within the subgroup.

^cModel base-case scenario assumed that opportunity costs were borne equally across the full population, with a threshold of US$150 000 per QALY

^dNet health impact (QALYs) in the subgroup for every 100,000 persons, used to compare subgroups of varying population sizes

Looking at net health gains, health increases due to alteplase treatment and opportunity costs were observed in all subgroups (Fig. 2). Non-Hispanic White individuals experienced the greatest net health gains, driven by the greater number of patients treated with alteplase compared with other subgroups, along with the increased risk of ischemic stroke at later ages (Table 1). When modeled in the US population aged ≥ 65 years, all net health gains increased in magnitude across all subgroups. Non-Hispanic Black, Hispanic and API individuals aged ≥ 65 years gained more benefit from treatment than White individuals. Notably, AIAN subgroups benefited the least from treatment. This was primarily due to the reduced QALE at birth compared with other subgroups, which reduced the size of the eligible elderly population. Relative health gains increased with higher SVI levels (Q5 subgroups) across all races and ethnicities, with the exception of API individuals. This was mainly caused by the lower QALE at birth in the five SVI quintile subgroups compared with other racial and ethnic subgroups, which allowed for a larger increase in health relative to baseline. Whereas the effect observed in API individuals was caused by the higher inpatient mortality seen in more socially vulnerable than less vulnerable subgroups. Using the Atkinson index, alteplase treatments were estimated to reduce total US health inequalities by 0.0001% annually.

Fig. 2.

Total net health gains per 100,000 persons (QALY) a total net health gains by subgroup per 100,000 persons in the US population and b total net health gains per 100,000 persons aged ≥ 65 years

Net health gains were observed in all subgroups across alternative opportunity cost thresholds of US$50,000 and US$100,000 per QALY (Online Resource Tables 3 and 4). Owing to positive health impacts resulting from opportunity costs, the total net health gain of alteplase increased as opportunity cost thresholds became stricter. This is driven by the fact that alteplase was dominant, thereby creating cost savings that led to larger health gains under lower opportunity cost thresholds.

Scenario analysis results were presented as the change in social welfare from alteplase treatment across levels of inequality aversion, with higher values meaning that greater value was placed on reducing health disparities (Fig. 3). All scenarios generated a curve with a positive slope above the x-axis, suggesting improved social welfare. Across health system scenarios, increasing alteplase utilization to reach 5% more patients post-ischemic stroke in a timely manner had the largest overall impact on reducing health disparities across the overall US population. In the scenario in which all racial and ethnic subgroups had an equal chance of receiving alteplase, non-Hispanic Black patients experienced the greatest improvement in health gains relative to the base case. The scenario in which the most deprived (current top 15% of the US population with the largest health disparities) had a 20% greater use of alteplase treatment generated the largest impact on reducing health disparities within geographic regions. This scenario was also the most equity-improving, as more value is placed on reducing health disparities, as evidenced by the curve with the largest positive slope. Results were robust for varying of average age of ischemic stroke onset, cost effectiveness of alteplase across subgroups, and opportunity cost distribution assumptions (Online Resource Fig. 2).

Fig. 3.

Social welfare EDEH QALY impact of alteplase across different scenarios

Discussion

In this first-ever ischemic stroke DCEA, we quantified the impact of alteplase administered within 3 h of stroke symptom onset on the health and equity of the US population. We adapted an existing CEA to capture health effects on 25 equity-relevant subgroups based on race, ethnicity and geography. Ischemic stroke risks, outcomes and receipt of treatment were updated by current distributional impacts found in the literature. In the underlying CEA, alteplase administration within 3 hours of ischemic stroke symptom onset provided a greater health benefit and was cost-saving compared with SOC. When scaled to the US population, the DCEA demonstrated that alteplase was cost saving to the US healthcare system (US$1.7 billion per year) and increased overall US population health (34,236 QALYs gained). When the cost savings of alteplase are used for interventions that improve health, alteplase treatments improved US population health by a total of 45,606 QALYs and reduced overall health inequality by 0.0001% annually. These effects were seen in all equity-relevant subgroups, with more vulnerable subgroups experiencing greater relative health benefit from alteplase. Owing to the cost saving to the US health system and the greater benefits observed in patients currently experiencing disparities in stroke care, these results highlight the importance of treatment with a thrombolytic in combating these disparities. When applied to individuals aged ≥ 65 years, who were most at risk of ischemic stroke, even greater positive population health and equity effects were observed. The results were robust across all modeled health system scenarios, with the largest impact on reducing health disparities in the USA observed when more patients received alteplase.

The underlying updated CEA yielded similar results to the CEA published by Boudreau et al. [16]. In both the current study and the study by Boudreau et al, alteplase was cost saving (US$30,697 vs US$25,000, respectively) and provided more health improvement (0.51 vs 0.39 QALYs, respectively) compared with SOC [16]. The larger QALY gain reported in our CEA was driven by the updates made to inputs that increased the long-term mortality risk and utility difference for patients who were disabled versus not disabled by ischemic stroke. Our results showing the gain of 45,606 QALYs and 0.0001% reduction of overall health inequality were comparable to those of previously published similarly designed analyses of an Alzheimer’s disease treatment (28,197 QALYs gained and 0.0009% reduction), and smaller in magnitude than those of a COVID-19 treatment (53,252 QALYs gained and 0.003% reduction) [48, 49]. Given the findings of a new study, which were not available at the time our analysis was completed, researchers found the US population inequality aversion level to be similar to that of the UK estimate used in our analysis [53]. When ischemic stroke incidence was scaled to the US population and adjusted for race and ethnicity, our estimation aligned with a study that captured 97% of all US ischemic stroke hospitalization in 2019 [54]. Specifically, scaled to the 2019 population, our estimate was 522,932 ischemic stroke cases versus 552,476 [54]. The centering of our modeled health system scenarios around impacts to alteplase treatment (i.e., more patients arriving within 3 hours or closing racial and ethnic treatment disparities) is justified by the ~20% gap in use of thrombolytics within 3 hours of early stroke presentations seen nationally [54].

Limitations

This analysis had some limitations, including the lack of direct data regarding SVI level impacts on ischemic stroke risk, costs or outcomes. Instead, we converted median household income impact on ischemic stroke inpatient mortality to capture the effect of geography. Ideally, SVI would have been used directly because it considers multiple social determinants of health. We acknowledge that SOC may be an outdated comparator given current stroke treatment options. However, low treatment rates of thrombectomy and IV thrombolysis continue to persist, even when patients arrive in time eligible for treatment [54]. Given that the US population inequality aversion estimate was not available at the time of the model’s development, a UK proxy was used. However, the EDEH plot (Fig. 3) was used to test the sensitivity of our findings to inequality aversion, with robust results across all inequality aversion levels. Aligned with other published DCEAs in the US setting, the assumption to distribute opportunity costs equally according to subgroup population size was also a limitation because the real-world distribution in the USA was not available [25, 27, 28, 48]. However, because opportunity costs were cost saving in the analysis, a different distribution would likely not have had a significant impact on results. Further, no additional costs were assumed for increased alteplase use in the scenarios. Future work could look to add costs and distributional impacts of health system interventions to improve ischemic stroke care. Although not considered in this analysis but reflective of the current ischemic stroke treatment landscape, thrombectomy is an option for many patients with ischemic stroke who are able to travel to a certified primary stroke center for care; however, this may be difficult for patients living in rural areas. Finally, we acknowledge that while this model sought to capture the best available information on disparities across the equity pathway to support equity analysis, there are many complex factors that shape disparities in stroke that could not be empirically explored in our analysis. The intersectionality of many factors is at the root of disparities (i.e., patient preferences on treatment choice, social and cultural factors affecting treatment consent, and structural racism), and future work should combine our findings with further information on public health interventions to address additional root causes of stroke disparities.

Conclusions

Results of this analysis highlight that increased efforts to deliver alteplase treatments in a timely manner, by health systems or population health decision-makers, could be very useful in reducing stroke disparities in historically marginalized populations. Further, the information generated here could help to inform health equity initiatives in hospitals and health plans. Generally, we hope that this analysis instills confidence in decision-makers that alteplase treatment is a cost-saving option to reduce health disparities and increase overall population health.

In this DCEA, we sought to quantify the health equity impacts of alteplase treatment for ischemic stroke. We estimate that alteplase treatments improved health in all equity-relevant subgroups, regardless of race, ethnicity and geography, with greater relative health gains observed in more vulnerable populations than in less vulnerable populations. Alteplase was also predicted to have reduced overall US health disparities by 0.0001% annually. Importantly, this small reduction of overall US health disparities was seen when slightly over 70,000 patients were treated with alteplase annually, highlighting the potential for further reductions if more patients have access to treatment. The findings of this modeling analysis of equity impacts support that not only is improved access to alteplase cost-saving, but more importantly, it could be a viable way to reduce health disparities broadly and in ischemic stroke. With this information, decision-makers should increase efforts to improve access to timely receipt of alteplase to the general population, especially in the most vulnerable subgroups within society.

Supplementary Information

Below is the link to the electronic supplementary material.

Declarations

Author contributions

TM & SK contributed to study concept and design, analysis and interpretation of data, development of the economic model, drafting of the manuscript and critical revision of the paper for important intellectual content. EM contributed to analysis and interpretation of data, analysis and editing of the manuscript.

Conflict of interest

TM and SK are employees and shareholders of Genentech Inc. EM was an employee of Genentech Inc. at the time this study was conducted.

Funding

This study was funded by Genentech, Inc., South San Francisco, CA, USA. Genentech, Inc. were involved in conducting the study.

Role of funder/sponsor

Publication of study results was not contingent on the sponsor’s approval or censorship of the manuscript.

Data availability statement

Data were obtained from publicly available data sets and other public information from the peer-reviewed literature.

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication (from patients/participants)

Not applicable.

Code availability

While all the data used in this study are publicly available, the model itself is proprietary to Genentech, Inc.