Abstract
Background:
Studies report that 25–50% of stroke survivors in the United States experience aphasia. This study aimed to characterize where services for aphasia were initiated and the discharge disposition among fee-for-service (FFS) Medicare beneficiaries.
Methods:
Data included 100% of FFS Medicare claims for Home Health Agency (HHA), Skilled Nursing Facility (SNF), Inpatient, Outpatient, and Carrier files. The sample included Medicare beneficiaries who incurred an inpatient claim between January 1, 2016 and October 1, 2019 with stroke as the primary diagnosis.
Results:
We identified 745,917 unique FFS Medicare beneficiaries with a primary stroke diagnosis; 90% were over age 65. Approximately 79% were non-Hispanic White, 12% were non-Hispanic Black, and 6% were Hispanic. Among all patients with a stroke hospitalization, 14.5% were diagnosed with aphasia within 90 days, with racial/ethnic variations. Between 50% and 55% of people with aphasia (PWA) had their initial encounter in an inpatient hospital facility, 15%–20% in a skilled nursing facility, and 8%–12% in a rehabilitation facility. Roughly 10–15% of PWA were discharged home, 30% to a skilled nursing facility, 40% to a long-term care/rehabilitation center, and 10% to home health. A smaller percentage of non-Hispanic Black PWA (9.9%) were discharged home compared to non-Hispanic White PWA (13.6%). Similarly, a slightly larger portion of non-Hispanic Black PWA were discharged to a long-term care/rehabilitation facility (41.4%) than their non-Hispanic White PWA counterparts (40%).
Conclusions:
The rate of aphasia remained relatively unchanged across the four-year period evaluated. Initial encounters for their aphasia varied across settings and the locations to which they were discharged after their acute care stay.
Keywords: Stroke, aphasia, Medicare
Background
According to the American Speech Language and Hearing Association (American Speech Language Hearing Association, 2024) approximately 180,000 Americans acquire post-stroke aphasia and there are currently about two million Americans living with the condition. Aphasia is an acquired language disorder that often occurs after a stroke that impacts spoken language expression, written expression, spoken language comprehension and reading comprehension (American Speech Language Hearing Association, 2024). Studies of rates of post-stroke aphasia after stroke, report estimates ranging from 25–50% of all stroke survivors have the condition (Berthier, 2005; Dickey et al., 2010; Engelter et al., 2006; Flowers et al., 2016; Gialanella & Prometti, 2009; Grönberg et al., 2022). Although post-stroke aphasia is most common among older adults, 15% to 25% of individuals with aphasia are under the age of 25 (Ellis, Hardy, et al., 2017; Engelter et al., 2006). Studies reporting different rates of have varied significantly in stroke sample size, approaches utilized to identify aphasia, measurement timing (acute, subacute, chronic), geographic location/country, and data collection process (Ellis, Hardy, et al., 2017). Additionally, data utilized to determine rates of aphasia has been collected from single hospitals, multi-hospital health systems, and state or country stroke registries (Ellis, Hardy, et al., 2017; Palmer et al., 2019). There is concern that the variations in samples, sample sizes and procedures use to determine rates of aphasia have resulted in significant differences in reported rates (Palmer et al., 2019).
Large national administrative datasets primarily utilized for billing purposes offer significant value in determining rates of conditions such as aphasia (Cohen, 2014). Large national administrative databases are critical to health services research and allow researchers to ask research questions using large population groups from multiple states or regions of the US (Cohen, 2014). The use of large administrative databases to study rates of aphasia and other related communication disorders has been limited. One prior study conducted by Ellis et al. 2018 using data from the Healthcare Cost and Utilization Project (HCUP) was limited to individuals with aphasia who were discharged from US hospitals (Ellis, Hardy, et al., 2017). The study included 152,972 adults with stroke discharged from hospitals in Oregon, Arizona, Colorado, Florida, Kentucky, North Carolina, South Carolina and Arkansas between 2011–2012. Among those discharged with stroke, 28,086 (18.4%) were discharged with aphasia. Unfortunately, because the length of stay for stroke care is traditionally short, many individuals with aphasia are diagnosed after the post-acute discharge level of care. Consequently, the numbers presented by Ellis et al were limited and likely resulted in a lower rate than expected (Ellis, Hardy, et al., 2017). A second study by Wu, et al. calculated aphasia prevalence among 4,339,156 adults with stroke using data from the HCUP National Inpatient Sample (Wu et al., 2020). They found that 16.93% had aphasia and aphasia was associated with longer length of stays and higher costs.
Despite the significant value of using large administrative databases, a primary limitation of such use is the lack of training in health services research among researchers studying the condition. (Morris et al., 2019). Traditionally, researchers studying aphasia have come from clinical disciplines such as speech-language pathology, neurology, neuropsychology among others. Consequently, partnerships with non-clinical disciplines such as epidemiologists, health economists, and implementation scientists are needed to conduct this research. One such database offering researchers an avenue for the study of large numbers of individuals with communication disorders such as aphasia is Medicare. The Centers for Medicare and Medicaid Services offers researchers access to Medicare administrative claims databases for the study of health-related conditions (Lichtman et al., 2015; Mues et al., 2017). Medicare data collected primarily from US adults over 65 offers an opportunity to study health-related conditions in the US and provide real-world evidence about the extent of specific conditions. More importantly, Medicare data offers a chance to explore conditions such as aphasia among a large cohort of individuals all seeking treatment for the condition.
Medicare data offers several strengths that are conducive to the study of aphasia and other communication disorders. First, Medicare datasets include longitudinal data from millions of US adults (Lichtman et al., 2015; Mues et al., 2017). Second, Medicare beneficiaries are followed until death, and their billing data includes a near-complete picture of their receipt of healthcare services across all the healthcare settings where they receive services (Lichtman et al., 2015; Mues et al., 2017). Third, the administrative data allows researchers to examine rates of disease conditions, the cost of care, healthcare delivery patterns, and the cost-effectiveness of care received (Mues et al., 2017). This paper aimed to characterize the receipt of SLP services for aphasia among US Medicare beneficiaries receiving care after a stroke.
This work emphasized where services for aphasia were initiated and their discharge disposition for two specific reasons. First, best practice recommendations for aphasia indicate that services should begin in the acute care stage, at which time people with aphasia (PWA) should receive information about their condition and options for treatment (Simmons-Mackie et al., 2016). More importantly, PWA should not be discharged from services without a means of communicating their needs and wishes whether by verbal, written or technological (smart phones, augmentative and alternative communication (AAC) or speech-generating devices) approaches. Finally, studies show that stroke patients who receive care based on established clinical care guidelines have better long-term outcomes (Cadilhac et al., 2017). Second, studies show that the site of post-acute stroke care is related to improved outcomes. Stroke patients who receive post-acute care that includes inpatient rehabilitation had better functional gains than those who received care through a skilled nursing facility, home health, or outpatient rehabilitation (Chan et al., 2013). Less is known about discharge disposition among stroke survivors with aphasia. However, the presence of aphasia can dictate the discharge location of stroke survivors (González-Fernández et al., 2013). Additionally, the presence of aphasia can be complicated by functional motor status, the presence of dysphagia, and non-linguistic cognitive issues, which in turn are believed to influence where PWAs are discharged (Ginex et al., 2023).
Understanding discharge disposition is important as the discharge location is a primary indicator of a patient’s recovery level, functional independence, and overall quality of care. Discharge disposition can be influenced by the patient’s available insurance, clinical presentation, hospital length of stay for stroke and pre-stroke function (McCartney et al., 2026; Waddell et al., 2025). Further, discharge disposition after acute stroke care is known to vary based on race/ethnicity and residence (rural vs urban) of the patient (Cheng et al., 2025; Man et al., 2024; Onukwugha & Mullins, 2007). Therefore, understanding where service is initiated and continued throughout the recovery continuum is important for understanding disparities in aphasia outcomes. In addition, having a clear understanding of where, when, and how services are received for conditions such as aphasia is critical for advocacy and leveraging the necessary resources for long-term aphasia management.
Methods
This study was reviewed and approved by the Institutional Review Board (IRB).
Data source
Data for this project comprised a 100% sample of Medicare claims filed between 2016 and 2019. The Medicare Master Beneficiary Summary File (MBSF) contained information on beneficiary characteristics such as age, race, sex, and entitlement codes. Claims included Outpatient, Skilled Nursing Facility (SNF), Home Health Agency (HHA), Inpatient, and Carrier files. Outpatient claims included services from hospital outpatient departments, clinics, and ambulatory surgical centers. SNF claims include post-acute care, therapy, rehabilitation, and nursing services. HHA claims covered skilled home health care services, including nursing, physical, occupational, and speech therapy. Inpatient claims included short-term acute care, long-term acute care, inpatient rehabilitation, and inpatient psychiatric care. Carrier files consisted of originating from individual practitioners or supplier organizations, rather than hospitals or other institutional facilities.
To identify the place of service within inpatient claims, the last four digits of the CMS Certification Number (CCN) – a unique six-digit identifier assigned by Medicare-certified providers – was used to determine the type of facility (see Table A2). To identify the location of service within carrier claims consist of claims, the place of service (POS) code was used to identify the location where the service was rendered. Each POS code corresponds to a specific type of service location, such as a physician’s office (POS 11), inpatient hospital (POS 21), outpatient hospital (POS 22), skilled nursing facility (POS 31), or home (POS 12). This information was used to classify services across care settings, even when the billing provider was not a facility.
Sample
The index hospitalization was defined as an acute care hospital admission for a first-time ischemic or hemorrhagic stroke (See Table A1). The presence of stroke was identified from inpatient claims listing stroke as the primary diagnosis between January 1, 2016, and October 1, 2019. While traditional Medicare operates transparently through a claim-based reimbursement process, allowing all care, providers, and diagnoses to be viewed within the claims, Medicare Advantage functions under a capitated payment system. Under this system, private insurance companies receive a fixed monthly payment per enrollee to cover all necessary healthcare services. Since these private insurers manage care internally, they do not submit detailed claims to Medicare. Medicare only collects encounter data from MA plans that summarize the services provided rather than detailing each claim. Therefore, all beneficiaries not continuously enrolled in traditional fee-for-service (FFS) Medicare for 12 months were excluded. Post-stroke aphasia was identified for beneficiaries with a diagnosis code (aphasia R13.0, I69.320, I69.920) in the IP, OP, Carrier, HHA, or SNF claims files within three months (90 days) of the admissions date of the index hospitalization.
Analysis: To characterize the first-time stroke and aphasia samples, frequency values for beneficiary-level age, sex, entitlement group, discharge disposition, and location of first aphasia claim were calculated. Given the proven validity of the Research Triangle Institute racial classification, the RTI race variable was used for classification. Entitlement codes included Old Age and Survivors Insurance (OASI), Disability Insurance Benefits (DIB), End-stage Renal Disease (ESRD), Both DIB and ESRD and Beneficiary Insured Due to Part B Immunosuppressive Drug (PBID). Due to CMS confidentiality restrictions that prohibit reporting cell sizes below 11, DIB, ESRD, and DIB/ESRD were combined into a single category. Dual Medicare–Medicaid eligibility was also examined as a beneficiary characteristic. Although dual eligibility was not uncommon in the overall sample, cross-classification of dual eligibility by race/ethnicity and other analytic characteristics, including discharge disposition and initial service location, resulted in cells containing fewer than 11 beneficiaries. In accordance with CMS data-use requirements, these cells were suppressed. Percentages corresponding to suppressed cells were also not reported because they could permit calculation of the underlying cell counts. Discharge disposition was determined using the Medicare inpatient discharge status variable from the index hospitalization claim. Because Medicare includes more than 40 discharge status codes, individual codes were collapsed into seven clinically meaningful categories: Home, Transfer to Acute Care Hospital, Skilled Nursing Facility, Long-Term Care/Rehabilitation Facility, Home Health, Death, and Hospice/Other Facility (Appendix Table A3). Finally, Charlson Comorbidity Index (CCI) values were calculated for all FFS beneficiaries using diagnoses listed in the OP, IP, and Carrier files following the methodology of Cenzer, et al. (2013).
Results
Characteristics of the Medicare fee-for-service beneficiaries with first-time stroke are listed in Table 1 for non-Hispanic White, non-Hispanic Black, Hispanic, and Unknown, Other, Asian/Pacific Islander, American Indian, Alaskan Native (AIAN) stroke survivors. These data reflect admissions between January 1, 2016, and October 1, 2019. Most stroke survivors were non-Hispanic White (78%) with 12% non-Hispanic Black, 6% Hispanic, and 4% AIAN. Roughly 90% of beneficiaries were over 65 and received Medicare through the OASI (Old-Age and Survivors Insurance) program with slight variations between racial and ethnic groups (non-Hispanic White 93%, non-Hispanic Black 78%, Hispanic 85%, AIAN 91%). Non-Hispanic Blacks had the largest portion of females (57%), while AIANs were split roughly equally between the sexes (50% female, 50% male). Over 41% of non-Hispanic White and 43% AIAN stroke survivors had more than six comorbidities on the CCI compared to 53% of non-Hispanic Black and 48% of Hispanic stroke survivors. Between 30% and 40% of stroke survivors were discharged directly home, and 17% to 20% were discharged to a skilled nursing facility.
Table 1.
Characteristics of Medicare fee-for-service stroke survivors.
| Non-Hispanic White (N = 580,972, 77.89%) | Non-Hispanic Black (N = 89,286, 11.97%) | Hispanic (N = 42,888, 5.75%) | Unknown, Other, Asian/Pacific Islander, American Indian, Alaska Native (N = 32771, 4.39%) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| N | PCT | N | PCT | N | PCT | N | PCT | ||
| Age | <65 | 38,742 | 6.67 | 19,330 | 21.65 | 6621 | 15.44 | 2953 | 9.01 |
| ≥65 | 542,230 | 93.33 | 69,956 | 78.35 | 36,267 | 84.56 | 29,818 | 90.99 | |
| Sex | Male | 265,100 | 45.63 | 38,758 | 43.41 | 20,487 | 47.77 | 16,378 | 49.98 |
| Female | 315,872 | 54.37 | 50,528 | 56.59 | 22,401 | 52.23 | 16,393 | 50.02 | |
| Entitlement | OASI | 541,953 | 93.28 | 69,865 | 78.25 | 36,208 | 84.42 | 29,789 | 90.9 |
| DIB, ESRD, DIB/ESRD, PBID | 39,019 | 6.72 | 19,421 | 21.75 | 6680 | 15.58 | 2982 | 9.1 | |
| Charlson Comorbidity Index | 0–1 | 62,412 | 10.74 | 6946 | 7.78 | 4122 | 9.61 | 3299 | 10.07 |
| 2–3 | 124,604 | 21.45 | 14,166 | 15.87 | 7980 | 18.61 | 6816 | 20.8 | |
| 4–5 | 155,103 | 26.7 | 20,643 | 23.12 | 10,235 | 23.86 | 8624 | 26.32 | |
| 6+ | 238,853 | 41.11 | 47,531 | 53.23 | 20,551 | 47.92 | 14,032 | 42.82 | |
| Discharge Destination | Home | 191,455 | 33.05 | 25,250 | 28.42 | 14,863 | 34.78 | 10,500 | 32.1 |
| Transfer to Acute Care | 10,851 | 1.87 | 1786 | 2.01 | 878 | 2.05 | 774 | 2.37 | |
| SNF | 115,652 | 19.96 | 19,102 | 21.5 | 7218 | 16.89 | 5939 | 18.16 | |
| LTC/Rehab | 110,049 | 19 | 19,955 | 22.46 | 8011 | 18.74 | 6565 | 20.07 | |
| Home Health | 69,615 | 12.02 | 12,654 | 14.24 | 5889 | 13.78 | 3990 | 12.2 | |
| Died | 41,567 | 7.17 | 5932 | 6.68 | 3468 | 8.11 | 3066 | 9.37 | |
| Hospice/Other Facility | 40,147 | 6.93 | 4174 | 4.7 | 2413 | 5.65 | 1872 | 5.72 | |
Characteristics and rate of aphasia
Characteristics of the Medicare fee-for-service with a diagnosis of aphasia within three months of a stroke are listed in Table 2. Since the analysis strategy required a lead of three months to verify aphasia diagnosis, these data only reflect January 1, 2016 through September 30, 2019. This strategy revealed 107,753 PWA or 14.45% of stroke survivors. Among those non-Hispanic White stroke survivors identified (N = 580,972), 83,249 (14.33%) had aphasia. Similarly, 16.20% (N = 14,466) of non-Hispanic Black, 12.79% (N = 5,488) of Hispanic, and 13.88%% (4,550) of AIAN had aphasia. Most PWAs were over 65 years old (94% non-Hispanic White, 79% non-Hispanic Black, 88% Hispanic, 92% AIAN), and about half were female (56% non-Hispanic White, 56% non-Hispanic Black, 52% Hispanic, 50% AIAN).
Table 2.
Characteristics of Medicare fee-for-service PWA.
| Non-Hispanic White (N = 83,249,77.26%) | Non-Hispanic Black (N = 14,466,13.43%) | Hispanic (N = 5,488,5.09%) | Unknown, Other, Asian/Pacific Islander, American Indian, Alaska Native (N = 4,550,4.22%) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| N | PCT | N | PCT | N | PCT | N | PCT | ||
| Age | <65 | 4951 | 5.95 | 3008 | 20.79 | 665 | 12.12 | 359 | 7.89 |
| ≥65 | 78,298 | 94.05 | 11,458 | 79.21 | 4823 | 87.88 | 4191 | 92.11 | |
| Sex | Male | 36,554 | 43.91 | 6376 | 44.08 | 2633 | 47.98 | 2271 | 49.91 |
| Female | 46,695 | 56.09 | 8090 | 55.92 | 2855 | 52.02 | 2279 | 50.09 | |
| Entitlement | OASI | 78,271 | 94.02 | 11,448 | 79.14 | 4815 | 87.74 | 4192 | 92.13 |
| DIB, ESRD, DIB/ESRD, PBID | 4978 | 5.98 | 3018 | 20.86 | 673 | 12.26 | 358 | 7.87 | |
| Charlson Comorbidity Index | 0–1 | 5813 | 6.98 | 702 | 4.85 | 316 | 5.76 | 250 | 5.49 |
| 2–3 | 13,697 | 16.45 | 1598 | 11.05 | 706 | 12.86 | 680 | 14.95 | |
| 4–5 | 22,771 | 27.35 | 3155 | 21.81 | 1322 | 24.09 | 1216 | 26.73 | |
| 6+ | 40,968 | 49.21 | 9011 | 62.29 | 3144 | 57.29 | 2404 | 52.84 | |
| Initial Service Location | Inpatient facility | 42,052 | 50.55 | 7361 | 50.92 | 3009 | 54.87 | 2511 | 55.16 |
| Rehabilitation facility | 8067 | 9.70 | 1615 | 11.17 | 609 | 11.11 | 374 | 8.22 | |
| Skilled nursing facility | 14,544 | 17.48 | 2795 | 19.33 | 841 | 15.34 | 718 | 15.77 | |
| Home Health | 6299 | 7.57 | 1093 | 7.56 | 393 | 7.17 | 337 | 7.40 | |
| Outpatient facility | 6607 | 7.94 | 947 | 6.55 | 329 | 6.00 | 367 | 8.06 | |
| Office | 3570 | 4.29 | 461 | 3.19 | 223 | 4.07 | 174 | 3.82 | |
| Other | 2049 | 2.46 | 184 | 1.27 | 80 | 1.46 | 71 | 1.56 | |
| Discharge Destination | Home | 11,307 | 13.64 | 1416 | 9.85 | 649 | 11.89 | 630 | 13.87 |
| Transfer to Acute Care | 2106 | 2.54 | 409 | 2.84 | 164 | 3 | 135 | 2.97 | |
| Skilled nursing facility | 23,456 | 28.29 | 4556 | 31.69 | 1516 | 27.77 | 1277 | 28.12 | |
| Rehabilitation facility | 33,238 | 40.09 | 5949 | 41.38 | 2268 | 41.55 | 1807 | 39.78 | |
| Home Health | 8209 | 9.9 | 1512 | 10.52 | 569 | 10.42 | 441 | 9.71 | |
| Died | 1604 | 1.93 | 202 | 1.4 | 116 | 2.12 | 99 | 2.18 | |
| Other facility | 2984 | 3.6 | 334 | 2.32 | 177 | 3.24 | 153 | 3.37 | |
Initial service encounter and discharge disposition
As seen in Figure 1 and Table 2, 50.6% of non-Hispanic White, 50.9% of non-Hispanic Black, 54.9% of Hispanic, and 55.2% AIAN PWA were identified in an inpatient facility while 17.5% of non-Hispanic White, 19.3% of non-Hispanic Black, 15.3% of Hispanic, and 15.8% of AIAN were identified in a skilled nursing facility. Nearly 10% of non-Hispanic White PWA, 11.2% of non-Hispanic Black PWA, 11.1% of Hispanic PWA, and 8.2% of AIAN PWA were identified in a rehabilitation facility. About 14% of non-Hispanic White, 12% of Hispanic, and 14% of AIAN PWA were discharged directly home, but only 10% of non-Hispanic Black PWA were discharged home (Table 2). About 30% of each group was discharged to a skilled nursing facility, 40 to 42% to a long-term care or rehabilitation facility, and 10% to 12% were discharged to home health.
Figure 1.
Initial service identification by race/ethnicity.
Discussion
We believe this is the first study designed to examine the location of initial services and discharge disposition using a sample of Medicare beneficiaries with aphasia. One prior study evaluated the cost of aphasia care among Medicare beneficiaries in the state of South Carolina, however, none have evaluated a national sample of beneficiaries (Ellis et al., 2012). In this study, we utilized Medicare data to calculate the rate of aphasia among Medicare beneficiaries rather than partially weighted national samples utilized to generate national estimates. Additionally, aphasia was identified using diagnoses appearing in Medicare claims within 90 days of the index stroke hospitalization. Therefore, the observed rate likely reflects beneficiaries whose aphasia was recognized and documented during the early post-stroke period and may underestimate the total number of stroke survivors who experienced aphasia but were diagnosed after the 90-day ascertainment window or whose condition was not captured in claims. Additionally, we identified the PWA first service encounter for aphasia and their discharged disposition. We found that among Medicare beneficiaries, the rate of aphasia after stroke (14.5%) remained relatively unchanged across the four years evaluated. Similarly, the general sociodemographic profile of PWA remained relatively unchanged. We also found that approximately 50% of PWA were initially seen in an inpatient facility. Finally, the most common discharge destinations among PWA were rehabilitation facilities and skilled nursing facilities. The process by which Medicare data can be linked across healthcare systems, hospitals and individual rehabilitation units offers a greater opportunity to identify PWA and process their trajectory of care. The tracking across Medicare claims reduces the likelihood that individuals with mild symptomology may be missed during short inpatient and never identified as having aphasia (Ellis, Hardy, et al., 2017). As noted in our prior work using national data (Ellis, Hardy, et al., 2017), the observed rate of aphasia is lower than the traditionally reported 25–50% (Berthier, 2005; Dickey et al., 2010; Engelter et al., 2006; Flowers et al., 2016; Gialanella & Prometti, 2009; Grönberg et al., 2022). Calculating a consistent rate or prevalence estimate may remain difficult given the many factors that impact reports of aphasia (e.g., sample size, aphasia measurement approach, timing of measurement, geographic location, etc). More importantly, this study further highlights the significant difficulty of determining the true burden of aphasia because most samples have some limitations that preclude determining an accurate nationwide estimate. Additionally, claims-based analyses may not capture the many individuals with very mild symptoms of aphasia such as word retrieval who are discharged home without services or the stroke survivors who do not prioritize in the recovery process.
Other factors that should be carefully considered are related to the sample itself. Approximately 90% of individuals experiencing stroke and those with post-stroke aphasia were ≥ age 65. Therefore, the observed rates of aphasia in this study are lower than those reported in studies with a greater representation of younger adults with the condition (Berthier, 2005; Dickey et al., 2010; Engelter et al., 2006; Flowers et al., 2016; Gialanella & Prometti, 2009; Grönberg et al., 2022). Consequently, the sample is more representative of older adults and those with Medicare benefits. Additionally, coding accuracy issues for both stroke and aphasia as well as aphasia being a secondary diagnosis after stroke, may reduce the identification of some individuals with the condition (Ellis, Hardy, et al., 2017) Therefore, the reported rates should only be considered in the context of the Medicare population.
We also found that approximately 50–55% of PWA were initially seen for services within inpatient facilities. This suggests that even though aphasia management guidelines and best practice recommendation developed by aphasia experts call for all patients with stroke to be screened for communication deficits, only about half receive such services for the condition (Burton et al., 2023; Simmons-Mackie et al., 2016). The optimal window to begin aphasia rehabilitation is not entirely clear, however most clinical practice guidelines advocate for early identification and initiation of aphasia management strategies (Nouwens, et al., 2015). More specifically this finding suggests screening for communication disorders in all individuals with stroke does not consistently occur. Formal screening tools for aphasia can lead to early detection of aphasia particularly for those individuals with mild aphasia that may not be easily identified (Lavoie et al., 2025). Early identification is also critical to prediction of recovery and understanding the clinical evolution of the communication disorder (Glize et al., 2015). Further study is needed to determine if this potential later identification and initiation of care contributes to worse aphasia outcomes.
Regarding discharge disposition, the majority of PWA were discharged to skilled nursing or rehabilitation facilities. It is notable that non-Hispanic Black PWA were less likely to be discharged home than other racial-ethnic groups. This observation aligns with our previous work that showed non-Hispanic Black PWA received greater service utilization during longer lengths of stays in acute care US hospital settings (Ellis & Peach, 2017). In addition, Skolarus et al. found greater intensity of speech therapy settings among non-Hispanic Blacks in hospital settings, yet lower intensity treatments in outpatient settings where higher intensity and more functional treatments are offered (Skolarus et al., 2017). Similar to observations of first encounters for treatment for aphasia, additional work is required that links timing and location of care to aphasia outcomes to further explore the relationship.
There are specific limitations to this work that must be carefully considered. Cohen (2014) notes that the use of Medicare and other administrative databases are designed primarily for billing and not research purposes. Secondary use as reported in this study can be impacted by coding accuracy related to primary and secondary diagnoses. Mues et al. (2017) also notes that while the longitudinal nature of Medicare data captures longer periods of health medical management the source is primarily limited to adults over the age of 65 (Mues et al., 2017). Similarly, the data does not include critical behavioral information such as diet and exercise that contribute to stroke risk and subsequently post-stroke conditions like aphasia. Medicare data also does not capture the outcomes of critical tests such as laboratory tests that guide medical management and determine overall stroke severity (Mues et al., 2017). Additionally, use of Medicare data and the health services approach utilized does not comprehensively capture all services that the individuals utilized nor a description of the intensity of those services. Regarding treatment for aphasia specifically, some specific claims include a wide range of settings that differ in some degree in their care roles. For example, inpatient claims include: acute care hospitals, critical access hospitals, inpatient rehabilitation facilities, inpatient psychiatric facilities, and long-term care hospitals. These different hospitals although inpatient facilities have widely different missions. Similarly, Medicare claims data may not delineate the influence of claims submitted or influenced by financial incentives which in turn may be susceptible to measurement error. Finally, the complexity of Medicare claims themselves and linkage needed to capture conditions such as aphasia over time can increase the likelihood of error and ultimately misinterpretation. We are unable to include individuals included in Medicare Advantage or other Medicare managed care plans since their encounters are not reported in entirety to CMS.
Conclusions
In conclusion, this study provides a comprehensive analysis of the rate of aphasia among Medicare FFS beneficiaries following first-time stroke. Findings indicate that the proportion of the sample with aphasia remained relatively stable over time, with approximately 14.5% of stroke survivors receiving a diagnosis of aphasia within three months of hospitalization. While Medicare data offers unique advantages in tracking post-stroke aphasia among Medicare FFS beneficiaries, limitations related to coding accuracy, the exclusion of younger stroke survivors, and the underdiagnosis of mild aphasia must be considered when interpreting these findings. These results underscore the need for improved identification and documentation of aphasia in clinical settings to ensure adequate resources, rehabilitation services, and policy efforts that address the long-term needs of individuals with post-stroke aphasia. Future research should explore additional population-based datasets and alternative methodologies to refine prevalence estimates and enhance the understanding of aphasia burden across diverse patient groups.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the NIH/NIDCD Grant Number [5 R01 DC021065-03] awarded to the first author.
Appendix
Table A1.
Codes used to identify Stroke.
| G45.0 G46.8 | G45.1 G45.2 G97.31 | G45.8 | G45.9 | G46.0 | G46.1 | G46.2 | G46.3 | G46.4 | G46.5 | G46.6 | G46.7 | |
| G97.32 I60.4 | I60.00 I60.01 I60.50 | I60.02 | I60.10 | I60.11 | I60.12 | I60.20 | I60.21 | I60.22 | I60.30 | I60.31 | I60.32 | |
| I60.51 I61.8 | I60.52 I60.6 I61.9 I63.00 | I60.7 I63.011 | I60.8 | I60.9 | I61.0 | I61.1 | I61.2 | I61.3 | I61.4 | I61.5 | I61.6 | |
| I63.012 I63.10 | I63.013 I63.111 I63.112 | I63.019 | I63.113 | I63.02 | I63.031 I63.119 | I63.032 | I63.039 | I63.09 | ||||
| I63.12 I63.212 | I63.131 I63.213 | I63.132 I63.219 | I63.133 I63.22 | I63.231 | I63.139 | I63.19 | I63.20 | I63.211 | ||||
| I63.232 I63.319 | I63.233 I63.321 | I63.239 I63.322 | I63.29 I63.323 | I63.30 | I63.311 I63.329 | I63.312 | I63.313 | |||||
| I63.331 | I63.332 | I63.333 | I63.339 | I63.341 | I63.342 | I63.343 | ||||||
| I63.349 | I63.39 | I63.40 | I63.411 | I63.412 | I63.413 | |||||||
| I63.419 I63.433 | I63.421 I63.439 | I63.422 I63.441 | I63.423 I63.442 | I63.429 I63.443 | I63.431 | I63.432 | ||||||
| I63.449 I63.522 | I63.49 I63.523 | I63.50 | I63.511 I63.529 | I63.512 I63.531 | I63.513 I63.532 | I63.519 | I63.521 | |||||
| I63.533 I63.8 | I63.539 I63.81 | I63.89 | I63.541 I63.9 I66.01 | I63.542 I66.02 | I63.543 | I63.549 | I63.59 I63.6 | |||||
| I66.03 I67.841 | I66.09 I67.848 | I66.11 | I66.12 I66.13 | I66.19 | I66.21 | I66.22 | I66.23 | I66.29 I66.3 | I66.8 I66.9 | |||
| I67.89 | I97.810 | I97.811 | I97.820 | I97.821 | ||||||||
Table A2.
Last four digits of the CMS certification Number (CCN) used to identify setting of aphasia Diagnosis.
| 0001–0879 | Short-term (General and Specialty) Hospitals |
|---|---|
| 0880–0899 | Reserved for hospitals participating in ORD demonstration project |
| 0900–0999 | Multiple Hospital Component in a Medical Complex (Numbers Retired) |
| 1000–1199 | Federally Qualified Health Centers |
| 1200–1224 | Alcohol/Drug Hospitals (Numbers Retired) |
| 1225–1299 | Medical Assistance Facilities |
| 1300–1399 | Critical Access Hospitals |
| 1400–1499 | Continuation of Community Mental Health Centers (4900–4999 series) |
| 1500–1799 | Hospices |
| 1800–1989 | Federally Qualified Health Centers |
| 1990–1999 | Religious Non-medical Health Care Institutions formerly Christian Science Sanatoria (Hospital Services) |
| 2000–2299 | Long-Term Hospitals (Excluded from PPS) |
| 2300–2499 | Hospital Based Renal Dialysis Facilities |
| 2500–2899 | Independent Renal Dialysis Facilities |
| 2900–2999 | Independent Special Purpose Renal Dialysis Facility |
| 3000–3024 | Formerly Tuberculosis Hospitals (Numbers Retired) |
| 3025–3099 | Rehabilitation Hospitals (Excluded from PPS) |
| 3100–3199 | Home Health Agencies |
| 3200–3299 | Continuation of Comprehensive Outpatient Rehabilitation Facilities (4800–4899) Series |
| 3300–3399 | Children’s Hospitals (Excluded from PPS) |
| 3400–3499 | Continuation of Rural Health Clinics (Provider-based) (3975–3999) Series |
| 3500–3699 | Hospital Based Satellite Renal Dialysis Facilities |
| 3700–3799 | Hospital Based Special Purpose Renal Dialysis Facility |
| 3800–3974 | Rural Health Clinics (Free-Standing) |
| 3975–3999 | Rural Health Clinics (Provider-Based) |
| 4000–4499 | Psychiatric Hospitals (Excluded from PPS) |
| 4500–4599 | Comprehensive Outpatient Rehabilitation Facilities |
| 4600–4799 | Community Mental Health Centers |
| 4800–4899 | Continuation of Comprehensive Outpatient Rehabilitation Facilities (4500–4599 Series) |
| 4900–4999 | Continuation of Community Mental Health Centers (4600–4799) Series |
| 5000–6499 | Skilled Nursing Facilities |
| 6500–6989 | Outpatient Physical Therapy Services |
| 6990–6999 | Numbers Reserved formerly Christian Science Sanatoria (Skilled Nursing Services) |
| 7000–8499 | Continuation of Home Health Agencies (3100–3199) Series |
| 8500–8899 | Continuation of Rural Health Clinics (Provider-Based) (3400–3499) Series |
| 8900–8999 | Continuation of Rural Health Clinics (Free-Standing) (3800–3974) Series |
| 9000–9799 | Continuation of Home Health Agencies (8000–8499) Series |
Table A3.
Medicare discharge status code grouping.
| Analytic Category | Medicare Discharge Status Codes |
|---|---|
| Home | 01 Home/self-care |
| Home Health | 06 Home with home health services |
| Transfer to Acute Care Hospital | 02 Short-term hospital; 05 Another type of institution |
| Skilled Nursing Facility | 03 SNF |
| Long-Term Care/Rehabilitation Facility | 62 Inpatient rehabilitation facility; 63 Long-term care hospital; 61 Swing-bed hospital; 65 Psychiatric hospital; 50 Hospice medical facility (if not grouped elsewhere) |
| Hospice/Other Facility | 50 Hospice-home; 51 Hospice-medical facility; 04 Intermediate care facility; 07 Left against medical advice; other institutional destinations |
| Died | 20 Expired |
Footnotes
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
This study utilized the Limited Data Set (LDS) from the Centers for Medicare & Medicaid Services (CMS). CMS makes these files available to researchers as allowed by federal laws and regulations and CMS policy. LDS files contain beneficiary-level health information and are considered identifiable files. Usage of LDS files requires a signed LDS Data Use Agreement (DUA) between CMS and the LDS requester. Requesters are also required to provide a research purpose as part of their request. CMS DUA prohibits all disclosure, sharing, and publication of these data under Section 1106(a) of the Social Security Act (42 U.S.C.§ 1306(a)). Violators will face criminal penalties under Section 1106(a) of the Social Security Act (42 U.S.C.§ 1306(a)), including a fine not exceeding $10,000 or imprisonment not exceeding 5 years, or both, may apply to disclosures of information that are covered by Section 1106 and that are not authorized by regulation or by Federal law.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
This study utilized the Limited Data Set (LDS) from the Centers for Medicare & Medicaid Services (CMS). CMS makes these files available to researchers as allowed by federal laws and regulations and CMS policy. LDS files contain beneficiary-level health information and are considered identifiable files. Usage of LDS files requires a signed LDS Data Use Agreement (DUA) between CMS and the LDS requester. Requesters are also required to provide a research purpose as part of their request. CMS DUA prohibits all disclosure, sharing, and publication of these data under Section 1106(a) of the Social Security Act (42 U.S.C.§ 1306(a)). Violators will face criminal penalties under Section 1106(a) of the Social Security Act (42 U.S.C.§ 1306(a)), including a fine not exceeding $10,000 or imprisonment not exceeding 5 years, or both, may apply to disclosures of information that are covered by Section 1106 and that are not authorized by regulation or by Federal law.