Alzheimer’s disease and related dementias in rural medicare populations: a scoping review

Abstract

Background

Rural populations in the U.S. face a disproportionate burden of Alzheimer’s Disease and Related Dementias (ADRD), characterized by delayed diagnosis, limited access to care, and high mortality. Medicare data, given their extensive coverage of older adults and ability to capture longitudinal care trajectories, are a critical resource for understanding these disparities. However, no previous review has systematically synthesized evidence specific to rural Medicare beneficiaries with ADRD. This scoping review maps the existing evidence and highlights critical areas where further rural ADRD research is needed.

Methods

We conducted a systematic search on PubMed, MEDLINE, CINAHL, Scopus, and Web of Science from January 1, 2000 to March 5, 2025. Peer-reviewed studies were included if they examined ADRD outcomes in rural Medicare populations. Information on study designs, health outcomes, population characteristics, rurality definitions, risk factors, access to care, quality of services, healthcare utilization, statistical methods, and policies or interventions were extracted and synthesized.

Results

Thirty-three studies were included, most published after 2019 (72.7%). The predominant study designs were cohort (60.6%) and cross-sectional (30.3%), with heavy reliance on Medicare Fee-for-Service data (84.8%). The literature focused primarily on care delivery (30.3%) and hospitalization outcomes (21.2%), whereas far fewer studies examined ADRD incidence, prevalence, mortality, medication use, and dementia subtypes. Lifestyle factors were assessed in 18.2%, whereas environmental exposures were rarely studied (3.0%). Methodologically, studies relied largely on simple regression approaches, used inconsistent rurality definitions, and rarely evaluated policy interventions.

Conclusions

Rural Medicare beneficiaries with ADRD remain underrepresented in research despite their disproportionate burden. Future studies should address inconsistent rural definitions, limited consideration of medication use, lifestyle and environmental exposures (natural and built), and rural-specific policy evaluations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-026-07031-7.

Background

Rural residents make up less than 15% of the U.S. population, yet they represent more than one in five adults aged 65 years and older, the age group at greatest risk for Alzheimer’s disease and related dementias (ADRD) [1]. Accordingly, rural communities in the U.S. face a disproportionately high burden of ADRD compared to urban areas [2]. Rural areas often experience higher age-adjusted mortality rates [3], rapid increases in ADRD prevalence [4], and more limited access to timely and specialized dementia care compared to urban areas [5]. Moreover, rural populations often face low educational attainment, social isolation, high chronic disease burden, depopulation, and persistent racial inequality. These conditions further constrain early detection, specialist access, and long-term support [6, 7]. Consequently, these factors make ADRD in rural U.S. a multidimensional public health challenge that places substantial burden on patients, caregivers, and the healthcare system [8].

Over the past two decades, rural–urban gaps in ADRD outcomes have grown wider [9]. Between 1999 and 2019, ADRD mortality increased at a faster pace in rural counties than in their urban counterparts, with rural mortality rates shifting from 7% higher than urban rates in 1999 to nearly 20% higher by 2019 [9, 10]. Post-diagnosis survival in rural and non-metropolitan counties averages about 1.5 months shorter compared to urban areas, while rural patients spend a significantly greater proportion of post-diagnosis life in nursing homes and less in community residences [11].

Medicare, a federal health insurance program in the U.S., is a primary source of health coverage for nearly all American adults aged 65 years and older, including those living with ADRD [12]. It provides near-universal, nationally standardized coverage for the population at greatest risk for ADRD. This program funds acute and post-acute care, and supports long-term services such as skilled nursing, hospice, home health, and dementia special care units [13]. Given the aging U.S. population, the economic burden of ADRD was estimated at approximately $290 billion in 2019 [14]. A substantial share of this burden falls on Medicare, with average end-of-life costs for a person with dementia exceeding $287,000 over the last five years of life, compared to about $175,000 for heart disease and $173,000 for cancer [15]. Yet broad coverage has not translated into equitable access or outcomes.

Evidence suggests that rural Medicare beneficiaries with ADRD follow distinct care trajectories compared with their urban counterparts, including lower hospice enrollment [16], reduced use of home health services [17], higher rates of potentially preventable hospitalizations [18], and greater reliance on emergency and institutional care at the end of life [19]. Despite these disparities, the

intersection of rural residence and Medicare remains an underexplored area in the ADRD literature. Prior reviews have examined rural-urban differences in ADRD outcomes [8, 18], but none have systematically synthesized evidence focused specifically on rural Medicare beneficiaries within a single, policy-relevant system. By restricting our scoping review to rural U.S. Medicare beneficiaries, we address this gap and provide a focused examination of ADRD diagnosis, specialist access, and health care utilization. We will highlight recurring patterns and evidence gaps by addressing the following objectives:

1. Characterize outcomes, access, quality, and utilization of care.

2. Summarize study designs, statistical methods, and rurality classification systems.

3. Identify population characteristics and common risk factors.

4. Describe policy intervention evaluations.

5. Highlight knowledge gaps in each of the above domains to guide future investigations aimed at improving ADRD research in rural Medicare populations.

Methods

Study design

This scoping review followed the methodological framework outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for Scoping Reviews [20]. The PRISMA checklist is provided in Supplementary File 1.

Search strategy

A comprehensive literature search was conducted across five major databases (i.e., PubMed, MEDLINE, CINAHL, Scopus, and Web of Science) from January 1, 2000, to March 5, 2025. The search was limited to U.S.-based, English-language, peer-reviewed studies. Search terms combined Medical Subject Headings and free-text keywords in three domains: (i) ADRD (e.g., Alzheimer’s disease, dementia, ADRD), (ii) Medicare (e.g., Medicare, Centers for Medicare and Medicaid [CMS], Fee-for-service (FFS)), and (iii) rurality (e.g., rural, rurality, remote, non-metropolitan).

Eligibility criteria

Studies were included if they: (i) focused on ADRD as the primary outcome; (ii) used Medicare data as the main or linked data source; (iii) included a rurality component in the design, subgroup analysis, or outcome stratification; (iv) were conducted in U.S. populations and written in English; (v) were peer-reviewed. Studies were excluded if they were conference proceedings, review articles, dissertations, editorials, commentaries and other non–peer-reviewed literature or grey literature.

Screening process and study selection

All retrieved studies were imported into Covidence online tool (https://www.covidence.org/), where duplicates were automatically removed. Screening was conducted in two sequential stages. In the first stage, titles and abstracts were independently screened by two reviewers (NK and SA), excluding studies without a primary focus on ADRD or lacking a rural component. In the second stage, the same two reviewers thoroughly evaluated the full texts of the remaining studies against predefined inclusion and exclusion criteria. Discrepancies were resolved through weekly meetings and, if necessary, consultation with a third reviewer (AM).

Data extraction

After finalizing studies that met all eligibility criteria, a standardized data extraction form was developed in Microsoft Excel (Microsoft Corporation, Redmond, WA, U.S.) and applied consistently across all included studies. For each study, we extracted data on publication details (e.g., title, first author, year), Medicare dataset characteristics, geographic scope, study design (e.g., cross-sectional, cohort, ecological), population demographics (e.g., age, sex, race/ethnicity), rurality classification method, and risk factors (e.g., demographics, socioeconomics, environmental, lifestyle, comorbidities). We also extracted information on health outcomes (e.g., prevalence, incidence, hospitalizations, care delivery, medication use, mortality), healthcare access and quality, utilization patterns (e.g., emergency department visits, skilled nursing, institutional care, telehealth use), statistical methods, policies or interventions, and reported limitations. Data extraction was performed by one reviewer (NK) and independently verified by a second reviewer (SA). The populated spreadsheet, including all extracted variables and their descriptions, is provided in Supplementary File 2.

Data synthesis

The extracted data were grouped and summarized into thematic categories aligned with the scoping review objectives. This structured synthesis facilitated the identification of recurring determinants of ADRD among Medicare beneficiaries in rural populations and underscored critical research gaps that warrant further investigation.

Results

Overview of literature search

Our search initially identified 2083 studies across the five databases. A total of 1252 duplicates were automatically removed by Covidence, leaving 831 studies for screening. During title and abstract screening stage, 667 studies were excluded, primarily due to not focusing on ADRD, lacking a rural component, or were not peer-reviewed publications. The remaining 164 studies underwent full text screening to assess eligibility. Of these, 131 studies were excluded for the following reasons: the primary outcome was not ADRD (n = 53), absence of a rurality component (n = 38), no use of Medicare data (n = 23), non–peer-reviewed studies (n = 17). Ultimately, 33 studies met all inclusion criteria and were retained for this scoping review. Figure 1 illustrates the PRISMA flowchart summarizing the study selection process.

Fig. 1.

PRISMA flowchart summarizing the study selection process

Descriptive characteristics

The selected studies were published between 2001 and 2025. While only 4 studies were published before 2017, publication volume accelerated thereafter, with a sharp increase since 2017 (n = 29, 87.7%). Figure 2a shows this temporal trend, depicting a pronounced post-2017 rise in publications.

Fig. 2.

Characteristics of the included studies: (a) publication year (2001–2025), (b) health outcomes assessed, and (c) study design

Most studies (n = 26, 78.8%) analyzed data nationally, while others (n = 7, 21.2%) focused on state- or region-specific populations. For instance, state-specific analyses included Ohio [18], Arkansas and Louisiana [21], while regional studies explored Central Appalachia [4] and the U.S. Deep South [22].

All studies were limited to adults aged 65 years and older. Age distributions were reported in most studies (n = 30, 90.9%). Sex was also reported in most studies (n = 27, 81.8%), where women consistently comprised the majority of the ADRD population (55%–70%). Race and ethnicity were frequently included (n = 25, 75.7%), many of which demonstrated that rural Black, Hispanic, and Native American patients face disadvantages in diagnosis, access to care, and quality of services. Dual-eligible (Medicare and Medicaid) status was commonly used (n = 16, 48.5%) to capture low-income status, with notable variations in its use and interpretation across geographic contexts. In studies that directly compared rural and urban beneficiaries, rural populations were most frequently characterized by higher social and economic vulnerability, most commonly reflected by higher rates of dual eligibility and greater area-level disadvantage measures [2, 11].

The level of analysis varied across studies: more than half of studies (n = 19, 57.6%) used individual-level claims or survey data, eight studies (n = 8, 24.2%) conducted area-level analyses at the ZIP code, county, or sub-county levels and a smaller number (n = 6, 18.2%) used facility-level analyses (e.g., skilled nursing facilities, accountable care organizations).

In terms of data sources, most studies (n = 28, 84.8%) used Medicare FFS data. Some integrated other CMS-linked resources, such as the Outcome and Assessment Information Set (OASIS) (n = 7, 21.2%) or the Medicare Current Beneficiary Survey (MCBS) (n = 2, 6.0%) [23, 24], while only four studies (n = 4, 12.1%) incorporated Medicare Part D (prescription drug benefit) [22, 24–26].

Health outcomes and study designs

Most studies (n = 28, 84.8%) used an umbrella, claims-based ADRD definition without disaggregating dementia subtypes (e.g., vascular, Lewy body, frontotemporal, and mixed or unspecified dementias). A smaller subset of studies (n = 4, 12.1%) examined specific dementia subtypes, such as vascular dementia or Lewy body dementia [5, 27], while only one study (n = 1, 3.0%) focused exclusively on Alzheimer’s disease [13].

The most frequently studied outcome was care delivery, including transitions of care, home health service use, and support for patients and families (n = 10, 30.3%). Among studies that compared rural and urban beneficiaries, outcomes most often reflected lower use or delayed receipt of formal supportive services among rural beneficiaries (e.g., home health or post-acute services). For example, a national survey of U.S. primary care practices highlighted gaps that may contribute to weaker care delivery in rural settings, noting more limited access to specialists and fewer formal supportive resources [28].

Hospital-related outcomes were the next most common domain (n = 7, 21.2%) and included all-cause or dementia-specific hospitalizations and readmissions. Studies assessing hospital-related outcomes most often reported higher acute-care utilization or higher potentially preventable hospitalizations among rural populations, although results varied by hospitalization measure and study setting. For example, a study of community-dwelling veterans with dementia found a higher risk of preventable hospitalizations among individuals residing in the most rural counties compared with large metropolitan areas [5].

Studies examining ADRD prevalence and incidence reported heterogeneous but generally unfavorable rural patterns. Four studies (n = 4, 12.1%) examined ADRD prevalence alone [4, 18, 21, 27], while 3 studies (n = 3, 9.1%) examined incidence only [16, 23, 29], and only one study (n = 1, 3.0%) considered both prevalence and incidence (Fig. 2b) [2]. Across studies reporting rural–urban contrasts, patterns varied by outcomes and geographic scopes, with differences more often reflecting worse outcomes in rural areas. For instance, a national claims-based study reported higher risk-adjusted diagnostic incidence in rural counties and shorter survival among rural and micropolitan beneficiaries compared with metropolitan beneficiaries [2].

Mortality outcomes were examined in 4 studies (n = 4, 12.1%) [3, 11, 13, 17]. Across studies that reported rural–urban contrasts, mortality-related patterns generally indicated worse outcomes for rural beneficiaries. For example, a national Medicare cohort study found that rural and micropolitan residents survived approximately 1.5 months less than metropolitan residents after ADRD diagnosis. They also spent more time in nursing homes and less time in the community [11].

Medication use, particularly prescribing patterns [24, 30], inappropriate medications [25], and deprescribing [26], was the primary outcome in four studies (n = 4, 12.1%). Medication-related outcomes revealed important rural quality-of-care concerns. Two studies evaluated antipsychotic use in ADRD care: one assessed potentially inappropriate medication use among community-dwelling older adults with dementia [25], while the other described geographic variation in antidementia and antipsychotic prescribing patterns in nursing homes [30]. Deprescribing of acetylcholinesterase inhibitors was also examined in relation to facility factors influencing discontinuation practices [26]. One study investigated the impact of Medicare Part D on medication utilization and ethnoracial disparities in drug access [24]. Notably, in a community-dwelling study that examined rural–urban patterns, rural residents were slightly more represented among beneficiaries with potentially inappropriate medication use than among those without it [25].

The most common study design was retrospective cohort (n = 20, 60.6%), followed by cross-sectional (n = 10, 30.3%), ecological (n = 2, 6.1%) and randomized clinical trial (RCT) (n = 1, 3.0%) (Fig. 2c). Sample sizes varied greatly, ranging from 412 participants (caregivers) in the RCT study [31] to roughly 170 million beneficiaries (person-years) in a nationwide cross-sectional study [2]. Stratified by study design, cohort studies had a range of 1,186 to 3 million participants (SD = 823,122), while cross-sectional studies had a range of 1,245 to 170 million beneficiaries (SD = 52,572,796). Ecological studies analyzed 1,297,271 beneficiaries in Arkansas and Louisiana [21] and 4,932,759 million Medicare beneficiaries in Central Appalachia [4].

Rurality definitions

Rurality was defined inconsistently or measured using diverse classification systems. A large number of studies (n = 14, 42.4%) dichotomized rurality into binary rural vs. urban variables without providing a clear definition. Six studies (n = 6, 18.2%) used Rural–Urban Continuum Codes (RUCC) [2, 11, 17, 18, 21, 29], five studies (n = 5, 15.2%) used Rural–Urban Commuting Area (RUCA) codes [25, 28, 32–34], and three studies (n = 3, 9.1%) relied on Core-Based Statistical Areas (CBSA) [1, 35, 36]. Two studies (n = 2, 6.1%) used Urban Influence Codes (UIC) [3, 5]. Additionally, three studies (n = 3, 9.1%) applied ZIP code–based [19], county-level [37], or facility-based classifications [30]. Twenty-one studies (n = 21, 63.6%) included rural–urban comparisons, while the remaining (n = 12, 36.4%) focused exclusively on rural populations or conducted rural-stratified analyses without direct urban comparison.

Demographic and socioeconomic risk factors

All included studies assessed at least one demographic variable as a risk factor or adjustment variable. Nearly all studies restricted their populations to Medicare beneficiaries aged 65 years or older (n = 31,93.9%), though a few (n = 2, 6.1%) included younger adults with disabilities [17, 21]. Age (n = 31, 93.9%), sex (n = 30, 90.9%), race/ethnicity (n = 28, 84.8%), and marital status (n = 3, 9.1%) were the most frequently reported. Women made up the majority of the ADRD population (n = 25/30, 83.3%) in studies that reported sex. In studies reporting rural–urban differences, one study found that the mean age at ADRD diagnosis was slightly lower in rural than urban settings (82.5 vs. 82.9 years), and mean age at death in rural areas was marginally lower than urban counterparts (85.2 vs. 85.7 years) [2]. Another national cohort reported differences in racial/ethnic composition, with rural populations having a higher proportion of White beneficiaries and lower representation of minoritized groups (e.g., 91.2% White in rural vs. 85.1% in urban; 5.6% Black in rural vs. 7.9% in urban) [11].

Socioeconomic status was examined in nearly all studies, using indicators such as dual eligibility (n = 24, 72.7%), poverty and area deprivation indices (n = 14, 42.4%), educational attainment (n = 12, 36.4%), income (n = 6, 18.2%), insurance coverage (n = 6, 18.2%), and employment status (n = 2, 6.1%). Among studies using Medicare Part D data (n = 4, 12.1%), one study measured socioeconomic status using low-income subsidy enrollment (n = 1/4, 25.0%), an individual-level marker of financial vulnerability related to prescription drug coverage [25].

Where rural–urban descriptive profiles were reported, rural beneficiaries appeared more socioeconomically vulnerable. For instance, one study reported higher dual eligibility in rural than urban beneficiaries (34.2% vs. 27.1%) [2]. Other studies showed similar patterns in community-level indicators, with higher poverty (17.25% rural vs. 12.53% urban) and lower educational attainment (85.30% rural vs. 89.48% urban) [1], as well as higher social deprivation scores in rural settings (50.3 rural vs. 46.3 urban) [11]. Overall, many studies noted that rural residence often overlaps with economic disadvantage, exacerbating inequities in ADRD diagnosis, management, and service use.

Environmental, lifestyle, and comorbidity risk factors

Environmental factors were rarely analyzed. One study (n = 1, 3.0%) explicitly examined air quality, finding that wildfire-related PM2.5 exposure was associated with increased ADRD-related hospitalizations [19]. None of the studies in the reviewed literature analyzed built-environment amenities such as playgrounds, sports venues, or recreational facilities.

Comorbidity was occasionally addressed, with cardiovascular disease (n = 6, 18.2%), diabetes (n = 6, 18.2%), stroke (n = 5, 15.2%), depression (n = 4, 12.1%), and hypertension (n = 3, 9.1%) among the most common conditions included as covariates or explicitly analyzed. Multiple studies found that comorbidity burden was higher in rural patients and contributed to earlier institutionalization (n = 3, 9.1%), lower care continuity (n = 2, 6.1%), and higher healthcare costs (n = 2, 6.1%) [21, 36].

Lifestyle-related factors were infrequently examined (n = 6, 18.2%). Among these, nutrition-related behaviors, including poor appetite, weight loss, or mechanically altered diets were incorporated in one study (n=, 3.0%) [26]. One study used county-level physical inactivity prevalence and social association rates as proxies for social engagement (n = 1, 3.0%) [16]. Another study considered smoking status, sleep disorders, and depression/anxiety as covariates (n = 1, 3.0%) [23]. One study explicitly examined social participation in community events (n = 1, 3.0%) [4]. Overall, lifestyle risk factors were less frequently examined in rural Medicare ADRD populations, and when present, were drawn from secondary indicators rather than direct behavioral measures.

Access, quality of care, and healthcare utilization

Access to care was a central focus in most studies (n = 25, 75.8%). Rural Medicare beneficiaries with ADRD consistently demonstrated lower access to specialists (e.g., neurologists and geriatricians; often linked to rural workforce shortages and geographic barriers), fewer dementia assessments, and reduced availability of post-acute care. Telehealth adoption was evaluated in few studies (n = 4, 12.1%), which found that rural nursing homes and hospitals initially lagged urban counterparts during the early COVID-19 period, although utilization subsequently increased [1, 36].

Quality of care was assessed in 11 studies (n = 11, 33.3%) and measured through outcomes such as medication safety (n = 5, 15.2%; e.g., potentially inappropriate prescribing [25], antipsychotic use [30]), timeliness of hospice entry (n = 3, 9.1%), and continuity of home health services (n = 3, 9.1%). Preventive service uptake was reported in two studies (n = 2, 6.1%) (i.e., annual wellness visits [16] and cognitive assessments [34]).

Utilization patterns were examined in 18 studies (n = 18, 54.5%). Among these, most studies reported higher utilization of emergency departments (n = 7, 21.2%), skilled nursing facilities (n = 6, 18.2%), and institutional long-term care (n = 5, 15.2%), while fewer studies noted lower use of continuous in-home support (n = 4, 12.1%), hospice care (n = 5, 15.2%), or outpatient dementia management (n = 3, 9.1%).

Statistical approaches

All included studies employed quantitative methods, with a predominance of traditional statistical analyses. The most common approaches were logistic regression (n = 17, 51.5%) and linear regression models (n = 7, 21.2%). Cox proportional hazards models and negative binomial regression were each used in three studies (n = 3, 9.1%) to analyze survival outcomes and overdispersed count data. Poisson regression (n = 2, 6.1%) and Generalized Linear Models (n = 2, 6.1%) were less commonly employed. Despite this methodological breadth, only few studies (n = 4, 12.1%) explicitly modeled interactions involving rurality, race/ethnicity, Medicaid eligibility, or comorbidity burden. Additional details on the statistical analysis techniques used in the included studies are provided in Supplementary File 3.

Policy implications and interventions

Only 7 studies (n = 7, 21.2%) explicitly evaluated policies or interventions. Tzeng et al. examined the Medicare annual wellness visit, demonstrating higher dementia and cognitive impairment diagnosis rates, but lower uptake among rural beneficiaries [16]. Qin et al. assessed CMS telehealth expansions during COVID-19, documenting how nursing homes and hospitals expanded telemedicine and enabling services, which reduced preventable hospitalizations and improved access to mental health care [34]. The impact of Medicare Part D was examined by Lind et al., finding increased antidementia medication use but persistent racial and ethnic disparities [24]. The Medicare Alzheimer’s Disease Demonstration, a randomized case management program, was found to reduce caregiver hospitalizations [31]. Joyce et al. examined the influence of Dementia Special Care Unit policies and CMS Nursing Home Compare oversight and found improved prescribing practices, quality indicators, and reduced hospitalizations [38]. Connor et al. examined geographic variation under the Medicare Hospice Benefit, documenting wide state-level differences in hospice enrollment [13]. Cross et al. evaluated hospital–skilled nursing facility preferred referral networks shaped by CMS payment reforms, and found that ADRD patients were less likely to access preferred facilities [32].

Discussion

The National Institute on Aging (NIA) has emphasized advancing equity and improving ADRD care as central priorities [39]. Guided by these priorities, this scoping review synthesized evidence on ADRD exclusively among rural U.S. Medicare beneficiaries. Medicare’s near-universal coverage of older adults and longitudinal claims data enable assessment of diagnosis and care utilization across the continuum and align directly with CMS policy levers, making it well suited for evaluating rural inequities in ADRD care. Building upon this foundation, we identified 33 studies published through March 2025, with a notable rise after 2017. Most studies relied on Medicare FFS data and employed retrospective cohort or cross-sectional designs. Key gaps were evident: limited use of causal inference models and advanced machine learning approaches, underrepresentation of environmental and lifestyle-related risk factors, and evaluations of policy interventions. Moreover, the findings highlight the urgent need for standardized rurality metrics. By focusing on Medicare-specific, rural populations, this review consolidates the current evidence base and provides a foundation to guide future research aimed at advancing equity in ADRD for rural U.S. Medicare populations.

While several reviews have examined rural ADRD globally or through rural–urban comparison, none have exclusively focused on Medicare beneficiaries in rural U.S. Barth et al. reviewed interventions for diagnosing cognitive decline and dementia in rural settings, highlighting modalities such as telehealth, online/mobile tools, and telephone-based screening as promising strategies in improving access in underserved areas [40]. However, their work did not extend to post-diagnosis care trajectories, service utilization, or policy evaluations. A global scoping review of rural–urban ADRD disparities synthesized evidence across multiple countries and reported higher prevalence in rural areas [8]. However, it only included studies that compared rural with urban areas and discarded rural-only studies. Mollalo et al. quantified rural–urban differences in ADRD prevalence worldwide, finding a higher prevalence in rural areas especially in regions with lower health spending and educational attainment, but reported no statistically significant differences in the U.S [41]. Our study builds on this body of work by shifting the lens to Medicare beneficiaries in rural U.S. populations, where evidence remains sparse.

Most included studies were published after 2017. This increase coincides with several structural and policy developments, including expanded CMS initiatives such as the Meaningful Measures Initiative (launched in 2017) to enhance quality reporting across nursing homes, hospice, and home health settings [42, 43], and the growing availability of public-use Medicare datasets for research beginning in the mid-2010s [44, 45]. The publication surge also aligns with broader policy attention to ADRD, including successive updates to the U.S. National Plan to Address Alzheimer’s Disease [46], which increasingly emphasized surveillance, equity, and care infrastructure [47]. More recently, COVID-19 related telehealth coverage expansions further accelerated research using Medicare data [38, 39] that might have contributed to the observed increase in the number of publications after 2017 [48, 49].

Surprisingly, few studies addressed lifestyle-related variables such as physical activity, nutritional behavior, smoking, or alcohol use, likely due to limitations in claims-based data. This underrepresentation is particularly in rural contexts, where higher prevalence of obesity [50], tobacco use [51], and physical inactivity [52] are well-documented contributors to poor cognitive health, but have not been systematically incorporated into Medicare-based ADRD studies. In addition, environmental determinants of ADRD were rarely assessed. Only one study directly linked wildfire-related PM2.5 exposure to increased ADRD-related hospitalizations [19] and no studies evaluated built environments or infrastructure such as housing quality or transportation [53]. These omissions reflect challenges such as linking Medicare claims with external geospatial or environmental datasets that require some specialties and data governance coordination [54]. Moreover, the existing focus of Medicare-based ADRD research emphasizes clinical and utilization outcomes over place-based or environmental exposures [55], that despite their importance for rural communities, remain deprioritized in analysis [44]. As a result, key environmental and lifestyle factors continue to be overlooked, limiting understanding of how place-based exposures contribute to ADRD outcomes among rural Medicare populations.

Policy and intervention evaluations were relatively uncommon, with only seven studies directly addressing Medicare-based policies, and even fewer incorporating rural-specific analyses. Interventions such as the Medicare Annual Wellness Visit, CMS telehealth expansions, and the Medicare Alzheimer’s Disease Demonstration demonstrated measurable benefits. However, most evaluations did not stratify outcomes by rurality, limiting their applicability to underserved populations. This gap is consistent with prior research on ADRD policy, which has noted that large-scale Medicare interventions often demonstrate overall effectiveness but fail to capture differential effects across socially vulnerable subgroups [2, 56]. The underrepresentation of rural perspectives may be due to both structural limitations in claims data and the design of federal initiatives, which are typically developed for national scalability rather than tailored to rural delivery systems. Yet, given persistent evidence of rural inequities in ADRD diagnosis, service use, and end-of-life care [57], this lack of rural focus may exacerbate ADRD-related disparities.

Notably, rural Medicare beneficiaries with ADRD consistently demonstrated lower access to specialists such as neurologists and geriatricians [58]. This pattern likely reflects a combination of structural and systemic barriers rather than patient-level preference. Rural areas in the U.S. face persistent shortages of neurology and geriatrics providers, with specialists disproportionately concentrated in urban academic centers [8]. Long travel distances, limited transportation options, and higher out-of-pocket costs further restrict specialist utilization among older adults in rural communities [41]. In addition, rural primary care clinicians often manage ADRD in the absence of specialist support due to workforce shortages and limited referral networks [58]. Moreover, reimbursement structures and lower availability of specialty clinics accepting Medicare in rural settings may further constrain access [8].

This review highlights important strengths in literature, including the reliance on large, nationally representative datasets and the ability to examine longitudinal trends. However, several limitations constrain the evidence base. Reliance on administrative claims limits clinical details and may under-capture true ADRD status, particularly in rural areas where underdiagnosis is common [2]. Inconsistent rurality definitions was another major limitation that reduces generalizability and comparability. Moreover, most studies relied on Medicare FFS data and not Medicare Advantage (MA), that is delivered through private health plans. This distinction is critical because FFS and MA populations differ in demographics, health status, and patterns of healthcare utilization [59, 60]. As a result, findings derived primarily from FFS data may not fully generalize to the broader Medicare population.

Moreover, while medical and long-term care costs captured in Medicare data are substantial, they do not reflect the full burden of ADRD [61]. ADRD is a complex condition that imposes a significant burden on caregivers, including reductions in quality of life, psychological strain, and lost productivity [58]. Importantly, the literature suggests that the economic value of informal caregiving and caregiver-related impacts often exceeds direct medical expenditures, particularly in rural settings; these dimensions are not captured in Medicare claims and may lead to underestimation of the full societal burden of ADRD [58]. In addition, Medicare does not cover most long-term custodial care, which is instead largely financed through Medicaid or out-of-pocket spending. Although Medicare pays for a substantial share of ADRD-related medical costs, the exclusion of long-term care services from Medicare claims limits the ability to capture the full financial burden of ADRD, particularly in rural populations where reliance on long-term and informal care may be greater.

Future research should prioritize underrepresented variables in rural ADRD studies, including lifestyle-related risk factors, environmental exposures, and genetic influences. Given that ADRD reflects both biological vulnerability and contextual conditions, closer attention to gene–environment interactions is critical for understanding how genetic susceptibility interacts with disease onset and progression [62]. Notably, the NIA Data LINKAGE Program [63] provides opportunities for researchers to link Medicare claims with other datasets such as the Health and Retirement Study, National Health and Aging Trends Study, National Study of Caregiving, and the Master Beneficiary Summary File, enabling more complete measurement of socioeconomic factors, functional status, and caregiving context. Only a minority of studies to date have employed advanced spatial or longitudinal methods, and even fewer have integrated intersectional analyses examining how rurality interacts with race, dual eligibility, or comorbidity burden. Future work should therefore prioritize the designs that can capture the complex, multilevel influences on ADRD care access and outcomes.

In parallel, rigorous evaluations of rural-specific policies and interventions, particularly those aimed at early detection, continuity of care, and specialist access, are critical to guiding equitable dementia care delivery within Medicare. While we focused exclusively on U.S.-based studies to maintain consistency in Medicare policy contexts, international comparisons with countries such as the UK [64], Canada [65], Germany [66], and Australia [67], each with distinct health system structures but comparable aging challenges, could offer valuable perspectives on how different healthcare systems address rural ADRD care disparities.

Conclusions

Despite Medicare’s broad coverage, this review found that rural populations remain underrepresented in ADRD research. The review revealed that many studies used binary rural classifications or lacked formal definitions altogether. Moreover, few studies examined interactions between rurality and lifestyle risk factors or environmental exposures, key determinants that are particularly salient in developing ADRD risk in rural settings but remain largely absent from Medicare-based analyses. To advance equity in ADRD care, future research should adopt standardized and transparent rurality frameworks and leverage advanced causal inference and ML approaches to capture multilevel and heterogeneous effects. Moreover, rigorous evaluations of policy interventions that explicitly impact rural ADRD care delivery constraints are needed.

Abbreviations

ADRD

Alzheimer’s Disease and Related Dementias

CMS

Centers for Medicare & Medicaid Services

FFS

Fee-for-Service

MA

Medicare Advantage

NIA

National Institute on Aging

RCT

Randomized Clinical Trial

OASIS

Outcome and Assessment Information Set

SNF

Skilled Nursing Facility 

ML

Machine Learning

NIH

National Institutes of Health

Authors’ contributions

Concept and design: NK and AM, Collection and assembly of data: NK and SA, Data analysis and interpretation: NK and AM, Manuscript writing: NK and AM, Final approval of manuscript: All authors, Accountable for all aspects of the work: All authors.

Funding

This study did not receive any funding.

Data availability

All relevant data are included in the manuscript.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.