Alzheimer’s Disease and Related Dementias in Rural U.S. Medicare Populations: A Scoping Review

Alzheimer’s Disease and Related Dementias in Rural U.S. Medicare Populations: A Scoping Review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Alzheimer’s Disease and Related Dementias in Rural U.S. Medicare Populations: A Scoping Review Nima Kianfar, Sara Alsharayri, Abe Mollalo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7982204/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version 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, are a critical resource for understanding these disparities. However, no previous review has systematically synthesized evidence specific to rural Medicare beneficiaries. 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 inception to March 5, 2025. Peer-reviewed studies were included if they examined ADRD outcomes in rural Medicare populations. We extracted and synthesized 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. 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%). Cardiovascular disease and diabetes were the most frequently examined comorbidities, each reported in 18.2% of studies. Lifestyle factors were also assessed in 18.2%, whereas environmental exposures (3.0%) were rarely studied. Logistic regression was the most common statistical method (51.5%), followed by linear regression (21.2%) and Cox proportional models (9.1%). However, advanced techniques (e.g., machine learning and causal inference) were largely absent. Only 21.2% evaluated policy interventions. Conclusions Rural Medicare beneficiaries with ADRD remain underrepresented in research despite their disproportionate burden. Future studies should address key gaps, including inconsistent rural definitions, limited consideration of medication use, lifestyle and environmental exposures (natural and built), and rural-specific policy evaluations. There is also a critical need for more advanced methods to disentangle the complex, multilevel drivers of ADRD disparities. Clinical trial number: Not applicable. Geriatrics & Gerontology Neurology Psychiatry Health Policy Alzheimer’s disease and related dementias rural Medicare Health disparities Scoping review Figures Figure 1 Figure 2 Figure 3 1. 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 ]. As a result, 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 ]. Evidence suggests that underdiagnosis or delayed diagnosis in resource-limited settings contributes to these disparities [ 12 ]. 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 [ 13 ]. 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 [ 14 ]. Given the aging U.S. population, the economic burden of ADRD was estimated at approximately $ 290 billion in 2019 [ 15 ]. 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 [ 16 ]. Yet broad coverage has not translated into equitable access or outcomes. Evidence implies that rural Medicare beneficiaries with ADRD experience different care trajectories than their urban counterparts. They often experience lower rates of hospice enrollment [ 17 ], reduced use of home health services [ 18 ], higher rates of potentially preventable hospitalizations [ 19 ], and greater reliance on emergency and institutional care settings at the end of life [ 20 ]. The intersection of rurality and Medicare remains a critical yet underexplored blind spot in equitable ADRD care delivery. While prior reviews have explored rural-urban disparities of ADRD [ 8 , 19 ], none have systematically synthesized evidence specific to Medicare beneficiaries in rural areas. To address this gap, we conducted a comprehensive scoping review of previous studies to examine ADRD in rural populations among Medicare beneficiaries. We will highlight recurring patterns and evidence gaps by addressing the following objectives: Characterize outcomes, access, quality, and utilization of care. Summarize study designs, statistical methods, and rurality classification systems. Identify population characteristics and common risk factors. Describe policy intervention evaluations. Highlight knowledge gaps in each of the above domains to guide future investigations aimed at improving ADRD research in rural Medicare populations. 2. METHODS 2.1 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 [ 21 ]. The PRISMA checklist is provided in Supplementary File 1. 2.2. Search strategy A comprehensive literature search was conducted across five major databases (namely PubMed, MEDLINE, CINAHL, Scopus, and Web of Science) on March 5, 2025, to identify relevant studies. 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). 2.3. 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. 2.4. 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). 2.5. 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, caregiving, 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. 2.6. 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. 3. RESULTS 3.1. 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. 3.2. 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 2 a shows this temporal trend, depicting a pronounced post-2017 rise in publications. 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 [ 19 ], Arkansas and Louisiana [ 22 ], while regional studies explored Central Appalachia [ 4 ] and the U.S. Deep South [ 23 ]. Regarding population characteristics, all studies were limited to adults aged 65 years and older. Age distributions were reported in most studies (n = 30, 90.9%). Sex was 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 [ 24 ]. 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. 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 (ACOs)). 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%), while only four studies (n = 4, 12.1%) incorporated Medicare Part D (prescription drug benefit). 3.3. Health outcomes and study designs Most studies (n = 28, 84.8%) used ADRD, and few (n = 4, 12.1%) examined mixed dementia types, such as vascular dementia or Lewy body dementia [ 5 , 25 ], while only one study (n = 1, 3.0%) focused specifically on Alzheimer’s disease [ 14 ]. The most frequently studied outcome was caregiving-related measures, including transitions of care, home health service use, and support for patients and families (n = 10, 30.3%), followed by Hospital-related outcomes—such as all-cause or dementia-specific hospitalizations and readmissions (n = 7, 21.2%). Four studies (n = 4, 12.1%) examined ADRD prevalence alone, while 3 studies (n = 3, 9.1%) examined incidence only, and only one study (n = 1, 3.0%) considered both prevalence and incidence (Fig. 2 b) [ 2 ]. Mortality outcomes were examined in 4 studies (n = 4, 12.1%). Medication use—particularly prescribing patterns, inappropriate medications, and deprescribing—was the primary outcome in four studies (n = 4, 12.1%). Two studies evaluated antipsychotic use in ADRD care: one assessed potentially inappropriate medication use among community-dwelling older adults with dementia [ 26 ], while the other described geographic variation in antidementia and antipsychotic prescribing patterns in nursing homes [ 27 ]. Deprescribing of acetylcholinesterase inhibitors was also examined in relation to facility factors influencing discontinuation practices [ 28 ]. One study investigated the impact of Medicare Part D on medication utilization and ethnoracial disparities in drug access [ 29 ]. 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. 2 c). Sample sizes varied greatly, ranging from 412 participants (caregivers) in the RCT study [ 30 ] 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 [ 22 ] and 4,932,759 million Medicare beneficiaries in Central Appalachia [ 4 ]. 3.4. Rurality definitions Rurality was defined 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), five studies (n = 5, 15.2%) used Rural–Urban Commuting Area (RUCA) codes, and three studies (n = 3, 9.1%) relied on Core-Based Statistical Areas (CBSA). Two studies (n = 2, 6.1%) used Urban Influence Codes (UIC). Additionally, three studies (n = 3, 9.1%) applied state-specific, ZIP code–based, county-level, or facility-based classifications. 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. 3.5. 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, though a few (n = 2, 6.1%) included younger adults with disabilities. 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. Four studies (n = 4, 12.1%) specifically examined how age modified the relationship between rurality and access to diagnosis or services. 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%). Dual eligibility consistently served as a marker of healthcare vulnerability and was frequently associated with poorer outcomes or reduced access to hospice and outpatient care. Many studies noted that rural residence often overlaps with economic disadvantage, exacerbating inequities in ADRD diagnosis, management, and service use. Educational attainment, while less consistently reported, was often modeled as a proxy for cognitive reserve or health literacy. 3.6. 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 [ 20 ]. No 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%) [ 22 , 31 ]. 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 most common (n = 3, 9.1%). One study used county-level physical inactivity prevalence and social association rates as proxies for social engagement (n = 1, 3.0%). Another study considered smoking status, sleep disorders, and depression/anxiety as covariates (n = 1, 3.0%). One study explicitly examined social participation in community events (n = 1, 3.0%). Overall, lifestyle risk factors were less frequently included, and when present, were drawn from secondary indicators rather than direct behavioral measures. 3.7. 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, geriatricians), 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. 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, antipsychotic use), timeliness of hospice entry (n = 3, 9.1%), and continuity of home health services (n = 3, 9.1%). Preventive service uptake (e.g., annual wellness visits, cognitive assessments) was reported in two studies (n = 2, 6.1%). 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%). 3.8. 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. There was only one article (n = 1, 3.0%) for each of the following techniques: instrumental variable analysis (two-stage least squares), Fine-Gray competing risk regression, propensity score matching, trajectory analysis to model longitudinal trends, spatial regression techniques, probit regression and generalized estimating equations models to evaluate repeated measures and marginal effects. One study (n = 1, 3.0%) relied exclusively on descriptive statistics, with no inferential testing or modeling. Despite this methodological breadth, only few studies (n = 4, 12.1%) explicitly modeled interactions involving rurality, race/ethnicity, Medicaid eligibility, or comorbidity burden. Figure 3 illustrates the frequency of statistical analysis techniques used. 3.9. 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 [ 17 ]. 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 [ 32 ]. The impact of Medicare Part D was examined by Lind et al., finding increased antidementia medication use but persistent racial and ethnic disparities [ 29 ]. The Medicare Alzheimer’s Disease Demonstration, a randomized case management program, was found to reduce caregiver hospitalizations [ 30 ]. 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 [ 33 ]. Connor et al. examined geographic variation under the Medicare Hospice Benefit, documenting wide state-level differences in hospice enrollment [ 14 ]. 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 [ 34 ]. 4. DISCUSSION The National Institute on Aging (NIA) has emphasized advancing equity and improving ADRD care as central priorities [ 35 ]. Guided by NIA priorities, we conducted this scoping review to synthesize research on ADRD in rural U.S. populations using Medicare beneficiaries’ data that provide comprehensive, nationally representative coverage of older adults, enabling robust identification of ADRD patterns across diverse settings. 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 [ 36 ]. 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. [ 37 ]. Our study builds on this body of work by shifting the lens to Medicare beneficiaries in rural U.S. populations, where evidence remains sparse. Majority of included studies were published after 2017, implying a growing national interest in aging, rural health equity, and the role of Medicare in long-term care. This surge aligns with expanded CMS policies, such as telehealth coverage expansions (introduced at the start of the COVID-19 pandemic, 2020) [ 38 , 39 ], CMS’s Meaningful Measures Initiative (2017) to prioritize quality reporting in nursing homes, hospice, and home health settings [ 40 , 41 ], and increased availability of public-use Medicare datasets for research (2015) [ 42 , 43 ]. The post-2017 growth also coincides with heightened policy attention to ADRD, including ongoing annual updates to the U.S. National Plan to Address Alzheimer’s Disease [ 44 ], which increasingly prioritized surveillance, equity, and care infrastructure [ 45 ]. This temporal trend suggests that policy shifts, improved data accessibility, and broader recognition of ADRD as a public health priority have catalyzed research in this field. 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 [ 46 ], tobacco use [ 47 ], and physical inactivity [ 48 ] 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 [ 20 ] and no studies evaluated built environments or infrastructure such as housing quality or transportation [ 49 ]. These omissions reflect challenges such as linking Medicare claims with external geospatial or environmental datasets that require some specialties and data governance coordination [ 50 ]. Moreover, the existing focus of Medicare-based ADRD research emphasizes clinical and utilization outcomes over place-based or environmental exposures [ 51 ]—that despite their importance for rural communities, remain deprioritized in analysis [ 42 ]. 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 , 52 ]. 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 [ 53 ], this lack of rural focus may exacerbate ADRD-related disparities. Thus, future policy evaluations must move beyond aggregate outcomes to explicitly address rural health system constraints, including workforce shortages, geographic barriers to specialty care, and infrastructural limitations. 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 [ 54 , 55 ]. As a result, findings derived primarily from FFS data may not fully generalize to the broader Medicare population. 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 [ 56 ]. 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. Moreover, 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. Finally, while we focused exclusively on U.S.-based studies to maintain consistency in Medicare policy contexts, international comparisons with countries such as the UK [ 57 ], Canada [ 58 ], Germany [ 59 ], and Australia [ 60 ]—each with distinct health system structures but comparable aging challenges—could offer valuable perspectives on how different healthcare systems address rural ADRD care disparities. 5. 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 MCBS Medicare Current Beneficiary Survey OASIS Outcome and Assessment Information Set SNF Skilled Nursing Facility RUCC Rural–Urban Continuum Codes RUCA Rural–Urban Commuting Area Codes CBSA Core-Based Statistical Area GLM Generalized Linear Model ML Machine Learning GIS Geographic Information System NIH National Institutes of Health Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This study did not receive any funding 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. Acknowledgements Not applicable. Availability of data and materials All relevant data are included in the manuscript. References Wang N, Buchongo P, Chen J (2022) Rural and urban disparities in potentially preventable hospitalizations among US patients with Alzheimer’s Disease and Related Dementias: Evidence of hospital-based telehealth and enabling services. Prev Med 163:107223 Rahman M, White EM, Mills C, Thomas KS, Jutkowitz E (2021 July) Rural-urban differences in diagnostic incidence and prevalence of Alzheimer’s disease and related dementias. 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Alzheimers Dement 19(9):4276–4286 Okobi OE, Ajayi OO, Okobi TJ, Anaya IC, Fasehun OO, Diala CS et al The Burden of Obesity in the Rural Adult Population of America. Cureus [Internet]. 2021 June 20 [cited 2025 Aug 26]; Available from: https://www.cureus.com/articles/61370-the-burden-of-obesity-in-the-rural-adult-population-of-america Hahn EJ, Bucher A, Rademacher K, Beckett W, Taylor L, Darville A et al (2024 Sept) Tobacco use disparities in rural communities. J Rural Health 40(4):738–744 Mollalo A, Grekousis G, Florez H, Neelon B, Lenert LA, Alekseyenko AV (2025) Alzheimer’s Disease Dementia Prevalence in the United States: A County-Level Spatial Machine Learning Analysis. Am J Alzheimers Dis Dementias® 40:15333175251335570 Kramer M, Alekseyenko AV, Mollalo A (2025) The role of built and social environments in Alzheimer’s disease dementia prevalence in the United States: A machine learning approach. J Alzheimer’s Dis. Sept;13872877251372144. Redman S, Wagstaff L, Flanagan E, Trevino M, Opportunities for Health Services Research (HSR) and Patient-Centered Outcomes Research (PCOR) (2022) Identifying Environmental Data, Barriers, and : An Environmental Scan. Report for the Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health & Human Services Mollalo A, Knox S, Meng J, Benitez A, Lenert LA, Alekseyenko AV (2024) Geospatial Analysis of the Association Between Medicaid Expansion, Minimum Wage Policies, and Alzheimer’s Disease Dementia Prevalence in the United States. Information 15(11):688 Spoto F, Tian J, Hügel J, Ortega DT, Ritchie CS, Blacker D et al (2025) Quantifying Diagnostic Signal Decay in Dementia: A National Study of Medicare Hospitalization Data [Internet]. arXiv; [cited 2025 Aug 26]. Available from: https://arxiv.org/abs/2506.14669 Wiese LAK, Gibson A, Guest MA, Nelson AR, Weaver R, Gupta A et al (2023 Sept) Global rural health disparities in Alzheimer’s disease and related dementias: State of the science. Alzheimers Dement 19(9):4204–4225 Jacobson M, Ferido P, Zissimopoulos J (2024 June) Health care utilization before and after a dementia diagnosis in Medicare Advantage versus traditional Medicare. J Am Geriatr Soc 72(6):1856–1866 Miller EA, Decker SL, Parker JD (2016 July) Characteristics of Medicare Advantage and Fee-for-Service Beneficiaries Upon Enrollment in Medicare at Age 65. J Ambul Care Manage 39(3):231–241 Migliore L, Coppedè F (2022) Gene–environment interactions in Alzheimer disease: the emerging role of epigenetics. Nat Rev Neurol 18(11):643–660 Malzbender K, Barbarino P, Ferrell PB, Bradshaw A, Brookes AJ, Díaz C et al (2024) Validation, Deployment, and Real-World Implementation of a Modular Toolbox for Alzheimer’s Disease Detection and Dementia Risk Reduction: The AD-RIDDLE Project. J Prev Alzheimers Dis 11(2):329–338 Black SE, Budd N, Nygaard HB, Verret L, Virdi S, Tamblyn Watts L et al (2024 July) A Model Predicting Healthcare Capacity Gaps For Alzheimer’s Disease-Modifying Treatment in Canada. Can J Neurol Sci J Can Sci Neurol 51(4):487–494 Georges D, Rakusa E, Holtz AV, Fink A, Doblhammer G Dementia in Germany: epidemiology, trends and challenges. 2023 [cited 2025 Aug 27]; Available from: https://edoc.rki.de/handle/176904/11294 Low L, Laver K, Lawler K, Swaffer K, Bahar-Fuchs A, Bennett S et al (2021) We need a model of health and aged care services that adequately supports Australians with dementia. Med J Aust 214(2):66 Additional Declarations The authors declare no competing interests. Supplementary Files Supplementaryfile1.PRISMAchecklist.docx Supplementaryfile2.Extractiontable.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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06:50:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":161652,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristics of the included studies: (a) publication year (2001–2025), (b) health outcomes assessed, and (c) study design.\u003c/p\u003e","description":"","filename":"Figure2.Characteristicsoftheincludedstudiesapublicationyear20012025bhealthoutcomesassessedandcstudydesign..png","url":"https://assets-eu.researchsquare.com/files/rs-7982204/v1/33d3c3e36dc830da77eadcd5.png"},{"id":94825257,"identity":"3fd1591e-e496-4bf0-9a60-65cf429a24a8","added_by":"auto","created_at":"2025-10-31 06:50:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":140681,"visible":true,"origin":"","legend":"\u003cp\u003eThe frequency of statistical approaches used in included studies.\u003c/p\u003e","description":"","filename":"Figure3.Thefrequencyofstatisticalapproachesusedinincludedstudies..png","url":"https://assets-eu.researchsquare.com/files/rs-7982204/v1/7a75a44eb53bd6f89e8d9cc5.png"},{"id":94827363,"identity":"88c03e9a-7700-419f-aa71-903bdacc264a","added_by":"auto","created_at":"2025-10-31 06:57:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1170597,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7982204/v1/832a0699-e737-4109-8a74-85e9bd41fad5.pdf"},{"id":94777202,"identity":"9e87732a-3617-44c2-a9ce-b41a49e1f8d3","added_by":"auto","created_at":"2025-10-30 15:00:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":28473,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1.PRISMAchecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-7982204/v1/f20e93d6c95ef4599dee99a2.docx"},{"id":94777206,"identity":"684a1f52-43ac-4803-86b2-09747e8037c8","added_by":"auto","created_at":"2025-10-30 15:00:10","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":59629,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile2.Extractiontable.docx","url":"https://assets-eu.researchsquare.com/files/rs-7982204/v1/fe90e14e3db4a1e4e2910791.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAlzheimer’s Disease and Related Dementias in Rural U.S. Medicare Populations: A Scoping Review\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. BACKGROUND","content":"\u003cp\u003eRural 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) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As a result, rural communities in the U.S. face a disproportionately high burden of ADRD compared to urban areas [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Rural areas often experience higher age-adjusted mortality rates [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], rapid increases in ADRD prevalence [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and more limited access to timely and specialized dementia care compared to urban areas [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOver the past two decades, rural–urban gaps in ADRD outcomes have grown wider [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Evidence suggests that underdiagnosis or delayed diagnosis in resource-limited settings contributes to these disparities [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMedicare, 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 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Given the aging U.S. population, the economic burden of ADRD was estimated at approximately \u003cspan\u003e$\u003c/span\u003e290\u0026nbsp;billion in 2019 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A substantial share of this burden falls on Medicare, with average end-of-life costs for a person with dementia exceeding \u003cspan\u003e$\u003c/span\u003e287,000 over the last five years of life—compared to about \u003cspan\u003e$\u003c/span\u003e175,000 for heart disease and \u003cspan\u003e$\u003c/span\u003e173,000 for cancer [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Yet broad coverage has not translated into equitable access or outcomes. Evidence implies that rural Medicare beneficiaries with ADRD experience different care trajectories than their urban counterparts. They often experience lower rates of hospice enrollment [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], reduced use of home health services [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], higher rates of potentially preventable hospitalizations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and greater reliance on emergency and institutional care settings at the end of life [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe intersection of rurality and Medicare remains a critical yet underexplored blind spot in equitable ADRD care delivery. While prior reviews have explored rural-urban disparities of ADRD [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], none have systematically synthesized evidence specific to Medicare beneficiaries in rural areas. To address this gap, we conducted a comprehensive scoping review of previous studies to examine ADRD in rural populations among Medicare beneficiaries. We will highlight recurring patterns and evidence gaps by addressing the following objectives:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eCharacterize outcomes, access, quality, and utilization of care.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eSummarize study designs, statistical methods, and rurality classification systems.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eIdentify population characteristics and common risk factors.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDescribe policy intervention evaluations.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHighlight knowledge gaps in each of the above domains to guide future investigations aimed at improving ADRD research in rural Medicare populations.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e"},{"header":"2. METHODS","content":"\u003ch2\u003e2.1 Study design\u003cp\u003e\u003c/p\u003e\u003c/h2\u003e\u003cp\u003eThis scoping review followed the methodological framework outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for Scoping Reviews [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The PRISMA checklist is provided in Supplementary File 1.\u003c/p\u003e\u003ch2\u003e2.2. Search strategy\u003c/h2\u003e\u003cp\u003eA comprehensive literature search was conducted across five major databases (namely PubMed, MEDLINE, CINAHL, Scopus, and Web of Science) on March 5, 2025, to identify relevant studies. 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).\u003c/p\u003e\u003ch2\u003e2.3. Eligibility criteria\u003c/h2\u003e\u003cp\u003eStudies 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.\u003c/p\u003e\u003ch2\u003e2.4. Screening process and study selection\u003c/h2\u003e\u003cp\u003eAll retrieved studies were imported into Covidence online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.covidence.org/\u003c/span\u003e\u003cspan address=\"https://www.covidence.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), 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).\u003c/p\u003e\u003ch2\u003e2.5. Data extraction\u003c/h2\u003e\u003cp\u003eAfter 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, caregiving, 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.\u003c/p\u003e\u003ch2\u003e2.6. Data synthesis\u003c/h2\u003e\u003cp\u003eThe 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.\u003c/p\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Overview of literature search\u003c/h2\u003e\u003cp\u003eOur 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\u0026thinsp;=\u0026thinsp;53), absence of a rurality component (n\u0026thinsp;=\u0026thinsp;38), no use of Medicare data (n\u0026thinsp;=\u0026thinsp;23), non\u0026ndash;peer-reviewed studies (n\u0026thinsp;=\u0026thinsp;17). Ultimately, 33 studies met all inclusion criteria and were retained for this scoping review. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the PRISMA flowchart summarizing the study selection process.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Descriptive characteristics\u003c/h2\u003e\u003cp\u003eThe 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\u0026thinsp;=\u0026thinsp;29, 87.7%). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea shows this temporal trend, depicting a pronounced post-2017 rise in publications.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMost studies (n\u0026thinsp;=\u0026thinsp;26, 78.8%) analyzed data nationally, while others (n\u0026thinsp;=\u0026thinsp;7, 21.2%) focused on state- or region-specific populations. For instance, state-specific analyses included Ohio [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], Arkansas and Louisiana [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], while regional studies explored Central Appalachia [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and the U.S. Deep South [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRegarding population characteristics, all studies were limited to adults aged 65 years and older. Age distributions were reported in most studies (n\u0026thinsp;=\u0026thinsp;30, 90.9%). Sex was reported in most studies (n\u0026thinsp;=\u0026thinsp;27, 81.8%), where women consistently comprised the majority of the ADRD population (55%\u0026ndash;70%). Race and ethnicity were frequently included (n\u0026thinsp;=\u0026thinsp;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 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Dual-eligible (Medicare and Medicaid) status was commonly used (n\u0026thinsp;=\u0026thinsp;16, 48.5%) to capture low-income status, with notable variations in its use and interpretation across geographic contexts.\u003c/p\u003e\u003cp\u003eThe level of analysis varied across studies: more than half of studies (n\u0026thinsp;=\u0026thinsp;19, 57.6%) used individual-level claims or survey data, eight studies (n\u0026thinsp;=\u0026thinsp;8, 24.2%) conducted area-level analyses at the ZIP code, county, or sub-county levels and a smaller number (n\u0026thinsp;=\u0026thinsp;6, 18.2%) used facility-level analyses (e.g., skilled nursing facilities, accountable care organizations (ACOs)).\u003c/p\u003e\u003cp\u003eIn terms of data sources, most studies (n\u0026thinsp;=\u0026thinsp;28, 84.8%) used Medicare FFS data. Some integrated other CMS-linked resources, such as the Outcome and Assessment Information Set (OASIS) (n\u0026thinsp;=\u0026thinsp;7, 21.2%) or the Medicare Current Beneficiary Survey (MCBS) (n\u0026thinsp;=\u0026thinsp;2, 6.0%), while only four studies (n\u0026thinsp;=\u0026thinsp;4, 12.1%) incorporated Medicare Part D (prescription drug benefit).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Health outcomes and study designs\u003c/h2\u003e\u003cp\u003eMost studies (n\u0026thinsp;=\u0026thinsp;28, 84.8%) used ADRD, and few (n\u0026thinsp;=\u0026thinsp;4, 12.1%) examined mixed dementia types, such as vascular dementia or Lewy body dementia [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], while only one study (n\u0026thinsp;=\u0026thinsp;1, 3.0%) focused specifically on Alzheimer\u0026rsquo;s disease [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The most frequently studied outcome was caregiving-related measures, including transitions of care, home health service use, and support for patients and families (n\u0026thinsp;=\u0026thinsp;10, 30.3%), followed by Hospital-related outcomes\u0026mdash;such as all-cause or dementia-specific hospitalizations and readmissions (n\u0026thinsp;=\u0026thinsp;7, 21.2%). Four studies (n\u0026thinsp;=\u0026thinsp;4, 12.1%) examined ADRD prevalence alone, while 3 studies (n\u0026thinsp;=\u0026thinsp;3, 9.1%) examined incidence only, and only one study (n\u0026thinsp;=\u0026thinsp;1, 3.0%) considered both prevalence and incidence (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Mortality outcomes were examined in 4 studies (n\u0026thinsp;=\u0026thinsp;4, 12.1%).\u003c/p\u003e\u003cp\u003eMedication use\u0026mdash;particularly prescribing patterns, inappropriate medications, and deprescribing\u0026mdash;was the primary outcome in four studies (n\u0026thinsp;=\u0026thinsp;4, 12.1%). Two studies evaluated antipsychotic use in ADRD care: one assessed potentially inappropriate medication use among community-dwelling older adults with dementia [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], while the other described geographic variation in antidementia and antipsychotic prescribing patterns in nursing homes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Deprescribing of acetylcholinesterase inhibitors was also examined in relation to facility factors influencing discontinuation practices [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. One study investigated the impact of Medicare Part D on medication utilization and ethnoracial disparities in drug access [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe most common study design was retrospective cohort (n\u0026thinsp;=\u0026thinsp;20, 60.6%), followed by cross-sectional (n\u0026thinsp;=\u0026thinsp;10, 30.3%), ecological (n\u0026thinsp;=\u0026thinsp;2, 6.1%) and randomized clinical trial (RCT) (n\u0026thinsp;=\u0026thinsp;1, 3.0%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Sample sizes varied greatly, ranging from 412 participants (caregivers) in the RCT study [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] to roughly 170\u0026nbsp;million beneficiaries (person-years) in a nationwide cross-sectional study [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Stratified by study design, cohort studies had a range of 1,186 to 3\u0026nbsp;million participants (SD\u0026thinsp;=\u0026thinsp;823,122), while cross-sectional studies had a range of 1,245 to 170\u0026nbsp;million beneficiaries (SD\u0026thinsp;=\u0026thinsp;52,572,796). Ecological studies analyzed 1,297,271 beneficiaries in Arkansas and Louisiana [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and 4,932,759\u0026nbsp;million Medicare beneficiaries in Central Appalachia [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Rurality definitions\u003c/h2\u003e\u003cp\u003eRurality was defined or measured using diverse classification systems. A large number of studies (n\u0026thinsp;=\u0026thinsp;14, 42.4%) dichotomized rurality into binary rural vs. urban variables without providing a clear definition. Six studies (n\u0026thinsp;=\u0026thinsp;6, 18.2%) used Rural\u0026ndash;Urban Continuum Codes (RUCC), five studies (n\u0026thinsp;=\u0026thinsp;5, 15.2%) used Rural\u0026ndash;Urban Commuting Area (RUCA) codes, and three studies (n\u0026thinsp;=\u0026thinsp;3, 9.1%) relied on Core-Based Statistical Areas (CBSA). Two studies (n\u0026thinsp;=\u0026thinsp;2, 6.1%) used Urban Influence Codes (UIC). Additionally, three studies (n\u0026thinsp;=\u0026thinsp;3, 9.1%) applied state-specific, ZIP code\u0026ndash;based, county-level, or facility-based classifications. Twenty-one studies (n\u0026thinsp;=\u0026thinsp;21, 63.6%) included rural\u0026ndash;urban comparisons, while the remaining (n\u0026thinsp;=\u0026thinsp;12, 36.4%) focused exclusively on rural populations or conducted rural-stratified analyses without direct urban comparison.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Demographic and socioeconomic risk factors\u003c/h2\u003e\u003cp\u003eAll 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, though a few (n\u0026thinsp;=\u0026thinsp;2, 6.1%) included younger adults with disabilities. Age (n\u0026thinsp;=\u0026thinsp;31, 93.9%), sex (n\u0026thinsp;=\u0026thinsp;30, 90.9%), race/ethnicity (n\u0026thinsp;=\u0026thinsp;28, 84.8%), and marital status (n\u0026thinsp;=\u0026thinsp;3, 9.1%) were the most frequently reported. Women made up the majority of the ADRD population (n\u0026thinsp;=\u0026thinsp;25/30, 83.3%) in studies that reported sex. Four studies (n\u0026thinsp;=\u0026thinsp;4, 12.1%) specifically examined how age modified the relationship between rurality and access to diagnosis or services.\u003c/p\u003e\u003cp\u003eSocioeconomic status was examined in nearly all studies, using indicators such as dual eligibility (n\u0026thinsp;=\u0026thinsp;24, 72.7%), poverty and area deprivation indices (n\u0026thinsp;=\u0026thinsp;14, 42.4%), educational attainment (n\u0026thinsp;=\u0026thinsp;12, 36.4%), income (n\u0026thinsp;=\u0026thinsp;6, 18.2%), insurance coverage (n\u0026thinsp;=\u0026thinsp;6, 18.2%), and employment status (n\u0026thinsp;=\u0026thinsp;2, 6.1%). Dual eligibility consistently served as a marker of healthcare vulnerability and was frequently associated with poorer outcomes or reduced access to hospice and outpatient care. Many studies noted that rural residence often overlaps with economic disadvantage, exacerbating inequities in ADRD diagnosis, management, and service use. Educational attainment, while less consistently reported, was often modeled as a proxy for cognitive reserve or health literacy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Environmental, lifestyle, and comorbidity risk factors\u003c/h2\u003e\u003cp\u003eEnvironmental factors were rarely analyzed. One study (n\u0026thinsp;=\u0026thinsp;1, 3.0%) explicitly examined air quality, finding that wildfire-related PM2.5 exposure was associated with increased ADRD-related hospitalizations [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. No studies in the reviewed literature analyzed built-environment amenities such as playgrounds, sports venues, or recreational facilities.\u003c/p\u003e\u003cp\u003eComorbidity was occasionally addressed, with cardiovascular disease (n\u0026thinsp;=\u0026thinsp;6, 18.2%), diabetes (n\u0026thinsp;=\u0026thinsp;6, 18.2%), stroke (n\u0026thinsp;=\u0026thinsp;5, 15.2%), depression (n\u0026thinsp;=\u0026thinsp;4, 12.1%), and hypertension (n\u0026thinsp;=\u0026thinsp;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\u0026thinsp;=\u0026thinsp;3, 9.1%), lower care continuity (n\u0026thinsp;=\u0026thinsp;2, 6.1%), and higher healthcare costs (n\u0026thinsp;=\u0026thinsp;2, 6.1%) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLifestyle-related factors were infrequently examined (n\u0026thinsp;=\u0026thinsp;6, 18.2%). Among these, nutrition-related behaviors, including poor appetite, weight loss, or mechanically altered diets were most common (n\u0026thinsp;=\u0026thinsp;3, 9.1%). One study used county-level physical inactivity prevalence and social association rates as proxies for social engagement (n\u0026thinsp;=\u0026thinsp;1, 3.0%). Another study considered smoking status, sleep disorders, and depression/anxiety as covariates (n\u0026thinsp;=\u0026thinsp;1, 3.0%). One study explicitly examined social participation in community events (n\u0026thinsp;=\u0026thinsp;1, 3.0%). Overall, lifestyle risk factors were less frequently included, and when present, were drawn from secondary indicators rather than direct behavioral measures.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.7. Access, quality of care, and healthcare utilization\u003c/h2\u003e\u003cp\u003eAccess to care was a central focus in most studies (n\u0026thinsp;=\u0026thinsp;25, 75.8%). Rural Medicare beneficiaries with ADRD consistently demonstrated lower access to specialists (e.g., neurologists, geriatricians), fewer dementia assessments, and reduced availability of post-acute care. Telehealth adoption was evaluated in few studies (n\u0026thinsp;=\u0026thinsp;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.\u003c/p\u003e\u003cp\u003eQuality of care was assessed in 11 studies (n\u0026thinsp;=\u0026thinsp;11, 33.3%) and measured through outcomes such as medication safety (n\u0026thinsp;=\u0026thinsp;5, 15.2%; e.g., potentially inappropriate prescribing, antipsychotic use), timeliness of hospice entry (n\u0026thinsp;=\u0026thinsp;3, 9.1%), and continuity of home health services (n\u0026thinsp;=\u0026thinsp;3, 9.1%). Preventive service uptake (e.g., annual wellness visits, cognitive assessments) was reported in two studies (n\u0026thinsp;=\u0026thinsp;2, 6.1%).\u003c/p\u003e\u003cp\u003eUtilization patterns were examined in 18 studies (n\u0026thinsp;=\u0026thinsp;18, 54.5%). Among these, most studies reported higher utilization of emergency departments (n\u0026thinsp;=\u0026thinsp;7, 21.2%), skilled nursing facilities (n\u0026thinsp;=\u0026thinsp;6, 18.2%), and institutional long-term care (n\u0026thinsp;=\u0026thinsp;5, 15.2%), while fewer studies noted lower use of continuous in-home support (n\u0026thinsp;=\u0026thinsp;4, 12.1%), hospice care (n\u0026thinsp;=\u0026thinsp;5, 15.2%), or outpatient dementia management (n\u0026thinsp;=\u0026thinsp;3, 9.1%).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.8. Statistical approaches\u003c/h2\u003e\u003cp\u003eAll included studies employed quantitative methods, with a predominance of traditional statistical analyses. The most common approaches were logistic regression (n\u0026thinsp;=\u0026thinsp;17, 51.5%) and linear regression models (n\u0026thinsp;=\u0026thinsp;7, 21.2%). Cox proportional hazards models and negative binomial regression were each used in three studies (n\u0026thinsp;=\u0026thinsp;3, 9.1%) to analyze survival outcomes and overdispersed count data. Poisson regression (n\u0026thinsp;=\u0026thinsp;2, 6.1%) and Generalized Linear Models (n\u0026thinsp;=\u0026thinsp;2, 6.1%) were less commonly employed. There was only one article (n\u0026thinsp;=\u0026thinsp;1, 3.0%) for each of the following techniques: instrumental variable analysis (two-stage least squares), Fine-Gray competing risk regression, propensity score matching, trajectory analysis to model longitudinal trends, spatial regression techniques, probit regression and generalized estimating equations models to evaluate repeated measures and marginal effects. One study (n\u0026thinsp;=\u0026thinsp;1, 3.0%) relied exclusively on descriptive statistics, with no inferential testing or modeling. Despite this methodological breadth, only few studies (n\u0026thinsp;=\u0026thinsp;4, 12.1%) explicitly modeled interactions involving rurality, race/ethnicity, Medicaid eligibility, or comorbidity burden. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the frequency of statistical analysis techniques used.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.9. Policy implications and interventions\u003c/h2\u003e\u003cp\u003eOnly 7 studies (n\u0026thinsp;=\u0026thinsp;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 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The impact of Medicare Part D was examined by Lind et al., finding increased antidementia medication use but persistent racial and ethnic disparities [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The Medicare Alzheimer\u0026rsquo;s Disease Demonstration, a randomized case management program, was found to reduce caregiver hospitalizations [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Connor et al. examined geographic variation under the Medicare Hospice Benefit, documenting wide state-level differences in hospice enrollment [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Cross et al. evaluated hospital\u0026ndash;skilled nursing facility preferred referral networks shaped by CMS payment reforms, and found that ADRD patients were less likely to access preferred facilities [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThe National Institute on Aging (NIA) has emphasized advancing equity and improving ADRD care as central priorities [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Guided by NIA priorities, we conducted this scoping review to synthesize research on ADRD in rural U.S. populations using Medicare beneficiaries\u0026rsquo; data that provide comprehensive, nationally representative coverage of older adults, enabling robust identification of ADRD patterns across diverse settings. 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.\u003c/p\u003e\u003cp\u003eWhile several reviews have examined rural ADRD globally or through rural\u0026ndash;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 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, their work did not extend to post-diagnosis care trajectories, service utilization, or policy evaluations. A global scoping review of rural\u0026ndash;urban ADRD disparities synthesized evidence across multiple countries and reported higher prevalence in rural areas [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, it only included studies that compared rural with urban areas and discarded rural-only studies. Mollalo et al. quantified rural\u0026ndash;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. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Our study builds on this body of work by shifting the lens to Medicare beneficiaries in rural U.S. populations, where evidence remains sparse.\u003c/p\u003e\u003cp\u003eMajority of included studies were published after 2017, implying a growing national interest in aging, rural health equity, and the role of Medicare in long-term care. This surge aligns with expanded CMS policies, such as telehealth coverage expansions (introduced at the start of the COVID-19 pandemic, 2020) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], CMS\u0026rsquo;s Meaningful Measures Initiative (2017) to prioritize quality reporting in nursing homes, hospice, and home health settings [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and increased availability of public-use Medicare datasets for research (2015) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The post-2017 growth also coincides with heightened policy attention to ADRD, including ongoing annual updates to the U.S. National Plan to Address Alzheimer\u0026rsquo;s Disease [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], which increasingly prioritized surveillance, equity, and care infrastructure [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This temporal trend suggests that policy shifts, improved data accessibility, and broader recognition of ADRD as a public health priority have catalyzed research in this field.\u003c/p\u003e\u003cp\u003eSurprisingly, few studies addressed lifestyle-related variables such as physical activity, nutritional behavior, smoking, or alcohol use\u0026mdash;likely due to limitations in claims-based data. This underrepresentation is particularly in rural contexts, where higher prevalence of obesity [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], tobacco use [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], and physical inactivity [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] 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\u0026mdash;only one study directly linked wildfire-related PM2.5 exposure to increased ADRD-related hospitalizations [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and no studies evaluated built environments or infrastructure such as housing quality or transportation [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. These omissions reflect challenges such as linking Medicare claims with external geospatial or environmental datasets that require some specialties and data governance coordination [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Moreover, the existing focus of Medicare-based ADRD research emphasizes clinical and utilization outcomes over place-based or environmental exposures [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u0026mdash;that despite their importance for rural communities, remain deprioritized in analysis [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. 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.\u003c/p\u003e\u003cp\u003ePolicy 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\u0026rsquo;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 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], this lack of rural focus may exacerbate ADRD-related disparities. Thus, future policy evaluations must move beyond aggregate outcomes to explicitly address rural health system constraints, including workforce shortages, geographic barriers to specialty care, and infrastructural limitations.\u003c/p\u003e\u003cp\u003eThis 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 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. 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)\u0026mdash;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 [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. As a result, findings derived primarily from FFS data may not fully generalize to the broader Medicare population.\u003c/p\u003e\u003cp\u003eFuture 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\u0026ndash;environment interactions is critical for understanding how genetic susceptibility interacts with disease onset and progression [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. 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. Moreover, 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. Finally, while we focused exclusively on U.S.-based studies to maintain consistency in Medicare policy contexts, international comparisons with countries such as the UK [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], Canada [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], Germany [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], and Australia [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u0026mdash;each with distinct health system structures but comparable aging challenges\u0026mdash;could offer valuable perspectives on how different healthcare systems address rural ADRD care disparities.\u003c/p\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eDespite Medicare\u0026rsquo;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\u0026mdash;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.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eADRD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAlzheimer\u0026rsquo;s Disease and Related Dementias\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCMS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCenters for Medicare \u0026amp; Medicaid Services\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFFS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFee-for-Service\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMedicare Advantage\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNIA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Institute on Aging\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRCT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRandomized Clinical Trial\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMCBS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMedicare Current Beneficiary Survey\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOASIS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOutcome and Assessment Information Set\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSNF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSkilled Nursing Facility\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRUCC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRural\u0026ndash;Urban Continuum Codes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRUCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRural\u0026ndash;Urban Commuting Area Codes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCBSA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCore-Based Statistical Area\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGLM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGeneralized Linear Model\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eML\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMachine Learning\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGIS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGeographic Information System\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNIH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Institutes of Health\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study did not receive any funding\u003c/p\u003e\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\u003cp\u003eConcept 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.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\u003cp\u003eAll relevant data are included in the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang N, Buchongo P, Chen J (2022) Rural and urban disparities in potentially preventable hospitalizations among US patients with Alzheimer\u0026rsquo;s Disease and Related Dementias: Evidence of hospital-based telehealth and enabling services. Prev Med 163:107223\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRahman M, White EM, Mills C, Thomas KS, Jutkowitz E (2021 July) Rural-urban differences in diagnostic incidence and prevalence of Alzheimer\u0026rsquo;s disease and related dementias. Alzheimers Dement 17(7):1213\u0026ndash;1230\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCrouch E, Probst JC, Bennett K, Eberth JM (2019) Differences in Medicare Utilization and Expenditures in the Last Six Months of Life among Patients with and without Alzheimer\u0026rsquo;s Disease and Related Disorders. J Palliat Med 22(2):126\u0026ndash;131\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWing JJ, Rajczyk JI, Burke JF (2024) Geographic Variation of Prevalence of Alzheimer\u0026rsquo;s Disease and Related Dementias in Central Appalachia. Abner E, editor. 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Med J Aust 214(2):66\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Medical University of South Carolina","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Alzheimer’s disease and related dementias, rural, Medicare, Health disparities, Scoping review","lastPublishedDoi":"10.21203/rs.3.rs-7982204/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7982204/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eRural populations in the U.S. face a disproportionate burden of Alzheimer\u0026rsquo;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, are a critical resource for understanding these disparities. However, no previous review has systematically synthesized evidence specific to rural Medicare beneficiaries. This scoping review maps the existing evidence and highlights critical areas where further rural ADRD research is needed.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a systematic search on PubMed, MEDLINE, CINAHL, Scopus, and Web of Science from inception to March 5, 2025. Peer-reviewed studies were included if they examined ADRD outcomes in rural Medicare populations. We extracted and synthesized 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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThirty-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%). Cardiovascular disease and diabetes were the most frequently examined comorbidities, each reported in 18.2% of studies. Lifestyle factors were also assessed in 18.2%, whereas environmental exposures (3.0%) were rarely studied. Logistic regression was the most common statistical method (51.5%), followed by linear regression (21.2%) and Cox proportional models (9.1%). However, advanced techniques (e.g., machine learning and causal inference) were largely absent. Only 21.2% evaluated policy interventions.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eRural Medicare beneficiaries with ADRD remain underrepresented in research despite their disproportionate burden. Future studies should address key gaps, including inconsistent rural definitions, limited consideration of medication use, lifestyle and environmental exposures (natural and built), and rural-specific policy evaluations. There is also a critical need for more advanced methods to disentangle the complex, multilevel drivers of ADRD disparities.\u003c/p\u003e\u003ch2\u003eClinical trial number:\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"Alzheimer’s Disease and Related Dementias in Rural U.S. Medicare Populations: A Scoping Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 15:00:05","doi":"10.21203/rs.3.rs-7982204/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0e4c03f4-81bf-4597-b027-2ef80c06fcf8","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57111898,"name":"Geriatrics \u0026 Gerontology"},{"id":57111899,"name":"Neurology"},{"id":57111900,"name":"Psychiatry"},{"id":57111901,"name":"Health Policy"}],"tags":[],"updatedAt":"2025-10-30T15:00:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 15:00:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7982204","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7982204","identity":"rs-7982204","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}