Bridging Research, Policy, and Practice: Advancing Evidence-Informed Approaches for Dementia Care and Prevention: an expert review by the Health Policy Professional Interest Area from ISTAART

Abstract

Bridging dementia research and policy remains a global challenge, with gaps in governance, capacity, data infrastructure, and contextual fit limiting the development and implementation of effective national dementia plans. To inform this issue, the Health Policy Professional Interest Area of ISTAART convened an expert discussion with researchers, clinicians, policymakers, and advocates, identifying three recurring challenges: difficulties translating research into actionable guidance, limited evaluation of real-world impact, and persistent inequities in access to dementia care. Building on these insights, this narrative review applies the Evidence-Informed Policy and Practice framework to examine how evidence is sourced, interpreted, and implemented across diverse settings. Integrating peer-reviewed literature, policy documents, and practice-based insights, the review highlights structural and institutional determinants of evidence generation; the contextual factors shaping evidence use; and system-level constraints and enabling mechanisms influencing implementation. The review highlights inclusive evidence generation, tailored translation, and delivery systems designed to support global dementia policy.

1. INTRODUCTION

Bridging dementia research and policy remains a persistent global challenge. Despite advances in diagnostics, prevention strategies, and models of care, translating scientific knowledge into national policy and routine practice continues to lag [1]. This gap is particularly evident in the development and implementation of national dementia plans, which are key instruments for aligning research evidence with population needs and improving outcomes for people living with dementia (PLWD) and their care partners [1]. When evidence fails to inform policy, countries risk fragmented, inequitable, or poorly sustained responses, limiting the public health impact of dementia research. Importantly, bridging dementia research and policy is bidirectional: policy priorities should inform what evidence is generated, just as research should guide policy decisions [1].

The urgency of addressing this gap is underscored by demographic trends: dementia cases are projected to nearly triple worldwide, from 57 million in 2019 to 153 million by 2050 [2]. In response, the World Health Organization (WHO) launched the Global Action Plan on the Public Health Response to Dementia (2017–2025), which identifies seven priority areas, including risk reduction, diagnosis, treatment, care, caregiver support, and research [3]. While the Global Action Plan provides an overarching framework, national dementia plans remain the primary mechanism for translating evidence into strategies tailored to local sociocultural, economic, and health system contexts [4]. Yet progress remains uneven: as of 2024–2025, only about one quarter of WHO Member States have implemented national dementia plans, and approximately 65% of these countries are high-income. Adoption has been concentrated in Europe (21 countries), the Americas (9), and the Western Pacific (9) [4–5]. This pattern is inconsistent with epidemiological projections showing that the fastest growth in dementia prevalence is occurring in low- and middle-income countries (LMIC), particularly in regions such as sub-Saharan Africa, Latin America, and South and Southeast Asia. Africa has no formally adopted national dementia plans, although initiatives are underway in several countries, and other regions, including Southeast Asia, have only a small number. [2, 4–5].

A central barrier to achieving these goals is the persistent gap between research and policy, reflecting both implementation challenges and misalignment between evidence generation and policy priorities. In high-income countries (HICs), short-term funding cycles, fragmented service delivery, and limited mechanisms for integrating new evidence are common [1,6]. Interim evaluations of the Dementia Action Plan in Wales highlighted rushed funding processes and limited coordination as factors undermining sustained implementation [7]. In LMICs, under-resourced health systems, constrained research capacity, and limited access to diagnostic and long-term care services further widen inequities [1,6].

To better understand these challenges and identify opportunities for improvement, the Health Policy Professional Interest Area (PIA) of the International Society to Advance Alzheimer’s Research and Treatment (ISTAART) convened a focused expert discussion with 35 researchers, clinicians, policymakers, and advisors. The discussion explored both the translation of dementia evidence into policy and practice and how policy priorities shape research agendas. Three cross-cutting themes emerged: (1) difficulties translating research findings into actionable policy guidance; (2) limited assessment of the real-world impact of dementia interventions; and (3) persistent disparities in access to dementia care and services.

Informed by these insights, this narrative review aimed to examine the challenges and opportunities of bridging dementia research and policy by synthesizing peer-reviewed and policy-relevant evidence. The review focused on how dementia research has been mobilized into national policy and routine practice across the evidence–policy continuum in diverse contexts. National dementia plans were prioritized as the primary instrument through which evidence is adopted, adapted, and operationalized. Related domains (e.g., prevention research, biomarkers, AI, and service delivery innovations) were included only where they directly informed plan development, implementation, or monitoring.

2. METHODS

Evidence was sourced using a targeted, multi-source strategy that integrated peer-reviewed literature with policy and grey literature. Peer-reviewed studies were identified through major bibliographic databases (e.g., PUBMED and Scopus). Policy documents, national dementia plans, and implementation frameworks were identified through multilateral organizations (e.g., WHO, Organization for Economic Co-operation and Development - OECD), regional policy hubs (e.g., Alzheimer Europe, PAHO), national government websites, and advocacy organizations (e.g., ADI, Alzheimer’s Association). Advocacy initiatives and science–policy engagement programs were included to capture mechanisms through which evidence is translated into policy and practice across diverse contexts.

Search terms combined concepts related to dementia/ADRD with terms for policy, national dementia plans/strategies, evidence use, implementation, knowledge translation, governance, health systems, equity, and LMIC/HIC. We prioritized (i) reviews, frameworks, and empirical studies that explicitly addressed evidence-to-policy processes; (ii) policy analyses and evaluations of national dementia plans; and (iii) reports describing implementation mechanisms (e.g., delivery infrastructure, regulatory pathways, monitoring systems). Items were included if they provided conceptual, empirical, or practical insight into barriers and leverage points across at least one EIPP stage (sourcing, using, implementing evidence). We excluded studies that were not relevant to evidence-to-policy processes (e.g., purely mechanistic biomedical studies without translation implications).

Evidence was synthesized narratively using the Evidence-Informed Policy and Practice (EIPP) framework as an organizing structure [8]. We extracted and grouped findings by EIPP stage and by cross-cutting mechanisms (political/institutional context, inequities, incentives and data ecosystems, methodological fit, interpretive capacity, trust, delivery infrastructure, regulation, and learning systems). Illustrative examples, from HIC and LMIC, were selected to demonstrate recurring processes and actionable leverage points rather than to provide exhaustive country coverage. In parallel, we incorporated practice-based insights from an ISTAART Health Policy PIA expert discussion (n=35 participants), using the convening outputs to (i) triangulate recurring barriers across settings and (ii) identify implementation-relevant opportunities that appear underrepresented in the peer-reviewed literature. Themes from the convening were used to refine the review’s analytic emphasis and to guide selection of illustrative examples, while the overall synthesis remained anchored in published literature and policy documentation.

2.1. Conceptual Framework

In this paper, we use the EIPP framework to guide our analysis [8]. Unlike many traditional health policy frameworks that focus primarily on policy formulation, implementation, or evaluation, EIPP offers a systems-oriented, context-sensitive approach that explicitly integrates evidence generation, interpretation, and implementation into policy processes. It emphasizes that policymaking is dynamic and negotiated, shaped not only by research evidence but also by structural, political, economic, sociocultural, and organizational factors [8]. This makes EIPP particularly well-suited for complex public health challenges such as dementia, where the interplay between evidence and policy is multifaceted and often bidirectional.

The EIPP framework is organized around three interconnected stages—sourcing evidence, using (interpreting) evidence, and implementing evidence—each influenced by capacities at the individual, organizational, and systemic levels [8]. Central to EIPP is the iterative “adopt, adapt, and act” cycle, through which evidence-informed strategies are selected (adopt), modified to fit local contexts (adapt), and implemented in practice (act). This cycle reflects the evolving and negotiated nature of evidence use in real-world policy settings, highlighting the need for flexibility, responsiveness, and continuous feedback [8]. EIPP also accommodates multiple policymaking models, from knowledge-driven to politically motivated approaches, and recognizes that the uptake, or neglect, of evidence depends on contextual factors such as political priorities, leadership, governance arrangements, funding stability, and infrastructure [9]. Importantly, EIPP underscores the value of capacity-building across all levels: enhancing skills and leadership at the individual level, fostering supportive culture and systems at the organizational level, and strengthening governance, resource allocation, and cross-sector collaboration at the systemic level [8].

2.2. Application to Dementia Policy and Practice

The EIPP framework adds value beyond existing dementia policy reviews by providing a process-oriented lens that makes visible where evidence moves, or fails to move, between generation, decision-making, and implementation [8]. By distinguishing among these stages, EIPP enables clearer identification of whether barriers arise from gaps in evidence availability, misalignment between evidence and policy priorities, or constraints in implementation capacity. This distinction is particularly important in dementia, where evidence is heterogeneous (biomedical, public health, social care, and lived experience domains), governance is complex and multi-actor, and contextual variation, especially across LMIC and HIC settings, shapes whether evidence can be adapted and enacted in practice. Therefore, applying the EIPP framework to dementia policy and practice requires attention to the complexities involved in policy development and implementation [3].

Accordingly, we apply the framework across three analytically distinct but interconnected stages: sourcing, using, and implementing evidence. Sourcing evidence integrates clinical, epidemiologic, economic, implementation, and lived-experience data to inform national dementia plans and service priorities (Figure 1). Using evidence involves interpreting and contextualizing findings for local health systems, equity considerations, and feasibility, translating evidence into plan targets, guidance, and financing decisions [9–11]. Implementing evidence operationalizes plan priorities through workforce capacity, delivery infrastructure, regulation, monitoring, and cross-sector collaboration (Figure 1).

Figure 1. Evidence-Informed Policy and Practice (EIPP) framework applied to Alzheimer’s Disease and Related Dementia (ADRD).

Decisions to accept or reject ADRD evidence are shaped by political, organizational, financial, infrastructural, and leadership contexts. At each stage, sourcing, using, and implementing capacity building is essential across individual, organizational, and systemic levels. This includes diversifying evidence sources (clinical, epidemiological, and community data), integrating the lived experiences of PLWD and care partners, and ensuring inclusivity across populations. Effective dementia policy must be culturally responsive, flexible, and adaptable to diverse healthcare settings and evolving needs.

Across all stages, historical, social, and institutional factors, including stigma, ageism, workforce shortages, and fragmented care systems, simultaneously influence evidence generation, use, and implementation [12]. Structural inequities, for example, may shape both the production of evidence (through underrepresentation in research) and its uptake (through uneven adoption of new care models), while the lived experiences of PLWD and their care partners inform both evidence sourcing and the local adaptation of policies. This perspective is particularly relevant in dementia, where interventions often require iterative adaptation to changing cognitive, functional, psychosocial, and behavioral needs, as well as to local resource constraints.

By emphasizing the plurality of evidence types, the diversity of actors, and the contextual factors shaping uptake, the EIPP framework helps identify actionable leverage points, such as research design, policy alignment, and capacity-building, through which the relevance of research and the effectiveness of policy can be strengthened. With this lens, the following sections synthesize challenges and opportunities across the dementia evidence-to-policy continuum. While some challenges and opportunities, such as inequities, resource constraints, and contextual fit, can affect multiple EIPP stages (i.e., sourcing, using, and implementing), we discuss them in the stage where they are most critical, recognizing that they may also play a role in other stages.

3. SOURCING EVIDENCE: CHALLENGES AND OPPORTUNITIES IN GENERATING POLICY-RELEVANT DEMENTIA EVIDENCE

Sourcing evidence for dementia policy is shaped by political priorities, institutional capacity, academic incentive structures, and methodological norms. These forces determine whose knowledge counts, what questions are asked, and which outcomes are measured, with downstream consequences for equity, feasibility, and policy relevance.

3.1. Political and Institutional Context Shaping Resources to Generate Dementia Evidence

Political and institutional conditions strongly influence how dementia evidence is generated in the first place. Political polarization and ideological conflict can shape which research questions are funded, which scientific advisory bodies are empowered, and whether national surveillance systems receive stable investment [12–13]. For example, analyses of political polarization in public health show that polarized environments frequently destabilize scientific institutions, disrupt advisory committees, and erode public trust in technical agencies, conditions that directly limit researchers’ ability to generate reliable, long-term population health data [12–16]. In such contexts, dementia-relevant research may be deprioritized because it competes with politically salient issues, resulting in fewer funded projects, shortened study timelines, and weakened technical capacity [12–16].

Institutional fragmentation further weakens evidence production. When responsibilities for aging, public health, research, and social care are distributed across uncoordinated ministries, research agendas become siloed [16]. This limits the ability to link clinical, social, and administrative datasets, constrains national workforce studies, and impedes the development of harmonized measurement approaches [12–16]. The WHO similarly notes that fragmented governance and incompatible data systems remain primary obstacles to building the robust, comparable dementia datasets required for global research coordination [16]. These institutional barriers manifest in uneven national participation in dementia biomarker research, limited genomic data availability for underrepresented populations, and gaps in the routine collection of dementia-related indicators [16]. Institutional rules can also influence who is allowed to generate evidence. Research eligibility criteria that privilege researchers with secure, full-time university appointments, established research offices, or extensive administrative infrastructure disadvantage clinicians, community organizations, and researchers in low-resource settings, even when the latter hold essential contextual expertise. Scholarship on political determinants of health highlights how such structural rules embed inequities into knowledge production, perpetuating imbalances in who contributes to scientific agendas and whose perspectives are systematically underrepresented [12–17].

Political instability, characterized by frequent administrative turnover or institutional volatility, political violence, terrorist events, government legitimacy issues, and external geopolitical tensions [18], also shapes the continuity of dementia evidence-generation pipelines. Large-scale shifts in research budgets, administrative restructuring, or downsizing of public health agencies can interrupt ongoing studies, weaken technical capacity, and disrupt longitudinal cohorts that require multi-year consistency [19]. Such disruptions can also impact the global evidence ecosystem, creating cascading effects on the availability, continuity, and diversity of evidence used for national dementia planning. especially for less-resourced countries that rely on global collaborations, shared datasets, and international training pathways. Analyses of how political agenda reshapes national health infrastructures show that changes in governing coalitions often redirect or reduce long-term investments in epidemiology, aging research, and chronic disease surveillance [19]. Cross-national analyses of COVID-19 outcomes indicate that countries with lower political stability exhibit greater disruptions in mortality reporting, incomplete surveillance of noncommunicable diseases, and inconsistent participation in global data-sharing initiatives [20]. These instabilities make it difficult to maintain longitudinal aging cohorts or sustain biomarker and cognitive assessment programs, resulting in fragmented datasets that limit cross-country comparability and undermine dementia epidemiology [16].

The WHO Dementia Research Blueprint provides a global model for strengthening dementia evidence generation by creating research environments that are less vulnerable to political cycles and institutional volatility [16]. It emphasizes stable long-term funding mechanisms, transparent priority-setting processes, and interoperable data systems that anchor research agendas in population need and scientific opportunity, rather than shifting governmental priorities [16]. These drivers of research (i.e., protected scientific advisory structures, methodological harmonization, and coordinated governance) are designed to ensure that dementia research progresses with continuity, independence, and global comparability. Australia illustrates how these Blueprint principles can be effectively operationalized in practice. Over several decades, dementia has been treated as a stable national priority, supported by bipartisan political agreement and protected research investments such as the Boosting Dementia Research Initiative [21], which committed funds to strengthen discovery science and national research capacity. Among LMICs, Brazil offers a nuanced example of how dementia evidence generation can be strengthened even without a national dementia-specific research fund. Brazil benefits from stable, autonomous state-level research foundations, particularly São Paulo Research Foundation (FAPESP), whose constitutionally protected funding buffers scientific research from federal political turnover. In parallel, Brazil’s participation in internationally coordinated consortia, such as the 10/66 Dementia Research Group, supported by multi-country funding structures, has enabled standardized, robust epidemiological data collection that is less vulnerable to domestic political variability [22]. Additionally, dementia-relevant population studies, such as ELSI-Brazil, supported through the Ministry of Health research mechanisms, demonstrate how large aging cohorts can be sustained despite national administrative change. Together, these arrangements illustrate how HICs and LMICs can advance WHO Blueprint principles through multi-level institutional buffering and internationally anchored research governance, even in the absence of ring-fenced dementia-only funding.

This cross-national variation in political prioritization and institutional capacity also shapes whose data are collected and whose needs become visible in the evidence base, as discussed next.

3.2. Structural Inequities Influencing Whose Data are Captured, Measured, and Valued

Beyond institutions, structural determinants shape who produces evidence, what questions are prioritized, and which populations are represented [12–13]. Macro-level governance across economic, social, and policy domains influences research framing, the allocation of research resources, and visibility. Social value is often stratified by intersecting dimensions such as socioeconomic position, race/ethnicity, caste, gender, familial birth order, and cultural norms, which affect access to power, funding, and representation [23–24].

These inequities contribute to the underrepresentation of minority populations in dementia research, despite strong evidence that social and structural determinants accumulate over the life course to shape brain health and aging [23–24]. As a result, the needs and experiences of historically excluded groups are frequently overlooked, producing persistent gaps that limit equitable policy development [12–13, 23–24]. Importantly, these barriers are not solely resource-driven: epistemic bias, discrimination, and exclusionary scientific networks can shape whose expertise is considered valid. Addressing funding inequities without confronting these forms of bias is unlikely to produce inclusive evidence practices.

A key manifestation of these inequities is the persistent absence of systematic, disaggregated data, an evidence gap that limits understanding of population diversity and conceals differential risk, access, and outcomes [25]. In many LMICs, limited infrastructure, insufficient human resources, and restricted access to global research networks further constrain the production and dissemination of locally relevant evidence [22, 26,27]. Vietnam illustrates this challenge, with sparse epidemiological data, fragmented service delivery information, and limited research capacity [28]. Similar barriers appear across other LMIC regions [22, 26, 27]. For example, dementia prevention and risk-reduction studies in 10/66 Dementia Research Group countries, including China, India, and several Latin American nations, often rely on fragmented or outdated datasets [26,27]. These gaps reflect weaknesses in population-level surveillance, limited diagnostic capacity, under-resourced primary care systems, and the absence of routine cognitive screening, all of which contribute to incomplete prevalence estimates and weak policy foundations [27].

Beyond these data gaps, the quality and cultural appropriateness of available data also warrant caution. Many diagnostic tools and risk-factor measures were developed within HIC contexts and do not always translate accurately to LMIC settings and other underrepresented groups [26–27]. Although the 10/66 study diagnostic algorithm is more culturally sensitive than traditional clinical criteria, studies show it can still overestimate dementia in very low-literacy populations and remains unrepresentative of several regions, including much of sub-Saharan Africa [22, 26, 27]. Risk-factor data often rely on self-report, which is less reliable where health-care access is limited and conditions such as depression may be undiagnosed or culturally conceptualized differently, reducing comparability with HIC datasets [26,27]. These measurement limitations, particularly the lack of culturally appropriate cognitive assessments for low-literacy populations and underrepresented groups, including Indigenous communities [27, 29], introduce systematic bias into dementia risk estimates and obscure the true global distribution of risk.

Opportunities to address these challenges must operate at both the technical and governance levels. Equity-oriented sourcing requires (a) routine disaggregation and inclusive measurement, (b) capacity strengthening and network access in under-resourced settings, and (c) explicit strategies to counter epistemic exclusion so that lived and contextual knowledge informs what is studied and how findings are interpreted. Important progress is emerging across these domains. In the United States, recent national equity initiatives in dementia care research have emphasized building more inclusive and granular data infrastructures, highlighting the need for datasets that better capture racial, ethnic, and rural populations that remain underrepresented in existing national surveys [30]. Capacity-building programs in LMICs, such as Vietnam’s NIH/NIA-funded national dementia research network, which provides mentored pilot grants, research training workshops, and a nationwide collaborative platform, demonstrate how targeted investment can expand research competency, infrastructure, and access to global networks in resource-limited settings [31].

Moreover, improving representation in dementia research is not only a measurement challenge but also a question of power and participation. Addressing structural inequities requires approaches that redistribute decision-making authority and elevate the perspectives of historically excluded groups. Community engagement, participatory methods, and co-design offer concrete strategies to counter epistemic exclusion by ensuring that evidence reflects the lived realities, priorities, and cultural contexts of diverse populations. Authentic collaboration with stakeholders—including clinicians, care partners, policymakers, community members, and PLWD, enhances the relevance, legitimacy, and usability of findings [32–37]. In co-design, PLWD and community partners participate in every stage of research from setting priorities and shaping questions to interpreting results and disseminating findings [37–39], increasing the likelihood that evidence aligns with real-world needs.

Institutional and funder policies increasingly reinforce these participatory approaches. Funders such as Australia’s NHMRC [40] and MRFF [41] now require or strongly encourage consumer engagement, signaling a shift toward embedding end-user perspectives as a component of rigor and relevance. Community partners contribute essential historical and cultural insight; practitioners identify frontline system challenges; and PLWD and families help ensure that evaluation metrics capture what matters most [42–45]. The EMBED-Care program exemplifies this approach, using co-design with PLWD, care partners, and practitioners to guide the development and user testing of a digital palliative dementia care intervention [46]. This lens is especially important when routine or surveillance data, often limited in scope, are used for policymaking. Metrics such as “place of death,” while convenient, may overlook outcomes valued by PLWD and families, including symptom control, access to palliative care, and dignity at end of life [47]. Co-design helps identify, interpret, and refine indicators so system metrics better align with lived experience and policy goals [13,48].

Finally, co-research and lived-experience leadership models further strengthen these participatory frameworks by formally recognizing PLWD as epistemic partners. Global initiatives such as Dementia Alliance International and empowerment-focused co-research collaborations demonstrate how PLWD can serve as full research partners rather than passive subjects, helping to reshape research agendas and counter longstanding patterns of epistemic exclusion [48].

3.3. Academic Incentives, Funding and Open Science Ecosystems: Aligning Evidence Production With Policy and Equity

Alongside population-level inequities, the organization of scientific reward systems also shapes what evidence is produced, how quickly it is generated, and how accessible it becomes. Traditional academic reward systems prioritize high-impact publishing, theoretical novelty, methodological sophistication, and individual recognition [49]. These pressures can devalue pragmatic, community-informed, or implementation-focused research that is highly relevant for policy and practice. Researchers may also be reluctant to interpret findings beyond narrow scientific boundaries to maintain perceived objectivity, inadvertently limiting the usability of their work for decision-making [50]. Limited training in dissemination and implementation science further widens the gap between research production and real-world application.

Publication norms and costs reinforce these dynamics. Null or inconclusive results remain underreported, while research from LMICs is disproportionately absent from high-visibility journals [51–52]. High open-access fees and commercialization incentives can restrict dissemination and discourage data sharing, weakening collaborative research ecosystems [53].

Reforms in open science and publishing aim to counter these challenges. Mandatory data sharing for publicly funded research can enhance transparency, reproducibility, and accountability [54–55], while expanding interoperability across datasets, platforms, algorithms, metadata, and common data dictionaries further supports replication and cross-study comparison. Major dementia data platforms, including ADNI [56], GAAIN [57], the AD Knowledge Portal [58], ADDI [59], the Dementia Platform UK [60], and the Dementia Datahub [61], have accelerated biomarker discovery, participant-recruitment optimization, and disease-burden visualization [59,62]. Advances in machine learning and AI offer additional opportunities to generate timely, policy-relevant insights [63].

Importantly, these same open-science reforms also create a powerful opportunity to advance global equity in dementia evidence generation. By broadening who can contribute data and who can access shared resources, open-science infrastructures can help correct longstanding imbalances in representation. Although many large harmonized datasets remain dominated by HIC cohorts, emerging initiatives demonstrate how inclusive data architectures can diversify the global evidence base. The Harmonized Cognitive Assessment Protocol (HCAP) Network exemplifies this potential [64], showing that coordinated efforts across both HICs and LMICs, including India, Mexico, South Africa, Kenya, China, and Chile, can produce high-quality, comparable cognitive data that support more globally relevant analyses. Scaling such models through sustained HIC–LMIC partnerships, co-developed tools, and shared governance structures can help ensure that open-science advances translate into more equitable, representative, and impactful dementia research worldwide.

However, sustained progress requires governance frameworks that address privacy regulations, data storage costs, and cross-sector trust, particularly where academic, commercial, and public health incentives diverge [65]. Growing journal openness to publishing rigorously conducted null results may also help reduce publication bias and strengthen cumulative science.

Funding structures and partnership models also influence which types of evidence can be produced. Conventional grant cycles are often slow, fragmented, and risk-averse, limiting responsiveness and innovation. Alternative funding mechanisms, including pilot schemes, incubators, accelerators, venture capital initiatives, and grants that bypass rigid university affiliation requirements, can expand participation by providing seed funding, mentorship, and technical support to early-career or nontraditional investigators [66–68]. International joint-funding arrangements that require multidisciplinary collaboration can counter silos between institutions and sectors, as demonstrated by initiatives such as the Dementia Discovery Fund [69], the Davos Alzheimer’s Collaborative [70], the U.S. National Clinical Cohort Collaborative [71], and the Alzheimer’s Association Research Grants. Public–private partnerships (PPPs) can further bridge publicly funded research with private-sector capacity, enabling early stakeholder engagement, structured data sharing, and iterative feedback loops [72]. Examples include Neuronet (Efficiently Networking European Neurodegeneration Research) [73], and the Accelerating Medicines Partnership^® Program for Alzheimer's Disease (AMP^® AD), a precompetitive partnership among government, industry, and nonprofit organizations to transform the current model for developing new diagnostics and treatments for Alzheimer's disease [74]. Similarly, several LMIC initiatives illustrate the potential of alternative funding models and international partnerships to expand representation in the generation of dementia evidence. NIH collaborative international research funding mechanisms have created a network of research institutions, training workshops, and mentored pilot grants, significantly strengthening local capacity to produce policy-relevant evidence and connect to global research networks [75] Global Neurodegeneration Proteomics Consortium (GNPC) brings together international partners, including some LMIC institutions, to generate harmonized biomarker datasets and accelerate translational discovery through shared public–private infrastructure [76]. The Alzheimer’s Association and Global Brain Health Institute’s Capacity Building in International Dementia Research (CBIDR) program similarly provides LMIC-led teams with funding, training, and cross-regional mentorship to advance equitable dementia research capacity worldwide. [77]. Brazil also offers strong examples of LMIC-led innovation funding and international collaboration [78]. The São Paulo Research Foundation’s (FAPESP) SPRINT program (São Paulo Researchers in International Collaboration) provides seed funding and multi-year mobility grants designed to internationalize research teams and support the development of medium- and long-term collaborative projects with foreign institutions [78]. In addition, FAPESP administers several programs that promote university-industry partnerships and research translation, including the PAPPE–PIPE schemes, which support innovative research within small companies, and broader collaboration grants connecting universities, research institutes, and industry [78]. While not dementia-specific, these programs offer potential pathways for agencies like FAPESP to direct targeted investments toward urgent public health priorities such as dementia.

Notably, PPPs can introduce conflicts of interest, administrative complexity, and intellectual-property constraints that delay or restrict knowledge sharing [72]. Transparent governance structures, balanced data-sharing agreements, and flexible IP arrangements, as demonstrated in several AMP-AD collaborations, can help align commercial innovation with open-science goals and researcher autonomy [72,79].

3.4. Methodological Fit-for-Purpose: Expanding the Toolkit While Preserving Rigor and Relevance

Dementia research has historically emphasized individual-level biomedical and clinical outcomes, with comparatively less attention to population health, policy, and system-level interventions [80]. This creates a mismatch between the evidence produced and the evidence needed to support structural and social change [81–82]. Translating basic discoveries (e.g., molecular biology, neuroimaging) into clinical, humanistic, and economic outcomes remains challenging, delaying the policy and care impact of innovation [83].

Study design norms can further limit what is learnable. While RCTs are central for clinical efficacy, they are often infeasible for complex system-level interventions. In addition, there remains a dearth of community-based participatory research designs. Broadening the methodological toolkit, including pragmatic trials, natural experiments, adaptive and Bayesian designs, and rigorous causal inference studies, can enhance our ability to capture real-world effectiveness, scalability, and context [84–85]. Leveraging longitudinal registries for stratification [86] and embedding studies in routine care settings can increase generalizability and accelerate learning [87]. These approaches are especially important for addressing subgroup relevance, especially in diverse populations and real-world settings [88].

Intervention feasibility studies offer an interesting opportunity to address the variety of real-world settings and have direct policy implications. In many LMICs, interventions targeting modifiable risk factors and health system strengthening (e.g., education, vascular risk control, diet, physical activity, hearing and vision care, and social engagement) may be more feasible to evaluate and implement at scale than drug-dependent strategies that require specialized diagnostics (e.g., PET imaging), infusion capacity, and intensive monitoring. These population-level approaches may also be especially policy relevant because they generate co-benefits for broader noncommunicable disease agendas and economic development.

Population-level policies also require evaluation beyond efficacy, including cost-effectiveness, feasibility, and unintended harms. Dementia screening, for example, may raise concerns about false positives, overdiagnosis, and overtesting, which can discourage adoption even if benefits accrue for certain individuals or communities [89]. When evidence is not inclusive or context-sensitive, policies risk reinforcing inequities or exacerbating disparities [90].

Overall, a fit-for-purpose evidence base is essential for national dementia plans to include scalable, system-level interventions rather than focusing solely on clinical outcomes. Moreover, even robust evidence requires interpretive capacity, legitimacy, and communication pathways to become usable for decision-makers; the next section examines how evidence is translated and contested in policy settings.

4. USING EVIDENCE: CHALLENGES AND OPPORTUNITIES IN INTERPRETING AND APPLYING DEMENTIA EVIDENCE

Once evidence is generated and made accessible, “using” dementia evidence in policy and practice involves more than applying research findings. It requires interpretation, contextual adaptation, credible communication, and mobilization through institutions and networks operating under time pressure, shifting political priorities, and diverse societal values. In this stage, barriers and enabling strategies are tightly coupled: the same systems that constrain evidence use (e.g., fragmented institutions, limited analytic capacity, mistrust, and weak dissemination) also point to practical levers for improvement. To organize these challenges, we move from macro-level governance determinants to meso-level translation capacities, trust conditions, and boundary organizations that shape whether dementia evidence becomes usable policy and practice.

4.1. Governance and Political Context: How Evidence Translation Shapes National Dementia Plans

National dementia plans sit at the boundary between evidence interpretation and policy design. They are not neutral reflections of the epidemiological burden; rather, they codify how political systems prioritize dementia, which institutions are empowered to translate evidence, and whose expertise is legitimized in national agenda-setting. Governance arrangements, including regime type (e.g., democratic vs. authoritarian features), centralization, and administrative stability, shape the incentives and pathways through which evidence is interpreted and transformed into strategy. These forces influence who participates in plan development, which evidence is selected, and how uncertainty is managed.

Cross-national variability shows that resources alone do not guarantee effective dementia planning. Even among HICs, political and institutional dynamics strongly shape national dementia planning. Finland, Portugal, and the United Arab Emirates (UAE), all HICs, demonstrate this variation. Finland has embedded its national strategy within strong regional and municipal governance systems that support person-centered, preventive, community-based care [91]. Portugal has established legal protections and raised awareness of specialized care, yet gaps remain in implementing regional plans, coordinating health and social services, and strengthening early diagnosis and caregiver support [92]. By contrast, the UAE still lacks a formal national dementia plan and is only beginning a WHO-supported dementia response, underscoring that political prioritization, not wealth, drives progress [93]. Similar patterns appear across LMICs. Strong political will and proactive integration of dementia as a public health priority enabled Chile and Costa Rica to develop prompt, robust national dementia plans in response to high rates of population aging and support of universal healthcare system demonstrating emerging progress through new national dementia policies and expanding research capacity [94], Whereas countries such as Brazil and Peru have recently developed national dementia plans (2024–2026), progress in these settings has emerged despite constrained resources. In each case, national planning has been propelled by strong civil society leadership (e.g., APEAD in Peru, FEBRAZ in Brazil), long-term advocacy and awareness-building, and strategic regional, national, and international partnerships, including collaborations with ADI, GBHI, and academic groups. These partnerships helped strengthen technical capacity, supported situational analyses, and elevated dementia as a public-health priority. Similar coalition-building is also advancing planning efforts in countries such as Bolivia and Argentina, which, despite not yet having fully established national plans, are actively working toward them [95]. Together, these examples show that even under restrictive institutional and fiscal conditions, coordinated governance, empowered civil-society actors, and cross-sector alliances can create viable pathways for LMICs to advance national dementia planning. In contrast, Nepal has not yet established an overarching national dementia policy, and dementia remains only weakly integrated into government health priorities [96]. Dementia care is therefore largely borne by families, with limited formal service pathways; while Nepal does provide a financial subsidy for people diagnosed with dementia, multiple barriers, geographic access constraints, weak governance and service delivery capacity, low awareness and stigma, and an insufficiently trained workforce, continue to limit effective coverage [96].

These differences are amplified by governance structure. Centralized systems can coordinate national guidance rapidly but may limit stakeholder participation or local adaptation; decentralized systems can enable stronger local ownership yet often require complex coordination across levels of government and can produce uneven progress across regions. In either case, the key constraint is not simply the availability of funding but whether governance structures can sustain political prioritization, maintain institutional continuity, and support legitimate processes for translating evidence into policy design.

Despite governance constraints, both HICs and LMICs can advance dementia planning by strengthening the institutional conditions that protect evidence translation from short-term political pressures. Opportunities include: (1) stable technical leadership within ministries and civil service units that persists across political cycles; (2) formal cross-sector coordination (health, social care, aging, finance) to reduce institutional fragmentation; (3) transparent stakeholder participation to strengthen legitimacy and reduce backlash; and (4) structured situational analysis and priority-setting to anchor national planning in population need rather than political visibility. International scaffolding can also help governments progress under constraint (e.g., WHO-supported roadmaps), especially when coupled with local ownership and accountability mechanisms.

Once governance sets the rules, incentives, and actors that shape national priorities, the next challenge is whether dementia evidence can be made usable, interpretable, comparable, and adaptable across different policy audiences and real-world settings.

4.2. Evidence accessibility, interpretation, and role-based translation needs

Using evidence depends not only on what is produced, but on whether policy systems have the technical capacity to interpret it, the infrastructure to access it, and the tools to adapt it to local realities. Translation barriers emerge when evidence is inaccessible, overly technical, contradictory, or insufficiently relevant to local settings. These challenges are experienced differently across policy actors and levels of government, creating predictable bottlenecks in dementia evidence use.

Policymakers and implementers must appraise the quality of evidence and interpret findings in relation to policy goals, budget constraints, and local service realities—tasks that require technical expertise and contextual knowledge. Yet many decision-makers lack formal training in scientific methods, data interpretation, or evidence synthesis, limiting their ability to assess study quality, uncertainty, and applicability [97]. Access barriers, such as paywalls, privacy laws, intellectual property protections, and non-disclosure agreements, can further restrict the timely use of evidence, while technical jargon can alienate policymakers and community stakeholders [98]. Poor dissemination compounds these problems: even high-quality evidence often fails to reach decision-makers and underserved populations in usable formats.

Evidence use also differs by policymaker role and level of governance. Elected officials often operate under short time horizons and may require concise, actionable framing, whereas civil servants and technical staff may engage more deeply with uncertainty, evidentiary strength, and implementation feasibility. Local and subnational decision-makers, in turn, are frequently tasked with executing nationally mandated priorities and therefore prioritize operational considerations such as workforce capacity, procurement, and service workflows [97–99]. These differences imply that effective evidence translation requires tailoring products and engagement strategies to the needs of distinct policy actors rather than assuming a single “policymaker audience [87–89).

Even when evidence is accessible, it may be difficult to apply. Scientific reports can appear contradictory or uncertain due to methodological limitations and evolving knowledge [99]. Decision-makers often need to triangulate across quantitative findings, qualitative insights, patient diaries, and expert opinion, work that is challenging under political time constraints. Consequently, evidence is frequently mediated through intermediaries (e.g., the Congressional Research Service, science writers, lobbying groups, and expert panels such as National Academy reports), who translate complex findings into actionable briefs [100]. While these intermediaries can increase usability, their framing choices may introduce bias or oversimplification [101]. Communication breakdowns are worsened by inconsistent terminology and differing interpretations across scientific, policy, and public audiences.

Several practical mechanisms can improve usability without sacrificing nuance. Early and sustained dialogue, such as including policymakers on advisory groups, helps align research outputs with policy needs and clarifies what constitutes “actionable” evidence. Dissemination formats designed for non-specialist readers (e.g., “research in context” sections in journals) can summarize findings, limitations, and implications in accessible language. Open-access publishing can reduce information inequities by removing paywalls that constrain policymakers and government agencies, particularly in resource-limited settings. Together, these strategies shift evidence from being merely available to being interpretable, credible, and decision-relevant.

4.3. Contextual fit, reproducibility, and rapid learning infrastructures

A critical barrier to evidence use is poor contextual fit. Research conducted in controlled environments or foreign settings may not reflect local infrastructure, workforce capacity, cultural norms, or patterns of inequity, requiring adaptation before policies can be implemented [97]. Without such contextualization, even rigorous evidence may be dismissed as impractical or irrelevant, and findings may not hold across populations or care settings. Systemic problems, limited transparency in reporting and difficulty reproducing results, further complicate decisions about what to trust and scale. Industry-sponsored trials may show higher success rates due to stronger protocols and resources, but potential conflicts of interest require careful interpretation [102].

Improving contextual fit increasingly depends on infrastructures that support rapid learning in real-world settings. Real-time data sharing systems can reduce the lag between discovery and application by enabling timely, evidence-informed decisions [103]. Interoperability standards such as HL7’s Fast Healthcare Interoperability Resources (FHIR) and the FAIR principles improve the feasibility of linking and reusing health data at scale [104]. However, infrastructure alone is insufficient: real-time systems must be paired with methodological expertise to support appropriate analysis and interpretation. Lessons from large-scale real-world data initiatives, such as cardiovascular quality improvement programs like “Get With the Guidelines”, illustrate how routine data can inform practice when coupled with training and capacity-building for investigators and implementers. In dementia, privacy-preserving approaches and partnerships, such as industry–academic efforts using privacy-enhancing technologies, can enable ethical data exchange while mitigating re-identification risks and facilitating the sharing of both successful and unsuccessful approaches, reducing duplication and accelerating learning.

Yet even when evidence is accessible, interpretable, and context-sensitive, translation can stall if evidence lacks legitimacy, because trust in scientific institutions and policy processes shapes whether evidence is accepted as credible.

4.4. Trust, Legitimacy, and Public Understanding

Trust is a precondition for using evidence at scale. Without it, even strong evidence may be resisted, reframed as contested, or dismissed as politically motivated. Trust is shaped by institutional credibility, transparency, and whether communities view evidence and policy processes as aligned with lived priorities.

A persistent barrier to evidence-informed policymaking is declining public trust in science and institutions. This mistrust is amplified by a mismatch between scientific communication (which emphasizes uncertainty, nuance, and iterative learning) and political decision-making (which often demands clear, immediate, and defensible actions aligned with legislative or electoral goals) [105]. Researchers frequently lack training in communicating findings to non-specialist audiences, including policymakers and the public [106]. Misunderstanding of the scientific process and the role of uncertainty further widens the gap [107–110]. Media coverage can also flatten complexity, promoting quick fixes or one-size-fits-all solutions that generate backlash when subsequent evidence reveals limitations.

The stakes are particularly high in contested or rapidly evolving domains such as dementia biomarkers and assistive technologies, where evidence is fragmented and may lack cultural or contextual sensitivity for underrepresented groups [111–114]. Controversies can spill over into broader institutional credibility. For example, the accelerated approval of certain monoclonal antibodies for Alzheimer’s disease, despite modest efficacy and meaningful safety concerns, sparked public controversy and contributed to erosion of trust in regulatory processes [115–117]. When high-profile disputes occur, they can undermine confidence in both science and policy institutions and slow adoption of future innovations [118].

Trust is strengthened less by “more messaging” than by consistent transparency and stakeholder participation. Practices that openly communicate uncertainty, disclose conflicts of interest, and explain evidentiary thresholds can reduce perceptions of bias. Engagement approaches that include people living with dementia, care partners, clinicians, and community leaders can improve legitimacy and reduce the risk that evidence is perceived as imposed or misaligned with lived priorities—especially when technologies or biomarkers are introduced into routine care.

Trust and interpretation are rarely produced by researchers alone; they are shaped by intermediary actors and boundary organizations that translate evidence, convene stakeholders, and move information through policy networks.

4.5. Intermediary Organizations and Collaborative Mechanisms for Evidence Use: Mobilizing Evidence Through Relationships and Institutions

Across health systems, dementia evidence rarely moves directly from research to policy. Instead, it is mobilized through intermediary organizations that operate at the interface of science, policy, and community life. These boundary-spanning actors, ranging from advocacy groups and professional societies to regional research networks and formal policy units, play essential roles in translating complex findings, contextualizing evidence for local realities, and building coalitions that enable policy uptake. Their work demonstrates that, despite significant structural challenges, both HICs and LMICs can strengthen the use of dementia evidence through strategic institutional mechanisms.

In HICs, organizations such as the Alzheimer’s Impact Movement (AIM) [119], UsAgainstAlzheimer’s, and the Alzheimer’s Foundation of America (AFA) help make emerging science intelligible and actionable to policymakers. By synthesizing data, framing key uncertainties, and identifying feasible policy options, these groups reduce the cognitive and time burdens that often limit evidence use in legislative or administrative settings. When operating with transparency and clear safeguards for evidence integrity, they enhance the policy relevance of dementia science and help decision-makers navigate contested domains [120]. Yet the influence of such organizations also underscores the need for clear governance: selective framing, institutional bias, or advocacy priorities can unintentionally shape which evidence is elevated or overlooked, reinforcing the importance of responsible and transparent mediation [100–101].

Similar boundary-spanning work is increasingly visible across LMICs, where civil-society organizations and regional networks help bridge evidence with national and subnational policymaking. In India, the Alzheimer’s and Related Disorders Society of India (ARDSI) participates in national policy dialogues and contributes to global LMIC-focused dementia forums convened by the World Dementia Council. In Uganda, the Uganda Alzheimer Association plays a similar role by linking research insights to community needs and advocating for national recognition of dementia as a public health priority. Regional convenings, such as the “Dementia and Brain Aging in LMICs” conference in Nairobi, have further strengthened these linkages by bringing together policymakers and researchers across the African continent to identify local priorities and jointly interpret emerging evidence. In Brazil, organizations such as FEBRAZ have become central actors in translating scientific evidence into policy agendas. Through advocacy, dissemination, and direct engagement with legislators, FEBRAZ, together with global partners such as ADI, contributed to Senate hearings and helped advance the country’s first national dementia policy [121–122].

Collaborative and cross-sector models extend this translational function. The STRiDE program (Strengthening Responses to Dementia in Developing Countries) is one of the most prominent multi-country initiatives in LMICs and demonstrates how co-produced research, stakeholder engagement, and local knowledge integration can improve the interpretability and usability of dementia evidence. STRiDE-supported teams in Africa, Asia, and Latin America work directly with policymakers and community actors to ensure that evidence is aligned with system capacity, cultural expectations, and political feasibility. In Brazil, STRiDE strengthened national advocacy efforts and informed subnational planning efforts in states such as Rio Grande do Sul [123].

Formal research–policy mechanisms in HICs echo this approach. In the United Kingdom, the Policy Research Unit in Dementia and Neurodegeneration provides dedicated analytic support to government bodies and maintains continuous channels between evidence generators and policy implementers [124]. Emerging LMIC models reflect similar dynamics: in India, dementia specialists increasingly contribute to national guideline development, while regional multi-stakeholder dialogues hosted by the World Dementia Council facilitate cross-country learning among policymakers from India, Uganda, Kenya, and several Latin American countries [125].

Large research ecosystems also contribute to evidence mobilization by offering infrastructure that enables translation. In the United States, Alzheimer’s Disease Research Centers (ADRCs) integrate basic science, clinical research, and community outreach, creating pathways for evidence to inform care and policy. Internationally, collaborative consortia such as TREAT-AD, MODEL-AD, and the UK’s TIDE program harmonize tools and resources, reducing costs and making research outputs more readily usable across settings [126–127]. In LMICs, regional initiatives such as the PAHO/ADI “Time to Act on Dementia” campaign play a similar role in priming policy environments for evidence-informed action across the Americas [128]. In Brazil, civic initiatives like Alzheimerebook illustrate how public engagement, lived-experience leadership, and locally relevant evidence can reinforce one another to reshape policy discourse [129].

Taken together, these examples demonstrate that intermediary organizations, whether operating in high-resource or low-resource settings, expand the reach and relevance of dementia evidence by translating findings into policy language, adapting evidence to local systems and cultural contexts, and creating enduring networks that sustain political attention. Their actions reveal a critical opportunity: by strengthening boundary-spanning roles and fostering collaborative ecosystems, countries can handle many of the political, institutional, and capacity-related constraints that otherwise limit the use of dementia evidence in national and subnational policy processes.

While effective use of evidence is essential, it remains only a midpoint; ultimately, the capacity of systems to implement and sustain that evidence determines whether national dementia plans achieve population-level impact

5. IMPLEMENTING EVIDENCE: CHALLENGES AND OPPORTUNITIES IN TRANSLATING DEMENTIA EVIDENCE INTO PRACTICE

Implementation is the final and most failure-prone stage of the evidence-informed policy process because it tests whether systems and communities can operationalize evidence under real-world constraints. It requires feasibility, infrastructure, resources, leadership, timelines, and cultural alignment—where “theory meets reality” and complexity becomes most visible [9]. Many implementation barriers mirror earlier EIPP stages (capacity limitations [97,130], contextual misalignment [28,131–132], limited collaboration), but implementation adds distinct pressures: sustained resourcing, cross-sector execution, ethical and equity safeguards, regulatory fit, and robust monitoring and feedback mechanisms. Below, key constraints are paired with practical enabling strategies.

5.1. Adoption Pathways and System Readiness: Time, Resources, Regulation, and Cross-Sector Translation

Translating dementia evidence into practice depends not only on the strength of the research but on the system’s readiness to support adoption. Across settings, implementation is hindered when promising evidence faces unclear adoption pathways, insufficient financing, or fragmented regulatory oversight. A common challenge is the prolonged timeline from discovery to widespread uptake, with translational pathways averaging 17 years from initial research to sustained implementation [133–134]. Some delays are inherent to ensuring rigor, safety, and regulatory compliance, but many are avoidable, stemming from bureaucratic inefficiencies, slow contracting and patent processes, misaligned grant cycles, and limited long-term investment in late-stage or high-risk implementation work. Workforce gaps compound these barriers; researchers and practitioners often lack training in commercialization, regulatory navigation, scaling strategies, and business development [134–135]. The result is frequently a chain of disconnected, sequential efforts rather than coordinated adoption strategies that move evidence efficiently into real-world care.

Opportunities arise when systems build intentional, well-structured pathways for adoption, pairing evidence with predictable financing, regulatory clarity, and cross-sector coordination. Rather than generating funding, national strategies and long-term plans help align research, service delivery, and investment priorities, supporting continuity across the research-to-implementation continuum even as funding levels fluctuate. In the United States, for example, the National Alzheimer’s Project Act (NAPA) created a national dementia plan and an interagency advisory structure that coordinated federal priorities without itself generating research funds; subsequent increases in NIH/NIA Alzheimer’s investment were secured through congressional appropriations and budget processes [136–137]. Canada’s Canadian Consortium on Neurodegeneration in Aging (CCNA, 2014–2024) similarly illustrates how coordinated national structures can stabilize data infrastructure and foster multi-institutional collaboration, supporting the “last mile” of translation by aligning diverse stakeholders around shared objectives.

Incentive-based approaches also provide opportunities for innovation when paired with explicit adoption planning. Prize-based models such as the Longitude Prize on Dementia can catalyze technological development and public engagement; however, their long-term impact depends on follow-on financing, maturation of early products, and integration into existing health and social care pathways [138]. By aligning such incentives with procurement, reimbursement, workforce training, and service workflows from the outset, systems increase the likelihood that promising prototypes will survive beyond pilot phases.

Regulatory environments profoundly shape implementation timelines. Slow approvals, redundant paperwork, and unclear accountability can stall translation, particularly for emerging tools such as AI-based dementia diagnostics, where issues of transparency, bias, equity, and accountability remain contested and unresolved [139]. Streamlined regulatory processes, adaptive approval pathways, and coordinated ethical frameworks can accelerate responsible uptake without compromising safety. National examples such as the Dutch Alzheimer Centers, which leverage university-based technology transfer offices to support validation and scaling, demonstrate how aligned regulatory and institutional pathways can speed the adoption of diagnostic innovations [140]. Administrative simplification, such as reducing duplicative reporting and using digital platforms for routine regulatory tasks, can further reduce friction and implementation delay [141].

Implementation challenges are equally pronounced, though differently configured, in LMICs. Many LMICs face shortages of trained specialists, under-resourced delivery systems, and limited sustained financing. Yet these constraints also prompt practical, adaptable implementation pathways. One such example is the STRiDE anti-stigma toolkit, developed as an evidence-informed package to reduce dementia stigma and support community-level uptake across seven LMICs (Brazil, India, Indonesia, Jamaica, Kenya, Mexico, and South Africa). STRiDE’s model illustrates how implementation can occur through low-cost, train-the-trainer approaches embedded in routine primary care and community platforms. In Brazil, the STRiDE anti-stigma intervention was delivered to community health workers through primary-care structures, demonstrating a clear pathway from evidence, local workforce training, and routine service delivery [141–142]. This illustrates how LMICs can advance implementation not by replicating high-resource models but by translating evidence into accessible tools that leverage existing frontline systems.

National policy commitments can also strengthen system readiness. Brazil’s National Policy for Comprehensive Care for People with Alzheimer’s Disease and Other Dementias (signed in June 2024) exemplifies how government direction can guide workforce expectations, system-level planning, and cross-sector collaboration, laying the groundwork for operational rollout. In contexts where formal services remain limited, civil society organizations and community-embedded groups function as crucial implementation infrastructures. In Kenya, the Alzheimer’s and Dementia Organization Kenya (ADOK) founded by caregivers and recognized as an ADI chapter conducts awareness activities, caregiver support, and community-based engagement. Public reporting indicates that ADOK, alongside national stakeholders, has contributed to stigma reduction and earlier recognition of dementia, illustrating how “implementation through community delivery and engagement” can advance care pathways even in the absence of specialist-heavy systems.

Together, these examples show that implementation succeeds when systems create conditions that support adoption: coordinated governance, clear regulatory pathways, mechanisms for cross-sector collaboration, accessible tools that align with frontline capacity, and institutions capable of sustaining translation work. Whether in high-income or low-resource settings, the opportunity lies in moving beyond fragmented, sequential processes toward integrated adoption pathways that carry dementia evidence from discovery to real-world impact.

5.2. Equity and Ethics: Ensuring Interventions Are Effective and Inclusive in Real-World Settings

Implementation efforts falter when interventions are deployed without intentional adaptation or mechanisms to monitor equity, often reproducing the same structural inequities embedded in the evidence base. Ethical considerations are central to implementation because effectiveness, generalizability, and inclusivity are interdependent: interventions that work only for well-resourced or well-represented groups undermine both scientific validity and public trust. When research excludes marginalized populations or fails to account for structural barriers, the policies constructed from that evidence may inadvertently widen disparities rather than reduce them [143]. Even highly efficacious interventions may prove unacceptable or inaccessible in communities whose cultural norms, linguistic contexts, or service structures differ from those represented in the original research.

Addressing these challenges requires equity-forward implementation, an approach that embeds inclusivity from the outset rather than attempting superficial adjustments later. Planned adaptation involves designing delivery models that reflect local realities, including culturally and linguistically appropriate materials, attention to structural constraints such as transportation or out-of-pocket costs, and the use of community-defined outcomes to ensure relevance. Equity-relevant metrics incorporated during rollout can track differential reach, uptake, and benefit, providing early signals of unintended gaps. Capacity-building initiatives that support context-specific national planning help ensure that implementation strategies are grounded in local epidemiology, workforce capacity, and sociocultural norms rather than being imported wholesale from high-resource settings. The STRiDE program’s emphasis on national dementia planning and context-specific solutions exemplifies this approach, providing a template for embedding equity into implementation from the beginning [144].

Examples from both HIC and LMIC settings show how equity-sensitive implementation can translate evidence into action. Finland’s National Memory Programme (2012–2020) provides a HIC illustration of how national strategies can support equitable, locally responsive implementation. Rather than assuming uniform capacity across municipalities, Finland required local governments to adapt national aims—promoting brain health, improving attitudes toward memory disorders, ensuring timely treatment and rehabilitation, and strengthening professional competence- to their own community contexts. Implementation measures included integrating early detection of memory concerns into routine health checks and creating seamless care pathways to ensure that support, treatment, and rehabilitation were delivered “systematically and at the right time.” National updates through quality recommendations and the country’s broader shift toward a “memory-friendly society” further strengthened the alignment between national goals and community-level realities, demonstrating how national standards can coexist with local tailoring [145].

In LMICs, where disparities often stem from limited specialist capacity and constrained health-system infrastructure, equity-focused implementation frequently involves designing interventions that can be delivered by frontline, community-embedded actors. Programs such as STRiDE have emphasized locally driven solutions—such as situational analyses that illuminate context-specific barriers and the development of culturally grounded tools—to ensure that implementation aligns with system readiness. These approaches illustrate that equity is not an aspirational add-on but a practical strategy: interventions that resonate culturally, fit within existing delivery systems, and build local capacity are more likely to be sustained and to produce equitable impacts across diverse populations.

Taken together, these examples underscore an essential principle: equitable implementation is both an ethical imperative and a practical necessity. By integrating planned adaptation, culturally responsive delivery, local capacity building, and rigorous monitoring of equity impacts, health systems can ensure that dementia interventions—whether clinical, social, or digital—are effective and meaningful across the full range of real-world settings. Successful implementation, in this sense, is not merely about “scaling what works” but about ensuring what works, works for everyone.

5.3. Delivery Systems and Learning Infrastructure: Monitoring, Public Involvement, and Collaborative Capacity

Implementation breaks down when systems lack the ability to measure outcomes, learn from performance, and maintain the trust and capacity needed to deliver care at scale. Even the strongest dementia evidence cannot be translated into routine practice without the delivery infrastructure, workforce, coordination mechanisms, and community partnerships required to carry it forward. In many settings, monitoring and evaluation (M&E), the mechanism through which systems determine whether policies are effective, equitable, and sustainable, is chronically underfunded or omitted. Without clear metrics or feedback loops, programs may be scaled prematurely, modified without evidence, or abandoned without understanding why. Limited community involvement compounds these challenges. Interventions that fail to align with cultural norms, local expectations, or community trust often struggle to reach intended populations or achieve their desired impact [1]. Sustaining trust requires transparency, time, and participatory engagement, skills that many technical teams and research groups are not routinely trained to practice.

Implementation strengthens when systems operate as learning systems, with ongoing cycles of measurement, adaptation, and refinement. Embedded pragmatic research models offer a pathway to achieving this. The IMPACT Collaboratory, for example, supports pragmatic clinical trials embedded directly within care settings, pairing real-world evaluation with training, best-practice guidance, and interdisciplinary collaboration [146–147]. Rather than treating research and implementation as separate phases, this model closes the loop between evidence and practice, generating implementation-relevant knowledge under routine conditions while maintaining methodological rigor.

National implementation platforms can further reinforce this learning orientation by funding infrastructure that translates evidence into practice. The CDC’s Building Our Largest Dementia (BOLD) Infrastructure for Alzheimer’s Act illustrates that. BOLD primarily supports public health capacity building, funding state, local, territorial, and tribal recipients and Centers of Excellence to increase the adoption of evidence-informed approaches to risk reduction, early detection, and caregiving [148]. Although BOLD has established a structured implementation architecture, rigorous evaluation of population-level outcomes remains an important next step [149–150]. These examples demonstrate that systems capable of continuous learning, not one-time program rollouts, are more likely to sustain impact.

Similar principles are visible across LMICs, where implementation progresses most effectively when dementia-related actions are integrated into existing public-health and primary-care structures rather than through disease-specific vertical programs. In India, the National Programme for Health Care of the Elderly (NPHCE) delivers promotive, preventive, curative, and rehabilitative services for older adults across district and primary-care levels, creating a practical platform for incorporating dementia screening, referral, and caregiver support within routine services [151–154]. Likewise, Ayushman Arogya Mandirs (formerly Health and Wellness Centres) aim to deliver comprehensive primary health care, including non-communicable diseases, mental health, and elder/palliative care, offering a scalable entry point for dementia interventions in settings with limited specialist availability. Regionally, the e-DiVA project (Empowering Dementia Carers With an iSupport Virtual Assistant) demonstrates how digitally enabled caregiver support and locally led capacity building can extend reach and sustain engagement where formal dementia services are limited [155]. Across LMIC initiatives, several enabling mechanisms recur: embedding dementia actions within existing public-health and primary-care systems; pairing capacity building with implementation tools that can be delivered by non-specialists such as community health workers; and drawing on civil-society organizations to maintain policy attention and support caregivers. These patterns reflect actionable pathways for strengthening national dementia planning and implementation in resource-constrained settings.

Public–private partnerships provide additional opportunities by enabling iterative feedback loops and technical scaling. However, these partnerships only succeed when adoption requirements (e.g., workflows, governance, accountability, and long-term financing) are defined early, rather than treated as downstream concerns. When clear expectations are set at the outset, cross-sector collaborations can reduce bottlenecks and accelerate responsible.

Ultimately, implementation capacity determines whether national dementia plans translate into sustained, equitable services and measurable outcomes. Persistent barriers, delayed adoption, unstable resources, inequities, weak feedback loops, misaligned regulation, and limited collaborative capacity, are most effectively addressed when countries invest in durable implementation infrastructure: predictable funding pathways, public-health delivery platforms, embedded learning and evaluation systems, equity-forward adaptation strategies, and regulatory alignment that supports ethical and scalable adoption. Together, these approaches transform evidence from an isolated output into a living asset, one capable of evolving with system needs and supporting population-level impact over time.

6. CONCLUSION

Bridging dementia research and policy requires more than producing high-quality evidence. Across the EIPP continuum, persistent barriers arise from governance challenges, capacity limitations, coordination gaps, and misalignment between evidence and real-world service environments. Strengthening dementia policy is therefore a systems challenge shaped by whether evidence is generated inclusively, interpreted credibly, and implemented through delivery structures able to sustain change.

Using the EIPP framework, we identified where breakdowns occur. It illustrates how political priority, funding stability, data systems, workforce capability, and collaborative platforms influence evidence use. By differentiating challenges in evidence availability, use, and implementation capacity, EIPP moves beyond generic calls for “more research” and highlights leverage points for system improvement.

Experiences across settings show progress is possible but context dependent. LMICs face major constraints in data, workforce, and service infrastructure, yet capacity-building efforts, primary care integration, and community-anchored models provide viable pathways for evidence translation. Conversely, HICs demonstrate how policy instability, fragmented governance, and weak coordination can impede sustained implementation even in well-resourced systems.

Several overarching lessons emerge. Gaps in dementia policy arise less from absent evidence than from limitations in mobilizing and applying existing knowledge. LMIC experiences highlight the need for system-level capacity building and contextual adaptation. Evidence use also varies across governance levels and actor groups, necessitating translation approaches aligned with their incentives and decision-making environments. Finally, global dementia research ecosystems depend on institutional capacity and stable funding, making governance continuity critical for sustained evidence generation and implementation.

To strengthen evidence-informed dementia policy, several actions emerge across stakeholder groups. For policymakers, investments in data infrastructure, cross-sector coordination, and workforce development are foundational precursors to introducing complex interventions and clarifying actors’ roles. For practitioners, co-design, cultural adaptation, and early feasibility assessment help improve interventions’ usability and acceptability. For researchers, pragmatic and participatory designs, explicit attention to implementation barriers, and clear articulation of how findings inform real-world decisions enhance the policy relevance of evidence.

Ultimately, improving dementia policy requires aligning evidence production, interpretation, and implementation with the systems responsible for carrying them. By making these relationships explicit, the EIPP framework offers a practical roadmap for advancing more feasible, equitable, and context-responsive dementia policy across diverse settings.