The policy exposome of dementia: Gaps, opportunities, and research agenda.

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Learn more: PMC Disclaimer | PMC Copyright Notice Alzheimers Dement . 2026 Apr 14;22(4):e71362. doi: 10.1002/alz.71362 Search in PMC Search in PubMed View in NLM Catalog Add to search The policy exposome of dementia: Gaps, opportunities, and research agenda Clémence Kieny Clémence Kieny 1 Center for Primary Care and Public Health (Unisanté), Department of Epidemiology and Health Systems, University of Lausanne, Lausanne, Switzerland Find articles by Clémence Kieny 1, ✉ , Geraldine Marks Geraldine Marks 1 Center for Primary Care and Public Health (Unisanté), Department of Epidemiology and Health Systems, University of Lausanne, Lausanne, Switzerland 2 Institute of Health Law, University of Neuchâtel, Neuchâtel, Switzerland Find articles by Geraldine Marks 1, 2 , Matheo Bourgeois Matheo Bourgeois 1 Center for Primary Care and Public Health (Unisanté), Department of Epidemiology and Health Systems, University of Lausanne, Lausanne, Switzerland Find articles by Matheo Bourgeois 1 , Survana Alladi Survana Alladi 3 National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India Find articles by Survana Alladi 3 , Samer Atshan Samer Atshan 4 Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA Find articles by Samer Atshan 4 , Carol Brayne Carol Brayne 5 Cambridge Public Health, University of Cambridge, Cambridge, UK Find articles by Carol Brayne 5 , William Dow William Dow 6 School of Public Health, University of California, Berkeley, California, USA Find articles by William Dow 6 , Maria Glymour Maria Glymour 7 Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA Find articles by Maria Glymour 7 , David Knapp David Knapp 4 Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA Find articles by David Knapp 4 , Gill Livingston Gill Livingston 8 Division of Psychiatry, University College London, London, UK 9 Camden and Islington NHS Foundation Trust, London, UK Find articles by Gill Livingston 8, 9 , Minhaj Mahmud Minhaj Mahmud 10 Economic Research and Development Impact Department, Asian Development Bank, Manila, Philippines Find articles by Minhaj Mahmud 10 , Nathalie Monnet Nathalie Monnet 1 Center for Primary Care and Public Health (Unisanté), Department of Epidemiology and Health Systems, University of Lausanne, Lausanne, Switzerland Find articles by Nathalie Monnet 1 , Irene Papanicolas Irene Papanicolas 11 Department of Health Services, Policy and Practice, Center for Health System Sustainability, Brown University School of Public Health, Providence, Rhode Island, USA 12 Department of Health Policy, London School of Economics, London, UK Find articles by Irene Papanicolas 11, 12 , Elizabeth Platt Elizabeth Platt 13 Center for Public Health Law Research, Temple University Beasley School of Law, Philadelphia, Pennsylvania, USA Find articles by Elizabeth Platt 13 , David Rehkopf David Rehkopf 14 Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA 15 Center for Population Health Sciences, Stanford University School of Medicine, Stanford, California, USA 16 Department of Health Policy, Stanford University School of Medicine, Stanford, California, USA 17 Department of Medicine, Stanford University School of Medicine, Stanford, California, USA 18 Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA 19 Department of Sociology, Stanford University, Stanford, California, USA Find articles by David Rehkopf 14, 15, 16, 17, 18, 19 , Ritu Sadana Ritu Sadana 20 Ageing and Health Unit, Department of Maternal, Newborn, Child and Adolescent Health and Ageing, WHO, Geneva, Switzerland Find articles by Ritu Sadana 20 , Lindsay Wallace Lindsay Wallace 21 Department of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada Find articles by Lindsay Wallace 21 , Sebastian Walsh Sebastian Walsh 5 Cambridge Public Health, University of Cambridge, Cambridge, UK Find articles by Sebastian Walsh 5 , McKayla Wenner McKayla Wenner 4 Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA Find articles by McKayla Wenner 4 , Mauricio Avendano Mauricio Avendano 1 Center for Primary Care and Public Health (Unisanté), Department of Epidemiology and Health Systems, University of Lausanne, Lausanne, Switzerland 22 Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, USA Find articles by Mauricio Avendano 1, 22 Author information Article notes Copyright and License information 1 Center for Primary Care and Public Health (Unisanté), Department of Epidemiology and Health Systems, University of Lausanne, Lausanne, Switzerland 2 Institute of Health Law, University of Neuchâtel, Neuchâtel, Switzerland 3 National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India 4 Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA 5 Cambridge Public Health, University of Cambridge, Cambridge, UK 6 School of Public Health, University of California, Berkeley, California, USA 7 Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA 8 Division of Psychiatry, University College London, London, UK 9 Camden and Islington NHS Foundation Trust, London, UK 10 Economic Research and Development Impact Department, Asian Development Bank, Manila, Philippines 11 Department of Health Services, Policy and Practice, Center for Health System Sustainability, Brown University School of Public Health, Providence, Rhode Island, USA 12 Department of Health Policy, London School of Economics, London, UK 13 Center for Public Health Law Research, Temple University Beasley School of Law, Philadelphia, Pennsylvania, USA 14 Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA 15 Center for Population Health Sciences, Stanford University School of Medicine, Stanford, California, USA 16 Department of Health Policy, Stanford University School of Medicine, Stanford, California, USA 17 Department of Medicine, Stanford University School of Medicine, Stanford, California, USA 18 Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA 19 Department of Sociology, Stanford University, Stanford, California, USA 20 Ageing and Health Unit, Department of Maternal, Newborn, Child and Adolescent Health and Ageing, WHO, Geneva, Switzerland 21 Department of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada 22 Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, USA * Correspondence , Clémence Kieny, Center for Primary Care and Public Health (Unisanté), Département Épidémiologie et services de santé, Lausanne, Switzerland. Email: [email protected] ✉ Corresponding author. Revised 2026 Mar 9; Received 2025 Dec 22; Accepted 2026 Mar 11; Collection date 2026 Apr. © 2026 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. PMC Copyright notice PMCID: PMC13077446  PMID: 41979011 Abstract Government policies targeting key risk factors for cognitive decline are potential levers for preventing or delaying dementia. Policy is therefore a central component of the exposome—the cumulative exposures shaping dementia risk across the life course. While extensive research links individual risk factors to dementia, less attention has been paid to how public policies structure these risks. We introduce the concept of the policy exposome and summarize findings from a structured expert consultation conducted by the Gateway Exposome Coordinating center (GECC) to identify priority policy research domains. We argue that systematic collection and harmonization of policy data across countries and sub‐national regions can enable causal research on dementia. We propose a tiered research framework to guide policy data collection, prioritizing education, tobacco and alcohol control, air pollution, and old‐age social assistance. Using tobacco control as a case study, we illustrate how policies can be operationalized as exposures linkable to dementia outcomes. Keywords: causal inference, dementia prevention, policy, policy surveillance, research agenda 1. INTRODUCTION: WHY POLICY MATTERS FOR DEMENTIA RESEARCH Alzheimer's disease and related dementias represent one of the most significant public health challenges of the 21 st century. In 2019, an estimated 57 million people were living with dementia worldwide, a figure projected to increase to 153 million by 2050, 1 with disproportionately high increases projected in low‐ and middle‐income countries. The associated social, economic, and healthcare costs exceed $1.3 trillion annually. 2 Marked differences in dementia incidence across countries and over time indicate that, while dementia risk rises steeply with age, its population burden is also shaped by social, economic, and environmental conditions across the life course. These factors can shift dementia risk upward or downward at a given age, suggesting that policies targeting these determinants may help delay onset or reduce incidence. However, most research has focused on individual behaviors, 3 overlooking how broader policy interventions that shape these risk factors may influence dementia risk. Rose's prevention paradox posits that modest reductions in risk across an entire population can prevent more cases than large reductions limited to high‐risk individuals. 4 Population‐level interventions embody this principle by seeking to shift the distribution of risk factors through changes in the broader policy environments in which those factors arise. 5 The recent Lancet Commission suggests that almost half of dementia cases may be attributable to 14 modifiable factors. 6 As emphasized by the World Health Organization (WHO) Risk Reduction Guidelines for dementia, 7 most of these risk factors are modifiable as they arise from lifestyle choices amenable to policies and interventions, including tobacco and alcohol consumption, inactivity, and unhealthy diets. Yet, lack of data infrastructure linking policies to longitudinal data has hampered research on the most effective policy levers to reduce dementia risk. The 2024 Lancet Commission emphasized the value of quasi‐experimental designs and policy evaluation methods, which exploit variation in exposure to examine causal effects, and to understand how risk factors can change in response to government action (Figure 1 ). Quasi‐experimental studies thus serve a dual purpose: they allow evaluation of policies as population‐level interventions, and they enable the use of policy variation to identify the causal effects of underlying risk factors on cognition and dementia. FIGURE 1. Open in a new tab Directed acyclic graph illustrating how policy variation can be leveraged to study the impact of risk factors on dementia. To advance this agenda, we propose the concept of the “policy exposome”: the cumulative exposure to public policies that shape cognitive health and dementia risk across the life course. This extends the original exposome concept, first proposed to capture all environmental exposures over the life course, 8 by focusing on the legal and policy reforms that shape those environments. Policies act as exposures through multiple pathways, for example, education policies may shape cognitive reserve across the life course; environmental and housing regulations may influence exposure to pollution and green space; and tobacco control policies may reduce exposure to tobacco throughout the life‐course. Through these mechanisms, policies transform environments and shape behaviors through incentives, behavioral restrictions, and information. One of the key obstacles to research on the impact of policies is the lack of harmonized data on laws and policy reforms. Policy Surveillance , a field concerned with tracking changes in policy, aims to apply scientific methods to collect and analyze measures of policies and laws with potential public health consequences. 9 Documenting policy changes systematically is a major endeavor requiring rigorous methodological approaches to capture government decisions, documents, laws, and implementation activities and convert these into measures that can be linked to longitudinal data to analyze their impact on health outcomes. The Gateway Exposome Coordinating Center (GECC), funded by the U.S. National Institute on Aging, aims to advance research on the life‐course exposome and its role in Alzheimer's disease and related dementias, including the policy exposome. This article presents the findings of a major GECC policy domain expert consultation, with the aim of understanding how public policies shape brain health, identify research priorities, and develop harmonized data resources. First, we aim to articulate the rationale for positioning public policy as a central component of dementia research. To do so, we outline key methodological tools for measuring the policy exposome. Second, we describe the GECC's participatory prioritization of policy domains and summarize the consensus on identified research priorities. Our priority‐setting consultation is partly based on the methodology proposed by the WHO Child Health and Nutrition Research Initiative (CHNRI), 10 focusing specifically on public policy as a modifiable exposure shaping dementia. 2. MEASURING THE EFFECTS OF THE POLICY EXPOSOME: LEGAL MAPPING TOOLS AND SYSTEMATIC POLICY SURVEILLANCE Studying the effects of policy on public health outcomes requires a systematic and transparent approach to tracking policies and their variation across time and place. In this paper, we focus on laws as one formal component of the policy environment. While policies may extend beyond legal texts, and not all laws reflect current or implemented policy, analyzing laws provides a systematic entry point for capturing official rules that shape identified risk factors for dementia and cognitive decline. Despite growing interest in the health impact of policy, most analyses still rely on ad hoc data collection for individual projects, 11 and efforts to integrate legal data into public health research often lack the methodological rigor found in other areas of epidemiology. 12 A central challenge is the scarcity and limited accessibility of data across cross‐country contexts. Many studies that aim to assess the impact of legal reforms on health fail to document how their legal data were collected or assess the reliability of their data sources, raising concerns about reproducibility and validity. 13 In response, policy surveillance and legal epidemiology methods have gained traction. Defined as “the systematic, scientific collection and analysis of laws and policies of public health significance”, 9 these methods rely on replicable protocols, primary legal sources, well‐defined variables, and rigorous coding standards. These advances have been accompanied by a rise in the development of data platforms that help researchers integrate legal data into public health analyses. Some are designed primarily for academic research, providing harmonized information that can be linked to population studies, while other initiatives, led by international organizations, monitor compliance with standards or commitments across countries. Although these datasets share the common goal of converting legal text into structured, analyzable data, they differ in important respects: geographical scope and granularity (cross‐national vs. state or local); temporal coverage (continuous tracking vs. updates at fixed intervals); methods of data collection and transparency (surveys, more common in international organizations, vs. primary legal research or secondary sources); measures captured; level of detail (comprehensive mapping of legal texts vs. targeted provisions only); and accessibility (open and exportable datasets vs. restricted or summary reports). Discussions around best practices in policy surveillance offer guidance on improving the quality of legal data collection and reporting standards. 9 , 11 Emerging tools such as artificial intelligence may eventually assist in automating the extraction and documentation of policy data, yet their role remains experimental and requires evaluation. While advances in policy surveillance improve the rigor and comparability of legal data, there is also a need for local expertise to understand the context in which policies were implemented in each country or region. Laws may not always be effectively implemented, or their implementation may diverge from their original design. Often, successful implementation depends on enforcement, institutional capacity, and broader socioeconomic and cultural conditions, which are not captured in the law. Interdisciplinary research collaboration is also necessary to understand how policies operate in practice, for example, through qualitative studies or policy expert interviews. For example, policies may generate indirect or unintended consequences—through spillovers and system‐level responses (e.g., shifts in service quality, resource allocation, or composition of users)—that alter exposures beyond what is explicitly specified in the law. Studying the policy exposome therefore requires local and interdisciplinary engagement with experts familiar with the institutional and regional contexts to understand how populations were affected and through which mechanisms. 3. DEFINING PRIORITIES FOR POLICY DATA COLLECTION: A PARTICIPATORY AND EVIDENCE‐BASED PROCESS Given the vast array of public policies that may influence dementia risk, it is essential to guide data collection with clear, evidence‐informed priorities. To this end, the GECC's Policy Environment group undertook a prioritization process aimed at identifying the most relevant policy domains for dementia research. We followed a funnel‐shaped process, moving from broad inputs to focused consensus. Three complementary approaches informed the identification of plausible policies affecting dementia risk factors: (i) a targeted literature review of dementia risk factors and their modifiability through public policy; (ii) a series of virtual stakeholder town halls with researchers, clinicians, policy‐makers, and civil society actors to gather diverse perspectives; and (iii) an expert survey on research priorities at the intersection of policy, cognition, and dementia. Together, these inputs guided discussions at an in‐person expert workshop, which brought leading academics and policy actors together to agree on Tier 1 and Tier 2 priorities for the policy exposome . The expert survey aimed to reach stakeholders beyond the GECC's immediate networks. It was conducted in two stages, inspired by the CHNRI methodology. 14 From 3,641 potential respondents, 202 experts from 31 countries submitted 423 research questions, which were thematically grouped into 23 higher‐level research options. In the second stage, the experts scored the ten most common options against four criteria: (1) impact potential (ability to reduce incidence/improve outcomes); (2) innovation (novelty/new knowledge); (3) translational potential (likelihood of informing actionable policy); (4) feasibility (practicality given data/resources). We obtained 130 answers, which enabled us to rank the policy options by strength of support. Input from the virtual town halls and surveys were summarized in Table 1 (columns 1 and 2, respectively) and read alongside the Population Attributable Fractions (PAFs) reported by the Lancet Commission (Table 1 , column 3). 6 For the town halls (column 1), salience reflects whether a domain was raised repeatedly across sessions. For the survey (column 2), salience reflects how often experts proposed research questions in each domain; “high salience” indicates the largest share of submissions, whereas “moderate salience” reflects less frequent but still recurrent domains. To summarize the Lancet evidence, we grouped PAFs into two ranges: ≥5% and < 5%. Strong alignment emerged between town hall and survey input around unpaid care and care workforce, primary care screening, and air pollution policies. TABLE 1. Summary of input from townhall meetings, expert survey, and Lancet dementia report Theme (1) Townhalls (2) Expert Survey (3) Lancet PAF calculations 1 Informal care & aging in place policies High salience High salience Not discussed 2 Institutional care & dementia workforce policies High salience High salience Not discussed 3 Policies on social isolation & loneliness High salience Moderate salience Higher‐attributable fraction (≥5%) 4 Primary care screening policies High salience High salience Not discussed 5 Policies promoting physical activity, exercise, and fitness High salience Moderate salience Lower‐attributable fraction (< 5%) 6 Policies on air pollution High salience High salience Lower‐attributable fraction (< 5%) 7 Health insurance policies Moderate salience High salience Not discussed 8 Policies preventing hearing loss and supporting hearing aid use Moderate salience High salience Higher‐attributable fraction (≥5%) 9 Education & lifelong learning policies High salience Moderate salience Higher‐attributable fraction (≥5%) 10 Policies promoting mental health & addressing depression Moderate salience Moderate salience Lower‐attributable fraction (< 5%) Open in a new tab Note : Salience categories (“High” / “Moderate”) reflect the frequency and prominence of each policy domain in qualitative inputs from town halls and expert survey responses. PAFs are grouped into ≥5% (higher‐attributable fraction) and < 5% (lower‐attributable fraction), based on estimates from the 2024 Lancet commission. An expert consultation workshop was held in March 2025 during the GECC launch meeting. Senior researchers from public health, epidemiology, geography, demography, health economics, psychiatry, and experts in aging and dementia engaged in a structured discussion to synthesize earlier phases. The prioritization process was guided by several criteria, including: (a) relevance to dementia risk, particularly its alignment with the 14 modifiable risk factors identified by the Lancet Commission 6 ; (b) extent of policy variation across time and jurisdictions, which creates valuable opportunities for quasi‐experimental research designs; (c) feasibility of data collection and harmonization, taking into account the availability of legal or policy datasets and alignment with existing survey data; and (d) potential for population‐level impact. This iterative process resulted in a two‐tier prioritization structure. Tier 1 priorities reflect risk factors with relatively strong evidence and existing data that could be harmonized and linked to longitudinal aging studies within a short time frame (2025‐2027). Tier 2 priorities reflect areas in which policy action is likely to affect dementia risk or resilience, but for which additional conceptual development or primary data collection is needed (2027‐2029). This participatory process provides essential guidance on which policy domains the research and policy community consider most relevant for advancing dementia prevention. The expert input informed but did not strictly determine prioritization. In developing Tier 1 and Tier 2 domains, we also drew on the existing scientific literature, the GECC methodological expertise, the availability and quality of policy data across countries, and the feasibility of harmonizing data at scale. Together, these considerations enabled us to translate broad expert recommendations into a realistic set of priorities for policy data collection and integration into the Gateway Exposome research infrastructure. The following section outlines the specific policy domains selected as Tier 1 (short‐term) and Tier 2 (long‐term) priorities. 3.1. Tier 1 priorities for research on policies for dementia 3.1.1. Early‐life education policies A large body of evidence supports an association between educational attainment and late‐life cognitive function, including a reduced risk of dementia. 15 Education is thought to contribute to cognitive reserve both directly and indirectly via occupational complexity, higher income, health literacy, and healthier behaviors across the life course. 16 To move beyond association, researchers have increasingly exploited natural experiments—particularly changes in compulsory schooling laws (CSLs)—to estimate the causal effects of education on late‐life cognition. Early studies have found that additional years of schooling improve memory and executive function in older age, with often stronger effects observed among individuals from lower socioeconomic backgrounds. 17 However, more recent evidence from Sweden suggests that extending compulsory schooling has little or no effect on dementia risk in later life, 18 highlighting that the impact of these policies may depend on historical context, population characteristics, and the outcomes measured. Other policies—for example affecting the age of tracking, access to subsidized education, peer composition, or school quality (e.g., teacher qualifications, student‐teacher ratios)—may be equally important for long‐term cognitive outcomes but remain unexplored. 3.1.2. Tobacco control policies The relationship between smoking and dementia is well‐established, with smokers facing a higher risk of developing Alzheimer's disease and vascular dementia. 19 Moreover, smoking cessation in midlife has been associated with slower cognitive decline and lower dementia risk than continued smoking, 20 positioning tobacco control as a powerful instrument for prevention. The gradual implementation of tobacco control policies offers opportunities for quasi‐experimental research that can inform causal pathways between tobacco consumption and brain health. Although a direct causal link between tobacco control policies and dementia has not yet been established, many policies— for example, increased tobacco taxes, plain packaging, graphic health warnings, advertising bans, and smoke‐free public spaces—alter smoking behavior and reduce risk of related outcomes. 5 For example, previous research shows that comprehensive smoking bans reduce cardiovascular and respiratory disease, conditions that share common vascular pathways with dementia. 21 3.1.3. Alcohol control policies The 2024 Lancet Commission 6 estimated that eliminating excess alcohol consumption might prevent approximately 1% of global dementia cases. Recent meta‐analyses show that heavy drinking significantly increases the risk of all‐cause and early‐onset dementia. 22 While light‐to‐moderate drinking has sometimes been linked to reduced dementia risk, causality remains unproven and findings are vulnerable to confounding and survivor bias. 23 Alcohol control policies, including taxation, advertising bans, and availability restrictions, can reduce alcohol consumption and related harm. 22 To our knowledge, no study has directly assessed the impact of alcohol control policies on dementia incidence or cognitive decline. 3.1.4. Air pollution policies Air pollution, particularly fine particulate matter (PM 2 . 5 ), is a modifiable risk factor for dementia. 6 Exposure to airborne particulates from outdoor and indoor sources has been linked to increased dementia risk and cognitive decline. 24 These associations exhibit a nonlinear concentration‐response relationship, with steeper risk increases at lower pollution levels, suggesting that even modest reductions in ambient pollution could yield cognitive health benefits. The geographic variation in air quality legislation creates opportunities for quasi‐experimental research. Several policies—such as clean cookstoves, low‐emission zones, car‐use restrictions, and the postponement of nonessential polluting activities on high pollution days—have been shown to reduce pollutant exposures relevant to cognitive health. 5 However, few studies have directly assessed their impact on cognitive outcomes. A notable exception is Yao et al., 25 who exploit China's Clean Air Act as a natural experiment and find that the implementation of stringent pollution reduction targets was associated with a smaller decline in cognitive function scores among older adults compared to control regions, while reductions in PM 2 . 5 and SO 2 were linked to significant preservation of cognitive performance. 3.1.5. Old‐age pensions and retirement Evidence suggests that later retirement and longer working careers may lead to improved cognitive function. 26 The rationale for this hypothesis is that working longer may help maintain cognitively stimulating activities, social networks, and physical fitness, which may in turn help delay cognitive decline. On the other hand, longer working careers may also have detrimental effects by reducing time available to invest in health and engage in social activities. 27 Cross‐national and cross‐cohort variation in eligibility ages for retirement benefits create natural experiments to address this question. A long‐standing concern in earlier literature is selection: individuals in poorer health—and those at higher risk of cognitive decline—are more likely to retire earlier. Studies exploiting policy‐driven changes in pension eligibility help address this selection by inducing exogenous variation in retirement behavior that is unrelated to an individual's health status. 27 Overall, although results vary in magnitude, studies from high income countries with established pension support generally suggest that later retirement improves cognitive function. 26 , 27 However, effects are likely to depend on the nature and quality of work, and further research is required to understand how these relationships vary by occupation, working conditions, and individual characteristics, as well as to disentangle the underlying causal mechanisms. 3.2. Tier 2 priorities: High‐potential areas requiring new data collection Tier 1 policies are those identified as the most actionable priorities for advancing dementia research in the near term. They are ready for immediate integration into policy exposome research because they have strong evidence, available data, and potential for impact. Tier 2 priorities, by contrast, remain conceptually and empirically underdeveloped. We know less about which policy dimensions matter most, and data are often limited or nonexistent. Advancing research in these domains thus requires both conceptual work to identify the most relevant policy features and substantial primary data collection. Table 2 provides an overview of six policy domains that plausibly affect dementia risk yet remain insufficiently mature for robust study. Across domains, the strongest evidence concerns effects on proximal risk factors—such as blood‐pressure control, hearing‐aid uptake, or physical activity—while evaluations that follow these pathways through to cognitive outcomes are scarce. The table draws on a targeted scoping of recent reviews and representative empirical studies to identify the main mechanisms and policy levers. It is intended not as an exhaustive review but as a high‐level evidence map that clarifies where mechanisms are credible, what types of policy levers exist, and where the empirical chain from policy to late‐life cognition remains incomplete. Two cross‐cutting limitations emerge across these policy domains. First, causal identification is limited: few studies exploit policy variation (e.g., staged rollouts, eligibility thresholds) to estimate impacts on cognition or dementia incidence. Second, exposure measurement lacks harmonization, and follow‐up horizons are typically short, even though many dementia‐related risk factors have early‐life and midlife origins shaped by policy. TABLE 2. Comparative overview of Tier 2 policy priorities: High‐potential areas requiring new data collection [print in black and white] Policy examples Existing evidence Gaps Preventive health system policies Primary care access Routine screening for risk factors (e.g., hypertension, diabetes) Regular wellness visits Primary care access Greater primary‐care access/regular source of care is linked to lower odds of cognitive impairment. 28 Routine screening for risk factors (e.g., hypertension, diabetes) Strong evidence that tighter BP control reduces MCI/dementia outcomes in high‐risk adults. 29 Diabetes is associated with higher dementia risk. 30 Regular wellness visits : Medicare Annual Wellness Visits (AWV) associated with higher odds of early cognitive impairment detection. 31 Causal effects of primary‐care access on long‐term cognitive trajectories remain untested; most evidence is observational. Direct evidence that screening programs for risk factors lower dementia incidence is limited; Whether regular wellness visits change long‐term cognitive trajectories, treatment uptake, or care outcomes has not been established. Insurance coverage and pharmaceutical pricing Insurance policy coverage for hearing aids, statins, and antihypertensive medications Pharmaceutical price regulation and reimbursement—Policies controlling drug prices, reference pricing, or reimbursement ceilings. Hearing devices slow decline/are linked to lower long‐term cognitive decline 32 . Blood‐pressure–lowering therapy reduces risk of MCI/dementia 33 . Statins and cognition: evidence is mixed; newer review suggest possible risk reduction 34 . Coverage matters for uptake: hearing‐aid benefits vary by state/plan and coverage is associated with higher use 35 . Pharmaceutical price‐control and reimbursement policies (e.g., reference pricing, negotiated price ceilings, or inclusion on essential drug lists) influence affordability and adherence to medications for identified risk factors for dementia 36 . Very little causal evidence that variation in insurance coverage (eligibility, copays) or pharmaceutical pricing translates into population‐level cognitive benefits; quasi‐experimental evaluations of coverage expansions are needed. Lifelong learning and adult education Community education programs Workplace training Adult literacy programs Creative courses Participation in adult education/literacy and creative courses is associated with lower dementia risk in large cohorts. 37 , 38 Reviews on leisure/cognitive activities report broadly protective associations for later‐life cognition 39 Evidence is predominantly observational; causal effects and policy‐level impacts remain unclear, and harmonized cross‐national policy data are scarce. Nutrition and food environment policies Taxes on sugary drinks Sodium reformulation Marketing restrictions Sugary‐drink taxes: Real‐world SSB taxes raise prices and reduce purchases/sales. 40 Sodium reformulation: Many countries have national sodium‐reduction programs; reformulation is widespread and linked to population sodium‐intake reductions. 41 Marketing restrictions: Policies restricting unhealthy‐food marketing are associated with reduced purchases/exposure 42 and large declines in “high‐in” purchases under Chile's comprehensive law. 43 Across all three levers: no evaluation trace the full policy → diet change → brain/cognitive outcomes pathway; quasi‐experimental studies with long follow‐up are needed. Sodium reformulation: Limited causal links to downstream BP‐mediated cognitive outcomes at the population level. Marketing restrictions: Evidence on digital marketing and long‐term diet/cognition remains sparse; effects vary by policy design. Mental health policies Mandated depression screening Parity in insurance reimbursement Expanded psychological services Mandated depression screening: Evidence reviews supporting the U.S. Preventive Services Task Force (USPSTF) recommendation find moderate net benefit for adult screening via increased detection and treatment engagement. 44 Parity in insurance reimbursement (MHPAEA): Implementation was associated with higher outpatient mental‐health service use among adults and adolescents. 45 Expanded psychological services: Systematic review shows greater access to evidence‐based therapies with broadly effective outcomes at scale. 46 Causal impact on dementia risk is uncertain: Late‐life depression may be prodromal (reverse causality), and few studies link screening/parity/service expansions to long‐run cognition or dementia incidence. Physical activity and built environment Active transportation planning (cycling infrastructure) Public exercise facilities School‐based sports Active transport and facility access are associated with lower dementia risk in recent population‐based studies. 47 Proximity/access to neighborhood exercise facilities is associated with lower incident dementia risk and better cognitive health in older adults. 48 , 49 School‐based PA/PE programs and organized sports show benefits for executive function and academic performance in children. 50 , 51 Policy‐level effects (infrastructure, public facilities, school PE mandates) on long‐term cognitive outcomes remain untested. Need quasi‐experimental evaluations with validated exposure measures and long follow‐up to establish causality. Open in a new tab Abbreviations: BP, blood pressure, MCI, mild cognitive impairment; PA/PE, physical activity/physical education. Preventive health system policies—such as those expanding primary care access and routine screening—appear to improve early detection and management of dementia‐related risk factors, yet their effects on long‐run cognitive trajectories remain uncertain. Insurance coverage and pharmaceutical pricing policies govern access to interventions with well‐established clinical efficacy (e.g., antihypertensive therapy, hearing aids), and existing evidence shows that coverage, pricing, and cost‐sharing shape affordability and adherence to these treatments. 36 However, there is limited evidence that variation in coverage or pricing translates into population‐level cognitive benefits or addresses earlier determinants of later risk factors (e.g., noise exposure in youth or nutritional drivers of blood pressure in adolescence). Lifelong learning and adult education policies are promising later‐life levers for cognitive reserve, yet uncertainty remains over which policy features are most relevant and how they can be measured consistently across settings. Nutrition and food environment policies have undergone several reforms over recent decades, yet few studies examine their causal effects on dietary exposures and cognition. Mental health policies may have increased detection and service use (e.g., through parity laws), but their impact on long‐term cognitive outcomes remains unclear. Finally, while physical activity is consistently associated with healthier cognitive aging, there is little evidence on whether physical activity or built‐environment policies causally influence long‐term cognitive outcomes. Taken together, while plausible mechanisms link these risk factors to cognitive aging, there is little evidence that policies targeting these risk factors at any life stage influence dementia risk. This motivates the need for harmonized policy measures and quasi‐experimental evaluations that can link policy exposure to long‐term cognitive outcomes. 4. IDENTIFYING AND SELECTING POLICY DATABASES FOR HARMONIZATION: TOBACCO CONTROL POLICIES EXAMPLE Studying the impact of the policy exposome on cognitive decline requires both health outcome data and information on the policies individuals have been exposed to through their childhood, working life, and old age. The GECC aims to collect and disseminate these data in a harmonized format to support research on how public policies may protect against or delay cognitive decline. This section illustrates the process used to determine the scope and measures for harmonization, using the example of tobacco control policies. Tobacco control policy data are already being collected and available in various databases. However, these differ in structure, coverage, and usability for quasi‐experimental research. Figure 2 outlines the five‐step process used to identify and select tobacco control policy databases for harmonization with longitudinal aging studies. FIGURE 2. Open in a new tab Process used to identify and select tobacco control policy databases for harmonization with longitudinal ageing studies. 4.1. Step 1: Identification of existing tobacco control policy databases Through a broad Internet search and a non‐systematic review of scientific literature, we identified 14 relevant tobacco‐related policy databases, maintained by a variety of institutions such as the World Health Organization (WHO), the Centers for Disease Control and Prevention (CDC), public research institutes, and nongovernmental organizations (NGOs). 4.2. Step 2: Systematic comparison of key features To guide the selection of the most suitable databases, we developed a comparative framework defined by a set of criteria. These included geographical scope and granularity (cross‐national, national, or sub‐national), and temporal coverage (both historical span and frequency of updates). We also compared data format and accessibility, distinguishing open, exportable datasets from those limited to written reports. Further criteria concerned data collection methods and transparency, such as whether information is derived from primary legal research, surveys, or secondary compilations, and the extent to which coding rules are documented. 4.3. Step 3: Identification and selection of the tobacco policy fields covered in each database Tobacco control measures encompass a wide array of policies to reduce both demand and supply for tobacco products. The WHO Framework Convention on Tobacco Control (FCTC) provides an evidence‐based framework for demand‐side—such as taxation, advertising restrictions, labeling requirements, and smoke‐free environments—and supply‐side measures, including sales restrictions, anti‐illicit trade measures, and support for economically viable alternatives to tobacco cultivation. Based on this framework, we examined the scope of each identified databases to determine which offer the most relevant and comprehensive policy datasets. 4.4. Step 4: Definition of the scope for harmonization The scope for harmonization was defined along three dimensions: geographic, chronological, and substantive. To ensure alignment with existing Health and Retirement Study‐International Network of Studies (HRS‐INS) outcome data, priority was given to datasets covering policies implemented in U.S. states and European countries participating in the Survey of Health, Ageing and Retirement in Europe (SHARE) study, and the focus was placed on policies enacted since the 1990s. Walsh et al. 3 identify four policy areas (indoor smoking bans, excise taxes, advertising restrictions, and standardized packaging) as having the strongest robust evidence base for their effectiveness at reducing smoking prevalence. Based on this evidence and a review of quasi‐experimental studies, two policy types were selected for harmonization: excise taxes on cigarettes and indoor smoking bans. These policies show the strongest and most consistent effects in the literature and are available in existing datasets in formats suitable for generating individual‐level exposure measures and link them to cognitive outcomes. 4.5. Step 5: Selection of databases for harmonization We matched the scope for harmonization with the documented features of each database, to select those with the best balance of coverage, frequency of update, methodological transparency and quality, and format. At the time of this paper's publication, we have selected the appropriate databases and plan to: (i) secure permissions and negotiate public‐sharing or licensing terms; (ii) standardize and clean the data, deriving harmonized measures; (iii) prepare linkage to aging studies using common geographic and temporal identifiers; (iv) conduct quality assurance and cross‐validation against primary legal sources; (v) produce comprehensive documentation, codebooks, and guidance on appropriate use and known limitations; and (vi) distribute the datasets. This process is meant to provide a replicable prioritization strategy that the GECC can apply to other policy domains. 5. DISCUSSION: A POLICY AGENDA FOR DEMENTIA RESEARCH Dementia is a leading cause of disability and dependency in older age. Yet despite decades of research, effective strategies for prevention remain limited. The exposome framework, focused on the totality of environmental, social, and behavioral exposures across the life course, offers a promising lens for understanding how complex, cumulative risk factors amenable to policy contribute to dementia. Policy can serve both as a subject of analysis and as a methodological tool to better understand the causes of dementia. Researchers can study the direct effects of policy on cognitive outcomes, for example, by evaluating whether stricter air pollution or tobacco regulations can lower dementia incidence. Such analyses, however, must account for distinctive methodological challenges inherent to measuring dementia outcomes. Dementia incidence at older ages is shaped by competing risks, particularly mortality, and failure to account for selective survival, interval censoring, or competing events may bias effect estimates. 52 Moreover, cross‐national research faces additional challenges because dementia diagnosis and cognitive assessment are not fully harmonized across countries and health systems, reflecting differences in diagnostic criteria, case ascertainment, language and testing instruments. 53 In this context, intermediate outcomes and proximal risk factors—including validated dementia risk scores or the burden of modifiable risk factors—may provide complementary indicators of policy impact, particularly in settings where long‐term follow‐up or harmonized dementia ascertainment is limited. Researchers can also use policies as a source of variation for quasi‐experimental studies on the causal impact of risk factors on dementia risk, overcoming many common selection problems and confounding factors in conventional observational studies. Quasi‐experimental evidence based on harmonized policy data can provide the foundations for future experimental studies on the impact of policies in new historical or geographical contexts, thus supporting cumulative evidence building. A potential challenge is that policies often co‐occur and interact, making it difficult to isolate their independent impacts on cognition. Compiling policies across different domains into a single policy database can help address this challenge by enabling researchers to examine how policies jointly shape dementia risk and cognitive function—a step forward from most existing studies, which typically focus on a single policy in isolation. Realizing this potential depends on access to reliable policy exposure data. To date, most studies have relied on isolated, manually curated datasets, limiting comparability and replication. Progress will require the systematic collection of high‐quality, transparent, and well‐documented legal data that can be linked to longitudinal cohort studies. As our case study on tobacco control illustrates, creating such datasets requires careful attention to data quality and transparency, alignment with the relevant geographic and chronological scope, and formats that can be transformed into individual‐level exposure measures. Given the difficulty and cost of collecting high‐quality policy data, strategic prioritization is essential. We propose a tiered framework to guide initial efforts. Tier 1 domains—including early‐life education, tobacco and alcohol control, air pollution, and financial security—are well‐established policy levers, with potential links to dementia risk and data readiness. These should be prioritized for immediate integration with longitudinal studies to measure the exposome. Tier 2 domains—including lifelong learning, mental health, dietary policy, and health system policies—are conceptually promising but require further work to define exposure metrics and build datasets. This approach allows the field to make short‐term progress while laying out the foundation for broader future policy data collection. The proposed prioritization reflects scientific considerations of evidence strength, data readiness, and opportunities for causal identification. A potential limitation of our approach is that these criteria may not align with the priorities of policy‐makers, who must often deal with relatively short‐time electoral concern and limited political time horizons. Policies targeting early‐life exposures, for example, may produce cognitive health benefits only decades later, whereas policy‐makers mostly operate within shorter budgetary and electoral cycles. At the same time, studying such policies can help identify mechanisms that remain relevant for contemporary decision‐making. For example, evidence on the long‐term cognitive effects of education reforms may inform current debates on lifelong learning or cognitive enrichment at older ages. Different stakeholders—including policy‐makers, clinicians, affected communities, and the broader public—may reasonably assign different weights to short‐term versus long‐term impacts, prevention versus care, or population‐wide versus distributional effects. A comprehensive policy exposome agenda should acknowledge these differing considerations to ensure that research infrastructure development remains grounded in both scientific evidence and pragmatic realities. The present prioritization can therefore be viewed as one evidence‐based starting point rather than a definitive ranking for all decision‐making contexts. We call on researchers, funders, and policy‐makers to recognize the role of policy research as fundamental to understanding the causes of dementia and how it may be prevented. This aligns with the WHO Blueprint for dementia research, which urges investment in cross‐sector data infrastructures, harmonized measures, and methodological standards to accelerate prevention research. 54 Building robust data infrastructure, clear guidance, and interdisciplinary methods will enable researchers to generate more actionable evidence on the effects of policy on dementia and related determinants. CONFLICT OF INTEREST STATEMENT The authors declare no conflicts of interest. Author disclosures are available in the supporting information . DECLARATION OF GENERATIVE AI AND AI‐ASSISTED TECHNOLOGIES IN THE MANUSCRIPT PREPARATION PROCESS During the preparation of this work the authors used Chat GPT in order to improve readability of certain sections. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article. Supporting information Supporting Information ALZ-22-e71362-s001.pdf (3.8MB, pdf) ACKNOWLEDGMENTS This research was conducted as part of the Gateway Exposome Coordinating Center and was supported by the U.S. National Institute on Aging, Grant U24AG088894. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the World Health Organization. Open access publishing facilitated by Universite de Lausanne, as part of the Wiley ‐ Universite de Lausanne agreement via the Consortium Of Swiss Academic Libraries. REFERENCES 1. Nichols E, Steinmetz JD, Vollset SE, et al. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the global burden of disease study 2019. 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