Individuals, families, and societies around the world are concerned about dementia and other forms of cognitive impairment that affect many older adults. Recent advances in the science of aging have shown that brain changes typically begin years—if not decades—before people show symptoms, which suggests that a window of opportunity exists to prevent or delay the onset of these conditions (Bateman et al., 2012; Reiman et al., 2012; Ritchie et al., 2015; Sperling et al., 2011, 2014). Furthermore, emerging evidence that the incidence and prevalence of dementia are declining in high-income countries offers hope that public health interventions can be effective in preventing cognitive decline and dementia. Although the evidence base on how to prevent or delay these conditions has been limited at best—despite the many claims of success made in popular media and advertising—a growing body of prevention research is emerging.
The National Institute on Aging (NIA) initiated this study with the National Academies of Sciences, Engineering, and Medicine to take stock of the current state of knowledge on interventions for preventing cognitive decline and dementia, to help shape the messages NIA conveys to the broader public about these conditions, and to inform future actions and research in this area. The task for the committee charged with carrying out this study was to evaluate the existing evidence on interventions for preventing cognitive decline and dementia and, based on this evidence, to recommend the appropriate content for inclusion in public health messages, as well as priorities for future prevention research. NIA also asked the Agency for Healthcare Research and Quality (AHRQ) to commission and oversee a systematic review of the salient evidence, which was conducted
by the Minnesota Evidence-based Practice Center (EPC) (Kane et al., 2017). This extensive and thorough review provided the primary evidence base for the discussion and recommendations in this report.
An earlier AHRQ systematic review on this subject (Williams et al., 2010), published in 2010, concluded that there was insufficient evidence to make recommendations about interventions to prevent cognitive decline and dementia. Since then, the knowledge base—from interventional research but also from neurobiological studies on risk and compensatory factors for dementia, as well as mechanistic pathways—has advanced, and there is reason to believe that much more will be known about how to prevent cognitive decline and dementia in the next several years. Existing evidence also makes it possible to draw conclusions about what appears not to work. Even with recent advances, however, the available evidence from randomized controlled trials (RCTs)—the “gold standard” of evidence—is limited and has shortcomings. Therefore, the committee considered additional evidence from nonexperimental observational studies—primarily longitudinal population-based cohort studies—as well as studies of risk factors and neurobiological processes that support biological plausibility. Box 1-1 provides definitions of the key terminology used in this report.
Clinical Alzheimer’s-type dementia (CATD) is common and costly, affecting approximately 4 to 5 million adults in the United States (Hebert et al., 2013; Plassman et al., 2007) at an annual estimated cost of more than $200 billion (Alzheimer’s Association, 2015; Hurd et al., 2013). An even greater number of older Americans have mild cognitive impairment (MCI) without dementia (Plassman et al., 2008). Public health experts warn that the burden associated with Alzheimer’s disease could nearly triple by 2050 as the number of adults over age 65 grows (Hebert et al., 2013), increasing annual costs to more than $1 trillion in the United States (Alzheimer’s Association, 2015). Globally in 2015, 46.8 million people were living with dementia, costing an estimated $818 billion that year (Prince et al., 2015). In addition to CATD and MCI, cognitive changes in older adults, or age-related cognitive decline (ARCD), often occur as a typical part of aging (IOM, 2015).
Many people are very interested in what they can do to maintain their own and others’ brain health (David and Gelfeld, 2014). Consequently, this topic is frequently covered by popular media, and unproven claims about brain health are commonplace in advertising. Many countries now support dementia research and are highly interested in what steps can be taken to prevent cognitive decline and dementia in their populations. In 2015, The Lancet, in partnership with academic institutions and other organizations,
convened an International Commission on Dementia Care to examine the evidence and make globally focused, evidence-based recommendations on dementia prevention and care (Livingston and Frankish, 2015). During the 2016 G7 summit in Japan, an array of national science academies highlighted global brain research as a critical priority, including a call for global programs on diagnosing, preventing, and treating brain disorders such as Alzheimer’s and Parkinson’s diseases (G-Science Academies, 2016). Similarly, the World Health Organization’s (WHO’s) 2016 draft Global Action Plan on the Public Health Response to Dementia 2017-2025 includes prevention as an important action area for all countries (final version anticipated in 2017).
In 2010, NIA convened a state-of-the-science conference to evaluate the evidence on preventing Alzheimer’s disease and cognitive decline. Like the current report, that conference was based largely on an AHRQ systematic review (Williams et al., 2010), supplemented by presentations and interactions among experts in the field. According to the consensus statement emerging from that process, no firm conclusions could be drawn about any interventions to prevent these conditions (Daviglus et al., 2010). Since then, a variety of other efforts have focused on the evidence in this domain. For example, the Alzheimer’s Association recently examined the evidence on modifiable risk factors for cognitive decline and dementia (Baumgart et al., 2015). Likewise, a 2015 Institute of Medicine report took an in-depth look at the evidence on cognitive aging (IOM, 2015). Recently, AARP launched a Global Council on Brain Health to bring together scientists, health professionals, and others to develop evidence-based recommendations on lifestyle changes that may impact brain health (AARP, 2017).
This report incorporates the most recent evidence from a rapidly evolving field and stands out as uniquely focused on applying AHRQ’s highly refined systematic review process to assess what evidence is available on the effectiveness of interventions themselves—as opposed to focusing on potentially modifiable risk factors—and examining how that evidence might serve as a basis for public health messaging. Since 2010, moreover, much research has provided significant evidence that the underlying pathophysiology of dementia is usually heterogeneous, entailing a mix of different pathologies. Furthermore, as noted above, it is now understood that the pathophysiologic processes of dementia begin to develop many years before symptoms manifest (Bateman et al., 2012; Jack et al., 2010; Kawas et al., 2015; Reiman et al., 2012; Sonnen et al., 2008; Sperling et al., 2011, 2014). This enhanced understanding can inform future clinical trials, including large-scale research now being planned.
As noted above, emerging evidence suggests that both the prevalence and incidence of dementia are declining in high-income countries. The Health and Retirement Study (HRS), a nationally representative prospective cohort study in the United States, found that dementia among those aged 65 and older declined between 2000 and 2012 by 2.8 percentage points, from 11.6 percent in 2000 to 8.8 percent in 2012 (Langa et al., 2017). This decline occurred despite the fact that the 2012 cohort had significantly higher rates of self-reported cardiovascular risk factors, including obesity, hypertension, and diabetes, all of which have been associated with an increased dementia risk (Deckers et al., 2015). Similarly, the Framingham Heart Study, a longitudinal cohort study, reported a 20 percent decline in dementia incidence between 1997 and 2008 even as body mass index, diabetes prevalence, and population age increased (Satizabal et al., 2016). The Cognitive Function and Ageing Study (CFAS) in the United Kingdom also reported declining incidence rates over the past 20 years across all age groups (Matthews et al., 2016). This decline was most pronounced in men, with incidence rates declining much less dramatically in women.
These studies confirm the findings of earlier studies in the United States and Europe of a decline in dementia prevalence (Grasset et al., 2016; Manton et al., 2005; Schrijvers et al., 2012). Results of a recent study in Switzerland also suggest a decline in the age-adjusted burden of amyloid deposition (Kovari et al., 2014), lending further support to the idea that the prevalence of Alzheimer’s disease is declining. A recent study in the Netherlands, based on primary care records from 1992 to 2014, did not find evidence of a decline (van Bussel et al., 2017), but this may be due to increasing recognition and diagnosis of dementia by clinicians over the time period studied (Larson and Langa, 2017).
In contrast with the apparent trends in higher-income countries, the prevalence of Alzheimer’s disease and dementia may not be declining in low- and middle-income countries (Chan et al., 2013; Gao et al., 2016; Wu et al., 2015). For example, there is some evidence that dementia prevalence appears to be increasing in some East Asian countries that are rapidly industrializing (Prince et al., 2016), whose populations show increased cardiovascular risk factors and rates of smoking, obesity, and metabolic diseases (Prince et al., 2016; Wu et al., 2015). However, it should be noted that these increases may be due in part to changes in diagnostic criteria (Prince et al., 2016), and that additional research is essential to inform understanding of future epidemiologic trends in dementia in this region given the rapidly changing socioeconomic environment and rise of noncommunicable diseases (Wu et al., 2015).
The apparent paradox that dementia prevalence is declining in higher-
income countries despite increases in cardiovascular risk factors could be explained, at least in part, by improvements in treatments for diabetes and heart disease and the decline of diabetes-related complications (Larson et al., 2013). Another factor correlated with a lower risk for dementia in both the HRS and the Framingham Heart Study is rising levels of education among U.S. adults (Federal Interagency Forum on Aging-Related Statistics, 2012). Other socioeconomic and environmental factors, including environmental exposures to toxins early in life, have been associated with dementia risk (Seifan et al., 2015) and could also explain the above paradox if overall trends mask disparities in dementia risk across subpopulations within higher-income counties. In the Framingham Heart Study, for example, a decline in dementia incidence was found only for those participants with at least a high school diploma (Satizabal et al., 2016). But education is a marker for socioeconomic status, making it challenging to disentangle the effects of each. Variation in risk by socioeconomic status also was demonstrated in analyses by Yaffe and colleagues (2013), which used data from the Health, Aging, and Body Composition study. The authors found that variation in dementia risk between white and black participants was no longer statistically significant when the analysis accounted for differences in socioeconomic status, a finding that underscores the inherent vulnerability of populations living in poverty in terms of both dementia risk and access to care (Yaffe et al., 2013).
Population-based estimates of the percentage of Alzheimer’s cases attributable to a given factor suggest that seven potentially modifiable risk factors—diabetes, midlife hypertension, midlife obesity, insufficient physical activity, depression, smoking, and low educational attainment—may account for about one-third of cases in the United States and Europe. The same analysis indicated that even a modest 10 percent reduction in each of these risk factors could reduce the prevalence of Alzheimer’s disease in these regions by about 8 percent by 2050 (Norton et al., 2014). These findings underscore the potential promise and importance of learning more about interventions that work to prevent cognitive decline and dementia.
It is important to note, however, that despite the trend toward declining age-specific incidence of dementia in high-income countries, increased longevity and the rise in the birth rate during the baby boom (1946 to 1964) mean that the overall number of people with dementia, and therefore its societal burden, will likely increase dramatically in the coming decades (Larson and Langa, 2017; Larson et al., 2013). This burden is felt most acutely among minority (Mayeda et al., 2016) and economically disadvantaged populations (Yaffe et al., 2013), as well as among the oldest old (Gardner et al., 2013). Among the latter population, specifically those aged 90 and older, dementia incidence has been shown to increase exponentially by age (Corrada et al., 2010).
NIA asked the National Academies to convene an expert committee to evaluate current scientific evidence and make recommendations that could inform public health messaging on preventive interventions for cognitive decline and dementia, as well as recommendations for future research. Biographies of the committee members are in Appendix C. As noted above, the primary basis for the committee’s work was a systematic review, commissioned and overseen by AHRQ and conducted by the Minnesota EPC, of the evidence on interventions that might prevent, delay the onset of, or slow MCI and CATD and delay or slow ARCD (see Appendix A). In accordance with the committee’s statement of task (see Box 1-2), other, less common dementias, such as frontotemporal dementia and Lewy body dementia, were excluded from the analysis, as were dementias with a clear etiology (e.g., incident stroke, AIDS, traumatic brain injury), for which prevention efforts would be focused on avoidance of causative factors. Also in accordance with the statement of task, interventions targeting stroke risk factors were given particular attention since they may contribute to CATD, and conditions that coexist with CATD as components of mixed dementia, such as vascular contributions to dementia, were included within the study scope. However, cognitive impairment that is likely to be caused solely by vascular disease (pure vascular dementia) was excluded. The committee was asked to focus on prevention among cognitively healthy individuals and those with MCI; the study did not specifically address the effectiveness of interventions in slowing the rate of decline among individuals already diagnosed with dementia. In addition, as noted earlier, this study was focused on the effectiveness of interventions; the committee did not explicitly address the identification of risk factors, which have been examined in depth by other studies (e.g., IOM, 2015). The committee also did not examine the potential of public health policies (e.g., access to education, clean air) to prevent cognitive decline and dementia. While outside the scope of this study, the committee recognizes that the evidence base related to such societal-level interventions is inadequate, and interdisciplinary research to inform such policies would be valuable, particularly to benefit the most disadvantaged populations.
Of course, for any intervention to be effective, adherence is essential. One study of older adults in the United States found that as many as 50 percent of patients did not adhere to a regimen of chronic medications, incurring an estimated $100 billion in preventable costs (Osterberg and Blaschke, 2005). Indeed, the WHO (2003) has estimated that increasing adherence could have a far greater impact on health than improvements in medical treatments. Recognizing that there are many reasons why individuals discontinue treatments and other interventions (e.g., side effects),
designing effective messages and interventions to enhance adherence, while beyond the scope of this study, is an important aspect of preventing cognitive decline and dementia.
The committee first met in December 2015 to provide input into the design of the AHRQ systematic review. This phase of the study included an open session in which NIA presented the charge to the committee, leaders from the Minnesota EPC review team provided a draft review protocol and discussed it with committee members, and other stakeholders were invited to comment (see Appendix B for the agendas of all open sessions). Following the meeting, the committee authored a brief letter report outlining its recommendations for the review design (NASEM, 2015).
In the following months, the EPC conducted the systematic review. The committee then reconvened in October 2016 after the draft review had been released. This second meeting included a day-long public workshop with presentations on the draft AHRQ systematic review by leaders from the EPC; individuals living with dementia; academic scientists; and representatives of government agencies, advocacy groups, and professional associations.
In January 2017, after the final AHRQ systematic review had been published, the committee met for a third and final time to develop its report. This meeting included a brief open session with leaders from the EPC and other interested parties to discuss the final version of the AHRQ systematic review.
AHRQ Systematic Review Design
The AHRQ systematic review represents the most up-to-date and thorough review of the RCT evidence available. The review relied primarily on RCTs with a minimum 6-month follow-up period for intermediate outcomes; large prospective quasi-experimental cohort studies with comparator arms (n ≥250 per arm) were also included in the search conducted for the review, but little concrete evidence emerged from such studies. Box 1-3 details the methodology of the review, the key questions addressed, and the inclusion criteria (the list of cohort studies and search details can be found in Appendix E of Kane et al. ).1 The AHRQ systematic review has several strengths: (1) it is the product of a systematic and extensive effort to summarize the state of the evidence; (2) it employed clear criteria
1 Throughout this report, for easy visual identification, boxes that present material quoted directly from the AHRQ systematic review are presented with rounded corners.
for inclusion and exclusion of studies (see Box 1-3); and (3) it provides a helpful framework for cross-classifying the literature in three ways—by intervention, by population, and by outcomes (CATD, MCI, and ARCD).
A key step in the systematic review process was a quality assessment of the risk of bias for potentially eligible studies. The EPC created an instrument to assess the risk of bias from elements of study design including participant selection, method of randomization or selection, blinding, allocation concealment, and attrition.2 Studies were classified as having a low, moderate, or high overall risk of bias based on the collective risk of bias inherent in each domain and confidence in the study results given the study limitations. Studies identified as having a high risk of bias generally were not included in the analysis for the AHRQ systematic review. However, such studies were still considered by the committee in identifying future research priorities, as the committee believed that less stringent criteria were appropriate for this purpose compared with those applied in identifying interventions to recommend for communications with the public. Where such studies are discussed in the following chapters, the committee notes that they were designated as having a high risk of bias.
Limitations of the Existing Randomized Controlled Trial Evidence
As noted earlier, the AHRQ systematic review was based almost exclusively on RCTs, generally regarded as the “gold standard” for providing evidence about the effectiveness of interventions. Yet it is particularly challenging to conduct RCTs for interventions that likely should be provided in midlife3 for prevention of cognitive conditions that develop in older adulthood and often are highly comorbid with other later-life conditions. Indeed, the AHRQ systematic review makes clear that virtually all evidence on preventive interventions for such conditions has significant shortcomings: no interventions met the review’s criteria for high-strength evidence of benefit based on the quality and design of the trials, and the review concluded that only a few RCTs can be used to inform recommendations on public health messaging.
These shortcomings stem partially from the challenges inherent in conducting RCTs on preventing cognitive decline and dementia. Examples of challenges that limit the strength of the evidence generated by existing studies include initiation of interventions at later life stages that may be
2 More detailed information on the instrument used to assess risk of bias can be found in Appendix B of the 2017 AHRQ systematic review, Interventions to prevent age-related cognitive decline, mild cognitive impairment, and clinical Alzheimer’s-type dementia (Kane et al., 2017) (see Appendix A).
3 Different age windows are used in studies that enroll participants in midlife, generally ranging from ages 35 to 65.
outside the optimal window; follow-up periods that are too short; high attrition (dropout and death); small sample sizes and studies underpowered to detect changes in incidence of MCI and CATD; heterogeneity in outcome measures and assessment tools; a focus on individual interventions when combinations of interventions may be most beneficial and, conversely, difficulty detecting which components of multimodal interventions are most effective in which combination(s); and difficulty identifying appropriate control groups. Chapter 3 describes these challenges in detail and examines opportunities to address them, including making greater use of new trial designs (e.g., adaptive trials) and analytic approaches for reducing biases related to attrition (e.g., methods for addressing missing data).
Use of Supplemental Evidence
Acknowledging the limitations of the evidence examined in the AHRQ systematic review, the committee supplemented that evidence by applying the judgment and expertise of its members to incorporate, when appropriate, information from a variety of other sources in developing the recommendations presented in this report. This supplemental evidence included
- available observational data—primarily from longitudinal population-based cohort studies—and evidence from neurobiological studies that address the effectiveness of a class of interventions;
- information from studies of risk factors;
- information about intervention effects on intermediate outcomes (e.g., changes in brain structure and function) that may predict ARCD, MCI, and CATD;
- knowledge about whether/how an intervention would benefit or harm other organ systems; and
- other general harms and costs potentially associated with an intervention.
In Chapter 2, which addresses communication with the public, the committee focuses on classes of interventions for which at least some supportive RCT data were identified, supplemented by the additional sources described above. Observational data and neurobiological knowledge are discussed more extensively in Chapter 4, which addresses priorities for future research. In addition to proposing further research on cognitive training, blood pressure management, and increased physical activity, which the committee found to be supported by encouraging but inconclusive evidence, Chapter 4 considers a number of interventions for which there was insufficient evidence and provides recommendations for future research priorities in these areas.
For well-known reasons, observational studies such as those cited in this report are imperfect and must be interpreted with caution. They demonstrate associations but not causation (Andrade, 2014). Confounding is a notable concern with observational studies, as it can result in misinterpretation of findings when an unmeasured factor affects both the outcome and the risk factor that is being examined, giving the false appearance of a causal relationship. Like RCTs, such studies also may be affected by subject selection bias and many other potential biases that are related to how studies are designed and reported, how participants are followed, and the accuracy of data collection (Viswanathan et al., 2012). Interpreting the findings from studies reporting associations with the incidence of cognitive decline may be further constrained by the use of a limited set of cognitive tests; changes in diagnostic thresholds and the frequency of diagnostic testing; changes in measures used to assess cardiovascular health, such as blood pressure; and changes in the use of self-reported versus informant-reported measures in both observational studies and RCTs (Glynn et al., 1999; Langa et al., 2017). Observational studies may be affected as well by changing recruitment and data collection strategies over time (Langa et al., 2017) and the difficulty of predicting trajectories of chronic diseases (Jones and Greene, 2013). Finally, while these studies suggest links between cognitive outcomes and modifiable risk factors, interventions (e.g., vitamin E supplementation, use of statins) targeting risk factors identified through observational studies have, in a number of cases, failed to translate into treatment benefit in RCTs conducted to date. Studies incorporating the full set of social, behavioral, and medical factors that may influence the risk of dementia are lacking (Langa et al., 2017), but this lack may never be remedied by studies that meet the evidence criteria of the AHRQ systematic review.
This report is divided into four chapters. Following this introductory chapter, Chapter 2 provides the committee’s analysis of the available evidence and recommendations regarding communicating with the public about the three interventions—cognitive training, blood pressure management for people with hypertension, and increased physical activity—supported by encouraging but inconclusive evidence. Chapter 3 presents recommendations for cross-cutting methodological improvements for future studies that would enhance the overall strength of evidence in this domain. Chapter 4 details future research priorities to enhance confidence in and tailoring of messages on the above three interventions, as well as others the committee deems potentially promising and worthy of prioritizing in future research. That chapter also summarizes the findings of the AHRQ system-
atic review regarding interventions for which there was some evidence of harm or low-strength evidence of no benefit.
AARP. 2017. Global Council on Brain Health. http://www.aarp.org/health/brain-health/global-council-on-brain-health (accessed January 24, 2017).
Alzheimer’s Association. 2015. Changing the trajectory of Alzheimer’s disease: How a treatment by 2025 saves lives and dollars. https://www.alz.org/documents_custom/trajectory.pdf (accessed March 2, 2017).
Andrade, C. 2014. Cause versus association in observational studies in psychopharmacology. Journal of Clinical Psychiatry 75(8):e781-e784.
Bateman, R. J., C. Xiong, T. L. Benzinger, A. M. Fagan, A. Goate, N. C. Fox, D. S. Marcus, N. J. Cairns, X. Xie, T. M. Blazey, D. M. Holtzman, A. Santacruz, V. Buckles, A. Oliver, K. Moulder, P. S. Aisen, B. Ghetti, W. E. Klunk, E. McDade, R. N. Martins, C. L. Masters, R. Mayeux, J. M. Ringman, M. N. Rossor, P. R. Schofield, R. A. Sperling, S. Salloway, and J. C. Morris. 2012. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. New England Journal of Medicine 367(9):795-804.
Baumgart, M., H. M. Snyder, M. C. Carrillo, S. Fazio, H. Kim, and H. Johns. 2015. Summary of the evidence on modifiable risk factors for cognitive decline and dementia: A population-based perspective. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association 11(6):718-726.
Chan, K. Y., W. Wang, J. J. Wu, L. Liu, E. Theodoratou, J. Car, L. Middleton, T. C. Russ, I. J. Deary, H. Campbell, W. Wang, and I. Rudan. 2013. Epidemiology of Alzheimer’s disease and other forms of dementia in China, 1990-2010: A systematic review and analysis. The Lancet 381(9882):2016-2023.
Concato, J., N. Shah, and R. I. Horwitz. 2000. Randomized, controlled trials, observational studies, and the hierarchy of research designs. New England Journal of Medicine 342(25):1887-1892.
Corrada, M. M., R. Brookmeyer, A. Paganini-Hill, D. Berlau, and C. H. Kawas. 2010. Dementia incidence continues to increase with age in the oldest old: The 90+ Study. Annals of Neurology 67(1):114-121.
David, P., and V. Gelfeld. 2014. Brain Health Research Study. AARP Research. http://www.aarp.org/content/dam/aarp/research/surveys_statistics/health/2015/2014-Brain-HealthResearch-Study-AARP-res-gen.pdf (accessed March 2, 2017).
Daviglus, M., C. Bell, W. Berrettini, P. Bowen, E. Connolly, N. Cox, J. Dunbar-Jacob, E. Granieri, G. Hunt, K. McGarry, D. Patel, A. Potosky, E. Sanders-Bush, D. Silberberg, and M. Trevisan. 2010. National Institutes of Health state-of-the-science conference statement: Preventing Alzheimer’s disease and cognitive decline. NIH Consensus & State-of-the-Science Statements 27(4):1-30.
Deckers, K., M. P. van Boxtel, O. J. Schiepers, M. de Vugt, J. L. Munoz Sanchez, K. J. Anstey, C. Brayne, J. F. Dartigues, K. Engedal, M. Kivipelto, K. Ritchie, J. M. Starr, K. Yaffe, K. Irving, F. R. Verhey, and S. Kohler. 2015. Target risk factors for dementia prevention: A systematic review and Delphi consensus study on the evidence from observational studies. International Journal of Geriatric Psychiatry 30(3):234-246.
Federal Interagency Forum on Aging-Related Statistics. 2012. Older Americans 2012: Key indicators of well-being. https://agingstats.gov/docs/PastReports/2012/OA2012.pdf (accessed March 2, 2017).
G-Science Academies. 2016. G-Science Academies statement 2016: Understanding, protecting, and developing global brain resources. http://fpcj.jp/wp/wp-content/uploads/2016/05/3Three-Joint-Statements-of-G-Science-Academies-2016.pdf (accessed March 2, 2017).
Gao, S., A. Ogunniyi, K. S. Hall, O. Baiyewu, F. W. Unverzagt, K. A. Lane, J. R. Murrell, O. Gureje, A. M. Hake, and H. C. Hendrie. 2016. Dementia incidence declined in African-Americans but not in Yoruba. Alzheimer’s & Dementia 12(3):244-251.
Gardner, R. C., V. Valcour, and K. Yaffe. 2013. Dementia in the oldest old: A multi-factorial and growing public health issue. Alzheimer’s Research & Therapy 5(4):27.
Glynn, R. J., L. A. Beckett, L. E. Hebert, M. C. Morris, P. A. Scherr, and D. A. Evans. 1999. Current and remote blood pressure and cognitive decline. Journal of the American Medical Association 281(5):438-445.
Grasset, L., C. Brayne, P. Joly, H. Jacqmin-Gadda, K. Peres, A. Foubert-Samier, J. F. Dartigues, and C. Helmer. 2016. Trends in dementia incidence: Evolution over a 10-year period in France. Alzheimer’s & Dementia 12(3):272-280.
Hebert, L. E., J. Weuve, P. A. Scherr, and D. A. Evans. 2013. Alzheimer disease in the United States (2010-2050) estimated using the 2010 census. Neurology 80(19):1778-1783.
Hurd, M. D., P. Martorell, A. Delavande, K. J. Mullen, and K. M. Langa. 2013. Monetary costs of dementia in the United States. New England Journal of Medicine 368(14):1326-1334.
IOM (Institute of Medicine). 2015. Cognitive aging: Progress in understanding and opportunities for action. Washington, DC: The National Academies Press.
Jack, Jr., C. R., D. S. Knopman, W. J. Jagust, L. M. Shaw, P. S. Aisen, M. W. Weiner, R. C. Petersen, and J. Q. Trojanowski. 2010. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. The Lancet Neurology 9(1):119-128.
Jones, D. S., and J. A. Greene. 2013. The decline and rise of coronary heart disease: Understanding public health catastrophism. American Journal of Public Health 103(7):1207-1218.
Kane, R. L., M. Butler, H. A. Fink, M. Brasure, H. Davila, P. Desai, E. Jutkowitz, E. McCreedy, V. Nelson, J. R. McCarten, C. Calvert, E. Ratner, L. Hemmy, and T. Barclay. 2017. Interventions to prevent age-related cognitive decline, mild cognitive impairment, and clinical Alzheimer’s-type dementia. Comparative effectiveness review 188. Rockville, MD: Agency for Healthcare Research and Quality.
Kawas, C. H., R. C. Kim, J. A. Sonnen, S. S. Bullain, T. Trieu, and M. M. Corrada. 2015. Multiple pathologies are common and related to dementia in the oldest-old: The 90+ study. Neurology 85(6):535-542.
Kovari, E., F. R. Herrmann, C. Bouras, and G. Gold. 2014. Amyloid deposition is decreasing in aging brains: An autopsy study of 1,599 older people. Neurology 82(4):326-331.
Langa, K. M., E. B. Larson, E. M. Crimmins, J. D. Faul, D. A. Levine, M. U. Kabeto, and D. R. Weir. 2017. A comparison of the prevalence of dementia in the United States in 2000 and 2012. JAMA Internal Medicine 177(1):51-58.
Larson, E. B., and K. M. Langa. 2017. What’s the “take home” from research on dementia trends. PLoS Medicine 14(3):e1002236.
Larson, E. B., K. Yaffe, and K. M. Langa. 2013. New insights into the dementia epidemic. New England Journal of Medicine 369(24):2275-2277.
Livingston, G., and H. Frankish. 2015. A global perspective on dementia care: A Lancet Commission. The Lancet 386(9997):933-934.
Manton, K. C., X. L. Gu, and S. V. Ukraintseva. 2005. Declining prevalence of dementia in the U.S. elderly population. Advances in Gerontology 16:30-37.
Matthews, F. E., B. C. Stephan, L. Robinson, C. Jagger, L. E. Barnes, A. Arthur, and C. Brayne. 2016. A two decade dementia incidence comparison from the Cognitive Function and Ageing Study I and II. Nature Communications 7:11398.
Mayeda, E. R., M. M. Glymour, C. P. Quesenberry, and R. A. Whitmer. 2016. Inequalities in dementia incidence between six racial and ethnic groups over 14 years. Alzheimer’s & Dementia 12(3):216-224.
NASEM (National Academies of Sciences, Engineering, and Medicine). 2015. Considerations for the design of a systematic review of interventions for preventing clinical Alzheimer’s-type dementia, mild cognitive impairment, and age-related cognitive decline: Letter report. Washington, DC: The National Academies Press.
Norton, S., F. E. Matthews, D. E. Barnes, K. Yaffe, and C. Brayne. 2014. Potential for primary prevention of Alzheimer’s disease: An analysis of population-based data. The Lancet Neurology 13(8):788-794.
Osterberg, L., and T. Blaschke. 2005. Adherence to medication. New England Journal of Medicine 353(5):487-497.
Plassman, B. L., K. M. Langa, G. G. Fisher, S. G. Heeringa, D. R. Weir, M. B. Ofstedal, J. R. Burke, M. D. Hurd, G. G. Potter, W. L. Rodgers, D. C. Steffens, R. J. Willis, and R. B. Wallace. 2007. Prevalence of dementia in the United States: The Aging, Demographics, and Memory Study. Neuroepidemiology 29(1-2):125-132.
Plassman, B. L., K. M. Langa, G. G. Fisher, S. G. Heeringa, D. R. Weir, M. B. Ofstedal, J. R. Burke, M. D. Hurd, G. G. Potter, W. L. Rodgers, D. C. Steffens, J. J. McArdle, R. J. Willis, and R. B. Wallace. 2008. Prevalence of cognitive impairment without dementia in the United States. Annals of Internal Medicine 148(6):427-434.
Prince, M., A. Wimo, M. Guerchet, G. C. Ali, Y. T. Wu, and M. Prina. 2015. World Alzheimer report 2016: The global impact of dementia: An analysis of prevalence, incidence, cost and trends. https://www.alz.co.uk/research/WorldAlzheimerReport2015.pdf (accessed May 31, 2017).
Prince, M., G. C. Ali, M. Guerchet, A. M. Prina, E. Albanese, and Y. T. Wu. 2016. Recent global trends in the prevalence and incidence of dementia, and survival with dementia. Alzheimer’s Research & Therapy 8(1):23.
Reiman, E. M., Y. T. Quiroz, A. S. Fleisher, K. Chen, C. Velez-Pardo, M. Jimenez-Del-Rio, A. M. Fagan, A. R. Shah, S. Alvarez, A. Arbelaez, M. Giraldo, N. Acosta-Baena, R. A. Sperling, B. Dickerson, C. E. Stern, V. Tirado, C. Munoz, R. A. Reiman, M. J. Huentelman, G. E. Alexander, J. B. Langbaum, K. S. Kosik, P. N. Tariot, and F. Lopera. 2012. Brain imaging and fluid biomarker analysis in young adults at genetic risk for autosomal dominant Alzheimer’s disease in the presenilin 1 e280a kindred: A case-control study. The Lancet Neurology 11(12):1048-1056.
Ritchie, K., C. W. Ritchie, K. Yaffe, I. Skoog, and N. Scarmeas. 2015. Is late-onset Alzheimer’s disease really a disease of midlife? Alzheimer’s & Dementia: Translational Research & Clinical Interventions 1(2):122-130.
Satizabal, C. L., A. S. Beiser, V. Chouraki, G. Chene, C. Dufouil, and S. Seshadri. 2016. Incidence of dementia over three decades in the Framingham Heart Study. New England Journal of Medicine 374(6):523-532.
Schrijvers, E. M., B. F. Verhaaren, P. J. Koudstaal, A. Hofman, M. A. Ikram, and M. M. Breteler. 2012. Is dementia incidence declining?: Trends in dementia incidence since 1990 in the Rotterdam Study. Neurology 78(19):1456-1463.
Seifan, A., M. Schelke, Y. Obeng-Aduasare, and R. Isaacson. 2015. Early life epidemiology of Alzheimer’s disease—a critical review. Neuroepidemiology 45(4):237-254.
Sonnen, J. A., K. S. Montine, J. F. Quinn, J. A. Kaye, J. C. Breitner, and T. J. Montine. 2008. Biomarkers for cognitive impairment and dementia in elderly people. The Lancet Neurology 7(8):704-714.
Sperling, R. A., P. S. Aisen, L. A. Beckett, D. A. Bennett, S. Craft, A. M. Fagan, T. Iwatsubo, C. R. Jack, Jr., J. Kaye, T. J. Montine, D. C. Park, E. M. Reiman, C. C. Rowe, E. Siemers, Y. Stern, K. Yaffe, M. C. Carrillo, B. Thies, M. Morrison-Bogorad, M. V. Wagster, and C. H. Phelps. 2011. Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia 7(3):280-292.
Sperling, R. A., D. M. Rentz, K. A. Johnson, J. Karlawish, M. Donohue, D. P. Salmon, and P. Aisen. 2014. The A4 study: Stopping AD before symptoms begin? Science Translational Medicine 6(228):228fs213.
van Bussel, E. F., E. Richard, D. L. Arts, A. C. J. Nooyens, P. M. Coloma, M. W. M. de Waal, M. van den Akker, M. C. Biermans, M. M. Nielen, K. van Boven, H. Smeets, F. E. Matthews, C. Brayne, W. B. Busschers, W. A. van Gool, and E. P. Moll van Charante. 2017. Dementia incidence trend over 1992–2014 in the Netherlands: Analysis of primary care data. PLoS Medicine 14(3):e1002235.
Viswanathan, M., M. T. Ansari, N. D. Berkman, S. Chang, L. Hartling, L. M. McPheeters, P. L. Santaguida, T. Shamliyan, K. Singh, A. Tsertsvadze, and J. R. Treadwell. 2012. Assessing the risk of bias of individual studies in systematic reviews of health care interventions. 12EHC047-EF. Rockville, MD: Agency for Healthcare Research and Quality.
WHO (World Health Organization). 2003. Adherence to long-term therapies: Evidence for action. Geneva, Switzerland: WHO.
WHO. 2016. Draft global action plan on the public health response to dementia: Report by the director-general. Geneva, Switzerland: WHO.
Williams, J. W., B. L. Plassman, J. Burke, and S. Benjamin. 2010. Preventing Alzheimer’s disease and cognitive decline. Evidence Report/Technology Assessment 193:1-727.
Wu, Y. T., C. Brayne, and F. E. Matthews. 2015. Prevalence of dementia in East Asia: A synthetic review of time trends. International Journal of Geriatric Psychiatry 30(8):793-801.
Yaffe, K., C. Falvey, T. B. Harris, A. Newman, S. Satterfield, A. Koster, H. Ayonayon, and E. Simonsick. 2013. Effect of socioeconomic disparities on incidence of dementia among biracial older adults: Prospective study. BMJ 347:f7051.