2015-2022. This data set contains data from BRFSS.

In the United States, around 39 percent of people with Alzheimer’s are 75 to 84 years old. Additionally, around 26 percent of those with Alzheimer’s are aged 65 to 74 years. Alzheimer’s disease is a form of dementia which impacts memory, behavior, and thinking and can lead to symptoms becoming so severe that those with the disease require support with basic daily tasks. Alzheimer’s remains a relevant problem around the world. Alzheimer’s disease deaths Alzheimer’s is currently the seventh leading cause of death in the United States, causing more deaths than diabetes and kidney disease. While advances in medicine and increased access to treatment and care have caused decreases in many major causes of death, deaths from Alzheimer’s have risen in recent years. For example, from 2000 to 2021, deaths from stroke in the U.S. dropped by 2.8 percent, while deaths from Alzheimer’s increased 141 percent. Alzheimer’s disease worldwide Alzheimer’s is not only a problem in the United States but impacts every country around the globe. In 2018, there were an estimated 50 million people living with dementia worldwide. This figure is predicted to increase to some 152 million by the year 2050. Alzheimer’s does not only cause a significant amount of death but also has a significant economic impact. In 2018, cost estimates for Alzheimer’s care worldwide totaled around one trillion U.S. dollars, with this figure predicted to double by the year 2030.

The global dementia care products market is likely to be valued at US$ 29.1 million in 2023. During the period of assessment, the market is expected to flourish at a CAGR of 6.9% to reach US$ 56.7 million by 2033. The dementia care products industry is continuously observing major growth, attributed to the rising awareness about disease management, increased sales prospects, advancement in devices, and, improvement in the existing devices.

Data points	Key Statistics
Global Dementia Care Products Market CAGR (2023 to 2033)	6.9%
Anticipated Market Value (2023)	US$ 29.1 million
Global Dementia Care Products Market (2033)	US$ 56.7 million

Why is North America Emerging as an Opportunistic Dementia Care Products Market?

Region	North America
Market Share % (2022)	36.7%

Why is the Demand for Dementia Care Products Burgeoning in Europe?

Region	Europe
Market Share % (2022)	31.3%

How is Asia Pacific Bolstering the Demand for Dementia Care Products?

Countries	Market Share % (2023 to 2033)
China	9.2%
India	8.8%

Report Scope

Report Attribute	Details
Growth Rate	CAGR of 6.9% from 2023 to 2033
Base Year for Estimation	2022
Market Value in 2023	US$ 29.1 million
Market Value in 2033	US$ 56.7 million
Forecast Period	2023 to 2033
Quantitative Units	Revenue in US$ million and CAGR from 2023 to 2033
Report Coverage	Revenue Forecast, Company Ranking, Competitive Landscape, Growth Factors, Trends, and Pricing Analysis
Segments Covered	Product Type End User Region
Regions Covered	North America Latin America Europe Asia Pacific Middle East and Africa
Key Countries Profiled	The United States Canada Brazil Mexico Germany The United Kingdom France Spain Italy China Japan South Korea GCC South Africa Israel
Key Companies Profiled	Parentgiving, Inc. EasierLiving, LLC Find Memory Care Healthcare Products LLC Best Alzheimer's Products NRS Healthcare Buddi Ltd.
Customization	Available Upon Request

EWA-DB is a speech database that contains data from 3 clinical groups: Alzheimer's disease, Parkinson's disease, mild cognitive impairment, and a control group of healthy subjects. Speech samples of each clinical group were obtained using the EWA smartphone application, which contains 4 different language tasks: sustained vowel phonation, diadochokinesis, object and action naming (30 objects and 30 actions), picture description (two single pictures and three complex pictures).The total number of speakers in the database is 1649. Of these, there are 87 people with Alzheimer's disease, 175 people with Parkinson's disease, 62 people with mild cognitive impairment, 2 people with a mixed diagnosis of Alzheimer's + Parkinson's disease and 1323 healthy controls.For speakers who provided written consent (total number of 1003 speakers), we publish audio recordings in WAV format. We are also attaching a JSON file with ASR transcription, if available manual annotation (available for 965 speakers) and additional information about the speaker. For speakers who did not give their consent to publish the recording, only the JSON file is provided. ASR transcription is provided for all 1649 speakers. All 1649 speakers gave their consent to the provider to process their audio recordings. Therefore, it is possible for third party researchers to carry out their experiments also on the unpublished audio recordings through cooperation with the provider.

Aim: To estimate the prevalence of Alzheimer’s disease (AD) and other dementias in the USA using a nationally representative sample. Methods: The Aging, Demographics, and Memory Study sample was composed of 856 individuals aged 71 years and older from the nationally representative Health and Retirement Study (HRS) who were evaluated for dementia using a comprehensive in-home assessment. An expert consensus panel used this information to assign a diagnosis of normal cognition, cognitive impairment but not demented, or dementia (and dementia subtype). Using sampling weights derived from the HRS, we estimated the national prevalence of dementia, AD and vascular dementia by age and gender. Results: The prevalence of dementia among individuals aged 71 and older was 13.9%, comprising about 3.4 million individuals in the USA in 2002. The corresponding values for AD were 9.7% and 2.4 million individuals. Dementia prevalence increased with age, from 5.0% of those aged 71–79 years to 37.4% of those aged 90 and older. Conclusions: Dementia prevalence estimates from this first nationally representative population-based study of dementia in the USA to include subjects from all regions of the country can provide essential information for effective planning for the impending healthcare needs of the large and increasing number of individuals at risk for dementia as our population ages.

Alzheimer's Disease Neuroimaging Initiative (ADNI) is a multisite study that aims to improve clinical trials for the prevention and treatment of Alzheimer’s disease (AD).[1] This cooperative study combines expertise and funding from the private and public sector to study subjects with AD, as well as those who may develop AD and controls with no signs of cognitive impairment.[2] Researchers at 63 sites in the US and Canada track the progression of AD in the human brain with neuroimaging, biochemical, and genetic biological markers.[2][3] This knowledge helps to find better clinical trials for the prevention and treatment of AD. ADNI has made a global impact,[4] firstly by developing a set of standardized protocols to allow the comparison of results from multiple centers,[4] and secondly by its data-sharing policy which makes available all at the data without embargo to qualified researchers worldwide.[5] To date, over 1000 scientific publications have used ADNI data.[6] A number of other initiatives related to AD and other diseases have been designed and implemented using ADNI as a model.[4] ADNI has been running since 2004 and is currently funded until 2021.[7]

Source: Wikipedia, https://en.wikipedia.org/wiki/Alzheimer%27s_Disease_Neuroimaging_Initiative

Additional file1: Supplementary Table 1. ICD-10 Diagnostic Codes for Dementia [24].

Background: Trajectories of comorbidities among individuals at risk of Alzheimer’s disease (AD) may differ from those aging without AD clinical syndrome. Therefore, characterizing the comorbidity burden and pattern associated with AD risk may facilitate earlier detection, enable timely intervention, and help slow the rate of cognitive and functional decline in AD. This case-control study was performed to compare the prevalence of comorbidities between AD cases and controls during the 5 years prior to diagnosis (or index date for controls); and to identify comorbidities with a differential time-dependent prevalence trajectory during the 5 years prior to AD diagnosis.Methods: Incident AD cases and individually matched controls were identified in a United States claims database between January 1, 2000 and December 31, 2016. AD status and comorbidities were defined based on the presence of diagnosis codes in administrative claims records. Generalized estimating equations were used to assess evidence of changes over time and between AD and controls. A principal component analysis and hierarchical clustering was performed to identify groups of AD-related comorbidities with respect to prevalence changes over time (or trajectory), and differences between AD and controls.Results: Data from 186,064 individuals in the IBM MarketScan Commercial Claims and Medicare Supplementary databases were analyzed (93,032 AD cases and 93,032 non-AD controls). In total, there were 177 comorbidities with a ≥ 5% prevalence. Five main clusters of comorbidities were identified. Clusters differed between AD cases and controls in the overall magnitude of association with AD, in their diverging time trajectories, and in comorbidity prevalence. Three clusters contained comorbidities that notably increased in frequency over time in AD cases but not in controls during the 5-year period before AD diagnosis. Comorbidities in these clusters were related to the early signs and/or symptoms of AD, psychiatric and mood disorders, cerebrovascular disease, history of hazard and injuries, and metabolic, cardiovascular, and respiratory complaints.Conclusion: We demonstrated a greater comorbidity burden among those who later developed AD vs. controls, and identified comorbidity clusters that could distinguish these two groups. Further investigation of comorbidity burden is warranted to facilitate early detection of individuals at risk of developing AD.

Additional file 2: Supplementary Table 2. ICD-10 Diagnostic Codes for Behavioral Disturbances [24].

Bio project 734525 (NCBI) This dataset was considered competent in the project due to its characteristic nature of Host_disease, seen as patients with AD (Alzheimer's) Vs MCI (Mild Cognitive Impairment) Vs Healthy. Gut microbiome profiles of Alzheimer's disease patients with different cognitive states enabled us to compare the microbial dysbiosis between each of these patient populations giving us a clearer picture of the effect of the gut-brain microbiome axis in neurological disorders.

The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is a two-year clinical trial to evaluate whether lifestyle interventions that simultaneously target multiple risk factors protect cognitive function in older adults (age 60-79) at increased risk for cognitive decline. U.S. POINTER is the first such study to be conducted in a large group of Americans.

Recent studies have highlighted that the proteome can be used to identify potential biomarker candidates for Alzheimer’s disease (AD) in diverse cohorts. Furthermore, the racial and ethnic background of participants is an important factor to consider to ensure the effectiveness of potential biomarkers for representative populations. A promising approach to survey potential biomarker candidates for diagnosing AD in diverse cohorts is the application of machine learning to proteomics data sets. Herein, we leveraged six existing bottom-up proteomics data sets, which included non-Hispanic White, African American/Black, and Hispanic participants, to study protein changes in AD and cognitively unimpaired participants. Machine learning models were applied to these data sets and resulted in the identification of amyloid-β precursor protein (APP) and heat shock protein β-1 (HSPB1) as two proteins that have high ability to distinguish AD; however, each protein’s performance varied based upon the racial and ethnic background of the participants. HSPB1 particularly was helpful for generating high areas under the curve (AUCs) for African American/Black participants. Overall, HSPB1 improved the performance of the machine learning models when combined with APP and/or participant age and is a potential candidate that should be further explored in AD biomarker discovery efforts.

Data by medical encounter for the following conditions by age, race/ethnicity, and gender:
Alzheimer's Disease
Alzheimer's Disease and Related Dementias (ADRD)
Dementia
Neurocognitive Disorders
Parkinson's Disease

Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population.
Blank Cells: Rates not calculated for fewer than 11 events. Rates not calculated in cases where zip code is unknown. Geography not reported where there are no cases reported in a given year. SES: Is the median household income by SRA community. Data for SRAs only.
*The COVID-19 pandemic was associated with increases in all-cause mortality. COVID-19 deaths have affected the patterns of mortality, including those of ADRD conditions.

Data sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System (VRBIS). California Department of Health Care Access and Information (HCAI), Emergency Department Database and Patient Discharge Database, 2020. SANDAG Population Estimates, 2020 (vintage: 09/2022). Population estimates were derived using the 2010 Census and data should be considered preliminary. Prepared by: County of San Diego, Health and Human Services Agency, Public Health Services, Community Health Statistics Unit, February 2023.

2020 Community Profile Data Guide and Data Dictionary Dashboard: https://public.tableau.com/app/profile/chsu/viz/2020CommunityProfilesDataGuideandDataDictionaryDashboard_16763944288860/HomePage

This dataset contains data from WHO's data portal covering the following categories:

Adolescent, Ageing, Air pollution, Assistive technology, Child, Child mortality, Cross-cutting, Dementia diagnosis, treatment and care, Environment and health, Foodborne Diseases Estimates, Global Dementia Observatory (GDO), Global Health Estimates: Life expectancy and leading causes of death and disability, Global Information System on Alcohol and Health, Global Patient Safety Observatory, Global strategy, HIV, Health financing, Health systems, Health taxes, Health workforce, Hepatitis, Immunization coverage and vaccine-preventable diseases, Malaria, Maternal and newborn, Maternal and reproductive health, Mental health, Neglected tropical diseases, Noncommunicable diseases, Nutrition, Oral Health, Priority health technologies, Resources for Substance Use Disorders, Road Safety, SDG Target 3.8 | Achieve universal health coverage (UHC), Sexually Transmitted Infections, Tobacco control, Tuberculosis, Vaccine-preventable communicable diseases, Violence prevention, Water, sanitation and hygiene (WASH), World Health Statistics.

For links to individual indicator metadata, see resource descriptions.

The Harmonised Cognitive Assessment Protocol (HCAP) is part of the Healthy Cognitive Aging Project, a study examining how people's memory and thinking change as they get older. In England, HCAP is a sub-study of ELSA, the English Longitudinal Study of Ageing (ELSA), a longitudinal survey of ageing and quality of life among older people that explores the dynamic relationships between health and functioning, social networks and participation, and economic position as people plan for, move into and progress beyond retirement. (The main ELSA study is held under SN 5050.)

ELSA-HCAP1 took place in 2018 and interviewed ELSA core members aged 65 and over. It included a second, shorter interview with an informant, a family member or friend nominated by the ELSA core member to complete an interview on their behalf. ELSA-HCAP2 took place in 2023 and interviewed ELSA-HCAP1 sample members and additional ELSA core members aged 65 and over, and also included an informant interview.

The HCAP study originated with the Health and Retirement Study (HRS) in the United States, which is a sister study to ELSA, a longitudinal study of people aged 50 and over in the United States. Researchers on HRS developed the protocols for HCAP, in discussion with researchers from ELSA and other international studies, and fieldwork in the United States began while ELSA-HCAP in England was still in the planning stages.

The aim of ELSA-HCAP is to measure the prevalence of dementia and cognitive impairment among older people in the ELSA panel, in order to:

• Understand more about how widespread these conditions are in England and increase our understanding of dementia;
• Test how well the cognitive assessments used in this study can identify these conditions.
• Examine the 5-year change in cognitive function in the subset of respondents who take part across multiple waves of ELSA-HCAP.

HCAP scores developed by Alden Gross and colleagues - February 2024

For the third edition (February 2024), harmonised general and domain-specific cognitive scores were added from HCAP studies across six countries: China, England, India, Mexico, South Africa and the USA. The harmonised cognitive function scores have been developed by Alden Gross and colleagues. These scores empirically reflect comparable domains of cognitive function among older adults across the six countries, have high reliability and are useful for population-based research. The accompanying documentation includes a guidance file and the publication by Gross et al. (with supplement) that explains the scores and how they were derived. Each of the 1,273 participants in HCAP1 has a score on general cognitive function, executive function, language, orientation, and memory.

ELSA-HCAP2 and Family and Friends (Informant) data deposited - February 2025

For the fourth edition (February 2025), the ELSA-HCAP2 and Family and Friends (Informant) 2023 data and documentation were deposited. The ELSA-HCAP2 dataset contains 2,022 cases and the Family and Friends (Informant) dataset contains 1,807 cases. Data were collected between April to November 2023 for ELSA-HCAP2 and April to December 2023 for Family and Friends (Informants). ELSA-HCAP2 had an additional aim; to examine the 5-year change in cognitive function in the subset of respondents that took part in ELSA-HCAP1 in 2018. Users should note that the data submitted currently contains no survey weights, and the technical report is not yet available. Both elements will be added in due course.

Our ability to learn unfamiliar routes declines in typical and atypical ageing. The reasons for this decline, however, are not well understood. Here we used eye-tracking to investigate how ageing affects people’s ability to attend to navigationally relevant information and to select unique objects as landmarks. We created short routes through a virtual environment, each comprised of four intersections with two objects each, and we systematically manipulated the saliency and uniqueness of these objects. While salient objects might be easier to memorise than non-salient objects, they cannot be used as reliable landmarks if they appear more than once along the route. As cognitive ageing affects executive functions and control of attention, we hypothesised that the process of selecting navigationally relevant objects as landmarks might be affected as well. The behavioural data showed that younger participants outperformed the older participants and the eye-movement data revealed some systematic differences between age groups. Specifically, older adults spent less time looking at the unique, and therefore navigationally relevant, landmark objects. Both young and older participants, however, effectively directed gaze towards the unique and away from the non-unique objects, even if these were more salient. These findings highlight specific age-related differences in the control of attention that could contribute to declining route learning abilities in older age. Interestingly, route-learning performance in the older age group was more variable than in the young age group with some older adults showing performance similar to the young group. These individual differences in route learning performance were strongly associated with verbal and episodic memory abilities.Knowing where we are and how to get to places are fundamental features of successful everyday living. Although most of us rely automatically and unquestioningly on our wayfinding abilities, they are markedly impaired in people with Alzheimer's disease (AD), the most prevalent form of dementia. This project will identify the features of buildings that make them relatively harder or easier for people with AD to navigate. The knowledge gained will allow us to create dementia-friendly architectural guidelines for use in the design of residences for people with AD. Many people with AD eventually move from their familiar home environments into unfamiliar care homes. Unfortunately, the dramatic reduction in wayfinding skills commonly seen at the onset of AD is particularly marked when it comes to learning unfamiliar environments. Thus, people with AD would have an easier transition to new residences if these larger - and often more institutional - environments were designed to be dementia-friendly in terms of wayfinding. A psychological understanding of orientation and navigation could play a major role here but, unfortunately, current design-guidelines are mainly based on custom and practice, not theory and research. This project aims to improve matters through a series of experiments on navigation in people with AD. Our research is innovative in several ways: We will use Virtual Reality (VR) technology to simulate unfamiliar care home environments. VR lets us change environmental features and structures systematically, to monitor how these changes impact on learning to way-find over a period of several weeks. This would be impractical in real world settings. Additionally, by using state-of-the-art eye tracking technology to record gaze direction, we can pinpoint the types of cues people use to find their way through unfamiliar environments (www.spatial-cognition.org). Finally, our experiments will allow us not only to measure the way in which navigation abilities decline in people with AD, but also to identify the mechanisms underlying these declines. Successful navigation depends on learning to recognise places by identifying and remembering landmarks, environmental cues that are unique to each location. We will investigate this process in more detail. Our experiments will examine how AD impacts on landmark selection by comparing the performance of people with AD and healthy adults of a similar age (age-matched controls). Our participants will learn routes through virtual residences that include multiple intersections. We will systematically vary the features present at the intersections to determine whether people with AD have particular difficulties when the same distractor cues are present at more than one intersection, and/or when uninformative cues are nevertheless particularly noticeable (salient). Next, we will use VR to simulate what happens when people move into unfamiliar residences. Over several weeks, we will (a) teach people with AD and age-matched controls to navigate a number of different routes through the same environment, and (b) compare their ability to discover new routes through the same environment, based on knowledge of the routes they have just learned. VR allows for systematic comparisons of different floor plans, so we will be able to establish the kinds of architectural structure that either help or hinder wayfinding in people with AD. A key output of the research will be a set of empirically validated design guidelines that support effective wayfinding in people with AD. Because these principles will be widely applicable, we will work with architects, building standards agencies and care commissioning bodies to ensure that they are used to develop national standards for residential care home design. Our research will thus help to increase or preserve the independence and well-being of people with AD, avoiding a further loss of autonomy, dignity and control that is, in theory, preventable. A total of 80 participants (32 younger adults [17 females; mean age 24.25 +- 6.38 years; range, 18-40] and 48 older adults [24 females; mean age 73.28 +- 4.82 years; range, 66-82]) took part in the experiment. Participants were first administered a battery of cognitive tests to assess overall cognitive function, verbal and visual memory, and working memory. Thereafter, we tested the participants' route learning performance and measured their eye movements. Participants were shown videos of the routes through the virtual environment in the training phases. In the subsequent test phases, full-screen images of the four intersections were presented in a random order and participants had to indicate the movement direction required to continue along the route by pressing the corresponding arrow key using a standard keyboard.