In 2019, around four percent of females aged 70 to 74 years worldwide had dementia, compared to 39 percent of women aged 90 years and older. This statistic illustrates the prevalence of dementia among adults aged 40 years and older worldwide as of 2019, by gender and age.

In 2019, approximately 45 percent of women and 30 percent of men aged 90 years and above in Europe were living with dementia. In general, the share of women living with dementia in Europe was higher in comparison to men.

In 2022, there were around 549 deaths in which dementia was the underlying cause per 100,000 population among those aged 65 years and older in the United States. The death rate for dementia among senior women was around 465 per 100,000 population, compared to a rate of 600 among men. This statistic shows the rate of death for dementia among those aged 65 years and older in the U.S. from 2018 to 2022, by gender.

In 2020, there were over 189 thousand women who were care partners in Canada for people living with dementia. By 2050, that figure is expected to increase to almost 539 thousand. This statistic illustrates the number of care partners for people living with dementia in Canada in 2020 and a projection for 2050, by gender.

This dataset presents a comprehensive overview of Alzheimer’s disease. Alzheimer’s is the most common type of dementia and is a progressive disease affecting nearly 6 million people. Alzheimer’s disease involves parts of the brain that control thought, memory, and language. It can seriously affect a person’s ability to carry out daily activities. It begins with mild memory loss and can lead to loss of ability to carry a conversation and respond to the environment.

Here are several potential analyses that can be performed:

Prevalence Analysis: Explore the overall prevalence of Alzheimer's disease across different years and locations.

Demographic Trends: Examine the distribution of Alzheimer's cases by age, gender, and ethnicity. Analyze how the prevalence varies across different demographic groups.

Geospatial Mapping: Create maps to visualize the geographic distribution of Alzheimer's cases. Identify regions with higher or lower prevalence rates.

Temporal Trends: Investigate how the prevalence of Alzheimer's has changed over the years. Identify any significant temporal patterns or trends. Confidence Interval Analysis:

Age-specific Analysis: Analyze how Alzheimer's prevalence varies across different age groups. Identify any age-specific trends or patterns.

Gender and Ethnicity Insights: Investigate how Alzheimer's prevalence differs among genders and ethnicities.

Ethnicity-specific Analysis: Explore variations in Alzheimer's prevalence within different ethnic groups.

The United Nations declareds 2021–2030 the ‘Decade of Healthy Ageing ’. Both individuals and society suffer from increasing rates of Alzheimer’s disease and other types of dementia (ADs). In 2019, these diseases contributed to a loss of 33.1 million years of healthy life globally. However, existing research has not fully analyzed the relationship among socioeconomic data and ADs. This study was designed to explore the relationship between Alzheimer’s disease rates and socioeconomic conditions in 120 countries. We used mixed effect models to investigate the relationship between the rates of ADs and socioeconomic data. The data was obtained from global databases, including from The Global Burden of Disease and World Bank . The socioeconomic data included information onf gender inequality, wealth inequality, and countries’ overall wealth. This study is among the first studies to put forward statistical evidence of a significant association between AD and other dementias among the elderly and socioeconomic inequality. These findings could help to inform the policies to be designed to improve the quality of interventions for ADs. Date Submitted: 2022-07-20

Objective: A previous study reported a U-shaped association between fasting insulin and dementia in a 5-year follow-up of a male cohort, which was now re-investigated in a representative population of women followed over 34 years, and taking into account the incidence of diabetes. Methods: Fasting values for insulin and glucose were obtained from serum samples in 1212 non-diabetic women aged 38-60 at the 1968 baseline. Risk of dementia was assessed by Cox proportional hazard regression adjusting for insulin, glucose, and other covariates, and, in a second model, after censoring for incident cases of diabetes. Incident diabetes was considered as a third endpoint, for comparison with dementia. Results: Over 34 years, we observed 142 incident cases of dementia. The low tertile of insulin displayed excess risk for dementia, hazard ratio (HR) = 2.34, 95% confidence interval = (1.52, 3.58), compared to the medium tertile, but the high tertile of insulin did not, HR = 1.28 (0.81, 2.03). These...

This study explores the effectiveness of Automatic Speech Recognition (ASR) in building end-to-end automatic speech diagnosis and prediction models. We implemented three publicly available ASR engines including Xunfei, Tencent, and Aliyun, and compared the classifiability using the ADReSS-IS2020 public dataset (https://dementia.talkbank.org/). The dataset is a balanced subset selected from the Pitt corpus in the DementiaBank database with the effects of gender and age bias removed. The provided feature file name is composed of the ASR engine name and the data collection category. Our feature data file contains 157 native English-speaking participants, including 78 AD patients and 78 healthy individuals. The test set division for classification was officially provided, where the training set contained 108 participants and the test set contained 48 testers. The data columns contain the sex and label of the participants and the names of the extracted acoustic and textual features. Here we have used only textual features for all the experiments.

In 2020, women aged 65 years and older accounted for nearly 62 percent of dementia patients in Canada. This statistic illustrates the distribution of adults aged 65 years and older in Canada with dementia in 2020 and forecasts for 2030, 2040, and 2050, by gender.

Age, gender and number of encounter statistics for cohorts.

Background: Advances in machine learning (ML) technology have opened new avenues for detection and monitoring of cognitive decline. In this study, a multimodal approach to Alzheimer's dementia detection based on the patient's spontaneous speech is presented. This approach was tested on a standard, publicly available Alzheimer's speech dataset for comparability. The data comprise voice samples from 156 participants (1:1 ratio of Alzheimer's to control), matched by age and gender.Materials and Methods: A recently developed Active Data Representation (ADR) technique for voice processing was employed as a framework for fusion of acoustic and textual features at sentence and word level. Temporal aspects of textual features were investigated in conjunction with acoustic features in order to shed light on the temporal interplay between paralinguistic (acoustic) and linguistic (textual) aspects of Alzheimer's speech. Combinations between several configurations of ADR features and more traditional bag-of-n-grams approaches were used in an ensemble of classifiers built and evaluated on a standardised dataset containing recorded speech of scene descriptions and textual transcripts.Results: Employing only semantic bag-of-n-grams features, an accuracy of 89.58% was achieved in distinguishing between Alzheimer's patients and healthy controls. Adding temporal and structural information by combining bag-of-n-grams features with ADR audio/textual features, the accuracy could be improved to 91.67% on the test set. An accuracy of 93.75% was achieved through late fusion of the three best feature configurations, which corresponds to a 4.7% improvement over the best result reported in the literature for this dataset.Conclusion: The proposed combination of ADR audio and textual features is capable of successfully modelling temporal aspects of the data. The machine learning approach toward dementia detection achieves best performance when ADR features are combined with strong semantic bag-of-n-grams features. This combination leads to state-of-the-art performance on the AD classification task.

The dataset available here consists of Fractional Anisotropy (FA) and Mean Diffusivity (MD) images in Analyze format. There are FA & MD images for 55 subjects (some subjects have two timepoints, as indicated by a -1 or -2 following subject ID in the filename, please disregard timepoint 2 at this time), and a spreadsheet describing each subject’s age (at time of scan), gender, diagnosis (Normal Control, Alzheimer’s Dissease, or Frontotemporal Dementia), ApoE alleles, and Mini-Mental State Exam score. Data Acquisition Location: San Francisco VA Medical Center; Scanner Type: Siemens Bruker 4T, equipped with a birdcage transmit and eight channel receive coil. DTI was based on a dual spin-echo echo-planar imaging (EPI) sequence supplemented with parallel imaging acceleration (GRAPPA) with a factor 2 to reduce susceptibility distortions. Other imaging parameters were: TR/TE = 6000/77 ms; field of view 256 × 224 cm; 128 × 112 matrix size, yielding 2 × 2 mm2 in-plane resolution; 40 continuo...

Objective To evaluate the long-term trend of dementia mortality in China from 1990 to 2019.Methods Mortality data on dementia were collected from the Global Burden of Disease Study 2019. The Joinpoint regression model was utilized to analyze the mortality trends of dementia among Chinese population from 1990 to 2019. The online analysis tool of the age-period-cohort model, provided by the National Institutes of Health, was used to examine death data related to dementia among older adults.Results From 1990 to 2019, both the mortality rates and standardized mortality rates of dementia were higher in females than in males. Furthermore, the standardized mortality rates of dementia for both sexes exhibited W-shaped fluctuations. The estimated average annual percentage change (AAPC) in dementia mortality for male seniors was 0.32% (95% CI: 0.27%-0.37%), while for female seniors, it was 0.12% (95% CI: 0.06%-0.17%). The age effects demonstrated that the risk of death of dementia increased exponentially with age for both men and women starting from the age of 60. In each 5-year age group, from 60 to 64 years old to 90 to 94 years old, the relative risk (RR) of dementia-related death was 2.35 among male seniors and 2.36 among female seniors. The period effect indicated that the RR began to decrease since 2005. The cohort effect revealed an increase in mortality among later birth cohorts.Conclusion Since 1990, there has been a substantial disease burden among women and older populations in terms of dementia mortality. Moreover, the gender gap in dementia mortality is expected to narrow. Therefore, it is crucial to enhance the prevention and management of dementia from a comprehensive life cycle perspective.

The rate of men covered by the French national health insurance scheme for dementia and other cognitive-degenerative diseases (such as the Alzheimer's disease) amounted to around 6 people per 1,000, in 2017. This figure was for women nearly the double, that is, 12 people out of 1,000 during that year.

The aim of this study was to explore the intention to use and perceived satisfaction with digital tools for dementia prevention among people in the Dutch general public. Data was gathered via a questionnaire among cognitively unimpaired people and people with subjective cognitive decline. Recruitment took place in two waves with the rationale to enrich the study sample’s diversity in the second recruitment wave. Data from wave I and wave II were merged into one data set prior to analyses. The questionnaire covered gender, age, employment situation, educational attainment, perceived financial scarcity, health literacy, dementia risk, motivation for dementia-prevention-related behaviour change, digital proficiency, digital acceptability, acceptance and use of technology. A use case was included to assess the outcome measures intention to use and perceived satisfaction applied to a Dutch existing dementia prevention app for citizens (MijnBreincoach). Analyses included exploratory descriptives, correlations, backward stepwise regression using Generalized Linear Models with 5-fold cross-validation. The total study sample consists of n=673 participants.

Objective: To help determine whether mid-life obesity is a cause of dementia, and whether low BMI, low caloric intake and physical inactivity are causes or merely consequences of the gradual onset of dementia, we recorded these factors early in a large 20-year prospective study and related them to dementia detection rates separately during follow-up periods 0-4, 5-9, 10-14 and 15+ years.

Methods: 1,136,846 UK women, mean age 56 (SD=5) years, were recruited in 1996-2001 and asked about height, weight, caloric intake and inactivity. They were followed until 2017 by electronic linkage to National Health Service records, detecting hospital admissions with mention of dementia. Cox regression yielded adjusted rate ratios (RRs) for first dementia detection during particular follow-up periods.

Results: 15 years after the baseline survey only 1% were lost to follow-up and 89% remained alive with no detected dementia, of whom 18,695 had dementia detected later, at mean age 77 (SD=4) years. Dem...

The Betula Project is a longitudinal study of aging, memory and dementia. Data was collected between 1988 - 2014.

Then the study began with including 1000 randomly selected individs from the Umeå municipality in the age groups 20, 35, 40, 45, 50, 55, 60, 65, 70, 75 and 80 years. It was 100 in each age cohort, and the gender distribution was similar to the population with about as many men as women in the younger age groups, and about twice as many women as men in the age groups 70 and over. Five years later, wo more sample were included. The group has been monitored every five years: 1993, 1998, 2003, 2008 and 2013. For each sample, each participant completed a thorough physical examination with blood tests of a nurse and a careful examination of a memory tester. There are data collected from approximately 4,700 participants. On each occasion, data for about 2000 variables were collected for each participant, which includes data on demographics, health, illness, medication, social and cognitive factors.

The Betula project has received funding for research infrastructures (large databases) from the Swedish Research Council. This means a major effort to make the Betula database available to researchers outside the project through collaborations with researchers within the Betulaproject. To Betulas database, genetic data and biological samples are also connected.

Purpose:

Study how memory functions change during adult life, to identify risk factors for dementia and to identify early preclinical signs of dementia and determine the critical factors of successful aging.

BACKGROUND AND PURPOSE: Our purpose was to determine the association of cataract surgery with subsequent development of dementia in older adults with newly diagnosed cataract. METHODS: By using data from Taiwan National Health Insurance Research Database (NHIRD), a population-based cohort study including 491 226 subjects aged 70 or older with first-time diagnosis of cataract coded from 2000 to 2009 was conducted. After matching cataract patients receiving cataract surgery with cataract patients without receiving cataract surgery for age, sex, index date, Charlson Comorbidity Index score, interval between first coding of cataract diagnosis and index date, hypertension and diabetes mellitus, 113 123 patients in each cohort were enrolled. The main outcome measure was newly diagnosed dementia coded by neurologists or psychiatrists more than 365 days after cataract surgery. Incidence rate and hazard ratio of dementia were compared between the cataract surgery and cataract diagnosis cohorts. RESULTS: The incidence rate of dementia was 22.40 per 1000 person-years in the cataract surgery cohort and 28.87 per 1000 person-years in the cataract diagnosis cohort. The rate of dementia was significantly lower in the cataract surgery group (hazard ratio 0.77, 95% confidence interval 0.75-0.79, P ＜ 0.001). Female gender (P ＜ 0.001) and a shorter interval between the date of first coding of a cataract diagnosis and the date of cataract surgery (P = 0.009) were significantly associated with a lower incidence rate of dementia. CONCLUSION: In an NHIRD cohort of Taiwanese aged 70 years and older with a diagnosis of cataract, patients undergoing cataract surgery were associated with a reduced risk of subsequent dementia compared with those without cataract surgery.

The dataset was created to investigate the speech and cognitive performance of people with varying degrees of cognitive impairment, primarily dementia. The dataset contains a comprehensive set of data including the results of standardized neuropsychological tests (RBANS, ALBA, POBAV, MASTCZ), speech tasks focused on comprehension, memory, naming, and repetition, and demographic data (age, gender, education).

Participants were divided into four groups based on clinical assessment: healthy individuals, healthy individuals with possible mild cognitive impairment, patients with mild cognitive impairment, and patients with dementia. All recordings and examinations were managed as part of routine clinical practice in the neurological outpatient clinic – Memory Disorders Advisory Unit, at the Neurological Clinic of the Faculty Hospital Královské Vinohrady. The dataset containing 268 examinations was divided into a training and test part using stratification by clinical group, age, gender, and level of education to ensure an even distribution of these key characteristics in both parts of the data.

The aim of the dataset is to support the development of methods for automated detection of cognitive disorders based on speech analysis and cognitive performance. The data are suitable for research in the areas of clinical neuropsychology, computational linguistics, and machine learning. The dataset is intended for non-commercial research purposes.

The Epinettes database is the collection of clinical and neuropsychological data, acquired during routine care of patients consulting at the Memory Centre of the University Hospital of Geneva in Switzerland. The database comprises different diagnosis groups, hence allows observational study. It is a transversal data set, as it aims to compare the groups, as well as a longitudinal, as it aims to analyse the progression of each type of dementia.