METHODS: A literature review was conducted for systematic reviews, meta-analyses, and scoping reviews published between January 1, 2020 and January 1, 2023. Literature assessing individuals with pre-existing neurological diseases and COVID-19 infection was included. Information regarding infection severity was extracted, and potential limitations were identified.

RESULTS: Thirty-nine articles met inclusion criteria, with data assessing >3 million patients from 51 countries. 26/51 (50.9%) of countries analyzed were classified as high income, while the remaining represented middle-low income countries (25/51; 49.0%). A majority of evidence focused on the impact of cerebrovascular disease (17/39; 43.5%) and dementia (5/39; 12.8%) on COVID-19 severity and mortality. 92.3% of the articles (36/39) suggested a significant association between neurological conditions and increased risk of severe COVID-19 and mortality. Cerebrovascular disease, dementia, Parkinson's disease, and epilepsy were associated with increased COVID severity and mortality.

METHODS: We harmonised data from 13 longitudinal cohort studies of ageing in North America, South America, Europe, Africa, Asia, and Australia. Studies were eligible for inclusion if they had baseline data for social connection markers and at least two waves of cognitive scores. Follow-up periods ranged from 0 years to 15 years across cohorts. We included participants with cognitive data for at least two waves and social connection data for at least one wave. We then identified and excluded people with dementia at baseline. Primary outcomes were annual rates of change in global cognition and cognitive domain scores over time until final follow-up within each cohort study analysed by use of an individual participant data meta-analysis. Linear mixed models within cohorts used baseline social connection markers as predictors of the primary outcomes. Effects were pooled in two stages using random-effects meta-analyses. We assessed the primary outcomes in the main (partially adjusted) and fully adjusted models. Partially adjusted models controlled for age, sex, and education; fully adjusted models additionally controlled for diabetes, hypertension, smoking, cardiovascular risk, and depression.

FINDINGS: Of the 40 006 participants in the 13 cohort studies, we excluded 1392 people with dementia at baseline. 38 614 individual participants were included in our analyses. For the main models, being in a relationship or married predicted slower global cognitive decline (b=0·010, 95% CI 0·000-0·019) than did being single or never married; living with others predicted slower global cognitive (b=0·007, 0·002-0·012), memory (b=0·017, 0·006-0·028), and language (b=0·008, 0·000-0·015) decline than did living alone; and weekly interactions with family and friends (b=0·016, 0·006-0·026) and weekly community group engagement (b=0·030, 0·007-0·052) predicted slower memory decline than did no interactions and no engagement. Never feeling lonely predicted slower global cognitive (b=0·047, 95% CI 0·018-0·075) and executive function (b=0·047, 0·017-0·077) decline than did often feeling lonely. Degree of social support, having a confidante, and relationship satisfaction did not predict cognitive decline across global cognition or cognitive domains. Heterogeneity was low (I2=0·00-15·11%) for all but two of the significant findings (association between slower memory decline and living with others [I2=58·33%] and community group engagement, I2=37·54-72·19%), suggesting robust results across studies.

INTERPRETATION: Good social connections (ie, living with others, weekly community group engagement, interacting weekly with family and friends, and never feeling lonely) are associated with slower cognitive decline.

METHODS: This experimental study was done on a sample of 86 caregivers of elderly with dementia in 2018. The study sample was selected from memory clinic of Taleghani Hospital and randomly assigned into groups (intervention n = 43, control n = 43 groups). The well-being was measured using the World Health Organization - Five Well-Being Index (WHO-5), before and two months after the intervention. Cyberspace-based educational intervention was conducted for one month. The SPSS software version 23 was employed in data analysis.

RESULTS: The mean age of the caregivers in the intervention and control groups were (M = 51.95, SD = 10.90) and (M = 51.36, SD = 15.12) respectively. No significant difference was found between two groups in terms of age, gender and level of education. The results of analysis showed that while the well-being of the intervention group was significantly increased (t (38) = -11.38, P<0.001) the well-being in the control group was significantly reduced ( t(36) =4.71 , P<0.001).

METHODS: A nationwide survey was conducted among individuals aged ≥60 years. Cognition was assessed with the Identification and Intervention for Dementia in Elderly Africans (IDEA) tool. QoL of older caregivers was assessed using the Control, Autonomy, Self-Realization and Pleasure (CASP-19) questionnaire.

METHODS: Data were from the 10/66 Study. Individuals aged 65 years or older and without dementia at baseline were selected from China, Cuba, the Dominican Republic, Mexico, Peru, Puerto Rico, and Venezuela. Dementia incidence was assessed over 3-5 years, with diagnosis according to the 10/66 Study diagnostic algorithm. Discrimination and calibration were tested for five models: the Cardiovascular Risk Factors, Aging and Dementia risk score (CAIDE); the Study on Aging, Cognition and Dementia (AgeCoDe) model; the Australian National University Alzheimer's Disease Risk Index (ANU-ADRI); the Brief Dementia Screening Indicator (BDSI); and the Rotterdam Study Basic Dementia Risk Model (BDRM). Models were tested with use of Cox regression. The discriminative accuracy of each model was assessed using Harrell's concordance (c)-statistic, with a value of 0·70 or higher considered to indicate acceptable discriminative ability. Calibration (model fit) was assessed statistically using the Grønnesby and Borgan test.

FINDINGS: 11 143 individuals without baseline dementia and with available follow-up data were included in the analysis. During follow-up (mean 3·8 years [SD 1·3]), 1069 people progressed to dementia across all sites (incidence rate 24·9 cases per 1000 person-years). Performance of the models varied. Across countries, the discriminative ability of the CAIDE (0·52≤c≤0·63) and AgeCoDe (0·57≤c≤0·74) models was poor. By contrast, the ANU-ADRI (0·66≤c≤0·78), BDSI (0·62≤c≤0·78), and BDRM (0·66≤c≤0·78) models showed similar levels of discriminative ability to those of the development cohorts. All models showed good calibration, especially at low and intermediate levels of predicted risk. The models validated best in Peru and poorest in the Dominican Republic and China.

INTERPRETATION: Not all dementia prediction models developed in HICs can be simply extrapolated to LMICs. Further work defining what number and which combination of risk variables works best for predicting risk of dementia in LMICs is needed. However, models that transport well could be used immediately for dementia prevention research and targeted risk reduction in LMICs.

METHODS: Participants attended the workshop and completed pre- (Time 1) and post-workshop (Time 2) questionnaires consisting of validated measures exploring attitudes towards dementia and older people more broadly.

RESULTS: A total of 97 students were recruited. Attitudes towards people with dementia showed significant positive changes between Time 1 and Time 2, whereas no differences were found for attitudes towards older people.