The review highlights the fact that the causal mechanisms behind health inequalities are dependent on whether or not the dimension being analyzed closely reflects labor market success. Additionally, further research should strive to improve the statistical modeling of causality, as this might influence the conclusions drawn regarding the relative importance of health selection and social causation.

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Background To date, dementia prediction models have been exclusively developed and tested in high-income countries (HICs). However, most people with dementia live in low-income and middle-income countries (LMICs), where dementia risk prediction research is almost non-existent and the ability of current models to predict dementia is unknown. This study investigated whether dementia prediction models developed in HICs are applicable to LMICs. MethodsData 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.

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Introduction:With no treatment for dementia, there is a need to identify high risk cases to focus preventive strategies, particularly in low-and middle-income countries (LMICs) where the burden of dementia is greatest. We evaluated the risk of conversion from mild cognitive ompairment (MCI) to dementia in LMICs.Methods: Medline, Embase, PsycINFO, and Scopus were searched from inception until June 30, 2020. The search was restricted to observational studies, conducted in population-based samples, with at least 1 year follow-up. There was no restriction on the definition of MCI used as long as it was clearly defined. PROSPERO registration: CRD42019130958. Results:Ten thousand six hundred forty-seven articles were screened; n = 11 retained.Of the 11 studies, most were conducted in China (n = 7 studies), with only two studies from countries classified as low income. A qualitative analysis of n = 11 studies showed that similar to high-income countries the conversion rate to dementia from MCI was variable (range 6.0%-44.8%; average follow-up 3.7 years [standard deviation = 1.2]).

Aim Our aim was to investigate whether there has been a recent secular trend in the grip strength of older English adults, using data from the English Longitudinal Study of Ageing (ELSA). Findings We found evidence of a slight decline in mean grip strength between 2004 and 2013. This decline is equivalent to 65-year-olds' mean strength declining to that previously seen in individuals at age 69, and did not appear to be explained by differences in lifestyle risk factors. Message These findings are important since they raise the possibility that more recent cohorts of older people remain at similar, or possibly slightly greater, risk of the adverse consequences of weak muscle strength.

Background Higher physical activity (PA) has been linked to better health and functioning. Trajectories of PA and associated factors have been studied in older adults aged ≥65, but less is known about influences on PA change in the very old (aged ≥85). Objective To investigate factors associated with self-reported PA and PA change over time in very old adults. Methods 845 participants in the Newcastle 85+ Study were followed for health and functioning at 1.5-, 3-, and 5-year follow-up (wave 2 to 4). PA scores (range 0–18) and PA levels (low (PA scores 0–1), medium (2–6) and high (7–18)) were determined using a purpose-designed PA questionnaire. We used linear mixed models (LMM) to investigate factors associated with 5-year change in PA scores. Results Overall, men had higher mean PA scores than women (up to 2.27 points). The highest proportion of participants (42–48%) had medium levels of PA across the waves. Although most experienced decline—stability in moderate and increases in high PA levels were also observed. The fully adjusted LMM revealed a curvilinear annual decline in PA scores of 0.52 (0.13) (β (SE), p<0.001), which decelerated by 0.07 (0.02) points (p<0.01) over time. The factors associated with low PA scores at baseline were female gender, higher waist-hip ratio, and no alcohol intake. Better self-rated and cognitive health and having fewer diseases were associated with higher PA scores. None were associated with the rate of change in PA over time. Conclusion We observed a curvilinear trend and deceleration in PA scores decline in the very old. Men and those in better health and who drank alcohol were more physically active at baseline. None of the factors were associated with the rate of PA decline. Investigating those who maintain or increase levels of PA may inform interventions for at risk groups with PA decline.