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Background: This study aimed to determine if people living in communities with higher socio-economic deprivation are at an increased risk of cognitive and functional impairment even after controlling for the effects of individual socio-economic status. Methods: We analysed cross-sectional data from the Medical Research Council Cognitive Function and Ageing Study which consists of a community-based sample of Cambridgeshire, Gwynedd, Newcastle, Nottingham and Oxford. The study included 13004 men and women aged 65 years and over who were randomly selected from Family Health Services Authority computerized records after being stratified to ensure equal numbers of those aged 75 years and over and those under 75 years. The outcome measures were cognitive impairment (Mini-Mental State Exam 0-21) and functional impairment (Instrumental Activities of Daily Living and/or Activities of Daily Living disability). Results: Individuals living in more deprived areas, as measured by the Townsend deprivation score, were found to have a higher prevalence of cognitive impairment [odds ratio (OR) (most deprived versus least deprived quintile)=2.3; 95% confidence interval (CI)1.8-3.0; P<0.001] and functional impairment [OR (most deprived versus least)= 1.6; 95% CI 1.4-1.9; P<0.001] after controlling for age, sex, centre effects, education and social class. Conclusions: There is a significantly higher prevalence of cognitive impairment and functional impairment in elderly individuals living in socio-economically deprived areas regardless of their own socio-economic status. This evidence is of relevance for informing public health policy and those allocating resources for the long-term care of the elderly. [ABSTRACT FROM AUTHOR]

Background: Previous investigations of test re-test reliability of the Mini-Mental State Examination (MMSE) have used correlations and statistics such as Cronbach's α to assess consistency. In practice, the MMSE is usually used to group individuals into cognitive states. The reliability of this grouping (state based approach) has not been fully explored., Methods: MMSE data were collected on a subset of 2,275 older participants (≥ 65 years) from the population-based Medical Research Council Cognitive Function and Ageing Study. Two measurements taken approximately two months apart were used to investigate three state-based categorisations. Descriptive statistics were used to determine how many people remained in the same cognitive group or went up or down groups. Weighted logistic regression was used to identify predictive characteristics of those who moved group., Results: The proportion of people who remained in the same MMSE group at screen and follow-up assessment ranged from 58% to 78%. The proportion of individuals who went up one or more groups was roughly equal to the proportion that went down one or more groups; most of the change occurred when measurements were close to the cut-points. There was no consistently significant predictor for changing cognitive group., Conclusion: A state-based approach to analysing the reliability of the MMSE provided similar results to correlation analyses. State-based models of cognitive change or individual trajectory models using raw scores need multiple waves to help overcome natural variation in MMSE scores and to help identify true cognitive change.

Objectives: To estimate population norms for use in assessment of individuals in relation to their age-matched peers using true longitudinal patterns of decline., Design: Longitudinal study of 10 years of follow-up data from the Medical Research Council Cognitive Function and Ageing Study (MRC CFAS) on the most commonly used cognitive test across clinical and research settings., Setting: England and Wales., Participants: Thirteen thousand four people were seen in five sites at baseline, with follow up at 2, 5, and 10 years., Measurements: Mini-Mental State Examination (MMSE) score at three interviews over 10 years. A total of 42,777 MMSE scores were used in the analysis., Results: MMSE norms are presented according to age and split according to sex using longitudinal data. Potential cohort effects and dropout of individuals with low MMSE scores have been accounted for., Conclusion: It is likely that the cognitive MMSE scale will continue to be used in many settings and across the age range. The figures presented here can be used to plot individual performance and chart where there is change in the relative position of one individual compared with others.

Background: Factors associated with the loss of participants in long-term longitudinal studies of ageing, due to refusal or moves, have been discussed less than those with short term follow-up., Methods: In a population-based study of cognition and ageing (the Medical Research Council Cognitive Function and Ageing Study (MRC CFAS)), factors associated with dropout due to refusal and moving in the first follow-up period (over two years) are compared with factors associated with dropout over ten years. Participants at 10-year follow-up are compared with their age-standardised baseline contemporaries., Results: Some consistent trends are found over the longer term. Refusers tended to have poorer cognition, less years of education, not have a family history of dementia and be women. Characteristics of people who moved differed between waves, but the oldest and people in worse health moved more. When surviving and responding individuals at ten years are compared with those of the same age at baseline many differences are found. Individuals of lower social class, education, cognitive ability, in residential care, with sight/hearing problems and poor/fair self-reported health are less likely to be seen after 10 years of follow-up. Individuals report more health problems when they participate in multiple interviews., Conclusion: The characteristics of refusers in the longer term are similar to those refusing to participate over the shorter term. Long-term follow-up studies will under represent the disadvantaged and disabled but represent full health status of participating individuals better. There are advantages and disadvantages to both short-term and long-term follow-up.