CC75C group. Education and trajectories of cognitive decline over 9 years in very old people: methods and risk analysis

MRC Biostatistics Unit, Institute of Public Health, Robinson Way, University Forvie Site, Cambridge, CB2 0SR, UK.
Age and Ageing (Impact Factor: 3.64). 03/2009; 38(3):277-82. DOI: 10.1093/ageing/afp004
Source: PubMed


the investigation of cognitive decline in the older population has been hampered by analytical considerations. Most studies of older people over prolonged periods suffer from loss to follow-up, yet this has seldom been investigated fully to date. Such considerations limit our understanding of how basic variables such as education can affect cognitive trajectories.
we examined cognitive trajectories in a population-based cohort study in Cambridge, UK, of people aged 75 and over in whom multiple interviews were conducted over time. Cognitive function was assessed using the Mini-Mental State Examination (MMSE). Socio-demographic variables were measured, including educational level and social class. An age-based quadratic latent growth model was fitted to cognitive scores. The effect of socio-demographic variables was examined on all latent variables and the probability of death and dropout.
at baseline, age, education, social class and mobility were associated with cognitive performance. Education and social class were not related to decline or its rate of change. In contrast, poor mobility was associated with lower cognitive performance, increased cognitive decline and increased rate of change of cognitive decline. Gender, age, mobility and cognitive ability predicted death and dropout
contrary to much of the current literature, education was not related to rate of cognitive decline or change in this rate as measured by MMSE. Higher levels of education do not appear to protect against cognitive decline, though if the MMSE is used in the diagnostic process, individuals with less education may be diagnosed as having dementia somewhat earlier.

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Available from: Tom Dening, Apr 17, 2014
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    • "However, there are still some mixed findings, particularly for models that examine education and cognitive change [8–12], which may stem from methodological issues. For example, it is rare for longitudinal analyses of cognitive decline in the older population to account for death and dropout, measurement error of the cognitive phenotype, ceiling and floor effects in the cognitive test [13], and the possibility of cognitive recovery, especially from the mild cognitive impairment state [14]. "
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    ABSTRACT: Cognitive lifestyle measures such as education, occupation, and social engagement are commonly associated with late-life cognitive ability although their associations with cognitive decline tend to be mixed. However, longitudinal analyses of cognition rarely account for death and dropout, measurement error of the cognitive phenotype, and differing trajectories for different population sub-groups. This paper applies a joint latent class mixed model (and a multi-state model in a sensitivity analysis) that accounts for these issues to a large (n = 3,653), population-based cohort, Paquid, to model the relationship between cognitive lifestyle and cognitive decline. Cognition was assessed over a 20-year period using the Mini-Mental State Examination. Three cognitive lifestyle variables were assessed: education, mid-life occupation, and late-life social engagement. The analysis identified four latent sub-populations with class-specific longitudinal cognitive decline and mortality risk. Irrespective of the cognitive trajectory, increased social engagement was associated with a decreased mortality risk. High education was associated with the most favourable cognitive trajectory, and after adjusting for cognitive decline, with an increased mortality risk. Mid-life occupational complexity was also associated with more favourable trajectories but not with mortality risk. To realistically examine the link between cognitive lifestyle and cognitive decline, complex statistical models are required. This paper applies and compares in a sensitivity analysis two such models, and shows education to be linked to a compression of cognitive morbidity irrespective of cognitive trajectory. Furthermore, a potentially modifiable variable, late-life social engagement is associated with a decreased mortality risk in all of the population sub-groups.
    European Journal of Epidemiology 02/2014; 29(3). DOI:10.1007/s10654-014-9881-8 · 5.34 Impact Factor
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    • "We did not explicitly model the risk of dropout or death in this study. A recent study concerning education and cognitive decline [44] suggests this modelling approach may be a worthy avenue for future investigation in this area. A final point is that the MMSE is a crude screening tool for dementia. "
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    ABSTRACT: The purpose was to examine the relationship between different types of social networks and memory over 15 years of followup in a large cohort of older Australians who were cognitively intact at study baseline. Our specific aims were to investigate whether social networks were associated with memory, determine if different types of social networks had different relationships with memory, and examine if changes in memory over time differed according to types of social networks. We used five waves of data from the Australian Longitudinal Study of Ageing, and followed 706 participants with an average age of 78.6 years (SD 5.7) at baseline. The relationships between five types of social networks and changes in memory were assessed. The results suggested a gradient of effect; participants in the upper tertile of friends or overall social networks had better memory scores than those in the mid tertile, who in turn had better memory scores than participants in the lower tertile. There was evidence of a linear, but not quadratic, effect of time on memory, and an interaction between friends' social networks and time was apparent. Findings are discussed with respect to mechanisms that might explain the observed relationships between social networks and memory.
    Journal of aging research 08/2012; 2012(7208):856048. DOI:10.1155/2012/856048
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    • "Despite its intrinsic limitations for measuring subtle change in ability, the MMSE is frequently used to measure cognitive change over time. Several studies have measured change as the difference in two scores [6,7] whereas others have used data from multiple waves [8,9]. When monitoring cognitive test scores over time it is desirable to account for natural variation from measurement error and test re-test reliability. "
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    ABSTRACT: 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. 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. 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. 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.
    BMC Medical Research Methodology 09/2011; 11(1):127. DOI:10.1186/1471-2288-11-127 · 2.27 Impact Factor
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