A Latent Transition Analysis Model to Assess Change in Cognitive States over Three Occasions: Results from the Rush Memory and Aging Project

Social and Leisure Activities Predict Transitions in Cognitive Functioning in Older Mexican Adults: A Latent Transition Analysis of the Mexican Health and Aging Study

OBJECTIVES

Mexico has a rapidly aging population at risk for cognitive impairment. Social and leisure activities may protect against cognitive decline in older adults. The benefits of these behaviors may vary by patterns of cognitive impairment. The objectives of this study were to identify latent states of cognitive functioning, model the incidence of transitions between these states, and investigate how social and leisure activities were associated with state transitions over a 6-year period in Mexican adults aged 60 and older.

METHODS

We performed latent transition analyses to identify distinct cognitive statuses in the 2012 and 2018 waves of the Mexican Health and Aging Study (N = 9,091). We examined the transition probabilities between these states and their associations with social and leisure activities.

RESULTS

We identified 4 cognitive statuses at baseline: normal cognition (43%), temporal disorientation (30%), perceptual-motor function impairment (7%), and learning and memory impairment (20%). Various social and leisure activities were associated with reduced odds of death and disadvantageous cognitive transitions, as well as increased odds of beneficial transitions.

DISCUSSION

Mapping the effects of popular social and leisure activities onto common patterns in cognitive functioning may inform the development of more enjoyable and effective health-protective behavioral interventions.

Latent Cognitive Class at Enrollment Predicts Future Cognitive Trajectories of Decline in a Community Sample of Older Adults

BACKGROUND

Methods that can identify subgroups with different trajectories of cognitive decline are crucial for isolating the biologic mechanisms which underlie these groupings.

OBJECTIVE

This study grouped older adults based on their baseline cognitive profiles using a latent variable approach and tested the hypothesis that these groups would differ in their subsequent trajectories of cognitive change.

METHODS

In this study we applied time-varying effects models (TVEMs) to examine the longitudinal trajectories of cognitive decline across different subgroups of older adults in the Rush Memory and Aging Project.

RESULTS

A total of 1,662 individuals (mean age = 79.6 years, SD = 7.4, 75.4%female) participated in the study; these were categorized into five previously identified classes of older adults differing in their baseline cognitive profiles: Superior Cognition (n = 328, 19.7%), Average Cognition (n = 767, 46.1%), Mixed-Domains Impairment (n = 71, 4.3%), Memory-Specific Impairment (n = 274, 16.5%), and Frontal Impairment (n = 222, 13.4%). Differences in the trajectories of cognition for these five classes persisted during 8 years of follow-up. Compared with the Average Cognition class, The Mixed-Domains and Memory-Specific Impairment classes showed steeper rates of decline, while other classes showed moderate declines.

CONCLUSION

Baseline cognitive classes of older adults derived through the use of latent variable methods were associated with distinct longitudinal trajectories of cognitive decline that did not converge during an average of 8 years of follow-up.

Using Mixture Modeling to Construct Subgroups of Cognitive Aging in the Wisconsin Longitudinal Study

OBJECTIVES

Longitudinal surveys of older adults increasingly incorporate assessments of cognitive performance. However, very few studies have used mixture modeling techniques to describe cognitive aging, identifying subgroups of people who display similar patterns of performance across discrete cognitive functions. We employ this approach to advance empirical evidence concerning interindividual variability and intraindividual change in patterns of cognitive aging.

METHOD

We drew upon data from 3,713 participants in the Wisconsin Longitudinal Study (WLS). We used latent class analysis to generate subgroups of cognitive aging based on assessments of verbal fluency and episodic memory at ages 65 and 72. We also employed latent transition analysis to identify how individual participants moved between subgroups over the 7-year period.

RESULTS

There were 4 subgroups at each point in time. Approximately 3 quarters of the sample demonstrated continuity in the qualitative type of profile between ages 65 and 72, with 17.9% of the sample in a profile with sustained overall low performance at both ages 65 and 72. An additional 18.7% of participants made subgroup transitions indicating marked decline in episodic memory.

DISCUSSION

Results demonstrate the utility of using mixture modeling to identify qualitatively and quantitatively distinct subgroups of cognitive aging among older adults. We discuss the implications of these results for the continued use of population health data to advance research on cognitive aging.

Heterogeneity in Longitudinal Healthcare Utilisation by Older Adults: A Latent Transition Analysis of the Irish Longitudinal Study on Ageing

Background

Older adults likely exhibit considerable differences in healthcare need and usage. Identifying differences in healthcare utilisation both between and within individuals over time may support future service development.

Objectives

To characterise temporal changes in healthcare utilisation among a nationally representative sample of community-dwelling older adults.

Methods

A latent transition analysis of the first three waves of The Irish Longitudinal Study on Ageing (TILDA) (N = 6128) was conducted.

Results

Three latent classes of healthcare utilisation were identified, ‘primary care only’; ‘primary care and outpatient visits’ and ‘multiple utilisation’. The classes were invariant across all three waves. Transition probabilities indicated dynamic changes over time, particularly for the ‘primary care and outpatient visits’ and ‘multiple utilisation’ statuses.

Discussion

Older adults exhibit temporal changes in healthcare utilisation which may reflect changes in healthcare need and disease progression. Further research is required to identify the factors which influence movement between healthcare utilisation patterns.

Latent Class and Transition Analysis of Alzheimer's Disease Data

This study uses independent latent class analysis (LCA) and latent transition analysis (LTA) to explore accurate diagnosis and disease status change of a big Alzheimer's disease Neuroimaging Initiative (ADNI) data of 2,132 individuals over a 3-year period. The data includes clinical and neural measures of controls (CN), individuals with subjective memory complains (SMC), early-onset mild cognitive impairment (EMCI), late-onset mild cognitive impairment (LMCI), and Alzheimer's disease (AD). LCA at each time point yielded 3 classes: Class 1 is mostly composed of individuals from CN, SMC, and EMCI groups; Class 2 represents individuals from LMCI and AD groups with improved scores on memory, clinical, and neural measures; in contrast, Class 3 represents LMCI and from AD individuals with deteriorated scores on memory, clinical, and neural measures. However, 63 individuals from Class 1 were diagnosed as AD patients. This could be misdiagnosis, as their conditional probability of belonging to Class 1 (0.65) was higher than that of Class 2 (0.27) and Class 3 (0.08). LTA results showed that individuals had a higher probability of staying in the same class over time with probability >0.90 for Class 1 and 3 and probability >0.85 for Class 2. Individuals from Class 2, however, transitioned to Class 1 from time 2 to time 3 with a probability of 0.10. Other transition probabilities were not significant. Lastly, further analysis showed that individuals in Class 2 who moved to Class 1 have different memory, clinical, and neural measures to other individuals in the same class. We acknowledge that the proposed framework is sophisticated and time-consuming. However, given the severe neurodegenerative nature of AD, we argue that clinicians should prioritize an accurate diagnosis. Our findings show that LCA can provide a more accurate prediction for classifying and identifying the progression of AD compared to traditional clinical cut-off measures on neuropsychological assessments.