Longitudinal associations between different dementia diagnoses and medication use jointly accounting for dropout

Association of polypharmacy and motoric cognitive risk syndrome in older adults: A 4-year longitudinal study in China

BACKGROUND

Polypharmacy is related to motoric cognitive risk (MCR) syndrome is not fully understood. Therefore, our aim was to fill this gap in knowledge.

METHODS

This population-based prospective longitudinal study used data from the China Health and Retirement Longitudinal Study. It included 3827 dementia-free older adults (age >60 years) who were followed from 2011 to 2015. Data on subjective cognitive complaints, walking speed, and polypharmacy were reported by the participants. The cross-sectional relationship between polypharmacy and the MCR at baseline was examined using multinomial logistic regression, and Cox regression will be used to analyze the impact of polypharmacy on MCR over a four-year period.

RESULTS

At baseline, 492 (12.9%) participants had been diagnosed with MCR. With 4 years of follow-up, 304 (12.5%) were classified as having incident MCR. Controlling for microsystem factors, polypharmacy (OR: 1.273, 95 % CI: 1.051-1.541; p < .05) was associated with MCR at baseline and every unit increase in polypharmacy was linked to a 53.8% (HR:1.538, 95 % CI: 1.227-1.927; p < .001) increase more likely to developing incident MCR. They adjusted for meso/ exosystem and macrosystem factors, and these associations remained significant, coping resources are believed to have a role in the relationships between polypharmacy and MCR in older persons.

CONCLUSION

Polypharmacy is significantly associated with MCR incidence during 4 years of follow-up in older adults. Future studies should independently confirm this observation for potentially bringing clinical benefits to older people with polypharmacy.

Modeling of correlated cognitive function and functional disability outcomes with bounded and missing data in a longitudinal aging study

Longitudinal studies of correlated cognitive and disability outcomes among older adults are characterized by missing data due to death or loss to follow-up from deteriorating health conditions. The Mini-Mental State Examination (MMSE) score for assessing cognitive function ranges from a minimum of 0 (floor) to a maximum of 30 (ceiling). To study the risk factors of cognitive function and functional disability, we propose a shared parameter model to handle missingness, correlation between outcomes, and the floor and ceiling effects of the MMSE measurements. The shared random effects in the proposed model handle missingness (either missing at random or missing not at random) and correlation between these outcomes, while the Tobit distribution handles the floor and ceiling effects of the MMSE measurements. We used data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and a simulation study. By ignoring the MMSE floor and ceiling effects in the analyses of the CLHLS, the association of systolic blood pressure with cognitive function was not significant and the association of age with cognitive function was lower by 16.6% (from -6.237 to -5.201). By ignoring the MMSE floor and ceiling effects in the simulation study, the relative bias in the estimated association of female gender with cognitive function was 43 times higher (from -0.01 to -0.44). The estimated associations obtained with data missing at random were smaller than those with data missing not at random, demonstrating how the missing data mechanism affects the analytic results. Our work underscores the importance of proper model specification in longitudinal analysis of correlated outcomes subject to missingness and bounded values.

Factors associated with dropout in the longitudinal Vogel study of cognitive decline

Dementia, including Alzheimer's disease, is a growing problem worldwide. Prevention or early detection of the disease or a prodromal cognitive decline is necessary. By means of our long-term follow-up 'Vogel study', we aim to predict the pathological cognitive decline of a German cohort (mean age was 73.9 ± 1.55 years at first visit) with three measurement time points within 6 years per participant. Especially in samples of the elderly and subjects with chronic or co-morbid diseases, dropouts are one of the biggest problems of long-term studies. In contrast to the large number of research articles conducted on the course of dementia, little research has been done on the completion of treatment. To ensure unbiased and reliable predictors of cognitive decline from study completers, our objective was to determine predictors of dropout. We conducted multivariate analyses of covariance and multinomial logistic regression analyses to compare and predict the subject's dropout behaviour at the second visit 3 years after baseline (full participation, partial participation and no participation/dropout) with neuropsychiatric, cognitive, blood and lifestyle variables. Lower performance in declarative memory, attention and visual-spatial processing predicted dropout rather than full participation. Lower performance in visual-spatial processing predicted partial participation as opposed to full participation. Furthermore, lower performance in mini-mental status examination predicted whether subjects dropped out or participated partially instead of full participation. Baseline cognitive parameters are associated with dropouts at follow-up with a loss of impaired participants. We expect a bias into a healthier sample over time.

Predictors of attrition in a longitudinal population-based study of aging

BACKGROUND

Longitudinal studies predictably experience non-random attrition over time. Among older adults, risk factors for attrition may be similar to risk factors for outcomes such as cognitive decline and dementia, potentially biasing study results.

OBJECTIVE

To characterize participants lost to follow-up which can be useful in the study design and interpretation of results.

METHODS

In a longitudinal aging population study with 10 years of annual follow-up, we characterized the attrited participants (77%) compared to those who remained in the study. We used multivariable logistic regression models to identify attrition predictors. We then implemented four machine learning approaches to predict attrition status from one wave to the next and compared the results of all five approaches.

RESULTS

Multivariable logistic regression identified those more likely to drop out as older, male, not living with another study participant, having lower cognitive test scores and higher clinical dementia ratings, lower functional ability, fewer subjective memory complaints, no physical activity, reported hobbies, or engagement in social activities, worse self-rated health, and leaving the house less often. The four machine learning approaches using areas under the receiver operating characteristic curves produced similar discrimination results to the multivariable logistic regression model.

CONCLUSIONS

Attrition was most likely to occur in participants who were older, male, inactive, socially isolated, and cognitively impaired. Ignoring attrition would bias study results especially when the missing data might be related to the outcome (e.g. cognitive impairment or dementia). We discuss possible solutions including oversampling and other statistical modeling approaches.

Association between surgery with anesthesia and cognitive decline in older adults: Analysis using shared parameter models for informative dropout

Objectives/Goals:

The association between surgery with general anesthesia (exposure) and cognition (outcome) among older adults has been studied with mixed conclusions. We revisited a recent analysis to provide missing data education and discuss implications of biostatistical methodology for informative dropout following dementia diagnosis.

Methods/study population:

We used data from the Mayo Clinic Study of Aging, a longitudinal study of prevalence, incidence, and risk factors for mild cognitive impairment (MCI) and dementia. We fit linear mixed effects models (LMMs) to assess the association between anesthesia exposure and subsequent trajectories of cognitive z-scores assuming data missing at random, hypothesizing that exposure is associated with greater decline in cognitive function. Additionally, we used shared parameter models for informative dropout assuming data missing not at random.

Results:

A total of 1948 non-demented participants were included. Median age was 79 years, 49% were female, and 16% had MCI at enrollment. Among median follow-up of 4 study visits over 6.6 years, 172 subjects developed dementia, 270 died, and 594 participants underwent anesthesia. In LMMs, exposure to anesthesia was associated with decline in cognitive function over time (change in annual cognitive z-score slope = −0.063, 95% CI: (−0.080, −0.046), p < 0.001). Accounting for informative dropout using shared parameter models, exposure was associated with greater cognitive decline (change in annual slope = −0.081, 95% CI: (−0.137, −0.026), p = 0.004).

Discussion:

We revisited prior work by our group with a focus on informative dropout. Although the conclusions are similar, we demonstrated the potential impact of novel biostatistics methodology in longitudinal clinical research.

The Impact of Dementia Diagnosis on Patterns of Potentially Inappropriate Medication Use Among Older Adults

Background

Use of potentially inappropriate medications (PIM) among people with dementia is common. We assessed the patterns of medication use from 1-year before dementia diagnosis, to 1-year after dementia diagnosis, compared with patterns of medication use in people without dementia.

Methods

We conducted longitudinal study using the National Alzheimer's Coordinating Center data. Adults aged 65 years and older newly diagnosed with dementia (n = 2,418) during 2005-2015 were year, age, and sex matched 1:1 with controls. Generalized estimating equation models weighted for missingness and adjusted for 15 participant characteristics were fit.

Results

Among participants with dementia, number of medications reported 1-year prediagnosis was 8% lower than at diagnosis year (p < .0001) and 11% higher 1-year postdiagnosis compared with year of diagnosis (p < .0001). Among participants with dementia, the odds of PIM exposure, assessed using the 2015 Beers Criteria, was 17% lower 1-year prediagnosis (p < .0001) and 17% higher 1-year postdiagnosis (p = .006) compared with year of diagnosis. Among controls, there were approximately 6% more medications reported between consecutive years (p < .0001 each comparison) and the odds of PIM exposure increased 11% between consecutive years (p = .006 and p = .047). At each annual follow-up, participants with dementia had lower odds of PIM exposure than their controls (prediagnosis p < .0001, at diagnosis p = .0007, postdiagnosis p = .03, respectively). There were no differences in exposure to anticholinergic medications.

Conclusions

Number of medications and PIM use increased annually for participants with and without dementia. Persistent challenge of increasing PIM use in this group of older adults is of major concern and warrants interventions to minimize such prescribing.

Joint modeling of concurrent binary outcomes in a longitudinal observational study using inverse probability of treatment weighting for treatment effect estimation

PURPOSE

Correlated healthcare utilization outcomes may be encoded as binary outcomes in epidemiologic studies. We demonstrate how to account for correlation between concurrent binary outcomes and confounding by person characteristics when estimating a treatment effect in observational studies.

METHODS

We present a joint shared-parameter model, weighted by inverse probability of treatment weights (IPTW) to account for confounding. The model is evaluated in a simulation study that emulates the Medical Expenditure Panel Survey data and compared with a covariate-adjusted joint model and with separate outcome models (IPTW weighted and covariate adjusted).

RESULTS

For the IPTW-weighted joint model, relative bias in the estimated treatment effect on outcome 1 ranged from -0.057 to -0.033 and outcome 2 from -0.077 to -0.043. For the covariate-adjusted joint model, relative bias ranged from -0.010 to -0.083 for outcome 1 and from -0.087 to -0.110 for outcome 2. The covariate-adjusted joint model estimated the effect more closely than the covariate-adjusted separate model. The IPTW-weighted joint model estimated the effect more closely for outcome 1.

CONCLUSIONS

The IPTW-weighted joint model handles correlation between binary outcomes, adjusts for confounding, and estimates the treatment effect accurately in observational studies. We illustrate the contribution of person-specific effects in estimating personalized risk.

Version 3 of the National Alzheimer's Coordinating Center's Uniform Data Set

INTRODUCTION

In 2015, the US Alzheimer's Disease Centers (ADC) implemented Version 3 of the Uniform Data Set (UDS). This paper describes the history of Version 3 development and the UDS data that are freely available to researchers.

METHODS

UDS Version 3 was developed after years of coordination between the National Institute on Aging-appointed Clinical Task Force (CTF), clinicians from ∼30 ADCs, and the National Alzheimer's Coordinating Center (NACC). The CTF recognized the need for updates to align with the state of the science in dementia research, while being flexible to the diverse needs and diseases studied at the ADCs. Version 3 also developed a nonproprietary neuropsychological battery.

RESULTS

This paper focuses on the substantial Version 3 changes to the UDS forms related to clinical diagnosis and characterization of clinical symptoms to match updated consensus-based diagnostic criteria. Between March 2015 and March 2018, 4820 participants were enrolled using UDS Version 3. Longitudinal data were available for 25,337 of the 37,568 total participants using all UDS versions.

DISCUSSION

The results from utilization of the UDS highlight the possibility for numerous research institutions to successfully collaborate, produce, and use standardized data collection instruments for over a decade.