Reducing case ascertainment costs in U.S. population studies of Alzheimer's disease, dementia, and cognitive impairment-Part 1

Distinctive visual tasks for characterizing mild cognitive impairment and dementia using oculomotor behavior

Introduction

One's eye movement (in response to visual tasks) provides a unique window into the cognitive processes and higher-order cognitive functions that become adversely affected in cases with cognitive decline, such as those mild cognitive impairment (MCI) and dementia. MCI is a transitional stage between normal aging and dementia.

Methods

In the current work, we have focused on identifying visual tasks (such as horizontal and vertical Pro-saccade, Anti-saccade and Memory Guided Fixation tasks) that can differentiate individuals with MCI and dementia from their cognitively unimpaired healthy aging counterparts based on oculomotor Performance indices. In an attempt to identify the optimal combination of visual tasks that can be used to differentiate the participant groups, clustering was performed using the oculomotor Performance indices.

Results

Results of our study with a group of 60 cognitively unimpaired healthy aging individuals, a group with 60 individuals with MCI and a group with 60 individuals with dementia indicate that the horizontal and vertical Anti-saccade tasks provided the optimal combination that could differentiate individuals with MCI and dementia from their cognitively unimpaired healthy aging counterparts with clustering accuracy of ∼92% based on the saccade latencies. Also, the saccade latencies during both of these Anti-saccade tasks were found to strongly correlate with the Neuropsychological test scores.

Discussion

This suggests that the Anti-saccade tasks can hold promise in clinical practice for professionals working with individuals with MCI and dementia.

Prevalence of dementia in India: National and state estimates from a nationwide study

INTRODUCTION

Prior estimates of dementia prevalence in India were based on samples from selected communities, inadequately representing the national and state populations.

METHODS

From the Longitudinal Aging Study in India (LASI) we recruited a sample of adults ages 60+ and administered a rich battery of neuropsychological tests and an informant interview in 2018 through 2020. We obtained a clinical consensus rating of dementia status for a subsample (N = 2528), fitted a logistic model for dementia status on this subsample, and then imputed dementia status for all other LASI respondents aged 60+ (N = 28,949).

RESULTS

The estimated dementia prevalence for adults ages 60+ in India is 7.4%, with significant age and education gradients, sex and urban/rural differences, and cross-state variation.

DISCUSSION

An estimated 8.8 million Indians older than 60 years have dementia. The burden of dementia cases is unevenly distributed across states and subpopulations and may therefore require different levels of local planning and support.

HIGHLIGHTS

The estimated dementia prevalence for adults ages 60+ in India is 7.4%. About 8.8 million Indians older than 60 years live with dementia. Dementia is more prevalent among females than males and in rural than urban areas. Significant cross-state variation exists in dementia prevalence.

Feasibility of an online consensus approach for the diagnosis of cognitive impairment and dementia in rural South Africa

INTRODUCTION

We describe the development and feasibility of using an online consensus approach for diagnosing cognitive impairment and dementia in rural South Africa.

METHODS

Cognitive assessments, clinical evaluations, and informant interviews from Cognition and Dementia in the Health and Aging in Africa Longitudinal Study (HAALSI Dementia) were reviewed by an expert panel using a web-based platform to assign a diagnosis of cognitively normal, mild cognitive impairment (MCI), or dementia.

RESULTS

Six hundred thirty-five participants were assigned a final diagnostic category, with 298 requiring adjudication conference calls. Overall agreement between each rater's independent diagnosis and final diagnosis (via the portal or consensus conference) was 78.3%. A moderate level of agreement between raters' individual ratings and the final diagnostic outcomes was observed (average κ coefficient = 0.50).

DISCUSSION

Findings show initial feasibility in using an online consensus approach for the diagnosis of cognitive impairment and dementia in remote, rural, and low-resource settings.

Does Your Loved One With Cognitive Symptoms Need to See a Doctor? Check It Online

Widespread access to emerging information and communication technologies (ICT) allows its use for the screening of diseases in the general population. At the initiative of the Spanish Confederation of Associations of Families of People with Alzheimer’s disease and other dementias (CEAFA), a website (http://www.problemasmemoria.com) has been created that provides information about Alzheimer’s disease and includes questionnaires to be completed by family or friends concerned about memory problems of a relative. A cross-sectional, randomized, multicenter study was performed to evaluate feasibility, validity, and user satisfaction with an electronic method of completion vs. the current method of paper-based questionnaires for clinically dementia screening completed by the informants: the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) and the Alzheimer’s disease-8 screening test (AD8). A total of 111 pairs were recruited by seven memory clinics. Informants completed IQCODE and AD8 questionnaires both in their paper and electronic versions. The correlation between paper and electronic versions was significantly positive for IQCODE (r = 0.98; p < 0.001) and AD8 (r = 0.96; p < 0.001). The execution time did not differ significantly, and participants considered their use equally easy. This study shows that an electronic version of the IQCODE and AD8 questionnaires is suitable for its online use via the internet and achieves the same results as the traditional paper versions.

Identifying Medicare beneficiaries with dementia

BACKGROUND/OBJECTIVES

No data exist regarding the validity of International Classification of Disease (ICD)-10 dementia diagnoses against a clinician-adjudicated reference standard within Medicare claims data. We examined the accuracy of claims-based diagnoses with respect to expert clinician adjudication using a novel database with individual-level linkages between electronic health record (EHR) and claims.

DESIGN

In this retrospective observational study, two neurologists and two psychiatrists performed a standardized review of patients' medical records from January 2016 to December 2018 and adjudicated dementia status. We measured the accuracy of three claims-based definitions of dementia against the reference standard.

SETTING

Mass-General-Brigham Healthcare (MGB), Massachusetts, USA.

PARTICIPANTS

From an eligible population of 40,690 fee-for-service (FFS) Medicare beneficiaries, aged 65 years and older, within the MGB Accountable Care Organization (ACO), we generated a random sample of 1002 patients, stratified by the pretest likelihood of dementia using administrative surrogates.

INTERVENTION

None.

MEASUREMENTS

We evaluated the accuracy (area under receiver operating curve [AUROC]) and calibration (calibration-in-the-large [CITL] and calibration slope) of three ICD-10 claims-based definitions of dementia against clinician-adjudicated standards. We applied inverse probability weighting to reconstruct the eligible population and reported the mean and 95% confidence interval (95% CI) for all performance characteristics, using 10-fold cross-validation (CV).

RESULTS

Beneficiaries had an average age of 75.3 years and were predominately female (59%) and non-Hispanic whites (93%). The adjudicated prevalence of dementia in the eligible population was 7%. The best-performing definition demonstrated excellent accuracy (CV-AUC 0.94; 95% CI 0.92-0.96) and was well-calibrated to the reference standard of clinician-adjudicated dementia (CV-CITL <0.001, CV-slope 0.97).

CONCLUSION

This study is the first to validate ICD-10 diagnostic codes against a robust and replicable approach to dementia ascertainment, using a real-world clinical reference standard. The best performing definition includes diagnostic codes with strong face validity and outperforms an updated version of a previously validated ICD-9 definition of dementia.

Comparison of the predictive accuracy of multiple definitions of cognitive impairment for incident dementia: a 20-year follow-up of the Whitehall II cohort study

BACKGROUND

Studies generally use cognitive assessment done at one timepoint to define cognitive impairment in order to examine conversion to dementia. Our objective was to examine the predictive accuracy and conversion rate of seven alternate definitions of cognitive impairment for dementia.

METHODS

In this prospective study, we included participants from the Whitehall II cohort study who were assessed for cognitive impairment in 2007-09 and were followed up for clinically diagnosed dementia. Algorithms based on poor cognitive performance (defined using age-specific and sex-specific thresholds, and subsequently thresholds by education or occupation levels) and objective cognitive decline (using data from cognitive assessments in 1997-99, 2002-04, and 2007-09) were used to generate seven alternate definitions of cognitive impairment. We compared predictive accuracy using Royston's R 2, the Akaike information criterion (AIC), sensitivity, specificity, and Harrell's C-statistic.

FINDINGS

5687 participants, with a mean age of 65·7 years (SD 5·9) in 2007-09, were included and followed up for a median of 10·5 years (IQR 10·1-10·9). Over follow-up, 270 (4·7%) participants were clinically diagnosed with dementia. Cognitive impairment defined using both cognitive performance and decline had higher hazard ratios (from 5·08 [95% CI 3·82-6·76] to 5·48 [4·13-7·26]) for dementia than did definitions based on cognitive performance alone (from 3·25 [2·52-4·17] to 3·39 [2·64-4·36]) and cognitive decline alone (3·01 [2·37-3·82]). However, all definitions had poor predictive performance (C-statistic ranged from 0·591 [0·565-0·616] to 0·631 [0·601-0·660]), primarily due to low sensitivity (21·6-48·4%). A predictive model containing age, sex, and education without measures of cognitive impairment had better predictive performance (C-statistic 0·783 [0·758-0·809], sensitivity 74·2%, specificity 72·2%) than all seven definitions of cognitive impairment (all p<0·0001).

INTERPRETATION

These findings suggest that cognitive impairment in early old age might not be useful for dementia prediction, even when it is defined using longitudinal data on cognitive decline and thresholds of poor cognitive performance additionally defined by education or occupation.

FUNDING

National Institutes of Health, UK Medical Research Council.

Online Clinical Consensus Diagnosis of Dementia: Development and Validation

OBJECTIVES

To introduce cost-effective expert clinical diagnoses of dementia into population-based research using an online platform and to demonstrate their validity against in-person clinical assessment and diagnosis.

DESIGN

The online platform provides standardized data necessary for clinicians to rate participants on the Clinical Dementia Rating (CDR^® ). Using this platform, clinicians diagnosed 60 patients at a range of CDR levels at two clinical sites. The online consensus diagnosis was compared with in-person clinical consensus diagnosis.

SETTING

All India Institute of Medical Sciences (AIIMS), Delhi, and National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India.

PARTICIPANTS

Thirty patients each at AIIMS and NIMHANS with equal numbers of patients previously independently rated in person by experts as CDR is 0 (cognitively normal), CDR is 0.5 (mild cognitive impairment), and CDR is 1 or greater (dementia).

MEASUREMENTS

Multiple clinicians independently rate each participant on each CDR domain using standardized data and expert clinical judgment. The overall summary CDR is calculated by algorithm. When there are discrepancies among clinician ratings, clinicians discuss the case through a virtual consensus conference and arrive at a consensus overall rating.

RESULTS

Online clinical consensus diagnosis based on standardized interview data provides consistent clinical diagnosis with in-person clinical assessment and consensus diagnosis (κ coefficient = 0.76).

CONCLUSION

A web-based clinical consensus platform built on the Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India interview data is a cost-effective way to obtain reliable expert clinical judgments. A similar approach can be used for other epidemiological studies of dementia. J Am Geriatr Soc 68:S54-S59, 2020.

Learning From Clinical Consensus Diagnosis in India to Facilitate Automatic Classification of Dementia: Machine Learning Study

BACKGROUND

The Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) is the first and only nationally representative study on late-life cognition and dementia in India (n=4096). LASI-DAD obtained clinical consensus diagnosis of dementia for a subsample of 2528 respondents.

OBJECTIVE

This study develops a machine learning model that uses data from the clinical consensus diagnosis in LASI-DAD to support the classification of dementia status.

METHODS

Clinicians were presented with the extensive data collected from LASI-DAD, including sociodemographic information and health history of respondents, results from the screening tests of cognitive status, and information obtained from informant interviews. Based on the Clinical Dementia Rating (CDR) and using an online platform, clinicians individually evaluated each case and then reached a consensus diagnosis. A 2-step procedure was implemented to train several candidate machine learning models, which were evaluated using a separate test set for predictive accuracy measurement, including the area under receiver operating curve (AUROC), accuracy, sensitivity, specificity, precision, F1 score, and kappa statistic. The ultimate model was selected based on overall agreement as measured by kappa. We further examined the overall accuracy and agreement with the final consensus diagnoses between the selected machine learning model and individual clinicians who participated in the clinical consensus diagnostic process. Finally, we applied the selected model to a subgroup of LASI-DAD participants for whom the clinical consensus diagnosis was not obtained to predict their dementia status.

RESULTS

Among the 2528 individuals who received clinical consensus diagnosis, 192 (6.7% after adjusting for sampling weight) were diagnosed with dementia. All candidate machine learning models achieved outstanding discriminative ability, as indicated by AUROC >.90, and had similar accuracy and specificity (both around 0.95). The support vector machine model outperformed other models with the highest sensitivity (0.81), F1 score (0.72), and kappa (.70, indicating substantial agreement) and the second highest precision (0.65). As a result, the support vector machine was selected as the ultimate model. Further examination revealed that overall accuracy and agreement were similar between the selected model and individual clinicians. Application of the prediction model on 1568 individuals without clinical consensus diagnosis classified 127 individuals as living with dementia. After applying sampling weight, we can estimate the prevalence of dementia in the population as 7.4%.

CONCLUSIONS

The selected machine learning model has outstanding discriminative ability and substantial agreement with a clinical consensus diagnosis of dementia. The model can serve as a computer model of the clinical knowledge and experience encoded in the clinical consensus diagnostic process and has many potential applications, including predicting missed dementia diagnoses and serving as a clinical decision support tool or virtual rater to assist diagnosis of dementia.

Is personality associated with dementia risk? A meta-analytic investigation

This study provides a quantitative synthesis of the prospective associations between personality traits (neuroticism, extraversion, openness, agreeableness, conscientiousness) and the risk of incident Alzheimer's disease and related dementias. We conducted five separate meta-analyses with 8-12 samples (N = 30,036 to 33,054) that were identified through a systematic literature search following the MOOSE guidelines. Higher neuroticism (HR = 1.24, 95% CI [1.17, 1.31]) and lower conscientiousness (HR = 0.77, 95% CI [0.73, 0.81]) were associated with increased dementia risk, even after accounting for covariates such as depressive symptoms. Lower extraversion (HR = 0.92, 95% CI [0.86, 0.97]), openness (HR = 0.91, 95% CI [0.86, 0.96]), and agreeableness (HR = 0.90, 95% CI [0.83, 0.98]) were also associated with increased risk, but these associations were less robust and not significant in fully adjusted models. No evidence of publication bias was found. The strength of associations was unrelated to publication year (i.e., no evidence of winner's curse). Meta-regressions indicated consistent effects for neuroticism, openness, and conscientiousness across methods to assess dementia, dementia type, follow-up length, sample age, minority, country, and personality measures. The association of extraversion and agreeableness varied by country. Our findings indicate robust associations of neuroticism and conscientiousness with dementia risk.

Personality and Dementia Risk in England and Australia

Evidence for the relation between personality and dementia risk comes mainly from American samples. We tested whether personality-dementia links extend to populations from England and Australia. Data from the English Longitudinal Study of Ageing (ELSA; N = 6,887; Follow-up mean: 5.64 years) and the Household, Income and Labour Dynamics in Australia (HILDA; N = 2,778; Follow-up mean: 10.96 years) were analyzed using Cox PH models. In both samples, higher neuroticism was associated with increased dementia risk. In ELSA, lower conscientiousness was related to increased risk. In HILDA, conscientiousness had a similar effect but did not reach statistical significance. The present work found a consistent association for neuroticism and suggests similar personality-dementia links across demographic groups and high-income countries.

Design and Methodology of the Longitudinal Aging Study in India-Diagnostic Assessment of Dementia (LASI-DAD)

BACKGROUND/OBJECTIVES

To provide high-quality data on older adults in India that will enable an in-depth study of late-life cognition and dementia in India and cross-country analysis of risk factors for cognitive aging and dementia.

DESIGN

The Longitudinal Aging Study in India (LASI) is a nationally representative survey of health, economic, and social well-being of the Indian population aged 45 and older. Its large sample of more than 70,000 older adults represents not only the country as a whole but also each state. LASI-Diagnostic Assessment of Dementia (DAD) is an in-depth study of late-life cognition and dementia, drawing a subsample of over 3,000 LASI respondents aged 60 and older.

SETTING

Participants were interviewed at home or in a participating hospital according to their preferences.

PARTICIPANTS

Adults aged 60 and older (N = 3,224), along with 3,191 informants.

MEASUREMENTS

Respondents underwent a battery of cognitive tests, and informants were interviewed about their cognitive and health conditions. A common set of cognitive tests was selected to enable international comparisons, and additional cognitive tests suitable for illiterate and innumerate populations were also selected. Rich data on risk factors of dementia were collected through health examination, venous blood assays, and genotyping.

RESULTS

The response rate was 82.9%, varying across sex, education, and urbanicity. Data are available to other researchers.

CONCLUSION

LASI-DAD provides an opportunity to study late-life cognition and dementia and their risk factors in the older population in India and to gain further insights through cross-country analysis by pooling data from its international sister studies. J Am Geriatr Soc 68:S5-S10, 2020.

Frailty Screening Using the Electronic Health Record Within a Medicare Accountable Care Organization

BACKGROUND

The accumulation of deficits model for frailty has been used to develop an electronic health record (EHR) frailty index (eFI) that has been incorporated into British guidelines for frailty management. However, there have been limited applications of EHR-based approaches in the United States.

METHODS

We constructed an adapted eFI for patients in our Medicare Accountable Care Organization (ACO, N = 12,798) using encounter, diagnosis code, laboratory, medication, and Medicare Annual Wellness Visit (AWV) data from the EHR. We examined the association of the eFI with mortality, health care utilization, and injurious falls.

RESULTS

The overall cohort was 55.7% female, 85.7% white, with a mean age of 74.9 (SD = 7.3) years. In the prior 2 years, 32.1% had AWV data. The eFI could be calculated for 9,013 (70.4%) ACO patients. Of these, 46.5% were classified as prefrail (0.10 < eFI ≤ 0.21) and 40.1% frail (eFI > 0.21). Accounting for age, comorbidity, and prior health care utilization, the eFI independently predicted all-cause mortality, inpatient hospitalizations, emergency department visits, and injurious falls (all p < .001). Having at least one functional deficit captured from the AWV was independently associated with an increased risk of hospitalizations and injurious falls, controlling for other components of the eFI.

CONCLUSIONS

Construction of an eFI from the EHR, within the context of a managed care population, is feasible and can help to identify vulnerable older adults. Future work is needed to integrate the eFI with claims-based approaches and test whether it can be used to effectively target interventions tailored to the health needs of frail patients.

LASI-DAD study: a protocol for a prospective cohort study of late-life cognition and dementia in India

INTRODUCTION

Alzheimer's disease and related dementias can be considered the epidemic of the 21st century. Particularly, the predicted growth in the size of elderly populations in low-income and middle-income countries is expected to produce a dramatic surge in dementia prevalence and incidence. Although a rising burden of dementia presents an urgent challenge for India, previous efforts to study dementia in the country have relied on non-representative samples in geographically restricted regions. The Harmonised Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) will provide rich, population-representative data on late-life cognition and dementia and their risk factors for the first time in India.

METHODS

The LASI-DAD will recruit a sample of 3000 people aged 60+ years. Their family members or friends, whom respondents nominate as informants, participate in the computer-assisted personal interview. The study sample is drawn from the ongoing, nationally representative Longitudinal Aging Study in India, a multipurpose panel survey of aging. We aim to collect rich data on cognitive and neuropsychological tests, informant reports, and epidemiological data through a comprehensive geriatric assessment, and venous blood collection and assays. For a subsample, we collect neuroimaging data. Data collection is currently in progress in 14 States and Union Territories of India. Clinicians will provide clinical consensus diagnosis based on the Clinical Dementia Rating.

ETHICS AND DISSEMINATION

Ethics approval was obtained from the Indian Council of Medical Research and all collaborating institutions. Anonymised data will be available for the larger research community through a secured website hosted by the Gateway to Global Aging Data platform. Research findings from the LASI-DAD team will be disseminated through journal publications and presentations at professional conferences.

Cognitive Function in Low-Income and Low-Literacy Settings: Validation of the Tablet-Based Oxford Cognitive Screen in the Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community in South Africa (HAALSI)

OBJECTIVES

1. Assess validity of the Oxford Cognitive Screen (OCS-Plus), a domain-specific cognitive assessment designed for low-literacy settings, especially in low- and middle-income countries (LMIC); 2. Advance theoretical contributions in cognitive neuroscience in domain-specific cognitive function and cognitive reserve, especially related to dementia.

METHOD

In a cross-sectional study of a sample of 1,402 men and women aged 40-79 in the Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community in South Africa (HAALSI), we administered OCS-Plus along with health and sociodemographic assessments. HAALSI is a representative sample of older adults in Agincourt, South Africa contributing to normative understanding of cognition in LMIC. We report measure distributions, construct and external validity of the OCS-Plus.

RESULTS

OCS-Plus has excellent construct and external validity. Intra-class correlations between similar basic measures of orientation in OCS-Plus and in HAALSI assessments was 0.79, and groups of people performing well on the OCS-Plus verbal memory also showed superior performance on HAALSI verbal memory. The OCS-Plus scores showed consistent associations with age and education and domain-specific associations with alcohol and depression. Younger respondents and the more educated did better on all assessments.

DISCUSSION

The OCS-Plus represents a major methodological advance in dementia studies in LMICs, and enhances understanding of cognitive aging.

A two-decade comparison of prevalence of dementia in individuals aged 65 years and older from three geographical areas of England: results of the Cognitive Function and Ageing Study I and II

BACKGROUND

The prevalence of dementia is of interest worldwide. Contemporary estimates are needed to plan for future care provision, but much evidence is decades old. We aimed to investigate whether the prevalence of dementia had changed in the past two decades by repeating the same approach and diagnostic methods as used in the Medical Research Council Cognitive Function and Ageing Study (MRC CFAS) in three of the original study areas in England.

METHODS

Between 1989 and 1994, MRC CFAS investigators did baseline interviews in populations aged 65 years and older in six geographically defined areas in England and Wales. A two stage process, with screening followed by diagnostic assessment, was used to obtain data for algorithmic diagnoses (geriatric mental state-automated geriatric examination for computer assisted taxonomy), which were then used to estimate dementia prevalence. Data from three of these areas--Cambridgeshire, Newcastle, and Nottingham--were selected for CFAS I. Between 2008 and 2011, new fieldwork was done in the same three areas for the CFAS II study. For both CFAS I and II, each area needed to include 2500 individuals aged 65 years and older to provide power for geographical and generational comparison. Sampling was stratified according to age group (65-74 years vs ≥75 years). CFAS II used identical sampling, approach, and diagnostic methods to CFAS I, except that screening and assessement were combined into one stage. Prevalence estimates were calculated using inverse probability weighting methods to adjust for sampling design and non-response. Full likelihood Bayesian models were used to investigate informative non-response.

FINDINGS

7635 people aged 65 years or older were interviewed in CFAS I (9602 approached, 80% response) in Cambridgeshire, Newcastle, and Nottingham, with 1457 being diagnostically assessed. In the same geographical areas, the CFAS II investigators interviewed 7796 individuals (14,242 approached, 242 with limited frailty information, 56% response). Using CFAS I age and sex specific estimates of prevalence in individuals aged 65 years or older, standardised to the 2011 population, 8·3% (884,000) of this population would be expected to have dementia in 2011. However, CFAS II shows that the prevalence is lower (6·5%; 670,000), a decrease of 1·8% (odds ratio for CFAS II vs CFAS I 0·7, 95% CI 0·6-0·9, p=0·003). Sensitivity analyses suggest that these estimates are robust to the change in response.

INTERPRETATION

This study provides further evidence that a cohort effect exists in dementia prevalence. Later-born populations have a lower risk of prevalent dementia than those born earlier in the past century.

FUNDING

UK Medical Research Council.

Low cognitive ability and poor skill with numbers may prevent many from enrolling in Medicare supplemental coverage

Because traditional Medicare leaves substantial gaps in coverage, many people obtain supplemental coverage to limit their exposure to out-of-pocket costs. However, some Medicare beneficiaries may not be well equipped to navigate the complex supplemental coverage landscape successfully because of their lower cognitive ability or numeracy--that is, the ability to work with numbers. We found that people in the lower third of the cognitive ability and numeracy distributions were at least eleven percentage points less likely than those in the upper third to enroll in a supplemental Medicare insurance plan. This result means that many Medicare beneficiaries do not have the financial protections and other benefits that would be available to them if they were enrolled in a supplemental insurance plan. Our findings suggest that policy makers may want to consider alternatives tailored to these high-need groups, such as enhanced education and enrollment programs, simpler sets of plan choices, or even some type of automatic enrollment with an option to decline coverage.

Overview and findings from the rush Memory and Aging Project

The Memory and Aging Project is a longitudinal, epidemiologic clinical-pathologic cohort study of common chronic conditions of aging with an emphasis on decline in cognitive and motor function and risk of Alzheimer's disease (AD). In this manuscript, we first summarize the study design and methods. Then, we present data on: (1) the relation of motor function to cognition, disability, and death; (2) the relation of risk factors to cognitive and motor outcomes, disability and death; (3) the relation of neuropathologic indices to cognitive outcomes; (4) the relation of risk factors to neuropathologic indices; and (5) additional study findings. The findings are discussed and contextualized.

Cross-validation of brain structural biomarkers and cognitive aging in a community-based study

BACKGROUND

Population-based studies face challenges in measuring brain structure relative to cognitive aging. We examined the feasibility of acquiring state-of-the-art brain MRI images at a community hospital, and attempted to cross-validate two independent approaches to image analysis.

METHODS

Participants were 49 older adults (29 cognitively normal and 20 with mild cognitive impairment (MCI)) drawn from an ongoing cohort study, with annual clinical assessments within one month of scan, without overt cerebrovascular disease, and without dementia (Clinical Dementia Rating (CDR) < 1). Brain MRI images, acquired at the local hospital using the Alzheimer's Disease Neuroimaging Initiative protocol, were analyzed using (1) a visual atrophy rating scale and (2) a semi-automated voxel-level morphometric method. Atrophy and volume measures were examined in relation to cognitive classification (any MCI and amnestic MCI vs. normal cognition), CDR (0.5 vs. 0), and presumed etiology.

RESULTS

Measures indicating greater atrophy or lesser volume of the hippocampal formation, the medial temporal lobe, and the dilation of the ventricular space were significantly associated with cognitive classification, CDR = 0.5, and presumed neurodegenerative etiology, independent of the image analytic method. Statistically significant correlations were also found between the visual ratings of medial temporal lobe atrophy and the semi-automated ratings of brain structural integrity.

CONCLUSIONS

High quality MRI data can be acquired and analyzed from older adults in population studies, enhancing their capacity to examine imaging biomarkers in relation to cognitive aging and dementia.

Overview and findings from the religious orders study

The Religious Orders Study is a longitudinal clinical-pathologic cohort study of aging and Alzheimer's disease (AD). In this manuscript, we summarize the study methods including the study design and describe the clinical evaluation, assessment of risk factors, collection of ante-mortem biological specimens, brain autopsy and collection of selected postmortem data.

THE RESULTS

(1) review the relation of neuropathologic indices to clinical diagnoses and cognition proximate to death; (2) examine the relation of risk factors to clinical outcomes; (3) examine the relation of risk factors to measures of neuropathology; and (4) summarize additional study findings. We then discuss and contextualize the study findings.

Assessment of cognition using surveys and neuropsychological assessment: the Health and Retirement Study and the Aging, Demographics, and Memory Study

OBJECTIVES

This study examines the similarity of cognitive assessments using 1 interview in a large population study, the Health and Retirement Study (HRS), and a subsample in which a detailed neuropsychiatric assessment has been performed (Aging, Demographics, and Memory Study [ADAMS]).

METHODS

Respondents are diagnosed in ADAMS as demented, cognitively impaired without dementia (CIND), or as having normal cognitive function. Multinomial logistic analysis is used to predict diagnosis using a variety of cognitive and noncognitive measures from the HRS and additional measures and information from ADAMS.

RESULTS

The cognitive tests in HRS predict the ADAMS diagnosis in 74% of the sample able to complete the HRS survey on their own. Proxy respondents answer for a large proportion of HRS respondents who are diagnosed as demented in ADAMS. Classification of proxy respondents with some cognitive impairment can be predicted in 86% of the sample. Adding a small number of additional tests from ADAMS can increase each of these percentages to 84% and 93%, respectively.

DISCUSSION

Cognitive assessment appropriate for diagnosis of dementia and CIND in large population surveys could be improved with more targeted information from informants and additional cognitive tests targeting other areas of brain function.

Assessment of cognition in mild cognitive impairment: a comparative study

The demand for rapidly administered, sensitive, and reliable cognitive assessments that are specifically designed for identifying individuals in the earliest stages of cognitive decline (and to measure subtle change over time) has escalated as the emphasis in Alzheimer's disease clinical research has shifted from clinical diagnosis and treatment toward the goal of developing presymptomatic neuroprotective therapies. To meet these changing clinical requirements, cognitive measures or tailored batteries of tests must be validated and determined to be fit-for-use for the discrimination between cognitively healthy individuals and persons who are experiencing very subtle cognitive changes that likely signal the emergence of early mild cognitive impairment. We sought to collect and review data systematically from a wide variety of (mostly computer-administered) cognitive measures, all of which are currently marketed or distributed with the claims that these instruments are sensitive and reliable for the early identification of disease or, if untested for this purpose, are promising tools based on other variables. The survey responses for 16 measures/batteries are presented in brief in this review; full survey responses and summary tables are archived and publicly available on the Campaign to Prevent Alzheimer's Disease by 2020 Web site (http://pad2020.org). A decision tree diagram highlighting critical decision points for selecting measures to meet varying clinical trials requirements has also been provided. Ultimately, the survey questionnaire, framework, and decision guidelines provided in this review should remain as useful aids for the evaluation of any new or updated sets of instruments in the years to come.

Reducing case ascertainment costs in U.S. population studies of Alzheimer's disease, dementia, and cognitive impairment-Part 2

Dementia of the Alzheimer's type (DAT) is a major public health threat in developed countries where longevity has been extended to the eighth decade of life. Estimates of prevalence and incidence of DAT vary with what is measured, be it change from a baseline cognitive state or a clinical diagnostic endpoint, such as Alzheimer's disease. Judgment of what is psychometrically "normal" at the age of 80 years implicitly condones a decline from what is normal at the age of 30. However, because cognitive aging is very heterogeneous, it is reasonable to ask "Is 'normal for age' good enough to screen for DAT or its earlier precursors of cognitive impairment?" Cost containment and accessibility of ascertainment methods are enhanced by well-validated and reliable methods such as screening for cognitive impairment by telephone interviews. However, focused assessment of episodic memory, the key symptom associated with DAT, might be more effective at distinguishing normal from abnormal cognitive aging trajectories. Alternatively, the futuristic "Smart Home," outfitted with unobtrusive sensors and data storage devices, permits the moment-to-moment recording of activities so that changes that constitute risk for DAT can be identified before the emergence of symptoms.