A Public Health Analysis of the Relationship Between Loneliness, Isolation, and Dementia

Loneliness and isolation are now characterised as major public health problems largely because of reported associations with negative health outcomes including dementia. We adopt a public health perspective and review the relationship between loneliness/isolation and dementia focussing on how these concepts are defined, measured, and reported. We identified community based longitudinal studies which measured loneliness/isolation at baseline and dementia at follow up (minimum 12 months) published up to February 2021. We identified 12 papers for loneliness and 15 for isolation which demonstrated substantial heterogeneity in how exposure (loneliness/ isolation) and outcome (dementia) were measured and reported. For example, dementia was measured in 5 different ways: death, hospitalisation, clinical diagnosis, dementia screening tools or cognitive function. Evidence to support a relationship between loneliness/isolation and dementia is inconclusive largely because of this methodological heterogeneity. Using consistent exposure and outcome measures is a prerequisite for determining the health consequences of loneliness and isolation.

Communicating Risk: Developing an “Efficiency Index” for Dementia Screening Tests

Diagnostic and screening tests may have risks such as misdiagnosis, as well as the potential benefits of correct diagnosis. Effective communication of this risk to both clinicians and patients can be problematic. The purpose of this study was to develop a metric called the “efficiency index” (EI), defined as the ratio of test accuracy and inaccuracy, to evaluate screening tests for dementia. This measure was compared with a previously described “likelihood to be diagnosed or misdiagnosed” (LDM), also based on “numbers needed” metrics. Datasets from prospective pragmatic test accuracy studies examining four brief cognitive screening instruments (Mini-Mental State Examination; Montreal Cognitive Assessment; Mini-Addenbrooke’s Cognitive Examination (MACE); and Free-Cog) were analysed to calculate values for EI and LDM, and to examine their variation with test cut-off for MACE and dementia prevalence. EI values were also calculated using a modification of McGee’s heuristic for the simplification of likelihood ratios to estimate percentage change in diagnostic probability. The findings indicate that EI is easier to calculate than LDM and, unlike LDM, may be classified either qualitatively or quantitatively in a manner similar to likelihood ratios. EI shows the utility or inutility of diagnostic and screening tests, illustrating the inevitable trade-off between diagnosis and misdiagnosis. It may be a useful metric to communicate risk in a way that is easily intelligible for both clinicians and patients.

Validation of AD8-Philippines (AD8-P): A Brief Informant-Based Questionnaire for Dementia Screening in the Philippines

Aim. This study was aimed at validating the Filipino version of AD8 (AD8-P). Methods. Community-dwelling Filipino older persons aged ≥60 years, together with their informants, participated in this study. Psychologists independently interviewed the informants with AD8-P and administered the Filipino-validated Mini-Mental State Examination (MMSE-P) and Montreal Cognitive Assessment (MoCA-P) to the older persons. Neurologists and geriatrician conducted physical and neurological examination and Clinical Dementia Rating™ (CDR™) to determine cognitive diagnosis and were blinded with the results of AD8-P. Dementia was diagnosed based on DSM-IV-TR criteria. AD8-P discriminatory ability to screen for dementia was evaluated according to DSM-IV-TR diagnostic criteria for dementia. Results. A total of 366 community-dwelling Filipino older persons aged ≥60 years, 213 with normal cognition and 153 with dementia, and their informants were included in this study. Majority (90%) were at the mildest stage of dementia. Area under the receiver-operating-characteristic curve (AUROC) for AD8-P was 0.94 (95% CI 0.92 to 0.96), demonstrating excellent overall predictive power to screen for dementia. The optimal AD8-P cut-off score with best balance sensitivity (91.5%) and specificity (77.9%) was ≥3. Conclusion. AD8-P demonstrated good psychometric properties to screen for dementia, even at the earliest stage of cognitive decline.

Impact of Educational Level on Versions (Basic and Complete) of the Montreal Cognitive Assessment

<b><i>Background:</i></b> One of the most marked problems in the use of screening instruments for the diagnosis of dementia or cognitive impairment in the elderly is the influence of educational level on the results of psychometric tests. The Montreal Cognitive Assessment (MoCA) questionnaire is one of the most widely used dementia screening instruments internationally and with greater proven validity. There is a version of this instrument called MoCA “Basic” which was developed to reduce education bias. The aim of the study was to compare the psychometric characteristics of the MoCA, full versus basic, versions in older adults. <b><i>Method:</i></b> Participants (<i>N</i> = 214) completed both versions of the MoCA, and screening measures to corroborate their health status. <b><i>Results:</i></b> Internal consistency was satisfactory in both versions: MoCA full (0.79) and MoCA basic (0.76). The overall correlation between both tests was high (0.73). There was no relationship between the dimensions included in each version. Educational level and age explained 33.8% of the total variance in MoCA Full and 31.8% in MoCA Basic. Among educational levels, there are statistically significant differences in participants with &#x3c;6 years of education. <b><i>Conclusions:</i></b> The results confirm that both versions are reliable instruments and also show that in both versions the educational level of &#x3c;6 years of education continues to have an impact on performance. Therefore, it can be considered that the MoCA Basic version for the Ecuadorian population with &#x3c;6 years of education continues to imply literacy competencies.

A Pilot Tool of the Virtual Scenario Initial Dementia Cognitive Screening (VSIDCS) with a Cultural Exhibition for Improving the Standard Traditional Test

Dementia has become a serious global health problem for older people. In the past, primary screening for dementia was carried out by a paper test. These standard traditional tests (e.g., Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE)) have been used for many years. In addition to paper tests, is there another way to let people have better involvement and emotions during the test procedure? With the advancement of technology, the application of virtual reality (VR) and augmented reality (AR) have changed and improved many medical technologies. However, there are few applications of VR and AR in dementia screening. The purpose of this study was to apply VR and AR to construct a pilot tool for virtual scenario initial dementia cognitive screening (VSIDCS) with a cultural exhibition, to achieve better involvement and emotions in participants. There were three operating interfaces designed for the system: a VR screening interface, cognitive board, and AR recognition interface. There were twenty-four middle-aged people (Female 10 and Male 14 between 50 and 65 years of age and with an average age of 58.7 years) selected for the test. The results of the experiments showed that VSIDCS test scores are consistent with those of the MoCA and MMSE. Additionally, VSIDCS can induce better involvement and emotions than the MoCA and MMSE. Participants showed better enthusiasm and more positive experiences during the VSIDCS test.

Detection of dementia on voice recordings using deep learning: a Framingham Heart Study

Background Identification of reliable, affordable, and easy-to-use strategies for detection of dementia is sorely needed. Digital technologies, such as individual voice recordings, offer an attractive modality to assess cognition but methods that could automatically analyze such data are not readily available. Methods and findings We used 1264 voice recordings of neuropsychological examinations administered to participants from the Framingham Heart Study (FHS), a community-based longitudinal observational study. The recordings were 73 min in duration, on average, and contained at least two speakers (participant and examiner). Of the total voice recordings, 483 were of participants with normal cognition (NC), 451 recordings were of participants with mild cognitive impairment (MCI), and 330 were of participants with dementia (DE). We developed two deep learning models (a two-level long short-term memory (LSTM) network and a convolutional neural network (CNN)), which used the audio recordings to classify if the recording included a participant with only NC or only DE and to differentiate between recordings corresponding to those that had DE from those who did not have DE (i.e., NDE (NC+MCI)). Based on 5-fold cross-validation, the LSTM model achieved a mean (±std) area under the receiver operating characteristic curve (AUC) of 0.740 ± 0.017, mean balanced accuracy of 0.647 ± 0.027, and mean weighted F1 score of 0.596 ± 0.047 in classifying cases with DE from those with NC. The CNN model achieved a mean AUC of 0.805 ± 0.027, mean balanced accuracy of 0.743 ± 0.015, and mean weighted F1 score of 0.742 ± 0.033 in classifying cases with DE from those with NC. For the task related to the classification of participants with DE from NDE, the LSTM model achieved a mean AUC of 0.734 ± 0.014, mean balanced accuracy of 0.675 ± 0.013, and mean weighted F1 score of 0.671 ± 0.015. The CNN model achieved a mean AUC of 0.746 ± 0.021, mean balanced accuracy of 0.652 ± 0.020, and mean weighted F1 score of 0.635 ± 0.031 in classifying cases with DE from those who were NDE. Conclusion This proof-of-concept study demonstrates that automated deep learning-driven processing of audio recordings of neuropsychological testing performed on individuals recruited within a community cohort setting can facilitate dementia screening.