TY - JOUR T1 - CogDrisk, ANU-ADRI, CAIDE, and LIBRA Risk Scores for Estimating Dementia Risk. JF - JAMA Netw Open Y1 - 2023 A1 - Huque, Md Hamidul A1 - Kootar, Scherazad A1 - Eramudugolla, Ranmalee A1 - Han, S Duke A1 - Carlson, Michelle C A1 - Lopez, Oscar L A1 - Bennett, David A A1 - Peters, Ruth A1 - Anstey, Kaarin J KW - Aged KW - Aged, 80 and over KW - Alzheimer disease KW - Australia KW - Cohort Studies KW - Female KW - Heart Disease Risk Factors KW - Humans KW - Male KW - Risk Factors AB -

IMPORTANCE: While the Australian National University-Alzheimer Disease Risk Index (ANU-ADRI), Cardiovascular Risk Factors, Aging, and Dementia (CAIDE), and Lifestyle for Brain Health (LIBRA) dementia risk tools have been widely used, a large body of new evidence has emerged since their publication. Recently, Cognitive Health and Dementia Risk Index (CogDrisk) and CogDrisk for Alzheimer disease (CogDrisk-AD) risk tools have been developed for the assessment of dementia and AD risk, respectively, using contemporary evidence; comparison of the relative performance of these risk tools is limited.

OBJECTIVE: To evaluate the performance of CogDrisk, ANU-ADRI, CAIDE, LIBRA, and modified LIBRA (LIBRA with age and sex estimates from ANU-ADRI) in estimating dementia and AD risks (with CogDrisk-AD and ANU-ADRI).

DESIGN, SETTING, AND PARTICIPANTS: This population-based cohort study obtained data from the Rush Memory and Aging Project (MAP), the Cardiovascular Health Study Cognition Study (CHS-CS), and the Health and Retirement Study-Aging, Demographics and Memory Study (HRS-ADAMS). Participants who were free of dementia at baseline were included. The factors were component variables in the risk tools that included self-reported baseline demographics, medical risk factors, and lifestyle habits. The study was conducted between November 2021 and March 2023, and statistical analysis was performed from January to June 2023.

MAIN OUTCOMES AND MEASURES: Risk scores were calculated based on available factors in each of these cohorts. Area under the receiver operating characteristic curve (AUC) was calculated to measure the performance of each risk score. Multiple imputation was used to assess whether missing data may have affected estimates for dementia risk.

RESULTS: Among the 6107 participants in 3 validation cohorts included for this study, 2184 participants without dementia at baseline were available from MAP (mean [SD] age, 80.0 [7.6] years; 1606 [73.5%] female), 548 participants without dementia at baseline were available from HRS-ADAMS (mean [SD] age, 79.5 [6.3] years; 288 [52.5%] female), and 3375 participants without dementia at baseline were available from CHS-CS (mean [SD] age, 74.8 [4.9] years; 1994 [59.1%] female). In all 3 cohorts, a similar AUC for dementia was obtained using CogDrisk, ANU-ADRI, and modified LIBRA (MAP cohort: CogDrisk AUC, 0.65 [95% CI, 0.61-0.69]; ANU-ADRI AUC, 0.65 [95% CI, 0.61-0.69]; modified LIBRA AUC, 0.65 [95% CI, 0.61-0.69]; HRS-ADAMS cohort: CogDrisk AUC, 0.75 [95% CI, 0.71-0.79]; ANU-ADRI AUC, 0.74 [95% CI, 0.70-0.78]; modified LIBRA AUC, 0.75 [95% CI, 0.71-0.79]; CHS-CS cohort: CogDrisk AUC, 0.70 [95% CI, 0.67-0.72]; ANU-ADRI AUC, 0.69 [95% CI, 0.66-0.72]; modified LIBRA AUC, 0.70 [95% CI, 0.68-0.73]). The CAIDE and LIBRA also provided similar but lower AUCs than the 3 aforementioned tools (eg, MAP cohort: CAIDE AUC, 0.50 [95% CI, 0.46-0.54]; LIBRA AUC, 0.53 [95% CI, 0.48-0.57]). The performance of CogDrisk-AD and ANU-ADRI in estimating AD risks was also similar.

CONCLUSIONS AND RELEVANCE: CogDrisk and CogDrisk-AD performed similarly to ANU-ADRI in estimating dementia and AD risks. These results suggest that CogDrisk and CogDrisk-AD, with a greater range of modifiable risk factors compared with other risk tools in this study, may be more informative for risk reduction.

VL - 6 IS - 8 ER - TY - JOUR T1 - Examining Health and Wealth Correlates of Perceived Financial Vulnerability: A Normative Study. JF - Innovation in Aging Y1 - 2020 A1 - Peter A Lichtenberg A1 - Daniel Paulson A1 - Han, S Duke KW - Financial strain KW - Mental Health KW - Wealth AB -

Background and Objectives: Age-associated financial vulnerability was introduced because it was increasingly recognized that cognitively intact older adults experienced changes that rendered them financially vulnerable. In this study, we attempt to apply the construct of Age-Associated Financial Vulnerability to a measure of Perceived Financial Vulnerability and whether this perceived vulnerability is predicted by risk factors from the 4 categorical domains used to define Age-Associated Financial Vulnerability's impact.

Research Design and Methods: This study was part of the Health and Retirement Study (HRS) survey in 2018. The survey contained 7 experimental module items of Perceived Financial Vulnerability. One thousand three hundred fourteen participants completed the Perceived Financial Vulnerability measure. The sample was drawn from Waves 13 and 14 of the HRS (2016 and 2018, respectively). The measurement of Perceived Financial Vulnerability was developed on the basis of 7 questions assessing financial awareness and psychological vulnerability items regarding personal finance that were included in the 2018 HRS data collection. Predictors included measures of cognition, function/health, depression, and wealth. Predictor measures from 2016 were regressed on 2018 Perceived Financial Vulnerability scores.

Results: Six items of Perceived Financial Vulnerability had psychometric properties acceptable for a new measure. Responses revealed variability in Perceived Financial Vulnerability. Overall, 18% of variance was accounted for and measures from cognition, depression, assets, and functional abilities were all unique and significant predictors.

Discussion and Implications: This study represents both a conceptual and empirical contribution to our understanding of older adult's perceptions of financial vulnerability. The high levels of Perceived Financial Vulnerability found in this normative sample underscore the importance of context in understanding people's economic behaviors. For instance, more than one half of the sample indicated that they wished they had someone to talk to about their finances. This desire to talk with others is normative and yet often underappreciated.

VL - 4 IS - 4 ER -