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97 Exploring Urban-Rural Disparities in Alzheimer’s disease: Clinical characterization of a southern Nevada cohort
- Justin B Miller, Christina Wong, Jessica ZK Caldwell, Jeffrey L Cummings, Samantha E John, Jayde Powell, Kaley Brouwers, Jessica Rodrigues, Kimberly Cobos, Raelynn de la Cruz, Aaron Ritter
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 397-399
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Objective:
As the US population ages, the prevalence of Alzheimer’s disease and related dementias (AD/RD) is on the rise. This is especially true in rural America, where mortality rates due to AD/RD are rising faster than in metropolitan areas. To date, however, people living in rural communities are severely underrepresented in aging research. The Nevada Exploratory Alzheimer’s Disease Research Center (NVeADRC) seeks to address this gap. Here, we present preliminary cognitive data from our rural-dwelling cohort, as well as relevant demographic and clinical characteristics.
Participants and Methods:Individuals with normal cognition (NC), mild cognitive impairment (MCI), and dementia due to Alzheimer’s disease (AD) living in rural communities, defined as a rural-urban commuting area (RUCA) code of 4 or higher, were enrolled through either clinic or community outreach. Eligibility for the observational cohort required: age >55 years, primarily English-speaking, primary residence in a rural community, and availability of a study partner. Measures included the Uniform Data Set (v3), blood-based biomarkers, structural brain MRI, and portions of the PhenX Social Determinants of Health toolkit. Participants are seen at baseline and followed annually, with interim remote visits every 6 months. A multidisciplinary consensus diagnosis is rendered after each visit. Where feasible, a harmonized urban cohort followed by the Nevada Center for Neurodegeneration and Translational Neuroscience (CNTN) was used for comparison.
Results:Fifty-six rural-dwelling (age=70.4±7.1 years; edu=15.2±2.6 years; 61% female) and 148 urban-dwelling (age=72.9±6.8 years; edu=15.8±2.7 years; 46% female) older adults were included; age significantly differed between cohorts but education did not. The rural cohort was 46% NC (MoCA=26.8±2.3; CDRsob=0.3±0.6), 32% MCI (MoCA=22.8±3.1; CDRsob=1.2±1.0), and 22% AD (MoCA=16.9±5.5; CDRsob=5.2±3.0). The urban cohort was 39% NC (MoCA=26.4±2.6; CDRsob=0.3±0.8), 44% MCI (MoCA=22.3±3.1; CDRsob=2.0±1.5) and 17% AD (MoCA=18.6±3.9; CDRsob=4.7±2.3). Rural communities were significantly more disadvantaged, as measured by the Area Deprivation Index (ADI), than urban communities (rural ADI=6.3±2.6; urban ADI=3.4±2.3; p<.001). Fifty-percent of the rural cohort lives in a moderate to severely disadvantaged neighborhood (ADI Decile>7) compared to 12% of the urban cohort, and 11% of individuals in the rural cohort reported living more than 30 miles from the nearest medical facility. Across the combined cohort, education was significantly correlated with ADI deciles (r=-.30, p<.001), with people in the areas of highest disadvantage having the lowest education. Verbal memory was also inversely associated with ADI. There were no differences in clinical diagnosis as a function of ADI rank.
Conclusions:Living in a rural community conveys a multifaceted array of risks and benefits, some of which differ from urban settings. The literature to date suggests that older adults living in rural communities are at significantly increased risk for morbidity and mortality due to AD/RD, though it is unclear why. Preliminary data from the NVeADRC show that increasing levels of neighborhood disadvantage were associated with lower levels of education and worse verbal memory in this convenience sample. The combined effect of low education and increased disadvantage account for some of the urban-rural differences in mortality that have been reported, though additional research on representative samples in this underrepresented population is critical.
46 Comparison of Anxiety Measures in a Memory Clinic Sample
- Raelynn Mae de la Cruz, Jessica Rodrigues, Rachel M. Butler-Pagnotti, Filippo Cieri, Shehroo B. Pudumjee, Sonakshi Arora, Kimberly L. Cobos, Jessica Z. K. Caldwell, Lucille Carriere, Christina G. Wong
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 725-726
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Objective:
As the presentation of anxiety may differ between younger and older adults, it is important to select measures that accurately capture anxiety symptoms for the intended population. The 21-item Beck Anxiety Inventory (BAI) is widely used; however, its high reliance on somatic symptoms may result in artificial inflation of anxiety ratings among older adults, particularly those with medical conditions. The 30-item Geriatric Anxiety Scale (GAS) was specifically developed for older adults and has shown strong psychometric properties in community-dwelling and long-term care samples. The reliability and validity of the GAS in a memory clinic setting is unknown. The present study aimed to compare the psychometric properties of the GAS and the BAI in a memory disorder clinic sample.
Participants and Methods:Participants included 35 older adults (age=73.3±5.0 years; edu=15.3±2.8 years; 42% female; 89% non-Hispanic white) referred for a neuropsychological evaluation in a memory disorders clinic. In addition to the GAS and BAI, the Geriatric Depression Scale (GDS) and Montreal Cognitive Assessment (MoCA) were included. Cutoffs for clinically significant anxiety were based on published data for each measure. A dichotomous anxiety rating (yes/no) was created to examine inter-measure agreement; minimal anxiety was classified as “no” and mild, moderate and severe anxiety were classified as “yes.” Internal scale reliability was examined using Cronbach’s alpha. Convergent and discriminant validity were examined using Spearman rank correlation coefficients. Frequency distributions determined the proportion of yes/no anxiety ratings, and a McNemar test compared the proportion of anxiety classifications between the two measures.
Results:Both measures had excellent internal consistency (BAI: a=.88; GAS: a=.94). The BAI and GAS were highly correlated with each other (r=.79, p<.001) and positively correlated with a depression measure (BAI-GDS: r=.51, p=.002; GAS-GDS: r=.53, p=.001). Discriminant validity was supported by lower correlations between the anxiety measures and cognition (BAI-MoCA: r=.38, p=.061; GAS-MoCA: r=.34, p=.098). The BAI classified 14 participants as having anxiety (40%) and 21 participants as not having anxiety (60%), whereas the GAS classified 21 participants as having anxiety (60%) and 14 participants as not having anxiety (40%). The proportion of anxiety classifications were significantly different between the two measures (p =.016). For 28 participants (80%), there was agreement between the anxiety ratings. Seven participants (20%) were classified as having anxiety by the GAS, but not by the BAI; GAS items related to worry about being judged or embarrassed may contribute to discrepancies, as they were frequently endorsed by these participants and are unique to the GAS.
Conclusions:Results support that both anxiety measures have adequate psychometric properties in a clinical sample of older adult patients with memory concerns. It was expected that the BAI would result in higher classification of anxiety due to reliance on somatic symptoms; however, the GAS rated more participants as having anxiety. The GAS may be more sensitive to detecting anxiety in our sample, but formal anxiety diagnoses were not available in the current dataset. Future research should examine the diagnostic accuracy of the GAS in this population. Overall, preliminary results support consideration of the GAS in memory disorder evaluations.