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Published on: August 22, 2012
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Public Health
Lauren Bojarski1,2, Cassity High3, Christopher J McLouth1,4
1University of Kentucky College of Medicine Department of Neurology, Lexington, KY, USA.
Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 23, 2025
Summary
Rural older adults face higher cognitive decline risks. Different classification systems show varying predictive power for identifying these at-risk populations, impacting dementia research.
Area of Science:
- Gerontology
- Public Health
- Neuroscience
Background:
- Older adults in rural areas have increased risk for cognitive impairment and dementia due to factors like limited healthcare access and socioeconomic challenges.
- Rural classification is recognized by NIH as an indicator of under-represented group (URG) status, yet lacks a standardized research definition.
- Existing risk factors for cognitive decline and classification systems were compared to analyze URG status in rural populations.
Purpose of the Study:
- To compare various rurality and socioeconomic classification systems for their effectiveness in identifying populations at risk for cognitive impairment and dementia.
- To analyze the association between different disadvantage classifications and the prevalence of cognitive impairment in a cohort of older adults.
Main Methods:
- A cross-sectional analysis of 790 participants from the University of Kentucky Alzheimer's Disease Research Center Cohort.
- Utilized Health Resources and Services Administration (HRSA), Medically Underserved Areas/Health Professional Shortage Areas (MUA/HPSA), Area Deprivation Index (ADI), and Rural-Urban Continuum Code (RUCC) classifications.
- Examined participant demographics, ApoE4 status, and 15 medical conditions in relation to cognitive impairment prevalence and relative risk.
Main Results:
- Health Resources and Services Administration (HRSA) Rural, MUA/HPSA, and ADI National Percentiles (≥70%) were associated with higher relative risks for cognitive impairment (RR ~1.2) and additive risks (2.3-3.6).
- Area Deprivation Index (ADI) State Deciles (≥7th) and Rural-Urban Continuum Code (RUCC) (≥4) did not predict increased medical risks for cognitive impairment and dementia (RR<1.0).
- Risks for various conditions like cardiovascular disease, diabetes, and depression were variably distributed across classifications.
Conclusions:
- Different classification systems for disadvantaged populations yield distinct risk profiles for cognitive impairment and dementia.
- Accurate understanding and application of rurality and disadvantage classifications are crucial for identifying high-risk URG populations in cognitive decline and dementia research.
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