Alzheimer’s Disease in Illinois: Analyzing Disparities and Projected Trends
Bibliographic record
Abstract
Alzheimer's disease (AD) is a growing public health issue disproportionately affecting adults 65 years and older. This growing trend is accompanied by rising economic, social, emotional, and physical costs, both for patients and their caregivers. As the U.S. population ages, understanding disparities in AD prevalence particularly by gender and age has become increasingly important, particularly in high-burden states like Illinois. This review focuses on gender and age disparities in AD, with a specific emphasis on Illinois. This review integrates national and global trends with state-specific projections and explores modifiable and non-modifiable risk factors that may contribute to these disparities. We analyzed projections from the Illinois Department of Public Health and the Alzheimer's Association to assess AD prevalence by gender and age across Illinois' 102 counties from 2020 to 2030, disaggregated by gender and age. Rates were compared with U.S. and global trends. Risk factors such as diabetes, education, access to care, and socioeconomic status were reviewed in the context of these disparities. Women consistently show higher AD prevalence across age groups and regions, with the greatest increase in cases is projected among adults aged 75 to 84 years, particularly in regions with higher women populations and social vulnerability. If unaddressed, risk factors like lower education, rural residency, and limited healthcare access may worsen these disparities. Addressing them requires focused public health efforts that combine early screening, caregiver support, and regional resource allocation. Illinois serves as a case study for targeted interventions applicable to broader national strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".