Incidence and Prevalence of Alzheimer’s Disease in Medicare Beneficiaries
Bibliographic record
Abstract
INTRODUCTION: The availability of anti-amyloid therapy for mild cognitive impairment (MCI) due to Alzheimer's disease and mild Alzheimer's dementia (AD) has underscored the need for realistic estimates of the population with AD/MCI within the healthcare system to assure adequate preparedness. We hypothesize that administrative databases can provide real-world epidemiologic estimates reflecting the population with diagnosed (known) MCI and AD. This study was conducted to estimate diagnostic incidence and prevalence of AD and all-cause MCI among the Medicare fee-for-service (FFS) and Medicare Advantage (MA) beneficiaries in the United States. METHODS: This was a retrospective analysis of Medicare beneficiaries (aged 65 and older) with identified diagnoses of AD/MCI based on ≥ 2 diagnostic codes ≥ 30 days apart. Incidence/prevalence estimates were reported per 10,000 person-years. RESULTS: In FFS, AD incidence (2008-2018) decreased (138 to 104); MCI incidence increased (8 to 47), but the sum (MCI + AD) was relatively stable (146 to 151). Prevalence (2008-2017) increased for AD (318 to 354), and MCI (13 to 99). In MA (2016) epidemiological estimates were consistent with FFS. In 2017, older age, female sex and the Northeastern region were consistently associated with higher AD/MCI prevalence among FFS beneficiaries. CONCLUSION: In FFS, AD/MCI diagnostic prevalence increased over 10 years, especially for MCI; prevalence estimates in MA (2016) were comparable. Diagnostic prevalence in 2016 (FFS + MA) was 3.4% for AD and 0.85% for MCI. Our findings address the reality of Alzheimer's disease in clinical practice in the United States that is confronted by healthcare professionals, payors, healthcare decision-makers, patients, and caregivers, and may offer a realistic gauge for patient triage for treatment, healthcare resource allocation, and health-systems' operational prioritization. With the availability of anti-amyloid treatments, we anticipate that the population with diagnosed MCI/AD within the Medicare database may rise over time; therefore, periodic updates of incidence/prevalence estimates may provide support for timely healthcare decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".