Prevalence and Severity Distribution of Alzheimer’s Disease in the United States from the Health and Retirement Study
Bibliographic record
Abstract
Abstract Background There is limited data regarding Alzheimer’s disease (AD) prevalence by severity across the AD continuum. This study aimed to estimate prevalence of mild cognitive impairment (MCI) and mild, moderate, and severe AD in a representative sample of the United States (US) population. Method Data from the Health and Retirement Study (HRS), a bi‐annual national survey of older adults in the US, were analyzed (2014‐2018), applying cluster sampling weights. AD ascertainment from HRS was based on patient report of a clinical diagnosis (physician diagnosis‐based), or the test score of Modified Telephone Interview of Cognitive Status (TICS‐m) (cognitive performance‐based). Patients with MCI were identified with the cognitive performance‐based method only. MCI and AD severity stages were ascertained using a crosswalk between the scores of TICS‐m and the mini‐mental state examination (MMSE). Result The diagnosis‐based method identified 1.2%, 1.2%, 1.3% patients with AD in 2014, 2016 and 2018, respectively (2.2% pooled). The mean (SD) age was 79.4 (10.3) years, with 61% women and 22% had passed college education (combined across surveys). The cognitive performance‐based method identified 23%, 23%, 21% patients with MCI in 2014, 2016 and 2018, respectively (24% pooled), and 27%, 22%, 17% patients with AD in 2014, 2016 and 2018, respectively (30% pooled). For patients with MCI, the mean age was 67.3 (10.1) years, with 51% women and 36% passed college education; for patients with AD, the mean age was 72.8 (11.1) years, with 53% women and 28% had passed college education. Severity distributions are shown in Figure 1. Unweighted results were similar. Conclusion The cognitive performance‐based method identified a substantially higher prevalence of AD than the diagnosis‐based method and enabled identification of MCI prevalence using HRS data. The prevalence of MCI was consistently higher than AD dementia severity categories over the years (2014, 2016, and 2018). The discordance in AD prevalence estimation between cognitive performance‐based and diagnosis‐based methods underscores a need for better understanding of clinical practice patterns in AD diagnosis, use of clinical assessment tools, and severity classification in the US.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".