Annual Wellness Visits and Early Dementia Diagnosis Among Medicare Beneficiaries
Bibliographic record
Abstract
Importance: Early recognition of cognitive impairment is key to optimal dementia care. No previous research has examined the probability of developing mild cognitive impairment (MCI) or Alzheimer disease and related dementias (ADRD) at 5-year follow-up among older adult Medicare beneficiaries by receipt of an annual wellness visit (AWV). Objective: To assess the association of incident AWV with the first ADRD or MCI diagnosis among older adults with Medicare fee-for-service benefits. Design, Setting, and Participants: This retrospective population-based cohort study used 100% Texas fee-for-service Medicare data from 2015 to 2022. Participants comprised 549 516 community-dwelling Medicare beneficiaries aged 68 years or older in 2018, with complete Medicare fee-for-service Parts A and B and no Medicare Advantage plan enrollment for 2015 to 2018. Exposure: Medicare AWVs. Main Outcomes and Measures: The first MCI or ADRD diagnosis (reported as MCI or ADRD diagnosis, MCI diagnosis, and ADRD diagnosis) from the AWV index date in 2018 through December 31, 2022. Results: In this cohort study of 549 516 Medicare beneficiaries with no diagnosis of MCI or ADRD in 2015 to 2017 (mean [SD] age, 76.7 [6.6] years; 289 932 women [52.8%]), 66 433 (12.1%) had an incident AWV in 2018. Annual wellness visit recipients were more likely than those who did not receive an AWV to be female, to be non-Hispanic White (followed by Hispanic, non-Hispanic Black, and other), to have more education, to reside in a metropolitan area, to have more comorbidities, and to have a primary care professional in the 12 months before the AWV index date. After propensity score matching, AWV receipt was associated with a 21% increase in MCI diagnosis (hazard ratio, 1.21 [95% CI, 1.16-1.27]) and a 4% increase in ADRD diagnosis (hazard ratio, 1.04 [95% CI, 1.02-1.06]). The increase in MCI diagnosis associated with AWV was larger when the AWV was censored or treated as a time-dependent covariate in the follow-up period. Conclusions and Relevance: These findings indicate that AWV recipients had a timelier first MCI diagnosis than those who did not receive an AWV, but first ADRD diagnosis differed little. This study suggests that the Medicare AWV health policy may increase MCI identification, prompting more specialized care.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".