Annual Wellness Visits and Early Dementia Diagnosis Among Older Adults Receiving Medicare Benefits
Bibliographic record
Abstract
Abstract Background Early recognition of cognitive impairment (CI) and timely diagnoses of mild CI (MCI) and Alzheimer’s Disease and Related Dementia (ADRD) are key to optimal dementia care. No previous research has examined the effects of Medicare annual wellness visits (AWVs) on early CI recognition among Medicare beneficiaries. Here, we assessed the association between incident diagnosis at MCI, moderate or severe ADRD stages in 2017‐2020, and having a Medicare AWV in the three years prior to diagnosis. Method This population‐based, case‐control study used 100% Texas fee‐for‐service Medicare data from 2014 to 2020; population: Medicare beneficiaries diagnosed with incident MCI or ADRD from 2017 to 2020. The exposure is Medicare AWVs. The primary outcome is MCI, moderate or severe ADRD status by ICD‐9‐CM and ICD‐10‐CM codes. We performed a multinomial logistic regression model to examine the association. Result Medicare beneficiaries (n = 197,356) who had an AWV were 23‐66% more likely to be diagnosed at the MCI stage and 8‐30% more likely to be diagnosed at the moderate ADRD stage compared to diagnoses at the severe ADRD stage relative to those who did not have an AWV. There was a dose‐response relationship; as the number of AWV visits in the three years increased, the magnitude of the likelihood of early diagnosis increased. Conclusion This study suggests that receipt of a Medicare AWV is associated with increased identification of early CI.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".