Changes in Health Care Use by Mexican American Medicare Beneficiaries Before and After a Diagnosis of Dementia
Bibliographic record
Abstract
BACKGROUND: Evidence from predominantly non-Hispanic White cohorts indicates health care utilization increases before Alzheimer's disease and related dementias (ADRD) is diagnosed. We investigated trends in health care utilization by Mexican American Medicare beneficiaries before and after an incident diagnosis of ADRD. METHODS: Data came from the Hispanic Established Populations for the Epidemiological Study of the Elderly that has been linked with Medicare claims files from 1999 to 2016 (n = 558 matched cases and controls). Piecewise regression and generalized linear mixed models were used to compare the quarterly trends in any (ie, one or more) hospitalizations, emergency room (ER) admissions, and physician visits for 1 year before and 1 year after ADRD diagnosis. RESULTS: The piecewise regression models showed that the per-quarter odds for any hospitalizations (odds ratio [OR] = 1.62, 95% CI = 1.43-1.84) and any ER admissions (OR = 1.40, 95% CI = 1.27-1.54) increased before ADRD was diagnosed. Compared to participants without ADRD, the percentage of participants with ADRD who experienced any hospitalizations (27.2% vs 14.0%) and any ER admissions (19.0% vs 11.7%) was significantly higher at 1 quarter and 3 quarters before ADRD diagnosis, respectively. The per-quarter odds for any hospitalizations (OR = 0.88, 95% CI = 0.80-0.97) and any ER admissions (OR = 0.89, 95% CI = 0.82-0.97) decreased after ADRD was diagnosed. Trends for any physician visits before or after ADRD diagnosis were not statistically significant. CONCLUSIONS: Older Mexican Americans show an increase in hospitalizations and ER admissions before ADRD is diagnosed, which is followed by a decrease after ADRD diagnosis. These findings support the importance of a timely diagnosis of ADRD for older Mexican Americans.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".