Real-world healthcare resource utilization of Alzheimer’s disease in the early and advanced stages: a retrospective cohort study
Bibliographic record
Abstract
AimsTo compare all-cause and Alzheimer’s disease (AD)-related healthcare resource utilization (HCRU) by cognitive stage.Methods and MaterialsThis retrospective study analyzed insurance claims data linked to electronic health records (01/01/2015-12/31/2021). Patients with ≥1 cognitive assessment (Mini-Mental State Examination or Montreal Cognitive Assessment) and ≥1 medical or pharmacy claim for an AD diagnosis or AD medications were included. Inverse probability of treatment weighting (IPTW) was used to address potential confounding. All-cause and AD-related HCRU were summarized per patient per year (PPPY) and compared between early AD and advanced AD cohorts (defined according to cognitive scores) using generalized linear regression models; adjusted incidence rate ratios (IRRs), and 95% confidence intervals (CI) were reported.ResultsA total of 193 patients were included (median age: 82 years; 63.2% female), 108 with early AD and 85 with advanced AD, with similar mean follow up. All-cause HCRU, on average, was similar between early AD and advanced AD cohorts (37.4 PPPY and 38.9 encounters PPPY, respectively). For AD-related HCRU, patients with early AD had fewer encounters PPPY, on average, than patients with advanced AD (1.26 and 3.88 encounters, respectively). Following IPTW adjustment, the advanced AD cohort had significantly higher overall AD-related HCRU (IRR: 3.64 [95% CI: 1.96-6.75], p <0.001) and outpatient visits (IRR: 2.76 [95% CI: 1.68-4.54], p <0.001) compared to the early AD cohort.LimitationsThe relatively small sample size of patients with linked claims and cognitive score data limited the ability to assess contribution of all encounter types to HCRU trends, as well as generalizability to the broader AD population.ConclusionsAlthough all-cause HCRU was similar, patients with advanced AD incurred higher AD-related HCRU compared to patients living with early AD. Further research is needed to determine whether interventions earlier in disease progression can mitigate the AD-related healthcare burden for patients with advanced AD.
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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 | 0.003 | 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".