Health care utilization and cost differences across cognitively‐defined Alzheimer’s disease subgroups
Bibliographic record
Abstract
Abstract Background Existing studies on the health care utilization and costs associated with Alzheimer’s disease (AD) have treated individuals with AD as a homogeneous group, though recent evidence suggests individuals with AD may be classified into biologically distinct subgroups with differing genetic and clinical profiles. The objective of our study is to examine differences in healthcare utilization and costs across cognitively defined AD subgroups. Method We utilize data from the Adult Changes in Thought (ACT) study (1994 – 2020), a population‐based longitudinal study of aging and the incidence of and risk factors for dementia. We focus our study on individuals who developed incident AD and classify these individuals into one of six cognitively‐defined AD subgroups using previously described methods. We identify a sex‐ and birth year‐matched set of controls using a many‐to‐one matching method. Controls are assigned an index date equal to the AD onset date for their matched AD case. We examine utilization and costs in the year preceding AD onset and in the three years following. Our utilization outcomes include number of days in a month spent in a hospital inpatient, intensive care unit, or skilled nursing facility setting, and number of emergency department visits. We also examine monthly total health care costs and component costs, including outpatient, hospital inpatient, skilled nursing facility, and pharmacy costs. We utilize repeated measures generalized estimating equations to estimate health care utilization associated with each AD subgroup. To estimate the incremental costs associated with each subgroup, we use the Basu and Manning cost estimator. Result We find significant utilization and costs differences across the cognitively‐defined AD subgroups, driven primarily by differences in the use of hospital inpatient and skilled nursing facility services. We also find the highest utilization and costs among the group of individuals with substantial relative impairments across multiple cognitive domains. Conclusion Studies on the health care utilization and costs associated with AD miss important heterogeneity by examining individuals with AD in the aggregate. Our study suggests that individuals in the cognitively‐defined AD subgroups have distinct health care utilization and cost patterns leading up to and following AD onset.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".