Do Quality-Adjusted Life Years Discriminate Against the Elderly? An Empirical Analysis of Published Cost-Effectiveness Analyses
Bibliographic record
Abstract
OBJECTIVES: Critics of quality-adjusted life-years argue that it discriminates against older individuals. However, little empirical evidence has been produced to inform this debate. This study aimed to compare published cost-effectiveness analyses (CEAs) on patients aged ≥65 years and those aged <65 years. METHODS: We used the Tufts Cost-Effectiveness Analysis Registry to identify CEAs published in MEDLINE between 1976 and 2021. Eligible CEAs were categorized according to age (≥65 years vs <65 years). The distributions of incremental cost-effectiveness ratios (ICERs) were compared between the age groups. We used logistic regression to assess the association between age groups and the cost-effectiveness conclusion adjusted for confounding factors. We conducted sensitivity analyses to explore the impact of mixed age and age-unknown groups and all ICERs from the same CEAs. Subgroup analyses were also conducted. RESULTS: A total of 4445 CEAs categorized according to age <65 years (n = 3784) and age ≥65 years (n = 661) were included in the primary analysis. The distributions of ICERs and the likelihood of concluding that the intervention was cost-effective were similar between the 2 age groups. Adjusted odds ratios ranged from 1.132 (95% CI 0.930-1.377) to 1.248 (95% CI 0.970-1.606) (odds ratio >1 indicating that CEAs for age ≥65 years were more likely to conclude the intervention was cost-effective than those for age <65 years). Sensitivity and subgroup analyses found similar results. CONCLUSION: Our analysis found no systematic differences in published ICERs using quality-adjusted life-years between CEAs for individuals aged ≥65 years and those for individuals aged <65 years.
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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 | 0.231 | 0.601 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".