A Compendium Of Two Decades Of Economic Evaluations In Dementia And A Critique Of The Implied Use Of Resources
Bibliographic record
Abstract
Abstract Background This paper presents a unified source of all health economic evaluations related to four categories of resource allocation in dementia (identification, pharmaceuticals, non‐pharmacological interventions and management strategies) and, for health professionals (i.e. non‐economists), we provides guided critiques of implied trade‐offs across varying allocation decisions. Methods To take stock of the entirety of economic evidence, we systematically review, and quality assess, all peer reviewed cost‐effectiveness analyses published since 1999. We provides in‐depth expert guidance, and objective critical appraisal, through the variety of implied inferences related to potential allocation decisions and avoid sources of underlying biased. We explain how to interpret, and differentiate between, the two common forms of economic evidence (i.e., trial‐based or model‐based cost effectiveness analysis). We illustrate strengths and weaknesses of our included studies and, towards informing optimal resource allocation, we detail the opportunities and threats in relation to quality of evidence. Results Since 1999, we found 127 published economic evaluations (identification, 20%; pharmaceuticals, 45%; non‐pharmacological approaches, 18% and; management strategies, 17%). Internationally, the top five countries producing economic evaluations in dementia were: UK (33%); USA (19%); the Netherlands (10%) Canada (7%) and; (jointly) Germany/Sweden (5%). Over time, production of economic evidence appears relatively flat (averaging 1 to 2 publications per category) but significant upsurges in pharmaceutical‐related studies occurred in 2005 (n = 5), 2010 (n = 8) and 2012 (n = 8). Reporting is generally found to meet objective international standards, however, our critique of model‐based techniques raises concerns. Examining funding sources, industry‐funded pharmaceutical studies appear more likely to report more favourable cost effectiveness results, with these exclusively simulation‐based studies often purporting more cost savings (as compared to publicly‐funded studies). Notwithstanding concerns, the sum of evidence indicates that, compared to usual dementia care, alternative resource allocation can often result in increased quality‐of‐life, whilst also reducing costs. Conclusion In contrast to competing disease areas, dementia research has experienced a relatively low production of economic evidence. Given the considerable economic changes created by dementia, and potential for economic benefit highlight here, increasing the inclusion of high quality economic evaluations alongside dementia‐related research is required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.239 | 0.560 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.027 | 0.019 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".