The impact of hypertension prevention and modification on dementia burden: A systematic review of economic studies
Bibliographic record
Abstract
AIM: Neurological disorders account for the largest proportion of disability-adjusted life years globally, with dementia being the third leading cause. Hypertension has been identified as a priority, targetable risk factor for dementia. This study aimed to systematically review economic studies that examine the impact of hypertension prevention and control on the costs and outcomes of dementia. METHODS: An electronic literature search was conducted using MEDLINE, EMBASE, Scopus, Web of Science, EconLit, and grey literature sources. The inclusion criteria were: 1) economic evaluation studies, including both full and partial evaluations; 2) a primary focus on dementia; and 3) evaluation of the impact of preventing or modifying hypertension on the burden of dementia. The quality of included studies was assessed using the Consensus on Health Economic Criteria (CHEC) list. RESULTS: Twelve studies were included in the final review. Four studies were full economic evaluations, while eight were partial evaluations, with one reporting costs and seven reporting the impact on dementia prevalence. Nine studies considered hypothetical reductions in hypertension rate, while three evaluated applied hypertension-related interventions. Hypertension modification was associated with higher life expectancy and a higher average age of dementia onset. Full economic evaluations of specific hypertension modification interventions found that these interventions dominated (i.e. had lower costs and higher quality-adjusted life-years (QALY)) the status quo scenario or had an acceptable incremental cost-effectiveness ratio (ICER). CONCLUSIONS: Hypertension modification has the potential to reduce the burden of dementia in a cost-effective way. However, further economic evaluations of applied interventions are needed to determine real-world feasibility and cost-effectiveness.
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 | 0.018 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".