Association between hypertension and dementia risk in low- and middle-income countries: A systematic review
Bibliographic record
Abstract
Background: Dementia prevalence is rising most rapidly in low- and middle-income countries (LMICs), yet most evidence on risk factors such as hypertension stems from high-income settings. In LMICs, hypertension may have a greater impact due to its high prevalence and poor control. We systematically reviewed evidence on the association between blood pressure and dementia and cognition in LMICs, and compared findings across regions. Methods: We searched PubMed, Embase, PsycINFO, and Global Index Medicus and reviewed reference lists for relevant studies. We included longitudinal studies (follow-up ≥6 months) from LMICs on the association between systolic blood pressure or hypertension and incident dementia, mild cognitive impairment (MCI), or cognition, with a sample size of ≥500 individuals. Risk of bias was assessed using a modified Newcastle-Ottawa Scale. Results: Of 8709 screened articles, 26 were included: 19 from Asia, six from Latin America, and one from Africa. Operationalization of hypertension and cognitive outcome was heterogeneous across studies, ranging from using routine care data to triple blood pressure measurements and comprehensive cognitive screening with expert review and validation. Follow-up duration ranged from 7 months to 16 years. Hypertension was associated with a higher risk of incident dementia (RR 1.26, 95 %CI 1.03 - 1.53) and MCI (RR 1.19, 95 %CI 1.09 - 1.29). Due to limited number of studies per region, we were unable to compare effect sizes across geographical regions. Conclusion: Hypertension is associated with an increased risk of dementia and cognitive impairment in LMICs, but limited studies from Latin America and especially from Africa prevented reliable regional comparisons.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".