Hypertension prevalence and coverage and intellectual disability: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: People with intellectual disabilities (ID) frequently experience poorer health and lower treatment coverage compared to those without ID, yet differences in hypertension prevalence and treatment coverage remain unclear. OBJECTIVE: To estimate the pooled prevalence ratio (PR) of hypertension and hypertension treatment coverage comparing adults with and without ID. METHODS: We searched MEDLINE, Embase, PsychINFO, Global Health and Global Index Medicus on June 6, 2024. We included observational and intervention studies that estimated the prevalence of hypertension and/or treatment coverage. The risk of bias was assessed using the Newcastle-Ottawa Scale tool. We undertook a random-effects meta-analysis to estimate the pooled PR with 95 % confidence intervals (CI). Sources of heterogeneity were explored through sensitivity and subgroup analyses, and meta-regression. RESULTS: 21 studies from 10 countries across three regions were included. The pooled PR were 0.71 (95 % CI: 0.47-1.05) for hypertension and 0.61 (95 % CI: 0.47-0.81) for hypertension treatment coverage. Only one study adjusted for age; most reported unadjusted estimates, making them prone to confounding. 14 studies were rated as high risk of bias. Subgroup analysis and meta-regression revealed variability in the methods used to diagnose ID, with sample size emerging as the primary source of variability in the effect estimates. CONCLUSIONS: This systematic review showed that adults with ID have a similar prevalence of hypertension, but lower hypertension treatment coverage compared to those without disabilities. However, these results should be interpreted with caution due to the lack of adjustment for confounding in the association and variability in the diagnosis of ID.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".