Rural-urban difference in the prevalence of hypertension in West Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
Urbanisation is considered a major contributor to the rising prevalence of hypertension in West Africa, yet the evidence regarding rural-urban differences in the prevalence of hypertension in the region has been mixed. A systematic literature search of four electronic databases: PubMed, Embase, African Journals Online, and WHO's African Index Medicus; and reference lists of eligible studies was carried out. Original quantitative studies describing the rural-urban difference in the prevalence of hypertension in one or more countries in West Africa, and published in English language from the year 2000 to 2021 were included. A random effects meta-analysis model was used to estimate the odds ratio of hypertension in rural compared to urban locations. A limited sex-based random effects meta-analysis was conducted with 16 studies that provided sex-disaggregated data. Of the 377 studies screened, 22 met the inclusion criteria (n = 62,907). The prevalence of hypertension was high in both rural, and urban areas, ranging from 9.7% to 60% in the rural areas with a pooled prevalence of 27.4%; and 15.5% to 59.2% in the urban areas with a pooled prevalence of 33.9%. The odd of hypertension were lower in rural compared to urban dwellers [OR 0.74, 95% CI: 0.66-0.83; p < 0.001]. The pooled prevalence of hypertension was 32.6% in males, and 30.0% in females, with no significant difference in the odds of hypertension between the sexes [OR 0.91, 95% CI: 0.8-1.05, p = 0.196]. Comprehensive hypertension control policies are needed for both rural, and urban areas in West Africa, and for both sexes.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".