Measured and self-reported hypertension among women of reproductive age, Gambia, Kenya, Mozambique
Bibliographic record
Abstract
Problem: In sub-Saharan Africa, hypertension prevalence is usually estimated from participant recall. We assessed the accuracy of self-reported hypertension in women of reproductive age. Approach: In PRECISE (PREgnancy Care Integrating translational Science, Everywhere), an observational prospective cohort study, we recruited 1825 non-pregnant women of reproductive age, 610 in the Gambia, 609 in Kenya and 606 in Mozambique. We compared self-reported and measured hypertension (systolic blood pressure ≥ 140mmHg or diastolic blood pressure ≥ 90mmHg). We adjusted hypertension prevalence for age, body mass index, education, parity, and antihypertensive medicine and oral contraceptive use. Local setting: PRECISE was conducted in both urban and rural hospitals or clinics. Relevant changes: The women were generally in their late twenties and parous. Adjusted measured hypertension prevalence was higher in Mozambique (10.4%; 95% confidence interval, CI: 7.9-12.7) and the Gambia (9.3%; 95% CI: 6.6-12.6) than in Kenya (4.6%; 95% CI: 3.0-6.6). Self-reported hypertension prevalence was highest in the Gambia (12.9%; 95% CI: 10.2-15.9) versus Mozambique (4.2%; 95% CI: 2.8-5.7) or Kenya (6.7%; 95% CI: 5.0-8.6). Sensitivity of self-reported (versus measured) hypertension was less than 45% in all countries, with specificities more than 89%. Positive likelihood ratios were fair in the Gambia (3.70; 95% CI: 2.47-5.54), and good in Kenya (5.79; 95% CI: 3.36-9.98) and Mozambique (5.18; 95% CI: 2.56-10.46). All negative likelihood ratios were poor (≥ 0.20). Lessons learnt: Self-reported hypertension is unsuitable for population hypertension estimates among women of reproductive age in these countries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".