Low prevalence of high blood pressure in pregnant women in Burkina Faso: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: High blood pressure (HBP) during pregnancy causes maternal and fetal mortality. Studies regarding its prevalence and associated factors in frontline level health care settings are scarce. We thus aimed to evaluate the prevalence of HBP and its associated factors among pregnant women at the first level of the health care system in Burkina Faso. METHODS: This cross-sectional study was conducted in six health facilities between December 2018 and March 2019. HBP was defined as systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg. Multivariable logistic regression analysis was performed to identify factors associated with HBP. RESULTS: A total of 1027 pregnant women were included. The overall prevalence of HBP was 1.4% (14/1027; 95% confidence interval [CI] 0.7-2.3), with 1.6% (7/590; 95% CI 0.8-3.3) in rural and 1.2% (7/437; 95% CI 0.6- 2.5) in semi-urban areas. The prevalence was 0.7% (3/440; 95% CI 0.2-2.1) among women in the first, 1.5% (7/452; 95% CI 0.7-3.2) in the second and 3% (4/135; 95% CI 1.1-7.7) in the third trimester. In the multivariable analysis, pregnancy trimester, maternal age, household income, occupation, parity, and residential area were not associated with HBP during pregnancy. CONCLUSION: The prevalence of HBP among pregnant women at the first level of health system care is significantly lower compared to prevalence's from hospital studies. Public health surveillance, primary prevention activities, early screening, and treatment of HDP should be reinforced in all health facilities to reduce the burden of adverse pregnancy outcomes in Burkina Faso.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".