May Measurement Month 2019: an analysis of blood pressure screening results from the Democratic Republic of the Congo
Bibliographic record
Abstract
Abstract Hypertension, the foremost cause of global morbi-mortality, is linked with a high mortality from numerous cardiovascular endpoints. The May Measurement Month (MMM) campaign is an annual initiative of the International Society of Hypertension (ISH) to collect information on blood pressure (BP) and other risk factors for cardiovascular disease (CVD) in adults. MMM2019 in the Democratic Republic of the Congo (DRC) was an opportunistic cross-sectional survey of volunteers aged ≥18 years that took place in Kinshasa and Mbuji-Mayi after the training of observers to familiarize with the ISH ad hoc methods. We screened 29 857 individuals (mean age: 40 years; 40% female). Hypertension was present in 7624 (25.5%) individuals. Of them, 2520 (33.1%) were aware, 1768 (23.2%) on treatment with 910 (51.5%) controlled BP (systolic BP <140 mmHg and/or diastolic BP <90 mmHg). Of all hypertensives screened, 11.9% had controlled BP. Of all respondents, 16.7% had participated in MMM18 and 60.5% did not have their BP verified during the last year. Fasting, pregnancy, and underweight status were linked with lower BP levels whilst smoking, drinking, antihypertensive medication, previous stroke, diabetes as well as being overweight/obese were associated with higher BP levels. Our results reflect the high rate of hypertension in the DRC with low levels of awareness, treatment, and control. A nationally representative sample is required to establish the nationwide hypertension prevalence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".