May Measurement Month 2022: An Analysis of Blood Pressure Screening Findings from Sierra Leone
Bibliographic record
Abstract
Abstract Introduction Elevated blood pressure, a major modifiable risk factor for death and disability globally, remains a significant public health challenge despite increased knowledge of preventive measures. Its high prevalence and underdiagnosis, particularly in low-resource settings, necessitate effective interventions. Methods The May Measurement Month (MMM) initiative by the International Society of Hypertension (ISH) aims to raise awareness and promote screening programs. This study presents the findings of the MMM 2022 in Sierra Leone. We conducted an opportunistic cross-sectional study in Sierra Leone from May to September 2022, enrolling 818 participants aged 18 or older. Results Among the participants (mean age 38.0 years, predominantly Black), the prevalence of hypertension was 25.7% (210 out 818 participants). However, only 17% (140 out of 818) of the participants were previously diagnosed with hypertension. The findings revealed a wide range of antihypertensive medication use among the known hypertensives, ranging from no medication to the use of five or more medication classes. Approximately 43.6% of the known hypertensives were taking one antihypertensive medication, while 22.9% were taking two medications. A significant proportion, 42.9%, of the known hypertensives did not consistently adhere to their antihypertensive medication regimen, citing common reasons such as cost, availability, side effects, and forgetfulness. Conclusion This study highlights the importance of community-based screening in identifying undiagnosed hypertension and emphasizes the need for expanded or alternative screening and referral programs to increase awareness, promote preventive measures, and improve hypertension management in Sierra Leone.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".