Demographics and Clinical Correlates of Blood Pressure Among Ugandans at Risk for Stroke
Bibliographic record
Abstract
Background: Stroke risk factors are an emerging public health problem in Sub-Saharan Africa. This analysis examined demographic and clinical correlates of blood pressure (BP) in a Ugandan sample at risk for stroke. Methods: We conducted a cross-sectional analysis of demographics, stroke risk factors, and selected clinical variables associated with systolic blood pressure (SBP) and diastolic blood pressure (DBP). Demographics (age, gender, marital status, education level, rural/suburban/urban status, and employment status), stroke risk factors (diabetes, hyperlipidemia, obesity, smoking status, sedentary lifestyle, and problem alcohol use), and clinical variables associated with SBP and DBP were derived from the screening and baseline sample of a prospective, randomized effectiveness-implementation trial testing a novel stroke risk reduction approach (TargetEd manAgeMent Intervention (TEAM)) conducted across three Ugandan sites. We examined variables with respect to an established set of guidelines for hypertension (HTN) severity, the European Society of Cardiology and the European Society of Hypertension (ESC-ESH) Guideline. Results: Of the total sample of 247, the mean sample age was 55.4 years (standard deviation (SD) = 12.0), with a female predominance (n = 168, 68%). In addition to HTN, the most common sample stroke risk factors were hyperlipidemia (n = 199, 80.6%) and obesity (n = 98, 39.7%). The majority (n = 238, 96.4%) were prescribed at least one medication to treat HTN. Mean SBP and DBP at baseline were 143.0 (SD = 19.8, range 94.5 - 206) and 89.3 (SD = 14.0, range 61 - 136), respectively. ESC-ESH classifications were grouped into grades of increasing severity from the mildest (grade 1) to the most severe (grade 3). An additional < grade 1 was created to reflect individuals whose ESC-ESH scores dropped below grade 1 post-screening. There were few significant differences across ESC-ESH groups, except that having diabetes, being sedentary, and being a smoker were associated with higher ESC-ESH grades. Conclusions: To help reduce the stroke burden in Uganda, our findings support the importance of raising awareness of HTN and helping individuals to manage their HTN with both medications and lifestyle approaches. J Neurol Res. 2024;14(2):74-85 doi: https://doi.org/10.14740/jnr830
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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 | 0.000 |
| 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".