PREVALENCE OF CARDIOVASCULAR DISEASE RISK FACTORSIN SEMI-URBAN COMMUNITIES OF NORTH-CENTRAL NIGERIA
Bibliographic record
Abstract
Background:Several studies have estimated the prevalence of cardiovascular disease risk factors (CRFs) in various communities in Nigeria. However, few have investigated the prevalence patterns in semi-urban communitiesof North-Central Nigeria. We aim to determine the prevalence patterns of CRFs in this geographical location, as a result of the growing incidence of Sudden Cardiac Death (SCD) and Heart Failure (HF) in Nigeria.\n\nMethods:A cross-sectional epidemiological study was conducted. The study was conducted across nine (9) local government councils in the southern part of Benue state, one of the six (6) states that make up the North-Central region of Nigeria. Overall, 108 participants aged > 18 years participated in the study. Risk factors were estimated by collecting information about the participants age, weight, height, Body Mass Index (BMI), waist circumference, waist-hip ratio, systolic blood pressure, diastolic blood pressure, total cholesterol,HDL-cholesterol, triglyceride cholesterol, LDL-cholesterol, and fasting blood glucose. Questionnaires, results of laboratory and instrumental diagnosis were used to collect information about the variables.\n\nResults:The overall mean of age was 50.35 ± 22.02 years. Findings showed that the prevalence of the examined cardiovascular disease risk factors was as follows: hypertension – 55(52.4%), generalized obesity (BMI > 30) – 10(9.26%)abdominal obesity –35 (32.4%) diabetes –9 (8.3%), hypercholesterolemia – 17(15.7%). The result also indicated an increase in the prevalence of hypertension with an increase in age the indices of obesity increased significantly with age but later decreased slightly among the elderly.\n\nConclusion:Findings from the study revealed that about half of the population were hypertensive. Other key risk factors were also prevalent in this population. There is a call on relevant stakeholders for important preventive and control initiatives for awareness, as this population is at high risk of the complications that arise from the underlying disease conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".