Prevalence and access to care for cardiovascular risk factors in older people in Sierra Leone: a cross-sectional survey
Bibliographic record
Abstract
Introduction Prevalence of cardiovascular disease risk factors (CVDRFs) is increasing, especially in low-income countries. In Sierra Leone, there is limited empirical data on the prevalence of CVDRFs, and there are no previous studies on the access to care for these conditions. Methods This study in rural and urban Sierra Leone collected demographic, anthropometric measurements and clinical data from randomly sampled individuals over 40 years old using a household survey. We describe the prevalence of the following risk factors: diabetes, hypertension, dyslipidaemia, overweight or obesity, smoking and having at least one of these risk factors. Cascades of care were constructed for diabetes and hypertension using % of the population with the disease who had previously been tested ('screened'), knew of their condition ('diagnosed'), were on treatment ('treated') or were controlled to target ('controlled'). Multivariable regression was used to test associations between prevalence of CVDRFs and progress through the cascade for hypertension with demographic and socioeconomic variables. In those with recognised disease who did not seek care, reasons for not accessing care were recorded. Results Of 2071 people, 49.6% (95% CI 49.3% to 50.0%) of the population had hypertension, 3.5% (3.4% to 3.6%) had diabetes, 6.7% (6.5% to 7.0%) had dyslipidaemia, 25.6% (25.4% to 25.9%) smoked and 26.5% (26.3% to 26.8%) were overweight/obese; a total of 77.1% (76.6% to 77.5%) had at least one CVDRF. People in urban areas were more likely to have diabetes and be overweight than those living in rural areas. Moreover, being female, more educated or wealthier increased the risk of having all CVDRFs except for smoking. There is a substantial loss of patients at each step of the care cascade for both diabetes and hypertension, with less than 10% of the total population with the conditions being screened, diagnosed, treated and controlled. The most common reasons for not seeking care were lack of knowledge and cost. Conclusions In Sierra Leone, CVDRFs are prevalent and access to care is low. Health system strengthening with a focus on increased access to quality care for CVDRFs is urgently needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".