Geographic and Sociodemographic Disparities in Cardiovascular Risk in Burkina Faso: Findings from a Nationwide Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease (CVD) risk assessment is a critical step in the current approach to the primary prevention of CVD, particularly in low-income countries such as Burkina Faso. In this study, we aimed to assess the geographic and sociodemographic disparities of the ten-year cardiovascular risk in Burkina Faso. METHODS: We conducted a secondary analysis of the data from the first nationwide survey using the World Health Organization (WHO) STEPwise approach. Ten-year cardiovascular risk was determined using the WHO 2019 updated risk chart (WHO risk) as main outcome, and the Framingham risk score (FRS) and the Globorisk chart for secondary outcomes. We performed a modified Poisson regression model using a generalized estimating equation to examine the association between CVD risk and sociodemographic characteristics. RESULTS: A total of 3081 participants aged 30 to 64 years were included in this analysis. The overall age and sex-standardized mean of absolute ten-year cardiovascular risk assessed using the WHO risk chart was 2.5% (95% CI: 2.4-2.6), ranging from 2.3% (95% CI: 2.2-2.4) in Centre Est to 3.0% (95% CI: 2.8-3.2) in the Centre region. It was 4.6% (95% CI: 4.4-4.8) for FRS and 4.0% (95% CI: 3.8-4.1) for Globorisk. Regarding categorized CVD risk (absolute risk ≥10%), we found out that the age and sex-standardized prevalence of elevated risk was 1.7% (95% CI: 1.3-2.1) for WHO risk, 10.4% (95% CI: 9.6-11.2) for FRS, and 5.9% (95% CI: 5.1-6.6) for Globorisk. For all of the three risk scores, elevated CVD risk was associated with increasing age, men, higher education, urban residence, and health region (Centre). CONCLUSION: We found sociodemographic and geographic inequalities in the ten-year CVD risk in Burkina Faso regardless of risk score used. Therefore, population-wide interventions are needed to improve detection and management of adult in the higher CVD risk groups in Burkina Faso.
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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 | 0.001 | 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".