Dyslipidemia and its associated factors in Ethiopia: a systematic review and meta-analysis
Bibliographic record
Abstract
Dyslipidemia is a major risk factor for cardiovascular disease, with its prevalence steadily rising in both developed and developing nations. An unhealthy lifestyle significantly contributes to the development of dyslipidemia, with smoking being a well-known risk factor. A comprehensive search was conducted across several databases, including Google Scholar, Web of Science, African Journals Online (AJOL), HINARI, and PubMed/MEDLINE. Articles published up until June 24, 2024, were considered for inclusion. Data extraction and organization were carried out using Microsoft Excel, while analysis was performed using STATA/MP 17.0. The quality of the included studies was evaluated using the Newcastle–Ottawa Scale (NOS). To analyze the pooled data, a weighted inverse variance random effects model with a 95% confidence interval was applied. Heterogeneity among studies was assessed using Cochrane’s I2 statistics, and Egger’s test was conducted to detect potential publication bias. The association between dyslipidemia and its associated factors was examined using the log odds ratio, with a p-value of less than 0.05 considered statistically significant. A total of 44 articles involving 12,395 participants were included. The overall pooled prevalence of dyslipidemia in Ethiopia was 56.60% (95% CI 50.40–62.80). Dyslipidemia was observed across various population groups, with notable prevalence rates associated with different risk factors. Among individuals with insufficient physical activity, the prevalence was 30.12% (95% CI 22.53–37.70). In those who smoked cigarettes, it was observed in 6.81% (95% CI 4.27–9.34). Among chronic alcohol consumers, the prevalence of dyslipidemia was 15.75% (95% CI 9.65–21.86). Furthermore, 30.12% (95% CI 22.53–37.70) of dyslipidemia was reported among individuals with inadequate physical exercise. The prevalence of dyslipidemia in Ethiopia was 56.60%, indicating a significant public health concern. The condition is particularly prevalent among individuals with insufficient physical activity, smoking habits, and chronic alcohol consumption, suggesting strong associations with these modifiable risk factors. To reduce dyslipidemia, public health initiatives should focus on promoting physical activity, anti-smoking campaigns, and educating on the risks of excessive alcohol use. Health professionals should also prioritize early detection and management in high-risk groups to reduce long-term cardiovascular risks.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".