Prevalence and epidemiological pattern of abdominal aortic aneurysms in Africa: A systematic review
Bibliographic record
Abstract
Introduction: The incidence of abdominal aortic aneurysms (AAAs) in high-income countries has been declining in the last three decades. However, in most low-income and middle-income countries especially in Africa, little is known about its burden. The absence of screening services for AAA in African countries makes it difficult to detect and promptly manage AAA before rupture, which has significant implications for mortality. This study sought to systematically assess the prevalence of AAA amongst patients visiting hospitals in Africa and evaluate its epidemiological pattern. Materials and Methods: A systematic review was performed on the EMBASE, GLOBAL HEALTH, MEDLINE, and PUBMED databases. The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement standards and protocol registered with PROSPERO (CRD42020162214). A data extraction tool was used to get relevant information from these studies. Quality assessment and risk of bias were performed using the Newcastle Ottawa Scale for cross-sectional studies. Results were summarised in tables, figures, and a forest plot. A narrative synthesis approach of the articles was taken. Results: Two hundred and sixty-one studies were identified and after the exclusion of 246, a final 15 were deemed suitable for analysis. A total of 4012 participants were screened for AAA and of these, 129 cases were identified. The prevalence of AAA in these studies ranged from 0.7 to 6.4%. Male participants accounted for 115 (89.1%) of the cases. There was a wide age range (31-72 years) reflective of both its possible infective and degenerative aetiology. AAA was reported to be associated with hypertension, smoking, advanced age, coronary artery disease, and HIV infection. There was no association between AAA and diabetes. Over 50% of cases were identified incidentally. About one-third (23-54%) of the participants presented aortic rupture with a mortality rate ranging between 65 and 72%. Conclusions: AAA prevalence in Africa is probably higher than the current thinking as there is no baseline data to compare with. Aetiologically, AAA was shown to be associated with hypertension, smoking, coronary artery disease, and possibly infectious pathologies like HIV. Large epidemiological studies would help better characterise AAA in this setting. Lastly, efforts targeting the reduction of the risk factors for AAA would go a long way in reducing the burden of AAA.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".