Prevalence and associated factors of khat chewing among pregnant women: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background Khat (Catha edulis) is a stimulant plant, broadly cultivated and consumed in the Horn of Africa and the Arabian Peninsula. It contains Cathinone, which is an amphetamines-like chemical and causes various adverse outcomes for pregnant women and babies when it is consumed during pregnancy. Decisive estimates of the prevalence of khat chewing and related risk factors which may increase this practice have not been determined. Aim To determine the pooled prevalence and associated factors of khat chewing among pregnant women in the Horn Africa and the Arabian Peninsula countries with a view to informing targeted interventions for the region. Method The study protocol was prepared and registered on PROSPERO, ID CRD42021190837. A database search including Gray literature and Google scholar was explored to identify 667 studies. Finally, 14 studies were considered relevant for meta-analysis, after removing 259 duplicates, 388 unrelated topics and 6 studies with full text examination. The Newcastle-Ottawa Scale quality assessment tool was used to assess the quality of the studies. The pooled prevalence was determined by using the random-effect model and the p- values of ≤ 0.05 were considered stastically significant to examine associations. Statistical heterogeneity amongst the studies was assessed by Cochrane chi-square and the I 2 statistical test. Main Findings From the meta-analysis of 14 studies with 15,343 study participants, the pooled prevalence of khat chewing among pregnant women was 21.42%, 95% CI (14.49 - 29.29); (I 2 =99.05% (p<0.0001). The results of the meta-analysis demonstrated that pregnant women who had a khat chewing partner [OR 6.50 (95% CI 5.01, 8.43)]; low educational status [OR 2.53 (95% CI 2.24 - 2.85)], lived in rural area [OR 1.69 (95% CI 1.52 – 1.88)] or had a low level of income [OR 1.70 (95% CI 1.55 – 1.87)] were significantly more likely to chew khat during pregnancy. Conclusion The prevalence of khat chewing amongst pregnant women in the Horn of Africa and the Arabian Peninsula has never been measured before and was found to be high. Partners khat chewing status, maternal low educational and economic status were the main factors associated with the problem. Designing intervention strategies to specifically target these risk factors and reduce the burden of the problem for women and their babies 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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.044 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".