Prevalence of unintended pregnancy in the MENA region: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Unintended pregnancies pose significant public health challenges globally, particularly in the Middle East and North Africa (MENA) region, where cultural, religious and societal factors play the most substantial role. This systematic review and meta-analysis investigated the pooled prevalence and factors associated with unintended pregnancies in the MENA region. METHODS: We conducted a systematic review to identify relevant studies in Medical Literature Analysis and Retriaval System (MEDLINE), Embase and Scopus published on unintended pregnancies until July 2024. We included studies that were conducted on unintended pregnancy prevalence within MENA countries and employed suitable measurement tools. We analysed data from 40 studies involving 34 837 participants across the region, including Egypt, Iran, Saudi Arabia and Qatar. We used a random-effects model to estimate the pooled prevalence of unintended pregnancy. RESULTS: In this meta-analysis, we found that the overall prevalence of unintended pregnancy was 27.0% (95% CI 25.0% to 30.0%) in the MENA region, and the certainty of the evidence was moderate. Saudi Arabia had the highest prevalence of unintended pregnancy at 32.0% (95% CI 27.0% to 38.0%). A lower prevalence, 10.0% (95% CI 8.0% to 14.0%), was found in the studies that used validated tools compared with non-validated tools to measure unintended pregnancy. Between 2006 and 2010, the prevalence was 34.0% (95% CI 28.0% to 40.0%), the highest compared to other time periods . Age, rural areas, education, employment, economic status, parity, gravidity, history of miscarriage, previous pregnancies or abortion, non-use or failure of contraception methods, limited antenatal care, were associated with unintended pregnancies. CONCLUSION: Our findings suggest that the MENA region faces a substantial burden of unintended pregnancies, with variations among countries and over time. The results emphasise the need for evidence-based interventions to address this issue, focusing on factors associated with unintended pregnancy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".