Factors associated with female infertility in Ethiopia: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Infertility is a significant public health issue that affects couples worldwide. The impacts of infertility is notably higher in Ethiopia due to various factors, such as cultural stigmas surrounding infertility and inadequate infrastructure for diagnosis and treatment. Several fragmented primary studies have assessed factors associated with female infertility in Ethiopia; however, their findings have been controversial and inconclusive. This meta-analysis aimed to identify the factors associated with female infertility in Ethiopia. MATERIALS AND METHODS: A comprehensive search was conducted across multiple databases and search engines, including PubMed, African Journals Online, EBSCO, Google Scholar, and the Directory of Open Access Journals. Additionally, studies were searched from the institutional repositories of Ethiopian universities. Data analysis was performed using Stata version 17. The quality of the included studies was evaluated using the Newcastle-Ottawa quality assessment instrument. Heterogeneity and publication bias were assessed using I² and Egger's tests, respectively. A random effects model was employed to identify factors associated with female infertility. The PROSPERO registration number for this meta-analysis was CRD42024525437. RESULT: Six studies were included in the analysis. Factors associated with female infertility included having multiple sexual partners (odds ratio [OR] = 4.31, 95% confidence interval [CI] = 3.46-5.16), a history of sexually transmitted diseases (OR = 2.76, 95% CI = 1.61-3.91), alcohol-abusing partners (OR = 1.57, 95% CI = 1.25-1.89), Khat-abusive partners (OR = 1.85, 95% CI = 1.36-2.35), and women's age over 35 years (OR = 2.07, 95% CI = 1.32-2.81). CONCLUSION: Having multiple sexual partners, a history of sexually transmitted diseases, an alcohol-abusing partner, a khat-abusing partner, and being over the age of 35 were significantly associated with female infertility in Ethiopia. Addressing these risk factors through education, early intervention, lifestyle modifications, and partner involvement can help reduce the burden of infertility and improve the chances of successful conception. The findings underscore the need for further research on understudied factors contributing to female infertility in Ethiopia, including immune function, psychological health, environmental exposures, as well as endocrinological and gynecological conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".