The varying estimation of infertility in Ethiopia: the need for a comprehensive definition
Bibliographic record
Abstract
Abstract Background Infertility is a marginalized sexual and reproductive health issue in low-resource settings. Globally, millions are affected by infertility, but the lack of a universal definition makes it difficult to estimate the prevalence of infertility at the population level. Estimating the prevalence of infertility may inform targeted and accessible intervention, especially for a resource-limited country like Ethiopia. This study aims to estimate the prevalence of female infertility in Ethiopia using the Demographic and Health Survey (DHS) through two approaches: (i) the demographic approach and (ii) the current duration approach. Methods Data from 15,683 women were obtained through the 2016 Ethiopian DHS. The demographic approach estimates infertility among women who had been married/in a union for at least five years, had never used contraceptives, and had a fertility desire. The current duration approach includes women at risk of pregnancy at the time of the survey and determines their current length of time-at-risk of pregnancy at 12, 24, and 36 months. Logistic regression analysis estimated the prevalence of infertility and factors associated using the demographic approach. Parametric survival analysis estimated the prevalence of infertility using the current duration approach. All estimates used sampling weights to account for the DHS sampling design. STATA 14 and R were used to perform the statistical analysis. Results Using the demographic definition, the prevalence of infertility was 7.6% (95% CI 6.6–8.8). When stratified as primary and secondary infertility, the prevalence was 1.4% (95% CI 1.0-1.9) and 8.7% (95% CI 7.5–10.1), respectively. Using the current duration approach definition, the prevalence of overall infertility was 24.1% (95% CI 18.8–34.0) at 12-months, 13.4% (95% CI 10.1–18.6) at 24-months, and 8.8% (95% CI 6.5–12.3) at 36-months. Conclusion The demographic definition of infertility resulted in a lower estimate of infertility. The current duration approach definition could be more appropriate for the early detection and management of infertility in Ethiopia. The findings also highlight the need for a comprehensive definition of and emphasis on infertility. Future population-based surveys should incorporate direct questions related to infertility to facilitate epidemiological surveillance.
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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 | 0.035 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".