Epidemiology of unintended pregnancies: regional insights from sub-Saharan Africa
Bibliographic record
Abstract
Unintended pregnancy remains a significant public health issue globally, with sub-Saharan Africa experiencing a particularly severe burden. The aim of this study was to assess the prevalence and determinants of unintended pregnancy across 35 countries in sub-Saharan Africa. Data were obtained from the Demographic and Health Surveys (n = 373,298). Unintended pregnancies were defined as those that were either mistimed (i.e., occurring earlier than desired) or unwanted (i.e., not desired at all). Descriptive statistics were used to summarize the data, and multivariable logistic regression analysis was performed to examine the association between sociodemographic factors and unintended pregnancy. The analyses were conducted using Stata 16 software, with survey weights applied to account for the complex survey design. Descriptive analysis revealed that 27.6% of women reported their last pregnancy as unintended, with significant regional variation. The highest prevalence of unintended pregnancies was observed in South Africa (57.9%), while the lowest was in Burkina Faso (9.8%). In general, rural areas had a higher prevalence of unintended pregnancies compared to urban areas. Regression analysis identified significant sociodemographic factors associated with unintended pregnancy. Women in rural areas had lower odds of unintended pregnancy compared to urban residents (OR = 0.93, 95% CI 0.91–0.95). However, women with primary education were more likely to experience unintended pregnancies compared to those with no education (OR = 2.02, 95% CI 1.98–2.06), and those in the richest wealth quintile had significantly lower odds than those in the poorest quintile (OR = 0.81, 95% CI 0.79–0.84). Contraceptive use was also strongly associated with unintended pregnancy: women using no contraceptive method had 1.74 times higher odds of unintended pregnancy compared to those using modern methods (OR = 1.74, 95% CI 1.71–1.77). This study underscores the need for targeted, evidence-based interventions that address the sociodemographic factors contributing to unintended pregnancies in sub-Saharan Africa. Interventions should focus on improving access to contraceptive methods, promoting education, reducing socioeconomic disparitiesinequalities, and increasing media access to better inform reproductive health decisions. These efforts can help mitigate the high prevalence of unintended pregnancies in the region.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".