Prevalence and determinants of terminated and unintended pregnancies among married women: analysis of pooled cross-sectional surveys in Nigeria
Bibliographic record
Abstract
BACKGROUND: Induced pregnancy termination and unintended pregnancy are two commonly occurring phenomena in the discipline of women's reproductive health. In the present study, we explored cross-sectional data pooled from three rounds of Nigeria Demographic and Health Survey (NDHS) to understand the trends of prevalence of pregnancy termination and unintended pregnancy as well as the interplay of various sociodemographic and economic factors whereby these health issues occur. METHODS: Study participants were 79 825 currently married women aged 15-49 years. Data were collected from NDHS conducted in 2003, 2008 and 2013. Outcome variables were self-reported history of pregnancy termination and unintended pregnancy for the last birth. Data were analysed using descriptive and multivariable logistic regression methods. RESULTS: Mean (±SD) age of the respondents was 28.7 years (±9.6). The overall prevalence of pregnancy termination and unintended pregnancy were about 11%. Older women had increase in the odds of terminated pregnancies, compared with women aged 15-19 years, while the converse was true for unintended pregnancy in the adjusted model. Educated women had significant higher odds of terminated and unintended pregnancies compared with women with no formal education. Women with higher wealth index were more likely to have unintended and terminated pregnancies after adjusting for other covariates. Remarkably, women who had unintended pregnancy were 1.47 times as likely to have terminated pregnancy compared with those who had no unintended pregnancy (OR=1.47; 95% CI 1.30 to 1.65). Experience of intimate partner violence had significant association with terminated and unintended pregnancies. CONCLUSION: The findings of this study showed that unintended and terminated pregnancies remain part of the issues to be addressed if the goal of ensuring healthy lives and promoting the well-being for all at all ages must be met. Stakeholders in Nigerian healthcare system should protect the lives of women who are vulnerable to the fatal consequences of unsafe abortion, especially in cases of rape, sexual assault, incest and where continuing a pregnancy would endanger the lives of women.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".