Socioeconomic inequalities in modern contraceptive use among women in Benin: a decomposition analysis
Bibliographic record
Abstract
BACKGROUND: Contraceptive use is crucial to achieving Sustainable Development Goal 3. Evidence of socioeconomic inequality in the use of modern contraceptives is essential to address the developing inequality in its utilisation given the low prevalence of contraceptive use among women in Benin. This study examined the socioeconomic inequalities in modern contraceptive use among women in Benin. METHODS: We performed a cross-sectional analysis of the 2017-18 Benin Demographic and Health Survey data. A weighted sample of 7,360 sexually active women of reproductive age was included in the study. We used a concentration curve to plot the cumulative proportion of women using modern contraception. Decomposition analysis was conducted to determine factors accounting for the socioeconomic disparities in modern contraceptive use. RESULTS: We noted that the richest women had higher odds of modern contraceptive use (adjusted odds ratio [aOR] = 1.67, CI = 1.22-2.30) compared to the poorest women. Other factors that showed significant associations with modern contraception use were age, marital status, religious affiliation, employment status, parity, women's educational level, and ethnicity. We found that modern contraceptive use is highly concentrated among the rich, with rich women having a higher propensity of using modern contraception relative to the poor. Also, the disadvantaged to modern contraceptive use included the poor, those aged 45-49, married women, those working, those with four or more live births, rural residents, and women of Bariba and related ethnicity. Conversely, favourable concentration in modern contraceptive use was found among the rich, women aged 20-24, the divorced, women with two live births, the highly educated, those with media exposure, and women of Yoruba and related ethnicity. CONCLUSION: The study has shown that wealthy women are more likely to utilize contraceptives than the poor. This is because wealthy women could afford both the service itself and the travel costs to the health facility, hence overcoming any economic barriers to using modern contraception. Other factors such as age, marital status, religion, employment status, parity, mother's educational level, and ethnicity were associated with contraceptive use in Benin. The Benin government and other stakeholders should develop family planning intercession techniques that address both the supply and demand sides of the equation, with a focus on reaching the illiterate and under-resourced population without admittance to modern contraception.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".