Social consequences of COVID-19 on fertility preference consistency and contraceptive use among Nigerian women: insights from population-based data
Bibliographic record
Abstract
BACKGROUND: Emerging evidence from high income countries showed that the COVID-19 pandemic has had negative effects on population and reproductive health behaviour. This study provides a sub-Saharan Africa perspective by documenting the social consequences of COVID-19 and its relationship to fertility preference stability and modern contraceptive use in Nigeria. METHOD: We analysed panel data collected by Performance Monitoring for Action in Nigeria. Baseline and Follow-up surveys were conducted before the COVID-19 outbreak (November 2019-February 2020) and during the lockdown respectively (May-July 2020). Analysis was restricted to married non-pregnant women during follow-up (n = 774). Descriptive statistics and generalized linear models were employed to explore the relationship between selected social consequences of COVID-19 and fertility preferences stability (between baseline and follow-up) as well as modern contraceptives use. RESULTS: Reported social consequences of the pandemic lockdown include total loss of household income (31.3%), food insecurity (16.5%), and greater economic reliance on partner (43.0%). Sixty-eight women (8.8%) changed their minds about pregnancy and this was associated with age groups, higher wealth quintile (AOR = 0.38, CI: 0.15-0.97) and household food insecurity (AOR = 2.72, CI: 1.23-5.99). Fertility preference was inconsistent among 26.1%. Women aged 30-34 years (AOR = 4.46, CI:1.29-15.39) were more likely of inconsistent fertility preference compared to 15-24 years. The likelihood was also higher among women with three children compared to those with only one child (AOR = 3.88, CI: 1.36-11.08). During follow-up survey, 59.4% reported they would feel unhappy if pregnant. This was more common among women with tertiary education (AOR = 2.99, CI: 1.41-6.33). The odds increased with parity. The prevalence of modern contraceptive use was 32.8%. Women aged 45-49 years (AOR = 0.24, CI: 0.10-0.56) were less likely to use modern contraceptives than those aged 15-24 years. In contrast, the odds of contraceptive use were significantly higher among those with three (AOR = 1.82, CI: 1.03-3.20), four (AOR = 2.45, CI: 1.36-4.39) and at least five (AOR = 2.89, CI: 1.25-6.74) children. Unhappy disposition towards pregnancy (AOR = 2.48, CI: 1.724-3.58) was also a significant predictor of modern contraceptive use. CONCLUSION: Some social consequences of COVID-19 affected pregnancy intention and stability of fertility preference but showed no independent association with modern contraceptive use.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".