Change in childbearing intention, use of contraception, unwanted pregnancies, and related adverse events during the COVID-19 pandemic: Results from a panel study in rural Burkina Faso
Bibliographic record
Abstract
Evidence on how the COVID-19 pandemic has affected women's reproductive health remains scarce, particularly for low- and middle-income countries. Deleterious indirect effects seem likely, particularly on access to contraception and risk of unwanted pregnancies, but rigorous evaluations using quasi-experimental designs are lacking. Taking a diachronic perspective, we aimed to investigate the effects of the pandemic on four indicators of women's reproductive health: history of recent adverse events during pregnancy (past), use of contraception and unwanted pregnancies (present), and childbearing intentions (future). This study was conducted in four rural health districts of Burkina Faso: Banfora, Leo, Sindou and Tenado. Two rounds of household surveys (before and during the pandemic) were conducted in a panel of 696 households using standardized questionnaires. The households were selected using a stratified two-stage random sampling method. All women aged 15-49 years living in the household were eligible for the study. The same households were visited twice, in February 2020 and February 2021. The effects were estimated by fitting hierarchical regression models with fixed effects or random intercepts at the individual level. A total of 814 and 597 women reported being sexually active before and during the COVID-19 pandemic, respectively. The odds of not wanting (any more) children were two times higher during the pandemic than before (2.0, 95% CI [1.32-3.04]). Among those with childbearing intention, the average desired delay until the next pregnancy increased from 28.7 to 32.8 months. When comparing 2021 versus 2020, there was an increase in the adjusted odds ratio of contraception use (1.23, 95% CI [1.08-1.40]), unwanted pregnancies (2.07, 95% CI [1.01-4.25]), and self-reported history of miscarriages, abortions, or stillbirths in the previous 12 months (2.4, 95% CI [1.04-5.43]). Our findings in rural Burkina Faso do not support the predicted detrimental effects of COVID-19 on the use of family planning services in LMICs, but confirm that it negatively affects pregnancy intentions. Use of contraception increased significantly among women in the panel, but arguably not enough to avoid an increase in unwanted pregnancies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".