Impact of abortion reforms on reproductive and neonatal health in low- and middle-income countries
Bibliographic record
Abstract
Background: While restrictive abortion laws still prevail in most low-and middle-income countries (LMICs), many countries have reformed their abortion laws, expanding the grounds on which abortion can be performed legally.However, the implications of these reforms on women's access to and use of health services, as well as their health outcomes, is uncertain.This systematic review aimed to evaluate and synthesize empirical research evidence concerning the effects of abortion law reforms on women's health services and health outcomes in LMICs.Methods: We searched Medline, Embase, CINAHL, and Web of Science databases, as well as grey literature and reference lists of included studies.We included pre-post and quasiexperimental studies that aimed to estimate the causal effect of a change in abortion law on at least one of four outcomes: (i) use of and access to abortion services, (ii) fertility rates, (iii) maternal and/or neonatal morbidity and mortality, and (iv) contraceptive use.We assessed the quality of studies using the quasi-experimental study design series checklist and synthesized evidence through a narrative description.Results: Of the 2796 records identified by our search, we included thirteen studies in the review, which covered reforms occurring in Uruguay, Ethiopia, Mexico, Nepal, Chile, Romania, India, and Ghana.Studies employed pre-post, interrupted time series, difference-in-differences, and synthetic control designs.Legislative reforms from highly restrictive to relatively liberal were associated with reductions in fertility, particularly among women 20 to 34 years of age, as well as lower maternal mortality.Conclusion: Evidence regarding the impact of abortion reforms on other outcomes, as well as whether effects vary by socioeconomic status, is limited.Further research is required to strengthen the evidence-base for informing abortion legislation in LMICs.This review explicitly points to the need for rigorous quasi-experimental studies with sensitivity analyses to assess underlying assumptions.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".