Looking beyond the obvious: a critical systematic review and meta-analyses of risk factors for fertility problems in a globalized world
Bibliographic record
Abstract
Abstract Background Well-established risk factors for fertility problems such as smoking have been included in fertility awareness efforts globally. However, these efforts neglect risks that women in low and middle-income countries (LMIC) face. Objective To address this gap, we identified eight risk factors affecting women in LMIC and the aim of the current review was to estimate the impact of these risks on fertility. Methods We conducted systematic reviews and where data was available meta-analyses. We searched Medline, Embase, Cochrane library, regional databases and key organizational websites (1946-June 2016, updated January 2018, latest update taking place in 2021). Two researchers screened and extracted data independently. We included all study designs that assessed exposure to risk in clinical or community-based samples and excluded studies without control groups. The outcome of interest was fertility problems (inability to achieve pregnancy or live birth and neonatal death). We calculated pooled effect estimates from reported effect sizes or raw data. We assessed study quality using the Newcastle-Ottawa Scale. We registered the review with PROSPERO, registration number CRD42016048497. Results We identified 2,418 studies and included 61 (57 in meta-analyses). Results revealed a nine-fold increased risk of inability to become pregnant in genital tuberculosis (OR 8.91, CI 1.89-42.12) and almost threefold in HIV (OR 2.93, CI 1.95-4.42) and bacterial vaginosis (OR 2.81, CI 1.85-4.27). A twofold increased risk of tubal-factor infertility in Female Genital Mutilation/Cutting–Type II/III (OR 2.06, CI 1.03-4.15) and increased post-natal mortality in consanguinity (stillbirth, OR 1.28, CI 1.04-1.57; neonatal death, OR 1.57, CI 1.22-2.02). Strength and limitations Reliability of results was bolstered by a rigorous systematic review methodology that is replicable but limited by methodological shortcomings of the available primary studies and the small number of studies in each meta-analysis. Conclusions The risk factors investigated appeared to impact the reproductive process through multiple biological, behavioural, and clinical pathways. Additionally, infection and pelvic inflammatory disease seemed to be common pathways for several risk factors. The complex multifactorial risk profile can be addressed by LMIC using a global health framework to determine which risk factors are significant to their populations and how to tackle them. The subsequent health promotion encompassing these relevant health indicators could translate into more prevention and effective early detection of fertility problems in LMIC. Finally, the findings of multifactorial risk reinforced the need to put fertility as an agenda in global health initiatives.
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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 | 0.042 | 0.116 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.031 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".