The Association Between Parity and Cervical Cancer Risk: A Systematic Review
Bibliographic record
Abstract
Introduction: Cervical cancer remains the fourth most common cancer in women globally (Sung et al., 2021; World Health Organization, 2024). While persistent infection with high-risk human papillomavirus (HPV) is established as the necessary cause, it is insufficient for carcinogenesis (Walboomers et al., 1999). Parity (the number of live births) has long been suspected as a critical cofactor, but evidence has been inconsistent (Tekalegn et al., 2022). This review synthesizes the epidemiological evidence on this association. Methods: This systematic review was conducted adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines (Page et al., 2021). A systematic search of MEDLINE/PubMed, Scopus, HINARI, Google Scholar, and Science Direct was performed (Tekalegn et al., 2022). Inclusion criteria were case-control or cohort studies quantifying the association between parity and cervical cancer risk. The methodological quality of included studies was assessed using the Newcastle-Ottawa Scale (NOS) (Wells et al., 2000). Results: A total of 18 observational studies, comprising 17 case-control studies and one prospective cohort study, were included in the final synthesis. A recent, high-quality meta-analysis incorporating many of these studies (Tekalegn et al., 2022) reported a significant pooled odds ratio (OR) from 6,685 participants. The analysis showed that women with high parity had 2.65 times higher odds of developing cervical cancer compared to their low-parity counterparts (OR = 2.65, 95% CI: 2.08–3.38). This review confirms this finding and further highlights a significant dose-response relationship, with risk increasing progressively with each additional birth (Muñoz et al., 2002; Sharma and Pattanshetty, 2018). Discussion: The evidence confirms that high parity is a major, independent cofactor that promotes carcinogenesis, particularly in HPV-positive women (Muñoz et al., 2002). This association is not an artifact of confounding by sexual behavior. Proposed biological mechanisms include: (1) supraphysiological hormonal changes during pregnancy promoting HPV oncogene expression; (2) persistent eversion (ectropion) of the cervical transformation zone, increasing epithelial vulnerability (Jensen et al., 2013); (3) cervical trauma during childbirth facilitating viral persistence; and (4) localized, pregnancy-related immunomodulation that impairs viral clearance. Conclusion: High parity is a robust and significant risk factor for cervical cancer. This finding has direct implications for public health, identifying women with high parity as a high-risk group that should be prioritized for cervical screening and HPV vaccination programs, especially in resource-limited settings where both high parity and cervical cancer incidence are prevalent.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".