Birth Mode and Inflammatory Bowel Disease Risk Meta-Analysis

Over 13 million births from 22 studies were analyzed to assess the association between cesarean section and inflammatory bowel disease. Hana Taha authored this systematic review and meta-analysis, with results updated in April 2026. The work synthesizes observational data to evaluate risks for Crohn's disease and ulcerative colitis.

Use Cases

• Analyze pooled relative risks (RRs) and confidence intervals for the association between cesarean section and ulcerative colitis (UC).
• Compare hazard ratios (HRs) and odds ratios (ORs) for Crohn's disease (CD) from adjusted analyses across studies.
• Investigate regional variation in birth mode risk associations using subgroup results from countries like Denmark, Switzerland, and Germany.
• Assess publication bias and heterogeneity metrics reported from the fixed or random-effects models used in the meta-analysis.

Strengths

• Synthesis of 22 studies representing a population of over 13 million births.
• Provides multiple effect measures: pooled relative risks (RRs), hazard ratios (HRs), and odds ratios (ORs) with 95% confidence intervals.
• Includes subgroup analyses revealing regional variations in risk associations.

Limitations

• The 2.1 MB file size indicates the dataset is a document summarizing results, not the underlying raw study data.
• Temporal coverage of the included primary studies is not specified, limiting analysis of trends over time.
• Geographic representation is biased towards specific countries (e.g., Denmark, Switzerland, Norway, Germany, Australia).

Provenance

Source

Systematic review and meta-analysis authored by Hana Taha, published on figshare.

Collection Method

Data extracted from observational cohorts and case-control studies identified via PubMed, Scopus, CENTRAL, and Web of Science.

Time Range

Literature search covered studies published through June 2025.

Freshness

Last updated April 2026, with literature search conducted through June 2025.

Geography

Studies included data from multiple countries, with noted regional variation (e.g., Denmark, Switzerland, Norway, Germany, Australia).