Prevalence of Vitamin D Deficiency in Pregnant Women: Systematic Review and Meta-analysis
Bibliographic record
Abstract
CONTEXT: Vitamin D is an essential steroid prehormone whose effects have garnered significant interest during pregnancy and lactation. Adequate vitamin D levels are crucial for embryonic implantation, immune modulation, and preventing adverse maternal outcomes such as preeclampsia, gestational diabetes, and preterm birth. OBJECTIVES: To estimate the prevalence of vitamin D deficiency in each trimester of pregnancy. DATA SOURCES: This is a systematic review and meta-analysis, conducted according to the PRISMA guidelines. Online databases were searched from inception to July 2023, including Medline/PubMed, Embase, and LILACS. DATA EXTRACTION: All English, Spanish, and Portuguese articles that specified the prevalence of vitamin D deficiency in each of the 3 trimesters of pregnancy were included. All screening and data extraction were performed independently by 2 authors. Risk-of-bias assessment was performed using the Newcastle-Ottawa Scale. DATA ANALYSIS: A total of 2627 articles were retrieved, and after evaluating eligibility criteria, 116 full-text articles were included. These comprised observational studies, totaling 127 290 pregnant women who underwent serum 25(OH)D evaluation. Vitamin D deficiency is a common condition among pregnant women. The meta-analysis revealed that 68% (95% CI, 60%-76%) of pregnant women had 25(OH)D <30 ng/mL in the first trimester, 81% (95% CI, 74%-87%) in the second trimester, and 70% (95% CI, 64%-75%) in the third trimester, indicating a widespread deficiency of this vitamin. CONCLUSION: A thorough analysis of the studies included in this review shows that hypovitaminosis D during pregnancy is a highly prevalent condition that occurs in all 3 trimesters. Given this high prevalence, these findings may be valuable for planning public health policies aimed at pregnant women. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration no. CRD42022343256.
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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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".