Anaemia in pregnancy across Tanzania: A comprehensive review of prevalence, risk factors, and birth outcomes
Bibliographic record
Abstract
Problem considered Anaemia in pregnancy is a significant public health issue in Tanzania, linked to poor maternal and neonatal outcomes. Despite numerous regional studies, a comprehensive synthesis is needed. This study examined the prevalence, associated factors, and perinatal outcomes of anaemia among pregnant women in Tanzania. Methods A comprehensive literature search was conducted across Medline, EMBASE, PubMed, CINAHL, Web of Science, Scopus, PsycINFO, Science Direct, and grey literature sources, including manual searches for unpublished theses and dissertations from January 2010 to May 2025. Eligible studies reported prevalence and/or associated factors. Two independent reviewers screened, extracted data, and assessed study quality using PRISMA guidelines and the Modified Newcastle-Ottawa Scale. Findings were synthesised narratively through thematic grouping and interpretation. Results Twelve studies met the inclusion criteria, reporting anaemia prevalence from 20% to 83.5%, with a pooled estimate of 51.5% (95% CI: 43.1%–61.6%). Fourteen risk factors were identified, including low income, limited education, poor diet, younger age, short pregnancy intervals, multigravidity, advanced gestation, and infections (malaria, HIV). Nine protective factors emerged, such as higher education, food security, good knowledge and attitudes, employment, adequate ANC, and proximity to health facilities. Anaemia was also linked to low birth weight and preterm birth. Conclusion Anaemia in pregnancy remains prevalent in Tanzania, driven by socioeconomic, nutritional, and health factors. Targeted efforts to improve maternal nutrition, education, and ANC use are vital to reduce its impact on pregnancy outcomes.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".