Comparative study on the factors influencing pre-eclampsia symptoms at different pregnancy stages in Bangladeshi women: urban vs. rural perspectives
Bibliographic record
Abstract
BACKGROUND: In Bangladesh, pre-eclampsia poses a significant concern, evident in the low attendance (37%) for antenatal care (ANC). Despite efforts to reduce maternal and neonatal mortality, the latest Bangladesh Maternal Mortality Survey (BMMS-2016) indicates limited progress. Access to essential maternal and newborn health services, including ANC, remains constrained, highlighting the challenge of translating service coverage into improved outcomes. A research gap on pre-eclampsia symptoms such as severe headache, blurred vision, high blood pressure, and oedema emphasizes the need for targeted interventions for these symptoms early so that we can reduce the prevalence of pre-eclampsia and therefore maternal mortality in Bangladesh. The aim of this study is to investigate and compare the risk factors for three stages of pre-eclampsia among Bangladeshi women living in urban and rural areas. METHODS: The study utilized BMMS-2016 data, employing statistical analyses, including binary logistic regression, to identify associations. It assessed four pre-eclampsia symptoms prevalence across pregnancy stages, considering factors like maternal age, stillborn births, residency, ANC, healthcare facility delivery, education, and children. RESULTS: Logistic regression highlights key associations with pre-eclampsia symptoms. Urban mothers aged 36 + face the highest risk during delivery (AOR = 2) and the lowest in rural areas after delivery (AOR = 1.43). Two or more stillborn births increase the risk in urban delivery by 97%. Complete ANC raises odds, notably in urban pregnancy (AOR = 1.5) and rural post-delivery (AOR = 1.16). Skilled ANC providers elevate risks during all stages, with the highest in urban pregnancy (AOR = 1.54) and lowest after rural delivery (AOR = 1.28). Unskilled ANC associates with symptoms only during pregnancy. Healthcare facility delivery increases odds at all stages, particularly in rural delivery (AOR = 1.74) and urban pregnancy (AOR = 1.26). Multifetal gestation raises urban delivery risk (AOR = 2.11). Rural areas show higher chances during both pregnancy and delivery. Higher education in rural pregnancy and 2 to 3 birth order in urban delivery reduce odds of pre-eclampsia symptoms. CONCLUSIONS: Addressing pre-eclampsia symptoms in Bangladesh, especially among urban women, is urgent. Identified risk factors necessitate targeted interventions to enhance ANC and overall maternal health. Advocating findings to policymakers is crucial for effective policies, reducing pre-eclampsia and eclampsia, contributing to lower maternal mortality.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".