Factors Affecting the Utilization of Antenatal Care Services During Pregnancy in Bangladesh and 28 Other Low- and Middle-income Countries: A Meta-analysis of Demographic and Health Survey Data
Bibliographic record
Abstract
The study aimed to identify the factors influencing the utilization of antenatal care (ANC) services among pregnant women to fulfill the Sustainable Development Goals (SDG) for maternal mortality ratio (MMR) by 2030; we also investigated the consistency of these factors. We have used the Demographic and Health Survey (DHS) data from 29 developing countries for analysis. A binary logistic regression model was run using Demographic and Health Survey data from Bangladesh to determine the factors influencing ANC utilization in Bangladesh. In addition, a random-effects model estimation for metaanalysis was performed using DHS data from 29 developing to investigate the overall effects and consistency between covariates and the utilization of ANC services. Logistic regression revealed that residence (odds ratio [OR] 1.436; 95% confidence interval [CI] 1.238, 1.666), respondent's education (OR 3.153; 95% CI 2.204, 4.509), husband's education (OR 2.507; 95% CI 1.922, 3.271) wealth index (OR 1.485; 95% CI 1.256, 1.756), birth order (OR 0.786; 95% CI 0.684, 0.904), working status (OR 1.292; 95% CI 1.136, 1.470), and media access (OR 1.649; 95% CI 1.434, 1.896) were the main significant factors for Bangladesh. Meta-analysis showed that residence (OR 2.041; 95% CI 1.621, 2.570), respondent's age (OR 1.260; 95% CI 1.106, 1.435), respondent's education level (OR 2.808; 95% CI 2.353, 3.351), husband's education (OR 2.267; 95% CI 1.911, 2.690), wealth index (OR 2.715; 95% CI 2.199, 3.352), birth order (OR 1.722; 95% CI 1.388, 2.137), and media access (OR 2.474; 95% CI 2.102, 2.913) were the most conclusive factors in a subjects decision to attend ANC. Our results support the augmentation of maternal education and media access in rural areas with ANC services. Particular focus is needed for women from Afghanistan since they have a lower level of ANC services.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.038 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".