Evaluation of the causal effect of maternal education on the utilisation of maternal health services in Bangladesh using an observational study - a comparison of different propensity score methods and covariate adjustment
Bibliographic record
Abstract
Abstract Background Assessing maternal health care utilisation is imperative for the health of both mother and her child. Maternal education is an important determinant in subsequent maternal health care usage, according to research. There is a dearth of research on the causal relationship between maternal education and maternal health services as well as examining the performance of different propensity score methods for estimating absolute effects. Therefore, this study aims to estimate the effect of maternal education on usage of maternal health services minimising the confounding effect and to examine the ability of different propensity score approaches to estimate absolute effects of maternal education on usage of maternal health services. Methods We used data from a cross-sectional study conducted by icddr,b in Bangladesh. A total of 1300 recently delivered mothers were included in this study. We used four propensity score methods to estimate the causal effect of maternal education on receiving maternal health services, including propensity score (PS) matching, PS weighting (average treatment effect and average treatment effect among the treated), covariate adjustment using PS, as well as used multiple logistic regression. Results The study revealed highly imbalanced possible confounding factors for mothers' educational level, which might lead to erroneous estimates. All methods indicate a significant effect of maternal education on maternal health services where mothers exposed to secondary or higher levels of education were more likely to have four or more ANC, delivered at facility, PNC within 42 days as well as receiving all maternal health services compared to the mothers who have below secondary level of education. The PS weighting provides better estimates with a low range of confidence interval. Conclusions The results provided important insights on how well these techniques worked to reduce confounding variables and achieve precise estimates. All methods indicated a significant causal effect of maternal education on the usage of maternal health services. However, propensity score weighting method performed better in terms of providing more precise estimates with a narrower range of confidence intervals, indicating that this method may be a reliable approach for estimating the causal effect of maternal education on maternal health service utilisation.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".