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
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 compare 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 different propensity score (PS) methods to estimate the causal effect of maternal education on receiving maternal health services, including PS matching, PS weighting, covariate adjustment using PS, as well as used modified log-Poisson regression with and without multiple covariates. RESULTS: The study revealed highly imbalanced possible confounding factors for mother's educational level, which might lead to erroneous estimates. All methods indicated a significant effect of higher maternal education, whether measured as a continuous scale or a binary variable (secondary or higher vs. below secondary) on maternal health services, significantly increased the likelihood of receiving four or more ANC, delivered at facility, PNC within 42 days and receiving all maternal health services compared to the mothers with lower level of education. The PS weighting provided precise estimates with a low range of confidence interval. CONCLUSIONS: The results provided important insights on how well these techniques worked to reduce effect of confounding variables and achieve precise estimates. 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. However, careful consideration should be given to selecting the most appropriate method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".