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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».