Independent and cumulative effects of risk factors associated with stillbirths in 50 low- and middle-income countries: A multi-country cross-sectional study
Notice bibliographique
Résumé
Background Early identification of high-risk pregnancies could reduce stillbirths, yet remains a challenge in low- and middle-income countries (LMICs). This study aims to estimate the associations between easily observable risk factors and stillbirths, and construct a risk score which could be adopted in LMICs to identify pregnancies with high risk of stillbirths. Methods Using the most recent Demographic and Health Surveys from 50 low- and middle-income countries (LMICs) with available data between January 1, 2010 and December 31, 2021, we analysed a total of 22 factors associated with stillbirths in a series of single-adjusted and mutually adjusted logistic regression models. Upon identification of the risk factors with the strongest associations, we constructed a risk score on the basis of the magnitude of the β coefficient to examine the cumulative effects of risk factors on stillbirths. To assess whether the associations between risk scores and stillbirths were moderated by protective factors, we added an interaction term between the identified protective factor and risk scores to the regression model. We also conducted two sets of subgroup analyses for previous history of pregnancy and maternal age at pregnancy and four sets of supplementary analyses to test the robustness of the results. Findings Among the 795,642 women identified for analysis with at least one pregnancy within the five years before the survey, the most recent pregnancy of 8968 (1.13%) ended as stillbirths. Using a mutually adjusted regression model, we found that the top factors showing the strongest associations with stillbirths were short maternal height (odds ratio [OR]: 1.99, 95% confidence interval [CI]: 1.48–2.67, P < 0.001), interpregnancy interval less than six months (OR: 1.84, 95% CI: 1.42–2.38, P < 0.001), previous stillbirth history (OR: 1.55, 95% CI: 1.07–2.26, P < 0.020), low maternal education (OR: 1.50, 95% CI: 1.01–2.24, P = 0.045), and lowest household wealth (OR: 1.32, 95% CI: 1.08–1.61, P = 0.008). A female household head was a protective factor with an OR of 0.71 (95% CI: 0.55–0.90, P = 0.005). Single-adjusted models, subgroup analyses, and sensitivity analyses showed generally consistent results. We also found that the odds of stillbirths increased with a larger risk score with a P trend <0.001. Compared with women without any risk factors, women with a risk score of 5 or more were 4.11 (95% CI: 2.83–5.97, P < 0.001) times more likely to have their pregnancies ending up as stillbirths. However, these associations were weakened if the head of household was female. Interpretation Our study suggested that short maternal height, low socioeconomic status, previous stillbirth history, low maternal education, and very short interpregnancy interval had the strongest associations with stillbirths. The construction of risk scores using easily observable risk factors could be an effective way to identify high-risk pregnancies in resource-poor settings. Funding This research was supported by Sanming Project of Medicine in Shenzhen (NO. SZSM202111001) and China National Natural Science Foundation (NO. 72203119).
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».