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Enregistrement W2588652569 · doi:10.1371/journal.pone.0171573

Factors associated with the utilization of institutional delivery services in Bangladesh

2017· article· en· W2588652569 sur OpenAlexaff
Sanni Yaya, Ghose Bishwajit, Michael Ekholuenetale

Notice bibliographique

RevuePLoS ONE · 2017
Typearticle
Langueen
DomaineMedicine
ThématiqueGlobal Maternal and Child Health
Établissements canadiensUniversity of Ottawa
Organismes subventionnairesnon disponible
Mots-clésHealth facilityEnvironmental healthLogistic regressionService delivery frameworkMedicineMillennium Development GoalsRural areaHealth careDeveloping countryReproductive healthDemographyService (business)Health servicesBusinessEconomic growthPopulation

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: Bangladesh has made remarkable progress towards reducing its maternal mortality rate (MMR) over the last two decades and is one of the few countries on track to achieving the MMR-related Millennium Development Goals (MDG-5A). However, the provision of universal access to reproductive healthcare (MDG-5B) and the utilization of maternal healthcare services (MHS) such as institutional delivery, which are crucial to the reduction of maternal mortality, are far behind the internationally agreed-upon target. Effective policymaking to promote the utilization of MHS can be greatly facilitated by the identification of the factors that hinder service uptake. In this study, we therefore aim to measure the prevalence of institutional delivery services and explore the factors associated with their utilization in Bangladesh. METHODS: Data for this study were extracted from the 2011 Bangladesh Demographic and Health Survey (BDHS, 2011); participants were 7,313 women between the ages of 15 and 49 years, selected from both urban and rural households. Data were analyzed using Chi-square analysis, and conditional logistic regression. RESULTS: According to the findings, fewer than one in three women reported delivering at a health facility. The multivariable regression analysis showed that participants from rural areas were 46.9% less likely to have institutional deliveries compared to urban dwellers (OR = 0.531; p<0.001; 95%CI: 0.467-0.604), and participants aged between 30 and 49 years had a 23.6% higher prevalence of institutional delivery service utilization compared to those aged 15 to 29 years (OR = 1.236; p = 0.006; 95%CI: 1.062-1.437). Moreover, participants with higher educational attainment were about twice as likely to deliver at a standard health facility when compared to those without formal education (OR = 2.081; p<0.001; 95%CI: 1.650-2.624), and similarly, husbands with higher educational attainment exhibited an approximately 71% higher service utilization of institutional delivery facilities compared to those without formal education (OR = 1.709; p<0.001; 95%CI: 1.412-2.069). Wealth status was also a significant predictor of institutional delivery service use, with participants belonging to the highest economic stratum being more likely to receive skilled care compared to the lowest economic stratum (OR = 2.507; p<0.001; 95%CI: 2.118-2.968). In addition, results indicated that households of average economic class had a 27% higher level of institutional delivery service utilization compared to those of lower economic status (OR = 1.272; p = 0.011; 95%CI: 1.057-1.531). Furthermore, institutional health service use was 18% higher among participants who were aware of community clinical services compared to those who were hardly aware of these services (OR = 0.816; p = 0.012; 95%CI: 0.696-0.957). Lastly, the odds of utilizing delivery services was 1.553 times more likely for participants who use family planning compared to those who do not (p<0.001; 95%CI: 1.374-1.754), and 3.639 times more likely for those who receive antenatal care compared to those who do not (p<0.001; 95%CI: 3.074-4.308). These were found to be significant predictors of the choice of delivery services. DISCUSSION: Our results suggest that efforts towards reducing national maternal mortality in Bangladesh could be aided by investments into education, poverty reduction and the strengthening of reproductive healthcare services through community clinics, with particular focus on rural areas.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,101

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,094
Tête enseignante GPT0,275
Écart entre enseignants0,180 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations131
Publié2017
Routes d'admission1
Résumé présentoui

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