Trends in maternal and child health outcomes in a health systems intervention: a case of Obekai dispensary in western Kenya
Notice bibliographique
Résumé
Background Maternal, fetal and neonatal mortality are higher in low-income compared to high-income countries primarily due to weak health systems that impede access and utilization of health services. Despite significant improvements in maternal, neonatal and under five children indicators, in some low-income countries, including Kenya these indicators remain relatively high prompting the search for innovative interventions to catalyze the progress towards attaining the Sustainable Development Goal (SDG) 3 target by 2030. We describe the results of a study that assessed the impact of an innovative health systems approach on maternal, neonatal and under-five children outcomes. Methods This was a four year pre-post prospective study to describe trends in the outcomes through the implementation of the Enhanced Health Care (EHC) using the Find Link Treat and Retain (FLTR) strategy. This was implemented in the catchment population of a ‘level 2’ facility, Obekai dispensary in Busia County, Kenya between January 2016 and January 2019. The study population was pregnant women, newborns and under-five children identified and referred to the facility over the study period. Women were identified in the community by community health workers early in pregnancy and followed up in Obekai dispensary until delivery. The newborns were followed up for the whole period the mother baby dyads were in the study. The EHC was implemented on these pairs during the duration of study upto 2-3 years. An interrupted time series model for a single group was used to assess the effect of the intervention on the outcome. Results Attendance in the outpatient, under five and antenatal clinic increased by 76%, 37% and 54% respectively from 2015 to 2018. There was a 90% fully immunization coverage with 97% and 94% BCG and Polio coverage respectively among children studied. There was a 91% facility delivery rate among the pregnant women enrolled in the study. After introduction of FLTR the immunization uptake increased significantly per quarter at a rate of 29.2 (95% confidence interval, CI=20.1-38.3). There was an increase in facility delivery, antenatal (ANC) attendance and decrease in neonatal death after introduction on FLTR. Conclusions Maternal and under-five health indicators in Obekai improved over the study period following the implementation of the EHC package.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 | 0,001 |
| 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 ».