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Enregistrement W1881872272 · doi:10.1684/san.2007.0088

[Individual and environmental characteristics associated with immunization of children in rural areas in Burkina Faso: a multi-level analysis].

2008· article· en· W1881872272 sur OpenAlexaff
Drissa Sia, Jean‐François Kobiané, Blaise Sondo, Pierre Fournier

Notice bibliographique

RevuePubMed · 2008
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueVaccine Coverage and Hesitancy
Établissements canadiensUniversité de Montréal
Organismes subventionnairesnon disponible
Mots-clésPolitical scienceHumanitiesGeographyRural populationRural areaArt

Résumé

récupéré en direct d'OpenAlex

PROBLEM: National and international efforts to immunize children aim to remove barriers that hinder full vaccination programs and to reinforce factors promoting it. Despite Burkina Faso's participation in all international and sub-regional initiatives to protect mothers and children from vaccination-preventable communicable diseases, vaccination coverage there remains low and has grown irregularly, from 34.7% in 1993 to 29.3% in 1998 and 43.9% in 2003. The situation is even more critical in rural than in urban areas. OBJECTIVE: To analyze the contribution of individual and environmental characteristics associated with vaccination of children aged 12-23 months in rural areas in Burkina Faso. Study population and methods. Data from the 1998 DHS (Demographic and Health Survey) and the 1997 Health Ministry Statistical Yearbook were used with a multi-level approach. Analysis distinguished two levels corresponding to the data's hierarchical structure: characteristics of children and their family's environment (level 1) and the health system and social environment (level 2). The study included 805 children aged 12 to 23 months, living in 44 health districts. The dependent variable was the child's vaccination status and is dichotomous (completely vaccinated or not). Completely vaccinated children are those who have received the BCG, the three doses of DTCoq, oral polio, measles and yellow fever vaccines, according to either their vaccination cards or their mothers' statement. RESULTS: The likelihood of vaccination increased with the level of household wealth (OR [well-off/poor]=1.88; [CI: 1.15-3.06] and was strongly associated with use of health services (OR [Prenatal care and assisted delivery/none of these services]=5.64; [CI: 3.16-10.05]). Nevertheless, these 2 variables did not alone explain the differences in vaccination observed between districts. More than 37% of the variation for vaccination completeness can be attributed to differences between health districts. Resources appear to play a minor role but a 1% increase in the proportion of educated women in the district increased the odds of complete vaccination by a factor of 1.14 [CI: 1.01-1.27]. Discussion. Despite universal access to free vaccination, children from poor households are less likely to receive all their vaccines than children from well-off households. This is probably due to indirect costs that stem from vaccination; the financial barrier remains one of the most significant factors preventing complete vaccination. Previous utilization of prenatal care and institutional delivery is more related to dynamics or even interaction between individuals and the health system. In addition to their direct effects, the interrelation between population and health systems may constitute a vaccination culture that may play a major role in explaining vaccination completeness. The resources of the health system bear little relation to vaccination. They are necessary but not sufficient for good health services. The organizational dynamic of health teams, the leadership of health district supervisors and staff motivation are key elements in these processes but were not measured in this study. CONCLUSION: Adding resources to vaccination programs is always a challenge for a number of national healthcare systems. It is not, however, the only key to success. The organization of healthcare systems and the contacts and relationships they establish with their populations appear to be determinant. The local vaccination culture that results from this interaction may be a key to explaining the variations observed between the different health districts.

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 enseignants0,008
Score d'incertitude au seuil0,304

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
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 Opus0,023
Tête enseignante GPT0,221
Écart entre enseignants0,199 · 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

Citations22
Publié2008
Routes d'admission1
Résumé présentoui

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