Pediatric hospital admissions in Indigenous children: a population-based study in remote Australia
Notice bibliographique
Résumé
BACKGROUND: We analysed hospital admissions of a predominantly Aboriginal cohort of children in the remote Fitzroy Valley in Western Australia during the first 7 years of life. METHODS: All children born between January 1, 2002 and December 31, 2003 and living in the Fitzroy Valley in 2009-2010 were eligible to participate in the Lililwan Project. Of 134 eligible children, 127 (95%) completed Stage 1 (interviews of caregivers and medical record review) in 2011 and comprised our cohort. Lifetime (0-7 years) hospital admission data were available and included the dates, and reasons for admission, and comorbidities. Conditions were coded using ICD-10-AM discharge codes. RESULTS: Of the 127 children, 95.3% were Indigenous and 52.8% male. There were 314 admissions for 424 conditions in 89 (70.0%) of 127 children. The 89 children admitted had a median of five admissions (range 1-12). Hospitalization rates were similar for both genders (p = 0.4). Of the admissions, 108 (38.6%) were for 56 infants aged <12 months (median = 2.5, range = 1-8). Twelve of these admissions were in neonates (aged 0-28 days). Primary reasons for admission (0-7 years) were infections of the lower respiratory tract (27.4%), gastrointestinal system (22.7%), and upper respiratory tract (11.4%), injury (7.0%), and failure to thrive (5.4%). Comorbidities, particularly upper respiratory tract infections (18.1%), failure to thrive (13.6%), and anaemia (12.7%), were common. In infancy, primary cause for admission were infections of the lower respiratory tract (40.8%), gastrointestinal (25.9%) and upper respiratory tract (9.3%). Comorbidities included upper respiratory tract infections (33.3%), failure to thrive (18.5%) and anaemia (18.5%). CONCLUSION: In the Fitzroy Valley 70.0% of children were hospitalised at least once before age 7 years and over one third of admissions were in infants. Infections were the most common reason for admission in all age groups but comorbidities were common and may contribute to need for admission. Many hospitalizations were feasibly preventable. High admission rates reflect disadvantage, remote location and limited access to primary healthcare and outpatient services. Ongoing public health prevention initiatives including breast feeding, vaccination, healthy diet, hygiene and housing improvements are crucial, as is training of Aboriginal Health Workers to increase services in remote communities.
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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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 ».