A systematic review of the magnitude and cause of geographic variation in unplanned hospital admission rates and length of stay for ambulatory care sensitive conditions
Notice bibliographique
Résumé
BACKGROUND: Unplanned hospital admissions place a large and increasing strain on healthcare budgets worldwide. Many admissions for ambulatory care sensitive conditions (ACSCs) are thought to be preventable, a belief supported by significant geographic variations in admission rates. We conducted a systematic review of the evidence on the magnitude and correlates of geographic variation in ACSC admission rates and length of stay (LOS). METHODS: We performed a search of Medline and Embase databases for English language cross-sectional and cohort studies on 28th March 2013 reporting geographic variation in admission rates or LOS for patients receiving unplanned care across at least 10 geographical units for one of 35 previously defined ACSCs. Forward and backward citation searches were undertaken on all included studies. We provide a narrative synthesis of study findings. Study quality was assessed using a modified Newcastle-Ottawa scale. RESULTS: We included 39 studies comprising 25 on admission rates and 14 on LOS. Studies generally compared admission rates between regions (e.g. states) and LOS between hospitals. Most of the published research was undertaken in the US, UK or Canada and often focussed on patients with pneumonia, COPD or heart failure. 35 (90%) studies concluded that geographic variation was present. Primary care quality and secondary care access were frequently suggested as drivers of admission rate variation whilst secondary care quality and adherence to clinical guidelines were often listed as contributors to LOS variation. Several different methods were used to quantify variation, some studies listed raw data, failed to control for confounders and used naive statistical methods which limited their utility. CONCLUSIONS: The substantial geographical variations in the admission rates and LOS of potentially avoidable conditions could be a symptom of variable quality of care and should be a concern for clinicians and policymakers. Policymakers targeting a reduction in unplanned admissions could introduce initiatives to improve primary care access and quality or develop alternatives to admission. Those attempting to curb unnecessarily long LOS could introduce care pathways or guidelines. Methodological work on the quantification and reporting of geographic variation is needed to aid inter-study comparisons.
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 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 | 0,008 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,000 |
| Bibliométrie | 0,001 | 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 ».