The Determinants of Health Care Costs in Older Adults Undergoing Non-Elective Abdominal Surgery
Notice bibliographique
Résumé
Health care spending in Canada has been increasing faster than the rate of gross domestic product (GDP). A disproportionate amount of the health care spending is allocated to care of older adults. Non-elective abdominal surgery is an expensive area of care for older adults. Despite this, the factors associated with cost in this patient population remain unclear.\nOBJECTIVES\nThe primary objective of this study was to estimate the association between perioperative factors (age, American Society of Anesthetists (ASA) classification, operative severity (OS), frailty index (FI), complication severity) and health care costs among older adults undergoing non-elective abdominal surgery. The secondary objectives were: 1. to provide a comprehensive description of costs based on patient-level resource utilization; and 2. to examine the relationship between hospital costs and adverse events (non-fatal complication severity, mortality, and change in living arrangement). \nMETHODS\n\tThis study was an observational prospective cohort study. Over a 15 month period all patients 70 years or older who underwent non-elective abdominal surgery at the QEII Health Sciences Centre, Nova Scotia, were enrolled. Data were collected on patient demographics, investigations, treatments, and outcomes. Direct hospital health care costs (2012 $CAD) were calculated by tabulating patient-level resource use and assigning specific costs. The association between five perioperative factors and costs were analyzed using univariate non-parametric tests and multiple linear regression. The associations between adverse events and costs were assessed using univariate non-parametric tests and multiple linear regression.\nRESULTS\n\tDuring the study period, 212 patients who underwent abdominal surgery (median age 78 years (range 70-97)) were enrolled. The median costs of care were $9,166 (range $1,993-$104,403). The largest proportions of spending were non-procedural costs (65% [$2,176,875]) and intensive care costs (16% [$554,523]). The perioperative factors ASA classification (p=0.0010), OS (p<0.0001), FI (p=0.0002) and complication severity (p<0.0001) were all independently associated with health care costs, while age was not (p=0.5330). The following adverse events were independently associated with health care costs: non-fatal complication severity (p<0.0001), change in living arrangement (p=0.0002), and mortality (p=0.0337). Non-fatal complications had the strongest association with hospital costs (standardized β coefficient = 0.3931).\nCONCLUSION\nFour perioperative factors (ASA, OS, FI and complication severity) are associated with costs; therefore, representing a potential cost prediction model for this patient group. This study is important for health care administrators, identifying targets for cost reduction. Cost reduction strategies and research should concentrate on mitigating or preventing complications and high cost areas, such as non-procedural costs and intensive care, in order to achieve cost savings.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| 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 | 0,001 | 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».