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Enregistrement W6903088123 · doi:10.7939/r3-rqgd-cs75

Healthcare Utilization Associated with Management of Oral and Oropharyngeal Cancer in Alberta: Trends and Predictors

2023· dissertation· en· W6903088123 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity of Alberta Library · 2023
Typedissertation
Langueen
DomaineMedicine
ThématiqueHead and Neck Cancer Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEmergency departmentIncidence (geometry)Medical diagnosisHealth careCohortCancerAmbulatory careCohort study

Résumé

récupéré en direct d'OpenAlex

Background: The increasing incidence of oral cavity cancer (OCC) and oropharyngeal cancer (OPC), especially HPV-related OPC, is a concerning healthcare challenge. Statistics Canada's recent report indicates a substantial 13.9% increase in OPC incidence in 2020 compared to the average from 2015-2019. Managing these cancers is resource-intensive and complex, and patients often endure not only the challenges of cancer itself but also treatment complications, especially when diagnoses occur in late stages. Effective and timely management of treatment complications is crucial, as improper handling can lead to acute care needs and treatment interruptions, such as emergency department (ED) visits and unplanned hospitalizations (UH), which have been associated with poorer oncologic outcomes. Considering the rising incidence and prevalence of these cancers, it becomes crucial to assess the healthcare utilization associated with delivering high-quality care for patients. Understanding and evaluating the patterns of healthcare utilization can provide valuable insights to enhance patient care, optimize resource allocation, and improve overall treatment outcomes. Objectives: With this background, this study had three main objectives: 1) Investigate trends in hospitalization and visits of OCC and OPC patients in emergency department, outpatient clinic, and community offices 2) Identify predictors of acute care visits, including unplanned hospitalizations, 30-day hospital readmissions, and emergency department visits 3) Determine the primary diagnoses of patients admitted to hospitals, visited emergency departments, and outpatient clinics. 3 Methods: This retrospective, population-based cohort study utilized administrative data collected from all healthcare facilities in Alberta from 2010 to 2019. The study cohort consisted of adult patients (18 years old or older) diagnosed with a primary tumor of the OCC or OPC, identified through the Alberta Cancer Registry (ACR). To examine the cohort's healthcare utilization, the ACR cohort was linked with the Discharge Abstract database, National Ambulatory Care Reporting System, and Physician Claim dataset. The primary diagnosis of patients in each event was determined using diagnosis codes from each database. For data analysis, the study outcomes were assessed using statistical methods, including logistic and linear regression, as well as parametric and non-parametric tests, all conducted using SAS Enterprise Guide 7.1. Results: The final cohort consisted of 1,721 patients, 72.4% were male and 57.9% were between 45-65 years of age. OPC patients were diagnosed at a significantly younger age, with a mean age of 59.4 years, compared to OCC patients who had a mean age of 62.4 years (P-value < 0.05). During the study, 34% (582 individuals) of the patients had at least one visit to the ED, and 72% (1,244 patients) had at least one hospitalization visit. UHs constituted 48.1% of the overall 2,228 hospitalizations. Notably, outpatient clinic and community office visits showed a significant increase during the study period, with visits rising from 475 to 1,101 (β=0.20, P=0.01) and from 1,653 to 2,629 (β=0.31, P=0.02), respectively. Concurrently, ED visits decreased from 0.65 to 0.49 visits per patient, and the rate of UHs per patient decreased from 0.69 to 0.54 visits. The common diagnosis for UHs were palliative care and post-surgical recovery, while surgery-related complications were frequent causes of 30-day unplanned readmissions. In ED 4 visits diagnoses of dehydration, post-procedural infections, and nausea and vomiting were frequent. Predictors of UHs included cancer stage, material deprivation, and the chosen treatment modality, whereas cancer type and comorbidities emerged as key predictors for readmissions. Moreover, Predictors of ED visits included cancer stage, rural residence, high material deprivation scores, and treatments other than surgery or no treatment. Conclusion: The study's findings revealed a decrease in ED visits and UHs among cancer patients diagnosed between 2010 and 2017, accompanied by increased utilization of outpatient clinics and community offices, indicating a shift towards primary care settings for cancer-related care. Implementing a primary care model may have contributed to better patient management, reducing acute care visits and hospitalizations. Predictors of acute care events highlighted the importance of improving access to care for underprivileged patients and those in rural areas. It also showed Patients not receiving oncologic treatments and those undergoing radiation therapy need for close monitoring and intervention. Preventive strategies and patient education could help reduce avoidable ED visits, while monitoring and managing procedure-related complications can prevent subsequent hospital events.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut 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 enseignants0,099
Score d'incertitude au seuil0,199

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,003
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,019
Tête enseignante GPT0,260
Écart entre enseignants0,241 · 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 source (Gemma direct ou Codex distillé), 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

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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