Healthcare Utilization Associated with Management of Oral and Oropharyngeal Cancer in Alberta: Trends and Predictors
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".