Health Care Utilization among Adult Survivors of Childhood and Adolescent Cancer in Ontario
Bibliographic record
Abstract
Survivors of childhood and adolescent cancers are at increased risk for late effects including subsequent cancer, cardiotoxicities, and poor psychosocial health. Few studies have investigated if this translates into increased health care use and costs during survivorship. This thesis aimed to address key knowledge gaps in the field of survivorship and health care service utilization using population-based data in Ontario, Canada. First, we compared rates of health care use between survivors and matched controls across a diverse array of outcomes including hospitalizations, emergency department visits, primary care visits, and diagnostic investigations. We demonstrated higher rates of health care use across all outcomes among survivors compared to cancer-free controls. Our second study leveraged the matched-cohort and evaluated long-term health care costs from the perspective of the public payer. We found that survivors consistently incurred higher health care costs throughout long-term follow-up and in their final year of life, with hospitalizations as the most significant cost driver. Lastly, we focused on childhood and adolescent cancer survivors and compared acute health care utilization across varying models of long-term follow-up. In Ontario, there are three models of long-term follow-up care: specialized survivor clinics (AfterCare), care from an oncologist at a general cancer clinic, and care from a primary care physician. Within our survivor cohort, we observed sub-optimal adherence to any model of long-term follow-up care. We demonstrated that survivors with recent attendance at AfterCare Clinics was associated with a decrease in emergency department visits compared to survivors with no regular survivorship care. We did not observe any effect between AfterCare visits and rates of hospitalizations. Ultimately, to reduce morbidity, premature mortality, and subsequent acute health care use, greater adherence to long-term follow-up and surveillance strategies are needed. Our findings provide insight on health care use among this population for consideration by policymakers and will be useful to inform allocation of resources for this growing population of young Canadians.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".