Using administrative health data to inform health service planning for specialist cancer care in Nova Scotia, Canada
Bibliographic record
Abstract
IntroductionResearch has demonstrated that primary care providers can safely and effectively provide follow-up care after a person has received treatment for cancer. Yet, discharge to primary care after cancer treatment is variable, despite the fact that cancer systems are challenged to provide follow-up care given constrained cancer specialist resources. Objectives and ApproachTo inform cancer system planning, we examined (1) cancer centre routine follow-up (CC-FUP) care for prevalent cancer types and (2) changes in CC-FUP over time. From the Nova Scotia Cancer Registry, we identified all persons diagnosed in Nova Scotia, Canada, with an invasive breast, colorectal, gynecological, or prostate cancer between 01/01/2006 and 31/12/2013. We linked this dataset to cancer centre/clinic data and identified a non-metastatic cancer survivor cohort (n=12,267). Descriptive statistics were computed to describe patterns of care. Negative binomial regression was used to examine changes over time for both CC-FUP and all cancer centre visits, adjusting for other covariates. ResultsNearly half of survivors (48.4\%) had at least one CC-FUP visit, which varied by disease site (range: 30.2-62.4\%). Variation existed across providers, with six oncologists providing 34.7\% of the CC-FUP visits to the study population. Year of diagnosis was associated with receipt of CC-FUP care, with each successive calendar year associated with an 8\% increase in CC-FUP visits (IRR=1.08, 95\%CI=1.07-1.10). Similarly, each successive calendar year was associated with a 14\% increase in all cancer centre visits (IRR=1.14, 95\%CI=1.13-1.15). Results were shared with cancer system decision-makers at regular intervals to inform ongoing analyses. Conclusion/ImplicationsBoth the number of CC-FUP visits and all visits increased over time, with the latter at a greater rate. The increases were much higher than assumed by cancer system decision makers (2\% increase per year) for resource planning, demonstrating the value of population-based administrative data to informing health service planning.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".