Prevalence of multimorbidity in adults with cancer, and associated health service utilization in Ontario, Canada: a population-based retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: The majority of people with cancer have at least one other chronic health condition. With each additional chronic disease, the complexity of their care increases, as does the potential for negative outcomes including premature death. In this paper, we describe cancer patients' clinical complexity (i.e., multimorbidity; MMB) in order to inform strategic efforts to improve care and outcomes for people with cancer of all types and commonly occurring chronic diseases. METHODS: We conducted a population-based, retrospective cohort study of adults diagnosed with cancer between 2003 and 2013 (N = 601,331) identified in Ontario, Canada healthcare administrative data. During a five to 15-year follow-up period (through March 2018), we identified up to 16 co-occurring conditions and patient outcomes for the cohort, including health service utilization and death. RESULTS: MMB was extremely common, affecting more than 91% of people with cancer. Nearly one quarter (23%) of the population had five or more co-occurring conditions. While we saw no differences in MMB between sexes, MMB prevalence and level increased with age. MMB prevalence and type of co-occurring conditions also varied by cancer type. Overall, MMB was associated with higher rates of health service utilization and mortality, regardless of other patient characteristics, and specific conditions differentially impacted these rates. CONCLUSIONS: People with cancer are likely to have at least one other chronic medical condition and the presence of MMB negatively affects health service utilization and risk of premature death. These findings can help motivate and inform health system advances to improve care quality and outcomes for people with cancer and MMB.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".