Epidemiology of multimorbidity in childhood cancer survivors: a matched cohort study of inpatient hospitalisations in Western Australia
Bibliographic record
Abstract
BACKGROUND: Childhood cancer survivors (CCS) experience an elevated burden of health complications, underscoring the importance of understanding the patterns of multimorbidity to guide the management of survivors with complex medical needs. METHODS: We examined the patterns of hospitalisations with multimorbidity in 5-year CCS (n = 2938) and age- and sex-matched non-cancer comparisons (n = 24,792) using statewide records of inpatient admissions in Western Australia from 1987 to 2019. RESULTS: Multimorbidity rates were higher for CCS (10.6, 95%CI 10.2-10.9) than for non-cancer comparisons (3.2, 95%CI 3.2-3.3). CCS exhibited a significantly higher adjusted hazard ratio of multimorbidity, particularly when admitted for neoplasms (14.6, 95%CI 11.2-19.1), as well as blood (7.3, 95%CI 4.9-10.7), neurological and sensory (5.2, 95%CI 4.2-6.6), and cardiovascular (3.6, 95%CI 2.6-4.8) diseases. By the age of 55 years, chronic multimorbidity was more prevalent in survivors than in comparisons (14.5% vs. 5.3%). Psychiatric disorders were common comorbidities, particularly in those admitted for neurological and sensory (71.1%), endocrine (61.5%), and digestive (59.3%) diseases. Multimorbidity during hospitalisation increased the length of hospital stay (p < 0.05). Key condition clusters included (1) psychoactive substance dependence, alcohol misuse, and other mental disorders; (2) hypertension, diabetes, kidney disease, and musculoskeletal diseases; (3) epilepsy, hypothyroidism, and other liver diseases; and (4) hypertension, kidney disease, and other liver diseases. CONCLUSIONS: These findings suggest that exposure to cancer in childhood elevates the risk of multimorbidity. The reconfiguration of healthcare delivery to enhance personalised care and clinical integration is essential for effectively managing multimorbidity in this population.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".