Patterns of Prescription Medication Use Before Diagnosis of Early Age-Onset Colorectal Cancer: Population-Based Descriptive Study
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer (CRC) is estimated to be the fourth most common cancer diagnosis in Canada (except for nonmelanoma skin cancers) and the second and third leading cause of cancer-related death in male and female individuals, respectively. OBJECTIVE: The rising incidence of early age-onset colorectal cancer (EAO-CRC; diagnosis at less than 50 years) calls for a better understanding of patients' pathway to diagnosis. Therefore, we evaluated patterns of prescription medication use before EAO-CRC diagnosis. METHODS: We used linked administrative health databases in British Columbia (BC), Canada, to identify individuals diagnosed with EAO-CRC between January 1, 2010, and December 31, 2016 (hereinafter referred to as "cases"), along with cancer-free controls (1:10), matched by age and sex. We identified all prescriptions dispensed from community pharmacies during the year prior to diagnosis and used the Anatomical Therapeutic Chemical Classification system Level 3 to group prescriptions according to the drug class. A parallel assessment was conducted for individuals diagnosed with average age-onset CRC (diagnosis at age 50 years and older). RESULTS: We included 1001 EAO-CRC cases (n=450, 45% female participants; mean 41.0, SD 6.1 years), and 12,989 prescriptions were filled in the year before diagnosis by 797 (79.7%) individuals. Top-filled drugs were antidepressants (first; n=1698, 13.1%). Drugs for peptic ulcer disease and gastroesophageal reflux disease (third; n=795, 6.1%) were more likely filled by EAO-CRC cases than controls (odds ratio [OR] 1.4, 95% CI 1.2-1.7) and with more frequent fills (OR 1.8, 95% CI 1.7-1.9). We noted similar patterns for topical agents for hemorrhoids and anal fissures, which were more likely filled by EAO-CRC cases than controls (OR 7.4, 95% CI 5.8-9.4) and with more frequent fills (OR 15.6, 95% CI 13.1-18.6). CONCLUSIONS: We observed frequent prescription medication use in the year before diagnosis of EAO-CRC, including for drugs to treat commonly reported symptoms of EAO-CRC.
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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) | 0.000 | 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".