Inequities in the Time to Colon Cancer Diagnosis Among Individuals With Severe Psychiatric Illness
Bibliographic record
Abstract
INTRODUCTION: Early colon cancer detection is critical for improving outcomes. The diagnostic interval is a useful approach to conceptualizing time-to-diagnosis within the healthcare system and understanding the diagnostic journey. Adults with severe psychiatric illness (SPI) are less likely to participate in cancer screening and more likely to be diagnosed with advanced cancers. We investigated the association between having an SPI and the colon cancer diagnostic interval. METHODS: We conducted a cross-sectional study of adults diagnosed with colon cancer in Ontario, Canada between 2007 and 2019 using administrative health data. Individuals with healthcare encounters consistent with pre-existing major depression, schizophrenia, bipolar disorder, or other non-organic psychotic illnesses were considered as having SPI. Individuals with an SPI-related hospitalization were categorized as having an inpatient SPI; the rest were considered outpatient. We calculated the diagnostic interval as the number of days from first colon cancer-related healthcare encounter to cancer diagnosis. Diagnostic pathways were assessed descriptively, including whether diagnosis was made symptomatically or with no symptom recorded. Quantile regression (stratified by symptom status) was used to quantify the association between SPI status and the diagnostic interval. RESULTS: We identified 42,143 individuals with colon cancer: 40,884 with no history of mental illness, 835 with a history of outpatient SPI, and 424 with inpatient SPI. Adults with SPI were significantly more likely to be diagnosed symptomatically (inpatient: 89.9%, outpatient: 86.6%, no SPI: 80.9%, p < 0.001). Individuals with SPI experienced a significantly longer median symptomatic diagnostic interval and a similar median diagnostic interval when diagnosed with no symptom recorded, relative to those without a history of mental illness. After adjusting for covariates, the median symptomatic diagnostic interval was 48 days longer (95% CI 28, 68) among individuals with outpatient SPI and 55 days longer (95% CI 28, 82) among individuals with inpatient SPI compared to those with no SPI. CONCLUSION: Individuals with SPI were more likely to be diagnosed symptomatically and had longer symptomatic diagnostic intervals than those without. This study represents a first step in targeting and improving cancer diagnostic processes for individuals with SPI.
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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 | 0.002 |
| 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) | 0.003 | 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 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".