Modeling Reporting Delay in Cancer Incidence Counts in the Evolving US and Canadian Population–Based Cancer Registry Environment
Bibliographic record
Abstract
BACKGROUND: Cancer incidence data collected by cancer registries in the United States and Canada are submitted to the North American Association of Central Cancer Registries, which publishes annual case counts for the two countries. To allow time to collect and report cases, counts for a given diagnosis year are initially published two years after the end of that year and updated annually. Initial counts typically underreport cases compared with updated counts due to reporting delays, potentially biasing estimated incidence trends. METHODS: Existing methods for estimating "delay-adjusted" counts are modified for this heterogeneous group of registries exhibiting different patterns of reporting delay. The new method can be applied to individual registries and combined to produce delay-adjusted rates for the entire population, as well as for geographic or demographic subpopulations. RESULTS: Steps involved in estimating delay-adjusted counts are illustrated for liver and intrahepatic bile duct cancer in White males, in which delay-adjusted rates exhibit a stabilized trend, in contrast to the rapid decline seen in observed (unadjusted) rates. Additionally, the new delay model reveals reporting delays varying across cancer sites, race, and ethnicity. Finally, an extended model provides validated delay-adjusted rates from preliminary data that reduces reporting time from 2 years to 1 year. CONCLUSIONS: Adjusting for reporting delay provides more accurate estimates of cancer incidence trends. The proposed method addresses practical issues of model implementation and continues the evolution of delay adjustment in cancer registries. IMPACT: The new model extends the use of delay adjustment to an important source of cancer surveillance statistics.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".